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

Wang, Kun; Patkar, Sushant; Lee, Joo Sang; Gertz, E. Michael; Robinson, Welles; Ruppin, Eytan; Crawford, David R.; Schäffer, Alejandro A.; Ruppin, Eytan

doi:10.1158/2159-8290.cd-21-0887

Cited by 44 publications

(72 citation statements)

References 99 publications

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“…First, the expression of these 65 genes in TCGA database of LUAD (lung adenocarcinoma) cohort identified patients with poor prognosis and shorter survival (Figure 7F). Second, using a precomputed estimates of cell abundance or cell-type-specific signature based on deconvolving cell-type-specific gene expression in each sample from bulk expression 44 of LUAD patients, patients with higher average expression of the unique signature (65 genes) identified in Siah1a/2 ablated macrophages were also found to have increased myeloid cells (CD14 + ) infiltration (Figure 7G). The TCGA dataset of the LUAD was also used to identify which of the 65 genes are associated with worse survival.…”

Section: Resultsmentioning

confidence: 99%

“…Hazard ratios in TCGA-LUAD for each gene were computed in R with coxph() function using the same ‘high’ and ‘low’ gene expression categorizations described above. Wang et al developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell-type-specific gene expression in each sample from bulk expression given either precomputed estimates of cell abundance or cell-type-specific signature 44 . To deconvolve TCGA-LUAD and TCGA-SCC, they first estimated abundance of 11 cell types (macrophages/dendritic cells--CD14+, B cells--CD19+, CD4+T cells, CD8+ T cells, T regulatory cells, NK cells--CD56+, endothelial cells, fibroblasts, neutrophils, basophils, eosinophils and tissue-specific tumor cells) based on bulk methylation and then applied CODEFACS to the corresponding bulk gene expression.…”

Section: Methodsmentioning

confidence: 99%

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Control of alveolar macrophage differentiation by Siah1a/2 ubiquitin ligases limits carcinogen-induced lung adenocarcinoma

Scortegagna

Bradley

et al. 2022

Preprint

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Tumor microenvironment components, including T and myeloid cells, play important roles in lung adenocarcinoma (LADC) progression and response to therapy. Here, we identify a role for Siah1a/2 ubiquitin ligases in controlling alveolar macrophage (AM) differentiation and urethane-induced LADC. Genetic ablation of Siah1a/2 in AMs enriched their immature state, coinciding with increased pro-tumorigenic and inflammatory gene signatures and abundance of Siah1a/2 substrates NRF2 and β-catenin. Urethane administration to mice enriched the population of monocytic and immature-like AMs, which were more prevalent in the lungs of mice carrying Siah1a/2-ablated macrophages. While resembling transitional profibrotic macrophages often seen in lung fibrosis, enrichment of immature AMs coincided with the development of more frequent and larger lung tumors in urethane-treated mice harboring Siah1a/2 ablated macrophages compared with controls. Gene expression signature of Siah1a/2 ablated immature-like AMs is associated with increased infiltration of CD14+ immune cells and worse survival of LADC patients. Our findings identify Siah1a/2 as gatekeepers of cancer development by controlling AM differentiation and profibrotic phenotypes contributing to carcinogen-induced lung cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Control of alveolar macrophage differentiation by Siah1a/2 ubiquitin ligases limits carcinogen-induced lung adenocarcinoma

Scortegagna

Bradley

et al. 2022

Preprint

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“…Second, we should note that our analysis combined data on patients receiving different formulations of anti-PD1 and anti-PDL1 (aiming to increase the statistical power of the analysis), whereas the FDA has approved to date, only the usage of a high-TMB biomarker for the treatment of pembrolizumab, a specific anti-PD1. Consequently, we expect that our results would be further refined as single cell-based measurements of immune cells abundance and activity in different cancer types are computed, including the application of recently developed expression deconvolution software tools to obtain a better estimation of TME immune factors in each cancer type (9,23).…”

Section: Discussionmentioning

confidence: 99%

Immune Determinants of the Association between Tumor Mutational Burden and Immunotherapy Response across Cancer Types

Sinha

Valero

et al. 2022

Cancer Research

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The FDA has recently approved a high tumor mutational burden (TMB-high) biomarker, defined by {greater than or equal to}10 mutations/Mb, for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies have shown that this TMB-high biomarker is only able to stratify ICI responders in a subset of cancer types, and the mechanisms underlying this observation have remained unknown. The tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power), determining if it will be predictive of ICI response in a given cancer type. To systematically study this hypothesis, we inferred the levels of 31 immune-related factors characteristic of the TME of different cancer types in The Cancer Genome Atlas (TCGA). Integration of this information with TMB and response data of 2,277 patients treated with anti-PD1 identified key immune factors that determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterized cancer types with high TMB power. A model based on these two immune factors strongly predicted TMB power in a given cancer type during cross-validation and testing (Spearman Rho=0.76 & 1, respectively). Using this model, we predicted the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available. Our analysis indicates that TMB-high may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

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“…Additionally, for bioinformatics courses designed to focus more heavily on the computational theory behind various mixed cell population deconvolution methods, TIMER2.0 offers a useful platform for side-by-side comparison of data output by distinct analytic methods. Of note, these types of computational analyses have become increasingly popular in cancer research, not only for providing basic insights into tumor immunobiology but also for identifying prognostic signatures that predict cancer patient response to immunotherapy [ 26 ]. Pedagogical strategies that offer training in the use of these methods are therefore becoming essential tools for preparing students who wish to pursue basic and clinical research careers in oncology.…”

Section: Discussionmentioning

confidence: 99%

A Bioinformatic Approach to Enhance Undergraduate Student Understanding of the Cancer-Immunity Cycle

Hargadon

2022

J Canc Educ

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Recent advances in tumor immunology and cancer immunotherapy have generated significant interest in the field of immuno-oncology. With the promise of these advances comes an increasing need to train the next generation of scientists who will support ongoing basic and clinical research efforts in this field. At this time, however, there remains a documented underrepresentation of tumor immunology as a core content area in many undergraduate science curricula. This study introduces a novel pedagogical strategy that aimed to promote undergraduate student interest in tumor immunology in ways that support recent education guidelines published by the American Association of Immunologists, and it highlights the efficacy of this approach in enhancing student understanding of concepts relevant to the Cancer-Immunity Cycle. Using RNA-sequencing data obtained from clinical specimens catalogued in The Cancer Genome Atlas, students performed Kaplan–Meier survival analyses to identify Cancer-Immunity Cycle genes with prognostic significance. After correlating expression of such genes with tumor-infiltrating immune cell populations using a bioinformatic tool to deconvolute whole tumor-transcriptome data, students undertook an exercise that requires integration of course content and findings from the primary literature to generate hypotheses about the influence of genetic factors and immune cell types on the Cancer-Immunity Cycle and overall patient outcome. A pre-/post-project assessment instrument demonstrated the efficacy of this approach as a means of improving undergraduate student understanding of core cancer immunology concepts. This report describes these data and discusses potential ways in which the project can be adapted to extend its utility to broad and diverse student populations. Supplementary Information The online version contains supplementary material available at 10.1007/s13187-022-02221-4.

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

Cited by 44 publications

References 99 publications

Control of alveolar macrophage differentiation by Siah1a/2 ubiquitin ligases limits carcinogen-induced lung adenocarcinoma

Control of alveolar macrophage differentiation by Siah1a/2 ubiquitin ligases limits carcinogen-induced lung adenocarcinoma

Immune Determinants of the Association between Tumor Mutational Burden and Immunotherapy Response across Cancer Types

A Bioinformatic Approach to Enhance Undergraduate Student Understanding of the Cancer-Immunity Cycle

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