Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

• Published in: BMC medical genomics Vol. 13; no. Suppl 8; p. 111
• Main Authors: Borisov, Nicolas, Sorokin, Maxim, Tkachev, Victor, Garazha, Andrew, Buzdin, Anton
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18-09-2020; BioMed Central; BMC
• Subjects: Annotations; Artificial intelligence; Biomarkers; Bortezomib; Brain tumors; Breast cancer; Busulfan; Cancer diagnosis; Cancer genetics; Cancer therapies; Care and treatment; Chemotherapy; Clinical outcomes; Cyclophosphamide; Data collection; Datasets; DNA microarrays; Feature selection; Gene expression; Genes; Genomes; Genomics; Glioma; Gliomas; Hypotheses; Learning algorithms; Leukemia; Lung cancer; Machine learning; Microarrays; Molecular diagnostics; Monoclonal antibodies; Multiple myeloma; Oncology; Patient outcomes; Patients; Precision medicine; Principal components analysis; R&D; Research & development; Ribonucleic acid; RNA; RNA sequencing; Targeted cancer therapy; Taxanes; Temozolomide; Transcriptomics; Trastuzumab; Vincristine; RNA sequencing; Chemotherapy; Molecular diagnostics; Microarrays; Transcriptomics; Clinical oncology; Machine learning; Biomarkers detection; Gene expression; Personalized medicine; Cancer
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-020-00759-0

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn't allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.
We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.
We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.
We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others - microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.