In May, we kicked off our 2024 Illumination Tour in Cambridge, MA with an amazing group of speakers sharing how they are using spatial biology to ask and answer groundbreaking questions. The room was packed with researchers and translational scientists interested in learning more about how this powerful technology can transform their studies and deliver new insights. Read on to hear about the exciting science that was shared.
Begin with the End in Mind
An exciting application of spatial biology that was highlighted at the Cambridge event is reverse translational research, also known as “bedside to benchtop” research. This powerful approach has been described as a process that:
“…..[starts with] actual, real-life patient experiences in the clinic, or during a clinical trial, and works backward to uncover the mechanistic basis for these experiences and clinical observations. …each new patient observation stimulates new testable hypotheses that help refine and direct the next iteration of benchtop therapeutics research, which, in turn, leads to the next clinical trial and the next human experience.”
Enabled by multiplexed, spatial phenotyping and imaging on the Akoya platforms, this approach to biomarker discovery is poised to profoundly increase the precision of patient-tailored immunotherapy, define the reasons for response and non-response to treatment, and ultimately, improve the clinical success of immunotherapy.
Defining Patient Response in Metastatic Melanoma
With a goal of improving the success rate of immunotherapy, Dr. Pei Hsuan Chen of Bristol Myers Squibb described reverse translational research in which patient samples are the starting point for unraveling molecular mechanisms and defining the function of key proteins implicated in clinical outcomes.
Dr. Chen highlighted how immune checkpoint inhibitors (ICIs) have fundamentally changed the treatment paradigm for patients with advanced melanoma. A decade ago, prior to the advent of immune checkpoint inhibitors, melanoma patients with nonresectable metastatic disease had median survival times of 6–10 months with dacarbazine chemotherapy. Results published in 2021 from the CheckMate -067 trial showed that nearly half of patients with advanced melanoma treated with a combination of nivolumab (an antibody that targets the anti-PD1 receptor) and ipilimumab (an antibody that blocks CTLA-4) survived more than six years.
In 2022, the FDA approved Opdualag™ (nivolumab and relatlimab-rmbw), the first LAG-3 blocking antibody combination as treatment for patients with unresectable or metastatic melanoma. Clinical trial results showed that Opdualag more than doubled median progression free survival (PFS) when compared to nivolumab monotherapy (10.1 months versus 4.6 months).
As expression of LAG-3 does not consistently predict which patients will benefit from the combination of nivolumab and relatlimab, reverse translational research is being used to identify more predictive biomarkers. Specifically, highly multiplexed tissue imaging detects multiple phenotypes with spatial context with the goal of identifying biomarkers of response to anti-LAG-3 combination immunotherapy. Spatial imaging is performed on the PhenoCycler-Fusion using a 50-plex panel consisting of immune, functional, stromal, and tumor phenotypic markers. Dr. Chen noted that this method has been validated, showing high concordance with immunohistochemistry and high reproducibility.
In her conclusion, Dr. Chen described highly multiplexed spatial profiling as a great approach for reverse translational research to increase the clinical success of immunotherapy. She noted that the PhenoCycler platform has enabled spatial phenotyping of millions of cells, mapping interactions, and the discovery of rare cells and cellular neighborhoods at an unprecedented scale with use of barcoded antibodies across single FFPE tissue sections.
Identifying Predictive Biomarkers in Renal Cell Carcinoma
Similar to metastatic melanoma, ICIs have transformed the treatment of advanced renal cell carcinoma (RCC), delivering prolonged overall survival and achieving higher response rate. Approved treatments include combinations of ICIs with tyrosine kinase inhibitors, as well as dual ICI approaches. Given the various approved ICI-based treatments and the fact that predictive biomarkers approved in other tumor types such as tumor mutational burden and PD-L1 staining are not clinically actionable in RCC, there is a critical need to identify biomarkers that predict therapeutic response and resistance.
Dr. Razan Mohanna, MD, postdoctoral research fellow at Brigham and Women’s Hospital in Boston, described the use of reverse translational research in treatment-naïve patients with advanced RCC. The goal of the first study summarized by Dr. Mohanna was to identify biomarkers predictive of either response or resistance to nivolumab monotherapy. Primary RCC tumor tissues were analyzed by multiparametric immunofluorescence using the PhenoImager platform, and the density of CD8+PD-1+TIM-3-LAG-3- tumor infiltrating lymphocytes (TILs) was quantified by image analysis.
Results of the study showed that a higher density of CD8+PD-1+TIM-3-LAG-3- TIL (named the “IF biomarker”) was associated with better objective response rate (ORR) and longer PFS. Tumor cells that have PD-L1 expression ≥ 1% further separated the clinical outcomes of RCC patients.
The PhenoImager platform was also used to study whether PD-1 expression by regulatory T cells (Treg) can predict resistance to PD-1 blockade in RCC. Because inhibition of PD-1 signaling in Treg cells augments their immunosuppressive function, it was hypothesized that PD-1 expression on these cells would predict resistance to PD-1 inhibitors.
Using the PhenoImager platform, PD-1+ Tregs were phenotyped using multiparametric immunofluorescence in metastatic RCC tissues from the CheckMate-025 nivolumab trial. Results of the study showed that high expression of PD-1 by tumor-infiltrating regulatory T cells experienced shorter PFS and shorter overall survival (OS).
Spatial Biology as the Enabler of Reverse Translational Research
Immunotherapies based on ICIs are becoming increasingly important for the management of cancer. And while these treatments have delivered remarkable results for some patients, in some types of cancer, response rates remain frustratingly low.
As highlighted at the Cambridge stop on our Illumination Tour, reverse translation enabled by the Akoya spatial biology platforms is identifying the biology responsible for the highly variable responses to ICIs. Filling the current knowledge gap will ultimately lead to better outcomes for more patients.
To learn more:
- See how the PhenoCycler-Fusion 2.0 solution is helping provide insights into complex biological systems and gain a better understanding of cellular microenvironments
- See how the PhenoImager 2.0 solution is helping drive translational research and biomarker discovery with high throughput multiplexed imaging
- View our upcoming events, including additional Illumination Tour stops worldwide, for opportunities to connect with us
