PathAI, a global provider of AI-powered technology applied to pathology research, announced that machine learning models were developed and applied on NSCLC samples from the LCMC3 trial by Genentech, a member of the Roche Group, and participating study investigators to identify predictive/prognostic biomarkers in the tumor microenvironment (TME) and perform AI-powered pathologic response assessment. A global summary of the LCMC3 primary analysis will be presented at the World Conference on Lung Cancer Symposium Singapore that will take place from January 28-31, 2021 in an oral presentation by Dr. David Carbone of The Ohio State University (“Clinical/Biomarker Data for Neoadjuvant Atezolizumab in Resectable Stage IB-IIIB NSCLC: Primary Analysis in the LCMC3 Study”, January 29, 2021; Session OA06.06).
The LCMC3 study is a single arm trial that enrolled 181 participants with resectable, untreated stage IB to select IIIB NSCLC to investigate the pathologic response to atezolizumab as a neoadjuvant treatment. Biopsies were collected from all study subjects prior to neoadjuvant treatment, and surgical resections were collected after treatment from 159 subjects. Pathologic response is suggested to be associated with survival outcomes and is traditionally assessed upon evaluating the residual tumor following a course of neoadjuvant therapy. A major pathologic response (MPR), described as less than 10% viable tumor cells present in the post neoadjuvant treatment resections, was achieved in 30/144 (21%) subjects eligible for preliminary analysis, and a complete pathologic response, meaning that 0% tumor cells were present after neoadjuvant treatment, was observed in 10/144 (7%) eligible subjects. PathAI’s AI-powered quantification of cell and tissue features to characterize the tumor microenvironment and analyze pathologic response is ongoing and those results will be presented at a future meeting.
In preliminary analyses, the PathAI research platform was able to identify and quantify tissue- and cell-level features in digitized whole slide images of hematoxylin and eosin (H&E)- stained biopsies and resections. ML model comparison of the TME composition pre- and post-treatment revealed quantifiable changes in histopathologic features in response to atezolizumab treatment. Furthermore, even in subjects that did not achieve a major pathologic response, these early results suggested that there may be a reduction in tumor tissue after treatment. If confirmed, this result would correlate well with other outcomes from this primary analysis that showed a significant increase in CD3+/PD1+ T cells after atezolizumab treatment, and that the presence of this cell type in the TME before treatment was associated with an observed MPR.
The data presented at WCLC highlight the potential for AI-powered pathology to reveal the architecture of the TME with the granularity necessary to understand the effect of anti-cancer agents and the biology underlying a treatment response. As treatment options become increasingly personalizable, developing robust and accurate quantitative measurements of the TME, and pathologic response to treatment will work toward enabling oncologists to provide patients with appropriate needs.
