A platform for rapid patient-derived cutaneous neurofibroma organoid establishment and screening.

Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by neurofibromatosis type 1 syndrome. cNFs are benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement resulting in substantial socio-emotional repercussions. Currently, surgery and laser desiccation are the sole treatment options but may result in scarring and potential regrowth from incomplete removal. To identify effective systemic therapies, we introduce an approach to establish and screen cNF organoids. We optimized conditions to support the ex vivo growth of genomically diverse cNFs. Patient-derived cNF organoids closely recapitulate cellular and molecular features of parental tumors as measured by immunohistopathology, methylation, RNA sequencing, and flow cytometry. Our cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine.

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

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.

Genomic evolution shapes prostate cancer disease type.

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

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.

NFTest: automated testing of Nextflow pipelines.

MOTIVATION: The ongoing expansion in the volume of biomedical data has contributed to a growing complexity in the tools and technologies used in research with an increased reliance on complex workflows written in orchestration languages such as Nextflow to integrate algorithms into processing pipelines. The growing use of workflows involving various tools and algorithms has led to increased scrutiny of software development practices to avoid errors in individual tools and in the connections between them. RESULTS: To facilitate test-driven development of Nextflow pipelines, we created NFTest, a framework for automated pipeline testing and validation with customizability options for Nextflow features. It is open-source, easy to initialize and use, and customizable to allow for testing of complex workflows with test success configurable through a broad range of assertions. NFTest simplifies the testing burden on developers by automating tests once defined and providing a flexible interface for running tests to validate workflows. This reduces the barrier to rigorous biomedical workflow testing and paves the way toward reducing computational errors in biomedicine. AVAILABILITY AND IMPLEMENTATION: NFTest is an open-source Python framework under the GPLv2 license and is freely available at https://github.com/uclahs-cds/tool-NFTest. The call-sSNV Nextflow pipeline is available at: https://github.com/uclahs-cds/pipeline-call-sSNV.

PipeVal: light-weight extensible tool for file validation.

MOTIVATION: The volume of biomedical data generated each year is growing exponentially as high-throughput molecular, imaging and mHealth technologies expand. This rise in data volume has contributed to an increasing reliance on and demand for computational methods, and consequently to increased attention to software quality and data integrity. RESULTS: To simplify data verification in diverse data-processing pipelines, we created PipeVal, a light-weight, easy-to-use, extensible tool for file validation. It is open-source, easy to integrate with complex workflows, and modularized for extensibility for new file formats. PipeVal can be rapidly inserted into existing methods and pipelines to automatically validate and verify inputs and outputs. This can reduce wasted compute time attributed to file corruption or invalid file paths, and significantly improve the quality of data-intensive software. AVAILABILITY AND IMPLEMENTATION: PipeVal is an open-source Python package under the GPLv2 license and it is freely available at https://github.com/uclahs-cds/package-PipeVal. The docker image is available at: https://github.com/uclahs-cds/package-PipeVal/pkgs/container/pipeval.

Impact of PSMA PET on Prostate Cancer Management.

OPINION STATEMENT: PSMA-PET has been a practice-changing imaging biomarker for the management of men with PCa. Research suggests improved accuracy over conventional imaging and other PET radiotracers in many contexts. With multiple approved PSMA-targeting radiotracers, PSMA PET will become even more available in clinical practice. Its increased use requires an understanding of the prospective data available and caution when extrapolating from prior trial data that utilized other imaging modalities. Future trials leveraging PSMA PET for treatment optimization and management decision-making will ultimately drive its clinical utility.

Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective.

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Association of exercise with pan-cancer incidence and overall survival.

Lavery et al. show that the association between exercise and risk of cancer varied as a function of organ site and amount of exercise. Exercise was also associated with a longevity benefit regardless of a cancer diagnosis or not. This study further highlights the importance of exercise as an effective cancer preventive strategy.

HPV-negative head and neck cancers with adverse pathological features carry specific molecular changes that are associated with survival.

BACKGROUND: Adverse pathological features following surgery in head and neck squamous cell carcinoma (HNSCC) are strongly associated with survival and guide adjuvant therapy. We investigated molecular changes associated with these features. METHODS: We downloaded data from the Cancer Genome Atlas and Cancer Proteome Atlas HNSCC cohorts. We compared tumors positive versus negative for perineural invasion (PNI), lymphovascular invasion (LVI), extracapsular spread (ECS), and positive margins (PSM), with multivariable analysis. RESULTS: All pathological features were associated with poor survival, as were the following molecular changes: low cyclin E1 (HR = 1.7) and high PKC-alpha (HR = 1.8) in tumors with PNI; six of 13 protein abundance changes with LVI; greater tumor hypoxia and high Raptor (HR = 2.0) and Rictor (HR = 1.6) with ECS; and low p38 (HR = 2.3), high fibronectin (HR = 1.6), low annexin A1 (HR = 3.1), and high caspase-9 (HR = 1.6) abundances with PSM. CONCLUSIONS: Pathological features in HNSCC carry specific molecular changes that may explain their poor prognostic associations.

Prostate lineage-specific metabolism governs luminal differentiation and response to antiandrogen treatment.

Lineage transitions are a central feature of prostate development, tumourigenesis and treatment resistance. While epigenetic changes are well known to drive prostate lineage transitions, it remains unclear how upstream metabolic signalling contributes to the regulation of prostate epithelial identity. To fill this gap, we developed an approach to perform metabolomics on primary prostate epithelial cells. Using this approach, we discovered that the basal and luminal cells of the prostate exhibit distinct metabolomes and nutrient utilization patterns. Furthermore, basal-to-luminal differentiation is accompanied by increased pyruvate oxidation. We establish the mitochondrial pyruvate carrier and subsequent lactate accumulation as regulators of prostate luminal identity. Inhibition of the mitochondrial pyruvate carrier or supplementation with exogenous lactate results in large-scale chromatin remodelling, influencing both lineage-specific transcription factors and response to antiandrogen treatment. These results establish reciprocal regulation of metabolism and prostate epithelial lineage identity.

Prostate-Specific Antigen Response to Androgen Deprivation Therapy in the Neoadjuvant Setting for High-Risk Prostate Adenocarcinoma (PIRANHA): Pooled Analysis of Two Randomized Clinical Trials.

PURPOSE: A suboptimal prostate-specific antigen (PSA) response to neoadjuvant androgen deprivation therapy (ADT) among men who go on to receive definitive radiation therapy for prostate cancer might suggest the existence of castration-resistant disease or altered androgen receptor signaling. This in turn may portend worse long-term clinical outcomes, especially in men with high-risk disease. We set out to evaluate the prognostic impact of poor PSA response to neoadjuvant ADT in men with high-risk prostate cancer. METHODS AND MATERIALS: This was a post hoc analysis of the multicenter TROG 03.04 RADAR and PCS IV randomized clinical trials. Inclusion criteria for this analysis were patients with high-risk prostate cancer (defined as Gleason score ≥8, initial PSA ≥20 ng/mL, or cT3a disease or higher) who received definitive radiation therapy, at least 18 months of ADT, and had a preradiation therapy PSA level drawn after at least 3 months of neoadjuvant ADT. Poor PSA response was defined as PSA >0.5 ng/mL. Cox regression and Fine-Gray models were used to test whether poor PSA response was associated with metastasis-free survival, biochemical recurrence, prostate-cancer specific mortality, and overall survival. RESULTS: Nine hundred thirty men met inclusion criteria for this analysis. Median follow-up was 130 months (interquartile range [IQR], 89-154 months). After a median of 3 months (IQR, 3-4.2 months) of neoadjuvant ADT, the median PSA was 0.60 ng/mL (IQR, 0.29-1.59). Overall, 535 men (57%) had a PSA >0.5 ng/mL. Poor PSA response was associated with significantly worse metastasis-free survival (hazard ratio [HR], 3.93; P = .02), worse biochemical recurrence (subdistribution HR, 2.39; P = .003), worse prostate-cancer specific mortality (subdistribution HR, 1.50; P = .005), and worse overall survival (HR, 4.51; P = .05). CONCLUSIONS: Patients with PSA >0.5 mg/mL after at least 3 months of neoadjuvant ADT had worse long-term clinical outcomes and should be considered for treatment intensification.

Loss of LRP1B expression drives acquired chemo and radio-resistance in HPV-positive head and neck cancer.

OBJECTIVES: Although human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) patients typically experience excellent survival, 15-20 % of patients recur after treatment with chemotherapy and radiation. Therefore, there is a need for biomarkers of treatment failure to guide treatment intensity. MATERIALS AND METHODS: Whole genome sequencing was carried out on HPV+OPSCC patients who were primarily treated with concurrent chemotherapy (cisplatin) and radiation. We then explored whether the loss of LRP1Bwas sufficient to drive an aggressive phenotype, and promote a resistance to cisplatin and radiation therapy both in vitro using HPV+ cell lines (93VU147T, UMSCC47, UWO37 and UWO23) and in vivo. RESULTS: Through integrative genomic analysis of three HPV+OPSCC tumour datasets, we identified that deletion of LRP1B was enriched in samples that recurred following chemo-radiation. Knockdown using siRNA in four HPV+ cell lines (UWO23, UWO37, UMSCC47 and 93VU147T) resulted in increased proliferation of all cases. CRISPR/Cas9 deletion of LRP1B in the same cell line panel demonstrated increased proliferation, clonogenic growth and migration, as well as resistance to both cisplatin and radiation in LRP1B deleted cells compared to their respective non-targeting control cells. Cell line derived xenograft studies indicated that the LRP1B knockout tumours were more resistant to cisplatin and radiation therapy compared to their controls invivo. CONCLUSION: Taken together, our work implicates LRP1B deletion as a potential biomarker for identifying treatment resistant HPV+ OPSCC cases.

MYC is a regulator of androgen receptor inhibition-induced metabolic requirements in prostate cancer.

Advanced prostate cancers are treated with therapies targeting the androgen receptor (AR) signaling pathway. While many tumors initially respond to AR inhibition, nearly all develop resistance. It is critical to understand how prostate tumor cells respond to AR inhibition in order to exploit therapy-induced phenotypes prior to the outgrowth of treatment-resistant disease. Here, we comprehensively characterize the effects of AR blockade on prostate cancer metabolism using transcriptomics, metabolomics, and bioenergetics approaches. The metabolic response to AR inhibition is defined by reduced glycolysis, robust elongation of mitochondria, and increased reliance on mitochondrial oxidative metabolism. We establish DRP1 activity and MYC signaling as mediators of AR-blockade-induced metabolic phenotypes. Rescuing DRP1 phosphorylation after AR inhibition restores mitochondrial fission, while rescuing MYC restores glycolytic activity and prevents sensitivity to complex I inhibition. Our study provides insight into the regulation of treatment-induced metabolic phenotypes and vulnerabilities in prostate cancer.

Pan-Cancer Analysis of Postdiagnosis Exercise and Mortality.

PURPOSE: The impact of postdiagnosis exercise on cause-specific mortality in cancer survivors and whether this differs on the basis of cancer site is unclear. METHODS: We performed an analysis of 11,480 patients with cancer enrolled in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial. Patients with a confirmed diagnosis of cancer completing a standardized survey quantifying exercise after diagnosis were included. The primary outcome was all-cause mortality (ACM); secondary end points were cancer mortality and mortality from other causes. Cox models were used to estimate the cause-specific hazard ratios (HRs) for ACM, cancer, and noncancer mortality as a function of meeting exercise guidelines versus not meeting guidelines with adjustment for important clinical covariates. RESULTS: After a median follow-up of 16 years from diagnosis, 4,665 deaths were documented (1,940 due to cancer and 2,725 due to other causes). In multivariable analyses, exercise consistent with guidelines was associated with a 25% reduced risk of ACM compared with nonexercise (HR, 0.75; 95% CI, 0.70 to 0.80). Compared with nonexercise, exercise consistent with guidelines was associated with a significant reduction in cancer mortality (HR, 0.79; 95% CI, 0.72 to 0.88) and mortality from other causes (HR, 0.72; 95% CI, 0.66 to 0.78). The inverse relationship between exercise and cause-specific mortality varied by exercise dose. Exercise consistent with guidelines was associated with a reduced hazard of ACM for multiple cancer sites. Reduction in cancer mortality for exercisers was only observed in head and neck and renal cancer. CONCLUSION: In this pan-cancer sample of long-term cancer survivors, exercise consistent with guidelines was associated with substantial ACM benefit driven by both reductions in cancer and noncancer mortality. The cause-specific impact of exercise differed as a function of cancer site.

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.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Serrate RNA Effector Molecule (SRRT) Is Associated with Prostate Cancer Progression and Is a Predictor of Poor Prognosis in Lethal Prostate Cancer.

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRT's potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.

Drug screening at single-organoid resolution via bioprinting and interferometry.

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.