1
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Sohrabei S, Moghaddasi H, Hosseini A, Ehsanzadeh SJ. Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study. BMC Cancer 2024; 24:852. [PMID: 39026174 PMCID: PMC11256548 DOI: 10.1186/s12885-024-12575-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients. METHOD A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline. RESULTS Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models. CONCLUSION Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.
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Affiliation(s)
- Solmaz Sohrabei
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Moghaddasi
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, Health Information Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Seyed Jafar Ehsanzadeh
- Department of English Language, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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2
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Jacobo Jacobo M, Donnella HJ, Sobti S, Kaushik S, Goga A, Bandyopadhyay S. An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer. Sci Rep 2024; 14:3694. [PMID: 38355954 PMCID: PMC10866903 DOI: 10.1038/s41598-024-53999-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024] Open
Abstract
Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.
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Affiliation(s)
- Mauricio Jacobo Jacobo
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Hayley J Donnella
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Sushil Sobti
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Swati Kaushik
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Andrei Goga
- Department of Cell & Tissue Biology, University of California San Francisco, San Francisco, CA, 94143, USA
- Department of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Sourav Bandyopadhyay
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA.
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3
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Sun Z, Chung D, Neelon B, Millar-Wilson A, Ethier SP, Xiao F, Zheng Y, Wallace K, Hardiman G. A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data. Stat Med 2023; 42:5266-5284. [PMID: 37715500 DOI: 10.1002/sim.9911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/15/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023]
Abstract
In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).
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Affiliation(s)
- Zequn Sun
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | | | - Stephen P Ethier
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Feifei Xiao
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Kristin Wallace
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Gary Hardiman
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences and Institute for Global Food Security, Queen's University Belfast, Belfast, UK
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4
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Kong J, Kim J, Kim D, Lee K, Lee J, Han SK, Kim I, Lim S, Park M, Shin S, Lee WY, Yun SH, Kim HC, Hong HK, Cho YB, Park D, Kim S. Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer. PATTERNS (NEW YORK, N.Y.) 2023; 4:100736. [PMID: 37409049 PMCID: PMC10318368 DOI: 10.1016/j.patter.2023.100736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/21/2022] [Accepted: 03/28/2023] [Indexed: 07/07/2023]
Abstract
Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.
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Affiliation(s)
- JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Jinho Kim
- Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Inhae Kim
- Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Korea
| | - Seongsu Lim
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | | | - Woo Yong Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Seong Hyeon Yun
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Hee Cheol Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Hye Kyung Hong
- Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea
| | - Yong Beom Cho
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Korea
| | | | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
- Institute of Convergence Science, Yonsei University, Seoul 120-749, Korea
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang 37673, Korea
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5
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Ha D, Kong J, Kim D, Lee K, Lee J, Park M, Ahn H, Oh Y, Kim S. Development of bioinformatics and multi-omics analyses in organoids. BMB Rep 2023; 56:43-48. [PMID: 36284440 PMCID: PMC9887100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Indexed: 01/28/2023] Open
Abstract
Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].
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Affiliation(s)
- Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Kwanghwan Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Hyunsoo Ahn
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Youngchul Oh
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea,Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Korea,Corresponding author. Tel: +82-54-279-2348; Fax: +82-54-279-2199; E-mail:
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6
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Arna AB, Patel H, Singh RS, Vizeacoumar FS, Kusalik A, Freywald A, Vizeacoumar FJ, Wu Y. Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy. Front Oncol 2023; 12:1087989. [PMID: 36761420 PMCID: PMC9905851 DOI: 10.3389/fonc.2022.1087989] [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: 11/02/2022] [Accepted: 12/28/2022] [Indexed: 01/26/2023] Open
Abstract
DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases.
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Affiliation(s)
- Ananna Bhadra Arna
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Hardikkumar Patel
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ravi Shankar Singh
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Frederick S. Vizeacoumar
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Anthony Kusalik
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Franco J. Vizeacoumar
- Division of Oncology, College of Medicine, University of Saskatchewan and Saskatchewan Cancer Agency, Saskatoon, SK, Canada,*Correspondence: Yuliang Wu, ; Franco J. Vizeacoumar,
| | - Yuliang Wu
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada,*Correspondence: Yuliang Wu, ; Franco J. Vizeacoumar,
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7
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Yingtaweesittikul H, Wu J, Mongia A, Peres R, Ko K, Nagarajan N, Suphavilai C. CREAMMIST: an integrative probabilistic database for cancer drug response prediction. Nucleic Acids Res 2022; 51:D1242-D1248. [PMID: 36259664 PMCID: PMC9825458 DOI: 10.1093/nar/gkac911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.
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Affiliation(s)
| | - Jiaxi Wu
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Aanchal Mongia
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Rafael Peres
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Karrie Ko
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Chayaporn Suphavilai
- To whom correspondence should be addressed. Tel: +65 86213683; Fax: +65 68088292;
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8
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Hiort P, Hugo J, Zeinert J, Müller N, Kashyap S, Rajapakse JC, Azuaje F, Renard BY, Baum K. DrDimont: explainable drug response prediction from differential analysis of multi-omics networks. Bioinformatics 2022; 38:ii113-ii119. [PMID: 36124784 PMCID: PMC9486584 DOI: 10.1093/bioinformatics/btac477] [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] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. RESULTS We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AVAILABILITY AND IMPLEMENTATION DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pauline Hiort
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Julian Hugo
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Justus Zeinert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Nataniel Müller
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Spoorthi Kashyap
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Bernhard Y Renard
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
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Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.
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Affiliation(s)
- Lucia Trastulla
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
| | | | - Francisca Vazquez
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Francesco Iorio
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
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10
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Bui TBV, Burgering BMT, Goga A, Rugo HS, van 't Veer LJ. Biomarkers for Cyclin-Dependent Kinase 4/6 Inhibitors in the Treatment of Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Advanced/Metastatic Breast Cancer: Translation to Clinical Practice. JCO Precis Oncol 2022; 6:e2100473. [PMID: 35666959 DOI: 10.1200/po.21.00473] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have emerged as effective treatments for patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced/metastatic breast cancer (mBC). Dedicated research efforts have been undertaken to find predictive biomarkers of response or resistance to these therapies although no molecular biomarkers for mBC have reached the clinic so far. This review aims to summarize and evaluate the performance of biomarkers in predicting progression-free survival in phase II and III clinical trials of CDK4/6 inhibitors in HR+/HER2- mBC. METHODS For this narrative review, a structured literature search of PubMed, Embase, and the Cochrane library (CENTRAL) was performed. Phase II or III clinical trials of a CDK4/6 inhibitor in patients with HR+/HER2- mBC reporting on at least one molecular biomarker analysis of progression-free survival were included. Publications and selected conference abstracts were included up until November 2021. RESULTS Twenty-two articles reporting biomarker results of 12 clinical trials were included. Retinoblastoma protein status and cyclin E1 mRNA expression were promising baseline biomarkers, whereas PIK3CA circulating tumor DNA ratio on treatment relative to baseline, change in plasma thymidine kinase activity, and circulating tumor cell count were potential dynamic biomarkers of response. A number of biomarkers were unsuccessful, despite a strong mechanistic rationale, and others are still being explored. CONCLUSION Our review of clinical trials showed that there are a number of promising biomarkers at baseline and several dynamic biomarkers that might predict response to CDK4/6 inhibitors. Validation of these findings and assessment of clinical utility are crucial to make the final translation to clinical practice. Better understanding of disease heterogeneity and further elucidation of resistance mechanisms could inform future studies of rationally selected biomarkers.
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Affiliation(s)
- Tam Binh V Bui
- Faculty of Medicine (SUMMA), Utrecht University/University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA
| | - Boudewijn M T Burgering
- Center for Molecular Medicine, Molecular Cancer Research, University Medical Center Utrecht, the Netherlands.,Oncode Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Andrei Goga
- University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA.,Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA.,Department of Medicine, Division of Hematology and Oncology, University of California, San Francisco, San Francisco, CA
| | - Hope S Rugo
- University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA.,Department of Medicine, Division of Hematology and Oncology, University of California, San Francisco, San Francisco, CA
| | - Laura J van 't Veer
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA.,University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA
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11
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Zhang X, Kschischo M. MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes. PLoS One 2021; 16:e0261183. [PMID: 34914736 PMCID: PMC8675718 DOI: 10.1371/journal.pone.0261183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/24/2021] [Indexed: 11/18/2022] Open
Abstract
Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.
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Affiliation(s)
- Xiaoxiao Zhang
- Department of Mathematics and Technology, RheinAhrCampus, University of Applied Sciences Koblenz, Remagen, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Maik Kschischo
- Department of Mathematics and Technology, RheinAhrCampus, University of Applied Sciences Koblenz, Remagen, Germany
- * E-mail:
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12
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Mourragui SMC, Loog M, Vis DJ, Moore K, Manjon AG, van de Wiel MA, Reinders MJT, Wessels LFA. Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proc Natl Acad Sci U S A 2021; 118:e2106682118. [PMID: 34873056 PMCID: PMC8670522 DOI: 10.1073/pnas.2106682118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.
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Affiliation(s)
- Soufiane M C Mourragui
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Marco Loog
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Daniel J Vis
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Kat Moore
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Anna G Manjon
- Division of Cell Biology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Mark A van de Wiel
- Epidemiology and Biostatistics, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge CB2 0SR, United Kingdom
| | - Marcel J T Reinders
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands;
- Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
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13
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Miranda SP, Baião FA, Fleck JL, Piccolo SR. Predicting drug sensitivity of cancer cells based on DNA methylation levels. PLoS One 2021; 16:e0238757. [PMID: 34506489 PMCID: PMC8432830 DOI: 10.1371/journal.pone.0238757] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 06/28/2021] [Indexed: 01/22/2023] Open
Abstract
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.
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Affiliation(s)
- Sofia P. Miranda
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda A. Baião
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Julia L. Fleck
- Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Etienne, France
| | - Stephen R. Piccolo
- Department of Biology, Brigham Young University, Provo, Utah, United States of America
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14
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Yao H, Liang Q, Qian X, Wang J, Sham PC, Li MJ. Methods and resources to access mutation-dependent effects on cancer drug treatment. Brief Bioinform 2020; 21:1886-1903. [PMID: 31750520 DOI: 10.1093/bib/bbz109] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 07/31/2019] [Accepted: 08/01/2019] [Indexed: 12/13/2022] Open
Abstract
In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.
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Affiliation(s)
- Hongcheng Yao
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Qian Liang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xinyi Qian
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Junwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, USA
| | - Pak Chung Sham
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China.,Departments of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.,Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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15
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Kong J, Lee H, Kim D, Han SK, Ha D, Shin K, Kim S. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat Commun 2020; 11:5485. [PMID: 33127883 PMCID: PMC7599252 DOI: 10.1038/s41467-020-19313-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 10/07/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.
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Affiliation(s)
- JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea
| | - Heetak Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea
| | - Donghyo Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea
| | - Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea
| | - Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea
| | - Kunyoo Shin
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea.
- Institute of Convergence Science, Yonsei University, Seoul, 120-749, Korea.
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, Korea.
- Institute of Convergence Science, Yonsei University, Seoul, 120-749, Korea.
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16
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Mourragui S, Loog M, van de Wiel MA, Reinders MJT, Wessels LFA. PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinformatics 2020; 35:i510-i519. [PMID: 31510654 PMCID: PMC6612899 DOI: 10.1093/bioinformatics/btz372] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting. Results We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors. Availability and implementation PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Soufiane Mourragui
- Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.,Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands
| | - Marco Loog
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Mark A van de Wiel
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Marcel J T Reinders
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.,Computational Biology Center, Leiden University Medical Center, Leiden ZC, The Netherlands
| | - Lodewyk F A Wessels
- Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.,Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands
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17
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Lakiotaki K, Georgakopoulos G, Castanas E, Røe OD, Borboudakis G, Tsamardinos I. A data driven approach reveals disease similarity on a molecular level. NPJ Syst Biol Appl 2019; 5:39. [PMID: 31666984 PMCID: PMC6814739 DOI: 10.1038/s41540-019-0117-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 09/26/2019] [Indexed: 02/06/2023] Open
Abstract
Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer's disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a "statistical telescope" providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/.
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Affiliation(s)
| | | | - Elias Castanas
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, Heraklion, Greece
| | - Oluf Dimitri Røe
- Norwegian University of Science and Technology, Department of Clinical Research and Molecular Medicine, Trondheim, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Norway
- Clinical Cancer Research Center, Department of Clinical Medicine, Aalborg, Denmark
| | | | - Ioannis Tsamardinos
- Computer Science Department, University of Crete, Heraklion, Greece
- Gnosis Data Analysis PC, Heraklion Crete, Greece
- Institute of Computational and Applied Mathematics, Foundation for Research and Technology, Heraklion, Greece
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