Title: A novel patch sampling approach for grading and generating cervical disease maps
Funder: Cancer Research UK

Abstract: Background: Screening for cervical cancer and its precursors can be facilitated by the detection of HPV DNA. Multiple studies have demonstrated the higher sensitivity of this approach to HSIL. However primary HPV screening has a low positive predictive value on its own thus leading to an increase in the number of women that are referred to colposcopy with LSIL. This stems from the fact that mere presence of DNA doesn’t correspond to malignant disease. Furthermore, an increase in referrals to a subjective procedure like colposcopy may lead to unnecessary treatment, increasing the risk of neonatal complications due to prematurity. Aims: 1) Improve the specificity of HPV triage using the biomarkers (p16/ MCM and E4 i.e. HSIL and LSIL markers) by validating their use on our novel patch sampling approach 2) Incorporate machine learning to objectively and quantitatively analyse the images produced using this approach 3) To improve the sensitivity and specificity of Colposcopy by providing an objective, automated approach to disease stratification which is superior to current methods 4) To prospectively follow lesion dynamics - enabling conservative management of women and preventing the complications associated with excisional treatments of the cervix 5) To combine primary DNA testing and patch sampling i.e. all-in-one test Methods: We utilise a novel patch sampling approach to obtain the surface cells of the cervix (including the transformation zone), while preserving their spatial positions. Patients attending colposcopy have a pre and post-acetic acid application photo, interspersed by patch sampling. 300 patients with a high-grade smear (at time of sampling) and then histology proven HSIL will be recruited in one arm vs. 700 patients with LSIL. This patch is then probed with antibodies to the E4 protein (LSIL marker) and p16/MCM (HSIL). The signal for each antibody is then quantitatively analysed using a machine learning image analysis approach to identify clusters of cells i.e. lesions, which would enable objective diagnosis of high/ low-grade disease states. Use of Results: 1) Publish in relevant high-impact journal 2) Enable objective and quantitative triage of HPV positive (screen positive) women by incorporation into the screening algorithm 3) Personalised colposcopic management - due to the production of cervical disease maps using our approach colposcopic biopsies/ treatment can be personalised 4) Successful combination of DNA testing and patch sampling will mean that all testing can be done on one sample thus reducing the significant number of women lost to follow-up

Lay abstract: Background: Screening for cervical cancer and its precursors can be facilitated by the detection of HPV DNA. Multiple studies have demonstrated the higher sensitivity of this approach to HSIL. However primary HPV screening has a low positive predictive value on its own thus leading to an increase in the number of women that are referred to colposcopy with LSIL. This stems from the fact that mere presence of DNA doesn’t correspond to malignant disease. Furthermore, an increase in referrals to a subjective procedure like colposcopy may lead to unnecessary treatment, increasing the risk of neonatal complications due to prematurity. Aims: 1) Improve the specificity of HPV triage using the biomarkers (p16/ MCM and E4 i.e. HSIL and LSIL markers) by validating their use on our novel patch sampling approach 2) Incorporate machine learning to objectively and quantitatively analyse the images produced using this approach 3) To improve the sensitivity and specificity of Colposcopy by providing an objective, automated approach to disease stratification which is superior to current methods 4) To prospectively follow lesion dynamics - enabling conservative management of women and preventing the complications associated with excisional treatments of the cervix 5) To combine primary DNA testing and patch sampling i.e. all-in-one test Methods: We utilise a novel patch sampling approach to obtain the surface cells of the cervix (including the transformation zone), while preserving their spatial positions. Patients attending colposcopy have a pre and post-acetic acid application photo, interspersed by patch sampling. 300 patients with a high-grade smear (at time of sampling) and then histology proven HSIL will be recruited in one arm vs. 700 patients with LSIL. This patch is then probed with antibodies to the E4 protein (LSIL marker) and p16/MCM (HSIL). The signal for each antibody is then quantitatively analysed using a machine learning image analysis approach to identify clusters of cells i.e. lesions, which would enable objective diagnosis of high/ low-grade disease states. Use of Results: 1) Publish in relevant high-impact journal 2) Enable objective and quantitative triage of HPV positive (screen positive) women by incorporation into the screening algorithm 3) Personalised colposcopic management - due to the production of cervical disease maps using our approach colposcopic biopsies/ treatment can be personalised 4) Successful combination of DNA testing and patch sampling will mean that all testing can be done on one sample thus reducing the significant number of women lost to follow-up

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS