Moving Precision Cancer Medicine Into the Clinic
This is an amazing time to be an oncologist. Cancer medicine is in the midst of an exciting transition, shifting away from the organ-based practice that we’ve known for decades and toward precision cancer medicine (PCM)—the use of genomics to identify genes in patient tumors that we can target with therapy.
Nurturing this evolution is the federal Cancer Moonshot Initiative, which seeks to make a decade’s worth of progress in five years. Key concepts that underlie the Moonshot effort include “big data,” “data sharing” and “innovation.”
At the OSUCCC – James, we are making progress in all three areas in our attempts to help move PCM into the clinic.
Interpreting Big Data
Interpreting large sets of genetic data is part and parcel of precision cancer medicine. The ultimate purpose for analyzing big data is to identify the most effective therapy for our patients.
Recent research has shown that the presence in tumor cells of an alteration called microsatellite instability has important treatment implications. Microsatellites are short strings of repeated DNA bases. If the number of repeats in malignant cells differs from that of nearby healthy cells, the tumor is said to show microsatellite instability (MSI).
MSI is a surrogate marker for the silencing of key DNA repair mechanisms. MSI testing is commonly done when screening for Lynch syndrome, an inherited cancer syndrome that increases the lifetime risk for colorectal and other cancers. But MSI also occurs in spontaneous cancers, and strong evidence suggests that up to 80 percent of patients with MSI-positive tumors might respond well to immunotherapy.
There is little data available for the prevalence of MSI in most types of spontaneous tumors. At the same time, the growing use of next-generation sequencing (NGS) is making data available for tumor types not usually tested for MSI.
This raises the need for tools that can quickly and accurately analyze this data in multiple cancer types.
Fortunately, such tools are available. MANTIS (Microsatellite Analysis for Normal Tumor InStability) is a new algorithm designed to classify samples by MSI status utilizing existing NGS data.
Our team published a study in the journal Oncotarget showing that MANTIS had the highest overall sensitivity and specificity for detection of MSI status across six different tumor types. It also required less memory and had faster run times. Next, we will apply this to data from the Oncology Research Information Exchange Network to identify additional patients with MSI, which could lead to a novel immunotherapy for these patients.
Our ability to identify the gene mutations, or cancer variants, that drive tumor growth and progression lies at the heart of precision cancer medicine. But doing so requires a common language, standardized terminology and a level of description that facilitates their use in research and their ready interpretation in clinical practice.
Currently, genomic information collected by many databases is described and shared differently, which can create discrepancies, inconsistencies and information gaps that make the clinical or research use of variants difficult. For example, different terminology is often used to classify germline and somatic variants (see box).
Fortunately, progress is underway here, too. We contribute to the team effort of the Clinical Genome Resource (ClinGen), a national NIH initiative that represents 75 institutions.
Recently, ClinGen published guidelines in the journal Genome Medicine that attempt to standardize information about cancer variants that is collected and shared so that everyone is speaking the same language.
The guidelines suggest the use of at least 16 discrete data elements to define a mutation. Standardizing these elements creates a common language for uniformly describing mutations and what they mean for patients.
Ultimately, we use NGS and analyze big data to determine the best therapy for our patients. That relates to another Moonshot goal: developing ways to overcome cancer’s resistance to therapy.
Several OSUCCC – James researchers are working on the problems of drug and radiation resistance. Our team recently led a study published in the journal Molecular Cancer Therapeutics that demonstrated a mechanism by which lung and bladder cancer cells develop resistance to a new class of drugs called fibroblast growth factor receptor (FGFR) inhibitors.
Our findings in a laboratory model provide insights into how clinical trials for these agents could be further developed to prevento r overcome drug resistance, and how the information could be useful for developing innovative therapies. Through three active clinical trials at the OSUCCC – James, we are studying how patients respond to novel FGFR inhibitors.
Clearly, there is no routine cancer. Each patient is different, and each needs molecular testing. The Cancer Moonshot will focus our research efforts in ways that hasten the transition to precision cancer medicine, and that should produce better outcomes for all cancer patients.