No abstract

Cancer immunotherapies, such as atezolizumab, are proving to be a valuable therapeutic strategy across indications, including non-small cell lung cancer (NSCLC) and urothelial cancer (UC). Here, we describe a diagnostic assay that measures programmed-death ligand 1 (PD-L1) expression, via immunohistochemistry, to identify patients who will derive the most benefit from treatment with atezolizumab, a humanized monoclonal anti-PD-L1 antibody. We describe the performance of the VENTANA PD-L1 (SP142) Assay in terms of specificity, sensitivity, and the ability to stain both tumor cells (TC) and tumor-infiltrating immune cells (IC), in NSCLC and UC tissues. The reader precision, repeatability and intermediate precision, interlaboratory reproducibility, and the effectiveness of pathologist training on the assessment of PD-L1 staining on both TC and IC were evaluated. We detail the analytical validation of the VENTANA PD-L1 (SP142) Assay for PD-L1 expression in NSCLC and UC tissues and show that the assay reliably evaluated staining on both TC and IC across multiple expression levels/clinical cut-offs. The reader precision showed high overall agreement when compared with consensus scores. In addition, pathologists met the predefined training criteria (≥85.0% overall percent agreement) for the assessment of PD-L1 expression in NSCLC and UC tissues with an average overall percent agreement ≥95.0%. The assay evaluates PD-L1 staining on both cell types and is robust and precise. In addition, it can help to identify those patients who may benefit the most from treatment with atezolizumab, although treatment benefit has been demonstrated in an all-comer NSCLC and UC patient population.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Activation of the MET signalling pathway is critical in regulating multiple cellular processes underlying tumourigenic growth and has represented an attractive target for therapeutic intervention in cancer. Early stage clinical studies of multiple agents targeting this pathway have been undertaken, frequently in unselected patient cohorts with variable results. Promising data in patient subgroups in these studies indicate the need for predictive biomarkers to identify the patients most likely to benefit from these therapies. In this review, we discuss the current knowledge of mechanisms of MET activation, the status of the clinical evaluation of MET-targeted therapies, the associated efforts to identify and validate biomarkers, and the considerations and challenges for potential development of companion diagnostics.

Part of developing therapeutics is the need to identify patients who will respond to treatment. For HER2-targeted therapies, such as trastuzumab, the expression level of HER2 is used to identify patients likely to receive benefit from therapy. Currently, chromogenic immunohistochemistry on patient tumor tissue is one of the methodologies used to assess the expression level of HER2 to determine eligibility for trastuzumab. However, chromogenic staining is fraught with serious drawbacks that influence scoring, which is additionally flawed due to the subjective nature of human/pathologist bias. Thus, to advance drug development and precision medicine, there is a need to develop technologies that are more objective and quantitative through the collection and integration of larger data sets. In proof of concept experiments, we show multiplexed ion beam imaging (MIBI), a novel imaging technology, can quantitate HER2 expression on breast carcinoma tissue with known HER2 status and those values correlate with pathologist-determined IHC scores. The same type of quantitative analysis using the mean pixel value of five individual cells and total pixel count of the entire image was extended to a blinded study of breast carcinoma samples of unknown HER2 scores. Here, a strong correlation between quantitation of HER2 by MIBI analysis and pathologist-derived HER2 IHC score was identified. In addition, a comparison between MIBI analysis and immunofluorescence-based automated quantitative analysis (AQUA) technology, an industry-accepted quantitation system, showed strong correlation in the same blind study. Further comparison of the two systems determined MIBI was comparable to AQUA analysis when evaluated against pathologist-determined scores. Using HER2 as a model, these data show MIBI analysis can quantitate protein expression with greater sensitivity and objectivity compared to standard pathologist interpretation, demonstrating its potential as a technology capable of advancing cancer and patient diagnostics.