Moving Oncology Beyond ‘One Mutation, One Drug’
Over the last 15 years, precision medicine has taken initial steps in diagnosing oncogenic drivers and matching drugs that target oncogene dependencies. A new set of treatment paradigms followed with historically better, often remarkable outcomes for a group of patients with advanced and imminently fatal cancers.
However, despite their historical significance, these advances merely scratch the surface and have yet to solve the problem of cancer for the vast majority of malignant diseases. “Five years to develop a drug, five months to get resistance” has commonly been remarked, while other treatments fail to show any efficacy despite having a theranostic biomarker.
But viewed from a perspective with molecular insight, this experience is not surprising. The “one mutation-one drug approach” does not grasp a majority of cancers, which each carry a plurality of mutations and dozens to hundreds of chromosomal aberrations. These molecular disease characteristics upregulate oncogenes and eliminate tumor suppressors. Downstream gene expression renders unexpected results as the depth and breadth of transcriptional dysregulation and post-translational modifications comes into play. As a rule, hundreds or thousands of genes and dozens of pathways that normally control growth become dysregulated, making the one mutation-one drug view naïve and far short of reality. Thankfully, the next iteration of precision medicine can be more precise.
During the initial period of targeted therapy’s first clinical successes, significant advances in the understanding of cancer biology revealed a complex and profound heterogeneity among patients with the same pathologic diagnosis. Indeed, patients diagnosed with the same cancer and dominant molecular driver often respond differently to treatment. But so far, we have not embraced comprehensive molecular diagnosis in the clinic. Too often, parallel and downstream pathway aberrations that generate resistance to oncogene targeting go undiagnosed and untreated.
The fault lies in our previous scientific goals. Most of the evidence base in oncology emerged in the pre-genomic era of protocol-centric medicine where we sought to find the best treatment for a median patient with a given diagnosis, rather than the best treatment for each patient with a unique set of genomic aberrations. Unavoidably, our status quo is defined by a period of therapeutic opportunity which diminishes within two or three interventions, almost always blindly administered from a cookbook of guidelines which are sometimes relevant and sometimes not, after which oncologists and their patients still find themselves out of time for intervention – game over.
Yet, cancer biology has grown exponentially during this period and fortuitously this knowledge can be translated into actionable information for patient management. The widespread commercial availability of high-quality molecular profiling has rendered each patient’s cancer amenable to deep, multi-omic interrogation and understanding. Increasingly, an engineering-level view of patients’ cancers is feasible where the complexity and uniqueness of each patient’s cancer can be teased apart. Unprecedented mechanistic insights readily emerge from this deep and comprehensive molecular diagnosis.
Nevertheless, there are obstacles to capitalizing on this technology. First, oncologists are not quite cancer biologists. The knowledge base related to almost any mechanism of cancer is vast and not easily assimilated by busy clinicians. Also, molecular heterogeneity creates problems for studying cancer as a group of orphan or rare diseases, the N-of-1 conundrum. Not least, regulatory norms from the pre-genomic era have not kept pace with the therapeutic necessities that emanate from molecular diagnosis.
While the cultural habits of practice, drug licensing and reimbursement will need to be aligned, it is now possible to discover optimal strategies, avoid approaches that have virtually no chance at disease control, and translate newfound understanding of an individual’s cancer into superior disease control. Large cost savings for the health system will flow from simply avoiding treatments that will not work.
Biosimulation Predicts Therapy Responses with High Accuracy
Comprehensive signaling pathway analysis and its impact on various therapeutic options promises to empower oncologists in unprecedented ways. But how can oncologists and their patients bridge the molecular chasm?
Cellworks has built a biosimulation platform that provides a unified representation of biological knowledge curated from heterogeneous datasets relevant for all the molecular abnormalities in a given patient’s cancer. By doing so, Cellworks provides oncologists with a computational omics biology model (CBM) that serves as an artificial intelligence tool in the clinic capable of revealing oncogenic and transcriptional mechanisms underlying unsuspected drug vulnerabilities and sources of drug resistance. The CBM utilizes computational biology to render patients’ genomic abnormalities in software. Next Genome Sequencing (NGS) results are converted into in silico patient-specific protein networks to generate a virtual disease model. In this way, each patient’s cancer is biosimulated to embrace both the complexity and uniqueness of its malignant process.
The cancer’s disease network is then iteratively interrogated with individual drugs and combinations of drugs to predict response in the clinic with a Therapy Response Index (TRI) Score. For each patient, Cellworks constructs a comprehensive network map of the signaling pathway aberrations from the mutanome, chromosomal copy number aberrations, and gene expression data. Therapies are biosimulated against this network map to rank via TRI Score and presented in online and report form for the oncologist and their patient.
Therapy Biosimulation Can Improve Patient Outcomes
While most NGS providers limit their annotated reports to one mutation-one drug associations that address targeted therapeutics, Cellworks’ biosimulation platform permits an assessment of all cancer therapies, including radiation, chemotherapy and immunotherapy. Cellworks suggested therapies are provided alongside pathway analysis with associated PMID sources, highlighting key patient-specific pathways within the biological complexity. As such, biosimulation removes the molecular blindfold and provides an alternative to the guidelines that often amount to only a shot in the dark or a physician’s best guess.