Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we found a significant B cell association with extended OS with PD-L1 blockade, independent of CD8+ T cell signals. We then derived gene signatures corresponding to the dominant B cell subsets present in NSCLC from single-cell RNA-seq data. Importantly, we found increased plasma cell signatures to be predictive of OS in patients treated with atezolizumab, but not chemotherapy. B cells were also associated with the presence of tertiary lymphoid structures and organized lymphoid aggregates. Our results suggest an important contribution of B and plasma cells to PD-L1 blockade efficacy in NSCLC.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

11 Datasets 15 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001007703	This dataset includes full tumor transcriptomes from 891 advanced NSCLC tumors. These data originate from pre-treatment samples from two large randomized clinical trials for second-line non-small cell lung cancer (POPLAR and OAK). The patients in these trials were treated with either the PD-L1 inhibitor atezolizumab or chemotherapy.	unspecified	891
EGAD00001008390	This dataset contains log2(TPM + 1) for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).		-
EGAD00001008391	This dataset contains log2(TPM + 1) for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).		-
EGAD00001008548	Relevant clinical data for POPLAR including treatment arm, histology, overall survival, progression-free survival, and best confirmed overall response.		-
EGAD00001008549	Relevant clinical data for OAK including treatment arm, histology, overall survival, progression-free survival, and best confirmed overall response.		-
EGAD00001008550	Additional relevant biomarker data for OAK including PD-L1 tumor cell IHC by the 22C3 assay, tumor mutational burden status, and STK11, KEAP1, and EGFR mutation status.		-
EGAD00001008628	This dataset contains counts for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).		-
EGAD00001008629	This dataset contains counts per million for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).		-
EGAD00001008630	This dataset contains counts for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).		-
EGAD00001008631	This dataset contains counts per million for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).		-
EGAD50000001253	The investigators would like to correlate NGS data from OAK to immunophenotype designation performed manually based on a panCK/CD8 immunohistochemistry stain for a subset of squamous cell carcinomas.		1

Publications	Citations
The ectonucleotidase CD39 identifies tumor-reactive CD8<sup>+</sup> T cells predictive of immune checkpoint blockade efficacy in human lung cancer. Chow A, Uddin FZ, Liu M, Dobrin A, Nabet BY, Mangarin L, Lavin Y, Rizvi H, Tischfield SE, Quintanal-Villalonga A, Chan JM, Shah N, Allaj V, Manoj P, Mattar M, Meneses M, Landau R, Ward M, Kulick A, Kwong C, Wierzbicki M, Yavner J, Egger J, Chavan SS, Farillas A, Holland A, Sridhar H, Ciampricotti M, Hirschhorn D, Guan X, Richards AL, Heller G, Mansilla-Soto J, Sadelain M, Klebanoff CA, Hellmann MD, Sen T, de Stanchina E, Wolchok JD, Merghoub T, Rudin CM. Immunity 56: 2023 93-106.e6	81
Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy. He LN, Li H, Du W, Fu S, Luo L, Chen T, Zhang X, Chen C, Jiang Y, Wang Y, Wang Y, Yu H, Zhou Y, Lin Z, Zhao Y, Huang Y, Zhao H, Fang W, Yang Y, Zhang L, Hong S. iScience 26: 2023 107058	4
Deciphering transcriptomic determinants of the divergent link between PD-L1 and immunotherapy efficacy. Li A, Luo L, Du W, Yu Z, He L, Fu S, Wang Y, Zhou Y, Yang C, Yang Y, Fang W, Zhang L, Hong S. NPJ Precis Oncol 7: 2023 87	2
Upregulation of Indoleamine 2,3-Dioxygenase 1 in Tumor Cells and Tertiary Lymphoid Structures is a Hallmark of Inflamed Non-Small Cell Lung Cancer. Bessede A, Peyraud F, Le Moulec S, Cousin S, Cabart M, Chomy F, Rey C, Lara O, Odin O, Nafia I, Guegan JP, Italiano A. Clin Cancer Res 29: 2023 4883-4893	17
TROP2 Is Associated with Primary Resistance to Immune Checkpoint Inhibition in Patients with Advanced Non-Small Cell Lung Cancer. Bessede A, Peyraud F, Besse B, Cousin S, Cabart M, Chomy F, Rey C, Lara O, Odin O, Nafia I, Vanhersecke L, Barlesi F, Guégan JP, Italiano A. Clin Cancer Res 30: 2024 779-785	28
Interferon-stimulated neutrophils as a predictor of immunotherapy response. Benguigui M, Cooper TJ, Kalkar P, Schif-Zuck S, Halaban R, Bacchiocchi A, Kamer I, Deo A, Manobla B, Menachem R, Haj-Shomaly J, Vorontsova A, Raviv Z, Buxbaum C, Christopoulos P, Bar J, Lotem M, Sznol M, Ariel A, Shen-Orr SS, Shaked Y. Cancer Cell 42: 2024 253-265.e12	85
Redox<sup>high</sup> phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma. Wei XW, Lu C, Zhang YC, Fan X, Xu CR, Chen ZH, Wang F, Yang XR, Deng JY, Yang MY, Gou Q, Mei SQ, Luo WC, Zhong RW, Zhong WZ, Yang JJ, Zhang XC, Tu HY, Wu YL, Zhou Q. Oncoimmunology 13: 2024 2340154	14
Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer. Fang C, Arango Argoty GA, Kagiampakis I, Khalid MH, Jacob E, Bulusu KC, Markuzon N. J Transl Med 22: 2024 726	4
Dissecting transcriptome signals of anti-PD-1 response in lung adenocarcinoma. Lee K, Cha H, Kim J, Jang Y, Son Y, Joe CY, Kim J, Kim J, Lee SH, Lee S. Sci Rep 14: 2024 21096	1
T Cells Instruct Immune Checkpoint Inhibitor Therapy Resistance in Tumors Responsive to IL1 and TNFα Inflammation. Cho NW, Guldberg SM, Nabet BY, Yu JZ, Kim EJ, Hiam-Galvez KJ, Yee JL, DeBarge R, Tenvooren I, Ashitey NA, Lynce F, Dillon DA, Rosenbluth JM, Spitzer MH. Cancer Immunol Res 13: 2025 229-244	6
Systemic longitudinal immune profiling identifies proliferating Treg cells as predictors of immunotherapy benefit: biomarker analysis from the phase 3 CONTINUUM and DIPPER trials. Huang SW, Jiang W, Xu S, Zhang Y, Du J, Wang YQ, Yang KY, Zhang N, Liu F, Zou GR, Jin F, Wu HJ, Zhou YY, Zhu XD, Chen NY, Xu C, Qiao H, Liu N, Sun Y, Ma J, Liang YL, Liu X. Signal Transduct Target Ther 9: 2024 285	9
Analysis of PD1, LAG3, TIGIT, and TIM3 expression in human lung adenocarcinoma reveals a 25-gene signature predicting immunotherapy response. Guégan JP, Peyraud F, Dadone-Montaudie B, Teyssonneau D, Palmieri LJ, Clot E, Cousin S, Roubaud G, Cabart M, Leroy L, Lebreton C, Rey C, Lara O, Odin O, Brunet M, Vanhersecke L, Gruyters EO, Achour I, Belcaid L, Le Moulec S, Grellety T, Bessede A, Italiano A. Cell Rep Med 5: 2024 101831	15
Tumor microenvironment assessment-based signatures for predicting response to immunotherapy in non-small cell lung cancer. Wu J, Wang Y, Huang Z, Wu J, Sun H, Zhou R, Qiu W, Ye Z, Fang Y, Huang X, Wu J, Bin J, Liao Y, Shi M, Wang J, Liao W, Zeng D. iScience 27: 2024 111340	3
Mast cells boost anti-tumor potency of MAIT cells via inflammasome-dependent secretion of IL-18. Fan F, Wang J, Liu K, Zhang S, Gao J, Li X, Ma J, Zhao Y, Li T, Su H, Yang X, Han H, Huang Q, Zhang Y, Pan Y, Ye T, Hu H, Sun Y, Li F, Cao Z, Zhang Y, Zhang X, Chen H. Nat Commun 16: 2025 6074	2
Tumor immune microenvironment score predicts efficacy of immune checkpoint inhibitors-based regimens in advanced non-small cell lung cancer. Dai J, Yan H, Chen Y, Zhang Y, Huang Z, Ruan Z, Tian F, Qin H, Xu Q, Wang J, Li X, Cheng P, Zhu C, Yang N, Zeng L, Zhang Y. J Transl Med 23: 2025 1391	0