Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer.

While mutations affecting protein-coding regions have been examined across many cancers, structural variants at the genome-wide level are still poorly defined. Through integrative deep whole-genome and -transcriptome analysis of 101 castration-resistant prostate cancer metastases (109X tumor/38X normal coverage), we identified structural variants altering critical regulators of tumorigenesis and progression not detectable by exome approaches. Notably, we observed amplification of an intergenic enhancer region 624 kb upstream of the androgen receptor (AR) in 81% of patients, correlating with increased AR expression. Tandem duplication hotspots also occur near MYC, in lncRNAs associated with post-translational MYC regulation. Classes of structural variations were linked to distinct DNA repair deficiencies, suggesting their etiology, including associations of CDK12 mutation with tandem duplications, TP53 inactivation with inverted rearrangements and chromothripsis, and BRCA2 inactivation with deletions. Together, these observations provide a comprehensive view of how structural variations affect critical regulators in metastatic prostate cancer.

Prostate cancer research in the 21st century; report from the 2021 Coffey-Holden prostate cancer academy meeting.

INTRODUCTION: The 2021 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, "Prostate Cancer Research in the 21st Century," was held virtually, from June 24-25, 2021. METHODS: The CHPCA Meeting is organized by the Prostate Cancer Foundation as a unique discussion-oriented meeting focusing on critical topics in prostate cancer research envisioned to bridge the next major advances in prostate cancer biology and treatment. The 2021 CHPCA Meeting was virtually attended by 89 investigators and included 31 talks over nine sessions. RESULTS: Major topic areas discussed at the meeting included: cancer genomics and sequencing, functional genomic approaches to studying mediators of plasticity, emerging signaling pathways in metastatic castration resistant prostate cancer, Wnt signaling biology and the challenges of targeted therapy, clonal hematopoiesis, neuroendocrine cell plasticity and antitumor immunity, cancer immunotherapy and its synergizers, and imaging the tumor microenvironment and metabolism. DISCUSSION: This meeting report summarizes the research presented at the 2021 CHPCA Meeting. We hope that publication of this knowledge will accelerate new understandings and the development of new biomarkers and treatments for prostate cancer.

Prostate cancer incidence across stage, NCCN risk groups, and age before and after USPSTF Grade D recommendations against prostate-specific antigen screening in 2012.

BACKGROUND: We sought to determine the extent to which US Preventive Services Task Force (USPSTF) 2012 Grade D recommendations against prostate-specific antigen screening may have impacted recent prostate cancer disease incidence patterns in the United States across stage, National Comprehensive Cancer Network (NCCN) risk groups, and age groups. METHODS: SEER*Stat version 8.3.4 was used to calculate annual prostate cancer incidence rates from 2010 to 2015 for men aged ≥50 years according to American Joint Committee on Cancer stage at diagnosis (localized vs metastatic), NCCN risk group (low vs unfavorable [intermediate or high-risk]), and age group (50-74 years vs ≥75 years). Age-adjusted incidences per 100,000 persons with corresponding year-by-year incidence ratios (IRs) were calculated using the 2000 US Census population. RESULTS: From 2010 to 2015, the incidence (per 100,000 persons) of localized prostate cancer decreased from 195.4 to 131.9 (Ptrend  < .001) and from 189.0 to 123.4 (Ptrend  < .001) among men aged 50-74 and ≥75 years, respectively. The largest relative year-by-year decline occurred between 2011 and 2012 in NCCN low-risk disease (IR, 0.77 [0.75-0.79, P < .0001] and IR 0.68 [0.62-0.74, P < .0001] for men aged 50-74 and ≥75 years, respectively). From 2010-2015, the incidence of metastatic disease increased from 6.2 to 7.1 (Ptrend  < .001) and from 16.8 to 22.6 (Ptrend  < .001) among men aged 50-74 and ≥75 years, respectively. CONCLUSIONS: This report illustrates recent prostate cancer "reverse migration" away from indolent disease and toward more aggressive disease beginning in 2012. The incidence of localized disease declined across age groups from 2012 to 2015, with the greatest relative declines occurring in low-risk disease. Additionally, the incidence of distant metastatic disease increased gradually throughout the study period.

Genomic and clinical characterization of stromal infiltration markers in prostate cancer.

BACKGROUND: The progression of prostate cancer is a complex, multistep process that involves molecular alterations in cells of the tumor and the microenvironment, with associated interactions between the stroma and epithelium. Genomic expression analyses of stromal infiltration markers were performed to determine the significance thereof in prostate cancer. METHODS: Genome-wide expression profiles of formalin-fixed, paraffin-embedded radical prostatectomy samples were evaluated from a prospective registry cohort (n = 5239) and 3 retrospective institutional cohorts (n = 1135). Two independent stromal gene expression signatures implied stromal infiltration. Cox proportional hazards regression defined the association between stromal infiltration expression and metastasis-free survival (MFS). RESULTS: Stromal expression scores were correlated with stromal signature genes and with other key stromal markers (CAV1, VIM, and TAGLN), basal activity, and CD3 and CD4 immune biomarkers (r > 0.5 for all). The top decile of stromal expression was associated with high genomic risk scores (Decipher ≥ 0.6) , high Cancer of the Prostate Risk Assessment-Postsurgical scores, Gleason 9 to 10 disease, and a higher risk for metastasis (hazard ratio, 2.35; 95% CI, 1.37-4.02; P = .001). A higher stromal infiltration score was also associated with decreased expression of DNA repair genes and higher radiation sensitivity genomic scores. Postoperative radiation therapy (RT) was associated with an MFS benefit for patients with high stromal scores, but not for patients with low stromal scores (Pinteraction  = .02). CONCLUSIONS: Expression of stromal infiltration markers is correlated with prostate cancer aggressiveness/progression and may be predictive of a response to RT. Stromal infiltration markers should be studied and considered for incorporation into clinical prognostication and decision making.

Genome-wide CRISPR screen identifies HNRNPL as a prostate cancer dependency regulating RNA splicing.

Alternative RNA splicing plays an important role in cancer. To determine which factors involved in RNA processing are essential in prostate cancer, we performed a genome-wide CRISPR/Cas9 knockout screen to identify the genes that are required for prostate cancer growth. Functional annotation defined a set of essential spliceosome and RNA binding protein (RBP) genes, including most notably heterogeneous nuclear ribonucleoprotein L (HNRNPL). We defined the HNRNPL-bound RNA landscape by RNA immunoprecipitation coupled with next-generation sequencing and linked these RBP-RNA interactions to changes in RNA processing. HNRNPL directly regulates the alternative splicing of a set of RNAs, including those encoding the androgen receptor, the key lineage-specific prostate cancer oncogene. HNRNPL also regulates circular RNA formation via back splicing. Importantly, both HNRNPL and its RNA targets are aberrantly expressed in human prostate tumors, supporting their clinical relevance. Collectively, our data reveal HNRNPL and its RNA clients as players in prostate cancer growth and potential therapeutic targets.

Conservative management of low-risk prostate cancer among young versus older men in the United States: Trends and outcomes from a novel national database.

BACKGROUND: Management for men aged ≤55 years with low-risk prostate cancer (LRPC) is debated given quality-of-life implications with definitive treatment versus the potential missed opportunity for cure with conservative management. The objective of this study was to define rates of conservative management for LRPC and associated short-term outcomes in young versus older men in the United States. METHODS: The nonpublic Surveillance, Epidemiology, and End Results Prostate with Active Surveillance/Watchful Waiting (AS/WW) Database identified 50,302 men who were diagnosed with LRPC from 2010 through 2015. AS/WW rates in the United States were stratified by age (≤55 vs ≥56 years). Prostate cancer-specific mortality and overall mortality were defined by initial management type (AS/WW vs definitive treatment [referent]) and age. RESULTS: AS/WW utilization increased from 8.61% (2010) to 34.56% (2015) among men aged ≤55 years (P for trend <0.001) and from 15.99% to 43.81% among men aged ≥56 years (P for trend <.001). Among patients who had ≤2 positive biopsy cores, AS/WW rates increased from 12.90% to 48.78% for men aged ≤55 years and from 21.85% to 58.01% for men aged ≥56 years. Among patients who had ≥3 positive biopsy cores, AS/WW rates increased from 3.89% to 22.45% for men aged ≤55 years and from 10.05% to 28.49% for men aged ≥56 years (all P for trend <.001). Five-year prostate cancer-specific mortality rates were <0.30% across age and initial management type subgroups. CONCLUSIONS: AS/WW rates quadrupled for patients aged ≤55 years from 2010 to 2015, with favorable short-term outcomes. These findings demonstrate the short-term safety and increasing acceptance of AS/WW for both younger and older patients. However, there are still higher absolute rates of AS/WW in older patients (P < .001), suggesting some national ambivalence toward AS/WW in younger patients.

Very Early Salvage Radiotherapy Improves Distant Metastasis-Free Survival.

PURPOSE: Early salvage radiotherapy following radical prostatectomy for prostate cancer is commonly advocated in place of adjuvant radiotherapy. We aimed to determine the optimal definition of early salvage radiotherapy. MATERIALS AND METHODS: We performed a multi-institutional retrospective study of 657 men who underwent salvage radiotherapy between 1986 and 2013. Two comparisons were made to determine the optimal definition of early salvage radiotherapy, including 1) the time from radical prostatectomy to salvage radiotherapy (less than 9, 9 to 21, 22 to 47 or greater than 48 months) and 2) the level of detectable pre-salvage radiotherapy prostate specific antigen (0.01 to 0.2, greater than 0.2 to 0.5 or greater than 0.5 ng/ml). Outcomes included freedom from salvage androgen deprivation therapy, and biochemical relapse-free, distant metastases-free and prostate cancer specific survival. RESULTS: Median followup was 9.8 years. Time from radical prostatectomy to salvage radiotherapy did not correlate with 10-year biochemical relapse-free survival rates (R2 = 0.18). Increasing pre-salvage radiotherapy prostate specific antigen strongly correlated with biochemical relapse-free survival (R2 = 0.91). Increasing detectable pre-salvage radiotherapy prostate specific antigen (0.01 to 0.2, greater than 0.2 to 0.5 and greater than 0.5 ng/ml) predicted worse 10-year biochemical relapse-free survival (62%, 44% and 27%), freedom from salvage androgen deprivation therapy (77%, 66% and 49%), distant metastases-free survival (86%, 79% and 66%, each p <0.001) and prostate cancer specific survival (93%, 89% and 80%, respectively, p = 0.001). On multivariable analysis early salvage radiotherapy (prostate specific antigen greater than 0.2 to 0.5 ng/ml) was associated with a twofold increase in biochemical failure, use of salvage androgen deprivation therapy and distant metastases compared to very early salvage radiotherapy (prostate specific antigen 0.01 to 0.2 ng/ml). CONCLUSIONS: The duration from radical prostatectomy to salvage radiotherapy is not independently prognostic for outcomes after salvage radiotherapy and it should not be used to define early salvage radiotherapy. Grouping all patients with pre-salvage radiotherapy prostate specific antigen 0.5 ng/ml or less may be inadequate to define early salvage radiotherapy and it has a relevant impact on ongoing and future clinical trials.

Anatomical patterns of recurrence following biochemical relapse after post-prostatectomy salvage radiation therapy: a multi-institutional study.

OBJECTIVES: To characterise the frequency and detailed anatomical sites of failure for patients receiving post-radical prostatectomy (RP) salvage radiation therapy (SRT). PATIENTS AND METHODS: A multi-institutional retrospective study was performed on 574 men who underwent SRT between 1986 and 2013. Anatomical recurrence patterns were classified as lymphotrophic (lymph nodes only), osteotrophic (bone only), or multifocal if both were present. Isolated first failure sites were defined as sites of initial clinically detected recurrence that remained isolated for at least 3 months. RESULTS: The median follow-up after SRT was 6.8 years. The 8-year rates of local, regional, and distant failure for patients undergoing SRT were 2%, 6%, and 21%, respectively. Of the 22% men (128 of 574) who developed a clinically detectable recurrence, 17%, 50%, and 31% were lymphotrophic, osteotrophic, and multifocal, respectively. The trophic nature of metastases was prognostic for distant metastases-free survival (DMFS) and prostate cancer-specific survival (PCSS); the 10-year rates of DMFS were 18%, 5%, and 7% (P < 0.01), and PCSS were 78%, 68%, and 56% (P < 0.01), for lymphotrophic, osteotrophic, and multifocal failure patterns, respectively. CONCLUSIONS: We demonstrate that trophism for metastatic site has significant prognostic impact on PCSS in men treated with SRT. Radiographic local failure is an uncommon event after SRT when compared to historical data of patients treated with surgery monotherapy. However, distant failure remains a challenge in this patient population and warrants further therapeutic investigation.

Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis.

BACKGROUND: Postoperative radiotherapy has an important role in the treatment of prostate cancer, but personalised patient selection could improve outcomes and spare unnecessary toxicity. We aimed to develop and validate a gene expression signature to predict which patients would benefit most from postoperative radiotherapy. METHODS: Patients were eligible for this matched, retrospective study if they were included in one of five published US studies (cohort, case-cohort, and case-control studies) of patients with prostate adenocarcinoma who had radical prostatectomy (with or without postoperative radiotherapy) and had gene expression analysis of the tumour, with long-term follow-up and complete clinicopathological data. Additional treatment after surgery was at the treating physician's discretion. In each cohort, patients who had postoperative radiotherapy were matched with patients who had not had radiotherapy using Gleason score, prostate-specific antigen concentration, surgical margin status, extracapsular extension, seminal vesicle invasion, lymph node invasion, and androgen deprivation therapy. We constructed a matched training cohort using patients from one study in which we developed a 24-gene Post-Operative Radiation Therapy Outcomes Score (PORTOS). We generated a pooled matched validation cohort using patients from the remaining four studies. The primary endpoint was the development of distant metastasis. FINDINGS: In the training cohort (n=196), among patients with a high PORTOS (n=39), those who had radiotherapy had a lower incidence of distant metastasis than did patients who did not have radiotherapy, with a 10-year metastasis rate of 5% (95% CI 0-14) in patients who had radiotherapy (n=20) and 63% (34-80) in patients who did not have radiotherapy (n=19; hazard ratio [HR] 0·12 [95% CI 0·03-0·41], p<0·0001), whereas among patients with a low PORTOS (n=157), those who had postoperative radiotherapy (n=78) had a greater incidence of distant metastasis at 10 years than did their untreated counterparts (n=79; 57% [44-67] vs 31% [20-41]; HR 2·5 [1·6-4·1], p<0·0001), with a significant treatment interaction (pinteraction<0·0001). The finding that PORTOS could predict outcome due to radiotherapy treatment was confirmed in the validation cohort (n=330), which showed that patients who had radiotherapy had a lower incidence of distant metastasis compared with those who did not have radiotherapy, but only in the high PORTOS group (high PORTOS [n=82]: 4% [95% CI 0-10] in the radiotherapy group [n=57] vs 35% [95% CI 7-54] in the no radiotherapy group [n=25] had metastasis at 10 years; HR 0·15 [95% CI 0·04-0·60], p=0·0020; low PORTOS [n=248]: 32% [95% CI 19-43] in the radiotherapy group [n=108] vs 32% [95% CI 22-40] in the no radiotherapy group [n=140]; HR 0·92 [95% CI 0·56-1·51], p=0·76), with a significant interaction (pinteraction=0·016). The conventional prognostic tools Decipher, CAPRA-S, and microarray version of the cell cycle progression signature did not predict response to radiotherapy (pinteraction>0·05 for all). INTERPRETATION: Patients with a high PORTOS who had postoperative radiotherapy were less likely to have metastasis at 10 years than those who did not have radiotherapy, suggesting that treatment with postoperative radiotherapy should be considered in this subgroup. PORTOS should be investigated further in additional independent cohorts. FUNDING: None.

Independent surgical validation of the new prostate cancer grade-grouping system.

OBJECTIVE: To report the independent prognostic impact of the new prostate cancer grade-grouping system in a large external validation cohort of patients treated with radical prostatectomy (RP). PATIENTS AND METHODS: Between 1994 and 2013, 3 694 consecutive men were treated with RP at a single institution. To investigate the performance of and validate the grade-grouping system, biochemical recurrence-free survival (bRFS) rates were assessed using Kaplan-Meier tests, Cox-regression modelling, and discriminatory comparison analyses. Separate analyses were performed based on biopsy and RP grade. RESULTS: The median follow-up was 52.7 months. The 5-year actuarial bRFS for biopsy grade groups 1-5 were 94.2%, 89.2%, 73.1%, 63.1%, and 54.7%, respectively (P < 0.001). Similarly, the 5-year actuarial bRFS based on RP grade groups was 96.1%, 93.0%, 74.0%, 64.4%, and 49.9% for grade groups 1-5, respectively (P < 0.001). The adjusted hazard ratios for bRFS relative to biopsy grade group 1 were 1.98, 4.20, 5.57, and 9.32 for groups 2, 3, 4, and 5, respectively (P < 0.001), and for RP grade groups were 2.09, 5.27, 5.86, and 10.42 (P < 0.001). The five-grade-group system had a higher prognostic discrimination compared with the commonly used three-tier system (Gleason score 6 vs 7 vs 8-10). CONCLUSIONS: In an independent surgical cohort, we have validated the prognostic benefit of the new prostate cancer grade-grouping system for bRFS, and shown that the benefit is maintained after adjusting for important clinicopathological variables. The greater predictive accuracy of the new system will improve risk stratification in the clinical setting and aid in patient counselling.

Comparative Evaluation of LLMs in Clinical Oncology.

BACKGROUND: As artificial intelligence (AI) tools become widely accessible, more patients and medical professionals will turn to them for medical information. Large language models (LLMs), a subset of AI, excel in natural language processing tasks and hold considerable promise for clinical use. Fields such as oncology, in which clinical decisions are highly dependent on a continuous influx of new clinical trial data and evolving guidelines, stand to gain immensely from such advancements. It is therefore of critical importance to benchmark these models and describe their performance characteristics to guide their safe application to clinical oncology. Accordingly, the primary objectives of this work were to conduct comprehensive evaluations of LLMs in the field of oncology and to identify and characterize strategies that medical professionals can use to bolster their confidence in a model's response. METHODS: This study tested five publicly available LLMs (LLaMA 1, PaLM 2, Claude-v1, generative pretrained transformer 3.5 [GPT-3.5], and GPT-4) on a comprehensive battery of 2044 oncology questions, including topics from medical oncology, surgical oncology, radiation oncology, medical statistics, medical physics, and cancer biology. Model prompts were presented independently of each other, and each prompt was repeated three times to assess output consistency. For each response, models were instructed to provide a self-appraised confidence score (from 1 to 4). Model performance was also evaluated against a novel validation set comprising 50 oncology questions curated to eliminate any risk of overlap with the data used to train the LLMs. RESULTS: There was significant heterogeneity in performance between models (analysis of variance, P<0.001). Relative to a human benchmark (2013 and 2014 examination results), GPT-4 was the only model to perform above the 50th percentile. Overall, model performance varied as a function of subject area across all models, with worse performance observed in clinical oncology subcategories compared with foundational topics (medical statistics, medical physics, and cancer biology). Within the clinical oncology subdomain, worse performance was observed in female-predominant malignancies. A combination of model selection, prompt repetition, and confidence self-appraisal allowed for the identification of high-performing subgroups of questions with observed accuracies of 81.7 and 81.1% in the Claude-v1 and GPT-4 models, respectively. Evaluation of the novel validation question set produced similar trends in model performance while also highlighting improved performance in newer, centrally hosted models (GPT-4 Turbo and Gemini 1.0 Ultra) and local models (Mixtral 8×7B and LLaMA 2). CONCLUSIONS: Of the models tested on a standardized set of oncology questions, GPT-4 was observed to have the highest performance. Although this performance is impressive, all LLMs continue to have clinically significant error rates, including examples of overconfidence and consistent inaccuracies. Given the enthusiasm to integrate these new implementations of AI into clinical practice, continued standardized evaluations of the strengths and limitations of these products will be critical to guide both patients and medical professionals. (Funded by the National Institutes of Health Clinical Center for Research and the Intramural Research Program of the National Institutes of Health; Z99 CA999999.).

A prostate cancer gastrointestinal transcriptional phenotype may be associated with diminished response to AR-targeted therapy.

Background: Prostate cancer is a heterogenous disease, but once it becomes metastatic it eventually becomes treatment resistant. One mechanism of resistance to AR-targeting therapy is lineage plasticity, where the tumor undergoes a transformation to an AR-indifferent phenotype, most studied in the context of neuroendocrine prostate cancer (NEPC). However, activation of additional de- or trans-differentiation programs, including a gastrointestinal (GI) gene expression program, has been suggested as an alternative method of resistance. In this study, we explored the previously identified GI prostate cancer phenotype (PCa-GI) in a large cohort of metastatic castration-resistant prostate cancer (mCRPC) patient biopsy samples. Methods: We analyzed a dataset of 634 mCRPC samples with batch effect corrected gene expression data from the West Coast Dream Team (WCDT), the East Coast Dream Team (ECDT), the Fred Hutchinson Cancer Research Center (FHCRC) and the Weill Cornell Medical center (WCM). Survival data was available from the WCDT and ECDT cohorts. We calculated a gene expression GI score using the sum of z-scores of genes from a published set of PCa-GI-defining genes (N=38). Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards regression with endpoint overall survival from time of biopsy to death of any cause. Results: We found that the PCa-GI score had a bimodal distribution, identifying a distinct set of tumors with an activated GI expression pattern. Approximately 35% of samples were classified as PCa-GI high, which was concordant with prior reports. Liver metastases had the highest median score but after excluding liver samples, 29% of the remaining samples were still classified as PCa-GI high, suggesting a distinct phenotype not exclusive to liver metastases. No correlation was observed between GI score and proliferation, AR signaling, or NEPC scores. Furthermore, the PCa-GI score was not associated with genomic alterations in AR, FOXA1, RB1, TP53 or PTEN. However, tumors with MYC amplifications showed significantly higher GI scores (p=0.0001). Patients with PCa-GI tumors had a shorter survival (HR=1.5 [1.1-2.1], p=0.02), but this result was not significant after adjusting for the liver as metastatic site (HR=1.2 [0.82-1.7], p=0.35). Patients with PCa-GI low samples had a better outcome after androgen receptor signaling inhibitors (ASI, abiraterone or enzalutamide) than other therapies (HR=0.37 [0.22-0.61], p=0.0001) while the benefit of ASI was smaller and non-significant for PCa-GI high samples (HR=0.55 [0.29-1.1], p=0.07). A differential pathway analysis identified FOXA2 signaling to be upregulated PCa-GI high tumors (FDR = 3.7 × 10-13). Conclusions: The PCa-GI phenotype is prevalent in clinical mCRPC samples and may represent a distinct biological entity. PCa-GI tumors may respond less to ASI and could offer a strategy to study novel therapeutic targets.

Mechanistic Characterization of Cancer-associated Fibroblast Depletion via an Antibody-Drug Conjugate Targeting Fibroblast Activation Protein.

Cancer-associated fibroblasts (CAF) are a prominent cell type within the tumor microenvironment (TME) where they are known to promote cancer cell growth and survival, angiogenesis, drug resistance, and immunosuppression. The transmembrane prolyl protease fibroblast activation protein (FAP) is expressed on the surface of highly protumorigenic CAFs found in the stroma of nearly every cancer of epithelial origin. The widespread expression of FAP has made it an attractive therapeutic target based on the underlying hypothesis that eliminating protumorigenic CAFs will disrupt the cross-talk between components of TME resulting in cancer cell death and immune infiltration. This hypothesis, however, has never been directly proven. To eliminate FAP-expressing CAFs, we developed an antibody-drug conjugate using our anti-FAP antibody, huB12, coupled to a monomethyl auristatin E (huB12-MMAE) payload. After determining that huB12 was an effective targeting vector, we found that huB12-MMAE potently eliminated FAP-expressing cells as monocultures in vitro and significantly prolonged survival in vivo using a xenograft engineered to overexpress FAP. We investigated the effects of selectively eliminating CAFs using a layered, open microfluidic cell coculture platform, known as the Stacks. Analysis of mRNA and protein expression found that treatment with huB12-MMAE resulted in the increased secretion of the proinflammatory cytokines IL6 and IL8 by CAFs and an associated increase in expression of proinflammatory genes in cancer cells. We also detected increased secretion of CSF1, a cytokine involved in myeloid recruitment and differentiation. Our findings suggest that the mechanism of FAP-targeted therapies is through effects on the immune microenvironment and antitumor immune response. SIGNIFICANCE: The direct elimination of FAP-expressing CAFs disrupts the cross-talk with cancer cells leading to a proinflammatory response and alterations in the immune microenvironment and antitumor immune response.

A platform-independent AI tumor lineage and site (ATLAS) classifier.

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

Integrated analyses highlight interactions between the three-dimensional genome and DNA, RNA and epigenomic alterations in metastatic prostate cancer.

The impact of variations in the three-dimensional structure of the genome has been recognized, but solid cancer tissue studies are limited. Here, we performed integrated deep Hi-C sequencing with matched whole-genome sequencing, whole-genome bisulfite sequencing, 5-hydroxymethylcytosine (5hmC) sequencing and RNA sequencing across a cohort of 80 biopsy samples from patients with metastatic castration-resistant prostate cancer. Dramatic differences were present in gene expression, 5-methylcytosine/5hmC methylation and in structural variation versus mutation rate between A and B (open and closed) chromatin compartments. A subset of tumors exhibited depleted regional chromatin contacts at the AR locus, linked to extrachromosomal circular DNA (ecDNA) and worse response to AR signaling inhibitors. We also identified topological subtypes associated with stark differences in methylation structure, gene expression and prognosis. Our data suggested that DNA interactions may predispose to structural variant formation, exemplified by the recurrent TMPRSS2-ERG fusion. This comprehensive integrated sequencing effort represents a unique clinical tumor resource.

GTP Signaling Links Metabolism, DNA Repair, and Responses to Genotoxic Stress.

How cell metabolism regulates DNA repair is incompletely understood. Here, we define a GTP-mediated signaling cascade that links metabolism to DNA repair and has significant therapeutic implications. GTP, but not other nucleotides, regulates the activity of Rac1, a guanine nucleotide-binding protein, which promotes the dephosphorylation of serine 323 on Abl-interactor 1 (Abi-1) by protein phosphatase 5 (PP5). Dephosphorylated Abi-1, a protein previously not known to activate DNA repair, promotes nonhomologous end joining. In patients and mouse models of glioblastoma, Rac1 and dephosphorylated Abi-1 mediate DNA repair and resistance to standard-of-care genotoxic treatments. The GTP-Rac1-PP5-Abi-1 signaling axis is not limited to brain cancer, as GTP supplementation promotes DNA repair and Abi-1-S323 dephosphorylation in nonmalignant cells and protects mouse tissues from genotoxic insult. This unexpected ability of GTP to regulate DNA repair independently of deoxynucleotide pools has important implications for normal physiology and cancer treatment. SIGNIFICANCE: A newly described GTP-dependent signaling axis is an unexpected link between nucleotide metabolism and DNA repair. Disrupting this pathway can overcome cancer resistance to genotoxic therapy while augmenting it can mitigate genotoxic injury of normal tissues. This article is featured in Selected Articles from This Issue, p. 5.

Machine Learning & Molecular Radiation Tumor Biomarkers.

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.