Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images DOI Creative Commons
Sushant Patkar, Alex Chen, Alina Basnet

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Abstract Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach infer the tumor microenvironment (TME) composition directly from histopathology images NSCLC patients. We show that HistoTME accurately predicts expression 30 distinct type-specific molecular signatures whole slide images, achieving an average Pearson correlation 0.5 with ground truth on independent cohorts. Furthermore, find HistoTME-predicted and their underlying interactions improve prognostication patients receiving immunotherapy, AUROC 0.75[95% CI: 0.61-0.88] for predicting responses following first-line ICI treatment, utilizing external clinical cohort 652 Collectively, presents effective interrogating TME response, complementing PD-L1 expression, bringing us closer personalized immuno-oncology.

Language: Английский

Single-cell RNA sequencing and spatial transcriptomics reveal the heterogeneity and intercellular communication of cancer-associated fibroblasts in gastric cancer DOI Creative Commons
Xijie Zhang, Bo Ren, Бо Лю

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 18, 2025

Gastric cancer is a highly aggressive malignancy characterized by complex tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are key component of the TME, exhibit significant heterogeneity and play crucial roles in progression. Therefore, comprehensive understanding CAFs essential for developing novel therapeutic strategies gastric cancer. This study investigates characteristics functional information CAF subtypes explores intercellular communication between malignant epithelial cells (ECs) analyzing single-cell sequencing data from 24 samples. CellChat was employed to map communication, Seurat used integrate with spatial transcriptome reconstruct map. The relationship apCAFs analyzed using multicolor immunohistochemistry. Cells were categorized into nine distinct categories, revealing positive correlation proportions fibroblasts. Furthermore, six fibroblast subpopulations identified: inflammatory (iCAFs), pericytes, matrix (mCAFs), antigen-presenting (apCAFs), smooth muscle (SMCs), proliferative (pCAFs). Each these linked various biological processes immune responses. Malignant ECs exhibited heightened particularly subpopulations, through specific ligand-receptor interactions. High-density regions displayed exclusivity, pericytes serving as source iCAFs, mCAFs, apCAFs. Notably, showed increased interactions, certain pairs potentially impacting prognosis Multiplex immunohistochemistry (mIHC) confirmed close proximity Our provided characterization revealed intricate networks within TME. identified their interactions could serve potential targets.

Language: Английский

Citations

1
Comprehensive pan-cancer analysis indicates key gene of p53-independent apoptosis is a novel biomarker for clinical application and chemotherapy in colorectal cancer DOI Creative Commons
Jianing Yan,

Jingzhi Wang,

Min Miao

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: March 27, 2025

Background Schlafen11 (SLFN11) is a key gene in p53-independent apoptosis through ribosome stalling; however, systematic research has been conducted on its role the tumor immune microenvironment, clinical application, and immunotherapy response across pan-cancer. Method Public data were downloaded multi-omics approaches used to investigate relationship between expression level of SLFN11 spatial position, biological function, landscape, application values. Cell Counting Kit-8 assay quantitative real-time PCR validate drug sensitivity colorectal cancer samples. Result Our study revealed that was downregulated most cancers correlated with DNA repair, P53 pathway development progress by analysis. Dysregulated accompanied several cell infiltrations immune-related regulators, which can be promising screening prognostic biomarker chemotherapy predictive target for application. In vitro experiments proved useful diagnostic linked imatinib resistance cancer. Conclusion The substantial promise as valuable diagnosis indicator assessing effectiveness human cancers, deserves further additional basic trials prove.

Language: Английский

Citations

0
Deciphering genetic regulation at single-cell resolution in gastric cancer DOI Creative Commons
Chengxuan Chen, Leng Han

Cell Genomics, Journal Year: 2025, Volume and Issue: 5(4), P. 100846 - 100846

Published: April 1, 2025

Language: Английский

Citations

0
SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication DOI Creative Commons
Jianwen Liu, Li‐Tian Ma,

Fen Ju

et al.

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 12, 2025

Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis cellular demands consideration not only binding between ligands and receptors but also a series downstream signal transduction reactions within cells. Thanks to advancements in spatial transcriptomics technology, we are now able better decipher process microenvironment. Nevertheless, majority existing cell–cell algorithms fail take into account signals In this study, put forward SpaCcLink, method that takes influence individual cells systematically investigates patterns as well networks. Analyses conducted on real datasets derived from humans mice have demonstrated SpaCcLink can help identifying more relevant receptors, thereby enabling us decode genes signaling pathways influenced by communication. Comparisons with other methods suggest identify closely associated biological processes discover reliable ligand-receptor relationships. By means profound all-encompassing comprehension mechanisms underlying be achieved, which turn promotes deepens our understanding intricate complexity

Language: Английский

Citations

0
Immunotherapy for gastric cancer and liver metastasis: Is it time to bid farewell DOI

Ahmed Dehal

World Journal of Gastrointestinal Surgery, Journal Year: 2024, Volume and Issue: 16(8), P. 2365 - 2368

Published: Aug. 16, 2024

Patients with metastatic gastric cancer have a grim prognosis. Palliative chemotherapy offers limited survival improvement, but recent advancements in immunotherapy sparked hope. However, the effectiveness of patients liver metastases remains debated. This article reviews study by Liu

Language: Английский

Citations

1
Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images DOI Creative Commons
Sushant Patkar, Alex Chen, Alina Basnet

et al.

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: Dec. 19, 2024

Abstract Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach infer the tumor microenvironment (TME) composition directly from histopathology images NSCLC patients. We show that HistoTME accurately predicts expression 30 distinct type-specific molecular signatures whole slide images, achieving an average Pearson correlation 0.5 with ground truth on independent cohorts. Furthermore, find HistoTME-predicted and their underlying interactions improve prognostication patients receiving immunotherapy, AUROC 0.75 [95% CI: 0.61-0.88] for predicting responses following first-line ICI treatment, utilizing external clinical cohort 652 Collectively, presents effective interrogating TME response, complementing PD-L1 expression, bringing us closer personalized immuno-oncology.

Language: Английский

Citations

1
Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes DOI Creative Commons
Yidi Sun,

Zhuoyu Pan,

Ziyi Wang

et al.

Journal of Translational Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: Oct. 3, 2024

Language: Английский

Citations

0
Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images DOI Creative Commons
Sushant Patkar, Alex Chen, Alina Basnet

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Abstract Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach infer the tumor microenvironment (TME) composition directly from histopathology images NSCLC patients. We show that HistoTME accurately predicts expression 30 distinct type-specific molecular signatures whole slide images, achieving an average Pearson correlation 0.5 with ground truth on independent cohorts. Furthermore, find HistoTME-predicted and their underlying interactions improve prognostication patients receiving immunotherapy, AUROC 0.75[95% CI: 0.61-0.88] for predicting responses following first-line ICI treatment, utilizing external clinical cohort 652 Collectively, presents effective interrogating TME response, complementing PD-L1 expression, bringing us closer personalized immuno-oncology.

Language: Английский

Citations

0