Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma

Bi, Kevin; He, Meng Xiao; Bakouny, Ziad; Kanodia, Abhay; Napolitano, Sara; Wu, Jingyi; Grimaldi, Grace; Braun, David A.; Cuoco, Michael S.; Mayorga, Angie; DelloStritto, Laura; Bouchard, Gabrielle; Steinharter, John A.; Tewari, Alok K.; Vokes, Natalie I.; Shannon, Erin; Sun, Maxine; Park, Jihye; Chang, Steven L.; McGregor, Bradley A.; Haq, Rizwan; Denize, Thomas; Signoretti, Sabina; Guerriero, Jennifer L.; Vigneau, Sébastien; Rozenblatt-Rosen, Orit; Rotem, Asaf; Regev, Aviv; Choueiri, Toni K.; Allen, Eliezer M. Van

doi:10.1016/j.ccell.2021.02.015

Cited by 302 publications

(355 citation statements)

References 68 publications

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“…It is reported that there are crosstalks between TAMs and T cells, TAMs, and tumor cells. TAMs may interact with CD8 + T cells and tumor cells through receptor-ligand pairs, such as SPP1-CD44 ( 41 ). The crosstalks between TAMs and CD8 + T cells/tumor cells may be validated by using multiplex imaging analysis ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

Multi-Omics Analysis Showed the Clinical Value of Gene Signatures of C1QC+ and SPP1+ TAMs in Cervical Cancer

et al. 2021

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PurposeTo evaluate the value of C1QC+ and SPP1+ TAMs gene signatures in patients with cervical cancer.MethodsWe compare the C1QC+ and SPP1+ TAMs gene signatures with the M1/M2 gene signatures at single cell level and bulk RNA-seq level and evaluate which gene signature can clearly divide TAMs and patients with cervical cancer into distinct clinical subclusters better.ResultsAt single-cell level, C1QC+ and SPP1+ TAMs gene signatures, but not M1 and M2 gene signatures, could clearly divided TAMs into two subclusters in a colon cancer data set and an advanced basal cell data set. For cervical cancer data from TCGA, patients with C1QChigh and SPP1low TAMs gene signatures have the best prognosis, lowest proportion (34.21%) of locally advanced cervical cancer (LACC), and highest immune cell infiltration, whereas patients with C1QClow and SPP1high TAMs gene signatures have the worst prognosis, highest proportion (71.79%) of LACC and lowest immune cell infiltration. Patients with C1QChigh and SPP1low TAMs gene signature have higher expression of most of the Immune checkpoint molecules (ICMs) than patients with C1QClow and SPP1high TAMs gene signatures. The GSEA results suggested that subgroups of patients divided by C1QC+ and SPP1+ TAMs gene signatures showed different anti- or pro-tumor state.ConclusionC1QC+ and SPP1+ TAMs gene signatures, but not M1/M2 gene signatures, can divide cervical patients into subgroups with different prognosis, tumor stage, different immune cell infiltration, and ICMs expression. Our findings may help to find suitable treatment strategy for cervical cancer patients with different TAMs gene signatures.

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Section: Discussionmentioning

confidence: 99%

Multi-Omics Analysis Showed the Clinical Value of Gene Signatures of C1QC+ and SPP1+ TAMs in Cervical Cancer

et al. 2021

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“…The PBRM1 gene had the highest mutation frequency (45.83%, 154/336), followed by SMARCA (2.68%, 9/336). In primary and advanced RCC, the expression characteristics of cancer cell subsets and immune escape are associated with PBRM1 mutations ( Bi et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Gene Signature Associated With Bromodomain Genes Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

Qian

et al. 2021

Front. Genet.

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Bromodomain (BRD) proteins exhibit a variety of activities, such as histone modification, transcription factor recruitment, chromatin remodeling, and mediator or enhancer complex assembly, that affect transcription initiation and elongation. These proteins also participate in epigenetic regulation. Although specific epigenetic regulation plays an important role in the occurrence and development of cancer, the characteristics of the BRD family in renal clear cell carcinoma (KIRC) have not been determined. In this study, we investigated the expression of BRD family genes in KIRC at the transcriptome level and examined the relationship of the expression of these genes with patient overall survival. mRNA levels of tumor tissues and adjacent tissues were extracted from The Cancer Genome Atlas (TCGA) database. Seven BRD genes (KAT2A, KAT2B, SP140, BRD9, BRPF3, SMARCA2, and EP300) were searched by using LASSO Cox regression and the model with prognostic risk integration. The patients were divided into two groups: high risk and low risk. The combined analysis of these seven BRD genes showed a significant association with the high-risk groups and lower overall survival (OS). This analysis demonstrated that total survival could be predicted well in the low-risk group according to the time-dependent receiver operating characteristic (ROC) curve. The prognosis was determined to be consistent with that obtained using an independent dataset from TCGA. The relevant biological functions were identified using Gene Set Enrichment Analysis (GSEA). In summary, this study provides an optimized survival prediction model and promising data resources for further research investigating the role of the expression of BRD genes in KIRC.

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“…Fig. 2), we shortlisted 7 26,33–36,38,39 out of 10 data sets for signature score calculations using SCSE 22 , AUCell 21 , ssGSEA 19 and JASMINE.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

Noureen

Chen

et al. 2021

Preprint

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Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

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Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma

Cited by 302 publications

References 68 publications

Multi-Omics Analysis Showed the Clinical Value of Gene Signatures of C1QC+ and SPP1+ TAMs in Cervical Cancer

Multi-Omics Analysis Showed the Clinical Value of Gene Signatures of C1QC+ and SPP1+ TAMs in Cervical Cancer

Gene Signature Associated With Bromodomain Genes Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

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