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Gu X, Wei S, Lv X. Circulating tumor cells: from new biological insights to clinical practice. Signal Transduct Target Ther 2024; 9:226. [PMID: 39218931 PMCID: PMC11366768 DOI: 10.1038/s41392-024-01938-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 05/31/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
The primary reason for high mortality rates among cancer patients is metastasis, where tumor cells migrate through the bloodstream from the original site to other parts of the body. Recent advancements in technology have significantly enhanced our comprehension of the mechanisms behind the bloodborne spread of circulating tumor cells (CTCs). One critical process, DNA methylation, regulates gene expression and chromosome stability, thus maintaining dynamic equilibrium in the body. Global hypomethylation and locus-specific hypermethylation are examples of changes in DNA methylation patterns that are pivotal to carcinogenesis. This comprehensive review first provides an overview of the various processes that contribute to the formation of CTCs, including epithelial-mesenchymal transition (EMT), immune surveillance, and colonization. We then conduct an in-depth analysis of how modifications in DNA methylation within CTCs impact each of these critical stages during CTC dissemination. Furthermore, we explored potential clinical implications of changes in DNA methylation in CTCs for patients with cancer. By understanding these epigenetic modifications, we can gain insights into the metastatic process and identify new biomarkers for early detection, prognosis, and targeted therapies. This review aims to bridge the gap between basic research and clinical application, highlighting the significance of DNA methylation in the context of cancer metastasis and offering new avenues for improving patient outcomes.
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Affiliation(s)
- Xuyu Gu
- Department of Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shiyou Wei
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xin Lv
- Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
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2
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Fuchs V, Sobarzo A, Msamra M, Kezerle Y, Linde L, Sevillya G, Anoze A, Refaely Y, Cohen AY, Melamed I, Azriel A, Shoukrun R, Raviv Y, Porgador A, Peled N, Roisman LC. Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration. Clin Transl Oncol 2024; 26:2227-2239. [PMID: 38553659 PMCID: PMC11333550 DOI: 10.1007/s12094-024-03450-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/05/2024] [Indexed: 08/20/2024]
Abstract
PURPOSE In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations. METHODS A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages. RESULTS The main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to the long duration of chemotherapy treatment, which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies. CONCLUSIONS Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.
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Affiliation(s)
- Vered Fuchs
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ariel Sobarzo
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Maha Msamra
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Yarden Kezerle
- Institute of Pathology, Soroka University Medical Center, Beer-Sheva, Israel
| | - Liat Linde
- Biomedical Core Facility, Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Gur Sevillya
- Biomedical Core Facility, Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Alaa Anoze
- The Oncology Institute, Helmsley Cancer Center, Precision Oncology and Innovation, Shaare Zedek Medical Center, 12, Shmuel Beit St, 9103102, Jerusalem, Israel
| | - Yael Refaely
- Department of Cardiothoracic Surgery, Soroka University Medical Center, Beer-Sheva, Israel
| | | | - Israel Melamed
- Department of Neurosurgery, Soroka University Medical Center, Beer Sheva, Israel
| | - Amit Azriel
- Department of Neurosurgery, Soroka University Medical Center, Beer Sheva, Israel
| | - Rami Shoukrun
- Department of Ears, Nose & Throat, Head & Neck Surgery, Soroka University Medical Center, Beer Sheva, Israel
| | - Yael Raviv
- Pulmonary Institute, Soroka University Medical Center, Beer-Sheva, Israel
| | - Angel Porgador
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Nir Peled
- The Oncology Institute, Helmsley Cancer Center, Precision Oncology and Innovation, Shaare Zedek Medical Center, 12, Shmuel Beit St, 9103102, Jerusalem, Israel.
| | - Laila Catalina Roisman
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
- The Oncology Institute, Helmsley Cancer Center, Precision Oncology and Innovation, Shaare Zedek Medical Center, 12, Shmuel Beit St, 9103102, Jerusalem, Israel.
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3
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Williams MJ, Vázquez-García I, Tam G, Wu M, Varice N, Havasov E, Shi H, Satas G, Lees HJ, Lee JJK, Myers MA, Zatzman M, Rusk N, Ali E, Shah RH, Berger MF, Mohibullah N, Lakhman Y, Chi DS, Abu-Rustum NR, Aghajanian C, McPherson A, Zamarin D, Loomis B, Weigelt B, Friedman CF, Shah SP. Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609031. [PMID: 39229105 PMCID: PMC11370573 DOI: 10.1101/2024.08.21.609031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.
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Affiliation(s)
- Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, 10027, USA
| | - Grittney Tam
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michelle Wu
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nancy Varice
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Eliyahu Havasov
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gryte Satas
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hannah J Lees
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jake June-Koo Lee
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew A Myers
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Zatzman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Ali
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ronak H Shah
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael F Berger
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Neeman Mohibullah
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Dennis S Chi
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nadeem R Abu-Rustum
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Carol Aghajanian
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Brian Loomis
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Claire F Friedman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Tang M, Zhang Z, Wang P, Zhao F, Miao L, Wang Y, Li Y, Li Y, Gao Z. Advancements in precision nanomedicine design targeting the anoikis-platelet interface of circulating tumor cells. Acta Pharm Sin B 2024; 14:3457-3475. [PMID: 39220884 PMCID: PMC11365446 DOI: 10.1016/j.apsb.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/10/2024] [Accepted: 03/13/2024] [Indexed: 09/04/2024] Open
Abstract
Tumor metastasis, the apex of cancer progression, poses a formidable challenge in therapeutic endeavors. Circulating tumor cells (CTCs), resilient entities originating from primary tumors or their metastases, significantly contribute to this process by demonstrating remarkable adaptability. They survive shear stress, resist anoikis, evade immune surveillance, and thwart chemotherapy. This comprehensive review aims to elucidate the intricate landscape of CTC formation, metastatic mechanisms, and the myriad factors influencing their behavior. Integral signaling pathways, such as integrin-related signaling, cellular autophagy, epithelial-mesenchymal transition, and interactions with platelets, are examined in detail. Furthermore, we explore the realm of precision nanomedicine design, with a specific emphasis on the anoikis‒platelet interface. This innovative approach strategically targets CTC survival mechanisms, offering promising avenues for combatting metastatic cancer with unprecedented precision and efficacy. The review underscores the indispensable role of the rational design of platelet-based nanomedicine in the pursuit of restraining CTC-driven metastasis.
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Affiliation(s)
- Manqing Tang
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zhijie Zhang
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Ping Wang
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Feng Zhao
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Lin Miao
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yuming Wang
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yingpeng Li
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yunfei Li
- College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zhonggao Gao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
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5
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Kabeer F, Tran H, Andronescu M, Singh G, Lee H, Salehi S, Wang B, Biele J, Brimhall J, Gee D, Cerda V, O'Flanagan C, Algara T, Kono T, Beatty S, Zaikova E, Lai D, Lee E, Moore R, Mungall AJ, Williams MJ, Roth A, Campbell KR, Shah SP, Aparicio S. Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers. Genome Biol 2024; 25:191. [PMID: 39026273 PMCID: PMC11256464 DOI: 10.1186/s13059-024-03318-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states. RESULTS We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. CONCLUSIONS Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.
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Affiliation(s)
- Farhia Kabeer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Hoa Tran
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Mirela Andronescu
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Gurdeep Singh
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Hakwoo Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Beixi Wang
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Justina Biele
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - David Gee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Viviana Cerda
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Ciara O'Flanagan
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Teresa Algara
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Takako Kono
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Sean Beatty
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Elena Zaikova
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Daniel Lai
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Eric Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Richard Moore
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Roth
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Kieran R Campbell
- Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Samuel Aparicio
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada.
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Aalam SMM, Nguyen LV, Ritting ML, Kannan N. Clonal tracking in cancer and metastasis. Cancer Metastasis Rev 2024; 43:639-656. [PMID: 37910295 DOI: 10.1007/s10555-023-10149-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.
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Affiliation(s)
| | - Long Viet Nguyen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Megan L Ritting
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
- Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.
- Center for Regenerative Biotherapeutics, Mayo Clinic, Rochester, MN, USA.
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7
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Hynds RE, Huebner A, Pearce DR, Hill MS, Akarca AU, Moore DA, Ward S, Gowers KHC, Karasaki T, Al Bakir M, Wilson GA, Pich O, Martínez-Ruiz C, Hossain ASMM, Pearce SP, Sivakumar M, Ben Aissa A, Grönroos E, Chandrasekharan D, Kolluri KK, Towns R, Wang K, Cook DE, Bosshard-Carter L, Naceur-Lombardelli C, Rowan AJ, Veeriah S, Litchfield K, Crosbie PAJ, Dive C, Quezada SA, Janes SM, Jamal-Hanjani M, Marafioti T, McGranahan N, Swanton C. Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models. Nat Commun 2024; 15:4653. [PMID: 38821942 PMCID: PMC11143323 DOI: 10.1038/s41467-024-47547-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 03/28/2024] [Indexed: 06/02/2024] Open
Abstract
Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.
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Affiliation(s)
- Robert E Hynds
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Epithelial Cell Biology in ENT Research Group (EpiCENTR), Developmental Biology and Cancer, Great Ormond Street University College London Institute of Child Health, London, UK.
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David R Pearce
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ayse U Akarca
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Kate H C Gowers
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Oriol Pich
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - A S Md Mukarram Hossain
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Simon P Pearce
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Monica Sivakumar
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Assma Ben Aissa
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Eva Grönroos
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Deepak Chandrasekharan
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Krishna K Kolluri
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Rebecca Towns
- Biological Services Unit, University College London, London, UK
| | - Kaiwen Wang
- School of Medicine, University of Leeds, Leeds, UK
| | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Leticia Bosshard-Carter
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | | | - Andrew J Rowan
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Philip A J Crosbie
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Caroline Dive
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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8
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Sunil HS, O'Donnell KA. Capturing heterogeneity in PDX models: representation matters. Nat Commun 2024; 15:4652. [PMID: 38821926 PMCID: PMC11143235 DOI: 10.1038/s41467-024-47607-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/05/2024] [Indexed: 06/02/2024] Open
Affiliation(s)
- Hari Shankar Sunil
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kathryn A O'Donnell
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA.
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA.
- Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA.
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9
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Wen S, Lin X, Luo W, Pan Y, Liao F, Wang Z, Zhan B, Feng J, Huang H. Metabolic difference between patient-derived xenograft model of pancreatic ductal adenocarcinoma and corresponding primary tumor. BMC Cancer 2024; 24:485. [PMID: 38632504 PMCID: PMC11022326 DOI: 10.1186/s12885-024-12193-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Patients-derived xenograft (PDX) model have been widely used for tumor biological and pathological studies. However, the metabolic similarity of PDX tumor to the primary cancer (PC) is still unknown. METHODS In present study, we established PDX model by engrafting primary tumor of pancreatic ductal adenocarcinoma (PDAC), and then compared the tumor metabolomics of PC, the first generation of PDX tumor (PDXG1), and the third generation of PDX tumor (PDXG3) by using 1H NMR spectroscopy. Then, we assessed the differences in response to chemotherapy between PDXG1 and PDXG3 and corresponding metabolomic differences in drug-resistant tumor tissues. To evaluate the metabolomic similarity of PDX to PC, we also compared the metabolomic difference of cell-derived xenograft (CDX) vs. PC and PDX vs. PC. RESULTS After engraftment, PDXG1 tumor had a low level of lactate, pyruvate, citrate and multiple amino acids (AAs) compared with PC. Metabolite sets enrichment and metabolic pathway analyses implied that glycolysis metabolisms were suppressed in PDXG1 tumor, and tricarboxylic acid cycle (TCA)-associated anaplerosis pathways, such as amino acids metabolisms, were enhanced. Then, after multiple passages of PDX, the altered glycolysis and TCA-associated anaplerosis pathways were partially recovered. Although no significant difference was observed in the response of PDXG1 and PDXG3 to chemotherapy, the difference in glycolysis and amino acids metabolism between PDXG1 and PDXG3 could still be maintained. In addition, the metabolomic difference between PC and CDX models were much larger than that of PDX model and PC, indicating that PDX model still retain more metabolic characteristics of primary tumor which is more suitable for tumor-associated metabolism research. CONCLUSIONS Compared with primary tumor, PDX models have obvious difference in metabolomic level. These findings can help us design in vivo tumor metabolomics research legitimately and analyze the underlying mechanism of tumor metabolic biology thoughtfully.
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Affiliation(s)
- Shi Wen
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China
| | - Xianchao Lin
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China
| | - Wei Luo
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China
| | - Yu Pan
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China
| | - Fei Liao
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China
| | - Zhenzhao Wang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, No. 422, Siming South Road, Siming District, 361005, Xiamen, China
| | - Bohan Zhan
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, No. 422, Siming South Road, Siming District, 361005, Xiamen, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, No. 422, Siming South Road, Siming District, 361005, Xiamen, China.
| | - Heguang Huang
- Department of General Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Gulou District, 351001, Fuzhou, China.
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10
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Sud A, Parry EM, Wu CJ. The molecular map of CLL and Richter's syndrome. Semin Hematol 2024; 61:73-82. [PMID: 38368146 DOI: 10.1053/j.seminhematol.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/19/2024]
Abstract
Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.
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Affiliation(s)
- Amit Sud
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Erin M Parry
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA
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11
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Lewis MT, Caldas C. The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer. Cold Spring Harb Perspect Med 2024; 14:a041329. [PMID: 38052483 PMCID: PMC10982691 DOI: 10.1101/cshperspect.a041329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.
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Affiliation(s)
- Michael T Lewis
- Baylor College of Medicine, The Lester and Sue Smith Breast Center, Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom
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12
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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13
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Tabatabai A, Arora A, Höfmann S, Jauch M, von Tresckow B, Hansen J, Flümann R, Jachimowicz RD, Klein S, Reinhardt HC, Knittel G. Mouse models of diffuse large B cell lymphoma. Front Immunol 2023; 14:1313371. [PMID: 38124747 PMCID: PMC10731046 DOI: 10.3389/fimmu.2023.1313371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
Diffuse large B cell lymphoma (DLBCL) is a genetically highly heterogeneous disease. Yet, to date, the vast majority of patients receive standardized frontline chemo-immune-therapy consisting of an anthracycline backbone. Using these regimens, approximately 65% of patients can be cured, whereas the remaining 35% of patients will face relapsed or refractory disease, which, even in the era of CAR-T cells, is difficult to treat. To systematically tackle this high medical need, it is important to design, generate and deploy suitable in vivo model systems that capture disease biology, heterogeneity and drug response. Recently published, large comprehensive genomic characterization studies, which defined molecular sub-groups of DLBCL, provide an ideal framework for the generation of autochthonous mouse models, as well as an ideal benchmark for cell line-derived or patient-derived mouse models of DLBCL. Here we discuss the current state of the art in the field of mouse modelling of human DLBCL, with a particular focus on disease biology and genetically defined molecular vulnerabilities, as well as potential targeting strategies.
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Affiliation(s)
- Areya Tabatabai
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Aastha Arora
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Svenja Höfmann
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Maximilian Jauch
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Bastian von Tresckow
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Julia Hansen
- Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Center for Integrated Oncology Aachen Bonn, Cologne, Germany
- Center for Molecular Medicine, University of Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Ruth Flümann
- Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Center for Integrated Oncology Aachen Bonn, Cologne, Germany
- Center for Molecular Medicine, University of Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Ron D. Jachimowicz
- Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Center for Integrated Oncology Aachen Bonn, Cologne, Germany
- Center for Molecular Medicine, University of Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Mildred Scheel School of Oncology Aachen Bonn Cologne Düsseldorf (MSSO ABCD), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Sebastian Klein
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Hans Christian Reinhardt
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Gero Knittel
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, West German Cancer Center, German Cancer Consortium Partner Site Essen, Center for Molecular Biotechnology, University of Duisburg-Essen, Essen, Germany
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14
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Lee TW, Hunter FW, Tsai P, Print CG, Wilson WR, Jamieson SMF. Clonal dynamics limits detection of selection in tumour xenograft CRISPR/Cas9 screens. Cancer Gene Ther 2023; 30:1610-1623. [PMID: 37684549 PMCID: PMC10721547 DOI: 10.1038/s41417-023-00664-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/08/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Transplantable in vivo CRISPR/Cas9 knockout screens, in which cells are edited in vitro and inoculated into mice to form tumours, allow evaluation of gene function in a cancer model that incorporates the multicellular interactions of the tumour microenvironment. To improve our understanding of the key parameters for success with this method, we investigated the choice of cell line, mouse host, tumour harvesting timepoint and guide RNA (gRNA) library size. We found that high gRNA (80-95%) representation was maintained in a HCT116 subline transduced with the GeCKOv2 whole-genome gRNA library and transplanted into NSG mice when tumours were harvested at early (14 d) but not late time points (38-43 d). The decreased representation in older tumours was accompanied by large increases in variance in gRNA read counts, with notable expansion of a small number of random clones in each sample. The variable clonal dynamics resulted in a high level of 'noise' that limited the detection of gRNA-based selection. Using simulated datasets derived from our experimental data, we show that considerable reductions in count variance would be achieved with smaller library sizes. Based on our findings, we suggest a pathway to rationally design adequately powered in vivo CRISPR screens for successful evaluation of gene function.
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Affiliation(s)
- Tet Woo Lee
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.
| | - Francis W Hunter
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
- Oncology Therapeutic Area, Janssen Research and Development, Spring House, PA, USA
| | - Peter Tsai
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Cristin G Print
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - William R Wilson
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Stephen M F Jamieson
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.
- Department of Pharmacology and Clinical Pharmacology, University of Auckland, Auckland, New Zealand.
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15
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Özdemir Akdur P, Çiledağ N. Review of the relationship between tumor receptor subtypes and preference for visceral and/or serosal metastasis in breast cancer patients. Medicine (Baltimore) 2023; 102:e35798. [PMID: 37904368 PMCID: PMC10615421 DOI: 10.1097/md.0000000000035798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/04/2023] [Indexed: 11/01/2023] Open
Abstract
In this study, we investigated the molecular phenotype-cancer relationship that may favor the main metastatic tendencies of cancer by comparing the association of receptor subtypes with the presence of metastasis, serosal metastasis, and/or visceral metastases in patients diagnosed with breast cancer. In this study, we retrospectively evaluated 853 patients who were diagnosed with breast cancer and followed up at our hospital between 2017 and 2022. The probability of metastasis in the most common tumor group, the non-special type of invasive carcinoma was significantly higher than that in other tumor groups. We formed our groups according to estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 status. In addition, when we compared the receptor groups, no significant difference was found between the receptor groups (Table 1). When the entire breast cancer cohort was considered, the association of serosal metastasis was statistically significantly higher in the ER and/or PR (+) and, HER2 (-) receptor subgroup than in all other receptor groups (P < .006), and the association of visceral metastasis/visceral + serosal metastasis with the ER and/or PR (+) and, HER2 (-) receptor subgroup was significantly higher than that in all other receptor groups (P < .001) (Table 2). In this study, we aimed to investigate the possible relationship between molecular markers of the primary tumor and the preference for serosal and visceral metastases over distant metastases in a large cohort of patients to contribute to the improvement of the diagnosis and treatment of breast cancer, a heterogeneous disease group. To the best of our knowledge, our study is the first to statistically investigate the association between receptor subgroups and visceral, serosal, and serosal + visceral metastases as a group and to reach some conclusions.
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Affiliation(s)
- Pinar Özdemir Akdur
- SBU Dr. Abdurahman Yurtaslan Ankara Oncology Training and Research Hospital, Department of Radiology, Ankara, Turkey
| | - Nazan Çiledağ
- SBU Dr. Abdurahman Yurtaslan Ankara Oncology Training and Research Hospital, Department of Radiology, Ankara, Turkey
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16
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Nemati F, de Koning L, Gentien D, Assayag F, Henry E, Ait Rais K, Pierron G, Mariani O, Nijnikoff M, Champenois G, Nicolas A, Meseure D, Gardrat S, Servant N, Hupé P, Kamal M, Le Tourneau C, Piperno-Neumann S, Rodrigues M, Roman-Roman S, Decaudin D, Mariani P, Cassoux N. Patient Derived Xenografts (PDX) Models as an Avatar to Assess Personalized Therapy Options in Uveal Melanoma: A Feasibility Study. Curr Oncol 2023; 30:9090-9103. [PMID: 37887557 PMCID: PMC10604955 DOI: 10.3390/curroncol30100657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/13/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Uveal melanoma is the most common primary intraocular malignancy in adults. Up to 50% of UM patients develop metastatic disease, usually in the liver. When metastatic, the prognosis is poor, and few treatment options exist. Here, we investigated the feasibility of establishing patient-derived xenografts (PDXs) from a patient's tumor in order to screen for therapies that the patient could benefit from. Samples obtained from 29 primary tumors and liver metastases of uveal melanoma were grafted into SCID mice. PDX models were successfully established for 35% of primary patient tumors and 67% of liver metastases. The tumor take rate was proportional to the risk of metastases. PDXs showed the same morphology, the same GNAQ/11, BAP1, and SF3B1 mutations, and the same chromosome 3 and 8q status as the corresponding patient samples. Six PDX models were challenged with two compounds for 4 weeks. We show that, for 31% of patients with high or intermediate risk of metastasis, the timing to obtain efficacy results on PDX models derived from their primary tumors was compatible with the selection of the therapy to treat the patient after relapse. PDXs could thus be a valid tool ("avatar") to select the best personalized therapy for one third of patients that are most at risk of relapse.
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Affiliation(s)
- Fariba Nemati
- Laboratory of Preclinical Investigation, Translational Research Department, Institut Curie, PSL University Paris, 26 rue d’Ulm, CEDEX 05, 75248 Paris, France
| | - Leanne de Koning
- Translational Research Department, Institut Curie, PSL University Paris, 75248 Paris, France; (L.d.K.)
| | - David Gentien
- Genomics Platform, Translational Research Department, Institut Curie, PSL Research University, 75248 Paris, France
| | - Franck Assayag
- Laboratory of Preclinical Investigation, Translational Research Department, Institut Curie, PSL University Paris, 26 rue d’Ulm, CEDEX 05, 75248 Paris, France
| | - Emilie Henry
- Genomics Platform, Translational Research Department, Institut Curie, PSL Research University, 75248 Paris, France
| | - Khadija Ait Rais
- Department of Genetics, Institut Curie, PSL Research University, 75248 Paris, France
| | - Gaelle Pierron
- Department of Genetics, Institut Curie, PSL Research University, 75248 Paris, France
| | - Odette Mariani
- Biological Resource Center, Department of Pathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Michèle Nijnikoff
- Biological Resource Center, Department of Pathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Gabriel Champenois
- Department of Biopathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - André Nicolas
- Department of Biopathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Didier Meseure
- Department of Biopathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Sophie Gardrat
- Department of Biopathology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Nicolas Servant
- Institut Curie, INSERM U900, CBIO-Centre for Computational Biology, Mines Paris Tech, PSL-Research University, 75248 Paris, France
| | - Philippe Hupé
- Institut Curie, INSERM U900, CBIO-Centre for Computational Biology, Mines Paris Tech, PSL-Research University, 75248 Paris, France
| | - Maud Kamal
- Department of Drug Development and Innovation (D3i), Institut Curie, 75248 Paris, France
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation (D3i), Institut Curie, 75248 Paris, France
- INSERM U900 Research Unit, Institut Curie, 92064 Saint-Cloud, France
- Paris-Saclay University, 75248 Paris, France
| | - Sophie Piperno-Neumann
- Department of Medical Oncology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Manuel Rodrigues
- Department of Medical Oncology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Sergio Roman-Roman
- Translational Research Department, Institut Curie, PSL University Paris, 75248 Paris, France; (L.d.K.)
| | - Didier Decaudin
- Laboratory of Preclinical Investigation, Translational Research Department, Institut Curie, PSL University Paris, 26 rue d’Ulm, CEDEX 05, 75248 Paris, France
- Department of Medical Oncology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Pascale Mariani
- Department of Surgical Oncology, Institut Curie, PSL Research University, 75248 Paris, France
| | - Nathalie Cassoux
- Department of Oncological Ophthalmology, Institut Curie, Université Paris Cité, 75248 Paris, France
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17
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Daneshdoust D, Luo M, Li Z, Mo X, Alothman S, Kallakury B, Schlegel R, Zhang J, Guo D, Furth PA, Liu X, Li J. Unlocking Translational Potential: Conditionally Reprogrammed Cells in Advancing Breast Cancer Research. Cells 2023; 12:2388. [PMID: 37830602 PMCID: PMC10572051 DOI: 10.3390/cells12192388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/07/2023] [Accepted: 09/19/2023] [Indexed: 10/14/2023] Open
Abstract
Preclinical in vitro models play an important role in studying cancer cell biology and facilitating translational research, especially in the identification of drug targets and drug discovery studies. This is particularly relevant in breast cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (radiotherapy, chemotherapy, immunotherapy, and/or surgery) are relatively lacking. To be clinically relevant, a model must accurately replicate the biology and cellular heterogeneity of the primary tumor. Addressing these requirements and overcoming the limitations of most existing cancer cell lines, which are typically derived from a single clone, we have recently developed conditional reprogramming (CR) technology. The CR technology refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. This innovative approach fulfills many of these needs and offers an alternative that surpasses the deficiencies associated with traditional cancer cell lines. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have applied CR technology to conduct breast cancer research.
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Affiliation(s)
- Danyal Daneshdoust
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
| | - Mingjue Luo
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
| | - Zaibo Li
- Departments of Pathology, Wexner Medical Center, Ohio State University, Columbus, OH 43210, USA
| | - Xiaokui Mo
- Department of Biostatics and Bioinformatics, Wexner Medical Center, Ohio State University, Columbus, OH 43210, USA
| | - Sahar Alothman
- Departments of Oncology and Medicine, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Bhaskar Kallakury
- Departments of Pathology, Lombardi Comprehensive Cancer Center, Center for Cell Reprogramming, Georgetown University, Washington, DC 20057, USA
| | - Richard Schlegel
- Departments of Pathology, Lombardi Comprehensive Cancer Center, Center for Cell Reprogramming, Georgetown University, Washington, DC 20057, USA
| | - Junran Zhang
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
- Department of Radiation Oncology, Wexner Medical Center, Ohio State University, Columbus, OH 43210, USA
| | - Deliang Guo
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
- Department of Radiation Oncology, Wexner Medical Center, Ohio State University, Columbus, OH 43210, USA
| | - Priscilla A. Furth
- Departments of Oncology and Medicine, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Xuefeng Liu
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
- Departments of Pathology, Urology, and Radiation Oncology, Wexner Medical Center, Ohio State University, Columbus, OH 43210, USA
| | - Jenny Li
- Comprehensive Cancer Center, Ohio State University, Columbus, OH 43210, USA
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18
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Zehra T, Jaffar N, Shams M, Chundriger Q, Ahmed A, Anum F, Alsubaie N, Ahmad Z. Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods. Diagnostics (Basel) 2023; 13:3105. [PMID: 37835848 PMCID: PMC10572449 DOI: 10.3390/diagnostics13193105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 10/15/2023] Open
Abstract
Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.
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Affiliation(s)
- Talat Zehra
- Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan; (T.Z.); (N.J.)
| | - Nazish Jaffar
- Department of Pathology, Jinnah Sindh Medical University, Karachi 75001, Pakistan; (T.Z.); (N.J.)
| | - Mahin Shams
- Department of Pathology, United Medical and Dental College, Karachi 71500, Pakistan;
| | - Qurratulain Chundriger
- Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan; (Q.C.); (A.A.)
| | - Arsalan Ahmed
- Department of Pathology and Laboratory Medicine, Section of Histopathology, Aga Khan University Hospital, Karachi 3500, Pakistan; (Q.C.); (A.A.)
| | - Fariha Anum
- Research Department, Ziauddin University, Karachi 75600, Pakistan;
| | - Najah Alsubaie
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Zubair Ahmad
- Consultant Histopathologist, Sultan Qaboos Comprehensive Cancer Care and Research Centre, Seeb P.O. Box 556, Oman;
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19
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Cui Y, Yang G, Goodwin DR, O’Flanagan CH, Sinha A, Zhang C, Kitko KE, Shin TW, Park D, Aparicio S, Boyden ES. Expansion microscopy using a single anchor molecule for high-yield multiplexed imaging of proteins and RNAs. PLoS One 2023; 18:e0291506. [PMID: 37729182 PMCID: PMC10511132 DOI: 10.1371/journal.pone.0291506] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023] Open
Abstract
Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to existing ExM protocols is the equipping of different kinds of molecules, with different kinds of anchoring moieties, so they can all be pulled apart from each other by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables proteins and RNAs to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and reduces cost (by 2-10-fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this united ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructures, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues and may support the visualization of other kinds of biomolecular species as well. uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.
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Affiliation(s)
- Yi Cui
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Gaojie Yang
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Daniel R. Goodwin
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Ciara H. O’Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Anubhav Sinha
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, Massachusetts, United States of America
| | - Chi Zhang
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Kristina E. Kitko
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Tay Won Shin
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Demian Park
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Edward S. Boyden
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, MIT, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts, United States of America
- Koch Institute for Cancer Research, MIT, Cambridge, Massachusetts, United States of America
- Howard Hughes Medical Institute, MIT, Cambridge, Massachusetts, United States of America
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20
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Liu L, Wu M, Huang A, Gao C, Yang Y, Liu H, Jiang H, Yu L, Huang Y, Wang H. Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies. J Transl Med 2023; 21:611. [PMID: 37689699 PMCID: PMC10492358 DOI: 10.1186/s12967-023-04444-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/16/2023] [Indexed: 09/11/2023] Open
Abstract
BACKGROUND Recurrent or metastatic cervical cancer (r/m CC) often has poor prognosis owing to its limited treatment options. The development of novel therapeutic strategies has been hindered by the lack of preclinical models that accurately reflect the biological and genomic heterogeneity of cervical cancer (CC). Herein, we aimed to establish a large patient-derived xenograft (PDX) biobank for CC, evaluate the consistency of the biologic indicators between PDX and primary tumor tissues of patients, and explore its utility for assessing patient's response to conventional and novel therapies. METHODS Sixty-nine fresh CC tumor tissues were implanted directly into immunodeficient mice to establish PDX models. The concordance of the PDX models with their corresponding primary tumors (PTs) was compared based on the clinical pathological features, protein biomarker levels, and genomic features through hematoxylin & eosin staining, immunohistochemistry, and whole exome sequencing, respectively. Moreover, the clinical information of CC patients, RNA transcriptome and immune phenotyping of primary tumors were integrated to identify the potential parameters that could affect the success of xenograft engraftment. Subsequently, PDX model was evaluated for its capacity to mirror patient's response to chemotherapy. Finally, PDX model and PDX-derived organoid (PDXO) were utilized to evaluate the therapeutic efficacy of neratinib and adoptive cell therapy (ACT) combination strategy for CC patients with human epidermal growth factor receptor 2 (HER2) mutation. RESULTS We established a PDX biobank for CC with a success rate of 63.8% (44/69). The primary features of established PDX tumors, including clinicopathological features, the expression levels of protein biomarkers including Ki67, α-smooth muscle actin, and p16, and genomics, were highly consistent with their PTs. Furthermore, xenograft engraftment was likely influenced by the primary tumor size, the presence of follicular helper T cells and the expression of cell adhesion-related genes in primary tumor tissue. The CC derived PDX models were capable of recapitulating the patient's response to chemotherapy. In a PDX model, a novel therapeutic strategy, the combination of ACT and neratinib, was shown to effectively inhibit the growth of PDX tumors derived from CC patients with HER2-mutation. CONCLUSIONS We established by far the largest PDX biobank with a high engraftment rate for CC that preserves the histopathological and genetic characteristics of patient's biopsy samples, recapitulates patient's response to conventional therapy, and is capable of evaluating the efficacy of novel therapeutic modalities for CC.
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Affiliation(s)
- Liting Liu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Anni Huang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chun Gao
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yifan Yang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Liu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Han Jiang
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Long Yu
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yafei Huang
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Hui Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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21
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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22
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Chen A, Neuwirth I, Herndler-Brandstetter D. Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice. Cancers (Basel) 2023; 15:2989. [PMID: 37296949 PMCID: PMC10251926 DOI: 10.3390/cancers15112989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/10/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.
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Affiliation(s)
| | | | - Dietmar Herndler-Brandstetter
- Center for Cancer Research, Medical University of Vienna and Comprehensive Cancer Center, 1090 Vienna, Austria; (A.C.); (I.N.)
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23
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Cao C, Lu X, Guo X, Zhao H, Gao Y. Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings. Exp Cell Res 2023; 425:113538. [PMID: 36871856 DOI: 10.1016/j.yexcr.2023.113538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 03/06/2023]
Abstract
Breast cancer has become the highest incidence of cancer in women. It was extensively and deeply studied by biologists and medical workers worldwide. However, the meaningful results in lab researches cannot be realized in clinical, and a part of new drugs in clinical experiments do not obtain as good results as the preclinical researches. It is urgently that promote a kind of breast cancer research models that can get study results closer to the physiological condition of the human body. Patient-derived models (PDMs) originating from clinical tumor, contain primary elements of tumor and maintain key clinical features of tumor. So they are promising research models to facilitate laboratory researches translate to clinical application, and predict the treatment outcome of patients. In this review, we summarize the establishment of PDMs of breast cancer, reviewed the application of PDMs in clinical translational researches and personalized precision medicine with breast cancer as an example, to improve the understanding of PDMs among researchers and clinician, facilitate them to use PDMs on a large scale of breast cancer researches and promote the clinical translation of laboratory research and new drug development.
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Affiliation(s)
- Changqing Cao
- Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, China; State Key Laboratory of Cancer Biology, Biotechnology Center, School of Pharmacy, The Fourth Military Medical University, China
| | - Xiyan Lu
- Department of Outpatient, The Second Affiliated Hospital of Air Force Medical University, China
| | - Xinyan Guo
- State Key Laboratory of Cancer Biology, Biotechnology Center, School of Pharmacy, The Fourth Military Medical University, China
| | - Huadong Zhao
- Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, China.
| | - Yuan Gao
- State Key Laboratory of Cancer Biology, Biotechnology Center, School of Pharmacy, The Fourth Military Medical University, China.
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24
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Filis P, Kyrochristos I, Korakaki E, Baltagiannis EG, Thanos D, Roukos DH. Longitudinal ctDNA profiling in precision oncology and immunο-oncology. Drug Discov Today 2023; 28:103540. [PMID: 36822363 DOI: 10.1016/j.drudis.2023.103540] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 02/15/2023] [Indexed: 02/25/2023]
Abstract
Serial analysis of circulating tumor DNA (ctDNA) over the disease course is emerging as a prognostic, predictive and patient-monitoring biomarker. In the metastatic setting, several multigene ctDNA assays have been approved or recommended by regulatory organizations for personalized targeted therapy, especially for lung cancer. By contrast, in nonmetastatic disease, detection of ctDNA resulting from minimal residual disease (MRD) following multimodal treatment with curative intent presents major technical challenges. Several studies using tumor genotyping-informed serial ctDNA profiling have provided promising findings on the sensitivity and specificity of ctDNA in predicting the risk of recurrence. We discuss progress, limitations and future perspectives relating to the use of ctDNA as a biomarker to guide targeted therapy in metastatic disease, as well as the use of ctDNA MRD detection to guide adjuvant treatment in the nonmetastatic setting.
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Affiliation(s)
- Panagiotis Filis
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece; Department of Medical Oncology, Medical School, University of Ioannina, 45110 Ioannina, Greece
| | - Ioannis Kyrochristos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece; Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, D-80539 Munich, Germany
| | - Efterpi Korakaki
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece; Department of Physiology, Medical School, University of Ioannina, Ioannina 45110, Greece
| | - Evangelos G Baltagiannis
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece; Department of Surgery, University Hospital of Ioannina, Ioannina 45500, Greece
| | - Dimitris Thanos
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Dimitrios H Roukos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece; Department of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece.
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25
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Ring A, Nguyen-Sträuli BD, Wicki A, Aceto N. Biology, vulnerabilities and clinical applications of circulating tumour cells. Nat Rev Cancer 2023; 23:95-111. [PMID: 36494603 PMCID: PMC9734934 DOI: 10.1038/s41568-022-00536-4] [Citation(s) in RCA: 81] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/07/2022] [Indexed: 12/13/2022]
Abstract
In recent years, exceptional technological advances have enabled the identification and interrogation of rare circulating tumour cells (CTCs) from blood samples of patients, leading to new fields of research and fostering the promise for paradigm-changing, liquid biopsy-based clinical applications. Analysis of CTCs has revealed distinct biological phenotypes, including the presence of CTC clusters and the interaction between CTCs and immune or stromal cells, impacting metastasis formation and providing new insights into cancer vulnerabilities. Here we review the progress made in understanding biological features of CTCs and provide insight into exploiting these developments to design future clinical tools for improving the diagnosis and treatment of cancer.
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Affiliation(s)
- Alexander Ring
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Department of Medical Oncology and Hematology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bich Doan Nguyen-Sträuli
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Department of Gynecology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas Wicki
- Department of Medical Oncology and Hematology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicola Aceto
- Department of Biology, Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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26
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Echeverria GV, Cai S, Tu Y, Shao J, Powell E, Redwood AB, Jiang Y, McCoy A, Rinkenbaugh AL, Lau R, Trevarton AJ, Fu C, Gould R, Ravenberg EE, Huo L, Candelaria R, Santiago L, Adrada BE, Lane DL, Rauch GM, Yang WT, White JB, Chang JT, Moulder SL, Symmans WF, Hilsenbeck SG, Piwnica-Worms H. Predictors of success in establishing orthotopic patient-derived xenograft models of triple negative breast cancer. NPJ Breast Cancer 2023; 9:2. [PMID: 36627285 PMCID: PMC9831981 DOI: 10.1038/s41523-022-00502-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023] Open
Abstract
Patient-derived xenograft (PDX) models of breast cancer are an effective discovery platform and tool for preclinical pharmacologic testing and biomarker identification. We established orthotopic PDX models of triple negative breast cancer (TNBC) from the primary breast tumors of patients prior to and following neoadjuvant chemotherapy (NACT) while they were enrolled in the ARTEMIS trial (NCT02276443). Serial biopsies were obtained from patients prior to treatment (pre-NACT), from poorly responsive disease after four cycles of Adriamycin and cyclophosphamide (AC, mid-NACT), and in cases of AC-resistance, after a 3-month course of different experimental therapies and/or additional chemotherapy (post-NACT). Our study cohort includes a total of 269 fine needle aspirates (FNAs) from 217 women, generating a total of 62 PDX models (overall success-rate = 23%). Success of PDX engraftment was generally higher from those cancers that proved to be treatment-resistant, whether poorly responsive to AC as determined by ultrasound measurements mid-NACT (p = 0.063), RCB II/III status after NACT (p = 0.046), or metastatic relapse within 2 years of surgery (p = 0.008). TNBC molecular subtype determined from gene expression microarrays of pre-NACT tumors revealed no significant association with PDX engraftment rate (p = 0.877). Finally, we developed a statistical model predictive of PDX engraftment using percent Ki67 positive cells in the patient's diagnostic biopsy, positive lymph node status at diagnosis, and low volumetric reduction of the patient's tumor following AC treatment. This novel bank of 62 PDX models of TNBC provides a valuable resource for biomarker discovery and preclinical therapeutic trials aimed at improving neoadjuvant response rates for patients with TNBC.
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Affiliation(s)
- Gloria V Echeverria
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Lester and Sue Smith Breast Cancer Center and Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Shirong Cai
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Yizheng Tu
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jiansu Shao
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Emily Powell
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Abena B Redwood
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Yan Jiang
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Aaron McCoy
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Amanda L Rinkenbaugh
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rosanna Lau
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Alexander J Trevarton
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Chunxiao Fu
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rebekah Gould
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Lei Huo
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rosalind Candelaria
- Department of Breast Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Lumarie Santiago
- Department of Breast Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Deanna L Lane
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Gaiane M Rauch
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Wei T Yang
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jason B White
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jeffrey T Chang
- Department of Integrative Biology and Pharmacology, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Stacy L Moulder
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - W Fraser Symmans
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Susan G Hilsenbeck
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Helen Piwnica-Worms
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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Guo L, Kong D, Liu J, Zhan L, Luo L, Zheng W, Zheng Q, Chen C, Sun S. Breast cancer heterogeneity and its implication in personalized precision therapy. Exp Hematol Oncol 2023; 12:3. [PMID: 36624542 PMCID: PMC9830930 DOI: 10.1186/s40164-022-00363-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.
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Affiliation(s)
- Liantao Guo
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Deguang Kong
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Jianhua Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Ling Zhan
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Lan Luo
- Department of Breast Surgery, The Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, Guizhou, China
| | - Weijie Zheng
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
| | - Shengrong Sun
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
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28
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Abstract
The capacity of cells to organize complex biochemical reactions in intracellular space is a fundamental organizational principle of life. Key to this organization is the compartmentalization of the cytoplasm into distinct organelles, which is frequently achieved through intracellular membranes. Recent evidence, however, has added a new layer of flexibility to cellular compartmentalization. As such, in response to specific stimuli, liquid-liquid phase separations can lead to the rapid rearrangements of the cytoplasm to form membraneless organelles. Stress granules (SGs) are one such type of organelle that form specifically when cells are faced with stress stimuli, to aid cells in coping with stress. Inherently, altered SG formation has been linked to the pathogenesis of diseases associated with stress and inflammatory conditions, including cancer. Exciting discoveries have indicated an intimate link between SGs and tumorigenesis. Several pro-tumorigenic signaling molecules including the RAS oncogene, mTOR, and histone deacetylase 6 (HDAC6) have been shown to upregulate SG formation. Based on these studies, SGs have emerged as structures that can integrate oncogenic signaling and tumor-associated stress stimuli to enhance cancer cell fitness. In addition, growing evidence over the past decade suggests that SGs function not only to regulate the switch between survival and cell death, but also contribute to cancer cell proliferation, invasion, metastasis, and drug resistance. Although much remains to be learned about the role of SGs in tumorigenesis, these studies highlight SGs as a key regulatory hub in cancer and a promising therapeutic target.
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Affiliation(s)
- Min-Seok Song
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Elda Grabocka
- Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA.
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29
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Li S, Lee W, Heo W, Son HY, Her Y, Kim JI, Moon HG. AKR1C2 Promotes Metastasis and Regulates the Molecular Features of Luminal Androgen Receptor Subtype in Triple Negative Breast Cancer Cells. J Breast Cancer 2022; 26:60-76. [PMID: 36762781 PMCID: PMC9981988 DOI: 10.4048/jbc.2023.26.e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 01/07/2023] Open
Abstract
PURPOSE Patients with triple-negative breast cancer (TNBC) have an increased risk of distant metastasis compared to those with other subtypes. In this study, we aimed to identify the genes associated with distant metastasis in TNBC and their underlying mechanisms. METHODS We established patient-derived xenograft (PDX) models using surgically resected breast cancer tissues from 31 patients with TNBC. Among these, 15 patients subsequently developed distant metastases. Candidate metastasis-associated genes were identified using RNA sequencing. In vitro wound healing, proliferation, migration, and invasion assays and in vivo tumor xenograft and metastasis assays were performed to determine the functional importance of aldo-keto reductase family 1 member C2 (AKR1C2). Additionally, we used the METABRIC dataset to investigate the potential role of AKR1C2 in regulating TNBC subtypes and their downstream signaling activities. RESULTS RNA sequencing of primary and PDX tumors showed that genes involved in steroid hormone biosynthesis, including AKR1C2, were significantly upregulated in patients who subsequently developed metastasis. In vitro and in vivo assays showed that silencing of AKR1C2 resulted in reduced cell proliferation, migration, invasion, tumor growth, and incidence of lung metastasis. AKR1C2 was upregulated in the luminal androgen receptor (LAR) subtype of TNBC in the METABRIC dataset, and AKR1C2 silencing resulted in the downregulation of LAR classifier genes in TNBC cell lines. The androgen receptor (AR) gene was a downstream mediator of AKR1C2-associated phenotypes in TNBC cells. AKR1C2 expression was associated with gene expression pathways that regulate AR expression, including JAK-STAT signaling or interleukin 6 (IL-6). The levels of phospho-signal transducer and activator of transcription and IL-6, along with secreted IL-6, were significantly downregulated in AKR1C2-silenced TNBC cells. CONCLUSION Our data indicate that AKR1C2 is an important regulator of cancer growth and metastasis in TNBC and may be a critical determinant of LAR subtype features.
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Affiliation(s)
- Songbin Li
- Interdisciplinary Graduate Program in Cancer Biology, Seoul National University College of Medicine, Seoul, Korea
| | - Woochan Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Genomic Medicine Institute, Medical Research Center, Seoul, Korea
| | - Woohang Heo
- Center for Medical Innovation, Seoul National University Hospital, Seoul, Korea
| | - Hye-Youn Son
- Center for Medical Innovation, Seoul National University Hospital, Seoul, Korea
| | - Yujeong Her
- Interdisciplinary Graduate Program in Cancer Biology, Seoul National University College of Medicine, Seoul, Korea
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Genomic Medicine Institute, Medical Research Center, Seoul, Korea
| | - Hyeong-Gon Moon
- Cancer Research Institute, Seoul National University, Seoul, Korea.,Department of Surgery, Seoul National University Hospital, Seoul, Korea.,Department of Surgery, Seoul National University College of Medicine, Seoul, Korea.
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30
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Funnell T, O'Flanagan CH, Williams MJ, McPherson A, McKinney S, Kabeer F, Lee H, Salehi S, Vázquez-García I, Shi H, Leventhal E, Masud T, Eirew P, Yap D, Zhang AW, Lim JLP, Wang B, Brimhall J, Biele J, Ting J, Au V, Van Vliet M, Liu YF, Beatty S, Lai D, Pham J, Grewal D, Abrams D, Havasov E, Leung S, Bojilova V, Moore RA, Rusk N, Uhlitz F, Ceglia N, Weiner AC, Zaikova E, Douglas JM, Zamarin D, Weigelt B, Kim SH, Da Cruz Paula A, Reis-Filho JS, Martin SD, Li Y, Xu H, de Algara TR, Lee SR, Llanos VC, Huntsman DG, McAlpine JN, Shah SP, Aparicio S. Single-cell genomic variation induced by mutational processes in cancer. Nature 2022; 612:106-115. [PMID: 36289342 PMCID: PMC9712114 DOI: 10.1038/s41586-022-05249-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/17/2022] [Indexed: 12/15/2022]
Abstract
How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.
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Affiliation(s)
- Tyler Funnell
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ciara H O'Flanagan
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Steven McKinney
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Farhia Kabeer
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Hakwoo Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Leventhal
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tehmina Masud
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Peter Eirew
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Damian Yap
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Allen W Zhang
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Jamie L P Lim
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Beixi Wang
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Justina Biele
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Jerome Ting
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Vinci Au
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Michael Van Vliet
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Yi Fei Liu
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Sean Beatty
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Daniel Lai
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jenifer Pham
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Diljot Grewal
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Douglas Abrams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eliyahu Havasov
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samantha Leung
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoria Bojilova
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard A Moore
- Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Florian Uhlitz
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adam C Weiner
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elena Zaikova
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - J Maxwell Douglas
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Dmitriy Zamarin
- GYN Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sarah H Kim
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arnaud Da Cruz Paula
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jorge S Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Spencer D Martin
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yangguang Li
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Hong Xu
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Teresa Ruiz de Algara
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - So Ra Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Viviana Cerda Llanos
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - David G Huntsman
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jessica N McAlpine
- Department of Gynecology and Obstetrics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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31
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Elst L, Van Rompuy AS, Roussel E, Spans L, Vanden Bempt I, Necchi A, Ross J, Jacob JM, Baietti MF, Leucci E, Albersen M. Establishment and Characterization of Advanced Penile Cancer Patient-derived Tumor Xenografts: Paving the Way for Personalized Treatments. Eur Urol Focus 2022; 8:1787-1794. [PMID: 35537937 DOI: 10.1016/j.euf.2022.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Systemic treatments for penile squamous cell carcinoma (pSCC) are toxic and inefficient. Patient-based preclinical models are essential to study novel treatments. OBJECTIVE To establish a library of patient-derived tumor xenograft (PDX) models of human papillomavirus-positive (HPV+) and -negative (HPV-) pSCC and characterize these at the genomic and histological levels. DESIGN, SETTING, AND PARTICIPANTS Eighteen tumor samples from 14 patients with recurrent or metastatic pSCC were implanted in nude mice. A biobank of PDX tumors was established after passaging of patient samples (F0) for three generations (F1, F2, F3) and was characterized using histopathology and targeted next-generation sequencing (tNGS). Single-nucleotide polymorphism fingerprinting was used to confirm PDX genealogy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The engraftment rate, overall growth rate, and pSCC histomorphology were checked for each PDX generation. Staining for p40 (a pSCC marker) and p16 (a surrogate for HPV infection) was performed for F0 samples. The mutational profile according to a validated panel of 96 cancer genes was determined for F0 and F3 samples and compared to a larger tNGS database. RESULTS AND LIMITATIONS Including a previously established pilot model, 11 out of 18 tumor samples (61%) successfully engrafted in F1. The mean time from implantation in F1 to completion of F3 was 36 wk (standard deviation 18). Histological fidelity was demonstrated across generations. The patient mutational profiles were preserved in F3 and were representative of 277 pSCC samples in the Foundation Medicine database. The rapid progression of pSCC in patients from our selected high-risk cohort impeded the use of PDXs as avatars. CONCLUSIONS We successfully established the first library of 11 PDX models of HPV- and HPV+ pSCC. Our PDX models showed high engraftment rates and histological and genomic fidelity to the tumor tissue of origin. These models may help in paving the way towards the development of novel treatments. PATIENT SUMMARY We established 11 animal models based on tumor tissue from patients with penile cancer. These models could play a vital role in selection of novel treatments according to genetic mutations. In the future, therapies with confirmed preclinical effects may have a profound impact on the development of personalized treatments in penile cancer.
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Affiliation(s)
- Laura Elst
- Laboratory of Experimental Urology, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | | | - Eduard Roussel
- Laboratory of Experimental Urology, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Lien Spans
- Department of Human Genetics, University Hospitals Leuven, Leuven, Belgium
| | | | - Andrea Necchi
- San Raffaele Hospital and Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy; Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Jeffrey Ross
- Foundation Medicine, Cambridge, MA, USA; Upstate Medical University, Syracuse, NY, USA
| | | | - Maria-Francesca Baietti
- Trace, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium; Laboratory of RNA Cancer Biology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Eleonora Leucci
- Trace, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium; Laboratory of RNA Cancer Biology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium
| | - Maarten Albersen
- Laboratory of Experimental Urology, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Urology, University Hospitals Leuven, Leuven, Belgium.
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32
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Song SL, Li B, Carvalho MR, Wang HJ, Mao DL, Wei JT, Chen W, Weng ZH, Chen YC, Deng CX, Reis RL, Oliveira JM, He YL, Yan LP, Zhang CH. Complex in vitro 3D models of digestive system tumors to advance precision medicine and drug testing: Progress, challenges, and trends. Pharmacol Ther 2022; 239:108276. [DOI: 10.1016/j.pharmthera.2022.108276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022]
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33
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van den Bosch T, Derks S, Miedema DM. Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity. Cancers (Basel) 2022; 14:4986. [PMID: 36291770 PMCID: PMC9600040 DOI: 10.3390/cancers14204986] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022] Open
Abstract
Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.
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Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
| | - Sarah Derks
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
- Department of Medical Oncology, Amsterdam University Medical Centers—Location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
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Predictive validity in drug discovery: what it is, why it matters and how to improve it. Nat Rev Drug Discov 2022; 21:915-931. [PMID: 36195754 DOI: 10.1038/s41573-022-00552-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/11/2022] [Indexed: 11/08/2022]
Abstract
Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.
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35
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Zanella ER, Grassi E, Trusolino L. Towards precision oncology with patient-derived xenografts. Nat Rev Clin Oncol 2022; 19:719-732. [PMID: 36151307 DOI: 10.1038/s41571-022-00682-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 11/09/2022]
Abstract
Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.
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Affiliation(s)
| | - Elena Grassi
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, Italy.,Department of Oncology, University of Torino, Candiolo, Italy
| | - Livio Trusolino
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, Italy. .,Department of Oncology, University of Torino, Candiolo, Italy.
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36
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He F, Zhou X, Huang G, Jiang Q, Wan L, Qiu J. Establishment and Identification of Patient-Derived Xenograft Model for Oral Squamous Cell Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:3135470. [PMID: 36213829 PMCID: PMC9536988 DOI: 10.1155/2022/3135470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022]
Abstract
Oral squamous cell carcinoma is the most common head and neck malignancy with high morbidity and mortality. Currently, platinum-based chemotherapy is the conventional chemotherapy regimen for patients with oral squamous cell carcinoma. However, due to the heterogeneity of tumors and individual differences of patients, chemotherapy regimens lacking individualized evaluation of tumor patients are often less effective. Therefore, personalized tumor chemotherapy is one of the effective methods for the future treatment of malignant tumors. The patient-derived xenograft model is a relatively new tumor xenograft model that relies on immunodeficient mice. This model can better maintain various histological characteristics of primary tumor grafts, such as pathological structural features, molecular diversity, and gene expression profiles. Therefore, the patient-derived xenograft model combined with drug screening technology to explore new tumor chemotherapy is the critical research direction for future tumor treatment. This study successfully established the patient-derived xenograft model of oral squamous cell carcinoma. It was verified by hematoxylin-eosin staining and immunohistochemistry that the constructed patient-derived xenograft model retained the pathological and molecular biological characteristics of primary tumors. Our patient-derived xenograft model can be used further to study the oncological characteristics of oral squamous carcinoma and can also be applied to personalize the treatment of oral squamous carcinoma patients, providing a practical resource for screening chemotherapy drugs.
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Affiliation(s)
- Fei He
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Xiongming Zhou
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Gan Huang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Qingkun Jiang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Li Wan
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Jiaxuan Qiu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
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37
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Pallikonda HA, Turajlic S. Predicting cancer evolution for patient benefit: Renal cell carcinoma paradigm. Biochim Biophys Acta Rev Cancer 2022; 1877:188759. [PMID: 35835341 DOI: 10.1016/j.bbcan.2022.188759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
Abstract
Evolutionary features of cancer have important clinical implications, but their evaluation in the clinic is currently limited. Here, we review current approaches to reconstruct tumour subclonal structure and discuss tumour sampling method and experimental design influence. We describe clear-cell renal cell carcinoma (ccRCC) as an exemplar for understanding and predicting cancer evolutionary dynamics. Finally, we discuss how understanding cancer evolution can benefit patients.
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Affiliation(s)
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom; Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom; Melanoma and Kidney Cancer Team, Institute of Cancer Research, London, United Kingdom.
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38
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Eirew P, O'Flanagan C, Ting J, Salehi S, Brimhall J, Wang B, Biele J, Algara T, Lee SR, Hoang C, Yap D, McKinney S, Bates C, Kong E, Lai D, Beatty S, Andronescu M, Zaikova E, Funnell T, Ceglia N, Chia S, Gelmon K, Mar C, Shah S, Roth A, Bouchard-Côté A, Aparicio S. Accurate determination of CRISPR-mediated gene fitness in transplantable tumours. Nat Commun 2022; 13:4534. [PMID: 35927228 PMCID: PMC9352714 DOI: 10.1038/s41467-022-31830-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/01/2022] [Indexed: 11/09/2022] Open
Abstract
Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.
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Affiliation(s)
- Peter Eirew
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Jerome Ting
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Sohrab Salehi
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- AbCellera Biologics Inc., Vancouver, BC, Canada
| | - Beixi Wang
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Justina Biele
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- AbCellera Biologics Inc., Vancouver, BC, Canada
| | - Teresa Algara
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - So Ra Lee
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Corey Hoang
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- British Columbia Institute of Technology, Vancouver, BC, Canada
| | - Damian Yap
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Steven McKinney
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Cherie Bates
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Esther Kong
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Daniel Lai
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Sean Beatty
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Elena Zaikova
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Tyler Funnell
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen Chia
- Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Karen Gelmon
- Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Colin Mar
- Department of Diagnostic Radiology, BC Cancer, Vancouver, BC, Canada
| | - Sohrab Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
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39
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Li Z, Seehawer M, Polyak K. Untangling the web of intratumour heterogeneity. Nat Cell Biol 2022; 24:1192-1201. [PMID: 35941364 DOI: 10.1038/s41556-022-00969-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.
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Affiliation(s)
- Zheqi Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marco Seehawer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
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40
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The cell-line-derived subcutaneous tumor model in preclinical cancer research. Nat Protoc 2022; 17:2108-2128. [PMID: 35859135 DOI: 10.1038/s41596-022-00709-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 03/31/2022] [Indexed: 01/09/2023]
Abstract
Tumor-bearing experimental animals are essential for preclinical cancer drug development. A broad range of tumor models is available, with the simplest and most widely used involving a tumor of mouse or human origin growing beneath the skin of a mouse: the subcutaneous tumor model. Here, we outline the different types of in vivo tumor model, including some of their advantages and disadvantages and how they fit into the drug-development process. We then describe in more detail the subcutaneous tumor model and key steps needed to establish it in the laboratory, namely: choosing the mouse strain and tumor cells; cell culture, preparation and injection of tumor cells; determining tumor volume; mouse welfare; and an appropriate experimental end point. The protocol leads to subcutaneous tumor growth usually within 1-3 weeks of cell injection and is suitable for those with experience in tissue culture and mouse experimentation.
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41
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Guo Q, Spasic M, Maynard AG, Goreczny GJ, Bizuayehu A, Olive JF, van Galen P, McAllister SS. Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research. Nat Commun 2022; 13:3837. [PMID: 35788590 PMCID: PMC9252988 DOI: 10.1038/s41467-022-31536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 06/21/2022] [Indexed: 11/27/2022] Open
Abstract
Single-cell analysis methods are valuable tools; however, current approaches do not easily enable live cell retrieval. That is a particular issue when further study of cells that were eliminated during experimentation could provide critical information. We report a clonal molecular barcoding method, called SunCatcher, that enables longitudinal tracking and live cell functional analysis. From complex cell populations, we generate single cell-derived clonal populations, infect each with a unique molecular barcode, and retain stocks of individual barcoded clones (BCs). We develop quantitative PCR-based and next-generation sequencing methods that we employ to identify and quantify BCs in vitro and in vivo. We apply SunCatcher to various breast cancer cell lines and combine respective BCs to create versions of the original cell lines. While the heterogeneous BC pools reproduce their original parental cell line proliferation and tumor progression rates, individual BCs are phenotypically and functionally diverse. Early spontaneous metastases can also be identified and quantified. SunCatcher thus provides a rapid and sensitive approach for studying live single-cell clones and clonal evolution, and performing functional analyses.
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Affiliation(s)
- Qiuchen Guo
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Milos Spasic
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Adam G Maynard
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Gregory J Goreczny
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Amanuel Bizuayehu
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Jessica F Olive
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter van Galen
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Sandra S McAllister
- Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
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42
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Beyond Genetics: Metastasis as an Adaptive Response in Breast Cancer. Int J Mol Sci 2022; 23:ijms23116271. [PMID: 35682953 PMCID: PMC9181003 DOI: 10.3390/ijms23116271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/26/2022] [Accepted: 06/01/2022] [Indexed: 01/27/2023] Open
Abstract
Metastatic disease represents the primary cause of breast cancer (BC) mortality, yet it is still one of the most enigmatic processes in the biology of this tumor. Metastatic progression includes distinct phases: invasion, intravasation, hematogenous dissemination, extravasation and seeding at distant sites, micro-metastasis formation and metastatic outgrowth. Whole-genome sequencing analyses of primary BC and metastases revealed that BC metastatization is a non-genetically selected trait, rather the result of transcriptional and metabolic adaptation to the unfavorable microenvironmental conditions which cancer cells are exposed to (e.g., hypoxia, low nutrients, endoplasmic reticulum stress and chemotherapy administration). In this regard, the latest multi-omics analyses unveiled intra-tumor phenotypic heterogeneity, which determines the polyclonal nature of breast tumors and constitutes a challenge for clinicians, correlating with patient poor prognosis. The present work reviews BC classification and epidemiology, focusing on the impact of metastatic disease on patient prognosis and survival, while describing general principles and current in vitro/in vivo models of the BC metastatic cascade. The authors address here both genetic and phenotypic intrinsic heterogeneity of breast tumors, reporting the latest studies that support the role of the latter in metastatic spreading. Finally, the review illustrates the mechanisms underlying adaptive stress responses during BC metastatic progression.
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43
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Souto EP, Dobrolecki LE, Villanueva H, Sikora AG, Lewis MT. In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts. J Mammary Gland Biol Neoplasia 2022; 27:211-230. [PMID: 35697909 PMCID: PMC9433358 DOI: 10.1007/s10911-022-09520-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/19/2022] [Indexed: 11/24/2022] Open
Abstract
Historically, human breast cancer has been modeled largely in vitro using long-established cell lines primarily in two-dimensional culture, but also in three-dimensional cultures of varying cellular and molecular complexities. A subset of cell line models has also been used in vivo as cell line-derived xenografts (CDX). While outstanding for conducting detailed molecular analysis of regulatory mechanisms that may function in vivo, results of drug response studies using long-established cell lines have largely failed to translate clinically. In an attempt to address this shortcoming, many laboratories have succeeded in developing clinically annotated patient-derived xenograft (PDX) models of human cancers, including breast, in a variety of host systems. While immunocompromised mice are the predominant host, the immunocompromised rat and pig, zebrafish, as well as the chicken egg chorioallantoic membrane (CAM) have also emerged as potential host platforms to help address perceived shortcomings of immunocompromised mice. With any modeling platform, the two main issues to be resolved are criteria for "credentialing" the models as valid models to represent human cancer, and utility with respect to the ability to generate clinically relevant translational research data. Such data are beginning to emerge, particularly with the activities of PDX consortia such as the NCI PDXNet Program, EuroPDX, and the International Breast Cancer Consortium, as well as a host of pharmaceutical companies and contract research organizations (CRO). This review focuses primarily on these important aspects of PDX-related research, with a focus on breast cancer.
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Affiliation(s)
- Eric P Souto
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Lacey E Dobrolecki
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Hugo Villanueva
- Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Andrew G Sikora
- Department of Head and Neck Surgery, Division of Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Michael T Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, TX, 77030, USA.
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA.
- Baylor College of Medicine, One Baylor Plaza, BCM-600; Room N1210, Houston, TX, 77030, USA.
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44
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SIAH1 promotes senescence and apoptosis of nucleus pulposus cells to exacerbate disc degeneration through ubiquitinating XIAP. Tissue Cell 2022; 76:101820. [PMID: 35580525 DOI: 10.1016/j.tice.2022.101820] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Using clinical samples and database analysis, this study aimed to investigate the signaling pathways that mediated degeneration of nucleus pulposus cells (NPCs) in patients with intervertebral disc degeneration (IDD). METHODS NPCs were extracted from enucleated intervertebral discs of IDD patients, and the senescence, apoptosis, and extracellular matrix (ECM) synthesis levels of cells were confirmed by β-galactosidase (SA-β-gal), Western blot, and measurement of superoxide dismutase (SOD), malondialdehyde (MDA) and glutathione (GSH). The microarray expression profile of GSE56081 was downloaded to screen differentially expressed mRNAs. CO-IP and ubiquitination assays were used to determine the targeted regulation of XIAP by SIAH1. Methylation of mRNA was verified by m6A RIP and actinomycin D assays. RESULTS NPCs extracted from the enucleated intervertebral discs of IDD patients exhibited marked senescence, apoptosis, elevated levels of inflammation, and decreased ECM synthesis. The expression of SIAH1 was significantly elevated in NPCs of IDD patients, and SIAH1 knockdown reversed senescence, apoptosis, elevated levels of inflammation, and decreased ECM synthesis in NPCs of IDD patients. CO-IP and ubiquitination assays indicated that SIAH1 can target and ubiquitinate XIAP. Besides, MeRIP-qPCR and actinomycin experiments showed that METTL3-mediated m6A can methylate SIAH1 mRNA. CONCLUSION In IDD patients, SIAH1 can target and ubiquitinate XIAP, thereby mediating senescence, apoptosis, increased inflammation, and decreased ECM synthesis of NPCs, while METTL3-mediated m6A can methylate SIAH1 mRNA, producing harmful effects.
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45
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Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches. J Pers Med 2022; 12:jpm12060854. [PMID: 35743639 PMCID: PMC9224778 DOI: 10.3390/jpm12060854] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 02/07/2023] Open
Abstract
Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.
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Abdolahi S, Ghazvinian Z, Muhammadnejad S, Saleh M, Asadzadeh Aghdaei H, Baghaei K. Patient-derived xenograft (PDX) models, applications and challenges in cancer research. J Transl Med 2022; 20:206. [PMID: 35538576 PMCID: PMC9088152 DOI: 10.1186/s12967-022-03405-8] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/24/2022] [Indexed: 12/12/2022] Open
Abstract
The establishing of the first cancer models created a new perspective on the identification and evaluation of new anti-cancer therapies in preclinical studies. Patient-derived xenograft models are created by tumor tissue engraftment. These models accurately represent the biology and heterogeneity of different cancers and recapitulate tumor microenvironment. These features have made it a reliable model along with the development of humanized models. Therefore, they are used in many studies, such as the development of anti-cancer drugs, co-clinical trials, personalized medicine, immunotherapy, and PDX biobanks. This review summarizes patient-derived xenograft models development procedures, drug development applications in various cancers, challenges and limitations.
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Affiliation(s)
- Shahrokh Abdolahi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeinab Ghazvinian
- Department of Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Samad Muhammadnejad
- Cell-Based Therapies Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahshid Saleh
- Department of Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Asadzadeh Aghdaei
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kaveh Baghaei
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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47
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Jia Q, Chu H, Jin Z, Long H, Zhu B. High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther 2022; 7:145. [PMID: 35504878 PMCID: PMC9065032 DOI: 10.1038/s41392-022-00990-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/08/2022] [Indexed: 12/22/2022] Open
Abstract
With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.
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Affiliation(s)
- Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China
| | - Han Chu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.,Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Zheng Jin
- Research Institute, GloriousMed Clinical Laboratory Co., Ltd, Shanghai, 201318, China
| | - Haixia Long
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, China.
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48
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Hoge ACH, Getz M, Zimmer A, Ko M, Raz L, Beroukhim R, Golub TR, Ha G, David UB. DNA-based copy number analysis confirms genomic evolution of PDX models. NPJ Precis Oncol 2022; 6:30. [PMID: 35484194 PMCID: PMC9050710 DOI: 10.1038/s41698-022-00268-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 03/22/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic evolution of patient-derived xenografts (PDXs) may lead to their gradual divergence away of their tumors of origin. We previously reported the genomic evolution of the copy number (CN) landscapes of PDXs during their engraftment and passaging1. However, whether PDX models are highly stable throughout passaging2, or can evolve CNAs rapidly1,3, remains controversial. Here, we reassess the genomic evolution of PDXs using DNA-based CN profiles. We find strong evidence for genomic evolution in the DNA-based PDX data: a median of ~10% of the genome is differentially altered between matched primary tumors (PTs) and PDXs across cohorts (range, 0% to 73% across all models). In 24% of the matched PT-PDX samples, over a quarter of the genome is differentially affected by CN alterations. Moreover, in matched analyses of PTs and their derived PDXs at multiple passages, later-passage PDXs are significantly less similar to their parental PTs than earlier-passage PDXs, indicative of genomic divergence. We conclude that PDX models indeed evolve throughout their derivation and propagation, and that the phenotypic consequences of this evolution ought to be assessed in order to determine its relevance to the proper application of these valuable cancer models.
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Affiliation(s)
- Anna C H Hoge
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Michal Getz
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Zimmer
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Minjeong Ko
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Linoy Raz
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rameen Beroukhim
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Todd R Golub
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Gavin Ha
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Uri Ben David
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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49
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Bowes A, Tarabichi M, Pillay N, Van Loo P. Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers. J Pathol 2022; 257:466-478. [PMID: 35438189 PMCID: PMC9322001 DOI: 10.1002/path.5914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amy Bowes
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK
| | - Maxime Tarabichi
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Nischalan Pillay
- Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK.,Department of Histopathology, The Royal National Orthopaedic Hospital NHS Trust, London, UK
| | - Peter Van Loo
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Department of Genetics, The University of Texas MD Anderson Cancer Centre, Houston, USA.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Centre, Houston, USA
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50
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Mehmood S, Faheem M, Ismail H, Farhat SM, Ali M, Younis S, Asghar MN. ‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’. Front Mol Biosci 2022; 9:783494. [PMID: 35495618 PMCID: PMC9048735 DOI: 10.3389/fmolb.2022.783494] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 03/14/2022] [Indexed: 12/24/2022] Open
Abstract
In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.
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Affiliation(s)
- Sabba Mehmood
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
- *Correspondence: Sabba Mehmood, ; Muhammad Nadeem Asghar,
| | - Muhammad Faheem
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Hammad Ismail
- Department of Biochemistry & Biotechnology University of Gujrat, Gujrat, Pakistan
| | - Syeda Mehpara Farhat
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Mahwish Ali
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Sidra Younis
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Muhammad Nadeem Asghar
- Department of Medical Biology, University of Québec at Trois-Rivieres, Trois-Rivieres, QC, Canada
- *Correspondence: Sabba Mehmood, ; Muhammad Nadeem Asghar,
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