Amina, B. | Search bioRxiv

bioRxiv2022

CanSeer: A Method for Development and Clinical Translation of Personalized Cancer Therapeutics

Computational modeling and analysis of biomolecular network models annotated with cancer patient-specific multi-omics data can enable the development of personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain to be fully translated. In this work, we present "CanSeer" a novel multi-stage methodology for developing in silico models towards clinical translation of personalized cancer therapeutics. The proposed methodology integrates state-of-the-art dynamical analysis of biomolecular network models with patient-specific genomic and transcriptomic data to assess the individualized therapeutic responses to targeted drugs and their combinations. CanSeers translational approach employs transcriptomic data (RNA-seq based gene expressions) with genomic profile (CNVs, SMs, and SVs). Specifically, patient-specific cancer driver genes are identified, followed by the selection of druggable and/or clinically actionable targets for therapeutic interventions. To exemplify CanSeer, we have designed three case studies including (i) lung squamous cell carcinoma, (ii) breast invasive carcinoma, and (iii) ovarian serous cystadenocarcinoma. The case study on lung squamous cell carcinoma concluded that restoration of Tp53 activity together with an inhibition of EGFR as an efficacious combinatorial treatment for patients with Tp53 and EGFR cancer driver genes. The findings from the cancer case study helped identify personalized treatments including APR-246, APR-246+palbociclib, APR-246+osimertinib, APR-246+afatinib, APR-246+osimertinib+dinaciclib, and APR-246+afatinib+dinaciclib. The second case study on breast invasive carcinoma revealed CanSeers potential to elucidate drug resistance against targeted drugs and their combinations including KU-55933, afuresertib, ipatasertib, and KU-55933+afuresertib. Lastly, the ovarian cancer case study revealed the combinatorial efficacy of APR-246+carmustine, and APR-246+dinaciclib for treating ovarian serous cystadenocarcinoma. Taken together, CanSeer outlines a novel method for systematic identification of optimal tailored treatments with mechanistic insights into patient-to-patient variability of therapeutic response, drug resistance mechanism, and cytotoxicity profiling towards personalized medicine.

systems biology↗

bioRxiv2021

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a "zero-code" environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISONs modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.

systems biology↗