Abstract 167P
Background
Immune Checkpoint Inhibitors (ICIs) are pivotal in the treatment of late stage NSCLC. Currently, PDL1-high (TPS>=50%) patients generally receive ICI alone, while PDL1 low and negative patients typically receive chemoimmunotherapy (chemo-ICI). However, many patients in both groups fail to respond, suggesting opportunities for more effective intervention - particularly PDL1 high patients who may benefit from escalation to chemo-ICI. We developed an AI-based digital pathology immunotherapy response (PIRe) score based upon tumor infiltrating lymphocyte density (denTIL) and their spatial interactions with tumor cells (spaTIL).
Methods
Pre-treatment hematoxylin and eosin (H&E) stained whole slide images (WSIs) of patients with mostly late stage NSCLC (94.3% stage III/IV, 5.7% stage I/II) treated with ICI were collected from 3 institutions (D1-D3). Nuclei of tumor cells and TIL were segmented and classified automatically via the Picture Health Px platform for spaTIL and denTIL feature calculation. A neural network classifier was trained with these features on a training set (D1 + D2, N = 77) for primary adenocarcinoma and squamous cell carcinoma to predict objective response (OR). PIRe, PDL1 TPS, and a combined PIRe+PDL1 model were then tested on an external test set (D3, N=29) of ICI and chemo-ICI recipients.
Results
PIRe included 5 features, such as morphology of TIL clusters and abundance of TILs surrounding tumor cells. Overall test AUC was 0.71 with 0.82 for ICI and 0.66 for chemo-ICI. PIRe was significantly elevated among responders to ICI (p=0.04, n=11), but not to chemo-ICI (p=0.14, n=16), as assessed by one-sided Wilcoxon rank-sum test. When combined, PDL1 and PIRe consistently outperformed PDL1 TPS alone (Table). Table: 167P
AUC for ICI response prediction with different models for different treatment subgroups
AUC | All treatments (n=29) | ICI only (n=11) | chemo-ICI only (n=18) |
PIRe | 0.71 | 0.82 | 0.60 |
PDL1 (TPS) IHC | 0.76 | 0.75 | 0.81 |
PDL1 IHC + PIRe | 0.82 | 0.96 | 0.85 |
Conclusions
PIRe predicted ICI response in NSCLC patients, showing added value to PDL1. Further validation in larger cohorts to confirm these promising early findings is essential.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Picture Health.
Disclosure
A. Madabhushi: Financial Interests, Personal, Advisory Board, Serve on SAB and consult.: SimbioSys; Financial Interests, Personal, Advisory Board: Aiforia, Picture Health; Financial Interests, Personal, Full or part-time Employment: Picture Health; Financial Interests, Personal, Ownership Interest: Picture Health, Elucid Bioimaging, Inspirata Inc; Financial Interests, Personal, Royalties: Picture Health, Elucid Bioimaging; Financial Interests, Institutional, Funding: AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Eli Lilly. N. Braman: Financial Interests, Personal, Stocks/Shares: Picture Health, Tempus Labs. All other authors have declared no conflicts of interest.
Resources from the same session
173P - Unveiling a novel EpCAM-CD24+ circulating cells with unidentified origin associated with breast cancer distant metastasis
Presenter: Evgeniya Grigoryeva
Session: Poster session 08
174P - Prognostic value of the immune and metabolic profile in the response to neoadjuvant treatment with ICIs in triple-negative breast cancer patients (TNBC)
Presenter: Lucía Serrano García
Session: Poster session 08
175P - Utility of artificial intelligence (AI) in Ki67 scoring of a breast cancer (BC) patient population
Presenter: Xavier Pichon
Session: Poster session 08
176P - ERBB2 amplifications across sex, race, and cancer types
Presenter: Marc Machaalani
Session: Poster session 08
177P - HER2 testing in multiple solid tumors: Concordance between 3 scoring algorithms
Presenter: Wentao Yang
Session: Poster session 08
178P - PD-L1 expression in ER-low versus triple-negative (TN) advanced breast cancer (aBC), and according to phenotypic evolution from primary to recurrent disease
Presenter: Federica Miglietta
Session: Poster session 08
179P - Multimodal deep learning integrating MRI and molecular profiles for predicting outcomes in triple-negative breast cancer
Presenter: Seong Hwan Park
Session: Poster session 08
181P - Molecular characterization and immune microenvironment analysis of MSI-H patients with or without MMR gene mutations
Presenter: Mengxi Ge
Session: Poster session 08
182P - Multi-modal artificial intelligence outperforms image-based approaches for mutation prediction from H&E tissue images in colorectal cancer
Presenter: Marc Päpper
Session: Poster session 08