3142 Background: Previous studies of non-small cell lung cancer (NSCLC) have shown that TLS can be predictive of therapy response and a positive prognostic factor for survival. Currently, TLS identification is manually performed by pathologists with limited morphological criteria. Standardizing TLS detection with an automated DIA workflow could guide clinical trials in precision medicine by improving patient stratification. Here, we investigate the reproducibility and sensitivity of our DIA platform for evaluating TLS in LUAD using digital histopathology and machine learning. Methods: TLS were assessed by 3 pathologists on whole slide images (WSI) in a validation cohort of 22 LUAD samples using current TLS characterization criteria of dense lymphoid structures, the presence/absence of a germinal center, and high endothelial venules (HEVs). The intraclass correlation coefficient (ICC) was used to measure reproducibility between pathologists. The BostonGene DIA platform was used to train models for automated TLS detection. Quantitative measurements of area, lymphocyte number, and density of each TLS were obtained. A prospective cohort of 8 samples was used to compare pathologist and DIA identification of TLS. Normalized numbers of TLS in the tumor area were used for cohort stratification for overall survival (OS) analysis using the Kaplan-Meier method in an independent clinical cohort of 104 TCGA-LUAD patients. Results: A panel of 3 pathologists identified 326 unique TLS from 22 samples. Between-pathologist detection of TLS, independent of germinal center or HEV criteria, resulted in good reproducibility with an ICC of 0.77. Our DIA platform exhibited excellent reproducibility with an ICC of 0.94 when compared to validated prospective cohort annotation. In total, 155 and 189 TLS were identified by pathologists and our DIA platform, respectively. The DIA platform demonstrated a markedly improved sensitivity of 0.91 for TLS identification. Furthermore, OS analysis revealed that a TLS density greater than 0.94 TLS per mm2 of tumor assessed by DIA is a statistically significant independent biomarker of better OS in the LUAD cohort from TCGA. Conclusions: These results demonstrate the BostonGene DIA platform detects TLS in LUAD, with improved reproducibility and sensitivity over previous methods. Additionally, the DIA platform showed a TLS density greater than 0.94 TLS per mm2 of tumor is a positive prognostic marker for OS in LUAD. Standardized TLS DIA identification can be exploited in digital pathology applications for future clinical trials, informing clinicians of predictive and prognostic information during the decision-making process.
Lung cancer is the leading cause of cancer-related deaths worldwide. Surgery and chemoradiation are the standard of care in early stages of non-small cell lung cancer (NSCLC), while immunotherapy is the standard of care in late-stage NSCLC.The immune composition of the tumor microenvironment (TME) is recognized as an indicator for responsiveness to immunotherapy, although much remains unknown about its role in responsiveness to surgery or chemoradiation. In this pilot study, we characterized the NSCLC TME using mass cytometry (CyTOF) and bulk RNA sequencing (RNA-Seq) with deconvolution of RNA-Seq being performed by Kassandra, a recently published deconvolution tool. Stratification of patients based on the intratumoral abundance of B cells identified that the B-cell rich patient group had increased expression of CXCL13 and greater abundance of PD1 + CD8 T cells. The presence of B cells and PD1 + CD8 T cells correlated positively with the presence of intratumoral tertiary lymphoid structures (TLS).We then assessed the predictive and prognostic utility of these cell types and TLS
Although genomic analyses of clear cell renal cell carcinoma (ccRCC) patients have revealed targetable pathways that have led to FDA-approved therapies, the responses to these therapies remain limited. The significant success of immune checkpoint inhibitors and anti-angiogenic agents suggest that ccRCC has a unique tumor microenvironment composition and tumor behavior that influences therapeutic response. Here, we describe an integrated proteogenomic method to study intratumoral heterogeneity (ITH), microenvironment composition, tumor spatial behavior, and cellular communities in ccRCC. A unique AI-based segmentation platform for multiplex immunofluorescence (MxIF) was developed to analyze an entire tissue slide at single-cell resolution, including 70 regions of interest per slide, providing significant information regarding spatial architecture. Primary ccRCC tumors collected from patients were biopsied at multiple locations and subjected to MxIF (20 markers, n = 10 sites, 4 pts, ~1,000,000 cells), RNA-seq (n = 8 sites, 3 pts) and CyTOF (n = 21 sites, 6 pts), allowing integrated multi-omics analysis at the single-cell level. Integrated analysis showed that genomic intratumor heterogeneity (ITH) was remarkably similar across all regions biopsied from the same patient, and the cellular populations present within each region of the same patient were alike. However, some cell types such as TCM CD4 T cells and Tem CD38 and Tem PD1+CD69+CD38- CD8 T cells, and CD163+PDL1+LAMP+ showed great inter-patient differences in the proportion of these cell populations. Notably, 14 CD4 T cell, 13 CD8 T cell, and 10 macrophage subpopulations were identified across the ccRCC tumors. Moreover, 14 microenvironment proximity communities based on MxIF imaging analysis of ~1 million cells were identified. The presence of B cell- and T cell-enriched communities (e.g., CD8 T cell enrichment and T-cell enrichment at the tumor border) correlated with the expression of interferon-gamma, PD1, IL-6, IL-10, PD-L1, CXCL13, and others. Macrophage-enriched communities correlated with the expression of CXCL12 and PDGFRB. Finally, tertiary lymphoid structures within the “B cell-enriched” communities correlating with the expression of CXCL13 were found in two tumors collected from one patient, with subsequent validation via H&E staining. Further, B cell repertoire (BCR) analysis of RNA-seq of the tumors from this patient showed the presence of a large B cell clone in the tumors. In conclusion, via MxIF, 14 distinct spatial microenvironment communities with unique cytokine expression patterns were identified in ccRCC. Uncovering the spatial behavior of tumors can lead to the development of effective therapies personalized for each patient based on microenvironment composition and architecture. Citation Format: Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ekaterina Postovalova, Viktor Svekolkin, Ilia Galkin, Pavel Ovcharov, Diana Shamsutdinova, Vladimir Zyrin, Alexander Bagaev, Krystle Nomie, Felix Frenkel, Ravshan Ataullakhanov, James J. Hsieh. Integrated multiregional transcriptomic and multi-parameter single-cell imaging analysis of clear cell renal cell carcinoma elucidates diverse cellular communities present within the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2742.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.