Predictive diagnostics in oncology precision medicine: an interview with Douglas Adkins

Written by Douglas Adkins (Washington University School of Medicine in St Louis, MO, USA).

We recently had the opportunity to interview Douglas Adkins, MD (Washington University School of Medicine in St Louis, MO, USA). Adkins is a medical oncologist, specializing in cancers of the head and neck. He is also involved in clinical trials for new therapy modalities and novel diagnostics. In this interview, Adkins discusses the precision medicine field, it’s challenges and novel predictive technology in oncology.

Can you describe how the field of precision medicine has advanced since the launch of the Human Genome Project?

Since the completion of the Human Genome Project, there has been a boom in the amount of genomic information available in data repositories like The Cancer Genome Atlas and shared through peer-reviewed publications. The field began using this plethora of genomic data for both drug development (therapies) and treatment decisions (diagnostics).  With this growing genomic information, we learned not only more about the biology of disease, but also the mechanism of action of therapies used to treat disease.

How has the advent of precision medicine changed how you diagnose and treat patients?

On the therapy side, we’ve seen an incredible increase in the number of therapies available, but these therapies often only work in a subset of patients. Now, rather than giving all patients who present with a specific cancer the same therapy, we’re looking to diagnostic tools to provide information on which patients will respond to which therapies.

What are the current challenges in the field of precision medicine and what are potential steps to overcome them?

A good example to discuss are immunotherapies called immune checkpoint inhibitors. In patients with recurrent or metastatic disease, the KEYNOTE-048 trial showed that overall survival was better in patients treated with immunotherapy compared with patients treated with chemotherapy. However, many patients did not benefit from immunotherapy. The critical problem we have as physicians is how do we identify before treatment those patients destined to benefit from immunotherapy vs those who will not? A perfect predictive test would allow physicians to limit the use of immunotherapy to patients destined to benefit and offer alternative therapies to other patients. The current on-label diagnostic used to predict benefit from immunotherapy is the single-analyte, PD-L1, assessed by immunohistochemistry (IHC). Unfortunately, PD-L1 IHC is an imperfect predictor of tumor response to immunotherapy. Most patients with a PD-L1-positive test do not benefit from immunotherapy. Recent research reports across several cancers have showed that multidimensional biomarkers may be better predictors of tumor response with immunotherapy, but none of these tests are clinically available to practicing physicians.

Recently, the team at Cofactor Genomics approached us with their Predictive Immune Modeling platform that they believed could make an impact here, and we saw potential in their early results to provide a platform that could better inform physicians on making treatment decisions. This was the start of a collaboration and we are undertaking a clinical study to build a new diagnostic tool.

You recently presented this work at the Multidisciplinary Head and Neck Meeting (27–29 February 2020, Scottsdale, AZ, USA), where this new predictive technology was evaluated in a retrospective study. What were the key findings of that study?

In this retrospective study, 107 patients with recurrent or metastatic head and neck squamous-cell carcinoma treated with an immunotherapy were screened for enrollment. Patients were classified as responders or non-responders to the immunotherapy, according to RECIST criteria. Pre-treatment formalin-fixed paraffin-embedded tumor samples were analyzed with Cofactor’s ImmunoPrism Assay, alongside the on-label PD-L1 IHC 22C3 pharmDx assay at Combined Positive Score thresholds of ≥1. The Cofactor ImmunoPrism Assay is a novel multidimensional RNA-based diagnostic.

In the study, Cofactor’s technology maintained high sensitivity (80% for both ImmunoPrism Assay and PD-L1 IHC) while significantly decreasing the number of false positive test results (ImmunoPrism Assay: 52% vs PD-L1 IHC: 68%). This is an important difference, as it means that 15% of patients treated with immunotherapy based on a positive PD-L1 test would have been more appropriately treated with an alternate therapy based on the Cofactor ImmunoPrism Assay.

What do you believe are the advantages of developing better predictive diagnostics for immunotherapy?

The preliminary data in this study shows that we can develop better diagnostic tools for predicting therapy response. More specifically, building diagnostics with multidimensional biomarkers rather than single analyte biomarkers represents a promising new approach. Today, we’re working in the recurrent and metastatic disease setting, but as immunotherapy is undergoing evaluation as a component of curative treatments for newly diagnosed disease, these multianalyte predictive diagnostics will become even more important in the curative setting. As a physician, any opportunity I have to improve treatment outcomes is a win for my patients.

What are your predictions for the future of precision medicine in oncology and how would you like to see the field progress over the next 5–10 years?

I believe we will continue to see novel therapies developed at a rapid pace that will change our expectations of likelihood of survival. For example, what will be the next therapy that makes the broad and lasting impact we have recently seen with immunotherapy?

Additionally, we will see incredible growth in the development of technologies that can act as matchmakers between patients with a unique cancer and the increasing number of targeted therapies available. As precision medicine evolves to be closer to personalized medicine, understanding each patient’s unique molecular tumor profile, and how that information predicts which therapy will be effective, is essential. These advancements will be driven by collaboration between multiple parties – drug manufacturers’, clinicians, and diagnostic developers – to ensure we are all working together for the same goal of improving patient outcomes.

Interested in this topic? Visit our Spotlight on precision medicine in oncology!

The opinions expressed in this interview are those of the interviewee and do not necessarily reflect the views of Oncology Central,  Future Science Group or Washington University in St. Louis.

Learn more about Cofactor Genomics here.