Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti-PD-1 Therapy

Overview

Journal Cancer Discov

Specialty Oncology

Date 2022 Jan 5

PMID 34983745

Citations 27

Authors

Kun Wang

Sushant Patkar

Joo Sang Lee

E Michael Gertz

Welles Robinson

Fiorella Schischlik

David R Crawford

Alejandro A Schaffer

Eytan Ruppin

Affiliations

Soon will be listed here.

Abstract

Significance: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type-specific gene expression profiles and identify cell type-specific ligand-receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873.

Citing Articles

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Interferon-induced factor 16 is essential in metastatic melanoma to maintain STING levels and the immune responses upon IFN-γ response pathway activation.

Kobayashi Y, Bustos M, Hayashi Y, Yu Q, Hoon D J Immunother Cancer. 2024; 12(10).

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A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade.

Sahni S, Wang B, Wu D, Dhruba S, Nagy M, Patkar S Nat Commun. 2024; 15(1):8867.

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The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy.

Dhruba S, Sahni S, Wang B, Wu D, Rajagopal P, Schmidt Y bioRxiv. 2024; .

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