Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Epigenomic Blood-Based Early Detection of Pancreatic Cancer Employing Cell-Free DNA.

BACKGROUND & AIMS: Early detection of pancreatic cancer (PaC) can drastically improve survival rates. Approximately 25% of subjects with PaC have type 2 diabetes diagnosed within 3 years prior to the PaC diagnosis, suggesting that subjects with type 2 diabetes are at high risk of occult PaC. We have developed an early-detection PaC test, based on changes in 5-hydroxymethylcytosine (5hmC) signals in cell-free DNA from plasma. METHODS: Blood was collected from 132 subjects with PaC and 528 noncancer subjects to generate epigenomic and genomic feature sets yielding a predictive PaC signal algorithm. The algorithm was validated in a blinded cohort composed of 102 subjects with PaC, 2048 noncancer subjects, and 1524 subjects with non-PaCs. RESULTS: 5hmC differential profiling and additional genomic features enabled the development of a machine learning algorithm capable of distinguishing subjects with PaC from noncancer subjects with high specificity and sensitivity. The algorithm was validated with a sensitivity for early-stage (stage I/II) PaC of 68.3% (95% confidence interval [CI], 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). CONCLUSIONS: The PaC detection test showed robust early-stage detection of PaC signal in the studied cohorts with varying type 2 diabetes status. This assay merits further clinical validation for the early detection of PaC in high-risk individuals.

Clinical and genomic characterization of Low PSA Secretors: a unique subset of metastatic castration resistant prostate cancer.

BACKGROUND: Metastatic disease burden out of proportion to serum PSA has been used as a marker of aggressive phenotype prostate cancer but is not well defined as a distinct subgroup. We sought to prospectively characterize the molecular features and clinical outcomes of Low PSA Secretors. METHODS: Eligible metastatic castration resistant prostate cancer (mCRPC) patients without prior small cell histology underwent metastatic tumor biopsy with molecular characterization. Low PSA secretion was defined as serum PSA < 2, 5, or 10 ng/mL plus >5 metastases with radiographic progression at study entry. Clinical and molecular features were compared between low PSA vs. normal secretors in a post-hoc fashion. RESULTS: 183 patients were enrolled, including 15 (8%) identified as Low PSA Secretors using optimal PSA cut point of 5 ng/mL. Biopsies from Low PSA Secretors demonstrated higher t-SCNC and RB1 loss and lower AR transcriptional signature scores compared with normal secretors. Genomic loss of RB1 and/or TP53 was more common in Low PSA Secretors (80% vs. 41%). Overall survival (OS) was shorter in Low PSA Secretors (median OS = 26.7 vs. 46.0 months, hazard ratio = 2.465 (95% CI: 0.982-6.183). Progression-free survival (PFS) on post-biopsy treatment with AR-targeted therapy was shorter than with chemotherapy (median PFS 6.2 vs. 4.1 months). CONCLUSIONS: Low PSA secretion in relation to metastatic tumor burden may be a readily available clinical selection tool for de-differentiated mCRPC with molecular features consistent with t-SCNC. Prospective validation is warranted.

Down-regulation of ADRB2 expression is associated with small cell neuroendocrine prostate cancer and adverse clinical outcomes in castration-resistant prostate cancer.

OBJECTIVES: The net oncogenic effect of ß2-adrenergic receptor ADRB2, whose downstream elements induce neuroendocrine differentiation and whose expression is regulated by EZH2, is unclear. ADRB2 expression and associated clinical outcomes in metastatic castration-resistant prostate cancer (mCRPC) are unknown. METHODS AND MATERIALS: This was a retrospective analysis of a multi-center, prospectively enrolled cohort of mCRPC patients. Metastatic biopsies were obtained at progression, and specimens underwent laser capture microdissection and RNA-seq. ADRB2 expression was stratified by histology and clustering based on unsupervised hierarchical transcriptome analysis and correlated with EZH2 expression; an external dataset was used for validation. The association between ADRB2 expression and overall survival (OS) was assessed by log-rank test and a multivariable Cox proportional hazard model. RESULTS: One hundred and twenty-seven patients with progressive mCRPC had sufficient metastatic tumor for RNA-seq. ADRB2 expression was lowest in the small cell-enriched transcriptional cluster (P < 0.01) and correlated inversely with EZH2 expression (r = -0.28, P < 0.01). These findings were validated in an external cohort enriched for neuroendocrine differentiation. Patients with tumors harboring low ADRB2 expression (lowest quartile) had a shorter median OS than those with higher (9.5 vs. 20.5 months, P = 0.02). In multivariable analysis, low ADRB2 expression was associated with a trend toward shorter OS (HR for death = 1.54, 95%CI 0.98-2.44). Conversely, higher expression of upstream transcriptional regulator EZH2 was associated with shortened OS (HR for death = 3.01, 95%CI 1.12-8.09). CONCLUSIONS: Low ADRB2 expression is associated with neuroendocrine differentiation and is associated with shortened survival. EZH2 is a potential therapeutic target for preventing neuroendocrine transdifferentiation and improving outcomes in mCRPC. Further studies of agents targeting ß-adrenergic signaling are warranted.

Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance.

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50's utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance. TP53 gene alterations were more common in nonresponders, although this did not reach statistical significance (P = 0.055). AR gene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets-including those linked to low AR transcriptional activity and a stemness program-were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

Using Transcriptional Signatures to Find Cancer Drivers with LURE.

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.

Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study.

Purpose The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)-targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Methods Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. Metastatic biopsy specimens underwent independent, blinded pathology review along with RNA/DNA sequencing. Results A total of 202 consecutive patients were enrolled. One hundred forty-eight (73%) had prior disease progression on abiraterone and/or enzalutamide. The biopsy evaluable rate was 79%. The overall incidence of t-SCNC detection was 17%. AR amplification and protein expression were present in 67% and 75%, respectively, of t-SCNC biopsy specimens. t-SCNC was detected at similar proportions in bone, node, and visceral organ biopsy specimens. Genomic alterations in the DNA repair pathway were nearly mutually exclusive with t-SCNC differentiation ( P = .035). Detection of t-SCNC was associated with shortened overall survival among patients with prior AR-targeting therapy for mCRPC (hazard ratio, 2.02; 95% CI, 1.07 to 3.82). Unsupervised hierarchical clustering of the transcriptome identified a small-cell-like cluster that further enriched for adverse survival outcomes (hazard ratio, 3.00; 95% CI, 1.25 to 7.19). A t-SCNC transcriptional signature was developed and validated in multiple external data sets with > 90% accuracy. Multiple transcriptional regulators of t-SCNC were identified, including the pancreatic neuroendocrine marker PDX1. Conclusion t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival. The near-mutual exclusivity with DNA repair alterations suggests t-SCNC may be a distinct subset of mCRPC. Transcriptional profiling facilitates the identification of t-SCNC and novel therapeutic targets.