Advances in immunotherapy have dramatically changed the landscape for cancer researchers. With significant improvements in overall survival and recurrent free survival in some of the deadliest cancers, lives have been extended by years for some patients while other patients have not experienced benefit from immunotherapy treatment. For some cancer indications, such as non-small cell lung cancer, the FDA approved companion diagnostic test for PD-L1 by immunohistochemistry has predictive value on patient response. However, the test is not a perfect indicator of response as some PD-L1 negative patients will respond to therapy and, conversely, some PD-L1 positive patients will not. At the same time, the number of clinical trials examining immunotherapy and combination therapies with immunotherapy as a backbone has exceeded 1000 in 2017. The possibility of combination therapy brings many questions to light for the researcher and ultimately the physician on which therapy to choose, for how long, in what order and what potential combination. With these questions, comes the increasing need for predictive tests that can lead the physician to the right therapy at the right time.
Several tests are currently under investigation for their predictive properties on immunotherapy response across indications. Tumor mutational burden and microsatellite instability assess the overall level of mutations in a particular tumor while neoantigen discovery will illuminate the cell cycle errors driving a particular tumor’s growth. Immunohistochemistry of mismatch repair protein expression may also give some insight to determining a patient’s success on immunotherapy. Recently, efforts have focused on the tumor microenvironment as an indicator of response to immunotherapy. Specifically, “hot” tumors with prior involvement of the immune system have been suggested to have statistically higher response to immunotherapy and, therefore, proper assessment of various types of T cell response in the area of the tumor could provide insight into a patient’s prognosis under treatment protocols. The environmental makeup remains extremely complex, with some cells enhancing immunosuppression and others inducing potent anti-tumor responses. Furthermore, evaluation of proximity and classification of the differing types of T cells will be critical in assessing the status of the existing immune involvement. In order to assess the tumor microenvironment, researchers will have to go beyond measurement of variants of DNA and illustrate the protein interactions and overall tissue phenome characteristics surrounding the tumor.
In the case of standard immunohistochemistry, the challenge in assessment lies within the inherent semi-quantitative analysis output. Manual pathology assessment of a single IHC marker is constrained by the intensity and percent of cells stained. Additionally, standardization and reproducibility remain a challenge in the clinical trial setting. Conversely, immunohistochemistry allows for the evaluation of differential expression among the heterogeneity of the sample by which no other testing modality is suitable. Employing image analysis and machine learning from analytics software such as Definiens, allows for standardization of the data captured for a given marker, thereby making the analysis of the marker under study more robust and powerful. A standard IHC marker can be analyzed with image analysis tools to develop an enumeration algorithm to assess the overall intensity of the marker as well as the density of the marker in the tumor region (reported in cells/mm2). Subsequent validation efforts will show the correlation of the analysis using the software compared with the manual assessment consistently meet the preset criteria. Furthermore, the repeatability of the assay when using image analysis consistently meets criteria significantly better than manual assessment. Overall, the employment of image analysis for any given marker helps to improve pathology assessments and standardizes interpretation as well as systematically identifies the optimal cut-point threshold.
With the assessment of the immune contexture, the tools used for analysis are almost as important as the finding of a predictive biomarker itself. In the example of PD-L1, tremendous effort was applied in the validation to ensure repeatability of testing. From specimen stability to report templates, each step in the work flow needed careful examination. For this test, both technologists and pathologists were tested and certified for approved testing facilities. Any clinical trial assay needs to be reliable such that it ultimately can show association to response—withstanding the statistical analysis performed for the trial. While PD-L1 by immunohistochemistry has been successfully employed as a companion diagnostic for several therapies across a handful of indications, PD-L1 is only one biomarker among many others providing insights into the tumor microenvironment of a patient.
Historically, clinical trial assays in oncology focused on the biology of the tumor itself, ultimately honing in on one biomarker as a potential companion diagnostic. Focusing on the surrounding environment of the tumor introduces a new playing field for measurement of biomarkers. Not only does the paradigm change because we are looking tissue phenome characteristics surrounding the tumor, we are in need of a multiplexed solution that allows for the identification to various types of cells including, but not limited to, influential regulatory T cells. Multiplexed immunohistochemistry options are limited by the location of the marker as multiple membranous proteins are unable to produce differentiation of stain under traditional protocol conditions. Non-traditional multiplexed options exist such as MultiOmyxTM with the employment of image analysis to obtain images of multiple biomarkers on the same cell, enabling classification of immune cell phenotypes to differentiate cells in the tumor microenvironment (see image). Predictive tests of the future within immunotherapy will require markers that describe the tumor and its environment and will need to have tools to systematically assess the characterization that goes beyond qualitative assessments of the past.
Gina Wallar, PhD
Division Vice President for Pharma Services Sales