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PURPOSE Nearly all men with prostate cancer treated with androgen receptor (AR) signaling inhibitors (ARSIs) develop resistance via diverse mechanisms including constitutive activation of the AR pathway, driven by AR genomic structural alterations, expression of AR splice variants (AR-Vs), or loss of AR dependence and lineage plasticity termed neuroendocrine prostate cancer. Understanding these de novo acquired ARSI resistance mechanisms is critical for optimizing therapy. MATERIALS AND METHODS A novel liquid biopsy technology was used to collect mRNA from circulating tumor cells (CTCs) to measure expression of AR-Vs, AR targets, and neuroendocrine prostate cancer markers. An institutional review board–approved prospective cohort (N = 99) was used to identify patterns of gene expression. Two prospective multicenter phase II clinical trials of ARSIs for men with castration-resistant prostate cancer (ClinicalTrials.gov: NCT01942837 [enzalutamide, N = 21] and NCT02025010 [abiraterone, N = 27]) were used to further validate these findings. RESULTS Hierarchical clustering of CTC transcripts identified two distinct clusters. Cluster 2 (C2) exhibited increased expression of AR-regulated genes and was associated with worse overall survival (median 8.6 v 22.4 months; P < .01; hazard ratio [HR] = 3.45 [1.9 to 6.14]). In multivariable analysis, C2 was prognostic independent of other clinicopathologic variables. AR-V status was not significant when accounting for C2. Upon further validation in pooled multicenter phase II trials, C2 was associated with worse overall survival (15.2 months v not reached; P < .01; HR = 8.43 [2.74 to 25.92]), prostate-specific antigen progression-free survival (3.6 v 12 months; P < .01; HR = 4.64 [1.53 to 14.11]), and radiographic progression-free survival (2.7 v 40.6 months; P < .01; HR = 4.64 [1.82 to 17.41]). CONCLUSION We demonstrate that a transcriptional profile detectable in CTCs obtained from liquid biopsies can serve as an independent prognostic marker beyond AR-V7 in patients with metastatic prostate cancer and can be used to identify the emergence of multiple ARSI resistance mechanisms. This is currently being investigated in additional prospective trials.

Genomes encode for genes and the regulatory signals that enable those genes to be transcribed, and are continually shaped by evolution. Genomes, including those of human and yeast, encode for numerous regulatory elements and transcripts that have limited evidence of conservation or function. Here, we sought to create a genomic null hypothesis by quantifying the gene regulatory activity of evolutionarily naïve DNA, using RNA-seq of evolutionarily distant DNA expressed in yeast and computational predictions of random DNA activity in human cells and tissues. In yeast, we found that >99% of bases in naïve DNA expressed as part of one or more transcripts. Naïve transcripts are sometimes spliced, and are similar to evolved transcripts in length and expression distribution, indicating that stable expression and/or splicing are insufficient to indicate adaptation. However, naïve transcripts do not achieve the extreme high expression levels as achieved by evolved genes, and frequently overlap with antisense transcription, suggesting that selection has shaped the yeast transcriptome to achieve high expression and coherent gene structures. In humans, we found that, while random DNA is predicted to have minimal activity, dinucleotide content-matched randomized DNA is predicted to have much of the regulatory activity of evolved sequences, including active chromatin marks at between half (DNase I and H3K4me3) and 1/16th (H3K27ac and H3K4me1) the rate of evolved DNA, and the repression-associated H3K27me3 at about twice the rate of evolved DNA. Naïve human DNA is predicted to be more cell type-specific than evolved DNA and is predicted to generate co-occurring chromatin marks, indicating that these are not reliable indicators of selection. However, extreme high activity is rarely achieved by naïve DNA, consistent with these arising via selection. Our results indicate that evolving regulatory activity from naïve DNA is comparatively easy in both yeast and humans, and we expect to see many biochemically active and cell type-specific DNA sequences in the absence of selection. Such naïve biochemically active sequences have the potential to evolve a function or, if sufficiently detrimental, selection may act to repress them.

Background: 10% of newly diagnosed prostate cancer presents with metastases. Known as de novo metastatic castrate-sensitive prostate cancer (mCSPC), it is disproportionally responsible for &gt;50% of prostate cancer deaths. Cancer genotyping can identify vulnerabilities exploitable by targeted therapies, and promises to help prognosticate. However, tissue from de novo mCSPC is scarce; neither prostatectomy nor metastatic biopsy is standard, and it is unknown if diagnostic biopsies are representative of synchronous metastases. The potential for plasma circulating tumor DNA (ctDNA) to inform on tumor genotype is also unknown. Methods: We performed comprehensive pathological and genomic assessment of all spatially or phenotypically-distinct tumor foci (n=523) in 43 patients with de novo mCSPC who underwent prostatectomy, pelvic lymph node dissection, and plasma collection. Results: 91% (478/523) of tissue foci had evidence of prostate cancer by targeted DNA sequencing, with a median tumor fraction of 48%. When modeling random selection of a single primary foci (mirroring biopsy tissue availability in clinic), tumor fraction was &lt;20% in 19% of patients. Only 46% of plasma cell-free DNA samples prior to systemic therapy had a ctDNA fraction above 0.3% (limit of detection); median tumor fraction of 5% in samples with confirmed ctDNA. We observed recurrent alterations in major driver genes, including TP53, FOXA1, PTEN, and RB1, and the genomic landscape was very similar to published cohorts of castration-resistant prostate cancer (excluding AR). Primary site genomic heterogeneity was pervasive, including secondary (clonally distinct) prostate cancer populations in 14% of patients. Polyclonal seeding of metastases was detected in 26% of patients. Biallelic inactivation of TP53, PTEN, and/or RB1 was observed in 63% of tumors, and was frequently found in synchronous metastases and ctDNA. The two patients with compound disruption of TP53, PTEN, and RB1 experienced rapid progression to castration-resistance and death within two years of diagnosis, despite initial low-risk clinical features. Across the cohort, biallelic disruption of TP53 together with high-risk clinical features at diagnosis was associated with rapid progression (HR 4.64 (95% CI: 1.70-12.69); P = 0.003). Conclusions: One fifth of patients with de novo mCSPC have pervasive low tumor fraction in their primary tumor and blood plasma. Many tumors exhibit spatial heterogeneity within the primary site, with evidence of multiple clones seeding metastases. This data raises concerns about accurate tumor genotyping in routine clinical practice where needle biopsy specimens are the only available tissue for profiling. Nevertheless, some de novo mCSPC are marked by aggressive genomics and experience rapid progression to lethal disease, suggesting that tailored multi-focal genomic profiling can further segment the disease. Citation Format: Evan Warner, Kim Van der Eecken, Andrew J. Murtha, Edmond M. Kwan, Sarah W. Ng, Xinyi E. Chen, Cecily Q. Bernales, Grainne Donnellan, Elena Schonlau, Sofie Verbeke, Nicolaas Lumen, Jo Van Dorpe, Gillian Vandekerkhove, Elie Ritch, Matti Annala, Bram De Laere, Piet Ost, Alexander W. Wyatt. Multi-focal genomic dissection of synchronous primary and metastatic tissue from de novo metastatic prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 41.

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.