Background Better blood tests to elucidate the behaviour of metastatic castration-resistant prostate cancer (mCRPC) are urgently needed to drive therapeutic decisions. Plasma cell-free DNA (cfDNA) comprises normal and circulating tumour DNA (ctDNA). Low-pass whole-genome sequencing (lpWGS) of ctDNA can provide information on mCRPC behaviour. Objective To validate and clinically qualify plasma lpWGS for mCRPC. Design, setting, and participants Plasma lpWGS data were obtained for mCRPC patients consenting to optional substudies of two prospective phase 3 trials (FIRSTANA and PROSELICA). In FIRSTANA, chemotherapy-naïve patients were randomised to treatment with docetaxel (75 mg/m 2 ) or cabazitaxel (20 or 25 mg/m 2 ). In PROSELICA, patients previously treated with docetaxel were randomised to 20 or 25 mg/m 2 cabazitaxel. lpWGS data were generated from 540 samples from 188 mCRPC patients acquired at four different time points (screening, cycle 1, cycle 4, and end of study). Outcome measurements and statistical analysis lpWGS data for ctDNA were evaluated for prognostic, response, and tumour genomic measures. Associations with response and survival data were determined for tumour fraction. Genomic biomarkers including large-scale transition (LST) scores were explored in the context of prior treatments. Results and limitations Plasma tumour fraction was prognostic for overall survival in univariable and stratified multivariable analyses (hazard ratio 1.75, 95% confidence interval 1.08–2.85; p = 0.024) and offered added value compared to existing biomarkers (C index 0.722 vs 0.709; p = 0.021). Longitudinal changes were associated with drug response. PROSELICA samples were enriched for LSTs ( p = 0.029) indicating genomic instability, and this enrichment was associated with prior abiraterone and enzalutamide treatment but not taxane or radiation therapy. Higher LSTs were correlated with losses of RB1 / RNASEH2B , independent of BRCA2 loss. Conclusions Plasma lpWGS of ctDNA describes CRPC behaviour, providing prognostic and response data of clinical relevance. The added prognostic value of the ctDNA fraction over established biomarkers should be studied further. Patient summary We studied tumour DNA in blood samples from patients with prostate cancer. We found that levels of tumour DNA in blood were indicative of disease prognosis, and that changes after treatment could be detected. We also observed a “genetic scar” in the results that was associated with certain previous treatments. This test allows an assessment of tumour activity that can complement existing tests, offer insights into drug respo...

Background Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients’ risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer. Methods We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes. Results Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations. Conclusions Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

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<p><b>The proteinaceous skeletons of deep-sea Antipatharian 'black' corals are a new proxy archive that has shown promise for providing high resolution marine records in a wide range of waters. Although stable isotopes have been used successfully for palaeoceanographic reconstruction, there have been few studies of trace metals in these skeletons. In this thesis we study a suite of trace elements in black coral skeletons to assess their utility in paleoenvironmental reconstructions.</b></p> <p>Fifty black corals were sampled from the NIWA Invertebrate Collection, broadly distributed around New Zealand from ~25˚S to 47 ˚S and 165˚E to 155˚E. Small powder samples were taken from the outer layers of the coral skeleton, dissolved in nitric acid and analysed by ICP-MS using a new methodology developed as part of this thesis. We critically evaluate this new method, quantifying the limits on accuracy and intrinsic reproducibility, and comparing this to the range of concentrations found in coral specimens.</p> <p>A necessary step in evaluating the potential for trace elements to provide palaeoenvironmental information is to understand how trace elements become incorporated into the corals’ skeletons. Questions include whether uptake is passive or active (i.e. biologically mediated); and whether trace elements derive from the coral’s food (originating in the surface ocean) or from the ambient water in which the coral is growing.</p> <p>In this thesis we explore these questions by studying spatial patterns of coral trace elements to determine if they show similarity to surface or intermediate-depth ocean trace element distributions. We investigate the influence of several variables on coral trace elements including proximity to the NZ mainland, the depth at which the corals grew, regional oceanography, and primary productivity. We examine the enrichment of skeletal trace elements compared with both coral tissue and particulate organic material (POM). Finally, we examine subsets of the coral samples that control for several environmental variables in an attempt to isolate the effect of coral size and taxonomy on trace element concentrations. Replicate samples from the same specimens and from different specimens at the same site (or within a 25Km radius) were used to assess differences in trace element content within and between coral specimens, respectively.</p> <p>Our results indicate that black corals strongly enrich many trace elements (TE) in their skeletons and tissues (Br>Zn>Cd>Mo>V>U>Fe>Cu>Ni>I) at levels up to 10⁸ times higher than seawater concentrations. A large intrinsic variability in TE content was found between and within all black coral specimens. This variability largely obscures any relationship that may indicate a water column origin and mechanism of uptake. Coral taxonomy at a genus level strongly influences most TE concentrations, contributing up to 80% of the variation in some elements. Although largely inconclusive, the data hints at a combination of active and passive uptake pathways from ambient or surface seawater sources for some elements which might warrant further investigation.</p> <p>In order to advance this field of study further, we suggest that a better understanding is needed of the taxonomic control over trace element incorporation, including biomineralisation and biological utilisation of trace elements by black corals. We also note that there is a lack of data on trace element biogeochemical cycles in the waters around NZ, which would be important in order to better constrain the behaviour of the TEs in black corals and to better evaluate their paleoenvironmental utility.</p>

Background: We, and others, have shown that responses to therapies in metastatic castration-resistant prostate cancer (mCRPC) can be monitored using plasma cell-free DNA (cfDNA), and that this can be both quantitative and qualitative. In this study, we explored the utility of low pass whole genome sequencing (lpWGS) of cfDNA in mCRPC patients treated on two large prospectively collected Phase III trials of taxane chemotherapy, FIRSTANA (NCT01308567) and PROSELICA (NCT01308580). Methods: Plasma samples collated from the FIRSTANA and PROSELICA trials were evaluated. cfDNA was isolated and analysed with lpWGS using the Illumina NovaSeq 6000. Reads were aligned using BWA-MEM and quality controlled using Picard and FASTQC. Whole-genome copy number aberration (CNA) profiles were generated using ichorCNA, which also estimated tumor purity and ploidy. Log-rank tests were used for univariable survival comparisons, and Cox proportional hazard models were used for multivariable survival analysis. Results: Overall, lp-WGS data was generated from 528 samples acquired at three different timepoints (Baseline [SCR and C1], C4 and EOS), representing 188 unique patients. We observed CNA profiles in line with emergent mCRPC subtypes including highly aberrant, large-scale events which have been linked to homologous-repair deficient tumors. We found that CNAs in this cohort matched other published studies with frequent amplifications of AR (~30%), copy-gains of PI3KCA (~45%) and copy-loss of NKX3-1 (~75%), RB1 (~70%) and PTEN (~50%). We did not observe changes in baseline tumor purity or ploidy between the FIRSTANA and PROSELICA cohorts. Following calculation of a large-scale transition (LST) score (measuring CNA breakpoints), we observed that this was significantly elevated (Wilcox test, p=0.0073) in cases that had prior exposure to second line androgen deprivation (abiraterone or enzalutamide). Baseline tumor purity values were associated with several mCRPC clinical variables including ECOG performance status. High lpWGS tumor purity values were univariably associated with poorer overall survival in both FIRSTANA and PROSELICA cohorts (log-rank test p=0.0021 and 0.0009), and were also associated with significantly poorer RPFS and PSAPFS. Tumor purity was also associated (HR 1.66, p=0.036) with survival when assessed using multivariable models alongside other mCRPC clinical variables, including cell-free DNA concentration. Further, longitudinal changes in tumor purity on treatment can be illustrative of drug responses. Conclusions: In our analysis of this prospectively collected clinical trial data, we show mCRPC CNA profiles in cfDNA, and that these CNAs may be induced by prior aggressive treatments. We also found that tumor purity estimation can serve as a robust, competitive indicator of both overall survival and, assessed longitudinally, shows clear association with drug response. We envision that these techniques will enable improved strategies for monitoring mCRPC patients. Citation Format: George Seed, Semini Sumanasuriya, Claudia Bertan, Harry Parr, Gemma Fowler, Rossitza Christova, Lorna Pope, Jane Goodall, Maryou Lambros, Pasquale Rescigno, Penelope Flohr, Suzanne Carreira, Wei Yuan, Mustapha Chadjaa, Sandrine Mace, Johann De Bono. Discovery of genomic correlates and tumor purity as an independent clinical factor of poor outcome in advanced prostate cancer lpWGS CNA data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-270.