Comprehensive genomic characterization of prostate cancer has identified recurrent alterations in genes involved in androgen signaling, DNA repair, and PI3K signaling, among others. However, larger ...and uniform genomic analysis may identify additional recurrently mutated genes at lower frequencies. Here we aggregate and uniformly analyze exome sequencing data from 1,013 prostate cancers. We identify and validate a new class of E26 transformation-specific (ETS)-fusion-negative tumors defined by mutations in epigenetic regulators, as well as alterations in pathways not previously implicated in prostate cancer, such as the spliceosome pathway. We find that the incidence of significantly mutated genes (SMGs) follows a long-tail distribution, with many genes mutated in less than 3% of cases. We identify a total of 97 SMGs, including 70 not previously implicated in prostate cancer, such as the ubiquitin ligase CUL3 and the transcription factor SPEN. Finally, comparing primary and metastatic prostate cancer identifies a set of genomic markers that may inform risk stratification.
Nearly all prostate cancer deaths are from metastatic castration-resistant prostate cancer (mCRPC), but there have been few whole-genome sequencing (WGS) studies of this disease state. We performed ...linked-read WGS on 23 mCRPC biopsy specimens and analyzed cell-free DNA sequencing data from 86 patients with mCRPC. In addition to frequent rearrangements affecting known prostate cancer genes, we observed complex rearrangements of the AR locus in most cases. Unexpectedly, these rearrangements include highly recurrent tandem duplications involving an upstream enhancer of AR in 70%–87% of cases compared with <2% of primary prostate cancers. A subset of cases displayed AR or MYC enhancer duplication in the context of a genome-wide tandem duplicator phenotype associated with CDK12 inactivation. Our findings highlight the complex genomic structure of mCRPC, nominate alterations that may inform prostate cancer treatment, and suggest that additional recurrent events in the non-coding mCRPC genome remain to be discovered.
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•Linked-read genome sequencing of mCRPC resolves haplotypes and rearrangements•CDK12 inactivation is associated with a global tandem duplication phenotype•A majority of cases have duplications of an enhancer of the androgen receptor•Progression on androgen pathway inhibitors is associated with gains in AR and AR enhancer
Linked-read genome sequencing data from patients highlight that amplification of an enhancer upstream of the androgen receptor locus is a key feature of metastatic castration-resistant prostate cancer.
Despite advances and social progress, the exclusion of diverse groups in academia, especially science, technology, engineering, and mathematics (STEM) fields, across the US and Europe persists, ...resulting in the underrepresentation of diverse people in higher education. There is extensive literature about theory, observation, and evidence-based practices that can help create a more equitable, inclusive, and diverse learning environment. In this article, we propose the implementation of a Diversity, Equity, Inclusion, and Justice (DEIJ) journal club as a strategic initiative to foster education and promote action towards making academia a more equitable institution. By creating a space for people to engage with DEIJ theories* and strategize ways to improve their learning environment, we hope to normalize the practice and importance of analyzing academia through an equity lens. Guided by restorative justice principles, we offer 10 recommendations for fostering community cohesion through education and mutual understanding. This approach underscores the importance of appropriate action and self-education in the journey toward a more diverse, equitable, inclusive, and just academic environment. *Authors’ note: We understand that “DEIJ” is a multidisciplinary organizational framework that relies on numerous fields of study, including history, sociology, philosophy, and more. We use this term to refer to these different fields of study for brevity purposes.
Abstract Purpose PD-1 ligand (PD-L1) is expressed on both tumor cells (TCs) and tumor immune cell infiltrates (TIMC). In metastatic bladder cancer, increased TIMC PD-L1 positivity (PD-L1+) was ...correlated with better overall survival, however, in high grade T1 bladder tumors (HGT1) PD-L1+ on TCs and TIMCs, and correlation with outcome or pathological features remain unknown. Materials and Methods Formalin-fixed paraffin embedded (FFPE) tumor samples from 140 clinically annotated HGT1patients (pts) were retrieved. All pts were initially diagnosed with HGT1 by transurethral resection (TUR), subsequently received BCG, and had a median follow up of 7.4 years. PD-L1+ on the initial TUR was evaluated by immunohistochemistry using a mouse monoclonal anti-PD-L1 antibody (405.9A11). TC PD-L1+ was defined as 5% of TC membrane staining. TIMC PD-L1+ was scored on the extent of infiltrate and percentage of positive cells. Fisher's exact test assessed the associations of PD-L1+ with disease outcomes, carcinoma in situ (CIS) presence, and difference between HGT1 and MIBC. Results Among 140 HGT1 pts, TCs and TIMC PD-L1+ was seen in 6 (4%) pts and 48 (34.3%) pts, respectively. In a subset of 106 patients with adequate follow up, PD-L1+ was not correlated with disease outcomes on TCs (p=0.3) nor TIMC (p=0.47). PD-L1+ did not correlate with the presence of CIS. TC PD-L1+ was significantly less in HGT1 compared to MIBC(p<0.001). Conclusions PD-L1 is widely expressed on TIMCs, but not on TCs in HGT1. We did not find a correlation between PD-L1+ and outcome or CIS presence. TC PD-L1+ is significantly lower in HGT1 compared to MIBC.
While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray ...diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.
High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association ...of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progression (PD) in a cohort of HGT1 patients. Exome sequencing was performed on 62 HGT1 and 15 matched normal tissue samples. Both tumor only (TO) and paired analyses were performed, focusing on 95 genes known to be mutated in bladder cancer. Somatic mutations, copy-number alterations, mutation load, and mutation signatures were studied. Thirty-three GO, 10 R, 18 PD, and 1 unknown outcome patients were analyzed. Tumor mutational burden (TMB) was similar to muscle-invasive disease and was highest in GO, intermediate in PD, and lowest in R patients (
= 0.017). DNA damage response gene mutations were associated with higher TMB (
< 0.0001) and GO (
= 0.003). ERCC2 and BRCA2 mutations were associated with GO. TP53, ATM, ARID1A, AHR, and SMARCB1 mutations were more frequent in PD. Focal copy-number gain in CCNE1 and CDKN2A deletion was enriched in PD or R (
= 0.047;
= 0.06). APOBEC (46%) and COSMIC5 (34%) signatures were most frequent. APOBEC-A and ERCC2 mutant tumors (COSMIC5) were associated with GO (
= 0.047;
= 0.0002). pT1b microstaging was associated with a genomic cluster (
= 0.05) with focal amplifications of E2F3/SOX4, PVRL4, CCNE1, and TP53 mutations. Findings were validated using external public datasets. These findings require confirmation but suggest that management of HGT1 bladder cancer may be improved via molecular characterization to predict outcome. SIGNIFICANCE: Detailed genetic analyses of HGT1 bladder tumors identify features that correlate with outcome, e.g., high mutational burden, ERCC2 mutations, and high APOBEC-A/ERCC2 mutation signatures were associated with good outcome.
In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions ...only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble–function predictions, analogous to the achievements of AlphaFold with single-structure prediction.
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and ...cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R free and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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