Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34–7.3; HR = 2.24 and 95% CI = 1.28–3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11–4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution

Ki67 Is an Independent Predictor of Recurrence in the Largest Randomized Trial of 3 Radiation Fractionation Schedules in Localized Prostate Cancer

Purpose: To assess whether the cellular proliferation marker Ki67 provides prognostic information and predicts response to radiation therapy fractionation in patients with localized prostate tumors participating in a randomized trial of 3 radiation therapy fractionation schedules (74 Gy/37 fractions vs 60 Gy/20 fractions vs 57 Gy/19 fractions). Methods and Materials: A matched case–control study design was used; patients with biochemical/clinical failure >2 years after radiation therapy (BCR) were matched 1:1 to patients without recurrence using established prognostic factors (Gleason score, prostate-specific antigen, tumor stage) and fractionation schedule. Immunohistochemistry was used to stain diagnostic biopsy specimens for Ki67, which were scored using the unweighted global method. Conditional logistic regression models estimated the prognostic value of mean and maximum Ki67 scores on BCR risk. Biomarker–fractionation interaction terms determined whether Ki67 was predictive of BCR by fractionation. Results: Using 173 matched pairs, the median for mean and maximum Ki67 scores were 6.6% (interquartile range, 3.9%-9.8%) and 11.0% (interquartile range, 7.0%-15.0%) respectively. Both scores were significant predictors of BCR in models adjusted for established prognostic factors. Conditioning on matching variables and age, the odds of BCR were estimated to increase by 9% per 1% increase in mean Ki67 score (odds ratio 1.09; 95% confidence interval 1.04-1.15, P =.001). Interaction terms between Ki67 and fractionation schedules were not statistically significant. Conclusions: Diagnostic Ki67 did not predict BCR according to fractionation schedule in CHHiP; however, it was a strong independent prognostic factor for BCR

Multi-candidate immunohistochemical markers to assess radiation response and prognosis in prostate cancer: results from the CHHiP trial of radiotherapy fractionation

BACKGROUND: Protein markers of cellular proliferation, hypoxia, apoptosis, cell cycle checkpoints, growth factor signalling and inflammation in localised prostate tumours have previously shown prognostic ability. A translational substudy within the CHHiP trial of radiotherapy fractionation evaluated whether these could improve prediction of prognosis and assist treatment stratification following either conventional or hypofractionated radiotherapy. METHODS: Using case:control methodology, patients with biochemical or clinical failure after radiotherapy (BCR) were matched to patients without recurrence according to established prognostic factors (Gleason score, presenting PSA, tumour-stage) and fractionation schedule. Immunohistochemical (IHC) staining of diagnostic biopsy sections was performed and scored for HIF1α, Bcl-2, Ki67, Geminin, p16, p53, p-chk1 and PTEN. Univariable and multivariable conditional logistic regression models, adjusted for matching strata and age, estimated the prognostic value of each IHC biomarker, including interaction terms to determine BCR prediction according to fractionation. FINDINGS: IHC results were available for up to 336 tumours. PTEN, Geminin, mean Ki67 and max Ki67 were prognostic after adjusting for multiple comparisons and were fitted in a multivariable model (n = 212, 106 matched pairs). Here, PTEN and Geminin showed significant prediction of prognosis. No marker predicted BCR according to fractionation. INTERPRETATION: Geminin or Ki67, and PTEN, predicted response to radiotherapy independently of established prognostic factors. These results provide essential independent external validation of previous findings and confirm a role for these markers in treatment stratification. FUNDING: 10.13039/501100000289Cancer Research UK (BIDD) grant (A12518), 10.13039/501100000289Cancer Research UK (C8262/A7253), Department of Health, 10.13039/501100000771Prostate Cancer UK, 10.13039/100008719Movember Foundation, NIHR Biomedical Research Centre at 10.13039/100012139Royal Marsden/ICR

Methodology for tissue sample collection within a translational sub-study of the CHHiP trial (CRUK/06/016), a large randomised phase III trial in localised prostate cancer

Background: This article presents the methodology for tissue sample collection in Trans-CHHiP, the main translational study within the CHHiP (Conventional or Hypofractionated High dose intensity modulated radiotherapy in Prostate cancer, ISRCTN 97182923) trial. The CHHiP trial randomised 3216 men with localised prostate cancer to 3 different radiotherapy fractionation schedules. Trans-CHHiP aims to identify biomarkers of fraction sensitivity. Methods: We outline the process of tissue collection, including central review by a study-specific specialist uropathologist and comparison of the centrally-assigned Gleason grade group with that assigned by the recruiting-centre pathologist. Results: 2047 patients provided tissue from 107 pathology departments between August 2012 and April 2014. A highly motivated Clinical Trials Unit chasing samples and a central Trans-CHHiP group that regularly reviewed progress were important for successful sample collection. Agreement in Gleason grade group assigned by the recruiting centre pathologist and the central study-specific uropathologist occurred in 886 out of 1854 (47.8%) cases. Key lessons learned were the need for prospective consent for tissue collection when recruiting patients to the main trial, and the importance of Material Transfer Agreement (MTA) integration into the initial trial site agreement. Conclusions: This methodology enabled collection of 2047 patient samples from a large randomised radiotherapy trial. Central pathological review is important to minimise subjectivity in Gleason grade grouping and the impact of grade shift. Keywords: Sample collection methodology, Prostate cancer biopsies, Translational stud

A four-group urine risk classifier for predicting outcome in prostate cancer patients

Objectives: to develop a risk classifier using urine-derived extracellular vesicle RNA (UEV-RNA) capable of providing diagnostic information of disease status prior to biopsy, and prognostic information for men on active surveillance (AS). Patients and methods: post-digital rectal examination UEV-RNA expression profiles from urine (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based Continuation-Ratio model was built to generate four Prostate-Urine-Risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico Low-risk (PUR-2), Intermediate-risk (PUR-3), and High-risk (PUR-4) PCa. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation. Results: each PUR signature was significantly associated with its corresponding clinical category (p<0.001). PUR-4 status predicted the presence of clinically significant Intermediate or High-risk disease, AUC = 0.77 (95% CI: 0.70-0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n=87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (p<0.001; IQR HR = 2.86, 95% CI:1.83-4.47). PUR-4, when utilised continuously, dichotomised patient groups with differential progression rates of 10% and 60% five years post-urine collection (p<0.001, HR = 8.23, 95% CI:3.26-20.81). Conclusion: UEV-RNA can provide diagnostic information of aggressive PCa prior to biopsy, and prognostic information for men on AS. PUR represents a new & versatile biomarker that could result in substantial alterations to current treatment of PCa patients. This article is protected by copyright. All rights reserved