Dataset Information

Integrating genome-wide CRISPR immune screen with multi-omic clinical data reveals distinct classes of tumor intrinsic immune regulators.

ABSTRACT:

Background

Despite approval of immunotherapy for a wide range of cancers, the majority of patients fail to respond to immunotherapy or relapse following initial response. These failures may be attributed to immunosuppressive mechanisms co-opted by tumor cells. However, it is challenging to use conventional methods to systematically evaluate the potential of tumor intrinsic factors to act as immune regulators in patients with cancer.

Methods

To identify immunosuppressive mechanisms in non-responders to cancer immunotherapy in an unbiased manner, we performed genome-wide CRISPR immune screens and integrated our results with multi-omics clinical data to evaluate the role of tumor intrinsic factors in regulating two rate-limiting steps of cancer immunotherapy, namely, T cell tumor infiltration and T cell-mediated tumor killing.

Results

Our studies revealed two distinct types of immune resistance regulators and demonstrated their potential as therapeutic targets to improve the efficacy of immunotherapy. Among them, PRMT1 and RIPK1 were identified as a dual immune resistance regulator and a cytotoxicity resistance regulator, respectively. Although the magnitude varied between different types of immunotherapy, genetically targeting PRMT1 and RIPK1 sensitized tumors to T-cell killing and anti-PD-1/OX40 treatment. Interestingly, a RIPK1-specific inhibitor enhanced the antitumor activity of T cell-based and anti-OX40 therapy, despite limited impact on T cell tumor infiltration.

Conclusions

Collectively, the data provide a rich resource of novel targets for rational immuno-oncology combinations.

SUBMITTER: Hou J

PROVIDER: S-EPMC7887353 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Integrating genome-wide CRISPR immune screen with multi-omic clinical data reveals distinct classes of tumor intrinsic immune regulators.

Hou Jiakai J Wang Yunfei Y Shi Leilei L Chen Yuan Y Xu Chunyu C Saeedi Arash A Pan Ke K Bohat Ritu R Egan Nicholas A NA McKenzie Jodi A JA Mbofung Rina M RM Williams Leila J LJ Yang Zhenhuang Z Sun Ming M Liang Xiaofang X Rodon Ahnert Jordi J Varadarajan Navin N Yee Cassian C Chen Yiwen Y Hwu Patrick P Peng Weiyi W

Journal for immunotherapy of cancer 20210201 2

<h4>Background</h4>Despite approval of immunotherapy for a wide range of cancers, the majority of patients fail to respond to immunotherapy or relapse following initial response. These failures may be attributed to immunosuppressive mechanisms co-opted by tumor cells. However, it is challenging to use conventional methods to systematically evaluate the potential of tumor intrinsic factors to act as immune regulators in patients with cancer.<h4>Methods</h4>To identify immunosuppressive mechanisms ...[more]

PMID: 33589527

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Project description:BackgroundGenomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3'end mRNA sequencing of different lengths as predictors.ResultsThe prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.ConclusionsThe use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3'end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Dataset Information

Integrating genome-wide CRISPR immune screen with multi-omic clinical data reveals distinct classes of tumor intrinsic immune regulators.

Background

Methods

Results

Conclusions

Publications

Integrating genome-wide CRISPR immune screen with multi-omic clinical data reveals distinct classes of tumor intrinsic immune regulators.

Similar Datasets

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