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Schlenker R, Schwalie PC, Dettling S, Huesser T, Irmisch A, Mariani M, Martínez Gómez JM, Ribeiro A, Limani F, Herter S, Yángüez E, Hoves S, Somandin J, Siebourg-Polster J, Kam-Thong T, de Matos IG, Umana P, Dummer R, Levesque MP, Bacac M. Myeloid-T cell interplay and cell state transitions associated with checkpoint inhibitor response in melanoma. Med 2024:S2666-6340(24)00127-2. [PMID: 38593812 DOI: 10.1016/j.medj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/23/2023] [Accepted: 03/17/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND The treatment of melanoma, the deadliest form of skin cancer, has greatly benefited from immunotherapy. However, many patients do not show a durable response, which is only partially explained by known resistance mechanisms. METHODS We performed single-cell RNA sequencing of tumor immune infiltrates and matched peripheral blood mononuclear cells of 22 checkpoint inhibitor (CPI)-naive stage III-IV metastatic melanoma patients. After sample collection, the same patients received CPI treatment, and their response was assessed. FINDINGS CPI responders showed high levels of classical monocytes in peripheral blood, which preferentially transitioned toward CXCL9-expressing macrophages in tumors. Trajectories of tumor-infiltrating CD8+ T cells diverged at the level of effector memory/stem-like T cells, with non-responder cells progressing into a state characterized by cellular stress and apoptosis-related gene expression. Consistently, predicted non-responder-enriched myeloid-T/natural killer cell interactions were primarily immunosuppressive, while responder-enriched interactions were supportive of T cell priming and effector function. CONCLUSIONS Our study illustrates that the tumor immune microenvironment prior to CPI treatment can be indicative of response. In perspective, modulating the myeloid and/or effector cell compartment by altering the described cell interactions and transitions could improve immunotherapy response. FUNDING This research was funded by Roche Pharma Research and Early Development.
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Affiliation(s)
- Ramona Schlenker
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany.
| | | | - Steffen Dettling
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany
| | - Tamara Huesser
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Anja Irmisch
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marisa Mariani
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Julia M Martínez Gómez
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alison Ribeiro
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Florian Limani
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Sylvia Herter
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Emilio Yángüez
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Sabine Hoves
- Roche Innovation Center Munich, Roche Pharma Research and Early Development (pRED), Penzberg, Germany
| | - Jitka Somandin
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | | | | | | | - Pablo Umana
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
| | - Reinhard Dummer
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marina Bacac
- Roche Innovation Center Zurich, pRED, Schlieren, Switzerland
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Goetze S, van Drogen A, Albinus JB, Fort KL, Gandhi T, Robbiani D, Laforte V, Reiter L, Levesque MP, Xuan Y, Wollscheid B. Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA. Clin Proteomics 2024; 21:26. [PMID: 38565978 PMCID: PMC10988896 DOI: 10.1186/s12014-024-09478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.
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Affiliation(s)
- Sandra Goetze
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland.
| | - Audrey van Drogen
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - Jonas B Albinus
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Kyle L Fort
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | | | | | | | | | - Mitchell P Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Yue Xuan
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | - Bernd Wollscheid
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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Babu E, Sen S. Explore & actuate: the future of personalized medicine in oncology through emerging technologies. Curr Opin Oncol 2024; 36:93-101. [PMID: 38441149 DOI: 10.1097/cco.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings. RECENT FINDINGS To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation. SUMMARY The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.
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Affiliation(s)
- Erald Babu
- UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai, Kalina Campus, Mumbai, Maharashtra, India
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Hofman P, Berezowska S, Kazdal D, Mograbi B, Ilié M, Stenzinger A, Hofman V. Current challenges and practical aspects of molecular pathology for non-small cell lung cancers. Virchows Arch 2024; 484:233-246. [PMID: 37801103 PMCID: PMC10948551 DOI: 10.1007/s00428-023-03651-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023]
Abstract
The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.
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Affiliation(s)
- Paul Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France.
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France.
| | - Sabina Berezowska
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Daniel Kazdal
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Baharia Mograbi
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Marius Ilié
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Albrecht Stenzinger
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Véronique Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
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Xiao Y, Hu G, Xie N, Yin L, Pan Y, Liu C, Lou S, Zhu C. Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer. J Gene Med 2024; 26:e3673. [PMID: 38404059 DOI: 10.1002/jgm.3673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/16/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. METHODS We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. RESULTS The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. CONCLUSION Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.
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Affiliation(s)
- Ying Xiao
- Department of Emergency, Nanjing Jiangning Hospital, Nanjing, Jiangsu, China
| | - Ge Hu
- Hefei Cancer Hospital, Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Ning Xie
- Department of Emergency, Nanjing Jiangning Hospital, Nanjing, Jiangsu, China
| | - Liang Yin
- Department of Breast Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China
| | - Yaqiang Pan
- Department of Breast Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China
| | - Cong Liu
- Department of Emergency, Nanjing Jiangning Hospital, Nanjing, Jiangsu, China
| | - Shihan Lou
- Department of Emergency, Nanjing Jiangning Hospital, Nanjing, Jiangsu, China
| | - Cunzhi Zhu
- Department of Emergency, Nanjing Tianyinshan Hospital & The First Affiliated Hospital of China Pharmaceutical University, Nanjing, Jiangsu, China
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Zila N, Eichhoff OM, Steiner I, Mohr T, Bileck A, Cheng PF, Leitner A, Gillet L, Sajic T, Goetze S, Friedrich B, Bortel P, Strobl J, Reitermaier R, Hogan SA, Martínez Gómez JM, Staeger R, Tuchmann F, Peters S, Stary G, Kuttke M, Elbe-Bürger A, Hoeller C, Kunstfeld R, Weninger W, Wollscheid B, Dummer R, French LE, Gerner C, Aebersold R, Levesque MP, Paulitschke V. Proteomic Profiling of Advanced Melanoma Patients to Predict Therapeutic Response to Anti-PD-1 Therapy. Clin Cancer Res 2024; 30:159-175. [PMID: 37861398 DOI: 10.1158/1078-0432.ccr-23-0562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/17/2023] [Accepted: 10/18/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Despite high clinical need, there are no biomarkers that accurately predict the response of patients with metastatic melanoma to anti-PD-1 therapy. EXPERIMENTAL DESIGN In this multicenter study, we applied protein depletion and enrichment methods prior to various proteomic techniques to analyze a serum discovery cohort (n = 56) and three independent serum validation cohorts (n = 80, n = 12, n = 17). Further validation analyses by literature and survival analysis followed. RESULTS We identified several significantly regulated proteins as well as biological processes such as neutrophil degranulation, cell-substrate adhesion, and extracellular matrix organization. Analysis of the three independent serum validation cohorts confirmed the significant differences between responders (R) and nonresponders (NR) observed in the initial discovery cohort. In addition, literature-based validation highlighted 30 markers overlapping with previously published signatures. Survival analysis using the TCGA database showed that overexpression of 17 of the markers we identified correlated with lower overall survival in patients with melanoma. CONCLUSIONS Ultimately, this multilayered serum analysis led to a potential marker signature with 10 key markers significantly altered in at least two independent serum cohorts: CRP, LYVE1, SAA2, C1RL, CFHR3, LBP, LDHB, S100A8, S100A9, and SAA1, which will serve as the basis for further investigation. In addition to patient serum, we analyzed primary melanoma tumor cells from NR and found a potential marker signature with four key markers: LAMC1, PXDN, SERPINE1, and VCAN.
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Affiliation(s)
- Nina Zila
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
- Division of Biomedical Science, University of Applied Sciences FH Campus Wien, Vienna, Austria
| | - Ossia M Eichhoff
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Irene Steiner
- Center for Medical Data Science, Institute of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Thomas Mohr
- Department of Medicine I, Institute of Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Andrea Bileck
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Phil F Cheng
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Tatjana Sajic
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Sandra Goetze
- Department of Health Sciences and Technology, Institute of Translational Medicine, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich, Switzerland
| | - Betty Friedrich
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Patricia Bortel
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Johanna Strobl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - René Reitermaier
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Sabrina A Hogan
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julia M Martínez Gómez
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ramon Staeger
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Felix Tuchmann
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Sophie Peters
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Georg Stary
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Mario Kuttke
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | | | - Christoph Hoeller
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Rainer Kunstfeld
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Bernd Wollscheid
- Department of Health Sciences and Technology, Institute of Translational Medicine, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Reinhard Dummer
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lars E French
- Department of Dermatology and Allergy University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Christopher Gerner
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Verena Paulitschke
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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Roccuzzo G, Bongiovanni E, Tonella L, Pala V, Marchisio S, Ricci A, Senetta R, Bertero L, Ribero S, Berrino E, Marchiò C, Sapino A, Quaglino P, Cassoni P. Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update. Expert Rev Mol Diagn 2024; 24:49-66. [PMID: 38334382 DOI: 10.1080/14737159.2024.2314574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma. AREAS COVERED The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma. EXPERT OPINION While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.
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Affiliation(s)
- Gabriele Roccuzzo
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Eleonora Bongiovanni
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Luca Tonella
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Valentina Pala
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Sara Marchisio
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Alessia Ricci
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Rebecca Senetta
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Luca Bertero
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Simone Ribero
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Enrico Berrino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Anna Sapino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Pietro Quaglino
- Department of Medical Sciences, Section of Dermatology, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
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Menotti L, Vannini A. Oncolytic Viruses in the Era of Omics, Computational Technologies, and Modeling: Thesis, Antithesis, and Synthesis. Int J Mol Sci 2023; 24:17378. [PMID: 38139207 PMCID: PMC10743452 DOI: 10.3390/ijms242417378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Oncolytic viruses (OVs) are the frontier therapy for refractory cancers, especially in integration with immunomodulation strategies. In cancer immunovirotherapy, the many available "omics" and systems biology technologies generate at a fast pace a challenging huge amount of data, where apparently clashing information mirrors the complexity of individual clinical situations and OV used. In this review, we present and discuss how currently big data analysis, on one hand and, on the other, simulation, modeling, and computational technologies, provide invaluable support to interpret and integrate "omic" information and drive novel synthetic biology and personalized OV engineering approaches for effective immunovirotherapy. Altogether, these tools, possibly aided in the future by artificial intelligence as well, will allow for the blending of the information into OV recombinants able to achieve tumor clearance in a patient-tailored way. Various endeavors to the envisioned "synthesis" of turning OVs into personalized theranostic agents are presented.
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Affiliation(s)
- Laura Menotti
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
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10
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Franzoi MA, Bayle A, Vaz-Luis I. Changing cancer representations toward comprehensive portraits to empower patients in their care journey. Ann Oncol 2023; 34:1082-1087. [PMID: 37816461 DOI: 10.1016/j.annonc.2023.09.3117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Affiliation(s)
- M A Franzoi
- Cancer Survivorship Group, Inserm U981, Gustave Roussy, Villejuif.
| | - A Bayle
- Bureau Biostatistique et Epidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif; INSERM, Université Paris-Saclay, CESP U1018 Oncostat, labelisé Ligue contre le cancer, Villejuif, France
| | - I Vaz-Luis
- Cancer Survivorship Group, Inserm U981, Gustave Roussy, Villejuif
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11
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Tsimberidou AM, Kahle M, Vo HH, Baysal MA, Johnson A, Meric-Bernstam F. Molecular tumour boards - current and future considerations for precision oncology. Nat Rev Clin Oncol 2023; 20:843-863. [PMID: 37845306 DOI: 10.1038/s41571-023-00824-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/18/2023]
Abstract
Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.
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Affiliation(s)
- Apostolia M Tsimberidou
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Michael Kahle
- Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Henry Hiep Vo
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mehmet A Baysal
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amber Johnson
- Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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12
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Dondi A, Lischetti U, Jacob F, Singer F, Borgsmüller N, Coelho R, Heinzelmann-Schwarz V, Beisel C, Beerenwinkel N. Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer. Nat Commun 2023; 14:7780. [PMID: 38012143 PMCID: PMC10682465 DOI: 10.1038/s41467-023-43387-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023] Open
Abstract
Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.
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Affiliation(s)
- Arthur Dondi
- ETH Zurich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Ulrike Lischetti
- ETH Zurich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland.
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031, Basel, Switzerland.
| | - Francis Jacob
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031, Basel, Switzerland
| | - Franziska Singer
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
- ETH Zurich, NEXUS Personalized Health Technologies, Wagistrasse 18, 8952, Schlieren, Switzerland
| | - Nico Borgsmüller
- ETH Zurich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Ricardo Coelho
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031, Basel, Switzerland
| | - Viola Heinzelmann-Schwarz
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031, Basel, Switzerland
- University Hospital Basel, Gynecological Cancer Center, Spitalstrasse 21, 4031, Basel, Switzerland
| | - Christian Beisel
- ETH Zurich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland.
| | - Niko Beerenwinkel
- ETH Zurich, Department of Biosystems Science and Engineering, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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13
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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14
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Åkerlund E, Gudoityte G, Moussaud-Lamodière E, Lind O, Bwanika HC, Lehti K, Salehi S, Carlson J, Wallin E, Fernebro J, Östling P, Kallioniemi O, Joneborg U, Seashore-Ludlow B. The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients. NPJ Precis Oncol 2023; 7:111. [PMID: 37907613 PMCID: PMC10618545 DOI: 10.1038/s41698-023-00463-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023] Open
Abstract
Most patients with advanced ovarian cancer (OC) relapse and progress despite systemic therapy, pointing to the need for improved and tailored therapy options. Functional precision medicine can help to identify effective therapies for individual patients in a clinically relevant timeframe. Here, we present a scalable functional precision medicine platform: DET3Ct (Drug Efficacy Testing in 3D Cultures), where the response of patient cells to drugs and drug combinations are quantified with live-cell imaging. We demonstrate the delivery of individual drug sensitivity profiles in 20 samples from 16 patients with ovarian cancer in both 2D and 3D culture formats, achieving over 90% success rate in providing results six days after operation. In this cohort all patients received carboplatin. The carboplatin sensitivity scores were significantly different for patients with a progression free interval (PFI) less than or equal to 12 months and those with more than 12 months (p < 0.05). We find that the 3D culture format better retains proliferation and characteristics of the in vivo setting. Using the DET3Ct platform we evaluate 27 tailored combinations with results available 10 days after operation. Notably, carboplatin and A-1331852 (Bcl-xL inhibitor) showed an additive effect in four of eight OC samples tested, while afatinib and A-1331852 led to synergy in five of seven OC models. In conclusion, our 3D DET3Ct platform can rapidly define potential, clinically relevant data on efficacy of existing drugs in OC for precision medicine purposes, as well as provide insights on emerging drugs and drug combinations that warrant testing in clinical trials.
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Affiliation(s)
- Emma Åkerlund
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Greta Gudoityte
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | | | - Olina Lind
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | | | - Kaisa Lehti
- Department of Biomedical Laboratory Science, Norwegian University of Science and Technology NTNU, Trondheim, Norway
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Sahar Salehi
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Department of Pelvic Cancer, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
- Department of Women's and Children's Health, Division of Obstetrics and Gynecology, Karolinska Institutet, Stockholm, Sweden
| | - Joseph Carlson
- Department of Pathology and Laboratory Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Emelie Wallin
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Department of Pelvic Cancer, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Josefin Fernebro
- Department of Women's and Children's Health, Division of Obstetrics and Gynecology, Karolinska Institutet, Stockholm, Sweden
- Department of Pelvic Cancer, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Päivi Östling
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Olli Kallioniemi
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ulrika Joneborg
- Department of Pelvic Cancer, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
- Department of Women's and Children's Health, Division of Obstetrics and Gynecology, Karolinska Institutet, Stockholm, Sweden
| | - Brinton Seashore-Ludlow
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.
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15
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Massa C, Seliger B. Combination of multiple omics techniques for a personalized therapy or treatment selection. Front Immunol 2023; 14:1258013. [PMID: 37828984 PMCID: PMC10565668 DOI: 10.3389/fimmu.2023.1258013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/05/2023] [Indexed: 10/14/2023] Open
Abstract
Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.
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Affiliation(s)
- Chiara Massa
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Barbara Seliger
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
- Institute of Medical Immunology, Martin Luther University Halle-Wittenberg, Halle, Germany
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
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16
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Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform 2023; 24:bbad313. [PMID: 37651607 PMCID: PMC10516349 DOI: 10.1093/bib/bbad313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 09/02/2023] Open
Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
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Affiliation(s)
- Tasbiraha Athaya
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Rony Chowdhury Ripan
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
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17
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Koladiya A, Davis KL. Advances in Clinical Mass Cytometry. Clin Lab Med 2023; 43:507-519. [PMID: 37481326 DOI: 10.1016/j.cll.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.
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Affiliation(s)
- Abhishek Koladiya
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Center for Cancer Cell Therapy, Stanford University, Stanford, CA, USA.
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18
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Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel) 2023; 15:4164. [PMID: 37627192 PMCID: PMC10452610 DOI: 10.3390/cancers15164164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.
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Affiliation(s)
- Zijian Zhu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
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Abstract
Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.
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Affiliation(s)
- Dario Bressan
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Giorgia Battistoni
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Gregory J. Hannon
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
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20
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van der Burgt Y, Wuhrer M. The role of clinical glyco(proteo)mics in precision medicine. Mol Cell Proteomics 2023:100565. [PMID: 37169080 DOI: 10.1016/j.mcpro.2023.100565] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.
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Affiliation(s)
- Yuri van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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21
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Badoiu SC, Greabu M, Miricescu D, Stanescu-Spinu II, Ilinca R, Balan DG, Balcangiu-Stroescu AE, Mihai DA, Vacaroiu IA, Stefani C, Jinga V. PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis. Int J Mol Sci 2023; 24:8391. [PMID: 37176098 PMCID: PMC10179314 DOI: 10.3390/ijms24098391] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/15/2023] Open
Abstract
Renal cell carcinoma (RCC) represents 85-95% of kidney cancers and is the most frequent type of renal cancer in adult patients. It accounts for 3% of all cancer cases and is in 7th place among the most frequent histological types of cancer. Clear cell renal cell carcinoma (ccRCC), accounts for 75% of RCCs and has the most kidney cancer-related deaths. One-third of the patients with ccRCC develop metastases. Renal cancer presents cellular alterations in sugars, lipids, amino acids, and nucleic acid metabolism. RCC is characterized by several metabolic dysregulations including oxygen sensing (VHL/HIF pathway), glucose transporters (GLUT 1 and GLUT 4) energy sensing, and energy nutrient sensing cascade. Metabolic reprogramming represents an important characteristic of the cancer cells to survive in nutrient and oxygen-deprived environments, to proliferate and metastasize in different body sites. The phosphoinositide 3-kinase-AKT-mammalian target of the rapamycin (PI3K/AKT/mTOR) signaling pathway is usually dysregulated in various cancer types including renal cancer. This molecular pathway is frequently correlated with tumor growth and survival. The main aim of this review is to present renal cancer types, dysregulation of PI3K/AKT/mTOR signaling pathway members, crosstalk with VHL/HIF axis, and carbohydrates, lipids, and amino acid alterations.
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Affiliation(s)
- Silviu Constantin Badoiu
- Department of Anatomy and Embryology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Maria Greabu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Daniela Miricescu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Iulia-Ioana Stanescu-Spinu
- Department of Biochemistry, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, Sector 5, 050474 Bucharest, Romania;
| | - Radu Ilinca
- Department of Medical Informatics and Biostatistics, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Daniela Gabriela Balan
- Department of Physiology, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania; (D.G.B.); (A.-E.B.-S.)
| | - Andra-Elena Balcangiu-Stroescu
- Department of Physiology, Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania; (D.G.B.); (A.-E.B.-S.)
| | - Doina-Andrada Mihai
- Department of Diabetes, Nutrition and Metabolic Diseases, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd, 050474 Bucharest, Romania;
| | - Ileana Adela Vacaroiu
- Department of Nephrology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania;
| | - Constantin Stefani
- Department of Family Medicine and Clinical Base, Dr. Carol Davila Central Military Emergency University Hospital, 134 Calea Plevnei, 010825 Bucharest, Romania;
| | - Viorel Jinga
- Department of Urology, “Prof. Dr. Theodor Burghele” Hospital, 050653 Bucharest, Romania
- “Prof. Dr. Theodor Burghele” Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania
- Medical Sciences Section, Academy of Romanian Scientists, 050085 Bucharest, Romania
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22
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Wang Z, Li Y, Zhao W, Jiang S, Huang Y, Hou J, Zhang X, Zhai Z, Yang C, Wang J, Zhu J, Pan J, Jiang W, Li Z, Ye M, Tan M, Jiang H, Dang Y. Integrative multi-omics and drug-response characterization of patient-derived prostate cancer primary cells. Signal Transduct Target Ther 2023; 8:175. [PMID: 37121942 PMCID: PMC10149505 DOI: 10.1038/s41392-023-01393-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 05/02/2023] Open
Abstract
Prostate cancer (PCa) is the second most prevalent malignancy in males across the world. A greater knowledge of the relationship between protein abundance and drug responses would benefit precision treatment for PCa. Herein, we establish 35 Chinese PCa primary cell models to capture specific characteristics among PCa patients, including gene mutations, mRNA/protein/surface protein distributions, and pharmaceutical responses. The multi-omics analyses identify Anterior Gradient 2 (AGR2) as a pre-operative prognostic biomarker in PCa. Through the drug library screening, we describe crizotinib as a selective compound for malignant PCa primary cells. We further perform the pharmacoproteome analysis and identify 14,372 significant protein-drug correlations. Surprisingly, the diminished AGR2 enhances the inhibition activity of crizotinib via ALK/c-MET-AKT axis activation which is validated by PC3 and xenograft model. Our integrated multi-omics approach yields a comprehensive understanding of PCa biomarkers and pharmacological responses, allowing for more precise diagnosis and therapies.
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Affiliation(s)
- Ziruoyu Wang
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Yanan Li
- CAS Key Lab of Separation Sciences for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, China
| | - Wensi Zhao
- The Chemical Proteomics Center and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Shuai Jiang
- Department of Urology, Zhongshan Hospital, Fudan University, 200032, Shanghai, China
- Department of Urology, Zhongshan Hospital Wusong Branch, Fudan University, 200032, Shanghai, China
| | - Yuqi Huang
- The Chemical Proteomics Center and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Jun Hou
- Department of Urology, Zhongshan Hospital, Fudan University, 200032, Shanghai, China
| | - Xuelu Zhang
- Center for Novel Target and Therapeutic Intervention, Chongqing Medical University, 400016, Chongqing, China
| | - Zhaoyu Zhai
- Center for Novel Target and Therapeutic Intervention, Chongqing Medical University, 400016, Chongqing, China
| | - Chen Yang
- Department of Urology, Huashan Hospital, Fudan University, 200040, Shanghai, China
| | - Jiaqi Wang
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Jiying Zhu
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Jianbo Pan
- Center for Novel Target and Therapeutic Intervention, Chongqing Medical University, 400016, Chongqing, China
| | - Wei Jiang
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Zengxia Li
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Mingliang Ye
- CAS Key Lab of Separation Sciences for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, China.
| | - Minjia Tan
- The Chemical Proteomics Center and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.
| | - Haowen Jiang
- Department of Urology, Huashan Hospital, Fudan University, 200040, Shanghai, China.
| | - Yongjun Dang
- Key Laboratory of Metabolism and Molecular Medicine, The Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
- Center for Novel Target and Therapeutic Intervention, Chongqing Medical University, 400016, Chongqing, China.
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23
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Einhaus J, Rochwarger A, Mattern S, Gaudillière B, Schürch CM. High-multiplex tissue imaging in routine pathology-are we there yet? Virchows Arch 2023; 482:801-812. [PMID: 36757500 PMCID: PMC10156760 DOI: 10.1007/s00428-023-03509-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/22/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023]
Abstract
High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.
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Affiliation(s)
- Jakob Einhaus
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sven Mattern
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Brice Gaudillière
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
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24
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIREs Comput Mol Sci 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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25
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Chandrashekar K, Setlur AS, Sabhapathi C A, Raiker SS, Singh S, Niranjan V. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. Cancer Inform 2023; 22:11769351221147244. [PMID: 36714384 PMCID: PMC9880585 DOI: 10.1177/11769351221147244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 12/06/2022] [Indexed: 01/24/2023] Open
Abstract
Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
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Affiliation(s)
| | | | | | | | | | - Vidya Niranjan
- Vidya Niranjan, Department of
Biotechnology, R V College of Engineering, Bengaluru, Karnataka 560059, India.
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26
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Kasoju N, Remya NS, Sasi R, Sujesh S, Soman B, Kesavadas C, Muraleedharan CV, Varma PRH, Behari S. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSIT 2023; 11:11-30. [PMCID: PMC10089382 DOI: 10.1007/s40012-023-00380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/27/2023] [Indexed: 04/12/2024]
Abstract
Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.
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Affiliation(s)
- Naresh Kasoju
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - N. S. Remya
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Renjith Sasi
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - S. Sujesh
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Biju Soman
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. Kesavadas
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. V. Muraleedharan
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - P. R. Harikrishna Varma
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Sanjay Behari
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
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27
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Witte HM, Fähnrich A, Künstner A, Riedl J, Fliedner SMJ, Reimer N, Hertel N, von Bubnoff N, Bernard V, Merz H, Busch H, Feller A, Gebauer N. Primary refractory plasmablastic lymphoma: A precision oncology approach. Front Oncol 2023; 13:1129405. [PMID: 36923431 PMCID: PMC10008852 DOI: 10.3389/fonc.2023.1129405] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Introduction Hematologic malignancies are currently underrepresented in multidisciplinary molecular-tumor-boards (MTB). This study assesses the potential of precision-oncology in primary-refractory plasmablastic-lymphoma (prPBL), a highly lethal blood cancer. Methods We evaluated clinicopathological and molecular-genetic data of 14 clinically annotated prPBL-patients from initial diagnosis. For this proof-of-concept study, we employed our certified institutional MTB-pipeline (University-Cancer-Center-Schleswig-Holstein, UCCSH) to annotate a comprehensive dataset within the scope of a virtual MTB-setting, ultimately recommending molecularly stratified therapies. Evidence-levels for MTB-recommendations were defined in accordance with the NCT/DKTK and ESCAT criteria. Results Median age in the cohort was 76.5 years (range 56-91), 78.6% of patients were male, 50% were HIV-positive and clinical outcome was dismal. Comprehensive genomic/transcriptomic analysis revealed potential recommendations of a molecularly stratified treatment option with evidence-levels according to NCT/DKTK of at least m2B/ESCAT of at least IIIA were detected for all 14 prPBL-cases. In addition, immunohistochemical-assessment (CD19/CD30/CD38/CD79B) revealed targeted treatment-recommendations in all 14 cases. Genetic alterations were classified by treatment-baskets proposed by Horak et al. Hereby, we identified tyrosine-kinases (TK; n=4), PI3K-MTOR-AKT-pathway (PAM; n=3), cell-cycle-alterations (CC; n=2), RAF-MEK-ERK-cascade (RME; n=2), immune-evasion (IE; n=2), B-cell-targets (BCT; n=25) and others (OTH; n=4) for targeted treatment-recommendations. The minimum requirement for consideration of a drug within the scope of the study was FDA-fast-track development. Discussion The presented proof-of-concept study demonstrates the clinical potential of precision-oncology, even in prPBL-patients. Due to the aggressive course of the disease, there is an urgent medical-need for personalized treatment approaches, and this population should be considered for MTB inclusion at the earliest time.
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Affiliation(s)
- Hanno M Witte
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,Department of Hematology and Oncology, Federal Armed Forces Hospital, Ulm, Germany
| | - Anke Fähnrich
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Axel Künstner
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Jörg Riedl
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Stephanie M J Fliedner
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Niklas Reimer
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Nadine Hertel
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Veronica Bernard
- Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Hartmut Merz
- University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Hauke Busch
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.,Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
| | - Alfred Feller
- Hämatopathologie Lübeck, Reference Centre for Lymph Node Pathology and Hematopathology, Lübeck, Germany
| | - Niklas Gebauer
- Department of Hematology and Oncology, University Hospital of Schleswig-Holstein, Lübeck, Germany.,University Cancer Center Schleswig-Holstein, University Hospital of Schleswig- Holstein, Lübeck, Germany
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28
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Heesen P, Studer G, Bode B, Windegger H, Staeheli B, Aliu P, Martin-Broto J, Gronchi A, Blay JY, Le Cesne A, Fuchs B. Quality of Sarcoma Care: Longitudinal Real-Time Assessment and Evidence Analytics of Quality Indicators. Cancers (Basel) 2022; 15:cancers15010047. [PMID: 36612043 PMCID: PMC9817921 DOI: 10.3390/cancers15010047] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Sarcomas represent a large group of rare to very rare diseases, requiring complex management with a transdisciplinary approach. Overall progress has been hampered because of discipline, institution and network fragmentation, and there is no global data harmonization or quality standards. To report on and improve quality, a common definition of quality indicators (QIs) of sarcoma care as well as the capacity to assess longitudinal real-time data is required. An international advisory board of world-renowned sarcoma experts defined six categories of QIs, totaling more than 80 quality indicators. An interoperable (web-based) digital platform was then created combining the management of the weekly sarcoma board meeting with the sarcoma registry and incorporating patient-reported outcome measures (PROMs) into the routine follow-up care to assess the entire care cycle of the patient. The QIs were then programmed into the digital platform for real-time analysis and visualization. The definition of standardized QIs covering all physician- (diagnostics and therapeutics), patient- (PROMS/PREMS), and cost-based aspects in combination with their real-time assessment over the entire sarcoma care cycle can be realized. Standardized QIs as well as their real-time assessment and data visualization are critical to improving the quality of sarcoma care. By enabling predictive modelling and introducing VBHC, precision health care for a complex disease is on the horizon.
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Affiliation(s)
- Philip Heesen
- Medical Faculty, University of Zurich, 8091 Zurich, Switzerland
| | - Gabriela Studer
- Faculty of Medicine, University of Lucerne, 6002 Lucerne, Switzerland
| | - Beata Bode
- Medical Faculty, University of Zurich, 8091 Zurich, Switzerland
| | | | | | - Paul Aliu
- Swiss Sarcoma Network, 6000 Luzern, Switzerland
| | | | - Alessandro Gronchi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, 20133 Milano, Italy
| | - Jean-Yves Blay
- Department of Medical Oncology, Centre Léon-Bérard Lyon, 69008 Lyon, France
| | - Axel Le Cesne
- Department of Medical Oncology, Gustave Roussy Cancer Campus, 94805 Paris, France
| | - Bruno Fuchs
- Medical Faculty, University of Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Lucerne, 6002 Lucerne, Switzerland
- Correspondence:
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29
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Rodríguez Ruiz N, Abd Own S, Ekström Smedby K, Eloranta S, Koch S, Wästerlid T, Krstic A, Boman M. Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach. Front Oncol 2022; 12:984021. [DOI: 10.3389/fonc.2022.984021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
BackgroundThe increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.PurposeTo design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.MethodsDesign science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.ResultsThe analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.ImpactOur work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.ConclusionAugmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
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30
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Heinemann T, Kornauth C, Severin Y, Vladimer GI, Pemovska T, Hadzijusufovic E, Agis H, Krauth MT, Sperr WR, Valent P, Jäger U, Simonitsch-Klupp I, Superti-Furga G, Staber PB, Snijder B. Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine. Blood Cancer Discov 2022; 3:502-515. [PMID: 36125297 PMCID: PMC9894727 DOI: 10.1158/2643-3230.bcd-21-0219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 06/08/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022] Open
Abstract
Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's choice-based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments. SIGNIFICANCE We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476.
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Affiliation(s)
- Tim Heinemann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | | | - Yannik Severin
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Gregory I. Vladimer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Tea Pemovska
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Emir Hadzijusufovic
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Hermine Agis
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Maria-Theresa Krauth
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang R. Sperr
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria.,Ludwig Boltzmann Institute for Hematology and Oncology, Medial University of Vienna, Austria
| | - Peter Valent
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria.,Ludwig Boltzmann Institute for Hematology and Oncology, Medial University of Vienna, Austria
| | - Ulrich Jäger
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | | | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.,Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Philipp B. Staber
- Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
| | - Berend Snijder
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,Corresponding Author: Berend Snijder, Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland. Phone: 41-44-633-71-49; E-mail:
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31
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Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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Affiliation(s)
- Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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32
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Moch H, Amin MB, Berney DM, Compérat EM, Gill AJ, Hartmann A, Menon S, Raspollini MR, Rubin MA, Srigley JR, Hoon Tan P, Tickoo SK, Tsuzuki T, Turajlic S, Cree I, Netto GJ. The 2022 World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. Eur Urol 2022; 82:458-468. [PMID: 35853783 DOI: 10.1016/j.eururo.2022.06.016] [Citation(s) in RCA: 176] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 06/21/2022] [Indexed: 02/07/2023]
Abstract
The fifth edition of the World Health Organization (WHO) classification of urogenital tumours (WHO "Blue Book"), published in 2022, contains significant revisions. This review summarises the most relevant changes for renal, penile, and testicular tumours. In keeping with other volumes in the fifth edition series, the WHO classification of urogenital tumours follows a hierarchical classification and lists tumours by site, category, family, and type. The section "essential and desirable diagnostic criteria" included in the WHO fifth edition represents morphologic diagnostic criteria, combined with immunohistochemistry and relevant molecular tests. The global introduction of massive parallel sequencing will result in a diagnostic shift from morphology to molecular analyses. Therefore, a molecular-driven renal tumour classification has been introduced, taking recent discoveries in renal tumour genomics into account. Such novel molecularly defined epithelial renal tumours include SMARCB1-deficient medullary renal cell carcinoma (RCC), TFEB-altered RCC, Alk-rearranged RCC, and ELOC-mutated RCC. Eosinophilic solid and cystic RCC is a novel morphologically defined RCC entity. The diverse morphologic patterns of penile squamous cell carcinomas are grouped as human papillomavirus (HPV) associated and HPV independent, and there is an attempt to simplify the morphologic classification. A new chapter with tumours of the scrotum has been introduced. The main nomenclature of testicular tumours is retained, including the use of the term "germ cell neoplasia in situ" (GCNIS) for the preneoplastic lesion of most germ cell tumours and division from those not derived from GCNIS. Nomenclature changes include replacement of the term "primitive neuroectodermal tumour" by "embryonic neuroectodermal tumour" to separate these tumours clearly from Ewing sarcoma. The term "carcinoid" has been changed to "neuroendocrine tumour", with most examples in the testis now classified as "prepubertal type testicular neuroendocrine tumour".
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Affiliation(s)
- Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital Zuerich and University of Zuerich, Zuerich, Switzerland.
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Urology, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Daniel M Berney
- Barts Cancer Institute, Queen Mary University of London, London, UK; Department of Cellular Pathology, Barts Health NHS Trust, London, UK
| | - Eva M Compérat
- Department of Pathology, Medical University of Vienna, General Hospital of Vienna, Vienna, Austria
| | - Anthony J Gill
- Sydney Medical School, University of Sydney, Sydney, Australia; NSW Health Pathology, Department of Anatomical Pathology and Pathology Group Kolling Institute of Medical Research Royal North Shore Hospital St Leonards, Sydney, Australia
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Santosh Menon
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Maria R Raspollini
- Histopathology and Molecular Diagnostics, University Hospital Careggi, Florence, Italy
| | - Mark A Rubin
- Department for BioMedical Research (DBMR), Bern Center for Precision Medicine (BCPM), University of Bern and Inselspital, Bern, Switzerland
| | - John R Srigley
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Puay Hoon Tan
- Division of Pathology, Singapore General Hospital, Singapore
| | - Satish K Tickoo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Toyonori Tsuzuki
- Department of Surgical Pathology, Aichi Medical University Hospital, Nagakut, Japan
| | - Samra Turajlic
- The Francis Crick Institute and The Royal Marsden NHS Foundation Trust, London, UK
| | - Ian Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - George J Netto
- Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
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33
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Akhoundova D, Rubin MA. Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future. Cancer Cell 2022; 40:920-938. [PMID: 36055231 DOI: 10.1016/j.ccell.2022.08.011] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/22/2022] [Accepted: 08/11/2022] [Indexed: 12/17/2022]
Abstract
Next-generation DNA sequencing technology has dramatically advanced clinical oncology through the identification of therapeutic targets and molecular biomarkers, leading to the personalization of cancer treatment with significantly improved outcomes for many common and rare tumor entities. More recent developments in advanced tumor profiling now enable dissection of tumor molecular architecture and the functional phenotype at cellular and subcellular resolution. Clinical translation of high-resolution tumor profiling and integration of multi-omics data into precision treatment, however, pose significant challenges at the level of prospective validation and clinical implementation. In this review, we summarize the latest advances in multi-omics tumor profiling, focusing on spatial genomics and chromatin organization, spatial transcriptomics and proteomics, liquid biopsy, and ex vivo modeling of drug response. We analyze the current stages of translational validation of these technologies and discuss future perspectives for their integration into precision treatment.
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Affiliation(s)
- Dilara Akhoundova
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland; Department of Medical Oncology, Inselspital, University Hospital of Bern, 3010 Bern, Switzerland
| | - Mark A Rubin
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland; Bern Center for Precision Medicine, Inselspital, University Hospital of Bern, 3008 Bern, Switzerland.
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34
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Crowell HL, Chevrier S, Jacobs A, Sivapatham S, Bodenmiller B, Robinson MD. An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data. F1000Res 2022; 9:1263. [PMID: 36072920 PMCID: PMC9411975 DOI: 10.12688/f1000research.26073.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 11/24/2022] Open
Abstract
Mass cytometry (CyTOF) has become a method of choice for in-depth characterization of tissue heterogeneity in health and disease, and is currently implemented in multiple clinical trials, where higher quality standards must be met. Currently, preprocessing of raw files is commonly performed in independent standalone tools, which makes it difficult to reproduce. Here, we present an R pipeline based on an updated version of CATALYST that covers all preprocessing steps required for downstream mass cytometry analysis in a fully reproducible way. This new version of CATALYST is based on Bioconductor’s SingleCellExperiment class and fully unit tested. The R-based pipeline includes file concatenation, bead-based normalization, single-cell deconvolution, spillover compensation and live cell gating after debris and doublet removal. Importantly, this pipeline also includes different quality checks to assess machine sensitivity and staining performance while allowing also for batch correction. This pipeline is based on open source R packages and can be easily be adapted to different study designs. It therefore has the potential to significantly facilitate the work of CyTOF users while increasing the quality and reproducibility of data generated with this technology.
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Affiliation(s)
- Helena L. Crowell
- Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, 8057, Switzerland
| | - Stéphane Chevrier
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Andrea Jacobs
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Sujana Sivapatham
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | | | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, 8057, Switzerland
| | - Mark D. Robinson
- Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, 8057, Switzerland
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35
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Penault-Llorca F, Kerr KM, Garrido P, Thunnissen E, Dequeker E, Normanno N, Patton SJ, Fairley J, Kapp J, de Ridder D, Ryška A, Moch H. Expert opinion on NSCLC small specimen biomarker testing - Part 2: Analysis, reporting, and quality assessment. Virchows Arch 2022. [PMID: 35857103 DOI: 10.1007/s00428-022-03344-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 10/31/2022]
Abstract
The diagnostic work-up for non-small cell lung cancer (NSCLC) requires biomarker testing to guide therapy choices. This article is the second of a two-part series. In Part 1, we summarised evidence-based recommendations for obtaining and processing small specimen samples (i.e. pre-analytical steps) from patients with advanced NSCLC. Here, in Part 2, we summarise evidence-based recommendations relating to analytical steps of biomarker testing (and associated reporting and quality assessment) of small specimen samples in NSCLC. As the number of biomarkers for actionable (genetic) targets and approved targeted therapies continues to increase, simultaneous testing of multiple actionable oncogenic drivers using next-generation sequencing (NGS) becomes imperative, as set forth in European Society for Medical Oncology guidelines. This is particularly relevant in advanced NSCLC, where tissue specimens are typically limited and NGS may help avoid tissue exhaustion compared with sequential biomarker testing. Despite guideline recommendations, significant discrepancies in access to NGS persist across Europe, primarily due to reimbursement constraints. The use of increasingly complex testing methods also has implications for the reporting of results. Molecular testing reports should include clinical interpretation with additional commentary on sample adequacy as appropriate. Molecular tumour boards are recommended to facilitate the interpretation of complex genetic information arising from NGS, and to collaboratively determine the optimal treatment for patients with NSCLC. Finally, whichever testing modality is employed, it is essential that adequate internal and external validation and quality control measures are implemented.
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36
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Bertolini A, Prummer M, Tuncel MA, Menzel U, Rosano-González ML, Kuipers J, Stekhoven DJ, Beerenwinkel N, Singer F. scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics. PLoS Comput Biol 2022; 18:e1010097. [PMID: 35658001 PMCID: PMC9200350 DOI: 10.1371/journal.pcbi.1010097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/15/2022] [Accepted: 04/12/2022] [Indexed: 11/24/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study. Single-cell RNA sequencing (scRNA-seq) measures the expression levels across the genes expressed in each single cell. Thus, it is well suited to inform on the cell type composition and the function of cells in different tissues and diseases. However, it is challenging to correctly process and interpret the large amounts of data generated with scRNA-seq. To this end, we have developed an analysis workflow named scAmpi (Single Cell Analysis mRNA pipeline) that starts on the raw sequencing data and performs preprocessing, quality control, and subsequent analysis steps following state-of-the-art recommendations for scRNA-seq processing. The workflow removes low quality cells, assigns a cell type label to each cell, and visualizes the expression of individual genes of interest and functional pathways on the single cells. Moreover, in disease-related analyses scAmpi can link the observed gene expression to potential drug candidates that could be suited to treat the disease.
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Affiliation(s)
- Anne Bertolini
- ETH Zurich, NEXUS Personalized Health Technologies, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Michael Prummer
- ETH Zurich, NEXUS Personalized Health Technologies, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Mustafa Anil Tuncel
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - Ulrike Menzel
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - María Lourdes Rosano-González
- ETH Zurich, NEXUS Personalized Health Technologies, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Jack Kuipers
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - Daniel Johannes Stekhoven
- ETH Zurich, NEXUS Personalized Health Technologies, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | | | - Niko Beerenwinkel
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - Franziska Singer
- ETH Zurich, NEXUS Personalized Health Technologies, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- * E-mail:
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37
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Ingelfinger F, Beltrán E, Gerdes LA, Becher B. Single-cell multiomics in neuroinflammation. Curr Opin Immunol 2022; 76:102180. [DOI: 10.1016/j.coi.2022.102180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/21/2022] [Indexed: 11/17/2022]
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38
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Roelofsen L, Kaptein P, Thommen D. Multimodal predictors for precision immunotherapy. Immunooncol Technol 2022; 14:100071. [PMID: 35755892 PMCID: PMC9216437 DOI: 10.1016/j.iotech.2022.100071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Immune checkpoint blockade (ICB) unleashes immune cells to attack tumors, thereby inducing durable clinical responses in many cancer types. The number of patients responding to ICB is modest, however, and combination treatments are likely needed to overcome the multifaceted suppressive pathways active in the tumor microenvironment (TME). The development of precision immuno-oncology (IO) strategies allowing to identify the optimal treatment of each patient upfront is therefore a pivotal question in the field of cancer immunotherapy. Although single-parameter biomarkers can enrich for response to ICB, their predictive capacity is far from perfect and their clinical utility is complicated by their continuous nature and the difficulty to determine cut-offs that reliably distinguish responding patients from those without clinical benefit. The antitumor immune response that is induced or reinvigorated by immunotherapy is a complex cascade of events requiring the interplay of multiple cell types. To move towards precision IO, it is therefore essential to understand for each individual patient at which level(s) the antitumor immune response failed and how it can be therapeutically restored. Holistic approaches to profile human tumor microenvironments and treatment-induced responses may help to identify critical rate-limiting factors of antitumor immunity. These factors need to be translated into clinically applicable multimodal predictors that allow for the selection of the best IO treatment. This review discusses strategies to (i) create such holistic views of antitumor immunity, (ii) identify measurable parameters capturing the complexity of a patient's immune status, and (iii) facilitate the incorporation of precision IO research in the clinic.
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Affiliation(s)
| | | | - D.S. Thommen
- Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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39
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Vincent F, Nueda A, Lee J, Schenone M, Prunotto M, Mercola M. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat Rev Drug Discov. [DOI: 10.1038/s41573-022-00472-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2022] [Indexed: 12/29/2022]
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40
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Janowczyk A, Baumhoer D, Dirnhofer S, Grobholz R, Kipar A, de Leval L, Merkler D, Michielin O, Moch H, Perren A, Rottenberg S, Rubbia-Brandt L, Rubin MA, Sempoux C, Tolnay M, Zlobec I, Koelzer VH; Swiss Digital Pathology Consortium (SDiPath). Towards a national strategy for digital pathology in Switzerland. Virchows Arch 2022. [PMID: 35622144 DOI: 10.1007/s00428-022-03345-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/25/2022] [Accepted: 05/18/2022] [Indexed: 11/02/2022]
Abstract
Precision medicine is entering a new era of digital diagnostics; the availability of integrated digital pathology (DP) and structured clinical datasets has the potential to become a key catalyst for biomedical research, education and business development. In Europe, national programs for sharing of this data will be crucial for the development, testing, and validation of machine learning-enabled tools supporting clinical decision-making. Here, the Swiss Digital Pathology Consortium (SDiPath) discusses the creation of a Swiss Digital Pathology Infrastructure (SDPI), which aims to develop a unified national DP network bringing together the Swiss Personalized Health Network (SPHN) with Swiss university hospitals and subsequent inclusion of cantonal and private institutions. This effort builds on existing developments for the national implementation of structured pathology reporting. Opening this national infrastructure and data to international researchers in a sequential rollout phase can enable the large-scale integration of health data and pooling of resources for research purposes and clinical trials. Therefore, the concept of a SDPI directly synergizes with the priorities of the European Commission communication on the digital transformation of healthcare on an international level, and with the aims of the Swiss State Secretariat for Economic Affairs (SECO) for advancing research and innovation in the digitalization domain. SDPI directly addresses the needs of existing national and international research programs in neoplastic and non-neoplastic diseases by providing unprecedented access to well-curated clinicopathological datasets for the development and implementation of novel integrative methods for analysis of clinical outcomes and treatment response. In conclusion, a SDPI would facilitate and strengthen inter-institutional collaboration in technology, clinical development, business and research at a national and international scale, promoting improved patient care via precision medicine.
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41
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Unsal-Beyge S, Tuncbag N. Functional stratification of cancer drugs through integrated network similarity. NPJ Syst Biol Appl 2022; 8:11. [PMID: 35440787 DOI: 10.1038/s41540-022-00219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 01/21/2022] [Indexed: 11/30/2022] Open
Abstract
Drugs not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored networks modulated by several drugs across multiple cancer cell lines by integrating their targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing a similar mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation and found literature evidence for a set of drug pairs. Overall, network-level exploration of drug-modulated pathways and their deep comparison may potentially help optimize treatment strategies and suggest new drug combinations.
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42
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Kramer CJH, Vreeswijk MPG, Thijssen B, Bosse T, Wesseling J. Beyond the snapshot: optimizing prognostication and prediction by moving from fixed to functional multidimensional cancer pathology. J Pathol 2022; 257:403-412. [PMID: 35438188 PMCID: PMC9324156 DOI: 10.1002/path.5915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 11/10/2022]
Abstract
The role of pathology in patient management has evolved over time from the retrospective review of cells, tissue, and disease (‘what happened’) to a prospective outlook (‘what will happen’). Examination of a static, two‐dimensional hematoxylin and eosin (H&E)‐stained tissue slide has traditionally been the pathologist's primary task, but novel ancillary techniques enabled by technological breakthroughs have supported pathologists in their increasing ability to predict disease status and behaviour. Nevertheless, the informational limits of 2D, fixed tissue are now being reached and technological innovation is urgently needed to ensure that our understanding of disease entities continues to support improved individualized treatment options. Here we review pioneering work currently underway in the field of cancer pathology that has the potential to capture information beyond the current basic snapshot. A selection of exciting new technologies is discussed that promise to facilitate integration of the functional and multidimensional (space and time) information needed to optimize the prognostic and predictive value of cancer pathology. Learning how to analyse, interpret, and apply the wealth of data acquired by these new approaches will challenge the knowledge and skills of the pathology community. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- C J H Kramer
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - M P G Vreeswijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - B Thijssen
- Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - T Bosse
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - J Wesseling
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.,Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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43
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Monteiro MV, Zhang YS, Gaspar VM, Mano JF. 3D-bioprinted cancer-on-a-chip: level-up organotypic in vitro models. Trends Biotechnol 2022; 40:432-447. [PMID: 34556340 PMCID: PMC8916962 DOI: 10.1016/j.tibtech.2021.08.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 12/20/2022]
Abstract
Combinatorial conjugation of organ-on-a-chip platforms with additive manufacturing technologies is rapidly emerging as a disruptive approach for upgrading cancer-on-a-chip systems towards anatomic-sized dynamic in vitro models. This valuable technological synergy has potential for giving rise to truly physiomimetic 3D models that better emulate tumor microenvironment elements, bioarchitecture, and response to multidimensional flow dynamics. Herein, we showcase the most recent advances in bioengineering 3D-bioprinted cancer-on-a-chip platforms and provide a comprehensive discussion on design guidelines and possibilities for high-throughput analysis. Such hybrid platforms represent a new generation of highly sophisticated 3D tumor models with improved biomimicry and predictability of therapeutics performance.
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Affiliation(s)
- Maria V Monteiro
- Department of Chemistry, CICECO - Aveiro Institute of Materials, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
| | - Yu Shrike Zhang
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA 02139, USA
| | - Vítor M Gaspar
- Department of Chemistry, CICECO - Aveiro Institute of Materials, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
| | - João F Mano
- Department of Chemistry, CICECO - Aveiro Institute of Materials, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
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44
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Abstract
In the past decade, substantial advances have been made in understanding the biology of tumour-associated macrophages (TAMs), and their clinical relevance is emerging. A particular aspect that is becoming increasingly clear is that the interaction of TAMs with cancer cells and stromal cells in the tumour microenvironment enables and sustains most of the hallmarks of cancer. Therefore, manipulation of TAMs could enable improved disease control in a substantial fraction of patients across a large number of cancer types. In this Review, we examine the diversity of TAMs in various cancer indications and how this heterogeneity is being revisited with the advent of single-cell technologies, and then explore the current knowledge on the functional roles of different TAM states and the prognostic and predictive value of TAM-related signatures. We also review agents targeting TAMs that are currently being or will soon be tested in clinical trials, and how manipulations of TAMs can improve existing anticancer treatments. Finally, we discuss how TAM-targeting approaches could be further integrated into routine clinical practice, considering a precision oncology approach and viewing TAMs as a dynamic population that can evolve under treatment pressure.
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Letai A, Bhola P, Welm AL. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell 2022; 40:26-35. [PMID: 34951956 PMCID: PMC8752507 DOI: 10.1016/j.ccell.2021.12.004] [Citation(s) in RCA: 91] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/26/2021] [Accepted: 12/02/2021] [Indexed: 01/12/2023]
Abstract
Functional precision medicine is a strategy whereby live tumor cells from affected individuals are directly perturbed with drugs to provide immediately translatable, personalized information to guide therapy. The heterogeneity of human cancer has led to the realization that personalized approaches are needed to improve treatment outcomes. Precision oncology has traditionally used static features of the tumor to dictate which therapies should be used. Static features can include expression of key targets or genomic analysis of mutations to identify therapeutically targetable "drivers." Although a surprisingly small proportion of individuals derive clinical benefit from the static approach, functional precision medicine can provide additional information regarding tumor vulnerabilities. We discuss emerging technologies for functional precision medicine as well as limitations and challenges in using these assays in the clinical trials that will be necessary to determine whether functional precision medicine can improve outcomes and eventually become a standard tool in clinical oncology.
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Affiliation(s)
- Anthony Letai
- Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Patrick Bhola
- Harvard Medical School, Boston, MA 02215, USA; Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Alana L Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA.
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Popescu C, Belengeanu V. Principalele abordări de profilare moleculă în oncologie: tehnologie, avantaje şi limitări. Oncolog-Hematolog ro 2022; 4:34. [DOI: 10.26416/onhe.61.4.2022.7415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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Omenn GS, Lane L, Overall CM, Paik YK, Cristea IM, Corrales FJ, Lindskog C, Weintraub S, Roehrl MHA, Liu S, Bandeira N, Srivastava S, Chen YJ, Aebersold R, Moritz RL, Deutsch EW. Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project. J Proteome Res 2021; 20:5227-5240. [PMID: 34670092 DOI: 10.1021/acs.jproteome.1c00590] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States.,Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Young-Ki Paik
- Yonsei Proteome Research Center and Yonsei University, Seoul 03722, Korea
| | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | - Susan Weintraub
- University of Texas Health, San Antonio, San Antonio, Texas 78229-3900, United States
| | - Michael H A Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | | | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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Aleksakhina SN, Imyanitov EN. Cancer Therapy Guided by Mutation Tests: Current Status and Perspectives. Int J Mol Sci 2021; 22:ijms222010931. [PMID: 34681592 PMCID: PMC8536080 DOI: 10.3390/ijms222010931] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 12/11/2022] Open
Abstract
The administration of many cancer drugs is tailored to genetic tests. Some genomic events, e.g., alterations of EGFR or BRAF oncogenes, result in the conformational change of the corresponding proteins and call for the use of mutation-specific compounds. Other genetic perturbations, e.g., HER2 amplifications, ALK translocations or MET exon 14 skipping mutations, cause overproduction of the entire protein or its kinase domain. There are multilocus assays that provide integrative characteristics of the tumor genome, such as the analysis of tumor mutation burden or deficiency of DNA repair. Treatment planning for non-small cell lung cancer requires testing for EGFR, ALK, ROS1, BRAF, MET, RET and KRAS gene alterations. Colorectal cancer patients need to undergo KRAS, NRAS, BRAF, HER2 and microsatellite instability analysis. The genomic examination of breast cancer includes testing for HER2 amplification and PIK3CA activation. Melanomas are currently subjected to BRAF and, in some instances, KIT genetic analysis. Predictive DNA assays have also been developed for thyroid cancers, cholangiocarcinomas and urinary bladder tumors. There is an increasing utilization of agnostic testing which involves the analysis of all potentially actionable genes across all tumor types. The invention of genomically tailored treatment has resulted in a spectacular improvement in disease outcomes for a significant portion of cancer patients.
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Affiliation(s)
- Svetlana N. Aleksakhina
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, 197758 Saint-Petersburg, Russia;
- Department of Medical Genetics, St.-Petersburg Pediatric Medical University, 194100 Saint-Petersburg, Russia
| | - Evgeny N. Imyanitov
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, 197758 Saint-Petersburg, Russia;
- Department of Medical Genetics, St.-Petersburg Pediatric Medical University, 194100 Saint-Petersburg, Russia
- Correspondence: ; Tel.: +7-812-439-95-28
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Ringborg U, Berns A, Celis JE, Heitor M, Tabernero J, Schüz J, Baumann M, Henrique R, Aapro M, Basu P, Beets‐Tan R, Besse B, Cardoso F, Carneiro F, van den Eede G, Eggermont A, Fröhling S, Galbraith S, Garralda E, Hanahan D, Hofmarcher T, Jönsson B, Kallioniemi O, Kásler M, Kondorosi E, Korbel J, Lacombe D, Carlos Machado J, Martin‐Moreno JM, Meunier F, Nagy P, Nuciforo P, Oberst S, Oliveiera J, Papatriantafyllou M, Ricciardi W, Roediger A, Ryll B, Schilsky R, Scocca G, Seruca R, Soares M, Steindorf K, Valentini V, Voest E, Weiderpass E, Wilking N, Wren A, Zitvogel L. The Porto European Cancer Research Summit 2021. Mol Oncol 2021; 15:2507-2543. [PMID: 34515408 PMCID: PMC8486569 DOI: 10.1002/1878-0261.13078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 01/22/2023] Open
Abstract
Key stakeholders from the cancer research continuum met in May 2021 at the European Cancer Research Summit in Porto to discuss priorities and specific action points required for the successful implementation of the European Cancer Mission and Europe's Beating Cancer Plan (EBCP). Speakers presented a unified view about the need to establish high-quality, networked infrastructures to decrease cancer incidence, increase the cure rate, improve patient's survival and quality of life, and deal with research and care inequalities across the European Union (EU). These infrastructures, featuring Comprehensive Cancer Centres (CCCs) as key components, will integrate care, prevention and research across the entire cancer continuum to support the development of personalized/precision cancer medicine in Europe. The three pillars of the recommended European infrastructures - namely translational research, clinical/prevention trials and outcomes research - were pondered at length. Speakers addressing the future needs of translational research focused on the prospects of multiomics assisted preclinical research, progress in Molecular and Digital Pathology, immunotherapy, liquid biopsy and science data. The clinical/prevention trial session presented the requirements for next-generation, multicentric trials entailing unified strategies for patient stratification, imaging, and biospecimen acquisition and storage. The third session highlighted the need for establishing outcomes research infrastructures to cover primary prevention, early detection, clinical effectiveness of innovations, health-related quality-of-life assessment, survivorship research and health economics. An important outcome of the Summit was the presentation of the Porto Declaration, which called for a collective and committed action throughout Europe to develop the cancer research infrastructures indispensable for fostering innovation and decreasing inequalities within and between member states. Moreover, the Summit guidelines will assist decision making in the context of a unique EU-wide cancer initiative that, if expertly implemented, will decrease the cancer death toll and improve the quality of life of those confronted with cancer, and this is carried out at an affordable cost.
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