Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

Vasaikar, Suhas; Huang, Chen; Wang, Xiaojing; Petyuk, Vladislav; Savage, Sara R.; Wen, Bo; Dou, Yongchao; Zhang, Yun; Shi, Zhiao; Arshad, Osama A.; Gritsenko, Marina; Zimmerman, Lisa J.; McDermott, Jason; Clauss, Therese RW; Moore, Ronald J.; Zhao, Rui; Monroe, Matthew E.; Wang, Yiting; Chambers, Matthew; Slebos, Robbert J.C.; Lau, Ken S.; Mo, Qianxing; Li, Ding; Ellis, Matthew J.; Thiagarajan, Mathangi; Kinsinger, Christopher R.; Rodriguez, Henry; Smith, Richard; Rodland, Karin D.; Liebler, D.C.; Liu, Tao; Zhang, Bing; Pandey, Akhilesh; Paulovich, Amanda G.; Hoofnagle, Andrew N.; Mani, D. R.; Chan, Daniel W.; Ransohoff, David F.; Fenyö, David; Tabb, David L.; Levine, Douglas A.; Boja, Emily S.; Kuhn, Eric; White, Forest M.; Whiteley, Gordon A.; Zhu, Heng; Zhang, Hui; Shih, Ie‐Ming; Bavarva, Jasmin; Whiteaker, Jeffrey R.; Ketchum, Karen A.; Clauser, Karl R.; Ruggles, Kelly V.; Elburn, Kimberly; Hannick, Linda; Watson, Mark A.; Oberti, Mauricio; Mesri, Mehdi; Sanders, Melinda E.; Borucki, Melissa; Gillette, Michael A.; Snyder, M; Edwards, Nathan; Vatanian, Negin; Rudnick, Paul; McGarvey, Peter B.; Mertins, Philip; Townsend, R. Reid; Thangudu, Ratna R.; Rivers, Robert; Payne, Samuel H.; Davies, Sherri R.; Cai, Shuang; Stein, Stephen E.; Carr, Steven A.; Skates, Steven J.; Madhavan, Subha; Hiltke, Tara; Chen, Xian; Zhao, Yingming; Wang, Yue; Zhang, Zhen

doi:10.1016/j.cell.2019.03.030

Cited by 601 publications

(611 citation statements)

References 78 publications

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“…Neoantigen-based immunotherapy is complementary to ICIs since it has no specific requirement for patient's MSI status nor tumor mutation burden (TMB). Current studies on CRC genomics mainly focus on the dissection of tumor occurrence and metastasis mechanisms, and rarely analyze into tumor-specific neoantigens [6][7][8][9][10][11]. By integrating the mutation data of already existing CRC cohorts and combining the common HLA genotypes in this population [12,13], our study is expected to find the common neoantigens in CRC patients and facilitate further development of off-the-shelf neoantigen-based immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets

Chen

Liu

2019

Preprint

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This study was aimed to investigate the mutations in colorectal cancer (CRC) for recurrent neoantigen identification. A total of 1,779 samples with whole exome sequencing (WES) data were obtained from 7 published CRC cohorts. Common HLA genotypes were used to predict the probability of neoantigens at high frequency mutants in the dataset. Based on the WES data, we not only obtained the most comprehensive CRC mutation landscape so far, but also found 1550 mutation sites which could be identified in at least 5 or more patients, including KRAS G12D (8%), KRAS G12V (5.8%), PIK3CA E545K (3.5%), PIK3CA H1047R (2.5%) and BMPR2 N583Tfs*44 (2.8%). These mutations can also be recognized by multiple common HLA molecules as potential 'public' neoantigens. Many of these mutations also have high mutation rates in metastatic pan-cancers, suggesting their value as therapeutic targets in different cancer types. Overall, our analysis provides recurrent neoantigens as potential cancer immunotherapy targets.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets

Chen

Liu

2019

Preprint

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“…24 In line a recent publication described a negative correlation between increased glycolysis and CD8 T cell infiltration in colon cancer. 25 What is more, GLUT1 expression inversely correlated with numbers of CD3 + T cells in HNSCC and lung SCC. 26 Two molecular subgroups with low-and high OXPHOS were also identified in high-grade serous ovarian cancer.…”

Section: Warburg Tumors With High Glut1 Expression and Low T Cell Infmentioning

confidence: 99%

“…Good response to checkpoint blockade a 84,119,173,175 Renal cell carcinoma Lower T cell infiltration, inferior response to checkpoint blockade 24,172 Colon cancer Lower CD8 T cell infiltration 25 Head and neck squamous cell carcinoma Lower CD8 T cell infiltration 26 Lung squamous cell carcinoma Lower CD8 T cell infiltration 26 Lung adenocarcinoma Lower T cell infiltration 55 Lung carcinoma Impaired anti-tumor T cell response 17 Prostate cancer Reduced anti-tumoral Th1 cells 115 a A contradictory finding by Najjar et al 174 who found high tumor OXPHOS as a barrier to checkpoint blockade.…”

Section: Melanoma Decreased T Cell Activity Inferior Response To Chementioning

confidence: 99%

The immunological Warburg effect: Can a metabolic‐tumor‐stroma score (MeTS) guide cancer immunotherapy?

Siska

Singer

Evert

et al. 2020

Immunological Reviews

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The "glycolytic switch" also known as the "Warburg effect" is a key feature of tumor cells and leads to the accumulation of lactate and protons in the tumor environment.Intriguingly, non-malignant lymphocytes or stromal cells such as tumor-associated macrophages and cancer-associated fibroblasts contribute to the lactate accumulation in the tumor environment, a phenomenon described as the "Reverse Warburg effect." Localized lactic acidosis has a strong immunosuppressive effect and mediates an immune escape of tumors. However, some tumors do not display the Warburg phenotype and either rely on respiration or appear as a mosaic of cells with different metabolic properties. Based on these findings and on the knowledge that T cell infiltration is predictive for patient outcome, we suggest a metabolic-tumor-stroma score to determine the likelihood of a successful anti-tumor immune response: (a) a respiring tumor with high T cell infiltration ("hot"); (b) a reverse Warburg type with respiring tumor cells but glycolytic stromal cells; (c) a mixed type with glycolytic and respiring compartments; and (d) a glycolytic (Warburg) tumor with low T cell infiltration ("cold"). Here, we provide evidence that these types can be independent of the organ of origin, prognostically relevant and might help select the appropriate immunotherapy approach. K E Y W O R D Sacidification, GLUT, immunotherapies, lactate, T cell, Warburg

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“…The advancements of high-throughput "omics" technologies, such as genomics, epigenomics, transcriptomics, proteomics, protein modifications, glycomics, lipidomics, and metabolomics, have produced incredible volume of data [1][2][3][4][5][6] . It is predicted that the trend of generating large datasets will continue as novel technologies are developed and current approaches advance.…”

Section: Introductionmentioning

confidence: 99%

OmicsOne: Associate Omics Data with Phenotypes in One-Click

Zhang

2019

Preprint

Self Cite

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The rapid advancements of high-throughput "omics" technologies have brought huge amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset, and discovery of interesting genes, proteins, lipids, glycans, or metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed to analyze and visualize the data. However, integrating multiple omics data analysis strategies and approaches in a single data processing pipeline is still a challenge task.OmicsOne is a software developed in R, Python and Jupyter Notebook that can achieve statistical analysis, machine learning, and data visualization on multi-'omics' data by taking the advantages of integrating the useful tools from individual software packages. OmicsOne can simplify "omics" data analysis, and delineate molecules, or pathways associated to interested phenotypes.

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Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

Cited by 601 publications

References 78 publications

Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets

Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets

The immunological Warburg effect: Can a metabolic‐tumor‐stroma score (MeTS) guide cancer immunotherapy?

OmicsOne: Associate Omics Data with Phenotypes in One-Click

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