Abstract A14: Network-based stratification: Combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma
Authors: Hofree Matan , Shen John Paul , Carter Hannah , Gross Andrew , Ideker Trey 
Published: August 2014

Abstract
Background: Recent technological advances have drastically reduced the cost and increased the speed of DNA sequencing, developments that have been heralded as the beginning of a new era of personalized medicine. However, there still remains a pressing need to translate improved knowledge of cancer genomics into better clinical outcomes. Like many solid tumors, high grade serous ovarian carcinoma (HGS-OvCa) is a heterogeneous entity with a widely variable clinical course even among patients of the same stage and histological subtype.
Most prior attempts to stratify tumors into clinically meaningful subtypes have utilized gene expression profiles; however as a whole such methods have not reliably discovered subtypes with significant differences in survival or response to chemotherapy. There has been much effort to stratify tumors based on their somatic mutations; however this has proved difficult due to the great heterogeneity and sparseness of somatic mutations in most solid tumors. For example, in the TCGA HGS-OvCa cohort the average tumor had 61 non-silent mutations spread out over the ~20,000 genes in the human genome; most genes were mutated in one tumor only. Standard consensus clustering approaches applied to this sparse mutational data have repeatedly failed to produce a clinically valuable stratification of patients.
Methods: The technique of Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to produce a subdivision of patients into subtypes. Briefly, somatic mutations for each patient are represented as a profile of binary (1,0) states on genes, in which a ‘1’ indicates a mutated gene. For each patient independently we project the mutation profiles onto a human gene interaction network obtained from public databases. Next the technique of network propagation is applied to spread the influence of each subsampled mutation profile over its network neighborhood, resulting in a ‘network-smoothed’ profile. Following this ‘network-smoothing’, profiles are clustered into a predefined number of subtypes using the unsupervised technique of non-negative matrix factorization (NMF). This procedure is repeated for 1000 different subsamples using the technique of consensus clustering to promote robust cluster assignments. This method was repeated with three difference sources of network data: STRING, HumanNet, and PathwayCommons.
Results: Using the NBS technique four distinct subtypes were identified from the TCGA HGS-OvCa cohort of 330 exome sequenced and clinically-annotated tumors. The median overall survival (OS) in months for these four subtypes was 36, 48, 56, and not-yet-reached respectively, which was highly significant (Logrank p = 1.59 x 10-5). Subtypes were also predictive of platinum free interval (PFI), (Logrank p = 0.046). No significant differences between subtypes were found in age, tumor stage at presentation, or percentage of patients with an optimal surgical resection, and a cox proportional hazards model was significant even after taking those factors into account (Likelihood ratio test p = 3.3x10-4).
Comparing each subtype against the other three, the network of mutated genes for subtype 1, which had the worst overall survival and shortest platinum free interval, was enriched for over 20 genes in the fibroblast growth pathway. The network for subtype 2 was enriched in DNA damage response genes including ATM, ATR, BRCA1/2, RAD51 and Chek2, and also contained the majority of patients with BRCA1/2 germline mutations (15/20 and 5/6, respectively).
Conclusions: Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to stratify a cohort of HGS-OvCa tumors into subtypes with significant differences is OS and PFI.
Citation Format: Matan Hofree, John Paul Shen, Hannah Carter, Andrew Gross, Trey Ideker. Network-based stratification: Combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A14.