The Evolutionary Landscape of Localized Prostate Cancers Drives Clinical Aggression.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

Identifying high-confidence capture Hi-C interactions using CHiCANE.

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides.

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.

3D Functional Genomics Screens Identify CREBBP as a Targetable Driver in Aggressive Triple-Negative Breast Cancer.

Triple-negative breast cancers (TNBC) are resistant to standard-of-care chemotherapy and lack known targetable driver gene alterations. Identification of novel drivers could aid the discovery of new treatment strategies for this hard-to-treat patient population, yet studies using high-throughput and accurate models to define the functions of driver genes in TNBC to date have been limited. Here, we employed unbiased functional genomics screening of the 200 most frequently mutated genes in breast cancer, using spheroid cultures to model in vivo-like conditions, and identified the histone acetyltransferase CREBBP as a novel tumor suppressor in TNBC. CREBBP protein expression in patient tumor samples was absent in 8% of TNBCs and at a high frequency in other tumors, including squamous lung cancer, where CREBBP-inactivating mutations are common. In TNBC, CREBBP alterations were associated with higher genomic heterogeneity and poorer patient survival and resulted in upregulation and dependency on a FOXM1 proliferative program. Targeting FOXM1-driven proliferation indirectly with clinical CDK4/6 inhibitors (CDK4/6i) selectively impaired growth in spheroids, cell line xenografts, and patient-derived models from multiple tumor types with CREBBP mutations or loss of protein expression. In conclusion, we have identified CREBBP as a novel driver in aggressive TNBC and identified an associated genetic vulnerability in tumor cells with alterations in CREBBP and provide a preclinical rationale for assessing CREBBP alterations as a biomarker of CDK4/6i response in a new patient population. SIGNIFICANCE: This study demonstrates that CREBBP genomic alterations drive aggressive TNBC, lung cancer, and lymphomas and may be selectively treated with clinical CDK4/6 inhibitors.