Neoadjuvant Exercise Therapy in Prostate Cancer: A Phase 1, Decentralized Nonrandomized ControlledTrial.

Importance: Observational data have shown that postdiagnosis exercise is associated with reduced risk of prostate cancer death. The feasibility and tumor biological activity of exercise therapy is not known. Objective: To identify recommended phase 2 dose of exercise therapy for patients with prostate cancer. Design, Setting, and Participants: This single-center, phase 1a dose-finding trial was conducted at a tertiary cancer center using a patientcentric, decentralized platform and included 53 inactive men with treatment-naive localized prostate cancer scheduled to undergo surgical resection between June 2019 and January 2023. Data were analyzed in June 2024. Intervention: Six escalated exercise therapy dose levels ranging from 90 to 450 minutes per week of individualized, moderate-intensity treadmill walking, allocated using adaptive continual reassessment. All exercise therapy sessions were conducted remotely with real-time monitoring. Main Outcomes and Measures: Feasibility was evaluated by relative exercise dose intensity (REDI). A dose level was considered feasible if 70% or more of patients achieved an REDI of 75% or greater. Activity end points were changes in tumor cell proliferation (Ki67) and plasma prostate-specific antigen levels between pretreatment and postintervention. Safety and changes in patient physiology were also assessed. Results: A total of 53 men were enrolled (median [IQR] age, 61 [56-66] years). All dose levels were feasible (≥75% REDI). The mean (95% CI) changes in Ki67 were 5.0% (-4.3% to 14.0%) for 90 minutes per week, 2.4% (-1.3% to 6.2%) for 150 minutes per week, -1.3% (-5.8% to 3.3%) for 225 minutes per week, -0.2% (-4.0% to 3.7%) for 300 minutes per week, -2.6% (-9.2% to 4.1%) for 375 minutes per week, and 2.2% (-0.8% to 5.1%) for 450 minutes per week. Changes in prostate-specific antigen levels were 1.0 ng/mL (-1.8 to 3.8) for 90 minutes per week, 0.2 ng/mL (-1.1 to 1.5) for 150 minutes per week, -0.5 ng/mL (-1.2 to 0.3) for 225 minutes per week, -0.2 (-1.7 to 1.3) for 300 minutes per week, -0.7 ng/mL (-1.7 to 0.4) for 375 minutes per week, and -0.9 ng/mL (-2.4 to 0.7) for 450 minutes per week. No serious adverse events were observed. Overall, 225 minutes per week (approximately 5 minutes per treatment at 5 times weekly) was selected as the recommended phase 2 dose. Conclusions and Relevance: The results of this nonrandomized clinical trial suggest that neoadjuvant exercise therapy is feasible and safe with promising activity in localized prostate cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03813615.

Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes.

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

moPepGen: Rapid and Comprehensive Proteoform Identification.

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms.

Background: Intra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. Results: We characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. Conclusions: These findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes.

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Reimagining the machine learning life cycle to improve educational outcomes of students.

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs). Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Singleton consonant onset acquisition in monolingual Granada Spanish-speaking preschoolers with typical versus protracted phonological development: Impacts of word structure and feature constraints.

While consonant acquisition clearly requires mastery of different articulatory configurations (segments), sub-segmental features and suprasegmental contexts influence both order of acquisition and mismatch (error) patterns (Bérubé, Bernhardt, Stemberger & Ciocca, 2020). Constraints-based nonlinear phonology provides a comprehensive framework for investigating the impact of sub- and suprasegmental impacts on acquisition (Bernhardt & Stemberger, 1998). The current study adopted such a framework in order to investigate these questions for Granada Spanish. Single-word samples of monolingual preschoolers in Granada (29 typically developing; 30 with protracted phonological development) were transcribed by native Spanish speakers in consultation with an international team. Beta regression analyses showed significant effects of age, developmental group, and word structure variables (word length, stress, position of consonants and syllables within the word); salience, markedness and/or frequency across the phonological hierarchy accounted for many patterns. The study further demonstrates the impacts of sub- and suprasegmental constraints of the phonological system on consonant acquisition.

Glycoproteomics Identifies Plexin-B3 as a Targetable Cell Surface Protein Required for the Growth and Invasion of Triple-Negative Breast Cancer Cells.

Driven by the lack of targeted therapies, triple-negative breast cancers (TNBCs) have the worst overall survival of all breast cancer subtypes. Considering that cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBCs. Here, we performed N-glycoproteomics on six TNBCs and five normal control (NC) cell lines using hydrazide-based enrichment. Quantitative proteomics and integrative data mining led to the discovery of Plexin-B3 (PLXNB3), a previously undescribed TNBC-enriched cell surface protein. Furthermore, siRNA knockdown and CRISPR-Cas9 editing of in vitro and in vivo models show that PLXNB3 is required for TNBC cell line growth, invasion, and migration. Altogether, we provide insights into N-glycoproteome remodeling associated with TNBCs and functional evaluation of an extracted target, which indicate the surface protein PLXNB3 as a potential therapeutic target for TNBCs.

A pan-cancer compendium of chromosomal instability.

Chromosomal instability (CIN) results in the accumulation of large-scale losses, gains and rearrangements of DNA1. The broad genomic complexity caused by CIN is a hallmark of cancer2; however, there is no systematic framework to measure different types of CIN and their effect on clinical phenotypes pan-cancer. Here we evaluate the extent, diversity and origin of CIN across 7,880 tumours representing 33 cancer types. We present a compendium of 17 copy number signatures that characterize specific types of CIN, with putative aetiologies supported by multiple independent data sources. The signatures predict drug response and identify new drug targets. Our framework refines the understanding of impaired homologous recombination, which is one of the most therapeutically targetable types of CIN. Our results illuminate a fundamental structure underlying genomic complexity in human cancers and provide a resource to guide future CIN research.

Prostate cancer multiparametric magnetic resonance imaging visibility is a tumor-intrinsic phenomena.

Multiparametric magnetic resonance imaging (mpMRI) is an emerging standard for diagnosing and prognosing prostate cancer, but ~ 20% of clinically significant tumors are invisible to mpMRI, as defined by the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of one or two. To understand the biological underpinnings of tumor visibility on mpMRI, we examined the proteomes of forty clinically significant tumors (i.e., International Society of Urological Pathology (ISUP) Grade Group 2)-twenty mpMRI-visible and twenty mpMRI-invisible, with matched histologically normal prostate. Normal prostate tissue was indistinguishable between patients with visible and invisible tumors, and invisible tumors closely resembled the normal prostate. These data indicate that mpMRI-visibility arises when tumor evolution leads to large-magnitude proteomic divergences from histologically normal prostate.

Association of Prudent, Western, and Alternate Healthy Eating Index (AHEI-2010) dietary patterns with serum testosterone and sex hormone binding globulin levels in men.

PURPOSE: The association of dietary patterns with testosterone (T) and sex hormone binding globulin (SHBG) levels remains unclear. We investigated the associations of dietary patterns with T and SHBG levels to determine whether these associations vary by obesity status. METHODS: A cross-sectional analysis was conducted in 1376 middle-aged (≥ 40 years old) men of the Health Professionals Follow-up Study. Prudent (rich in whole grains and dietary fiber) and Western (rich in red meat and refined grains) diet scores were identified using principal component analysis. The Alternate Healthy Eating Index 2010 (AHEI-2010) score, a measure of overall diet quality, was defined based on foods and nutrients predictive of chronic disease risk. RESULTS: We identified a weak inverse association between AHEI-2010 and T levels (Ptrend = 0.07), but no associations with other dietary patterns. Null associations were observed between diet scores and SHBG. Obesity status appeared to modify the associations for the Prudent diet and AHEI-2010 with both T and SHBG (Pinteraction ≤ 0.05). T levels were lower (Q1 vs. Q4, 4.23 vs. 3.38) and SHBG higher (Q1 vs. Q4, 48.6 vs. 64.3) with adherence to a more prudent diet among obese men (Ptrends ≤ 0.05). CONCLUSION: We observed a weak inverse association between AHEI-2010 and T levels. Null associations were identified for SHBG. Obesity status seemed to modulate associations of T and SHBG levels with diet scores, especially the AHEI-2010 and prudent diets. However, this research question warrants further investigation in prospective studies.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry.

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

A clinically applicable integrative molecular classification of meningiomas.

Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2 wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.

Precision Radiotherapy: Reduction in Radiation for Oropharyngeal Cancer in the 30 ROC Trial.

BACKGROUND: Patients with human papillomavirus-related oropharyngeal cancers have excellent outcomes but experience clinically significant toxicities when treated with standard chemoradiotherapy (70 Gy). We hypothesized that functional imaging could identify patients who could be safely deescalated to 30 Gy of radiotherapy. METHODS: In 19 patients, pre- and intratreatment dynamic fluorine-18-labeled fluoromisonidazole positron emission tomography (PET) was used to assess tumor hypoxia. Patients without hypoxia at baseline or intratreatment received 30 Gy; patients with persistent hypoxia received 70 Gy. Neck dissection was performed at 4 months in deescalated patients to assess pathologic response. Magnetic resonance imaging (weekly), circulating plasma cell-free DNA, RNA-sequencing, and whole-genome sequencing (WGS) were performed to identify potential molecular determinants of response. Samples from an independent prospective study were obtained to reproduce molecular findings. All statistical tests were 2-sided. RESULTS: Fifteen of 19 patients had no hypoxia on baseline PET or resolution on intratreatment PET and were deescalated to 30 Gy. Of these 15 patients, 11 had a pathologic complete response. Two-year locoregional control and overall survival were 94.4% (95% confidence interval = 84.4% to 100%) and 94.7% (95% confidence interval = 85.2% to 100%), respectively. No acute grade 3 radiation-related toxicities were observed. Microenvironmental features on serial imaging correlated better with pathologic response than tumor burden metrics or circulating plasma cell-free DNA. A WGS-based DNA repair defect was associated with response (P = .02) and was reproduced in an independent cohort (P = .03). CONCLUSIONS: Deescalation of radiotherapy to 30 Gy on the basis of intratreatment hypoxia imaging was feasible, safe, and associated with minimal toxicity. A DNA repair defect identified by WGS was predictive of response. Intratherapy personalization of chemoradiotherapy may facilitate marked deescalation of radiotherapy.

Quantifying the influence of mutation detection on tumour subclonal reconstruction.

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

The association of diabetes with risk of prostate cancer defined by clinical and molecular features.

BACKGROUND: To prospectively examine the association between diabetes and risk of prostate cancer defined by clinical and molecular features. METHODS: A total of 49,392 men from the Health Professionals Follow-up Study (HPFS) were followed from 1986 to 2014. Data on self-reported diabetes were collected at baseline and updated biennially. Clinical features of prostate cancer included localised, advanced, lethal, low-grade, intermediate-grade, and high-grade. Molecular features included TMPRSS2: ERG and PTEN subtypes. Cox proportional hazards regression models were used to evaluate the association between diabetes and incidence of subtype-specific prostate cancer. RESULTS: During 28 years of follow-up, we documented 6733 incident prostate cancer cases. Relative to men free from diabetes, men with diabetes had lower risks of total (HR: 0.82, 95% CI: 0.75-0.90), localised (HR: 0.82, 95% CI: 0.74-0.92), low-and intermediate-grade prostate cancer (HR: 0.77, 95% CI: 0.66-0.90; HR: 0.77, 95% CI: 0.65-0.91, respectively). For molecular subtypes, the HRs for ERG-negative and ERG-positive cases were 0.63 (0.42-0.95) and 0.72 (0.46-1.12); and for PTEN-intact and PTEN-loss cases were 0.69 (0.48-0.98) and 0.52 (0.19-1.41), respectively. CONCLUSION: Besides providing advanced evidence for the inverse association between diabetes and prostate cancer, this study is the first to report associations between diabetes and ERG/PTEN defined prostate cancers.

Steerable ePCA: Rotationally Invariant Exponential Family PCA.

In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications require an accurate estimation of the covariance of the underlying 2-D clean images. For example, in X-ray free electron laser (XFEL) single molecule imaging, the covariance matrix of 2-D diffraction images is used to reconstruct the 3-D molecular structure. Accurate estimation of the covariance from low-photon-count images must take into account that pixel intensities are Poisson distributed, hence the classical sample covariance estimator is highly biased. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this paper we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflections. Our procedure, steerable ePCA, combines in a novel way two recently introduced innovations. The first is a methodology for principal component analysis (PCA) for Poisson distributions, and more generally, exponential family distributions, called ePCA. The second is steerable PCA, a fast and accurate procedure for including all planar rotations when performing PCA. The resulting principal components are invariant to the rotation and reflection of the input images. We demonstrate the efficiency and accuracy of steerable ePCA in numerical experiments involving simulated XFEL datasets and rotated face images from Yale Face Database B.

A community effort to create standards for evaluating tumor subclonal reconstruction.

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.