JAMMIT Analysis Defines 2 Semi-Independent Immune Processes Common to 29 Solid Tumors
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Curated by eLife
Evaluation Summary:
This study provides a sound and novel algorithm to analyze the massive cancer data and its findings greatly help inform novel cancer immunotherapy across various cancer types. Moreover, a 3-gene signature was established based upon Tc1, Tc17, and immune cold tumors to estimate the abundance of monocytic infiltrates that could potentially impact on the overall survival of cancer patients. It might be of great interest to the general audience of cancer biologists, immunologists and computational biologists.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewer remained anonymous to the authors.)
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Abstract
Immunophenotype of solid tumors has relevance to cancer immunotherapy, as not all patients respond optimally to treatment utilizing monoclonal antibodies. Bioinformatic studies have failed to clearly identify tumor immunophenotype in a way that encompasses a wide variety of tumor types and highlights fundamental differences among them, complicating prediction of patient clinical response. The novel JAMMIT algorithm was used to analyze mRNA data for 33 cancer types in The Cancer Genome Atlas (TCGA). We found that B cells and T cells constitute the principal source of variation in most patient cohorts, and that virtually all solid malignancies formed three hierarchical clustering patterns with similar molecular features. The second main source of variability in transcriptomic studies we attribute to monocytes. We identified the three tumor types as T C 1-mediated, T C 17-mediated and non-immunogenic immunophenotypes and used a 3-gene signature to approximate infiltration by agranulocytes. Methods of in silico validation such as pathway analysis, Cibersort and published data from treated cohorts were used to substantiate these findings. Monocytic infiltrate is found to be related to patient survival according to immunophenotype, important differences in some solid tumors are identified and deficiencies of common bioinformatic approaches relevant to diagnosis are detailed by this work.
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Evaluation Summary:
This study provides a sound and novel algorithm to analyze the massive cancer data and its findings greatly help inform novel cancer immunotherapy across various cancer types. Moreover, a 3-gene signature was established based upon Tc1, Tc17, and immune cold tumors to estimate the abundance of monocytic infiltrates that could potentially impact on the overall survival of cancer patients. It might be of great interest to the general audience of cancer biologists, immunologists and computational biologists.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewer remained anonymous to the authors.)
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Reviewer #1 (Public Review):
This paper documents the immune profiles of solid tumors using a novel JAMMIT algorithm applied to data from TCGA. This work is important because it could help inform cancer immunotherapy which shows great promise for treatment. The paper is well-written with interesting results.
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Reviewer #2 (Public Review):
In this manuscript, the authors have utilized a novel algorithm, JAMMIT for the mRNA data analysis of different cancers obtained from TCGA. They demonstrated that the main source of variation in the tumor microenvironment is inflicted through alterations in T and B cells. In addition, they have classified tumors based on the abundance of Tc1, Tc17, and immune inert immunophenotypes and used a 3-gene signature to evaluate the infiltration of monocytes. The strengths of this study include the use of IPA, Cibersort, and published patient data for the validation of computational findings. This study would help in the development of immune signatures as prognostic indicators and also in clinical decision-making.
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