Prostate cancer follows a long and heterogeneous disease course with incompletely understood aetiology1. Here we dissect the mutational processes shaping the genomes of 959 donors from the Pan Prostate Cancer Group and assess their clinical relevance. By integrating de novo extracted single-base substitution, insertion–deletion and copy-number signatures with six novel complex structural variant signatures, we identify eight integrated mutational footprints (IMFs) that collectively explain the mutational processes in 85% of primary prostate cancer genomes. IMFs were strongly influenced by regional biases in the genome, most prevalently androgen receptor-mediated mutagenesis and replication stress. Four IMFs, present in 37% of primary tumours, were significantly associated with shorter time to metastasis. These included reactive oxygen-species-driven mutagenesis and both canonical and non-canonical homologous recombination deficiency, the latter being enriched in patients of African ancestry. Extending to the metastatic setting, we found that IMFs predicted sensitivity to androgen receptor pathway inhibitors. Taken together, our study delineates the aetiologies and mutational processes that drive the genomic and clinical heterogeneity of prostate cancer, introduces IMFs as a unifying framework, and highlights their potential to improve both risk stratification and biomarker-guided treatment selection. Eight integrated mutational footprints collectively explain the mutational processes in 85% of primary prostate cancer genomes.

The inactivation of tumour suppressor genes is a key step in cancer development, and is usually achieved by homozygous loss. In prostate cancer, however, large genomic regions are often hemizygously lost, which complicates the identification of putative tumour suppressors in these regions. Here, we develop Epi2Hit, an integrative computational method that leverages whole genome sequencing, epigenomic profiling and gene expression to identify biallelic inactivation of tumour suppressor genes involving DNA methylation of promoter and enhancer regions of one allele and genomic loss of the other allele. We apply Epi2Hit to a cohort of 2,021 prostate cancers to discover tumour suppressor genes. In particular, we identify epigenetic biallelic inactivation of ZFHX3 at a recurrence level similar to TP53. Biallelic inactivation of ZFHX3, a transcriptional repressor, leads to upregulation of oncogenes, including MYC and a shorter time to metastasis. Finally, we provide evidence that epigenetic silencing as 2nd hit is particularly enriched in regions with nearby essential genes, precluding homozygous loss. Epigenetic biallelic inactivation in prostate cancer remains to be explored. Here, the authors develop a computational method Epi2Hit that integrates the hemizygous genomic disruptions with patterns of hypermethylation at regulatory CpG sites to identify biallelic inactivation in tumour suppressor genes.

Clinical trials show benefit of the combination of poly(ADP-ribose) polymerase inhibitors (PARPi) with androgen receptor (AR) pathway inhibitors (ARPi) in metastatic, castration-resistant prostate cancer. While benefit was evident in patients with tumours harbouring mutations in homologous recombination repair (HRR) genes, improved outcomes were also observed in the absence of such alterations. Although there is literature linking AR with DNA repair, the basis of the interaction between the AR and PARP is unclear. We show that benefit of the combination of ARPi and PARPi in prostate cancer models with no HRR mutations requires ARPi-responsive cells and a PARPi with PARP1-trapping activity, and does not involve an effect of PARPi treatment in modulating AR transcription. Combination benefit is driven by an increase in DNA double-strand breaks and micronuclei formation, which is not due to a control of HRR gene transcription by the AR. Also, we uncover a novel role of PARP1 modulating AR recruitment to chromatin in the presence of DNA damage. These data shed light on the interplay between PARP1 and the AR in dealing with genotoxic insults and provide a mechanism-of-action consistent with the clinical benefit of the combination of PARPi and ARPi in patients with prostate cancer.

Clear cell renal cell carcinoma (ccRCC) is characterised by significant genetic heterogeneity, which has diagnostic and prognostic implications. Very limited evidence is available regarding DNA methylation heterogeneity. We therefore generate sequence level DNA methylation data on 136 multi-region tumour and normal kidney tissue from 18 ccRCC patients, along with matched whole exome sequencing (85 samples) and gene expression (47 samples) data on a subset of samples. We perform a comprehensive systematic analysis of heterogeneity between patients, within a patient and within a sample. We demonstrate that bulk methylation data may be deconvoluted into cell-type-specific latent methylation components (LMCs), and that LMC1, which is likely to represent T cells, is associated with prognostic parameters. Differential epipolymorphism was noted between ccRCC and normal tissue in the promoter region of genes which are known to be associated with kidney cancer. This was externally validated in an independent cohort of 71 ccRCC and normal kidney tissues. Differential epipolymorphism in the gene promoter was a predictor of gene expression, after adjusting for average methylation. This represents the first evaluation of epipolymorphism in ccRCC and suggests that gains and losses in methylation disorder may have a functional relevance, gleaning important information on tumourigenesis.

Background: Bacteria play a suspected role in the development of several cancer types, and associations between the presence of particular bacteria and prostate cancer have been reported. Objective: To provide improved characterisation of the prostate and urine microbiome and to investigate the prognostic potential of the bacteria present. Design, setting, and participants: Microbiome profiles were interrogated in sample col-lections of patient urine (sediment microscopy: n = 318, 16S ribosomal amplicon sequencing: n = 46; and extracellular vesicle RNA-seq: n = 40) and cancer tissue (n = 204). Outcome measurements and statistical analysis: Microbiomes were assessed using anaerobic culture, population-level 16S analysis, RNA-seq, and whole genome DNA sequencing. Results and limitations: We demonstrate an association between the presence of bacteria in urine sediments and higher D'Amico risk prostate cancer (discovery, n = 215 patients, p < 0.001; validation, n = 103, p < 0.001, v2 test for trend). Characterisation of the bacterial community led to the (1) identification of four novel bacteria (Porphyromonas sp. nov., Varibaculum sp. nov., Peptoniphilus sp. nov., and Fenollaria sp. nov.) that were frequently found in patient urine, and (2) definition of a patient sub-group associated with metastasis development (p = 0.015, log-rank test). The presence of five specific anaerobic genera, which includes three of the novel isolates, was associated with cancer risk group, in urine sediment (p = 0.045, log-rank test), urine extracellular vesicles (p = 0.039), and cancer tissue (p = 0.035), with a meta-analysis hazard ratio for disease progression of 2.60 (95% confidence interval: 1.39-4.85; p = 0.003; Cox regression). A limitation is that functional links to cancer development are not yet established. Conclusions: This study characterises prostate and urine microbiomes, and indicates that specific anaerobic bacteria genera have prognostic potential. Patient summary: In this study, we investigated the presence of bacteria in patient urine and the prostate. We identified four novel bacteria and suggest a potential prognostic utility for the microbiome in prostate cancer. (C) 2022 Published by Elsevier B.V. on behalf of European Association of Urology.

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

Prostate cancer screening using prostate-specific antigen (PSA) has been shown to reduce mortality but with substantial overdiagnosis, leading to unnecessary biopsies. The identification of a highly specific biomarker using liquid biopsies, represents an unmet need in the diagnostic pathway for prostate cancer. In this study, we employed a method that enriches for methylated cell-free DNA fragments coupled with a machine learning algorithm which enabled the detection of metastatic and localized cancers with AUCs of 0.96 and 0.74, respectively. The model also detected 51.8% (14/27) of localized and 88.7% (79/89) of patients with metastatic cancer in an external dataset. Furthermore, we show that the differentially methylated regions reflect epigenetic and transcriptomic changes at the tissue level. Notably, these regions are significantly enriched for biologically relevant pathways associated with the regulation of cellular proliferation and TGF-beta signaling. This demonstrates the potential of circulating tumor DNA methylation for prostate cancer detection and prognostication.

Recent landmark studies have shown a survival benefit from using genomic profiling to guide targeted personalized therapy in men with metastatic castrate-refractory prostate cancer (mCRPC) [1]. However, the point at which genomic profiles should be acquired in the prostate cancer pathway remains unclear. Primary high-grade and/or locally advanced prostate cancer has a significant relapse rate despite radical therapy, with 30%–60% developing distant metastases, cancer-specific and all-cause mortality within a median of 6.5 years’ follow-up [2]. The UK National Prostate Cancer Audit has reported that more than a half of all new cancers diagnosed present with such aggressive disease that is National Institute of Health and Care Excellence [NICE] Cambridge Prognostic Group, CPG 4–5 or de novo metastatic [3]. This represents a large demographic of men with a high probability of progression to the mCRPC state, and in whom knowledge of their genomic status may help in managing their treatment. Here, we explored the evaluation of genomic mutation yield at diagnosis in these men by synergizing data from pre-biopsy imaging, guided biopsies and modern prognostic categorization. Patients referred to a NHS diagnostic service between January 2019 and December 2021 were screened using the pre-biopsy MRI report and presenting PSA level [4]. Eligible men (REC ethics approval 03/018) included those with raised PSA levels and MRI Prostate Imaging – Reporting and Data System (PI-RADS) 4–5 and/or evidence of ≥T3 disease or de novo metastatic disease. Biopsies were taken from image-targeted sites as well as systematic biopsies (no extra or specific research samples added) and prepared as standard (formalin-fixed paraffin-embedded [FFPE]). A uro-pathologist marked all cancer areas from target and non-target samples. Surplus-to-diagnosis FFPE sections matching the marked areas were sent for sequencing. The methods used for extraction, DNA isolation and sequencing are detailed in Appendix S1. For this study, a 350-gene capture panel (TWIST Biosciences) was used. A total of 62 men were recruited, of whom 52 (84%) had biopsy cancer content that were sufficient and met the required quality for sequencing. Cohort details are shown in Table S1. Of the 52 men, 37 (71%) had either CPG ≥4–5 (n = 30) or metastatic disease (n = 7) at diagnosis. These data confirm high yields of aggressive cancer detection by using pre-biopsy selection criteria of high PSA and MRI features and that samples for sequencing can be obtained without disrupting the standard diagnostic histopathology work-up. DNA damage repair (DDR) pathogenic mutations were detected in five individuals, with four harbouring BRCA2 (one germline) and one ATM (Table S2). All were in men with CPG 4–5/metastatic disease, representing 14% (5/37) of this subcohort. Currently based on published guidance the four BRCA2 mutations are potentially actionable with Poly (ADP-ribose) polymerase (PARP) inhibitors [5]. A further four cases were called BRCA2 or ATM loss by the mutation calling algorithm but were of uncertain clinical significance. In the phosphatidylinositol 3-kinase (PI3K)-Akt pathway, PTEN mutations were identified in four men and a further three were present in PI3K-related gene subunits, as well as one mutation in MTOR (total 8/52, 15%; Table S2). Seven were in men with CPG 4–5 or metastatic disease representing 19% of this subcohort (7/37). Based on eligibility for use of the novel AKT inhibitor capiversetib within the Capitello-281 trial (NCT04493853), mutations in PTEN and PIK3CA might be actionable, representing 10% (5/52 men) of the whole cohort and 11% (4/37 men) of the CPG 4–5/metastatic subcohort. Other pathogenic mutations identified are shown in Table S2, with the commonest being p53 (6/52 men; 12%). Focusing on the 37 men with CPG 4–5/metastatic disease, all but two were treated with combined androgen deprivation therapy (ADT) and radiotherapy or systemic therapy alone (ADT and chemotherapy). Follow-up was available for a median (range) of 26 (15–35) months. In this CPG 4–5/metastatic group overall, there were progression events in 12/37 men (32%): nine new or worsening metastases with four who eventually died from prostate cancer, three others developed rapid biochemical relapse after first-line treatment. Of the five men with DDR pathway mutations, two progressed: one with a BRCA2 mutation eventually died from disease and another developed new metastasis. Interestingly, one of the men with ATM loss of uncertain significance, relapsed rapidly after surgery. Of the men with PI3K-Akt mutations there was one death from prostate cancer and one progression despite treatment. Combining men with either a DDR (n = 4) or a PI3K pathogenic (n = 7) mutation, 4/11 (36%) progressed and/or died from disease (one individual had both BRCA2 and PI3K mutations). In men without any reported DDR or PI3K mutations (pathogenic or uncertain), 5/22 (23%) progressed in the follow-up period. We have shown that these steps are possible in a routine NHS diagnostic pathway and offer a rational way to maximize the detection of potentially actionable mutations while avoiding over-investigation. Using this method, DDR and PI3K mutations were detected in 11% and 19% of those with CPG 4–5/metastatic disease and these men seemed to be over-represented in cases that progressed over the short term (observational data). Our study was primarily descriptive as our aim was to assess feasibility and yield and we did not attempt to determine cost effectiveness. Nevertheless, as BRCA1/2 is already commissioned by NHS Genomics, incorporating routine profiling is already possible under NHS provisions. Any test is only of benefit, however, if it will alter management. In this regard, use of PARP inhibitors for treatment of BRCA-mutant tumours (including metastatic prostate cancer) are now approved by NICE (as well as the European Medicines Agency and US Food and Drug Administration) [6]. It could be argued that waiting for men to develop mCRPC before profiling may further reduce the numbers sequenced. However, our approach abrogates issues including (i) having to search off-site archives for archived material (ii) repeat sectioning and reporting and (iii) dealing with degrading DNA quality from stored FFPE. Furthermore, a priori knowledge of BRCA status may allow modifications at earlier stages of management and future proof for new indications for genomic profiling (including trials in earlier disease). BRCA2 status, for instance, is already an independent predictor of poor survival in the Predict Prostate algorithm [8]. In summary, we report a pragmatic method to optimize the selection of men for molecular profiling of prostate cancer at the point of diagnosis. Working within standard diagnostic pathways, our method could be adopted by any NHS (or other) unit without much extra resourcing. The upfront information could not only be used for future drug selection but could also influence decision making and personalized follow-up. We are grateful for help from Mrs Anne George in assisting with the study in its early phases and to the Cambridge Pathology Tissue Bank staff, especially Ms Elizabeth Cromwell who worked to collate and transfer samples to the Cancer Molecular Diagnostics Laboratory. We are also indebted to clincial colleagues who worked with the CUTRACT team to recruit men into this study with particular thanks to Mr Syed Shah and Mr Adham Ahmed. Professor Vincent J. Gnanapragasam acknowledges infrastructure and part funding from the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This project and publication are dedicated to the memory of Dr Charlie Massie. A very valued colleague and outstanding scientist, who was committed to the translation of bench to bedside but did not live to see this work come to fruition. Its success would not have been possible without his enthusiasm and championing of the multidisciplinary approach to prostate cancer research. None. Appendix S1. Supplementary methods and tables. Table S1. Demographics of the study population. Table S2. Pathogenic mutations detected in prostate biopsy samples and actionable status. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Background: PARP inhibition with androgen deprivation is an effective treatment for Prostate Cancer (PC). We investigated the effects of a two-week course of olaparib±degarelix in the “window” before radical prostatectomy (RP). Methods: Primary endpoint: level of PARP inhibition. Secondary endpoints: safety and feasibility of the trial design. Exploratory endpoints: changes in relevant biological pathways after olaparib±degarelix. Participants (Pt) with localised PC (D’Amico intermediate or high risk) were randomly allocated (1:1) to receive olaparib 300mg bid for two weeks ± degarelix (240mg s/c). Recruitment target was twenty evaluable Pts. Compliance (patient diaries) and toxicity (CTCAE v4.03) were assessed. Diagnostic biopsies with subsequent prostatectomy samples were analysed by IHC for PAR protein expression and whole transcriptome gene expression analysis was performed using NovaSeq. Gene mutation profiling was performed using a custom 350 gene panel, sequenced on Illumina NextSeq 2000 and analysed with in-house bioinformatics pipeline. Serum samples for PSA and testosterone were taken at baseline, day 15 and 42 post RP. Results: 24 patients were recruited, 23 evaluable for safety and 20 for primary endpoint (11 olaparib, 9 olaparib+degarelix). All related AE ≤ Grade (Gr) 2, majority Gr 1. Commonest AEs: fatigue, nausea & vomiting (olaparib) or injection site reaction, fatigue & nausea (olaparib+degarelix). No RP was delayed by an AE. PSA declined from baseline in all (8/8) Pt after olaparib+degarelix and 3/9 Pt after olaparib (-11, -17 & -47%). PAR protein expression decreased in both cohorts after treatment, with the mean reduction in PAR expression olaparib > olaparib+degarelix (p=0.01). After olaparib, Pt with greatest PSA change also had greatest reduction in PAR expression compared with other Pt (p=0.008); no correlation between PSA and PAR change was detected in olaparib+degarelix Pt. Pathogenic mutations (in BRCA2/ATM and ATM) were detected in two Pt. Upregulation of gene signatures, including homologous recombination signature, were noted following combined olaparib+degarelix treatment. Conclusions: We have previously reported that toxicities were predictable and manageable, with no drug-related delay in RP for Pt. Updated results confirm PARP inhibition (reduced PAR expression) in PC samples after olaparib and (to a lesser degree) olaparib+degarelix. Up-regulation of homologous recombination deficiency gene signature occurred after olaparib+degarelix. Further assessments of post-treatment tissues are ongoing to establish mechanisms of action of combination therapy across all genetic backgrounds. Citation Format: Harveer Dev, Mark Linch, Howard Kynaston, Greg Shaw, Krishna Narahari, Tatiana Hernández, Anne Warren, Alopa Malaviya, Vincent Gnanapragasam, Elizabeth Harrington, Niedzica Camacho, Silvia Glont, Massimo Sqautrito, Joshua Armenia, Luiza Moore, Robert Hanson, Toby Milne-Clarke, Shubha Anand, Charlie Massie, Nimish Shah, Simon Pacey. Updated analysis of “CANCAP03” – A study into the pharmacodynamic biomarker effects of olaparib (PARP inhibitor) ± degarelix (GnRH antagonist) given prior to radical prostatectomy [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B061.

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

Additional file 3. Subclonal analysis summary in multiple samples: Worksheet 1 summarises the number of clusters and clonal cell fraction for each patient after applying a multidimensional Bayesian Dirichlet process. Worksheet 2 reports the total number of patients included in the subclonal analysis and the location of normal samples in relation to the tumour sample.

Background Up to 80% of cases of prostate cancer present with multifocal independent tumour lesions leading to the concept of a field effect present in the normal prostate predisposing to cancer development. In the present study we applied Whole Genome DNA Sequencing (WGS) to a group of morphologically normal tissue ( n = 51), including benign prostatic hyperplasia (BPH) and non-BPH samples, from men with and men without prostate cancer. We assess whether the observed genetic changes in morphologically normal tissue are linked to the development of cancer in the prostate. Results Single nucleotide variants ( P = 7.0 × 10 –03 , Wilcoxon rank sum test) and small insertions and deletions (indels, P = 8.7 × 10 –06 ) were significantly higher in morphologically normal samples, including BPH, from men with prostate cancer compared to those without. The presence of subclonal expansions under selective pressure, supported by a high level of mutations, were significantly associated with samples from men with prostate cancer ( P = 0.035, Fisher exact test). The clonal cell fraction of normal clones was always higher than the proportion of the prostate estimated as epithelial ( P = 5.94 × 10 –05 , paired Wilcoxon signed rank test) which, along with analysis of primary fibroblasts prepared from BPH specimens, suggests a stromal origin. Constructed phylogenies revealed lineages associated with benign tissue that were completely distinct from adjacent tumour clones, but a common lineage between BPH and non-BPH morphologically normal tissues was often observed. Compared to tumours, normal samples have significantly less single nucleotide variants ( P = 3.72 × 10 –09 , paired Wilcoxon signed rank test), have very few rearrangements and a complete lack of copy number alterations. Conclusions Cells within regions of morphologically normal tissue (both BPH and non-BPH) can expand under selective pressure by mechanisms that are distinct from those occurring in adjacent cancer, but that are allied to the presence of cancer. Expansions, which are probably stromal in origin, are characterised by lack of recurrent driver mutations, by almost complete absence of structural variants/copy number alterations, and mutational processes similar to malignant tissue. Our findings have implications for treatment (focal therapy) and early detection approaches.

You have accessJournal of UrologyCME1 May 2022PD43-11 METHYLATION HETEROGENEITY IN CCRCC OFFERS INSIGHTS INTO TUMORIGENESIS AND BIOMARKER DEVELOPMENT Sabrina Rossi, Victoria Dombrowe, Sara Pita, Christopher Smith, Grant Stewart, Roland Schwarz, and Charlie Massie Sabrina RossiSabrina Rossi More articles by this author , Victoria DombroweVictoria Dombrowe More articles by this author , Sara PitaSara Pita More articles by this author , Christopher SmithChristopher Smith More articles by this author , Grant StewartGrant Stewart More articles by this author , Roland SchwarzRoland Schwarz More articles by this author , and Charlie MassieCharlie Massie More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002604.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 30% of patients with clear cell renal cell carcinoma (ccRCC) will develop a recurrence, however there are currently no validated prognostic biomarkers. This has been identified as a key research priority. Biomarker development has been hampered by significant genetic intra-tumoral heterogeneity (ITH), yet methylation ITH is largely uncharacterised. We therefore aim to evaluate methylation heterogeneity between patients, within patients and within individual tumour samples. METHODS: Multi-region samples were collected from fresh frozen nephrectomy specimens from individuals with ccRCC (Ethical approval: REC ID 03/018). Multiple tumour and normal samples were obtained from each patient (at least 5 samples per patient). Methylation analysis was performed using the Illumina EPIC-seq to obtain high-resolution sequence-level data. Matched samples were processed for copy-number alterations (whole exome sequencing) and gene expression (RNA-seq). An independent cohort of ccRCC tissue samples was sequenced for external validation. RESULTS: We sequenced 136 samples from 18 ccRCC patients, which consists of the largest multi-region methylation cohort to date. We observed extensive heterogeneity between patients, which dominated over within-patient heterogeneity. Although disordered methylation is believed to be a stochastic process, there were significant changes in methylation epipolymorphism between ccRCC and normal kidney at the promoter region of 47 known kidney cancer genes (including SLC16A3, MYC, WT1, KRT7 and KRT18). This finding was confirmed in an independent cohort of 71 ccRCC tumour and normal samples. Genes with significantly disordered methylation in tumour are more often associated with significant hypermethylation in ccRCC (87% genes hypermethylated) rather than hypomethylation, and are negatively correlated with gene expression, implying that methylation disorder may have a functional consequence. Several genes which have been implicated in ccRCC prognosis (including DKK2, SFRP1, CCND1, SERPINF1, SMAD3) demonstrated heterogeneous methylation patterns. CONCLUSIONS: Heterogeneous methylation between patients and within a sample may be a key determinant of the noted failure to validate prognostic genes in ccRCC. Disordered methylation and ITH may lead to inconsistent results amongst samples, which is exacerbated by different methylation platforms covering disparate CpGs within the promoter of genes. This key finding may help identify prognostic methylation markers in future. Source of Funding: Cancer Research UK © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e707 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sabrina Rossi More articles by this author Victoria Dombrowe More articles by this author Sara Pita More articles by this author Christopher Smith More articles by this author Grant Stewart More articles by this author Roland Schwarz More articles by this author Charlie Massie More articles by this author Expand All Advertisement PDF downloadLoading ...

Background: Germline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression.Objective: To find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors asso-ciated with such progression.Design, setting, and participants: Whole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) data -set.Outcome measurements and statistical analysis: A total of 15, 822 rare (MAF <1%) predicted-deleterious coding germline mutations were identified. Optimal multifactor and univariate Cox regression models were built to predict time to BCR after radical treatment, using germline variants grouped by functionally annotated gene sets. Models were tested for robustness using bootstrap resampling.Results and limitations: Optimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in "Hallmark"gene sets were consistently asso-ciated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset.Conclusions: We demonstrate for the first time that rare deleterious coding germline variants robustly associate with time to BCR after radical treatment, including cohort -independent validation. Our findings suggest that germline testing at diagnosis could aid clinical decisions by stratifying patients for differential clinical management.Patient summary: Prostate cancer patients with particular genetic mutations have a higher chance of relapsing after initial radical treatment, potentially providing opportu-nities to identify patients who might need additional treatments earlier.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).

AbstractBlood-based assays have shown increasing ability to detect circulating tumour DNA (ctDNA) in patients with early-stage cancer. However, detection of ctDNA in patients with non-small cell lung cancer (NSCLC) has continued to prove challenging. We performed retrospective analysis to quantify ctDNA levels in a cohort of 100 patients with early-stage NSCLC prior to treatment with curative intent enrolled in the LUCID study (NCT04153526). Where tumour tissue was available for whole exome sequencing, mutations identified were used to define patient-specific sequencing assays. For those 90 patients, plasma cell-free DNA was sequenced to high depth across capture panels targeting a median of 328 mutations specific to each patient. Data was analysed using Integration of Variant Reads (INVAR), detecting ctDNA in 66.7% of patients, including 52.7% (29 of 55) patients with stage I disease and >88% detection for patients with stage II and III disease (16/18 and 15/17). ctDNA was detected in plasma at fractional concentrations as low as 9.1×10−6, and in patients with tumour volumes as low as 0.23 cm3. A 36-gene sequencing panel (InVisionFirst-Lung™) was used to analyse plasma DNA in 27 samples including the 10 cases without tumour exome data, and detected ctDNA in 59% of samples tested (16 of 27). Across the entire cohort, detection rates were higher in squamous cell carcinoma patients compared to adenocarcinoma patients (81% vs. 59%). Detection of ctDNA prior to treatment was associated with significantly shorter time free from relapse, across all patients and in patient subgroups, with Hazard Ratios >11 for selected patient subsets. Our analysis indicates that for patients with stage I NSCLC, the median ctDNA fraction in plasma is approx. 12 parts per million (0.0012%). This indicates the limits of detection that would be required for ctDNA-based liquid biopsies to detect ctDNA in the majority of patients with early-stage NSCLC.

SUMMARYMetabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that aVHLloss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasionin vitroand metastasisin vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage.HighlightsBranched-chain amino acids catabolism is reprogrammed in ccRCC tumorsBCAT-dependent transamination supplies nitrogen forde novobiosynthesis of amino acids including aspartate and asparagine in ccRCCAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginineASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levelsSilencing of ASS1 impairs the metastatic potentialin vitroandin vivoof ccRCC cells