Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Volume 13 Supplement 8 Selected Topics in “Systems Biology and Bioinformatics” - 2019: medical genomics Research Open Access Published: 18 September 2020 Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments Nicolas Borisov  ORCID: orcid.org/0000-0002-1671-55241,2, Maxim Sorokin1,3, Victor Tkachev1, Andrew Garazha1 & Anton Buzdin1,2,3,4  BMC Medical Genomics volume 13, Article number: 111 (2020) Cite this article 163 Accesses 1 Altmetric Metrics details Abstract 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. Background Personalized approach provides important advantages in clinical oncology in terms of improved patient survival and lower drug toxicities [1, 2]. However, so far it can only cover a minor fraction of cancer patients [3, 4] due to lack of robust prognostic biomarkers for most of the treatments [5]. The proportion of patients eligible for personalized oncology slightly grows. For example, the percentage of US patients with cancer estimated to benefit from personalized prescriptions of targeted therapeutics was only 0.7% in 2006, and it had increased to ~ 5% in 2018 [4]. However, this progress could be more significant if more companion diagnostic tests would be available for the standardly used cancer drugs. In this regard, gene expression data, either obtained by RNA sequencing [1] or using microarrays [6], frequently provide an advantage over genomic tests. Several trials and clinical case reports were published recently evidencing high efficiency of gene expression-based prescriptions of cancer chemotherapeutics. Cancer gene expression data can be used per se or can be normalized on the available profiles of healthy human tissues [7]. Using transcriptomic data, bioinformatic models can be built for patient-oriented ranking of cancer drugs [8]. These models can be hypothesis-driven, e.g. based on the knowledge of the specific mechanisms of drugs anti-cancer activities [9,10,11]. Alternatively, hypothesis-free approaches like machine learning (ML) don’t need any theoretic background but instead strongly require sufficient training and validation datasets. Many ML methods may be used for such applications, e.g. decision trees [12, 13], random forests, RF [14, 15], linear [16], logistic [17], lasso [18, 19], ridge [15, 20] regressions, multi-layer perceptron, MLP [12, 15, 21, 22], support vectors machines [12, 13, 15, 23,24,25], adaptive boosting [26,27,28], as well as binomial naïve Bayesian [15] method. High-quality training and validation datasets are required to run both types of the above models. Nowadays there is a shortage of clinically annotated molecular data that would help developing ML-assisted diagnostic tools. The datasets available are usually considered too small for applying ML [23, 25, 26, 29,30,31,32,33]. Indeed, the figure of dozens or hundreds of annotated biosamples is negligible in comparison with ~ 20,000 protein coding genes measured in transcriptomic assays. Intelligent data filtering is, therefore, needed to reduce dimensionality of data [8]. However, a recent approach using dynamic feature extraction, or flexible data trimming, can significantly improve performances of ML-based methods for the real-world datasets [15, 25]. This study was performed to review available clinically annotated datasets of cancer transcriptomic profiles that may be suitable for applications in ML models. To our knowledge, this is the largest published collection of processed gene expression data coupled with case history excerpts indicating positive or negative response to certain treatment protocols for cancer patients. This manually curated collection of molecular datasets will be helpful for those working with the ML or artificial intelligence applications in oncology, as well as for the fundamental research and development of cancer biomarkers. Methods We curated GEO [34], TARGET [35] and TCGA [36] repositories to extract cancer gene expression profiles associated with the clinical outcomes of chemotherapeutic treatments. We attempted to build a knowledgebase of molecular datasets suitable for building ML classifiers of clinical responses on chemotherapy treatments (Table 1, Additional file 1). Every included dataset met the following criteria: at least 40 gene expression profiles present; data obtained for the same cancer type and using the same experimental platform every profile is linked with the case clinical history all cancers treated with at least one common drug or chemotherapy regimen treatment outcomes are available enabling to classify every case as either responder or non-responder. Table 1 Overview of selected transcriptomic datasets of responders/non-responders to cancer chemotherapy, responders (R) vs non-responders (NR) Full size table We used different approaches to discriminate between the treatment responders and non-responders. Where available, e.g. for the datasets extracted from the GEO repository, we used the responder/non-responder marks assigned by the authors of the original communications publishing these data. In many instances, the number of response groups was more than two and included groups like “partial responders”. However, most frequently binary ML-assisted drug response classifiers are needed that classify patients in only two classes: either respo (...truncated)