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TP53 and LRP1B Co-Wild Predicts Improved Survival for Patients with LUSC Receiving Anti-PD-L1 Immunotherapy.

ABSTRACT: Immunotherapy brought long-term benefits for partial patients with lung squamous cell carcinoma (LUSC). The predictor of anti-PD-L1 therapy was controversial and limited in LUSC. We aimed to explore novel biomarker for LUSC immunotherapy and the potential mechanism. Five hundred and twenty-five Chinese patients (Geneplus cohort) with LUSC underwent targeted sequencing and were involved to explore the genomic profiling. TP53 and LRP1B were the most frequently recurrent genes and correlated to higher tumor mutational burden (TMB). We observed that LUSC patients with TP53 and LRP1B co-wild (co-wild type) were associated with better survival of anti-PD-L1 therapy compared with TP53 mutant or LRP1B mutant (mutant type) in POPAR/OAK cohort. Copy-number variation (CNV) and whole genome doubling (WGD) data from TCGA LUSC cohort were obtained to assess the CNV events. There were fewer CNV alterations and lower chromosome instability in patients with TP53/LRP1B co-wild compared with those with TP53/LRP1B mutant. RNA expression data from the TCGA LUSC cohort were collected to explore the differences in RNA expression and tumor immune microenvironment (TIME) between mutant and co-wild groups. The TP53/LRP1B co-wild type had a significantly increased proportion of multiple tumor-infiltrating lymphocytes (TILs), including activated CD8 T cell, activated dendritic cell (DC), and effector memory CD8 T cell. Immune-related gene sets including checkpoint, chemokine, immunostimulatory, MHC and receptors were enriched in the co-wild type. In conclusion, TP53/LRP1B co-wild LUSC conferred an elevated response rate in anti-PD-L1 therapy and improved survival, which was associated with a chromosome-stable phenotype and an activated immune microenvironment.

SUBMITTER: Yu J

PROVIDER: S-EPMC9320428 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity.SignificanceThe weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.

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TP53 and LRP1B Co-Wild Predicts Improved Survival for Patients with LUSC Receiving Anti-PD-L1 Immunotherapy.

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