Title:Non-small Cell Lung Cancer Survival Estimation Through Multi-omic
Two-layer SVM: A Multi-omics and Multi-Sources Integrative Model
Volume: 18
Issue: 8
Author(s): Lorenzo Manganaro*, Gianmarco Sabbatini, Selene Bianco, Paolo Bironzo, Claudio Borile, Davide Colombi, Paolo Falco, Luca Primo, Shaji Vattakunnel, Federico Bussolino and Giorgio Vittorio Scagliotti
Affiliation:
- AizoOn Technology Consulting, Str. del Lionetto, 6, Torino, 10146, Italy
Keywords:
Multi-omics, multi-layer support vector machine, disease-free survival, machine learning, non-small cell lung cancer, predictive medicine.
Abstract:
Background: The new paradigm of precision medicine brought an increasing interest in survival
prediction based on the integration of multi-omics and multi-sources data. Several models have
been developed to address this task, but their performances are widely variable depending on the specific
disease and are often poor on noisy datasets, such as in the case of non-small cell lung cancer
(NSCLC).
Objective: The aim of this work is to introduce a novel computational approach, named multi-omic twolayer
SVM (mtSVM), and to exploit it to get a survival-based risk stratification of NSCLC patients from
an ongoing observational prospective cohort clinical study named PROMOLE.
Methods: The model implements a model-based integration by means of a two-layer feed-forward network
of FastSurvivalSVMs, and it can be used to get individual survival estimates or survival-based risk
stratification. Despite being designed for NSCLC, its range of applicability can potentially cover the full
spectrum of survival analysis problems where integration of different data sources is needed, independently
of the pathology considered.
Results: The model is here applied to the case of NSCLC, and compared with other state-of-the-art
methods, proving excellent performance. Notably, the model, trained on data from The Cancer Genome
Atlas (TCGA), has been validated on an independent cohort (from the PROMOLE study), and the results
were consistent. Gene-set enrichment analysis of the risk groups, as well as exome analysis, revealed
well-defined molecular profiles, such as a prognostic mutational gene signature with potential
implications in clinical practice.
