Evidence map›Paper›PMID 32134923›Full record

ArticlePloS one2020

Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma.

Jared Ostmeyer, Elena Lucas, Scott Christley, Jayanthi Lea, Nancy Monson, Jasmin Tiro, Lindsay G Cowell

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.4field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Epithelial Ovarian Cancer: Providing Evidence of Predisposition Genes.International journal of environmental research and public health · 2022
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. T Cell Receptor Repertoires AcquiredFrontiers in immunology · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Jared OstmeyerDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID 0000-0003-0405-6987
Elena LucasDepartment of Pathology, UT Southwestern Medical Center, Dallas, TX, United States of America.
Scott ChristleyDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America.
Jayanthi LeaDepartment of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, United States of America.
Nancy MonsonDepartment of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America.
Jasmin TiroDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America.
Lindsay G CowellDepartment of Population and Data Sciences, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID 0000-0003-1617-8244
Southwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We previously showed, in a pilot study with publicly available data, that T cell receptor (TCR) repertoires from tumor infiltrating lymphocytes (TILs) could be distinguished from adjacent healthy tissue repertoires by the presence of TCRs bearing specific, biophysicochemical motifs in their antigen binding regions. We hypothesized that such motifs might allow development of a novel approach to cancer detection. The motifs were cancer specific and achieved high classification accuracy: we found distinct motifs for breast versus colorectal cancer-associated repertoires, and the colorectal cancer motif achieved 93% accuracy, while the breast cancer motif achieved 94% accuracy. In the current study, we sought to determine whether such motifs exist for ovarian cancer, a cancer type for which detection methods are urgently needed. We made two significant advances over the prior work. First, the prior study used patient-matched TILs and healthy repertoires, collecting healthy tissue adjacent to the tumors. The current study collected TILs from patients with high-grade serous ovarian carcinoma (HGSOC) and healthy ovary repertoires from cancer-free women undergoing hysterectomy/salpingo-oophorectomy for benign disease. Thus, the classification task is distinguishing women with cancer from women without cancer. Second, in the prior study, classification accuracy was measured by patient-hold-out cross-validation on the training data. In the current study, classification accuracy was additionally assessed on an independent cohort not used during model development to establish the generalizability of the motif to unseen data. Classification accuracy was 95% by patient-hold-out cross-validation on the training set and 80% when the model was applied to the blinded test set. The results on the blinded test set demonstrate a biophysicochemical TCR motif found overwhelmingly in women with HGSOC but rarely in women with healthy ovaries, strengthening the proposal that cancer detection approaches might benefit from incorporation of TCR motif-based biomarkers. Furthermore, these results call for studies on large cohorts to establish higher classification accuracies, as well as for studies in other cancer types.

Indexed as

Biomarkers, TumorCarcinoma, Ovarian EpithelialCohort StudiesCystadenocarcinoma, SerousFemaleHumansLymphocytes, Tumor-InfiltratingMiddle AgedOvarian NeoplasmsOvaryPilot ProjectsReceptors, Antigen, T-CellBiomarkers, TumorReceptors, Antigen, T-Cell

Identifiers

PMID32134923
PMCPMC7058380
OpenAlexW3009865719

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.