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

Assessment of Ki67 in relation to radiotherapy (RT) fractionation and prognosis in the CHHiP (CRUK/06/016) trial.

98 Background: A uniform fractionation schedule is used to deliver external beam RT for localised prostate tumours Individualising fractionation according to tumour biology may improve outcomes. In addition recurrence rates after RT vary considerably and better prognostic markers are needed to guide treatment choices. This study aims to identify if the cell proliferation marker Ki67 predicts response to RT fractionation in CHHiP, a randomised trial of 3 RT fractionation schedules. It also aims to identify if Ki67 predicts prognosis. Methods: A matched case:control study design was used, patients with biochemical or clinical failure &gt; 2 years after RT (BCR) were matched to patients without recurrence according to established prognostic factors (Gleason score, PSA, Tumour-stage) and fractionation schedule. Immunohistochemical (IHC) staining of diagnostic biopsy sections was carried out using the MIB1 Ki67 antibody. Two independent investigators scored Ki67 using the unweighted global method (1) to derive a mean and maximum percentage of cells staining positive (mean Ki67 and maximum Ki67 respectively). Conditional logistic regression models were fitted with interaction terms between the biomarker and the fractionation schedules to determine whether Ki67 predicted BCR according to fractionation. Secondly models were fitted using the entire case:control study sample to estimate the prognostic value of Ki67 on risk of BCR. Results: Ki67 results were available for 173 matched pairs. The interaction terms between Ki67 and the fractionation schedules were not significant. However mean and maximum Ki67 were significant prognostic markers for BCR in a model adjusted for established prognostic factors. Conditioning on matching variables and age, the odds of BCR is estimated to increase by 9% per 1 point increase in mean Ki67 (OR = 1.09, 95%CI:1.04–1.15, p = 0.001). Conclusions: Ki67 did not predict BCR according to fractionation schedule in CHHiP, however it did predict prognosis independently of established prognostic factors. Additional IHC biomarkers are under evaluation. </jats:p

Tumour evolvability metrics predict recurrence beyond 10 years in locally-advanced prostate cancer

Abstract Cancers evolve obeying Darwinian laws and therefore the evolutionary paradigm lays the ground for predictive oncology. However, the predictive power of evolutionary metrics in cancer has been seldom tested. There is a need for quantitative measurements in controlled clinical trials with long term follow-up information. This is particularly true in locally-advanced prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 samples from 114 patients who took part in the prostate radiotherapy trials at The Royal Marsden Hospital, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using deep learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). We found that evolvability, measured as genetic divergence as well as morphological diversity, was a strong independent predictor of recurrence (respectively HR=72.06, 95% CI 2.97-1748.5, p=0.009 and HR=6.2, 95% CI 1.86-20.72, p=0.003). Combined, these two measurements together also identified a group of patients with half the median time to recurrence compared to the rest of the cohort (5.6 vs 11.5 years). We also found a small subset of MYC/FGFR1 amplified cases (4.4%) with particularly poor prognosis. The overall burden of chromosomal alterations correlated with higher Gleason score. We identified associations between 24 chromosomal arm copy number changes and Gleason score (e.g. -22q, +5p, +8q, +16p, +7p), and show that loss of chromosome 6p (encompassing the HLA locus) was correlated with markedly reduced immune cell infiltration. This study shows that combining genomics with AI-aided histopathology in clinical trials leads to the identification of novel clinical biomarkers.</jats:p

Tumour evolvability metrics predict recurrence beyond 10 years in locally-advanced prostate cancer

Abstract Cancers evolve obeying Darwinian laws and therefore the evolutionary paradigm lays the ground for predictive oncology. However, the predictive power of evolutionary metrics in cancer has been seldom tested. There is a need for quantitative measurements in controlled clinical trials with long term follow-up information. This is particularly true in locally-advanced prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 samples from 114 patients who took part in the prostate radiotherapy trials at The Royal Marsden Hospital, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using deep learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). We found that evolvability, measured as genetic divergence as well as morphological diversity, was a strong independent predictor of recurrence (respectively HR=72.06, 95% CI 2.97-1748.5, p=0.009 and HR=6.2, 95% CI 1.86-20.72, p=0.003). Combined, these two measurements together also identified a group of patients with half the median time to recurrence compared to the rest of the cohort (5.6 vs 11.5 years). We also found a small subset of MYC/FGFR1 amplified cases (4.4%) with particularly poor prognosis. The overall burden of chromosomal alterations correlated with higher Gleason score. We identified associations between 24 chromosomal arm copy number changes and Gleason score (e.g. -22q, +5p, +8q, +16p, +7p), and show that loss of chromosome 6p (encompassing the HLA locus) was correlated with markedly reduced immune cell infiltration. This study shows that combining genomics with AI-aided histopathology in clinical trials leads to the identification of novel clinical biomarkers.</jats:p

A four‐group urine risk classifier for predicting outcomes in patients with prostate cancer

Objectives To develop a risk classifier using urine‐derived extracellular vesicle (EV )‐RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS ). Patients and Methods Post‐digital rectal examination urine‐derived EV ‐RNA expression profiles (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) prostate cancer. 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 (area under the curve = 0.77, 95% confidence interval [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 (interquartile range hazard ratio [HR ] 2.86, 95% CI 1.83–4.47; P < 0.001). PUR ‐4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26–20.81; P < 0.001). Conclusion Urine‐derived EV‐RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS . PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer