There are more than 100 chemotherapy drugs available today with a regular cadence of new drugs being introduced each year. It’s fair to say that there is no such thing as “one size fits all” approach when it comes to dealing with cancer. There are endless individual and exogenous variables that make the same cancer a totally different experience across patients. We use the term “cancer” as a catch all phrase when in actuality there are well over 200 different forms of cancer that we can classify. Where the cancer arises, when in the course of persons life it occurs, and how long it has been able to afflict an individual before diagnosis are all factors that play a pivotal role each with massive implications with respect to prognosis and pathology. The same can be said for cancer treatment. A treatment regimen can work wonderfully for one person and then be completely ineffective for someone else. This reality can be extremely disheartening at times and represents why there is such an urgent need for discovering new ways for attacking this problem more precisely.
If we ask ourselves the question “why is it that there are no one size fits all approaches to therapy?” the answer is, we don’t exactly know. Could it be related to the spatial architecture of person’s tissue? The idea of examining a person’s tissue at a cellular level is a concept with a focus around understanding what determines cellular interactions and how these interactions may be indicative of a particular response. There is a growing understanding that immune cells function primarily in tissues, not blood1 and therefore it is important to set our sights on understanding their nuanced interactions within tissue and how these interactions may dictate treatment response versus treatment resistance. The emergence of new immunotherapies as the first line treatment for cancer, has necessitated the need for developing clinically useful biomarkers to select responders to a given therapy. This represent a necessary and critical step towards the advancement of such treatments. Immune checkpoint inhibitors (ICI) are a class of immunotherapy that have revolutionized the treatment landscape of oncology, but the response rate elicited by these drugs has stalled out around 20%-30%. So how is it that we can better identify those who will respond, what makes them ideal candidates for ICI therapy? A way of doing this is by developing predictive biomarkers of response using multiplex imaging. Multiplex imaging using immunofluorescence is a promising method to facilitate more quantitative, reproducible, precise, and objective assays capable of delivering a wider dynamic range while simultaneously querying more markers for more powerful spatial phenotyping. Results using this methodology are comparable with the results of monoplex immunofluorescence and chromogenic IHC staining.2 A recent systematic review and meta-analysis comparing different biomarker modalities for predicting clinical response to anti-PD1/PDL1 therapy demonstrated that protein spatial phenotyping with multiplex imaging had significantly higher diagnostic accuracy than other biomarkers including PD-L1 IHC.2 This means there is a clear avenue towards creating an assay rooted in multiplex imaging using immunofluorescence that can rapidly stratify patients to the ideal therapy.
The PhenoImager solution enables quick and accurate spatial phenotyping of tissues across a whole slide. This allows for the unbiased development of spatial signatures – measurements of the interactions and cell densities of tumor and immune cells in the tumor microenvironment. Akoya’s solution enables analysis of dozens of cell phenotypes and their spatial interactions from a single formalin-fixed, paraffin-embedded (FFPE) tissue section. By validating and standardizing the multiplex imaging workflow, spatial signatures can enter the clinic and make an impact on immunotherapy outcomes. The Multi-Institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) study supports this concept. The MITRE study was a multi-site collaboration that assessed the analytic performance of the end-to-end multiplex immunofluorescence workflow while focusing on PD-1/ PD-L1, it demonstrated the mIFs reproducibility as well as intra-laboratory and inter-laboratory concordance across multiple parameters.3
The concept and utility of Spatial Signatures represents a massive step towards streamlining how we approach cancer diagnosis and also how we tailor patient specific therapies by allowing us investigate the tumor immune microenvironment (TME).
PhenoCode Signature panels are designed with users in mind to help them keep pace with the ever-changing combination therapy landscape. They also provide flexibility, allowing for the easy integration of one additional marker to a 5-plex panel. Here we present five examples of PhenoCode Signature panels and they cell phenotypes or specific research questions they are uniquely suited to examine.
Akoya’s commitment to accelerating the spatial signature development process has removed the hard part and guess work associated with developing biomarker panels by recently launching PhenoCode™ Signature Panels. These customizable panels contain key markers for phenotyping the tumor microenvironment and immune status. When combined with the high-speed and robust imaging of the PhenoImager platforms, a rapid, quantitative, end-to-end spatial phenotyping workflow is enabled. The workflow accelerates development and validation of predictive signatures and prognostic biomarkers for immuno-oncology applications. The number of clinical laboratories applying these multiplex imaging-based spatial biology workflows is increasing as well as the number of cancer types being analyzed. The PhenoImager platform continues to establish itself as the standard when generating robust and reproducible spatial phenotypic signatures, providing the level of accuracy and performance needed to support clinical trials and testing.4,5 There are many other spatial signatures waiting to be discovered, each of which with the potential to drive us, again to the same outcome – creating effective therapies.
- Farber D, L. Tissues, not blood, are where immune cells act. Nature. 27 May 2021, Vol 593, 506-509
- Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195–1204.
- Taube JM, Roman K, Engle EL, et al. Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study. Journal for Immuno Therapy of Cancer 2021
- Abdullahi Sidi F, Bingham V, Craig SG, et al. PDL1 Multiplex and Quantitative Image Analysis for Molecular Diagnostics. Cancers (Basel). 2020;13(1):29, 2020 Dec 23.
- Hoyt CC Multiplex Immunofluorescence and Multispectral Imaging: Forming the Basis of a Clinical Test Platform for Immuno-Oncology. Front. Mol. Biosci. 2021; 8:674747.
Author: James DeRosa, MPH