Neuronal Activity Promotes Glioma Growth through Neuroligin-3 Secretion.

Active neurons exert a mitogenic effect on normal neural precursor and oligodendroglial precursor cells, the putative cellular origins of high-grade glioma (HGG). By using optogenetic control of cortical neuronal activity in a patient-derived pediatric glioblastoma xenograft model, we demonstrate that active neurons similarly promote HGG proliferation and growth in vivo. Conditioned medium from optogenetically stimulated cortical slices promoted proliferation of pediatric and adult patient-derived HGG cultures, indicating secretion of activity-regulated mitogen(s). The synaptic protein neuroligin-3 (NLGN3) was identified as the leading candidate mitogen, and soluble NLGN3 was sufficient and necessary to promote robust HGG cell proliferation. NLGN3 induced PI3K-mTOR pathway activity and feedforward expression of NLGN3 in glioma cells. NLGN3 expression levels in human HGG negatively correlated with patient overall survival. These findings indicate the important role of active neurons in the brain tumor microenvironment and identify secreted NLGN3 as an unexpected mechanism promoting neuronal activity-regulated cancer growth.

An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

AZGP1 deficiency promotes angiogenesis in prostate cancer.

BACKGROUND: Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive. METHOD: AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples. RESULT: Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues. CONCLUSION: AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

Transcriptome and genome evolution during HER2-amplified breast neoplasia.

BACKGROUND: The acquisition of oncogenic drivers is a critical feature of cancer progression. For some carcinomas, it is clear that certain genetic drivers occur early in neoplasia and others late. Why these drivers are selected and how these changes alter the neoplasia's fitness is less understood. METHODS: Here we use spatially oriented genomic approaches to identify transcriptomic and genetic changes at the single-duct level within precursor neoplasia associated with invasive breast cancer. We study HER2 amplification in ductal carcinoma in situ (DCIS) as an event that can be both quantified and spatially located via fluorescence in situ hybridization (FISH) and immunohistochemistry on fixed paraffin-embedded tissue. RESULTS: By combining the HER2-FISH with the laser capture microdissection (LCM) Smart-3SEQ method, we found that HER2 amplification in DCIS alters the transcriptomic profiles and increases diversity of copy number variations (CNVs). Particularly, interferon signaling pathway is activated by HER2 amplification in DCIS, which may provide a prolonged interferon signaling activation in HER2-positive breast cancer. Multiple subclones of HER2-amplified DCIS with distinct CNV profiles are observed, suggesting that multiple events occurred for the acquisition of HER2 amplification. Notably, DCIS acquires key transcriptomic changes and CNV events prior to HER2 amplification, suggesting that pre-amplified DCIS may create a cellular state primed to gain HER2 amplification for growth advantage. CONCLUSION: By using genomic methods that are spatially oriented, this study identifies several features that appear to generate insights into neoplastic progression in precancer lesions at a single-duct level.

Epidemiological Profile and Clinicopathological, Therapeutic, and Prognostic Characteristics of Nasopharyngeal Carcinoma in Northern Morocco.

BACKGROUND: Nasopharyngeal carcinoma is a multifactorial disease mainly affecting the Asian and North African populations including Morocco. This study aimed to determine the epidemiological profile of nasopharyngeal carcinoma in Northern Morocco as well as its clinicopathological, therapeutic, and prognostic characteristics. METHODS: 129 patients with nasopharyngeal carcinoma followed at the regional center of oncology of Tangier in the period between April 2017 and July 2019, and diagnosed elsewhere from March 2000 to February 2019, were included in this study. Statistical analysis of the data was realized using Statistical Package for the Social Sciences (SPSS) software. RESULTS: Nasopharyngeal carcinoma (NPC) represented 5% of all cases with a median age of 50. The most affected age group was 40-54 years (41.1%). Of all patients, 65.9% were men and 34.1% were women with a sex ratio of 1.93 (Male/Female). Undifferentiated nasopharyngeal carcinomas were the most common histological type affecting 96.12% of patients. At diagnosis, the majority of patients (82.2%) had an advanced stage of NPC (III, VIa, b, c) including 5.4% of metastatic cases (IVc). Most cases (86%) had lymph node involvement with cervical mass being the most common clinical presentation. 81.4% of patients received radiotherapy combined with chemotherapy. Among these patients, 54.3% had concurrent radiochemotherapy preceded by induction chemotherapy. The 5-year overall survival (OS) was 86.8% for all patients. It represented 91.3% for early stages, 87.9% for locally advanced stages, and 57.1% for the metastatic stage significantly. The disease-free survival (DFS) at 5 years was 87.6% knowing that relapse occurred in 16 cases. CONCLUSIONS: Nasopharyngeal carcinoma is a particular disease with a late declaration. It is common in Morocco as is the case in other endemic areas with a high prevalence. Patients' survival is significantly influenced by disease staging.

ImmunoGlobe: enabling systems immunology with a manually curated intercellular immune interaction network.

BACKGROUND: While technological advances have made it possible to profile the immune system at high resolution, translating high-throughput data into knowledge of immune mechanisms has been challenged by the complexity of the interactions underlying immune processes. Tools to explore the immune network are critical for better understanding the multi-layered processes that underlie immune function and dysfunction, but require a standardized network map of immune interactions. To facilitate this we have developed ImmunoGlobe, a manually curated intercellular immune interaction network extracted from Janeway's Immunobiology textbook. RESULTS: ImmunoGlobe is the first graphical representation of the immune interactome, and is comprised of 253 immune system components and 1112 unique immune interactions with detailed functional and characteristic annotations. Analysis of this network shows that it recapitulates known features of the human immune system and can be used uncover novel multi-step immune pathways, examine species-specific differences in immune processes, and predict the response of immune cells to stimuli. ImmunoGlobe is publicly available through a user-friendly interface at www.immunoglobe.org and can be downloaded as a computable graph and network table. CONCLUSION: While the fields of proteomics and genomics have long benefited from network analysis tools, no such tool yet exists for immunology. ImmunoGlobe provides a ground truth immune interaction network upon which such tools can be built. These tools will allow us to predict the outcome of complex immune interactions, providing mechanistic insight that allows us to precisely modulate immune responses in health and disease.

How many human proteoforms are there?

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.

Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.

Longitudinal Monitoring of Antibody Responses against Tumor Cells Using Magneto-nanosensors with a Nanoliter of Blood.

Each immunoglobulin isotype has unique immune effector functions. The contribution of these functions in the elimination of pathogens and tumors can be determined by monitoring quantitative temporal changes in isotype levels. Here, we developed a novel technique using magneto-nanosensors based on the effect of giant magnetoresistance (GMR) for longitudinal monitoring of total and antigen-specific isotype levels with high precision, using as little as 1 nL of serum. Combining in vitro serologic measurements with in vivo imaging techniques, we investigated the role of the antibody response in the regression of firefly luciferase (FL)-labeled lymphoma cells in spleen, kidney, and lymph nodes in a syngeneic Burkitt's lymphoma mouse model. Regression status was determined by whole body bioluminescent imaging (BLI). The magneto-nanosensors revealed that anti-FL IgG2a and total IgG2a were elevated and sustained in regression mice compared to non-regression mice (p < 0.05). This platform shows promise for monitoring immunotherapy, vaccination, and autoimmunity.

Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection.

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were <30% on tissue sections and spatial resolution was 200 µm (or greater). Limits of detection for proteins and peptides on tissue were in the micromolar to millimolar range. Protein identification was only possible for proteins present in high abundance in the tissue. These results provide a baseline for the application of MALDI-MSI towards the discovery of new candidate biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI-MSI workflows.

Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments.

Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.

AshwaMAX and Withaferin A inhibits gliomas in cellular and murine orthotopic models.

Glioblastoma multiforme (GBM) is an aggressive, malignant cancer Johnson and O'Neill (J Neurooncol 107: 359-364, 2012). An extract from the winter cherry plant (Withania somnifera ), AshwaMAX, is concentrated (4.3 %) for Withaferin A; a steroidal lactone that inhibits cancer cells Vanden Berghe et al. (Cancer Epidemiol Biomark Prev 23: 1985-1996, 2014). We hypothesized that AshwaMAX could treat GBM and that bioluminescence imaging (BLI) could track oral therapy in orthotopic murine models of glioblastoma. Human parietal-cortical glioblastoma cells (GBM2, GBM39) were isolated from primary tumors while U87-MG was obtained commercially. GBM2 was transduced with lentiviral vectors that express Green Fluorescent Protein (GFP)/firefly luciferase fusion proteins. Mutational, expression and proliferative status of GBMs were studied. Intracranial xenografts of glioblastomas were grown in the right frontal regions of female, nude mice (n = 3-5 per experiment). Tumor growth was followed through BLI. Neurosphere cultures (U87-MG, GBM2 and GBM39) were inhibited by AshwaMAX at IC50 of 1.4, 0.19 and 0.22 µM equivalent respectively and by Withaferin A with IC50 of 0.31, 0.28 and 0.25 µM respectively. Oral gavage, every other day, of AshwaMAX (40 mg/kg per day) significantly reduced bioluminescence signal (n = 3 mice, p < 0.02, four parameter non-linear regression analysis) in preclinical models. After 30 days of treatment, bioluminescent signal increased suggesting onset of resistance. BLI signal for control, vehicle-treated mice increased and then plateaued. Bioluminescent imaging revealed diffuse growth of GBM2 xenografts. With AshwaMAX, GBM neurospheres collapsed at nanomolar concentrations. Oral treatment studies on murine models confirmed that AshwaMAX is effective against orthotopic GBM. AshwaMAX is thus a promising candidate for future clinical translation in patients with GBM.

Characterizing deformability and surface friction of cancer cells.

Metastasis requires the penetration of cancer cells through tight spaces, which is mediated by the physical properties of the cells as well as their interactions with the confined environment. Various microfluidic approaches have been devised to mimic traversal in vitro by measuring the time required for cells to pass through a constriction. Although a cell's passage time is expected to depend on its deformability, measurements from existing approaches are confounded by a cell's size and its frictional properties with the channel wall. Here, we introduce a device that enables the precise measurement of (i) the size of a single cell, given by its buoyant mass, (ii) the velocity of the cell entering a constricted microchannel (entry velocity), and (iii) the velocity of the cell as it transits through the constriction (transit velocity). Changing the deformability of the cell by perturbing its cytoskeleton primarily alters the entry velocity, whereas changing the surface friction by immobilizing positive charges on the constriction's walls primarily alters the transit velocity, indicating that these parameters can give insight into the factors affecting the passage of each cell. When accounting for cell buoyant mass, we find that cells possessing higher metastatic potential exhibit faster entry velocities than cells with lower metastatic potential. We additionally find that some cell types with higher metastatic potential exhibit greater than expected changes in transit velocities, suggesting that not only the increased deformability but reduced friction may be a factor in enabling invasive cancer cells to efficiently squeeze through tight spaces.

Examination of the role of mutualism in immune evasion.

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

Prospector Heads: Generalized Feature Attribution for Large Models & Data.

Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ML models in scientific and biomedical domains. Current methods for feature attribution, which rely on "explaining" the predictions of end-to-end classifiers, suffer from imprecise feature localization and are inadequate for use with small sample sizes and high-dimensional datasets due to computational challenges. We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods that can be applied to any encoder and any data modality. Prospector heads generalize across modalities through experiments on sequences (text), images (pathology), and graphs (protein structures), outperforming baseline attribution methods by up to 26.3 points in mean localization AUPRC. We also demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data. Through their high performance, flexibility, and generalizability, prospectors provide a framework for improving trust and transparency for ML models in complex domains.

Anterior gradient 2 (AGR2): blood-based biomarker elevated in metastatic prostate cancer associated with the neuroendocrine phenotype.

BACKGROUND: Anterior gradient 2 (AGR2) is associated with metastatic progression in prostate cancer cells as well as other normal and malignant tissues. We investigated AGR2 expression in patients with metastatic prostate cancer. METHODS: Blood was collected from 44 patients with metastatic prostate cancer separated as: castration sensitive prostate cancer (CSPC, n = 5); castration resistant prostate cancer (CRPC, n = 36); and neuroendocrine-predominate CRPC defined by PSA ≤ 1 ng/ml in the presence of wide-spread metastatic disease (NE-CRPC, n = 3). AGR2 mRNA levels were measured with RT-PCR in circulating tumor cell (CTC)-enriched peripheral blood. Plasma AGR2 levels were determined via ELISA assay. AGR2 expression was modulated in prostate cancer cell lines using plasmid and viral vectors. RESULTS: AGR2 mRNA levels are elevated in CTCs and strongly correlated with CTC enumeration. Plasma AGR2 levels are elevated in all sub-groups. AGR2 levels vary independently to PSA and change in some patients in response to androgen-directed and other therapies. Plasma AGR2 levels are highest in the NE-CRPC sub-group. A correlation between AGR2, chromagranin A (CGA), and neuron-specific enolase (NSE) expression is demonstrated in prostate cancer cell lines. CONCLUSIONS: We conclude that AGR2 expression is elevated at the mRNA and protein level in patients with metastatic prostate cancer. In particular, we find that AGR2 expression is associated features consistent with neuroendocrine, or anaplastic, prostate cancer, exemplified by an aggressive clinical phenotype without elevation in circulating PSA levels. Further studies are warranted to explore the mechanistic and prognostic implications of AGR2 expression in this patient population.

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas.

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images.

Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.

Peptide identification from mixture tandem mass spectra.

The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of single-peptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.