November 19, 2019 | Kenna Mills-Shaw believes that precision oncology is neither an illusion nor a panacea. But while there have been some dramatic wins for specific, molecularly-defined tumors, scaling precision oncology will require a highly integrated decision support platform, she believes.
Mills-Shaw is the Executive Director for the Sheikh Khalifa Bin Zayed al Nahyan Institute for Personalized Cancer Therapy at the MD Anderson Cancer Center in Houston. Previously she led the National Cancer Institute’s role in The Cancer Genome Atlas (TCGA) Program.
Editor's Note: Mills-Shaw is speaking in the Precision Medicine program at the upcoming Molecular Medicine Tri-Conference in San Francisco, March 1-4. On behalf of Diagnostics World News, Emily Le, a conference producer Cambridge Healthtech Institute, spoke to her about precision oncology. Their conversation has been edited for length and clarity.
What does precision medicine mean to you?
Precision medicine and personalized medicine are definitely distinct, and we are currently really focused mostly on precision medicine. In our case, I define that as using distinct actionable biomarkers that are predictive of response (or resistance) in a given patient and matching that biomarker to a targeted agent (or avoidance of that agent). Personalized medicine would go much deeper than tumor biomarker data alone.
What are the most significant advances in precision medicine over the past five years?
Routine, affordable deep NGS sequencing on tumor, germline and cfDNA.
As we continue to sequence more and more patients, and an increasing amount of genomic territory (including moving into transcriptomes), we are finding that physicians are welcoming assistance in trying to understand in real time what information is actionable for a specific patient. It is impossible for any single clinician to remember—or have the time to review—the literature for every alteration found in a patient’s report, so a large fraction of the work we do now is to try to make our NGS reports intelligible for clinicians, giving them not only the level of evidence for actionability, but also an accurate and timely representation of the possible clinical interventions that might be available for their patients. In addition, we are always trying to improve our understanding of why patients respond, or fail to respond, to different therapies. By combining discovery genomics with clinical molecular data, we are collecting data on tens of thousands of patients, their genomics, and their response data to improve patient outcomes.
What are the biggest challenges and opportunities that lie ahead?
Continued challenges in reimbursement and having sufficient evidence to merit testing and reimbursement for testing and appropriate matched therapies. I also would like to see us focus on truly selecting the right patients with clearly actionable alterations to enrich our opportunities for success. By treating patients in an unmatched fashion (i.e. all comers) or patients with variants of unknown significance on targeted agents, we are likely diluting our potential impact, and this is a significant hazard to the field in that it maps more to the hype than the science.
What is the role of decision support platform in precision medicine?
The knowledge in the field is constantly changing. Our understanding of biology, the treatment choices (either via approved treatments or clinical trials) and the number of alterations/types of assays we utilize in precision medicine are all constantly evolving. It is truly impossible for any given clinician to be able to maintain accurate information in real-time or even have sufficient time to perform the research for every alteration for every patient. Decision support brings the relevant information and level of evidence to the clinician in real time- to enable each clinician to make precise and personalized recommendations for treatment for each patient in real time as the science of medicine evolves.
What is your advice for the scientists in this field to move precision medicine forward?
We must continue to develop models and better understand the world outside of single point mutations. Right now so many of our “matches” are based on a single alteration at a single point in time and how that would map to a single alteration. This is not consistent with our understanding of tumor biology or tumor evolution. We need to proactively go after the complex biology of tumors if we are going to shift the field.