T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates.

Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE

Original Publication: [Lausanne : Frontiers Research Foundation]

Early Detection of Cancer* ; Genes, T-Cell Receptor beta* ; Papanicolaou Test* ; Vaginal Smears*; Alphapapillomavirus/*immunology ; Papillomavirus Infections/*diagnosis ; Precancerous Conditions/*diagnosis ; T-Lymphocytes/*immunology ; Uterine Cervical Neoplasms/*diagnosis; Adult ; Alphapapillomavirus/genetics ; Alphapapillomavirus/pathogenicity ; Complementarity Determining Regions/genetics ; Female ; Gene Expression Profiling ; High-Throughput Nucleotide Sequencing ; Human Papillomavirus DNA Tests ; Humans ; Machine Learning ; Middle Aged ; Papillomavirus Infections/immunology ; Papillomavirus Infections/virology ; Precancerous Conditions/immunology ; Precancerous Conditions/virology ; Predictive Value of Tests ; Proof of Concept Study ; Reproducibility of Results ; Risk Assessment ; Risk Factors ; T-Lymphocytes/virology ; Transcriptome ; Uterine Cervical Neoplasms/immunology ; Uterine Cervical Neoplasms/virology

Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor β chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Christley, Ostmeyer, Quirk, Zhang, Sirak, Giuliano, Zhang, Monson, Tiro, Lucas and Cowell.)

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UL1 TR003163 United States TR NCATS NIH HHS

Keywords: cervical cancer screening; cervical cancer surveillance; immune repertoire; machine learning; regression biomarker

0 (Complementarity Determining Regions)

Date Created: 20210419 Date Completed: 20210914 Latest Revision: 20220223

20240829

PMC8050337

10.3389/fimmu.2021.624230

33868241