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Distinct T cell receptor repertoire diversity of clinically defined high-grade serous ovarian cancer treatment subgroups.

ABSTRACT: In patients with high-grade serous ovarian cancer (HGSC), it is unclear which genomic features are related to complete gross resection (R0), which is typically associated with better clinical outcomes, or response to neoadjuvant chemotherapy (NACT). In this study, we evaluated T cell receptor (TCR) repertoire diversity in primary and metastatic tumor samples (n = 90) from clinically well-annotated patients with HGSC who achieved R0 or received NACT with excellent or poor response based on a laparoscopic triage algorithm. TCR sequencing followed by an integrative analysis with comprehensive multi-omics data identified higher TCR diversity (e.g., higher number of unique productive sequences and less clonal relatedness) in the R0 than NACT groups. We found enrichment of specific TCR? genes usage, distinct mutual exclusiveness and co-occurrence pattern of TCR? genes among the groups. We also found significantly positive correlations between clonal relatedness and neoantigens, copy number variations, and mutation load in the groups.

SUBMITTER: Lee S

PROVIDER: S-EPMC7840469 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Distinct T cell receptor repertoire diversity of clinically defined high-grade serous ovarian cancer treatment subgroups.

Lee Sanghoon S Zhao Li L Little Latasha D LD Westin Shannon N SN Jazarei Amir A AA Fleming Nicole D ND Zhang Jianhua J Futreal P Andrew PA Sood Anil K AK

iScience 20210109 2

In patients with high-grade serous ovarian cancer (HGSC), it is unclear which genomic features are related to complete gross resection (R0), which is typically associated with better clinical outcomes, or response to neoadjuvant chemotherapy (NACT). In this study, we evaluated T cell receptor (TCR) repertoire diversity in primary and metastatic tumor samples (n = 90) from clinically well-annotated patients with HGSC who achieved R0 or received NACT with excellent or poor response based on a lapa ...[more]

PMID: 33537658

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Project description:Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier.We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77).Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort.Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.

Project description:PurposeOvarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.MethodsAn unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.ResultsWe found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them-DSPG3 and LOX-were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.ConclusionsWe distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Dataset Information

Distinct T cell receptor repertoire diversity of clinically defined high-grade serous ovarian cancer treatment subgroups.

Publications

Distinct T cell receptor repertoire diversity of clinically defined high-grade serous ovarian cancer treatment subgroups.

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