Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out

Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones

The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors

Evolution and oncological outcomes of a contemporary radical prostatectomy practice in a UK regional tertiary referral centre

Objective To investigate the clinical and pathological trends over a ten-year period for robotic-assisted laparoscopic prostatectomy (RALP) in a UK regional tertiary referral centre. Patients and Methods 1500 consecutive patients underwent RALP between October 2005 and January 2015. Prospective data was collected on clinic-pathological details at presentation as well as surgical outcomes and compared over time. Results The median(range) age of patients throughout the period was 62(35-78) years. The proportion of pre-operative high-grade cases (Gleason sum 8-10) rose from 4.6% in 2005-2008 to 18.2% in 2013-2015 (p<0.0001). In the same periods the proportion of clinical stage T3 cases operated on rose from 2.4% to 11.4% (p<0.0001). Median PSA at diagnosis did not alter significantly. Overall 11.6% of men in 2005-2008 were classified pre-operatively as high-risk by NICE criteria, compared to 33.6% in 2013-2015 (p<0.0001). The corresponding proportions for low-risk cases were 48.6% and 17.3% respectively. Final surgical pathology demonstrated an increase in tumour stage, Gleason grade and nodal status across time. The proportion of pT3 cases rose from 43.2% in 2005-2008 to 55.5% in 2013-15 (p=0.0007), Gleason grade 9-10 tumours increased from 1.8% to 9.1% (p=0.0002) and positive nodal status increased from 1.6% to 12.9% (p<0.0001) between the same periods. Despite this, positive surgical margin rates showed a downward trend in all pT groups across the different eras (p=0.72). Conclusion This study suggests that the patient profile for RALP in our unit is changing, with increasing proportions of higher-stage and more advanced disease being referred and operated on. Surgical margin outcomes however have remained good.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/bju.1351

Development and Validation of a 28-gene Hypoxia-related Prognostic Signature for Localized Prostate Cancer.

BACKGROUND: Hypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer. METHOD: Hypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON). RESULTS: A 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P \u3c .05), with borderline significances achieved in the other two (P \u3c .1). The signature also predicted biochemical recurrence in patients receiving post-prostatectomy radiotherapy (n = 130, P = .007) or definitive radiotherapy alone (n = 248, P = .035). Lastly, the signature predicted metastasis events in a pooled cohort (n = 631, P = .002). Prognostic significance remained after adjusting for clinic-pathological factors and commercially available prognostic signatures. The signature predicted benefit from hypoxia-modifying therapy in bladder cancer patients (intervention-by-signature interaction test P = .0026), where carbogen and nicotinamide was associated with improved survival only in hypoxic tumours. CONCLUSION: A 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients

Evolution and oncological outcomes of a contemporary radical prostatectomy practice in a UK regional tertiary referral centre.

OBJECTIVE: To investigate the clinical and pathological trends, over a 10-year period, in robot-assisted laparoscopic prostatectomy (RALP) in a UK regional tertiary referral centre. PATIENTS AND METHODS: In all, 1 500 consecutive patients underwent RALP between October 2005 and January 2015. Prospective data were collected on clinicopathological details at presentation as well as surgical outcomes and compared over time. RESULTS: The median (range) age of patients throughout the period was 62 (35-78) years. The proportion of preoperative high-grade cases (Gleason score 8-10) rose from 4.6% in 2005-2008 to 18.2% in 2013-2015 (P < 0.001). In the same periods the proportion of clinical stage T3 cases operated on rose from 2.4% to 11.4% (P < 0.001). The median prostate-specific antigen (PSA) level at diagnosis did not alter significantly. Overall, 11.6% of men in 2005-2008 were classified preoperatively as high-risk by National Institute for Health and Care Excellence criteria, compared with 33.6% in 2013-2015 (P < 0.001). The corresponding proportions for low-risk cases were 48.6% and 17.3%, respectively. Final surgical pathology showed an increase in tumour stage, Gleason grade, and nodal status over time. The proportion of pT3 cases rose from 43.2% in 2005-2008 to 55.5% in 2013-2015 (P < 0.001), Gleason score 9-10 tumours increased from 1.8% to 9.1% (P < 0.001) and positive nodal status increased from 1.6% to 12.9% (P < 0.001) between the same periods. Despite this, positive surgical margin rates showed a downward trend in all pT groups across the different eras (P = 0.72). CONCLUSION: This study suggests that the patient profile for RALP in our unit is changing, with increasing proportions of higher stage and more advanced disease being referred and operated on. However, surgical margin outcomes have remained good.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/bju.1351

Identification of potential therapeutic targets in prostate cancer through a cross-species approach.

Genetically engineered mouse models of cancer can be used to filter genome-wide expression datasets generated from human tumours and to identify gene expression alterations that are functionally important to cancer development and progression. In this study, we have generated RNAseq data from tumours arising in two established mouse models of prostate cancer, PB-Cre/PtenloxP/loxP and p53loxP/loxPRbloxP/loxP, and integrated this with published human prostate cancer expression data to pinpoint cancer-associated gene expression changes that are conserved between the two species. To identify potential therapeutic targets, we then filtered this information for genes that are either known or predicted to be druggable. Using this approach, we revealed a functional role for the kinase MELK as a driver and potential therapeutic target in prostate cancer. We found that MELK expression was required for cell survival, affected the expression of genes associated with prostate cancer progression and was associated with biochemical recurrence

HES5 silencing is an early and recurrent change in prostate tumourigenesis.

Prostate cancer is the most common cancer in men, resulting in over 10 000 deaths/year in the UK. Sequencing and copy number analysis of primary tumours has revealed heterogeneity within tumours and an absence of recurrent founder mutations, consistent with non-genetic disease initiating events. Using methylation profiling in a series of multi-focal prostate tumours, we identify promoter methylation of the transcription factor HES5 as an early event in prostate tumourigenesis. We confirm that this epigenetic alteration occurs in 86-97% of cases in two independent prostate cancer cohorts (n=49 and n=39 tumour-normal pairs). Treatment of prostate cancer cells with the demethylating agent 5-aza-2'-deoxycytidine increased HES5 expression and downregulated its transcriptional target HES6, consistent with functional silencing of the HES5 gene in prostate cancer. Finally, we identify and test a transcriptional module involving the AR, ERG, HES1 and HES6 and propose a model for the impact of HES5 silencing on tumourigenesis as a starting point for future functional studies.The authors are grateful to study volunteers for their participation and staff at the Welcome Trust Clinical Research Facility, Addenbrooke’s Clinical Research Centre, Cambridge. They also thank the NIHR Cambridge Biomedical Research Centre, the DOH HTA (ProtecT grant), and the NCRI/MRC (ProMPT grant) for help with the bio-repository, The University of Cambridge, Hutchison Whampoa Limited and Cancer Research UK for funding. They are grateful to the CRUK Cambridge Institute Genomics and Bioinformatics Core Facilities. Cross-validation of HES5 methylation includes the use of data generated by the TCGA Research Network.This is the final version of the article. It was originally published in the Endocrine-Related Cancer, April 1, 2015 22 131-144 doi: 10.1530/ERC-14-0454