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.

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.

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. Yet 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 two decades have seen an explosion of multi-omics 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 towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define 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.

A flexible electronic strain sensor for the real-time monitoring of tumor regression

Assessing the efficacy of cancer therapeutics in mouse models is a critical step in treatment development. However, low-resolution measurement tools and small sample sizes make determining drug efficacy in vivo a difficult and time-intensive task. Here, we present a commercially scalable wearable electronic strain sensor that automates the in vivo testing of cancer therapeutics by continuously monitoring the micrometer-scale progression or regression of subcutaneously implanted tumors at the minute time scale. In two in vivo cancer mouse models, our sensor discerned differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours following therapy initiation. These short-term regression measurements were validated through histology, and caliper and bioluminescence measurements taken over weeklong treatment periods demonstrated the correlation with longer-term treatment response. We anticipate that real-time tumor regression datasets could help expedite and automate the process of screening cancer therapies in vivo.

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.

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.

Modular fluorescent nanoparticle DNA probes for detection of peptides and proteins

Fluorescently labeled antibody and aptamer probes are used in biological studies to characterize binding interactions, measure concentrations of analytes, and sort cells. Fluorescent nanoparticle labels offer an excellent alternative to standard fluorescent labeling strategies due to their enhanced brightness, stability and multivalency; however, challenges in functionalization and characterization have impeded their use. This work introduces a straightforward approach for preparation of fluorescent nanoparticle probes using commercially available reagents and common laboratory equipment. Fluorescent polystyrene nanoparticles, Thermo Fisher Scientific FluoSpheres, were used in these proof-of-principle studies. Particle passivation was achieved by covalent attachment of amine-PEG-azide to carboxylated particles, neutralizing the surface charge from - 43 to - 15 mV. A conjugation-annealing handle and DNA aptamer probe were attached to the azide-PEG nanoparticle surface either through reaction of pre-annealed handle and probe or through a stepwise reaction of the nanoparticles with the handle followed by aptamer annealing. Nanoparticles functionalized with DNA aptamers targeting histidine tags and VEGF protein had high affinity (EC50s ranging from 3 to 12 nM) and specificity, and were more stable than conventional labels. This protocol for preparation of nanoparticle probes relies solely on commercially available reagents and common equipment, breaking down the barriers to use nanoparticles in biological experiments.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01451-6.

Synthesis, characterization and bioactivity of thio-acetamide modified ZnO nanoparticles embedded in zinc acetate matrix

ZnO nanoparticles embedded in zinc acetate matrix were synthesized by chemical route. The effect of thio-acetamide concentration during its synthesis was probed by structural, morphological, optical and bioactivity studies. XRD characterization indicated the formation of dominant phase of zinc acetate along with the low intensity peak of wurtzite ZnO. Morphological transition from bulky-like feature to flower-like feature via flake-like feature, is evidenced with increasing thio-acetamide molar concentrations. The optical band gap of samples decreased from ∼3.29 to 3.24 eV whereas the emitted color shifted from near green to blue region with increasing of molar concentration of thio-acetamide from 0 to 30% in the sample. The nanoparticles exhibited antimicrobial activity against seven (7) common human pathogenic bacteria including drug resistant varieties K. pneumonaie and S. aureus. The nanoparticles formed pores in the biological model membranes made from egg-phosphatidyl choline. Our study reveals that the thio-acetamide modified ZnO nanoparticles embedded in zinc acetate matrix could be used as potential drug lead to fight drug resistance against K. pneumoniae and S. aureus.

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.

Geostatistical visualization of ecological interactions in tumors

Recent advances in our understanding of cancer progression have highlighted the roles played by molecular heterogeneity and by the tumor microenvironment in driving drug resistance and metastasis. The coupling of single-cell measurement technologies with algorithms, such as t-sne and SPADE, have enabled deep investigation of tumor heterogeneity. However, such techniques only capture molecular heterogeneity and do not enable the quantification nor visualization of intercellular interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. Novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment are critically needed. Here, we take a tumor ecology perspective to examine how predation, mutualism, commensalism, and parasitism may impact tumor development and spatial patterning. We additionally quantify local spatial heterogeneity and the emergent global spatial behavior of the models using geostatistics. By visualizing emergent spatial patterns we demonstrate the potential utility of a geostatistical analysis in differentiating amongst cell-cell interactions in the tumor microenvironment. These studies introduce both an ecological framework for characterizing intercellular interactions in cancer and a novel way of quantifying and visualizing spatial patterns in cancer.

PTR Explorer: An approach to identify and explore Post Transcriptional Regulatory mechanisms using proteogenomics

Integration of transcriptomic and proteomic data should reveal multi-layered regulatory processes governing cancer cell behaviors. Traditional correlation-based analyses have demonstrated limited ability to identify the post-transcriptional regulatory (PTR) processes that drive the non-linear relationship between transcript and protein abundances. In this work, we ideate an integrative approach to explore the variety of post-transcriptional mechanisms that dictate relationships between genes and corresponding proteins. The proposed workflow utilizes the intuitive technique of scatterplot diagnostics or scagnostics, to characterize and examine the diverse scatterplots built from transcript and protein abundances in a proteogenomic experiment. The workflow includes representing gene-protein relationships as scatterplots, clustering on geometric scagnostic features of these scatterplots, and finally identifying and grouping the potential gene-protein relationships according to their disposition to various PTR mechanisms. Our study verifies the efficacy of the implemented approach to excavate possible regulatory mechanisms by utilizing comprehensive tests on a synthetic dataset. We also propose a variety of 2D pattern-specific downstream analyses methodologies such as mixture modeling, and mapping miRNA post-transcriptional effects to explore each mechanism further. This work suggests that the proposed methodology has the potential for discovering and categorizing post-transcriptional regulatory mechanisms, manifesting in proteogenomic trends. These trends subsequently provide evidence for cancer specificity, miRNA targeting, and identification of regulation impacted by biological functionality and different types of degradation. (Supplementary Material - https://github.com/arunima2/PTRE_PSB_2020).

Abstract 2451: Physical, genomic, and proteomic characterization of a cancer cell line panel in an integrated dataset

Introduction: Recent work has linked cancer phenotype (e.g., metastatic ability) to physical properties (e.g., nuclear deformability), as well as genetic alterations (e.g., mutations in lamin genes affecting nuclear stiffness). However, while the genomics of immortalized human cell lines - important models for studying cancer disease pathogenesis and progression - have been relatively well studied, an integrative analysis of physical cell phenotypes with genomic, transcriptomic, and proteomics assays, spanning tissues of origins and culture environments (CE), has been lacking. The Physical Sciences in Oncology Network (PS-ON), a trans-disciplinary project established by the NCI, has generated a curated open dataset that is accessible to researchers via various database interfaces to address this need.

Methods: A panel of 39 cancerous and non-malignant cell lines frequently used in cancer research were selected to cover a set of nine tissues: breast, lung, pancreas, brain, prostate, colon, ovary, blood, and skin. Thirty of these cell lines (from all tissues except blood) were assayed by traction and atomic force microscopy and imaging to measure their cell morphology, proliferation, motility, and nuclear volume. Measurements were performed in seven CE of varying physical properties (e.g., stiffness) to recapitulate a range of realistic tissue conditions and frequently used reagents. All 39 cell lines were assayed via whole exome sequencing, mRNA-seq, and miRNA-seq (in a single CE), while nine cell lines were subjected to proteomic analysis (across seven CEs).

Results: All raw measurements and summary metrics from the study were incorporated into a data model compatible with established data stores (e.g., GDC, miRBase) and integrated in an open-access relational database (RDB). We performed several integrative analyses using the RDB. A broad set of interactions emerged between cell line CE characteristics (e.g., integrin ligands), the phenotypes of cells (e.g., motility) and gene expression, with effect-size varying by cell-line tissue of origin and cancer diagnosis. For instance, motility was significantly (FDR < 5%) correlated with expression of a small set of genes across tissues (e.g., BNP1 and PFKP in both skin and prostate cancers).

Conclusion: This PS-ON cell line characterization allows correlative analyses across CE, cells’ morphological and proliferation properties, and omics data. The integrated RDB facilitates these analyses via a unified interface and data model for the large collection of files comprising the resource. We demonstrated the utility of the RDB by using it to infer the relationship between differential gene expression, CE and resulting phenotypes, with potentially important downstream effects on hallmarks of cancer malignancy.

Citation Format: Milen Nikolov, Brian White, Adrian Pegoraro, Debra Hope, Mariam Eljanne, James Eddy, Paul Janmey, Parag Mallick, Kristen Dang. Physical, genomic, and proteomic characterization of a cancer cell line panel in an integrated dataset [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2451.

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows

A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.

Semantic workflows for benchmark challenges: Enhancing comparability, reusability and reproducibility

Benchmark challenges, such as the Critical Assessment of Structure Prediction (CASP) and Dialogue for Reverse Engineering Assessments and Methods (DREAM) have been instrumental in driving the development of bioinformatics methods. Typically, challenges are posted, and then competitors perform a prediction based upon blinded test data. Challengers then submit their answers to a central server where they are scored. Recent efforts to automate these challenges have been enabled by systems in which challengers submit Docker containers, a unit of software that packages up code and all of its dependencies, to be run on the cloud. Despite their incredible value for providing an unbiased test-bed for the bioinformatics community, there remain opportunities to further enhance the potential impact of benchmark challenges. Specifically, current approaches only evaluate end-to-end performance; it is nearly impossible to directly compare methodologies or parameters. Furthermore, the scientific community cannot easily reuse challengers' approaches, due to lack of specifics, ambiguity in tools and parameters as well as problems in sharing and maintenance. Lastly, the intuition behind why particular steps are used is not captured, as the proposed workflows are not explicitly defined, making it cumbersome to understand the flow and utilization of data. Here we introduce an approach to overcome these limitations based upon the WINGS semantic workflow system. Specifically, WINGS enables researchers to submit complete semantic workflows as challenge submissions. By submitting entries as workflows, it then becomes possible to compare not just the results and performance of a challenger, but also the methodology employed. This is particularly important when dozens of challenge entries may use nearly identical tools, but with only subtle changes in parameters (and radical differences in results). WINGS uses a component driven workflow design and offers intelligent parameter and data selection by reasoning about data characteristics. This proves to be especially critical in bioinformatics workflows where using default or incorrect parameter values is prone to drastically altering results. Different challenge entries may be readily compared through the use of abstract workflows, which also facilitate reuse. WINGS is housed on a cloud based setup, which stores data, dependencies and workflows for easy sharing and utility. It also has the ability to scale workflow executions using distributed computing through the Pegasus workflow execution system. We demonstrate the application of this architecture to the DREAM proteogenomic challenge.

Loss of ER retention motif of AGR2 can impact mTORC signaling and promote cancer metastasis

Anterior gradient 2 (AGR2) is a member of the protein disulfide isomerase (PDI) family, which plays a role in the regulation of protein homeostasis and the unfolded protein response pathway (UPR). AGR2 has also been characterized as a proto-oncogene and a potential cancer biomarker. Cellular localization of AGR2 is emerging as a key component for understanding the role of AGR2 as a proto-oncogene. Here, we provide evidence that extracellular AGR2 (eAGR2) promotes tumor metastasis in various in vivo models. To further characterize the role of the intracellular-resident versus extracellular protein, we performed a comprehensive protein-protein interaction screen. Based on these results, we identify AGR2 as an interacting partner of the mTORC2 pathway. Importantly, our data indicates that eAGR2 promotes increased phosphorylation of RICTOR (T1135), while intracellular AGR2 (iAGR2) antagonizes its levels and phosphorylation. Localization of AGR2 also has opposing effects on the Hippo pathway, spheroid formation, and response to chemotherapy in vitro. Collectively, our results identify disparate phenotypes predicated on AGR2 localization. Our findings also provide credence for screening of eAGR2 to guide therapeutic decisions.

Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images

Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). We synthesize a workflow which (a) intelligently samples the training data by automatically selecting only image areas that display visible disease-relevant tissue state and (b) isolates regions most pertinent to the trained CNN prediction and translates them to observable and qualitative features such as color, intensity, cell and tissue morphology and texture. We use the Cancer Genome Atlas’s Breast Invasive Carcinoma (TCGA-BRCA) histology dataset to build a model predicting patient attributes (disease stage and node status) and the tumor proliferation challenge (TUPAC 2016) breast cancer histology image repository to help identify disease-relevant tissue state (mitotic activity). We find that our enhanced CNN based workflow both increased patient attribute predictive accuracy (~2% increase for disease stage and ~10% increase for node status) and experimentally proved that a data-driven CNN histology model predicting breast invasive carcinoma stages is highly sensitive to features such as color, cell size, and shape, granularity, and uniformity. This work summarizes the need for understanding the widely trusted models built using deep learning and adds a layer of biological context to a technique that functioned as a classification only approach till now.

A blood biomarker for monitoring response to anti-EGFR therapy

BACKGROUND AND OBJECTIVE

To monitor therapies targeted to epidermal growth factor receptors (EGFR) in non-small cell lung cancer (NSCLC), we investigated Peroxiredoxin 6 (PRDX6) as a biomarker of response to anti-EGFR agents.

METHODS

We studied cells that are sensitive (H3255, HCC827) or resistant (H1975, H460) to gefitinib. PRDX6 was examined with either gefitinib or vehicle treatment using enzyme-linked immunosorbent assays. We created xenograft models from one sensitive (HCC827) and one resistant cell line (H1975) and monitored serum PRDX6 levels during treatment.

RESULTS

PRDX6 levels in cell media from sensitive cell lines increased significantly after gefitinib treatment vs. vehicle, whereas there was no significant difference for resistant lines. PRDX6 accumulation over time correlated positively with gefitinib sensitivity. Serum PRDX6 levels in gefitinib-sensitive xenograft models increased markedly during the first 24 hours of treatment and then decreased dramatically during the following 48 hours. Differences in serum PRDX6 levels between vehicle and gefitinib-treated animals could not be explained by differences in tumor burden.

CONCLUSIONS

Our results show that changes in serum PRDX6 during the course of gefitinib treatment of xenograft models provide insight into tumor response and such an approach offers several advantages over imaging-based strategies for monitoring response to anti-EGFR agents.

Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma

INTRODUCTION

Despite apparently complete surgical resection, approximately half of resected early-stage lung cancer patients relapse and die of their disease. Adjuvant chemotherapy reduces this risk by only 5% to 8%. Thus, there is a need for better identifying who benefits from adjuvant therapy, the drivers of relapse, and novel targets in this setting.

METHODS

RNA sequencing and liquid chromatography/liquid chromatography-mass spectrometry proteomics data were generated from 51 surgically resected non-small cell lung tumors with known recurrence status.

RESULTS

We present a rationale and framework for the incorporation of high-content RNA and protein measurements into integrative biomarkers and show the potential of this approach for predicting risk of recurrence in a group of lung adenocarcinomas. In addition, we characterize the relationship between mRNA and protein measurements in lung adenocarcinoma and show that it is outcome specific.

CONCLUSIONS

Our results suggest that mRNA and protein data possess independent biological and clinical importance, which can be leveraged to create higher-powered expression biomarkers.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Multi-lectin Affinity Chromatography and Quantitative Proteomic Analysis Reveal Differential Glycoform Levels between Prostate Cancer and Benign Prostatic Hyperplasia Sera

Currently prostate-specific antigen is used for prostate cancer (PCa) screening, however it lacks the necessary specificity for differentiating PCa from other diseases of the prostate such as benign prostatic hyperplasia (BPH), presenting a clinical need to distinguish these cases at the molecular level. Protein glycosylation plays an important role in a number of cellular processes involved in neoplastic progression and is aberrant in PCa. In this study, we systematically interrogate the alterations in the circulating levels of hundreds of serum proteins and their glycoforms in PCa and BPH samples using multi-lectin affinity chromatography and quantitative mass spectrometry-based proteomics. Specific lectins (AAL, PHA-L and PHA-E) were used to target and chromatographically separate core-fucosylated and highly-branched protein glycoforms for analysis, as differential expression of these glycan types have been previously associated with PCa. Global levels of CD5L, CFP, C8A, BST1, and C7 were significantly increased in the PCa samples. Notable glycoform-specific alterations between BPH and PCa were identified among proteins CD163, C4A, and ATRN in the PHA-L/E fraction and among C4BPB and AZGP1 glycoforms in the AAL fraction. Despite these modest differences, substantial similarities in glycoproteomic profiles were observed between PCa and BPH sera.

The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis

In this review, we discuss the interaction between cancer and markers of inflammation (such as levels of inflammatory cells and proteins) in the circulation, and the potential benefits of routinely monitoring these markers in peripheral blood measurement assays. Next, we discuss the prognostic value and limitations of using inflammatory markers such as neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios and C-reactive protein measurements. Furthermore, the review discusses the benefits of combining multiple types of measurements and longitudinal tracking to improve staging and prognosis prediction of patients with cancer, and the ability of novel in silico frameworks to leverage this high-dimensional data.

Data Conversion with ProteoWizard msConvert

Recent advances in proteome informatics have led to an explosion in tools to analyze mass spectrometry data. These tools operate across the analysis pipeline doing everything from assessing quality control to matching peptides to spectra to quantification. Unfortunately, the vast majority of these tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Consequently, the first step in many protocols is the conversion of data from vendor-specific binary files to open-format files. This protocol details the use of ProteoWizard's msConvert and msConvertGUI software for this conversion, taking format features, coding options, and vendor particularities into account. We specifically describe the various options available when doing conversions and the implications of each option.

Longitudinal Multiplexed Measurement of Quantitative Proteomic Signatures in Mouse Lymphoma Models Using Magneto-Nanosensors

Cancer proteomics is the manifestation of relevant biological processes in cancer development. Thus, it reflects the activities of tumor cells, host-tumor interactions, and systemic responses to cancer therapy. To understand the causal effects of tumorigenesis or therapeutic intervention, longitudinal studies are greatly needed. However, most of the conventional mouse experiments are unlikely to accommodate frequent collection of serum samples with a large enough volume for multiple protein assays towards single-object analysis. Here, we present a technique based on magneto-nanosensors to longitudinally monitor the protein profiles in individual mice of lymphoma models using a small volume of a sample for multiplex assays.

Methods: Drug-sensitive and -resistant cancer cell lines were used to develop the mouse models that render different outcomes upon the drug treatment. Two groups of mice were inoculated with each cell line, and treated with either cyclophosphamide or vehicle solution. Serum samples taken longitudinally from each mouse in the groups were measured with 6-plex magneto-nanosensor cytokine assays. To find the origin of IL-6, experiments were performed using IL-6 knock-out mice.

Results: The differences in serum IL-6 and GCSF levels between the drug-treated and untreated groups were revealed by the magneto-nanosensor measurement on individual mice. Using the multiplex assays and mouse models, we found that IL-6 is secreted by the host in the presence of tumor cells upon the drug treatment.

Conclusion: The multiplex magneto-nanosensor assays enable longitudinal proteomic studies on mouse tumor models to understand tumor development and therapy mechanisms more precisely within a single biological object.

Building trans-omics evidence: using imaging and 'omics' to characterize cancer profiles

Utilization of single modality data to build predictive models in cancer results in a rather narrow view of most patient profiles. Some clinical facet s relate strongly to histology image features, e.g. tumor stages, whereas others are associated with genomic and proteomic variations (e.g. cancer subtypes and disease aggression biomarkers). We hypothesize that there are coherent "trans-omics" features that characterize varied clinical cohorts across multiple sources of data leading to more descriptive and robust disease characterization. In this work, for l 05 breast cancer patients from the TCGA (The Cancer Genome Atlas), we consider four clinical attributes (AJCC Stage, Tumor Stage, ER-Status and PAM50 mRNA Subtypes), and build predictive models using three different modalities of data (histopathological images, transcriptomics and proteomics). Following which, we identify critical multi-level features that drive successful classification of patients for the various different cohorts. To build predictors for each data type, we employ widely used "best practice" techniques including CNN-based (convolutional neural network) classifiers for histopathological images and regression models for proteogenomic data. While, as expected, histology images outperformed molecular features while predicting cancer stages, and transcriptomics held superior discriminatory power for ER-Status and PAM50 subtypes, there exist a few cases where all data modalities exhibited comparable performance. Further, we also identified sets of key genes and proteins whose expression and abundance correlate across each clinical cohort including (i) tumor severity and progression (incl. GABARAP), (ii) ER-status (incl.ESRl) and (iii) disease subtypes (incl. FOXCl). Thus, we quantitatively assess the efficacy of different data types to predict critical breast cancer patient attributes and improve disease characterization.

Global Transcriptome Analysis of RNA Abundance Regulation by ADAR in Lung Adenocarcinoma

Despite tremendous advances in targeted therapies against lung adenocarcinoma, the majority of patients do not benefit from personalized treatments. A deeper understanding of potential therapeutic targets is crucial to increase the survival of patients. One promising target, ADAR, is amplified in 13% of lung adenocarcinomas and in-vitro studies have demonstrated the potential of its therapeutic inhibition to inhibit tumor growth. ADAR edits millions of adenosines to inosines within the transcriptome, and while previous studies of ADAR in cancer have solely focused on protein-coding edits, > 99% of edits occur in non-protein coding regions. Here, we develop a pipeline to discover the regulatory potential of RNA editing sites across the entire transcriptome and apply it to lung adenocarcinoma tumors from The Cancer Genome Atlas. This method predicts that 1413 genes contain regulatory edits, predominantly in non-coding regions. Genes with the largest numbers of regulatory edits are enriched in both apoptotic and innate immune pathways, providing a link between these known functions of ADAR and its role in cancer. We further show that despite a positive association between ADAR RNA expression and apoptotic and immune pathways, ADAR copy number is negatively associated with apoptosis and several immune cell types' signatures.

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ADAR potentially regulates the mRNA abundance of thousands of genes.

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Editing of the APOL1 3′ UTR is associated with its upregulation and patient poor overall survival.

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ADAR-regulated genes are enriched for apoptosis and immune pathways.

Lung cancer is the most deadly cancer globally and current targeted treatments only benefit a minority of patients. Inhibiting the ADAR oncogene has shown promising preclinical results; however, little is known about ADAR's functions in cancer. We investigate a key function of ADAR, mRNA regulation via RNA editing, and provide evidence that it is linked to tumor immunity and cell death in human lung adenocarcinoma. Our results provide a motivation to explore combination immunotherapies that include ADAR inhibition.

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.

Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Background

Quantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike ‘omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms.

Methods

We obtained ten blood plasma “case” samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes.

Results

Although we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly (p < 1E-01) altered expression levels between cases and controls, with no proteins retaining statistical significance after false discovery correction.

Conclusions

Technical, practical, and study design considerations are essential to maximize information obtained in limited pre-diagnostic samples of cancer patients. This study provides a framework that estimates biological effect sizes critical for consideration in designing studies for pre-diagnostic blood-based biomarker detection.

Electronic supplementary material

The online version of this article (10.1186/s40364-017-0110-y) contains supplementary material, which is available to authorized users.

JUN-Mediated Downregulation of EGFR Signaling Is Associated with Resistance to Gefitinib in EGFR-mutant NSCLC Cell Lines

Mutations or deletions in exons 18-21 in the EGFR) are present in approximately 15% of tumors in patients with non-small cell lung cancer (NSCLC). They lead to activation of the EGFR kinase domain and sensitivity to molecularly targeted therapeutics aimed at this domain (gefitinib or erlotinib). These drugs have demonstrated objective clinical response in many of these patients; however, invariably, all patients acquire resistance. To examine the molecular origins of resistance, we derived a set of gefitinib-resistant cells by exposing lung adenocarcinoma cell line, HCC827, with an activating mutation in the EGFR tyrosine kinase domain, to increasing gefitinib concentrations. Gefitinib-resistant cells acquired an increased expression and activation of JUN, a known oncogene involved in cancer progression. Ectopic overexpression of JUN in HCC827 cells increased gefitinib IC₅₀ from 49 nmol/L to 8 μmol/L (P < 0.001). Downregulation of JUN expression through shRNA resensitized HCC827 cells to gefitinib (IC₅₀ from 49 nmol/L to 2 nmol/L; P < 0.01). Inhibitors targeting JUN were 3-fold more effective in the gefitinib-resistant cells than in the parental cell line (P < 0.01). Analysis of gene expression in patient tumors with EGFR-activating mutations and poor response to erlotinib revealed a similar pattern as the top 260 differentially expressed genes in the gefitinib-resistant cells (Spearman correlation coefficient of 0.78, P < 0.01). These findings suggest that increased JUN expression and activity may contribute to gefitinib resistance in NSCLC and that JUN pathway therapeutics merit investigation as an alternate treatment strategy. Mol Cancer Ther; 16(8); 1645-57. ©2017 AACR.

Platelet count as a predictor of metastasis and venous thromboembolism in patients with cancer

Platelets are anucleate cells in the blood at concentrations of 150,000 to 400,000 cells/µL and play a key role in hemostasis. Several studies have suggested that platelets contribute to cancer progression and cancer-associated thrombosis. In this review, we provide an overview of the biochemical and biophysical mechanisms by which platelets interact with cancer cells and review the evidence supporting a role for platelet-enhanced metastasis of cancer, and venous thromboembolism (VTE) in patients with cancer. We discuss the potential for and limitations of platelet counts to discriminate cancer disease burden and prognosis. Lastly, we consider more advanced diagnostic approaches to improve studies on the interaction between the hemostatic system and cancer cells.

A Robust Protocol for Protein Extraction and Digestion

Proteins play a key role in all aspects of cellular homeostasis. Proteomics, the large-scale study of proteins, provides in-depth data on protein properties, including abundances and post-translational modification states, and as such provides a rich avenue for the investigation of biological and disease processes. While proteomic tools such as mass spectrometry have enabled exquisitely sensitive sample analysis, sample preparation remains a critical unstandardized variable that can have a significant impact on downstream data readouts. Consistency in sample preparation and handling is therefore paramount in the collection and analysis of proteomic data.Here we describe methods for performing protein extraction from cell culture or tissues, digesting the isolated protein into peptides via in-solution enzymatic digest, and peptide cleanup with final preparations for analysis via liquid chromatography-mass spectrometry. These protocols have been optimized and standardized for maximum consistency and maintenance of sample integrity.

Dual transcript and protein quantification in a massive single cell array

Recently, single-cell molecular analysis has been leveraged to achieve unprecedented levels of biological investigation. However, a lack of simple, high-throughput single-cell methods has hindered in-depth population-wide studies with single-cell resolution. We report a microwell-based cytometric method for simultaneous measurements of gene and protein expression dynamics in thousands of single cells. We quantified the regulatory effects of transcriptional and translational inhibitors on cMET mRNA and cMET protein in cell populations. We studied the dynamic responses of individual cells to drug treatments, by measuring cMET overexpression levels in individual non-small cell lung cancer (NSCLC) cells with induced drug resistance. Across NSCLC cell lines with a given protein expression, distinct patterns of transcript-protein correlation emerged. We believe this platform is applicable for interrogating the dynamics of gene expression, protein expression, and translational kinetics at the single-cell level - a paradigm shift in life science and medicine toward discovering vital cell regulatory mechanisms.

Single cell dynamic phenotyping

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

Abstract A23: Characterization of diverging molecular and phenotypic responses under changing extracellular matrix stiffness in MCF10A cells

Tissue stiffness has emerged as an important regulator in the maintenance of normal cellular processes. In general, solid tumors are known to be much stiffer than normal tissue, with increased extracellular matrix (ECM) density being indicative of a higher risk for many cancers, including those of the breast. Over the course of breast cancer progression, the ECM becomes increasingly stiffer, a phenomenon which is known to be a driver of malignancy. In a 3D culture system using self-assembling peptide (SAP) gels, we grew MCF10A acini under a dynamic range of stiffness environments (140-5800 Pa). Under normal breast tissue elasticity, mammary epithelial cells form rounded, polarized acini with cleared lumens. However, in tumor-like stiffnesses, the same cells form non-polarized, collapsed structures that invade into the gel. To investigate the specific genes and pathways affected by stiffness, we have conducted tandem mass spectrometry to identify protein expression changes in MCF10A cells grown on SAP gels of increasing stiffness. Interestingly, the resulting analysis showed that cells grown on higher stiffnesses exhibit greater variation in protein expression. These divergent force-regulated molecular pathways are involved in a variety of cellular functions, including cell adhesion and migration, metabolism, and the immune response, among others. This study underscores the significance of mechanical forces exerted by the ECM, and signifies the first large-scale proteomic screen carried out to analyze heterogeneous cellular responses to changing stiffness environments.

Citation Format: Sonya Liu, Russell Bainer, Yoshihiro Yui, Colin Flinders, Parag Mallick, Valerie Weaver, Shannon Mumenthaler. Characterization of diverging molecular and phenotypic responses under changing extracellular matrix stiffness in MCF10A cells. [abstract]. In: Proceedings of the AACR Special Conference: Function of Tumor Microenvironment in Cancer Progression; 2016 Jan 7–10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2016;76(15 Suppl):Abstract nr A23.

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.

Epigenetic changes mediated by polycomb repressive complex 2 and E2a are associated with drug resistance in a mouse model of lymphoma

Background

The genetic origins of chemotherapy resistance are well established; however, the role of epigenetics in drug resistance is less well understood. To investigate mechanisms of drug resistance, we performed systematic genetic, epigenetic, and transcriptomic analyses of an alkylating agent-sensitive murine lymphoma cell line and a series of resistant lines derived by drug dose escalation.

Methods

Dose escalation of the alkylating agent mafosfamide was used to create a series of increasingly drug-resistant mouse Burkitt’s lymphoma cell lines. Whole genome sequencing, DNA microarrays, reduced representation bisulfite sequencing, and chromatin immunoprecipitation sequencing were used to identify alterations in DNA sequence, mRNA expression, CpG methylation, and H3K27me3 occupancy, respectively, that were associated with increased resistance.

Results

Our data suggest that acquired resistance cannot be explained by genetic alterations. Based on integration of transcriptional profiles with transcription factor binding data, we hypothesize that resistance is driven by epigenetic plasticity. We observed that the resistant cells had H3K27me3 and DNA methylation profiles distinct from those of the parental lines. Moreover, we observed DNA methylation changes in the promoters of genes regulated by E2a and members of the polycomb repressor complex 2 (PRC2) and differentially expressed genes were enriched for targets of E2a. The integrative analysis considering H3K27me3 further supported a role for PRC2 in mediating resistance. By integrating our results with data from the Immunological Genome Project (Immgen.org), we showed that these transcriptional changes track the B-cell maturation axis.

Conclusions

Our data suggest a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B-cell epigenome; these alterations attenuate alkylating agent treatment-induced apoptosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0305-0) contains supplementary material, which is available to authorized users.

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.

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.

Abstract PR09: Extracellular stiffness cues drive spatial reorganization of the genome to globally constrain RNA abundance

The stiffness of the extracellular matrix (ECM) drives mechanosignaling that regulates tissue development and malignancy. We previously showed that a stiff ECM disrupts tissue organization and enhances malignant progression by inducing cell invasion and migration. However, the specific transcriptional and molecular events in which mechanotransduction directs these phenotypes are not well understood. To clarify this process, we used a combination of genome-scale approaches to monitor changes in gene expression and protein abundance as a function of acinar morphogenesis and tissue homeostasis in three dimensional extracellular matrix hydrogels with tunable stiffness. Elevated ECM stiffness perturbed tissue homeostasis and reverted the transcriptional phenotype of differentiated mammary acini to resemble that observed in rapidly proliferating nonpolarized mammary cell aggregates. These findings suggest that tissue tension induces cellular changes that directly reflect higher-order tissue organizational states. We found that these changes involve the spatial rearrangement of peripheral chromatin, and that the expression levels of multiple histone deacetylases increase in organized tissues concurrently with elevated nuclear heterochromatin content, an effect that is abrogated in rigid ECM conditions. We support these observations by mapping mechanoresponsive peripheral heterochromatin elements via ChIPseq, enabling us to directly identify dynamic regions containing genes whose transcriptional activity is responsive to mechanical cues. Finally, using a combination of genomic, imaging, and molecular biology techniques we demonstrated that ECM compliance and tissue organization significantly influences global RNA abundance. Notably, this model presents formidable conceptual and practical challenges for the interpretation of genomic data. Collectively, this work indicates that tissue organization is critically dependent on the cellular mechanical environment, which qualitatively and quantitatively shapes the epigenetic and transcriptional landscape by mechanisms that have not yet been elucidated.

Note: This abstract was not presented at the conference.

Citation Format: Russell Bainer, Yoshihiro Yui, Shannon Mumenthaler, Parag Mallick, Lin Liu, Hua-Jun Wu, Ondrej Podlaha, Franziska Michor, Jan Liphardt, Jonathan Licht, Valerie Weaver. Extracellular stiffness cues drive spatial reorganization of the genome to globally constrain RNA abundance. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR09.

Abstract LB-036: Neuronal activity-regulated secretion of neuroligin-3 promotes glioma growth

Active neurons exert a mitogenic effect on normal neural precursor and oligodendroglial precursor cells, the putative cellular origins of high-grade glioma (HGG). We demonstrate that active neurons similarly promote HGG proliferation and growth in vivo using optogenetic control of cortical neuronal activity in a patient-derived pediatric glioblastoma orthotopic xenograft model. Activity-regulated mitogen(s) are secreted, as the conditioned medium from optogenetically stimulated cortical slices promoted proliferation of pediatric and adult patient-derived HGG cultures. The synaptic protein neuroligin-3 (NLGN3) was identified as the leading candidate mitogen; soluble NLGN3 was sufficient and necessary to promote robust HGG cell proliferation. NLGN3 induced PI3K-mTOR pathway activity and feed-forward expression of NLGN3 in glioma cells, providing mechanistic insight into its surprising role as a mitogen. 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.

Citation Format: Humsa Venkatesh, Tessa Johung, Viola Caretti, Parag Mallick, Michelle Monje. Neuronal activity-regulated secretion of neuroligin-3 promotes glioma growth. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-036. doi:10.1158/1538-7445.AM2015-LB-036

The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.