Baseline pathology image analyses by deep learning approaches to predict the onset of ESR1 mutations, an exploratory study on samples collected in PADA-1, a UCBG/GINECO phase 3 trial
Worldwide Cancer Research
Institute Curie
Background Pathology image analysis by deep neural networks has been successfully applied to the detection of numerous mutations (EGFR in NSCLC, BRCA…) already present in tumor cells; these approaches have however never been used to predict whether a resistance mutation will appear later during therapy. Mutations in ESR1, the gene encoding for estrogen receptor (ER) alpha, are rare (<5%) in untreated ER+ HER2- metastatic breast cancers before therapy but are “acquired” in ~40% of cancers as a mechanism of resistance to aromatase inhibitors (AI). Interestingly, these mutations are sensitive to selective ER degraders (SERDs). Predicting which cancer is more likely to develop an ESR1 mutation could transform ER+ breast cancer care, as it will allow targeting high-risk patients and even guide the use of SERDs before ESR1 mutations become detectable. Challenge We hypothesize that digitalized and combined hematoxylin-eosin (H&E), ER and PR (progesterone receptor) immunohistochemistry (IHC) analyses of pre-treatment tumor tissue by deep neural networks could accurately predict the later onset of ESR1 mutations during AI-based therapy. Methods 588 tumor samples, obtained with each patient consent before the start of first line AI + palbociclib therapy, have been collected as part of the PADA-1 trial (PI: FC Bidard; NCT03079011). These samples are fully annotated: patient and tumor characteristics, treatment response, PFS, OS and ESR1 mutation status by ctDNA analysis collected every 2 months during therapy, until tumor progression. Centralized H&E stained slides, ER and PR IHC slides will be digitalized and then processed by our deep learning Multiple Instance Learning workflow, based on tiling, encoding, attention scoring, tile aggregation, and slide classification. Deliverables We will establish validated algorithm predicting the individual risk to develop an “acquired” ESR1 mutations while on AI + Palbociclib. The prediction of treatment outcomes (PFS, OS) will also be assessed as a by-product of this analysis.
6.1 Pharmaceuticals