14P Integrated genomic analysis of whole genomes to derive a biomarker of durable response to immunotherapy in melanoma

Background: Adjuvant treatment has become more personalized in resectable NSCLC.New biomarkers are needed to predict treatment outcomes.In this study, we looked for a broadly applicable biomarker using deep learning on hematoxylin and eosin (H&E) histology.Based on recommendations [Salgado et al., Ann Oncol 2015], we focused on analyzing tumor infiltrating lymphocytes in the stromal (sTILs) and the intratumoral (iTILs) compartment.We assessed TILs fully automated at slide and tumor bulk level, guided by manual annotations of the tumor bulk.We hypothesized high iTIL and sTIL density being associated with improved disease-free survival (DFS) in the total study population.Methods: In this retrospective cohort study, 106 chemo-naive patients with resectable NSCLC were identified in two cohorts, based on recurrence (n45, cohort A) versus no recurrence (n61, cohort B).Whole mount tumor slides were stained with H&E and digitalized.Deep learning models were used to compute tissue masks [J.Spronck et al., PLMR 2023] and for TIL detection [HoverNet, Med Imag Anal.2019].Tumor bulk was manually annotated to exclude distant tumor lets.Distribution of iTIL and sTIL densities in cohort A and B were compared using Mann-Whitney U based on absence of normal distribution patterns.DFS was measured by log-rank and cox regression to correct for covariates after a binary distinction based on median TIL densities. Results:The slide and annotation iTIL density were significantly higher in cohort B versus cohort A (slide: 300 vs 232 TILs/mm2, p0.007 and annotation: 318 vs 230 TILs/mm2, p0.013).The slide and annotation sTIL density were also higher in cohort B versus A (slide: 1489 vs 1153 TILs/mm2, p0.035 and annotation: 1753 vs 1469, p0.030).DFS was significantly higher in patients with a high slide iTIL density compared to patients with a low slide iTIL density (HR 0.44; 95% CI 0.24 to 0.81; P<0.009).DFS remained significant after correction for age, gender, smoking history co-morbidity and clinical stage, but not for ECOG performance. Conclusions:The iTIL density detected on slide level could serve as favorable prognostic factor in patients with resectable NSCLC, potentially being a biomarker for adjuvant treatment.

Authors

Institutions

Publication Details

Journal
Immuno-Oncology Technology
Published
2024-12-01
DOI
https://doi.org/10.1016/j.iotech.2024.100897
Primary Topic
Immunotherapy and Immune Responses
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

14P Integrated genomic analysis of whole genomes to derive a biomarker of durable response to immunotherapy in melanoma

Benjamin Shum, Irene Lobón, A.C. Gault, Sharon H. Nahm et al.
Immuno-Oncology Technology
Immunotherapy and Immune Responses
article

14P Integrated genomic analysis of whole genomes to derive a biomarker of durable response to immunotherapy in melanoma

Benjamin Shum, Irene Lobón, A.C. Gault, Sharon H. Nahm, Kevin Litchfield, Dennis E. Egan, Husayn Ahmed Pallikonda, Qinghua Jiang, Samra Turajlic, Tom Lesluyes, L.M. Pickering, Leila Khoja, Alberto Fusi, Paul C. Lorigan, Sarah Danson, James Larkin, Emilia Peleva, Peter Van Loo, S. El Badri, Rabia Fidan
article en

Abstract

Background: Adjuvant treatment has become more personalized in resectable NSCLC.New biomarkers are needed to predict treatment outcomes.In this study, we looked for a broadly applicable biomarker using deep learning on hematoxylin and eosin (H&E) histology.Based on recommendations [Salgado et al., Ann Oncol 2015], we focused on analyzing tumor infiltrating lymphocytes in the stromal (sTILs) and the intratumoral (iTILs) compartment.We assessed TILs fully automated at slide and tumor bulk level, guided by manual annotations of the tumor bulk.We hypothesized high iTIL and sTIL density being associated with improved disease-free survival (DFS) in the total study population.Methods: In this retrospective cohort study, 106 chemo-naive patients with resectable NSCLC were identified in two cohorts, based on recurrence (n45, cohort A) versus no recurrence (n61, cohort B).Whole mount tumor slides were stained with H&E and digitalized.Deep learning models were used to compute tissue masks [J.Spronck et al., PLMR 2023] and for TIL detection [HoverNet, Med Imag Anal.2019].Tumor bulk was manually annotated to exclude distant tumor lets.Distribution of iTIL and sTIL densities in cohort A and B were compared using Mann-Whitney U based on absence of normal distribution patterns.DFS was measured by log-rank and cox regression to correct for covariates after a binary distinction based on median TIL densities. Results:The slide and annotation iTIL density were significantly higher in cohort B versus cohort A (slide: 300 vs 232 TILs/mm2, p0.007 and annotation: 318 vs 230 TILs/mm2, p0.013).The slide and annotation sTIL density were also higher in cohort B versus A (slide: 1489 vs 1153 TILs/mm2, p0.035 and annotation: 1753 vs 1469, p0.030).DFS was significantly higher in patients with a high slide iTIL density compared to patients with a low slide iTIL density (HR 0.44; 95% CI 0.24 to 0.81; P<0.009).DFS remained significant after correction for age, gender, smoking history co-morbidity and clinical stage, but not for ECOG performance. Conclusions:The iTIL density detected on slide level could serve as favorable prognostic factor in patients with resectable NSCLC, potentially being a biomarker for adjuvant treatment.

Immuno-Oncology TechnologyVol. 24
University College Dublin (IE), St George's, University of London (GB), Barts Health NHS Trust (GB), The Francis Crick Institute (GB), Cancer Research UK (GB), Freeman Hospital (GB), Weston Park Cancer Centre (GB), University of Oxford (GB), Sheffield Teaching Hospitals NHS Foundation Trust (GB), Birmingham Dental Hospital (GB), Royal Marsden Hospital (GB), The Christie NHS Foundation Trust (GB), Genomics (United Kingdom) (GB), University College London (GB)
Zero hunger
Openalex Percentile: Top 70%
Immunotherapy and Immune Responses
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.