Utilizing Multidimensional Immune Modeling to Advance Precision Medicine
By David Messina, PhD
Today, it’s hard to predict which patients will respond to a therapy. Disease, particularly cancers, are not the result of one defect. Rather, multiple small changes often drive a disease phenotype. The traditional approach of relying on isolated, single analyte biomarkers is often insufficient to capture this complex biology, leaving clinicians without adequate diagnostic tests to help ensure their patients get the right treatment the first time, every time.
Multidimensional models such as Health Expression Models represent multiple facets of biology — looking at both the presence or absence of RNA, as well as the dynamic expression levels that can be influenced by the state of the disease, environmental effects, the therapy, and other clinical variables. They can capture a more detailed readout of the tumor microenvironment and comprehensively represent the rich complexity of the immune system’s interaction with a tumor.
By comparing RNA from an individual patient’s biopsy to a database of Health Expression Models representing key immune cell types, doctors can better quantify the immune composition, enabling them to more accurately predict that patient’s response to therapy. For example, a recent publication found that “[in] melanoma patients, ratios of CD8+/CD4+ lower than 2 predicted lack of response to [anti-PD1] treatment (0%) (p = 0.006), while CD8+/CD4+ ratios higher than 2.7 had an 81.3% response rate (p = 0.0001)”*. When the typical overall response rate to a leading anti-PD1 treatment is less than 50%**, using a CD8+/CD4+ ratio-based assay could lead to a significant improvement in treatment decisions and patient benefit. Furthermore, immunotherapies have a high cost, on the order of $150,000 per year, and so better treatment decisions around these powerful but expensive drugs can reduce rising costs of healthcare that affect us all.
Clinicians are embracing technological advances like Health Expression Models that enable better prediction of patient response, better treatment decisions, and better outcomes for patients. As more complex treatment regimes such as combination therapies become available and further outpace the limits of traditional single-analyte diagnostics, multidimensional approaches to guide treatment will become not only more valuable, but a necessity.
Questions about Cofactor or our product offerings? Reach out to schedule a time to speak with one of our Project Scientists today.
* Uryvaev, A., Passhak, M., Hershkovits, D., Sabo, E., & Bar-Sela, G. (2018). The role of tumor-infiltrating lymphocytes (TILs) as a predictive biomarker of response to anti-PD1 therapy in patients with metastatic non-small cell lung cancer or metastatic melanoma. Medical Oncology, 35(3), 25. https://doi.org/10.1007/s12032-018-1080-0
** Long GV, Schachter J, Ribas A, et al. 4-year survival and outcomes after cessation of pembrolizumab (pembro) after 2-years in patients (pts) with ipilimumab (ipi)-naive advanced melanoma in KEYNOTE-006. J Clin Oncol. 36, 2018 (suppl; abstr 9503).
