Gene Expression-Based Predictive Markers for Paclitaxel Treatment in ER+ and ER− Breast Cancer

One of the objectives of precision oncology is to identify patient’s responsiveness to a given treatment and prevent potential overtreatments through molecular profiling. Predictive gene expression biomarkers are a promising and practical means to this purpose. The overall response rate of paclitaxel drugs in breast cancer has been reported to be in the range of 20–60% and is in the even lower range for ER-positive patients. Predicting responsiveness of breast cancer patients, either ER-positive or ER-negative, to paclitaxel treatment could prevent individuals with poor response to the therapy from undergoing excess exposure to the agent. In this study, we identified six sets of gene signatures whose gene expression profiles could robustly predict nonresponding patients with precisions more than 94% and recalls more than 93% on various discovery datasets (n = 469 for the largest set) and independent validation datasets (n = 278), using the previously developed Multiple Survival Screening algorithm, a random-sampling-based methodology. The gene signatures reported were stable regardless of half of the discovery datasets being swapped, demonstrating their robustness. We also reported a set of optimizations that enabled the algorithm to train on small-scale computational resources. The gene signatures and optimized methodology described in this study could be used for identifying unresponsiveness in patients of ER-positive or ER-negative breast cancers

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

Abstract Background Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. Results To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. Conclusions In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework

Additional file 1 of Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

Supplementary figures and tables. Figure S1. The AUCs of precision recall curves of our proposed method when the number of dimensions K increases. Figure S2. Performance comparison of our proposed method and existing network-based methods, evaluated by IntOGen list. Figure S3. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RV , λ LV and λ RU are fixed and λ LU varies. Figure S4. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RU , λ LU and λ RV are fixed and λ LV varies. Figure S5. Performance comparison of our proposed method and existing network-based methods, applied on GBM, COADREAD and BRCA datasets and evaluated by IntOGen list. Figure S6. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by CGC list. Figure S7. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by IntOGen list. Figure S8. Performance comparison of our proposed method and existing network-based methods with network information from both iRefIndex and String v10. Table S1. Fisher’s exact test on the top scored candidates of BRCA results for CGC and IntOGen benchmarking genes. Table S2. Fisher’s exact test on the top scored candidates of GBM results for CGC and IntOGen benchmarking genes. Table S3. The full list of the top 200 genes detected by our model on GBM dataset. Table S4. The full list of the top 200 genes detected by our model on COADREAD dataset. Table S5. The full list of the top 200 genes detected by our model on BRCA dataset. Table S6. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on COADREAD dataset. Table S7. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on BRCA dataset. (PDF 5670kb