A Digital Health framework for Personalized medicine: Development of a new algorithm for identifying concise actionable driver gene panels and application in Cancer and Rheumatoid Arthritis

Abstract

Precision medicine, enabled by next-generation sequencing (NGS), has shown tremendous potential for use in a clinical setting for disease diagnosis and treatment. The biggest promise is to make treatments more precise and tailored for individual patients, departing from the one-size-fits-all approach. However, the wider translation of genomic panels into clinical practice and its implementation into a digital health framework is met with challenges. Conventional gene panels based on frequently occurring mutations benefit only a subset of patients. Hence, there is an urgent need to expand the scope of this to all patients, for which new methods are required to be developed so as to identify key actionable gene panels in all patients.

We address this gap in this work and present a new algorithm, PreDDs (Precision Driver Panels), to identify gene mutations that drive the disease by integrating genomics, transcriptomics, genome-wide protein-protein interactions and precision networks. Our unbiased network method combines both the gene alterations and the perturbed gene expressions in the functional context to give a comprehensive molecular-level view of the pathological drivers in individual patients, which we refer to as ipanels. Our algorithm shows superior performance when compared to the existing methods. It gives patient-wise concise gene panels that encapsulate major molecular perturbations in the disease. We observe that PrOPs is able to capture many gold-standard genes that represent the altered pathways in the diseases studied. Further, we add a computational workflow to identify ‘actionable’ genes from the panels and associate them with known ‘actions’ in terms of the available drugs to modify the effect of the alterations in the panel genes. This end-to-end pipeline constitutes a framework in digital health and enables its application in a clinical setting, where the pipeline takes in exome and bulk transcriptome sequences as inputs and produces a personalized report indicating the key driver genes in that patient as well as druggable genes with suggested action that guides the clinician in the decision-making process.

We developed and tested the algorithm on individual patient data from 6 different cancer cohorts from The Cancer Genome Atlas (TCGA) - Breast Adenocarcinoma, Colon Adenocarcinoma, Glioblastoma, Hepatocellular Carcinoma, Lung Adenocarcinoma and Skin Melanoma. This resulted in gene panels that were unique to the individuals in the cohort, with the panel sizes ranging from 1 to 9. Apart from identifying most of the known driver genes like TP53, APC, EGFR, RAS and RAF, we also identified a number of rare driver genes that would have been otherwise missed at the population level. This includes genes like ID2 in melanoma that was mutated in only one patient sample but was correctly identified by PrOPs to be a driver gene in that patient. This also happens to be a COSMIC signature gene in Melanoma. We extend the functional significance of the panel genes to the phenotype by formulating a risk score based on the network connectivity of the panel genes and the perturbed genes and the respective hazard ratio calculated from the studied cohort. This score gives insights into the survival status of the individual. Further, 92% of the patients studied had at least one actionable gene in their panel.

Next, we expand the framework to generalize it to other diseases outside the oncology domain and select an autoimmune disease - Rheumatoid Arthritis (RA) as an example. We refer to the expanded framework as PreDD (Precision Disease Drivers). We generated a new South Indian cohort involving patients with Rheumatoid arthritis, in collaboration with a hospital in this region. The major risk factor for this autoimmune disease is the variations in the genes. PreDD precisely identified genes that are relevant to the disease. We observe that PreDD genes are also gold standards studied in this disease, indicating the potential of our algorithm to be used as a general driver gene identification tool.

In the final part of the thesis, we present an ordinary differential equation (ODE) model of the signalling pathways based on the panels identified above in both the Cancer and Rheumatoid arthritis cohorts. The results from the model simulations highlight the importance of the identified mutations and the effect they have on the relevant pathways and processes.

Identifying these driver mutation profiles that influence the disease progression of each patient is necessary to understand their disease risk and to develop personalised treatment regimes. Individuals exhibit very high heterogeneity in their mutational profiles, making it essential to address the driver mutations in each patient. The insights gained from this thesis have a high potential to be applied to real-world data and translated to clinical practice to provide a platform for personalized care.