Spatial: the final frontier?

Study shows spatial biology might be critical for predicting response to immuno-oncology treatment

Jeffrey Bouley
MENLO PARK, Calif.—Akoya Biosciences Inc., which also bills itself as The Spatial Biology Company, announced this summer that an in-depth comparison of immuno-oncology biomarker types conducted by scientists at four universities determined that multiplex immunofluorescence with spatial characterization significantly outperformed other biomarker testing approaches—gene expression profiling, tumor mutational burden assessment and immunohistochemistry among them—for predicting patient response to treatments targeting PD-1/PD-L1.
The study was published in JAMA Oncology under the title “Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis.”
As Akoya explains, multiplex immunofluorescence, a new type of biomarker assay, allows investigators “to simultaneously analyze the expression of many proteins in individual cells within the tumor microenvironment, preserving critical information about which cells are active and how they are spatially distributed relative to each other.” The company makes it possible to do this type of analysis with the CODEX System, an ultra-high multiplexing platform for biomarker discovery, and Phenoptics, a high-throughput multiplexing platform for translational and clinical research.
“Immunotherapies targeting PD-1 or PD-L1 have proven remarkably effective for treating cancer in some patients, but there remains a paucity of accurate biomarkers that can differentiate responders from non-responders,” the company noted in a news release about the study’s publication. “Identifying the patients most likely to respond to these therapies is an important step in ensuring optimal outcomes for all patients. To date, several assays have been developed with the potential to predict response based on genetic signatures, gene expression, and immunohistochemistry. Although these assays are helpful in limited situations, there is a need for options that are better at predicting response across a larger percentage of cases.”
The study, conducted in collaboration with scientists at Johns Hopkins University, Yale University, Vanderbilt University and Northwestern University, reviewed published data from more than 50 studies covering more than 10 types of cancer and over 8,000 patients. Statistical analyses were performed to assess the performance and predictive value of each type of biomarker. While tumor mutational burden, gene expression profiling and immunohistochemistry had comparable performance to each other for differentiating between responders and non-responders, multiplex immunofluorescence had considerably better performance metrics, according to Akoya. Specifically, it had fewer false positives, meaning it was less likely to predict positive response in a patient who would not ultimately respond to therapy.
“This meta-analysis of previous studies clearly demonstrates the potential for using multiplex immunofluorescence to generate more comprehensive and reliable data to better predict response to anti-PD-1/PD-L1 treatments,” said Cliff Hoyt, vice president of translational and scientific affairs at Akoya and a co-author of the paper. “This contributes to growing evidence that spatial resolution of tumor biomarkers is essential for an accurate view of cancer biology, and the Akoya team is excited to help researchers continue down this promising avenue of investigation.”
“Immunotherapies represent the latest advance in cancer treatment, and this important study shows that the multiplex immunofluorescence technology underlying our systems can serve to more accurately stratify patients for optimal outcomes,” added Brian McKelligon, CEO of Akoya. “Of the approaches available for spatially resolving biomarkers, the Phenoptics platform is uniquely suited to fulfill the most critical needs in translational research and clinical applications. Our end-to-end solutions put researchers in the best position to drive precision immuno-oncology forward in the coming years.”
The main outcomes and measure in the study were summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV)—as well as positive and negative likelihood ratios (LR+ and LR−) for each assay modality.
When each modality was evaluated with sROC curves, multiplex immunohistochemistry/immunofluorescence (mIHC/IF) had a significantly higher AUC (0.79) compared with PD-L1 immunohistochemistry (AUC, 0.65, P < .001), gene expression profiling (AUC, 0.65, P = .003) and tumor mutational burden (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 immunohistochemistry and/or gene expression profiling plus tumor mutational burden, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR−, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches.
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