Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
['Borisov|Nicolas|N|0000-0002-1671-5524', 'Sorokin|Maxim|M|', 'Tkachev|Victor|V|', 'Garazha|Andrew|A|', 'Buzdin|Anton|A|']
D000970:Antineoplastic Agents D020869:Gene Expression Profiling D006801:Humans D000069550:Machine Learning D009369:Neoplasms D000077982:Progression-Free Survival D016896:Treatment Outcome
Cancer Gene expression Chemotherapy Machine learning Molecular diagnostics RNA sequencing Microarrays Biomarkers detection Clinical oncology Personalized medicine Transcriptomics