Abstract 5705: Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer

Background: In recent years, a relationship between the tumor microenvironment (TME) and patient response to targeted cancer immunotherapy has been suggested. We applied machine-learning algorithms on H&E stained tissue to study the TME in metastatic non-small cell lung cancer (NSCLC) patients. Our goal was to identify digital pathology (DP) features associated with outcome under combination treatment or monotherapy with atezolizumab (atezo), an anti-PD-L1 therapy, and relate those features to other data modalities. We analyzed patient data from two phase 3 clinical trials, OAK (docetaxel versus atezo in 2L+ NSCLC) and IMpower150 (bevacizumab, carboplatin, and paclitaxel (BCP) versus BCP+atezo (ABCP) in advanced 1L non-squamous NSCLC).

Methods: As part of our effort to build a DP-based tumor-immune microenvironment atlas, digitized H&E images were registered onto the PathAI research platform. Over 200K annotations from 90 pathologists were used to train convolutional neural networks (CNNs) that classify slide-level human-interpretable features (HIFs) of cells and tissue structures from images and deployed on images from OAK and IMpower150. HIFs and PD-L1 status were associated with outcome in all samples in each arm in OAK and results were validated in IMpower150, using Cox proportional hazard models. Bulk RNAseq was run using samples extracted from the same area as the H&E slide.

Results: We identified a composite feature capturing the ratio of immune cells to fibroblasts in the stroma predictive of both overall survival (OS) (HR=0.74 p=0.0046) and progression-free survival (PFS) (HR=0.87 p=0.14). While patients primarily benefit from atezo if they are PD-L1 high, we found that even PD-L1 negative patients benefited from atezo when enriched for this feature (22C3 PD-L1 assay: OS HR=0.59 p=0.015, PFS HR=0.8 p=0.25; SP142 PD-L1 assay: OS HR=0.74 p=0.12, PFS HR=0.88 p=0.45). We thus recognized a DP feature that was predictive for positive outcome with atezo treatment, independent of PD-L1 levels. This association was then validated in IMpower150 comparing ABCP to BCP, both overall (OS HR=0.69 p=0.012) and in PD-L1 negative patients (SP263 assay OS HR=0.56 p=0.034). Integrating with RNAseq, patients enriched for this DP feature showed similar enrichment for B and T gene signatures and depletion in CAF-related gene signatures, thus showing the harmonization of TME between different data modalities.

Conclusions: Using a deep learning-based assay for quantifying pathology features of the TME from H&E images in two NSCLC trials, we identified a novel biomarker predictive of outcome to PD-L1 targeting therapy, even in PD-L1 low & negative patients. Importantly, our work shows how different data modalities (DP, gene expression) can be integrated to further our understanding of the TME.

Citation Format: Aditi Qamra, Minu K. Srivastava, Eloisa Fuentes, Ben Trotter, Raymond Biju, Guillaume Chhor, James Cowan, Steven Gendreau, Webster Lincoln, Lisa McGinnis, Luciana Molinero, Namrata S. Patil, Amber Schedlbauer, Katja Schulze, Adam Stanford-Moore, Laura Chambre, Ilan Wapinski, David S. Shames, Hartmut Koeppen, Stephanie Hennek, Jane Fridlyand, Jennifer M. Giltnane, Assaf Amitai. Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5705.

Abstract CT112: AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC)

Background: PD-L1 expression evaluated by immunohistochemistry (IHC) is a well-established predictor of anti-PD-L1/PD-1 cancer immunotherapy (CIT). The Phase II LCMC3 (NCT02927301) study evaluated pre-operative treatment (tx) with atezolizumab (anti-PD-L1) in pts with untreated early stage resectable NSCLC, achieving a 20% major pathologic response (MPR) rate (primary efficacy pts, n=143). A digital PD-L1 scoring method was developed to assess PD-L1 expression as a potential predictive marker for MPR in squamous and non-squamous tumor samples from LCMC3.

Methods: Manual scoring was used to determine PD-L1 status on pre-tx biopsy samples using the tumor proportion score (TPS) with a positive threshold of TPS≥50 (22C3). Binary results were correlated with MPR and stratified by squamous/non-squamous histology. A digital pathology workflow for automated PD-L1 scoring was developed to yield a precise continuous PD-L1 TPS. Deep convolutional neural networks trained using pathologist annotations were used to detect individual cells within the tumor and tumor microenvironment and quantify their PD-L1 expression. These cell type predictions were used to compute a digital PD-L1 TPS. LCMC3 pts with available digital and manual PD-L1 scores were then used to assess the role of PD-L1 expression in predicting MPR.

Results: PD-L1 scores were available for pre-tx biopsies from 108 pts. No significant difference in scores was seen between histological subtypes. At cutoff (Oct 15, 2021), TPS≥50 was seen in 41 (non-squamous, n=26 [39%]; squamous, n=15 [36%]) of 108 pts and was associated with MPR in non-squamous (odds ratio [OR], 28.6; P<0.001; Fisher’s exact test) but not squamous histology (OR, 1.3; P=1.0). Continuous digital PD-L1 scores (range: 0-100) were highly correlated with local manual PD-L1 scores (range: 0-100) for squamous (n=42, Pearson r=0.90, P<0.001) and non-squamous stained histology slides (n=66, Pearson r=0.90, P<0.001). Continuous digital and manual PD-L1 TPS on pre-tx biopsies (n=108) were predictive of MPR (digital: area under the receiver operating curve (AUROC)=0.678, logistic regression [LR] P=0.01; manual: AUROC=0.675, LR P=0.003). Strikingly, when pts were stratified by histology, PD-L1 scores were predictive of MPR from pre-tx biopsies for non-squamous samples (n=66; digital: AUROC=0.821, LR P=0.002; manual: AUROC=0.819, LR P=0.001) but not for squamous samples (n=42; digital: AUROC=0.519, LR P=0.93; manual: AUROC=0.506, LR P=0.90), despite no significant difference in MPR rate between the 2 groups.

Conclusions: These findings support using digitally assessed PD-L1 IHC as a centralized and standardized scoring system and suggest that tumor histological subtype could be an important factor in the utility of PD-L1 as a predictive biomarker for neoadjuvant CIT in early stage NSCLC.

Citation Format: John Abel, Christopher Rivard, Filip Kos, Guillaume Chhor, Yi Liu, Jennifer Giltnane, Sara Hoffman, Murray Resnick, Cyrus Hedvat, Amaro Taylor-Weiner, Farah Khalil, Alan Nicholas, Gregory A. Fishbein, Lynette M. Sholl, Natasha Rekhtman, Stephanie Hennek, Ilan Wapinski, Ann Johnson, Michael Montalto, Katja Schulze, Bruce E. Johnson, David P. Carbone, Konstantin Shilo, Andrew H. Beck, Sanja Dacic, William D. Travis, Ignacio Wistuba. AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT112.

Abstract LB016: Deep learning identifies pathobiological features within H&E images associated with genomic alterations and progression on anti-PD(L)1 in HUDSON, an AstraZeneca-sponsored Phase II clinical trial

Introduction Machine learning (ML) models offer the potential to provide rich, quantitative characterizations of the tumor and tumor micro-environment (TME). Here we deployed a machine learning-based approach to the analysis of H&E images from HUDSON (NCT03334617), an AstraZeneca Phase II Platform clinical trial, to identify and quantify cellular composition and tissue architecture features in the TME that are associated with genomic alterations and time to progression on anti-PD(L)1 therapies.

Methods PathAI previously trained ML models on non-small cell lung carcinoma (NSCLC) samples from commercial and clinical datasets to identify cell types and tissue regions within the TME. With no additional training, the models were deployed on 169 digitized whole slide images (WSIs) of H&E-stained biopsies from an international, multi-site AstraZeneca-sponsored Phase II clinical trial of novel anti-cancer agents in subjects with metastatic NSCLC. Biopsies were across multiple body sites, and taken both pre- and post-checkpoint progression. ML models generated human interpretable features (HIFs) that characterize the cell composition and tissue architecture from each biopsied sample. HIFs from baseline samples that met minimum image quality thresholds (n=89) were clustered to reduce redundancy and were tested for association with weeks to progression on anti-PD(L)1 therapy using Cox regression analysis.

Results The PathAI ML models were successfully deployed on WSIs from the HUDSON clinical trial. Following correction for biopsy timing and location, a total of 59 HIFs were found to be significantly associated (p <0.05) with weeks to progression on anti-PD(L)1 therapy, including features related to plasma cell infiltration, proportion of cancer cells, presence of macrophages and fibroblasts, and blood vessel compression. Features characterizing both plasma cells and blood vessels were also found to be significantly associated with any class I HLA locus loss of heterozygosity.

Conclusions PathAI models were able to identify TME-associated features from WSIs from a Phase II clinical trial which were associated with therapy failure and genomic alterations. These results suggest the power of deploying pre-trained ML-based systems in a clinical trial setting to identify pathobiological features associated with tumor characteristics and time to progression from only H&E images.

Citation Format: Laura Dillon, Marylens Hernandez, Ben Glass, Guillaume Chhor, Sara Hoffman, Varsha Chinnaobireddy, Sai Chowdary Gullapally, Kris Sachsenmeier, Andy Beck, Jason Hipp. Deep learning identifies pathobiological features within H&E images associated with genomic alterations and progression on anti-PD(L)1 in HUDSON, an AstraZeneca-sponsored Phase II clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB016.

Abstract PO-072: Robust deployment of ML models quantifying the H&E tumor microenvironment in NSCLC subjects from an AstraZeneca-sponsored phase II clinical trial

Introduction Machine learning models offer the potential to provide rich, quantitative characterizations of the tumor and tumor micro-environment (TME); however, historically it has been difficult to generalize trained models to new sets of clinical trial samples from trials not used in training. Here we evaluate the ability to deploy a machine learning based model (ML Model) for the identification of non-small cell lung tissue regions and lymphocytes within the tumor and TME on H&E stained images from clinical trial samples with no additional model training. Methods The ML model was previously trained on both squamous cell carcinoma and lung adenocarcinoma non-small cell lung carcinoma (NSCLC) samples from commercial and clinical datasets. The ML model was deployed on an AstraZeneca-sponsored phase II clinical trial of novel anti-cancer agents in patients with metastatic NSCLC. In order to validate the predictions of lymphocytes from the H&E stained images, we established a reference dataset for manual vs digital concordance consisting of 300, 150 × 150-micron–sized “frames” sampled from the trial dataset, removing frames of inadequate tissue quality or with presence of artifacts. For each frame, we collected exhaustive annotations from 5 pathologists to produce quantitative estimates of lymphocytes. Altogether, 43,932 annotations were collected and used to compute pathologist consensus scores for each frame. These scores were then correlated with each individual pathologist (inter-reader agreement) and with the PathAI-derived automated scores for evaluation of manual vs digital agreement. Results The PathAI system was successfully deployed on 169 H&E stained images from the phase II clinical trial to exhaustively identify all tumor associated lymphocytes from each whole slide image. In total, PathAI classified 2,859,796 lymphocytes, with an average number of 16,922 lymphocytes per image. We used frames-based validation to determine the correlation between the automated scoring and consensus scoring from pathologists hand labeling individual lymphocytes within image frames. The PathAI platform showed strong correlation between reference-based consensus scores (r2 = 0.84, CI [0.80 – 0.87]) and the ML model, which was similar to the level of agreement achieved between individual pathologists (r2 = 0.80, CI [0.76 – 0.85]). Conclusions The PathAI system showed strong generalizability for the identification of lymphocytes within the tumor and TME from H&E stained images from NSCLC clinical trial samples. These results suggest the power of deploying ML-based systems broadly for the automated, single cell resolution characterization of disease pathology from clinical trial material.

Citation Format: Ben Glass, Laura Dillon, Guillaume Chhor, Sara Hoffman, Varsha Chinnaobireddy, Sai Chowdary Gullapally, Andy Beck, Jason Hipp. Robust deployment of ML models quantifying the H&E tumor microenvironment in NSCLC subjects from an AstraZeneca-sponsored phase II clinical trial [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-072.

Artificial intelligence and dermatology: opportunities, challenges, and future directions

The application of artificial intelligence (AI) to medicine has considerable potential within dermatology, where the majority of diagnoses are based on visual pattern recognition. Opportunities for AI in dermatology include the potential to automate repetitive tasks; optimize time-consuming tasks; extend limited medical resources; improve interobserver reliability issues; and expand the diagnostic toolbox of dermatologists. To achieve the full potential of AI, however, developers must aim to create algorithms representing diverse patient populations; ensure algorithm output is ultimately interpretable; validate algorithm performance prospectively; preserve human-patient interaction when necessary; and demonstrate validity in the eyes of regulatory bodies.

Artificial intelligence and dermatology: opportunities, challenges, and future directions

The application of artificial intelligence (AI) to medicine has considerable potential within dermatology, where the majority of diagnoses are based on visual pattern recognition. Opportunities for AI in dermatology include the potential to automate repetitive tasks; optimize time-consuming tasks; extend limited medical resources; improve interobserver reliability issues; and expand the diagnostic toolbox of dermatologists. To achieve the full potential of AI, however, developers must aim to create algorithms representing diverse patient populations; ensure algorithm output is ultimately interpretable; validate algorithm performance prospectively; preserve human-patient interaction when necessary; and demonstrate validity in the eyes of regulatory bodies.