Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.

SummarySubunits of the SWI/SNF chromatin remodeling complex are mutated in a significant proportion of human cancers. Malignant rhabdoid tumors (MRTs) are lethal pediatric cancers characterized by a deficiency in the SWI/SNF subunit SMARCB1. Here, we employ an integrated molecular profiling and chemical biology approach to demonstrate that the receptor tyrosine kinases (RTKs) PDGFRα and FGFR1 are coactivated in MRT cells and that dual blockade of these receptors has synergistic efficacy. Inhibitor combinations targeting both receptors and the dual inhibitor ponatinib suppress the AKT and ERK1/2 pathways leading to apoptosis. MRT cells that have acquired resistance to the PDGFRα inhibitor pazopanib are susceptible to FGFR inhibitors. We show that PDGFRα levels are regulated by SMARCB1 expression, and assessment of clinical specimens documents the expression of both PDGFRα and FGFR1 in rhabdoid tumor patients. Our findings support a therapeutic approach in cancers with SWI/SNF deficiencies by exploiting RTK coactivation dependencies.

The purpose of this randomized trial was to determine whether coronary artery bypass graft surgery patients and their caregivers who received telehealth follow-up had greater improvements in anxiety levels from pre-surgery to 3 weeks after discharge than did those who received standard care. Secondary outcomes included changes in depressive symptoms and patients' contacts with physicians. No group differences were noted in changes in patients' anxiety and depressive symptoms, but patients in the telehealth group had fewer physician contacts (p = .04). Female caregivers in the telehealth group had greater decreases in anxiety than those in standard care (p < .001), and caregivers of both genders in the telehealth group had greater decreases in depressive symptoms (p = .03).

Elucidating the wiring diagram of the human cell is one of the central goals of the post-genomic era. Here, we integrate genome engineering, confocal imaging, mass spectrometry and data science to systematically map protein localization in live cells and protein interactions under endogenous expression conditions. For this, we generated a library of 1,311 CRISPR-edited cell lines harboring fluorescent tags that also serve as handles for affinity capture, and applied a new machine learning framework to encode the interaction and localization profiles of each protein. Our approach provides a data-driven description of the molecular and spatial networks that organize the human proteome. We show that unsupervised clustering of these networks delineates functional groups and facilitates biological discovery, while hierarchical analyses uncover the core features that template cellular architecture. Furthermore, we discover that localization signatures are remarkably predictive of protein function, and often contain enough information to identify molecular interactions. Paired with a fully interactive website (opencell.czbiohub.org), OpenCell is a resource for the quantitative cartography of human cellular organization.

ObjectivesTo develop a risk classifier using urine‐derived extracellular vesicle (EV)‐RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS).Patients and MethodsPost‐digital rectal examination urine‐derived EV‐RNA expression profiles (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO‐based continuation ratio model was built to generate four prostate urine risk (PUR) signatures for predicting the probability of normal tissue (PUR‐1), D'Amico low‐risk (PUR‐2), intermediate‐risk (PUR‐3), and high‐risk (PUR‐4) prostate cancer. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub‐cohort (n = 87) for prognostic evaluation.ResultsEach PUR signature was significantly associated with its corresponding clinical category (P < 0.001). PUR‐4 status predicted the presence of clinically significant intermediate‐ or high‐risk disease (area under the curve = 0.77, 95% confidence interval [CI] 0.70–0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub‐cohort (n = 87), groups defined by PUR status and proportion of PUR‐4 had a significant association with time to progression (interquartile range hazard ratio [HR] 2.86, 95% CI 1.83–4.47; P < 0.001). PUR‐4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26–20.81; P < 0.001).ConclusionUrine‐derived EV‐RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS. PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer.

Background:Androgen receptor (AR)-gene amplification, found in 20–30% of castration-resistant prostate cancer (CRPCa) is proposed to develop as a consequence of hormone-deprivation therapy and be a prime cause of treatment failure. Here we investigate AR-gene amplification in cancers before hormone deprivation therapy.Methods:A tissue microarray (TMA) series of 596 hormone-naive prostate cancers (HNPCas) was screened for chromosome X and AR-gene locus-specific copy number alterations using four-colour fluorescence in situ hybridisation.Results:Both high level gain in chromosome X (⩾4 fold; n=4, 0.7%) and locus-specific amplification of the AR-gene (n=6, 1%) were detected at low frequencies in HNPCa TMAs. Fluorescence in situ hybridisation mapping whole sections taken from the original HNPCa specimen blocks demonstrated that AR-gene amplifications exist in small foci of cells (⩽600 nm, ⩽1% of tumour volume). Patients with AR gene-locus-specific copy number gains had poorer prostate cancer-specific survival.Conclusion:Small clonal foci of cancer containing high level gain of the androgen receptor (AR)-gene develop before hormone deprivation therapy. Their small size makes detection by TMA inefficient and suggests a higher prevalence than that reported herein. It is hypothesised that a large proportion of AR-amplified CRPCa could pre-date hormone deprivation therapy and that these patients would potentially benefit from early total androgen ablation.

Background:Prostate cancer diagnosis is routinely made by the histopathological examination of formalin fixed needle biopsy specimens. Frequently this is the only cancer tissue available from the patient for the analysis of diagnostic and prognostic biomarkers. There is, therefore, an urgent need for methods that allow the high-throughput analysis of these biopsy samples using immunohistochemical (IHC) markers and fluorescence in situ hybridisation (FISH) analysis based markers.Methods:A method that allows the construction of tissue microarrays (TMAs) from diagnostic prostate needle biopsy cores has previously been reported. However, the technique only allows the production of low-density biopsy TMAs with a maximum of 20 cores per TMA. Here two methods are presented that allow the rapid and uniform production of biopsy TMAs containing between 54 and 72 biopsy cores. IHC and FISH techniques were used to detect biomarker status.Results:Biopsy TMAs were constructed from prostate needle biopsy specimens taken from 102 patients entered into an active surveillance trial and 201 patients in a radiotherapy trial. The detection rate for cancer in slices of these biopsy TMAs was 66% and 79% respectively. Slices of a biopsy TMA prepared from biopsies from active surveillance patients were used to detect multiple IHC markers and to score TMPRSS2-ERG fusion status in a FISH-based assay.Conclusions:The construction of biopsy TMAs provides an effective method for the multiplex analysis of IHC and FISH markers and for their assessment as prognostic biomarkers in the context of clinical trials.