De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

Pooled -omic data from patient samples have enabled construction of cellular interaction modules that provides a system-level understanding of disease etiology. This new conceptualization of disease has led to discoveries of previously unknown mechanisms and has significantly expanded opportunities for therapeutic targeting (Iborra-Egea et al. 2017; Sharma et al. 2018). Specifically, system and network science has pinpointed novel pharmacological targets and opportunities for drug repurposing or drug–drug synergies (Zhao and Iyengar 2012). Advancements owing to system biology have been even more pronounced in oncology. The reconstruction of complex cancer disease modules describes tumor biology at the system level, which is particularly important for such a polygenic and dynamic disease (Zielinski et al. 2017; Lin et al. 2019).Despite these recent advancements, a truly individualized system approach has yet to be applied to individualized medicine. Oncology patients in particular experience highly variable disease phenotypes and drug responses. The need for precision approaches in oncology has therefore been well established, with numerous scientists and clinicians calling for innovation (Aronson and Rehm 2015; Relling and Evans 2015; Carrasco-Ramiro et al. 2017; Werner et al. 2017). Patient-derived xenograft (PDX) models and clinical studies have highlighted the heterogeneity of tumor mechanistic properties and therapeutic responses (Chiron et al. 2014; Dagogo-Jack and Shaw 2018; Xu et al. 2019). Some of this variability can be captured with patient stratification through disease subtype classification or biomarker testing, but the majority of inter-patient variability remains unexplained (Dagogo-Jack and Shaw 2018). The lack of broadly applicable biomarkers indicates that unique system-level interactions are at play within single patients. System and network biology is poised to capture these phenomena well, but new theoretical frameworks and computational approaches must be implemented to make such precision network biology a reality.Existing methodologies extract disease modules (or “disease networks”), which are highly perturbed subnetworks of the larger cellular interactome where disease gene interactions occur (Menche et al. 2015). Previous approaches have attempted to detect and prioritize individualized cancer drivers, but these algorithms infer their individualized analyses from cohort-level disease modules (Bashashati et al. 2012; Cho et al. 2016; Reyna et al. 2018). For example, the LIONESS algorithm uses aggregate disease modules generated by existing approaches to linearly interpolate individual sample modules (Kuijjer et al. 2019). We hypothesize that although some individual patient disease activity is recapitulated in the cohort disease modules, there are additional unexplored interactions detectable only at the individual patient level, which dictate patient-specific mechanisms, phenotypes, and therapeutic responses. We additionally suspect that at the gene level, there are patient-specific variations in pathogenicity. This is because patients possess highly varied basal cellular environments and mutations (The Cancer Genome Atlas Network 2012; Dagogo-Jack and Shaw 2018). Given that current approaches rely heavily or exclusively on cohort-inferred disease modules, we suspect that inter-patient variability and precision have been underrepresented.Although practical, using features inferred across the cohort fail to capture patient individualized features by disregarding rare unique factors within a patient. A new approach is needed to truly infer individualized disease modules that accurately recapitulate individualized disease. In this study, we examined on the collective actions of mutated genes to try to understand individualized disease at a deeper level. We hypothesized that cohort disease modules are poorly representative of individualized disease and that new insights in precision medicine would reveal themselves once we zoomed in on individual patients. Thus, we set out, first, to create a robust pipeline for individualized disease module construction and, second, to use these disease modules to characterize individualize disease pathobiology and therapeutics.