1
|
Al Assaad M, Shin N, Sigouros M, Manohar J, Antysheva Z, Kotlov N, Kiriy D, Nikitina A, Kleimenov M, Tsareva A, Makarova A, Fomchenkova V, Dubinina J, Boyko A, Almog N, Wilkes D, Escalon JG, Saxena A, Elemento O, Sternberg CN, Nanus DM, Mosquera JM. Deciphering the origin and therapeutic targets of cancer of unknown primary: a case report that illustrates the power of integrative whole-exome and transcriptome sequencing analysis. Front Oncol 2024; 13:1274163. [PMID: 38318324 PMCID: PMC10838960 DOI: 10.3389/fonc.2023.1274163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/18/2023] [Indexed: 02/07/2024] Open
Abstract
Cancer of unknown primary (CUP) represents a significant diagnostic and therapeutic challenge, being the third to fourth leading cause of cancer death, despite advances in diagnostic tools. This article presents a successful approach using a novel genomic analysis in the evaluation and treatment of a CUP patient, leveraging whole-exome sequencing (WES) and RNA sequencing (RNA-seq). The patient, with a history of multiple primary tumors including urothelial cancer, exhibited a history of rapid progression on empirical chemotherapy. The application of our approach identified a molecular target, characterized the tumor expression profile and the tumor microenvironment, and analyzed the origin of the tumor, leading to a tailored treatment. This resulted in a substantial radiological response across all metastatic sites and the predicted primary site of the tumor. We argue that a comprehensive genomic and molecular profiling approach, like the BostonGene© Tumor Portrait, can provide a more definitive, personalized treatment strategy, overcoming the limitations of current predictive assays. This approach offers a potential solution to an unmet clinical need for a standardized approach in identifying the tumor origin for the effective management of CUP.
Collapse
Affiliation(s)
- Majd Al Assaad
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Nara Shin
- BostonGene Corporation, Waltham, MA, United States
| | - Michael Sigouros
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Jyothi Manohar
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | | | | | - Daria Kiriy
- BostonGene Corporation, Waltham, MA, United States
| | | | | | | | | | | | | | | | - Nava Almog
- BostonGene Corporation, Waltham, MA, United States
| | - David Wilkes
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Joanna G. Escalon
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Ashish Saxena
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Olivier Elemento
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Cora N. Sternberg
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - David M. Nanus
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Juan Miguel Mosquera
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States
| |
Collapse
|
2
|
Chevalier A, Guo T, Gurevich NQ, Xu J, Yajima M, Campbell JD. Characterization of highly active mutational signatures in tumors from a large Chinese population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.03.23297964. [PMID: 37961450 PMCID: PMC10635259 DOI: 10.1101/2023.11.03.23297964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The majority of mutational signatures have been characterized in tumors from Western countries and the degree to which mutational signatures are similar or different in Eastern populations has not been fully explored. We leveraged a large-scale clinical sequencing cohort of tumors from a Chinese population containing 25 tumor types and found that the highly active mutational signatures were similar to those previously characterized1,2. The aristolochic acid signature SBS22 was observed in four soft tissue sarcomas and the POLE-associated signature SBS10 was observed in a gallbladder carcinoma. In lung adenocarcinoma, the polycyclic aromatic hydrocarbon (PAH) signature SBS4 was significantly higher in males compared to females but not associated with smoking status. The UV-associated signature SBS7 was significantly lower in cutaneous melanomas from the Chinese population compared to a similar American cohort. Overall, these results add to our understanding of the mutational processes that contribute to tumors from the Chinese population.
Collapse
Affiliation(s)
- Aaron Chevalier
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Bioinformatics Program, Boston University, Boston, Massachusetts
| | - Tao Guo
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Department of Mathematics & Statistics, Boston University, Boston, Massachusetts
| | - Natasha Q. Gurevich
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Bioinformatics Program, Boston University, Boston, Massachusetts
| | - Jingwen Xu
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Department of Mathematics & Statistics, Boston University, Boston, Massachusetts
| | - Masanao Yajima
- Department of Mathematics & Statistics, Boston University, Boston, Massachusetts
| | - Joshua D. Campbell
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Bioinformatics Program, Boston University, Boston, Massachusetts
| |
Collapse
|
3
|
Chen T, Tang C, Zheng W, Qian Y, Chen M, Zou Q, Jin Y, Wang K, Zhou X, Gou S, Lai L. VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants. BIOINFORMATICS ADVANCES 2023; 3:vbad107. [PMID: 37701675 PMCID: PMC10493178 DOI: 10.1093/bioadv/vbad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/24/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023]
Abstract
Summary Next-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization. Availability and implementation The source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.
Collapse
Affiliation(s)
- Tao Chen
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Chengcheng Tang
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Wei Zheng
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Yanan Qian
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Min Chen
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Qingjian Zou
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Yinge Jin
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Kepin Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China
| | - Xiaoqing Zhou
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
| | - Shixue Gou
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China
- Guangzhou National Laboratory, Guangzhou 510005, China
| | - Liangxue Lai
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, South China Institute of Large Animal Models for Biomedicine, School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
- Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya 572000, China
| |
Collapse
|
4
|
Ahmad A, Imran M, Ahsan H. Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics 2023; 15:1630. [PMID: 37376078 DOI: 10.3390/pharmaceutics15061630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.
Collapse
Affiliation(s)
- Anas Ahmad
- Julia McFarlane Diabetes Research Centre (JMDRC), Department of Microbiology, Immunology and Infectious Diseases, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, Cumming School of Medicine, Foothills Medical Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Mohammad Imran
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane 4102, Australia
| | - Haseeb Ahsan
- Department of Biochemistry, Faculty of Dentistry, Jamia Millia Islamia, New Delhi 110025, India
| |
Collapse
|
5
|
Hamy AS, Abécassis J, Driouch K, Darrigues L, Vandenbogaert M, Laurent C, Zaccarini F, Sadacca B, Delomenie M, Laas E, Mariani O, Lam T, Grandal B, Laé M, Bieche I, Vacher S, Pierga JY, Brain E, Vallot C, Hotton J, Richer W, Rocha D, Tariq Z, Becette V, Meseure D, Lesage L, Vincent-Salomon A, Filmann N, Furlanetto J, Loibl S, Dumas E, Waterfall JJ, Reyal F. Evolution of synchronous female bilateral breast cancers and response to treatment. Nat Med 2023; 29:646-655. [PMID: 36879128 PMCID: PMC10033420 DOI: 10.1038/s41591-023-02216-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 01/10/2023] [Indexed: 03/08/2023]
Abstract
Synchronous bilateral breast cancer (sBBC) occurs after both breasts have been affected by the same germline genetics and environmental exposures. Little evidence exists regarding immune infiltration and response to treatment in sBBCs. Here we show that the impact of the subtype of breast cancer on levels of tumor infiltrating lymphocytes (TILs, n = 277) and on pathologic complete response (pCR) rates (n = 140) differed according to the concordant or discordant subtype of breast cancer of the contralateral tumor: luminal breast tumors with a discordant contralateral tumor had higher TIL levels and higher pCR rates than those with a concordant contralateral tumor. Tumor sequencing revealed that left and right tumors (n = 20) were independent regarding somatic mutations, copy number alterations and clonal phylogeny, whereas primary tumor and residual disease were closely related both from the somatic mutation and from the transcriptomic point of view. Our study indicates that tumor-intrinsic characteristics may have a role in the association of tumor immunity and pCR and demonstrates that the characteristics of the contralateral tumor are also associated with immune infiltration and response to treatment.
Collapse
Affiliation(s)
- Anne-Sophie Hamy
- Department of Medical Oncology, Institut Curie, Université Paris Cité, Paris, France
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
| | - Judith Abécassis
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
- INRIA, Université Paris-Saclay, CEA, Palaiseau, France
| | - Keltouma Driouch
- Pharmacogenomics Unit, Department of Genetics, PSL University, Institut Curie, Paris, France
| | - Lauren Darrigues
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
| | - Mathias Vandenbogaert
- Translational Research Department, Institut Curie Research Center, PSL University, Paris, France
- INSERM U830, Institut Curie, PSL University, Paris, France
| | - Cecile Laurent
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
| | - Francois Zaccarini
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
| | - Benjamin Sadacca
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
- INSERM U830, Institut Curie, PSL University, Paris, France
| | - Myriam Delomenie
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
| | - Enora Laas
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
| | - Odette Mariani
- Biological Resource Center, Department of Pathology, Department of Diagnostic and Theranostic Medicine, Institut Curie, PSL University, Paris, France
| | - Thanh Lam
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
- Department of Gynecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland
| | - Beatriz Grandal
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France
| | - Marick Laé
- Biological Resource Center, Department of Pathology, Department of Diagnostic and Theranostic Medicine, Institut Curie, PSL University, Paris, France
- Department of Pathology, Centre Henri Becquerel, INSERM U1245, UNIROUEN, University of Normandie, Rouen, France
| | - Ivan Bieche
- Pharmacogenomics Unit, Department of Genetics, PSL University, Institut Curie, Paris, France
- INSERM U1016, Faculty of Pharmaceutical and Biological Sciences, Université de Paris Cité, Paris, France
| | - Sophie Vacher
- Pharmacogenomics Unit, Department of Genetics, PSL University, Institut Curie, Paris, France
| | - Jean-Yves Pierga
- Department of Medical Oncology, Institut Curie, Université Paris Cité, Paris, France
| | - Etienne Brain
- Department of Medical Oncology, Institut Curie, Université Paris Cité, Paris, France
| | - Celine Vallot
- Translational Research Department, Institut Curie Research Center, PSL University, Paris, France
- CNRS UMR3244, Institut Curie, PSL University, Paris, France
| | - Judicael Hotton
- Department of Surgical Oncology, Institut Godinot, Reims, France
| | - Wilfrid Richer
- Translational Research Department, Institut Curie Research Center, PSL University, Paris, France
- Translational Immunotherapy Team, INSERM U932, Institut Curie, PSL University, Paris, France
| | - Dario Rocha
- Translational Immunotherapy Team, INSERM U932, Institut Curie, PSL University, Paris, France
| | - Zakia Tariq
- Pharmacogenomics Unit, Department of Genetics, PSL University, Institut Curie, Paris, France
| | - Veronique Becette
- Biological Resource Center, Department of Pathology, Department of Diagnostic and Theranostic Medicine, Institut Curie, PSL University, Paris, France
| | - Didier Meseure
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | - Laetitia Lesage
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | | | | | - Sibylle Loibl
- German Breast Group, Neu-Isenburg, Germany
- Centre for Haematology and Oncology/Bethanien, Frankfurt am Main, Germany
| | - Elise Dumas
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France
| | - Joshua J Waterfall
- Translational Research Department, Institut Curie Research Center, PSL University, Paris, France.
- INSERM U830, Institut Curie, PSL University, Paris, France.
| | - Fabien Reyal
- Residual Tumor & Response to Treatment Laboratory, RT2Lab, Translational Research Department, Paris, INSERM, U932 Immunity and Cancer, Institut Curie, Université de Paris, Paris, France.
- Department of Breast, Gynecological and Reconstructive Surgery, Institut Curie, Université de Paris Cité, Paris, France.
| |
Collapse
|
6
|
Li B, Zhang F, Niu Q, Liu J, Yu Y, Wang P, Zhang S, Zhang H, Wang Z. A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model. MOLECULAR THERAPY. NUCLEIC ACIDS 2022; 31:224-240. [PMID: 36700042 PMCID: PMC9843270 DOI: 10.1016/j.omtn.2022.12.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.
Collapse
Affiliation(s)
- Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Fengbin Zhang
- Department of Gastroenterology and Hepatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Qikai Niu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Siqi Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Huamin Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China,Corresponding author: Huamin Zhang, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China,Corresponding author: Zhong Wang, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| |
Collapse
|
7
|
Zheng T. DETexT: An SNV detection enhancement for low read depth by integrating mutational signatures into TextCNN. Front Genet 2022; 13:943972. [PMID: 36246660 PMCID: PMC9554618 DOI: 10.3389/fgene.2022.943972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022] Open
Abstract
Detecting SNV at very low read depths helps to reduce sequencing requirements, lowers sequencing costs, and aids in the early screening, diagnosis, and treatment of cancer. However, the accuracy of SNV detection is significantly reduced at read depths below ×34 due to the lack of a sufficient number of read pairs to help filter out false positives. Many recent studies have revealed the potential of mutational signature (MS) in detecting true SNV, understanding the mutational processes that lead to the development of human cancers, and analyzing the endogenous and exogenous causes. Here, we present DETexT, an SNV detection method better suited to low read depths, which classifies false positive variants by combining MS with deep learning algorithms to mine correlation information around bases in individual reads without relying on the support of duplicate read pairs. We have validated the effectiveness of DETexT on simulated and real datasets and conducted comparative experiments. The source code has been uploaded to https://github.com/TrinaZ/extra-lowRD for academic use only.
Collapse
Affiliation(s)
- Tian Zheng
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
- Institute of Data Science and Information Quality, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Tian Zheng,
| |
Collapse
|
8
|
Lin PC, Tsai YS, Yeh YM, Shen MR. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care. Biomolecules 2022; 12:biom12081133. [PMID: 36009026 PMCID: PMC9405970 DOI: 10.3390/biom12081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.
Collapse
Affiliation(s)
- Peng-Chan Lin
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yi-Shan Tsai
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yu-Min Yeh
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Meng-Ru Shen
- Institute of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Pharmacology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Correspondence: ; Tel.: +886-6-235-3535
| |
Collapse
|
9
|
Gorlova OY, Kimmel M, Tsavachidis S, Amos CI, Gorlov IP. Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes. Oncotarget 2022; 13:756-767. [PMID: 35634240 PMCID: PMC9132259 DOI: 10.18632/oncotarget.28231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/03/2022] [Indexed: 01/25/2023] Open
Abstract
Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.
Collapse
Affiliation(s)
- Olga Y. Gorlova
- 1Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA,Correspondence to:Olga Y. Gorlova, email:
| | - Marek Kimmel
- 2Department of Statistics, Rice University, Houston, TX 77005, USA
| | | | | | - Ivan P. Gorlov
- 1Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| |
Collapse
|
10
|
Xia N, Yang N, Shan Q, Wang Z, Liu X, Chen Y, Lu J, Huang W, Wang Z. HNRNPC regulates RhoA to induce DNA damage repair and cancer-associated fibroblast activation causing radiation resistance in pancreatic cancer. J Cell Mol Med 2022; 26:2322-2336. [PMID: 35277915 PMCID: PMC8995438 DOI: 10.1111/jcmm.17254] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/29/2022] [Accepted: 02/17/2022] [Indexed: 12/24/2022] Open
Abstract
Pancreatic cancer (PC) is one of the most lethal types of cancer due to its asymptomatic nature in the early stages and consequent late diagnosis. Its mortality rate remains high despite advances in treatment strategies, which include a combination of surgical resection and adjuvant therapy. Although these approaches may have a positive effect on prognosis, the development of chemo- and radioresistance still poses a significant challenge for successful PC treatment. Heterogeneous nuclear ribonucleoprotein C1/C2 (HNRNPC) and RhoA have been implicated in the regulation of tumour cell proliferation and chemo- and radioresistance. Our study aims to investigate the mechanism for HNRNPC regulation of PC radiation resistance via the RhoA pathway. We found that HNRNPC and RhoA mRNA and protein expression levels were significantly higher in PC tissues compared to adjacent non-tumour tissue. Furthermore, high HNRNPC expression was associated with poor patient prognosis. Using HNRNPC overexpression and siRNA interference, we demonstrated that HNRNPC overexpression promoted radiation resistance in PC cells, while HNRNPC knockdown increased radiosensitivity. However, silencing of RhoA expression was shown to attenuate radiation resistance caused by HNRNPC overexpression. Next, we identified RhoA as a downstream target of HNRNPC and showed that inhibition of the RhoA/ROCK2-YAP/TAZ pathway led to a reduction in DNA damage repair and radiation resistance. Finally, using both in vitro assays and an in vivo subcutaneous tumour xenograft model, we demonstrated that RhoA inhibition can hinder the activity of cancer-related fibroblasts and weaken PC radiation resistance. Our study describes a role for HNRNPC and the RhoA/ROCK2-YAP/TAZ signalling pathways in mediating radiation resistance and provides a potential therapeutic target for improving the treatment of PC.
Collapse
Affiliation(s)
- Ning Xia
- Department of RadiologyRuijin Hospital Luwan BranchShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Nannan Yang
- Department of RadiologyRuijin Hospital Luwan BranchShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Qungang Shan
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Ziyin Wang
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Xiaoyu Liu
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Yingjie Chen
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Jian Lu
- Department of RadiologyRuijin Hospital Luwan BranchShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Wei Huang
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zhongmin Wang
- Department of RadiologyRuijin Hospital Luwan BranchShanghai Jiao Tong University School of MedicineShanghaiChina
- Department of Interventional RadiologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| |
Collapse
|