SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

• Published in: Cancers 2021-06, Vol.13 (13), p.3106
• Main Authors: Kalakoti, Yogesh, Yadav, Shashank, Sundar, Durai
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Algorithms; Cancer; Cell cycle; Datasets; Deep learning; Gene expression; Genomes; Genomics; Learning algorithms; Lung cancer; Machine learning; Medical prognosis; Neural networks; Patients; Precision medicine; Principal components analysis; Risk groups; Values
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers13133106

The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient’s survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.