Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Wang, Zeya; Morris, Jeffrey S.; Cao, Shaolong; Ahn, Jaeil; Liu, Rongjie; Tyekucheva, Svitlana; Li, Bo; Lü, Wei; Tang, Ximing; Wistuba, Ignacio I.; Bowden, Michaela; Mucci, Lorelei A.; Loda, Massimo; Parmigiani, Giovanni; Holmes, Connor; Wang, Wenyi

doi:10.1101/146795

Cited by 22 publications

(34 citation statements)

References 21 publications

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“…There are potential limitations to DeClust. The strategy of computational dissection of tumor gene expression data into three compartments by DeClust and other methods [15,31,54] still underestimates the complexity of cellular heterogeneity comprising tumors and TMEs. The resolution of deconvolution could be improved by dissection into more detailed components in the future.…”

Section: Discussionmentioning

confidence: 99%

“…By modeling cancer transcriptomic data as a mixture of three cellular compartments (cancer, immune, and stromal compartments) and stratifying samples into multiple subtypes based on the cancer cellular compartment, our algorithm outputs the cancer cell-intrinsic subtype for each sample as well as the fraction and estimated reference expression profile for each cellular compartment. The output cancer cell-intrinsic subtype reference profiles are for the dataset, not for individuals as ISOpure [14] and DeMixT [15] do.…”

Section: Introductionmentioning

confidence: 99%

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A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

et al. 2020

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Background: Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment. Methods: We developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data. Results: DeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data. Conclusions: DeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

et al. 2020

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“…5g, h), suggesting that tumor cellintrinsic Stat1-Ido1 promotes CRC progression. Moreover, we employed the DeMixT algorithm 38 to deconvolute tumor cellintrinsic and stromal expression of Ido1 in TCGA data. We further stratified tumors into CMS1-4 consensus molecular 1 IFIT3 USP18 IFI44 ISG15 OAS3 STAT1 OASL IFIT1 IRF7 IDO1 LYZ LGALS3BP MUC1 SERPINB2 REG4 MUC2 MUC17 PAPPA2 CHGA CHGB SYP REG3A BMI1 OLFM4 TFF3 LRIG1 DEFA5 DEFA6 LGR5 ASCL2 IFIT3 USP18 IFI44 ISG15 OAS3 STAT1 OASL IFIT1 IRF7 IDO1 LYZ LGALS3BP MUC1 SERPINB2 REG4 MUC2 MUC17 PAPPA2 CHGA CHGB SYP REG3A BMI1 OLFM4 TFF3 LRIG1 DEFA5 DEFA6 LGR5 subtypes 39 and reinvestigated immune cell marker expression.…”

Section: Tumor Cell-intrinsic Stat1 Suppresses Immune Cell Activationmentioning

confidence: 99%

IDO1+ Paneth cells promote immune escape of colorectal cancer

et al. 2020

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Tumors have evolved mechanisms to escape anti-tumor immunosurveillance. They limit humoral and cellular immune activities in the stroma and render tumors resistant to immunotherapy. Sensitizing tumor cells to immune attack is an important strategy to revert immunosuppression. However, the underlying mechanisms of immune escape are still poorly understood. Here we discover Indoleamine-2,3-dioxygenase-1 (IDO1) + Paneth cells in the stem cell niche of intestinal crypts and tumors, which promoted immune escape of colorectal cancer (CRC). Ido1 expression in Paneth cells was strictly Stat1 dependent. Loss of IDO1 + Paneth cells in murine intestinal adenomas with tumor cell-specific Stat1 deletion had profound effects on the intratumoral immune cell composition. Patient samples and TCGA expression data suggested corresponding cells in human colorectal tumors. Thus, our data uncovered an immune escape mechanism of CRC and identify IDO1 + Paneth cells as a target for immunotherapy.

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“…In contrast, deconvolution methods can computationally estimate cell type proportions, including closely related cell subsets, and can also impute cell type‐specific gene expression patterns from bulk tissue transcriptomes . For example, we recently introduced CIBERSORTx, a method that extends CIBERSORT to infer both cellular abundance and cell‐type‐specific gene expression profiles from bulk‐tissue RNA admixtures without physical cell isolation .…”

Section: In Silico Methods For Determining Tissue Compositionmentioning

confidence: 99%

Computational approaches for characterizing the tumor immune microenvironment

2019

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Summary Recent advances in high‐throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor‐associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor‐associated immune system in bulk tissues and at the single‐cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Cited by 22 publications

References 21 publications

A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

IDO1+ Paneth cells promote immune escape of colorectal cancer

Computational approaches for characterizing the tumor immune microenvironment

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