Abstract
BackgroundSurvival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. Neighborhood component analysis (NCA), a supervised feature selection algorithm selected relevant features from multi-omics datasets retrieved from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. The drug response framework used regression and unsupervised clustering (K-means) to segregate samples into responders and non-responders based on their predicted IC50 values (Z-score).ResultsThe survival prediction framework was highly effective in categorizing patients into risk subtypes with an accuracy of 94%. Compared to single-omics and early integration approaches, our drug response prediction models performed significantly better and were able to predict IC50 values (Z-score) with a mean square error (MSE) of 1.154 and an overall regression value of 0.92, showing a linear relationship between predicted and actual IC50 values.ConclusionThe proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.
Highlights
Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient
Large scale collaborative efforts such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium have led to numerous reports related to interim analyses of gene expression, somatic mutation, copy number variation (CNV) and protein expression data in the literature [12,13,14,15,16]
Multi-omics integration improves survival prediction in breast cancer (BRCA) patients The Neighborhood component analysis (NCA) selected 246 six-omics feature set along with clinical features like age, gender, days to the last followup, pathologic stage, the number of affected lymph nodes, tumor stage, lymph node metastasis, metastatic stage and histological type were fed into neural networkbased survival prediction model to classify the patients into two classes, i.e., high-risk class and low-risk class
Summary
Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. We have proposed a late multi omics integrative framework that robustly quantifies survival and drug response for breast cancer patients with a focus on the relative predictive ability of available omics datatypes. A Neural network framework, fed with NCA selected features, was used to develop survival and drug response prediction models for breast cancer patients. Malik et al BMC Genomics (2021) 22:214 This implies the limitations in the current understanding of cancer complexity and the need for models that efficiently simulate the diversity of human tumor biology in a preclinical arrangement. Multi-omics data integration has emerged as a promising approach for the prediction of clinical outcomes and identification of biomarkers in several cancer studies [17,18,19,20]. A refined integrative approach to handle these diverse datasets coherently is required
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
