Statistical analysis plan for the TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation of potentially clinically significant prostate cancer) multicentre randomised controlled trial

Background: The TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation) trial assesses the clinical and cost-effectiveness of two biopsy procedures in terms of detection of clinically significant prostate cancer (PCa). This article describes the statistical analysis plan (SAP) for the TRANSLATE randomised controlled trial (RCT). Methods/design: TRANSLATE is a parallel, superiority, multicentre RCT. Biopsy-naïve men aged ≥ 18 years requiring a prostate biopsy for suspicion of possible PCa are randomised (computer-generated 1:1 allocation ratio) to one of two biopsy procedures: transrectal (TRUS) or local anaesthetic transperineal (LATP) biopsy. The primary outcome is the difference in detection rates of clinically significant PCa (defined as Gleason Grade Group ≥ 2, i.e. any Gleason pattern ≥ 4 disease) between the two biopsy procedures. Secondary outcome measures are th eProBE questionnaire (Perception Part and General Symptoms) and International Index of Erectile Function (IIEF, Domain A) scores, International Prostate Symptom Score (IPSS) values, EQ-5D-5L scores, resource use, infection rates, complications, and serious adverse events. We describe in detail the sample size calculation, statistical models used for the analysis, handling of missing data, and planned sensitivity and subgroup analyses. This SAP was pre-specified, written and submitted without prior knowledge of the trial results. Discussion: Publication of the TRANSLATE trial SAP aims to increase the transparency of the data analysis and reduce the risk of outcome reporting bias. Any deviations from the current SAP will be described and justified in the final study report and results publication. Trial registration: International Standard Randomised Controlled Trial Number ISRCTN98159689, registered on 28 January 2021 and registered on the ClinicalTrials.gov (NCT05179694) trials registry

Mechanistic insight into the reaction catalysed by bacterial type II dehydroquinases

DHQ2 (type II dehydroquinase), which is an essential enzyme in Helicobacter pylori and Mycobacterium tuberculosis and does not have any counterpart in humans, is recognized to be an attractive target for the development of new antibacterial agents. Computational and biochemical studies that help understand in atomic detail the catalytic mechanism of these bacterial enzymes are reported in the present paper. A previously unknown key role of certain conserved residues of these enzymes, as well as the structural changes responsible for triggering the release of the product from the active site, were identified. Asp89*/Asp88* from a neighbouring enzyme subunit proved to be the residue responsible for the deprotonation of the essential tyrosine to afford the catalytic tyrosinate, which triggers the enzymatic process. The essentiality of this residue is supported by results from site-directed mutagenesis. For H. pylori DHQ2, this reaction takes place through the assistance of a water molecule, whereas for M. tuberculosis DHQ2, the tyrosine is directly deprotonated by the aspartate residue. The participation of a water molecule in this deprotonation reaction is supported by solvent isotope effects and proton inventory studies. MD simulation studies provide details of the required motions for the catalytic turnover, which provides a complete overview of the catalytic cycle. The product is expelled from the active site by the essential arginine residue and after a large conformational change of a loop containing two conserved arginine residues (Arg109/Arg108 and Arg113/Arg112), which reveals a previously unknown key role for these residues. The present study highlights the key role of the aspartate residue whose blockage could be useful in the rational design of inhibitors and the mechanistic differences between both enzymesFinancial support from the Comunidad de Madrid (S2010-BMD-2457 to F.G.), Xunta de Galicia (10PXIB2200122PR and GRC2010/12 to C.G.-B.) and the Spanish Ministry of Science and Innovation (SAF2009-13914-C02-02 to F.G. and SAF2010-15076 to C.G.-B.) is 5076 to CGB and BFU2008-01588/BMC to MJvR) is gratefully acknowledged. C.C. and A.P. thank the Spanish Ministry of Science and Innovation for their respective FPU fellowshipsS

Synthesis of 3-alkyl enol mimics inhibitors of type II dehydroquinase: factors influencing their inhibition potency

Several 3-alkylaryl mimics of the enol intermediate in the reaction catalyzed by type II dehydroquinase were synthesized to investigate the effect on the inhibition potency of replacing the oxygen atom in the side chain by a carbon atom. The length and the rigidity of the spacer was also studied. The inhibitory properties of the reported compounds against type II dehydroquinase from Mycobacterium tuberculosis and Helicobacter pylori are also reported. The binding modes of these analogs in the active site of both enzymes were studied by molecular docking using GOLD 5.0 and dynamic simulations studiesFinancial support from the Xunta de Galicia (10PXIB2200122PR and GRC2010/12) and the Spanish Ministry of Science and Innovation (SAF2010-15076) is gratefully acknowledged. BB, AS and AP thank the Spanish Ministry of Science and Innovation for FPU fellowshipsS

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

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

Purpose It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with i) stable PCa post-primary or salvage RT (sPCa), ii) progressing PCa post-RT (pPCa), and iii) de novo metastatic PCa (mPCa). Experimental Design A cohort of 19 patients with diagnostic prostate biopsies (n=6 sPCa, n=5 pPCa, n=8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3’RNA sequencing (3’RNAseq) (n=19), nanoString analysis (n=12) and Illumina 850k methylation (n=8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes (DEGs) and methylated genomic regions (MGRs). Results 18 of 19 samples provided useable 3’RNAseq data. Principal Component Analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ∼600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p<0.0001) in an external cohort. Conclusions 3’RNAseq, nanoString and 850K-methylation analyses are each achievable from historical FFPE diagnostic pre-treatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation

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