Abstract

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.

Highlights

  • The primary cause of cervical cancer is persistent infection with human papillomaviruses (HPV) [1,2,3]

  • We aimed to determine whether T cell receptor (TCR) repertoire profiling could be conducted on cervical cytology samples, the sample type routinely collected for cervical cancer screening and surveillance and on which cytology tests and hrHPV tests are conducted

  • Applying a machine learningbased method we previously developed [45,46,47, 50], we identified a biophysicochemical motif in the complementaritydetermining region 3 (CDR3) of TCR b chains (TCRB) whose presence was associated with lower-risk follow-up test results with 95% accuracy by leave-one-out cross-validation (19 of 20 trajectories correctly predicted)

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Summary

Introduction

The primary cause of cervical cancer is persistent infection with human papillomaviruses (HPV) [1,2,3]. In low Human Development Index settings, it ranks second for both measures [6]. This is due in part to the fact that current vaccines cover only nine of the most oncogenic HPV types out of over 200 types that have been identified [3]. Even within the guideline-approved age groups, vaccination rates remain low. Together, these facts mean that cervical cancer prevalence and mortality will not decrease significantly in the short term [7,8,9]. General population screening for cervical cancer must continue for the foreseeable future

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