Updates on immunotherapy for colorectal cancer

Kalyan, Aparna; Kircher, Sheetal Mehta; Shah, Hiral; Mulcahy, Mary F.; Benson, Al B.

doi:10.21037/jgo.2018.01.17

Cited by 156 publications

(118 citation statements)

References 80 publications

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“…In colon adenocarcinoma (COAD) and stomach adenocarcinoma (STAD), patients with high-MSI cancer (MSI-H) showed a significantly higher infiltration of antitumor and tumor helper cells, such as Tc, γδ T, NK, and DC ( Figure S6A), but a significantly lower infiltration of tumor suppressor cells (Tr1 and neutrophils) and CD8 naïve cells ( Figure S6b). These factors may partially explain the better outcomes of patients with MSI-H colorectal cancer undergoing immunotherapy [27] . Furthermore, we observed that some immune cell types showed stage-related profiles in cancers.…”

Section: Case Study Of Immucellai Application To Tcga Pan-cancer Datamentioning

confidence: 99%

ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

Miao

Zhang

Lei

et al. 2019

Preprint

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The distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. There are many T-cell subsets with specific function, however current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here we introduce Immune Cell Abundance Identifier (ImmuCellAI), a novel gene set signature-based method, for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both our sequencing data with flow cytometry results and public expression data indicated that ImmuCellAI can estimate immune cells with superior accuracy than other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets revealed that the abundance of dendritic cells (DC), cytotoxic T, and gamma delta T cells was significantly higher both in comparisons of on-treatment vs. pre-treatment and responders vs. non-responders.Meanwhile, we built an ImmuCellAI result-based model for predicting the immunotherapy response with high accuracy (AUC 0.80~0.91). These results demonstrated the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. The ImmuCellAI online server is freely available at http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/.

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Section: Case Study Of Immucellai Application To Tcga Pan-cancer Datamentioning

confidence: 99%

ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

Miao

Zhang

Lei

et al. 2019

Preprint

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“…Despite the significant advances of custom treatment method, the high migration and invasion capacity have been a bottle-neck for eliminating the mortality, which kept the 5-year survival rate of CRC under 12% [2][3][4]. In addition, several other factors were also identified to influence CRC prognosis, such as health-related quality of life [5], genome stability [6,7], aberrant gene expression [8,9], etc.…”

Section: Introductionmentioning

confidence: 99%

A Six-LncRNA Expression Signature Associated with Prognosis of Colorectal Cancer Patients

Zhao

Shang

et al. 2018

Cell Physiol Biochem

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Background/Aims: Colorectal cancer (CRC) is one of the most common malignant tumor with high migration and invasion capacity. Long non-coding RNAs (lncRNAs) have been identified to influence multiple cancers progression through competitively binding microRNAs (miRNAs). In this study, we proposed to develop a lncRNA-based signature for CRC survival outcomes. Methods: LncRNA expression profiles of CRC patients were extracted from the Gene Expression Omnibus (GEO) data sets GSE38832 (training set) and GSE29621 (testing set) . Associations between lncRNA expression and CRC disease free survival (DFS) were evaluated through univariate Cox regression analysis, and prognosis signature constructed by combination of weighted lncRNA expression values were obtained through multivariate Cox regression analysis. Robustness of the prognosis signature was evaluated through receiver operating characteristics analysis in the testing set. Results: A weighted prognosis signature of six lncRNAs, including LINC01583, LINC00276, LUNAR1, DKFZp434J0226, SFTA1P and OGFOD3, was yielded from multivariate Cox regression analysis. Samples with significantly different DFS dislayed distinct signatures, indicating considerable predictory accuracy of this expression signature. Conclusion: Robustness of the prognosis signature was evaluated in the testing set through Kaplan-Meier and receiver operating characteristics (ROC) analysis. Furthermore, functional enrichment analysis of lncRNAs suggested significant enrichment of cancer related pathways. Our results revealed the promise of lncRNAs as prognostic biomarkers.

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“…Most importantly, due to the inverse correlation between MSI and aneuploidy in CRC, our results suggest that this BCS value could be established as a cutoff to define the edge between low and high aneuploid tumors. In fact, while high aneuploid tumors show poor response to immunotherapy, it has been suggested that CMS1 microsatellite unstable tumors are likely to show a positive response to immune checkpoints inhibitors (Kalyan, Kircher, Shah, Mulcahy, & Benson, 2018;Le et al, 2015). However, BCS was not associated with overall survival in patients after relapse (data not shown).…”

Section: Discussionmentioning

confidence: 96%

CNApp: quantification of genomic copy number alterations in cancer and integrative analysis to unravel clinical implications

Franch-Expósito

Bassaganyas

Vila-Casadesús

et al. 2018

Preprint

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Somatic copy number alterations (CNAs) are a hallmark of cancer. Although CNA profiles have been established for most human tumor types, their precise role in tumorigenesis as well as their clinical and therapeutic relevance remain largely unclear.Thus, computational and statistical approaches are required to thoroughly define the interplay between CNAs and tumor phenotypes. Here we developed CNApp, a userfriendly web tool that offers sample-and cohort-level computational analyses, allowing a comprehensive and integrative exploration of CNAs with clinical and molecular variables. By using purity-corrected segmented data from multiple genomic platforms, CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples.We applied CNApp to a pan-cancer dataset of 10,635 genomes from TCGA showing that CNA patterns classify cancer types according to their tissue-of-origin, and that broad and focal CNA scores positively correlate in samples with low amounts of wholechromosome and chromosomal arm-level imbalances. Moreover, using the hepatocellular carcinoma cohort from the TCGA repository, we demonstrate the reliability of the tool in identifying recurrent CNAs, confirming previous results.Finally, we establish machine learning-based models to predict colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and specific genomic imbalances. In summary, CNApp facilitates data-driven research and provides a unique framework for the first time to comprehensively assess CNAs and perform integrative analyses that enable the identification of relevant clinical implications. CNApp is hosted at http://cnapp.bsc.es.Pan-cancer; Colorectal cancer; Hepatocellular carcinoma computational approach for CNA analysis in cancer is GISTIC2.0 (Mermel et al., 2011), which is a gene-centered probabilistic method that enables to define the boundaries of recurrent putative driver CNAs in large cohorts (Beroukhim et al., 2010).Nevertheless, despite ongoing progress on identifying CNAs, to our knowledge none of the existing software packages is readily available for integrative analyses to unveil their biological and clinical implications.To address this issue, we developed CNApp, the first open-source application to quantify CNAs and integrate genomic profiles with molecular and clinical variables.CNApp is a web-based tool that provides the user with high-quality interactive plots and statistical correlations between CNAs and annotated variables in a fast and easy-toexplore interface. In particular, CNApp uses purity-corrected genomic segmented data from multiple genomic platforms to redefine CNA profiles, to compute CNA scores based on the number, length and amplitude of broad and focal genomic alterations, to assess differentially altered genomic regions, and to perform machine learning-based predictions to classify tumor samples. To exemplify the applicability and performance of CNApp, we used publicly available se...

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Updates on immunotherapy for colorectal cancer

Cited by 156 publications

References 80 publications

ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

A Six-LncRNA Expression Signature Associated with Prognosis of Colorectal Cancer Patients

CNApp: quantification of genomic copy number alterations in cancer and integrative analysis to unravel clinical implications

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