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Multi-omic Cancer Therapy Biosimulation Explained

Authored by
Vishwas Joseph, Sr. Biomodeling Model Integration & Validation Scientist, Cellworks

Cancer cells are endowed with capabilities for uninhibited proliferation, invasion and metastasis. Historically, cancers have been classified based on the tissue of origin. Although this practice has been useful in terms of differentiating cancer types, we now realize the approach in the current context has limitations in terms of usage of therapeutic options available and the associated clinical response is poor.

Precision Medicine aims to eradicate these limitations by elucidating the aberrations in the patient’s genome that are responsible for driving the cancer, a tissue agnostic approach. Researchers today use Next Generation Sequencing (NGS) reports to identify these genomic aberrations, which can then result in drug targetable therapeutic options. Although this approach is proven to be useful, the success rate is limited to only a small cohort of individuals expressing specific aberrations.

For example, Erlotinib is recommended for lung cancer patients having EGFR mutations, which is present in only 17% of the cohort in a recent study. Hence, there is a clear need to look beyond the one mutation one drug approach and account for various pathway-associated consequences of mutations, copy-number alterations, mRNA expression, gene fusion and methylation to both tumor-promoting oncogenes and tumor-suppressor genes.

Cellworks Omics Biosimulation

At Cellworks, we recognize and address this need via a tissue agonistic approach using our unique omics biosimulation platform. Our biosimulation platform is a unified representation of a simulated healthy control system, developed using biological knowledge of known signaling pathways, curated from heterogeneous datasets, and applied to finding cures. Patient’s aberrations such as mutations, copy-number variations, gene fusions and epigenetic regulation are captured and converted by the computational system into patient-specific dynamic protein networks to generate a simulated virtual disease model. The patient-specific disease models are then interrogated using a drug library via simulations on the Cellworks model to predict therapy response.

Multi-omic Biosimulation Applied to ALL

Acute lymphoblastic leukemia (ALL) is an aggressive hematological malignancy for which optimal therapeutic approaches are poorly characterized. In a recent study on Early T-cell precursor acute lymphoblastic leukemia  (ETP-ALL), we used the Cellworks biosimulation model in conjunction with genomic data from cell lines and individual patients to generate disease-specific protein network maps that were used to identify unique characteristics associated with the mutational profiles of ETP-ALL compared to non-ETP-ALL (T-ALL) cases and simulated therapeutic responses to a digital library of FDA-approved and investigational agents. 

The Cellworks model was able to classify ETP-ALL patients with an accuracy of 89.66% with prediction sensitivity and specificity being 93% and 87%, respectively. In addition, 87 unique targeted combination therapies were identified in 56 out of 62 (convert to %) patients despite actionable mutations being present in only 37% of ETP-ALL patients.

Benefits of Multi-omic Biosimulation

It is becoming increasingly clear that an omics biosimulation platform can be visualized as a solution to existing bottle-necks in our quest to understand cancer and decipher actionable therapies. Cellworks tissue-agnostic approach not only provides more efficient means of classification of cancer than the conventional tissue specific classification, but also goes beyond the one mutation one drug approach to provide combination therapies with relevant rationale. With its proven high accuracy predictions, the Cellworks biosimulation platform is capable of predicting response to existing SOC and aims to provide significant advantages that translates to marked reduction in cost to patient/payor by being an efficient decision support to clinicians administering therapy and to contribute significantly in patient outcomes.

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