Abstract 2752
Background
The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations.
Methods
Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n = 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n = 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8+ was tested on n = 163 durvalumab-treated and n = 199 non-IO-treated samples.
Results
A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster.
Conclusions
Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings.
Clinical trial identification
All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562.
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Boehringer Ingelheim, MedImmune, Definiens AG.
Disclosure
M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmüller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Schäpe: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.
Resources from the same session
2171 - CCND1 Amplification Contributes to Immunosuppression in Head and Neck Squamous Cell Carcinoma and the Association with a Poor Response to Immune Checkpoint Inhibitors
Presenter: Chloe Huang
Session: Poster Display session 3
Resources:
Abstract
2624 - Efficacy of PD-1/PD-L1 inhibitors in the treatment of non-small cell lung cancer patients with sensitive genes mutation
Presenter: Hui-Juan Cui
Session: Poster Display session 3
Resources:
Abstract
3494 - Neutrophil to Lymphocyte Ratio (NLR) kinetics as predictors of outcomes in metastatic renal cell carcinoma (mRCC) and non-small cell lung cancer (NSCLC) patients treated with nivolumab (N).
Presenter: Audrey Simonaggio
Session: Poster Display session 3
Resources:
Abstract
3964 - Predictive markers of checkpoint inhibitor activity in adult metastatic solid tumours
Presenter: Alexandra Pender
Session: Poster Display session 3
Resources:
Abstract
3041 - Blood-based TMB (bTMB) correlates with tissue-based TMB (tTMB) in a multi-cancer Phase I IO Cohort
Presenter: Daniel Araujo
Session: Poster Display session 3
Resources:
Abstract
3910 - Analysis of Molecular Profile Complexities for Immunotherapy Decision Support
Presenter: Robert Dóczi
Session: Poster Display session 3
Resources:
Abstract
4836 - The Role of Tumor Neoantigens in the Differential Response to Immunotherapy (IO) in EGFR and BRAF Mutated Lung Cancers - Quantity or Quality?
Presenter: Katrina Case
Session: Poster Display session 3
Resources:
Abstract
1929 - Impact of previous corticosteroid (CS) exposure on efficacy of Programmed Cell Death-(Ligand) 1 blockade in patients with advanced Non-Small-Cell Lung Cancer (NSCLC): a single Center retrospective analysis
Presenter: Fabrizio Nelli
Session: Poster Display session 3
Resources:
Abstract
2601 - Comparison 18F-FDG-PET/CT criteria for prediction of therapy response and clinical outcome in patients with metastatic melanoma treated with Ipilimumab and PD-1 inhibitors
Presenter: Sabrina Vari
Session: Poster Display session 3
Resources:
Abstract
3628 - Predictive model for survival in advanced non-small-cell lung cancer (NSCLC) treated with frontline pembrolizumab
Presenter: Xabier Mielgo Rubio
Session: Poster Display session 3
Resources:
Abstract