Search

Radical radiotherapy, often in combination with hormone ablation, is a safe and effective treatment option for localised or locally-advanced prostate cancer. However, up to 30% of patients with locally advanced PCa will go on to develop biochemical failure, within 5 years, following initial radiotherapy. Improving radiotherapy response is clinically important since patients exhibiting biochemical failure develop castrate-resistant metastatic disease for which there is no curative therapy and median survival is 8–18 months. The aim of this research was to determine if loss of PTEN (highly prevalent in advanced prostate cancer) is a novel therapeutic target in the treatment of advanced prostate cancer. Previous work has demonstrated PTEN-deficient cells are sensitised to inhibitors of ATM, a key regulator in the response to DSBs. Here, we have shown the role of PTEN in cellular response to IR was both complex and context-dependent. Secondly, we have confirmed ATM inhibition in PTEN-depleted cell models, enhances ionising radiation-induced cell killing with minimal toxicity to normal prostate RWPE-1 cells. Furthermore, combined treatment significantly inhibited PTEN-deficient tumour growth compared to PTEN-expressing counterparts, with minimal toxicity observed. We have further shown PTEN loss is accompanied by increased endogenous levels of ROS and DNA damage. Taken together, these findings provide pre-clinical data for future clinical evaluation of ATM inhibitors as a neoadjuvant/adjuvant in combination with radiation therapy in prostate cancer patients harbouring PTEN mutations.

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

BACKGROUND: Approximately 4-25% of patients with early prostate cancer develop disease recurrence following radical prostatectomy. OBJECTIVE: To identify a molecular subgroup of prostate cancers with metastatic potential at presentation resulting in a high risk of recurrence following radical prostatectomy. DESIGN, SETTING, AND PARTICIPANTS: Unsupervised hierarchical clustering was performed using gene expression data from 70 primary resections, 31 metastatic lymph nodes, and 25 normal prostate samples. Independent assay validation was performed using 322 radical prostatectomy samples from four sites with a mean follow-up of 50.3 months. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Molecular subgroups were identified using unsupervised hierarchical clustering. A partial least squares approach was used to generate a gene expression assay. Relationships with outcome (time to biochemical and metastatic recurrence) were analysed using multivariable Cox regression and log-rank analysis. RESULTS AND LIMITATIONS: A molecular subgroup of primary prostate cancer with biology similar to metastatic disease was identified. A 70-transcript signature (metastatic assay) was developed and independently validated in the radical prostatectomy samples. Metastatic assay positive patients had increased risk of biochemical recurrence (multivariable hazard ratio [HR] 1.62 [1.13-2.33]; p=0.0092) and metastatic recurrence (multivariable HR=3.20 [1.76-5.80]; p=0.0001). A combined model with Cancer of the Prostate Risk Assessment post surgical (CAPRA-S) identified patients at an increased risk of biochemical and metastatic recurrence superior to either model alone (HR=2.67 [1.90-3.75]; p<0.0001 and HR=7.53 [4.13-13.73]; p<0.0001, respectively). The retrospective nature of the study is acknowledged as a potential limitation. CONCLUSIONS: The metastatic assay may identify a molecular subgroup of primary prostate cancers with metastatic potential. PATIENT SUMMARY: The metastatic assay ma

Background: Approximately 4–25% of patients with early prostate cancer develop disease recurrence following radical prostatectomy. Objective: To identify a molecular subgroup of prostate cancers with metastatic potential at presentation resulting in a high risk of recurrence following radical prostatectomy. Design, setting, and participants: Unsupervised hierarchical clustering was performed using gene expression data from 70 primary resections, 31 metastatic lymph nodes, and 25 normal prostate samples. Independent assay validation was performed using 322 radical prostatectomy samples from four sites with a mean follow-up of 50.3 months. Outcome measurements and statistical analysis: Molecular subgroups were identified using unsupervised hierarchical clustering. A partial least squares approach was used to generate a gene expression assay. Relationships with outcome (time to biochemical and metastatic recurrence) were analysed using multivariable Cox regression and log-rank analysis. Results and limitations: A molecular subgroup of primary prostate cancer with biology similar to metastatic disease was identified. A 70-transcript signature (metastatic assay) was developed and independently validated in the radical prostatectomy samples. Metastatic assay positive patients had increased risk of biochemical recurrence (multivariable hazard ratio [HR] 1.62 [1.13–2.33]; p = 0.0092) and metastatic recurrence (multivariable HR = 3.20 [1.76–5.80]; p = 0.0001). A combined model with Cancer of the Prostate Risk Assessment post surgical (CAPRA-S) identified patients at an increased risk of biochemical and metastatic recurrence superior to either model alone (HR = 2.67 [1.90–3.75]; p < 0.0001 and HR = 7.53 [4.13–13.73]; p < 0.0001, respectively). The retrospective nature of the study is acknowledged as a potential limitation. Conclusions: The metastatic assay may identify a molecular subgroup of primary prostate cancers with metastatic potential. Patient summary: The metastat