1320P - Digital pathology predicts PD-L1 expression in metastatic NSCLC patients treated with immunotherapy

Background

Among numerous efforts to discover new biomarkers, PD-L1 expression stands alone, demonstrating its unique, although not perfect, ability to predict response to immune checkpoint inhibitors (ICIs). A specific morphology can be associated with PD-L1 expression, introducing new scenarios for ICIs response.

Methods

I3LUNG is a global EU-project, aiming to predict response to ICIs through artificial intelligence (AI). Six countries (Italy, Germany, Greece, Spain, US and Israel) collected digital pathology slides (DPS), genomics, radiomics, and other pt characteristics. Square tiles [299x299px] at 10x magnification were extracted from each whole slide. A Generative Adversarial Networks (GANs) and normalization were used to reduce batch effect. A foundation model pretrained using Self Supervised Learning was employed to convert each tile into a vector. To focus on the tumor areas, we used uncertainty gated model. Attention Based Multiple Instance Learning, a deep learning method was performed for classification. Among the 5 centers able to provide DPS, one center was held out and the model was trained through 4-fold cross validation (1-fold, 1 center) to define hyperparameters. Two models were developed: one to distinguish between pt with high PD-L1 (³50%) vs low/negative (<50%) and another one between positive (³1%) and negative (<1%) pt.

Results

2188 pt were enrolled in the I3LUNG retrospective cohort treated in first or further line IO. Among, 474 pt with available DPS and PD-L1 status were considered for the analysis. PD-L1 expression was high, low (1-49%) and negative in 145 (37%), 129 (32%) and 127 (31%) pt within the training and 24 (33%), 23 (32%) and 26 (35%) among the validation set. PD-L1 high vs low/negative status through DPS were able to be predicted with an area under the curve (AUC) of 0.70; while for PD-L1 positive vs negative an AUC of 0.68.

Conclusions

This is the largest series to date demonstrating a correlation between morphological features and PD-L1 expression in lung cancer. This rapid and generalizable model suggests that PD-L1 high and negative have different morphological phenotypes. Within I3LUNG the integration of genomic and radiomic data is expected to improve the ability to assess patient prognosis.

Clinical trial identification

NCT05537922.

Editorial acknowledgement

Legal entity responsible for the study

Fondazione IRCCS Istituto Nazionale dei Tumori (INT).

Funding

European Commission, Grant Horizon Europe, Project ID: 101057695.

Disclosure

A. Prelaj: Financial Interests, Personal, Other, Training of personnel: AstraZeneca, Italfarma; Financial Interests, Personal, Invited Speaker, The Hive Project: Discussant: Roche; Financial Interests, Personal, Advisory Board, Advisory board in Lung Cancer project: BMS; Financial Interests, Personal, Other, Travel Grant: Janssen; Financial Interests, Personal, Advisory Board: Janssen, AstraZeneca; Financial Interests, Personal, Invited Speaker: Medsir; Financial Interests, Institutional, Local PI: Bayer, BMS, Lilly, MSD; Financial Interests, Institutional, Coordinating PI: AstraZeneca, Spectrum. C. Proto: Financial Interests, Personal, Advisory Board: Italfarmaco, AstraZeneca, BMS, MSD, Janssen; Financial Interests, Institutional, Funding: Novartis. A. Meya: Financial Interests, Personal, Other, Employee: MEDSIR. E. Felip: Financial Interests, Personal, Advisory Board: AbbVie, Amgen, AstraZeneca, Bayer, BeiGene, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, F. Hoffmann-La Roche, Gilead, GSK, Janssen, Merck Serono, Merck Sharp & Dohme, Novartis, Peptomyc, Regeneron, Sanofi, Takeda, Turning Point, Pfizer, Genmab; Financial Interests, Personal, Invited Speaker: Amgen, Daiichi Sankyo, Genentech, Janssen, Medical Trends, Medscape, Merck Serono, PeerVoice, Pfizer, Sanofi, Takeda, AstraZeneca, Bristol Myers Squibb, Eli Lilly, F. Hoffmann-La Roche, Merck Sharp & Dohme; Financial Interests, Personal, Member of Board of Directors, Independent member: Grifols; Financial Interests, Institutional, Local PI, Clinical Trial: AstraZeneca AB, AbbVie, Amgen, Bayer Consumer Care AG, BeiGene, Boehringer Ingelheim GmbH, Bristol Myers Squibb International Corporation, Daiichi Sankyo Inc., Exelixis Inc., F. Hoffmann-La Roche Ltd., Genentech Inc., GSK Research and Development Limited, Janssen Cilag International NV, Merck Sharp & Dohme Corp, Merck KGAA, Mirati Therapeutics Inc., Novartis Pharmaceutica SA, Pfizer, Takeda Pharmaceuticals International; Financial Interests, Institutional, Local PI: Nuvalent; Non-Financial Interests, Leadership Role, President (2021-2023): SEOM (Sociedad Espanola de Oncologia Medica); Non-Financial Interests, Member, Member of Scientific Committee: ETOP (European Thoracic Oncology Platform); Non-Financial Interests, Member, Member of the Scientiffic Advisory Committee: CAC Hospital Universitari Parc Taulí. M. 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All other authors have declared no conflicts of interest.