The greatest challenge in oncology is refractory cancer. One-third of all cancer patients die of refractory disease. In my blood cancer patient population, 80% ultimately die of cancer. Over the last 5 years, next generation DNA sequencing has unveiled three important characteristics of cancer: (1) high number of genomic mutations (also called mutanome), (2) highly heterogeneous mutanomes among cancers (even within the same tissue type), and (3) subclonal architecture with survival-of-the-fittest clonal evolution. These features explain the disparate disease responses in cancer patients and the lack of cure with one-size-fits-all treatment in many patients.
This challenge forged a partnership between an academic medical center and a computational biology company to invent new trials processes for introducing precision diagnostics and therapies to patients and the market.
The computational biology method includes software code based on tens of thousands of PubMed references that simulate the intracellular activities of thousands of known biological entities. Furthermore, code is written to reproduce tens of thousands of interactions among the known biological entities. Each patient’s cancer mutanome, which typically includes hundreds of gene mutations, is inputted into the computational biology system. A protein network map is then generated based on each patient’s cancer mutanome. Consequent mutant proteins are modeled as gain-of-function/activated or loss-of-function/inactivated. The patient’s cancer model/avatar is then used to quantify changes in cell growth characteristics (proliferation, viability and apoptosis) are quantified using growth curves. Drugs of interest are applied to the model and examined for reductions in cancer cell growth. Drugs with dose-dependent decreases in cell growth are predicted to remit the cancer.
The chief technology innovations in our approach are (A) the ability to include hundreds to thousands of gene abnormalities and (B) the ability to map the interacting nature of the multitude of gene abnormalities. This is the first technology capable of performing these tasks and in the clinic. These innovations generate new knowledge for understanding cancer biology, patient selection, and drug activity.
This method has been validated in several retrospective studies (Medina, et al. American Society of Hematology 2016 oral presentation). These studies have led to greater understanding of blood cancers and identification of unique biomarker signatures associating with drug response. Based on retrospective proof, we initiated a prospective validation trial called iCare for Cancer Patients (https://clinicaltrials.gov/show/NCT02435550). This study is powered to report predictive values of the computational biology method in predicting response to chemotherapy of choice. A follow-up prospective clinical trial (called iCare 2) is designed as a randomized phase 2 trial and is now under review at the FDA (Figure). These studies are necessary to validate the clinical accuracy and utility of a computational biology method. To our knowledge this is the only precision oncology computational biology method in randomized testing for validation.
From this effort, several innovative reinventions of the trials process have arisen:
- A rapid workflow for N=1 treatment decision-making in cancer,
- Rapid method of repurposing older drugs for new indications. These discoveries joined with clinical proof-points help pharmaceutical companies reinvigorate older assets,
- Ability to identify novel biomarker signatures that explain drug sensitivity or resistance. These biomarker signatures not only further our understanding of cancer biology and pharmacology, but also represent patentable intellectual property (IP) for creation of companion diagnostics,
- A virtual clinical trials method using large, publicly available datasets of cancer patients to test clinical efficacy of investigational new drugs. Drugs generating improvement in virtual Kaplan-Meier survival curves are fast-tracked for human application. Drugs generating no improvement in Kaplan-Meier survival curves are scrutinized further with other pre-clinical examinations. In general, the earlier an investigational agent is identified as a poor performer, the better it is to conduct further structure activity relationship (SAR) studies or scuttle the effort and focus on more promising assets.
- Rapid pharmacogenomics screen for predicting drug metabolism and adverse events. These data help prepare the patient and treating physician for what to expect. Prophylactic measures such as enhancing anti-emetic strategy can be employed. These pharmacogenomics screening profiles represent new IP that for companion diagnostics with every drug.
- Monitoring cancer burden by saliva or finger stick. Non-invasive specimens from cancer patients can be measured rapidly (within days) for cancer burden using ultra-high sensitivity droplet digital polymerase chain reaction (ddPCR). This method not only permits rapid determination of biologic activity, but also permits early remodeling and readjustment of anti-cancer treatment strategy. There is a growing agreement among oncologists that, in the future, refractory cancers will be managed by a myriad of drugs based on cancer subclonal evolutions.