Oncotarget Volume 11, Issue 27 published ‘Clonality and antigen-specific responses shape the prognostic effects of tumor-infiltrating T cells in ovarian cancer’ by Tsuji et al. which reported that to delineate the complexity of anti-tumor T-cell responses
Credit: Correspondence to – Kunle Odunsi – [email protected]
Oncotarget Volume 11, Issue 27 published “Clonality and antigen-specific responses shape the prognostic effects of tumor-infiltrating T cells in ovarian cancer” by Tsuji et al. which reported that to delineate the complexity of anti-tumor T-cell responses, the author’s utilized a computational method for de novo assembly of sequences from CDR3 regions of 369 high-grade serous ovarian cancers from TCGA, and then applied deep TCR-sequencing for analyses of paired tumor and peripheral blood specimens from an independent cohort of 99 ovarian cancer patients.
In the validation cohort, the authors’ discovered that patients with low T-cell infiltration but low diversity or focused repertoires had clinical outcomes almost indistinguishable from highly-infiltrated tumors.
They also found that the degree of divergence of the peripheral repertoire from the TIL repertoire, and the presence of detectable spontaneous anti-tumor immune responses are important determinants of clinical outcome.
Also that the prognostic significance of TILs in ovarian cancer is dictated by T-cell clonality, degree of overlap with peripheral repertoire, and the presence of detectable spontaneous anti-tumor immune response in the patients.
These immunological phenotypes defined by the TCR repertoire may provide useful insights for identifying “TIL-low” ovarian cancer patients that may respond to immunotherapy.
“These immunological phenotypes defined by the TCR repertoire may provide useful insights for identifying “TIL-low” ovarian cancer patients that may respond to immunotherapy.”
Dr. Kunle Odunsi from The Center for Immunotherapy as well as The Department of Gynecologic Oncology at Roswell Park Comprehensive Cancer Center said, “The presence of tumor-infiltrating lymphocytes (TILs) is a key determinant of clinical outcome in a wide range of solid tumors including ovarian cancer.“
High-throughput next-generation sequencing has made it possible to read the entire CDR3 to uniquely identify specific T cell clones and to estimate the absolute frequency of T cell clones in tumor tissue from the copy number of TCR sequences.
The importance of TCR repertoire in shaping anti-tumor immunity in ovarian cancer was recently demonstrated using unbiased functional analysis of TCR repertoires from TILs derived from two patients.
Tumor reactivity was revealed in 0–5% of tested TCRs indicating that the vast majority of T cells infiltrating ovarian tumors were irrelevant for tumor recognition.
To determine how the TCR repertoire of TILs shapes the prognosis of ovarian cancer patients, the authors’ utilized a new computational method for de novo assembly of sequences from CDR3 regions using paired-end RNA-seq data from the Cancer Genome Atlas study of high-grade serous ovarian cancers.
The author’s examined TCR repertoire in the context of the degree of tumor infiltration by T cells, spontaneous immune responses against bona fide TAAs, and clinical outcome.
The Odunsi Research Team concluded in their Oncotarget Research Paper that despite these limitations, this study highlights the extraordinary diversity of the T-cell repertoire in ovarian cancer patients, and demonstrates that pre-existing immunity against cancer antigen is a critical prerequisite to correctly understand the prognostic significance of the T-cell repertoire in the tumor and peripheral blood of patients with ovarian cancer.
They have distilled TCR repertoire information into candidate biomarkers that may critically influence the prognosis of ovarian cancer patients.
Conceptually, ovarian cancers may not fit into the classic paradigm of ?cold’ and ?hot’ based on the number of T cells they contain, but also by the TCR repertoire information, which serves as a surrogate for tumor recognition.
The latest technologies put these prognostic features in clinical reach not only for predicting prognosis but potentially for determining the best immunotherapeutic strategy for each patient.
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Correspondence to – Kunle Odunsi – [email protected]
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Ryan James Jessup
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