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.

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).

Challenges in proteogenomics: a comparison of analysis methods with the case study of the DREAM proteogenomics sub-challenge.

BACKGROUND: Proteomic measurements, which closely reflect phenotypes, provide insights into gene expression regulations and mechanisms underlying altered phenotypes. Further, integration of data on proteome and transcriptome levels can validate gene signatures associated with a phenotype. However, proteomic data is not as abundant as genomic data, and it is thus beneficial to use genomic features to predict protein abundances when matching proteomic samples or measurements within samples are lacking. RESULTS: We evaluate and compare four data-driven models for prediction of proteomic data from mRNA measured in breast and ovarian cancers using the 2017 DREAM Proteogenomics Challenge data. Our results show that Bayesian network, random forests, LASSO, and fuzzy logic approaches can predict protein abundance levels with median ground truth-predicted correlation values between 0.2 and 0.5. However, the most accurately predicted proteins differ considerably between approaches. CONCLUSIONS: In addition to benchmarking aforementioned machine learning approaches for predicting protein levels from transcript levels, we discuss challenges and potential solutions in state-of-the-art proteogenomic analyses.

Integrative cancer patient stratification via subspace merging.

MOTIVATION: Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease. RESULTS: Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cancer dataset and show that by integrating gene expression, microRNA and DNA methylation data, our proposed method can produce clinically useful subtypes of breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA breast cancer patients and validate these results in three additional breast cancer datasets. We also compare our approach with previous integrative clustering approaches and obtain comparable or superior results. AVAILABILITY AND IMPLEMENTATION: https://github.com/michaelsharpnack/GrassmannCluster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

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.

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.

E2f8 mediates tumor suppression in postnatal liver development.

E2F-mediated transcriptional repression of cell cycle-dependent gene expression is critical for the control of cellular proliferation, survival, and development. E2F signaling also interacts with transcriptional programs that are downstream of genetic predictors for cancer development, including hepatocellular carcinoma (HCC). Here, we evaluated the function of the atypical repressor genes E2f7 and E2f8 in adult liver physiology. Using several loss-of-function alleles in mice, we determined that combined deletion of E2f7 and E2f8 in hepatocytes leads to HCC. Temporal-specific ablation strategies revealed that E2f8's tumor suppressor role is critical during the first 2 weeks of life, which correspond to a highly proliferative stage of postnatal liver development. Disruption of E2F8's DNA binding activity phenocopied the effects of an E2f8 null allele and led to HCC. Finally, a profile of chromatin occupancy and gene expression in young and tumor-bearing mice identified a set of shared targets for E2F7 and E2F8 whose increased expression during early postnatal liver development is associated with HCC progression in mice. Increased expression of E2F8-specific target genes was also observed in human liver biopsies from HCC patients compared to healthy patients. In summary, these studies suggest that E2F8-mediated transcriptional repression is a critical tumor suppressor mechanism during postnatal liver development.

Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes.

Co-expression analysis has been employed to predict gene function, identify functional modules, and determine tumor subtypes. Previous co-expression analysis was mainly conducted at bulk tissue level. It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation. Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors. We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. Moreover, gene members of some protein complexes are co-expressed only at the bulk level, while those of other complexes are co-expressed at both single-cell and bulk levels. Finally, we identified a set of co-expressed genes that can predict the survival of glioblastoma patients. Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential.

Redeployment of Myc and E2f1-3 drives Rb-deficient cell cycles.

Robust mechanisms to control cell proliferation have evolved to maintain the integrity of organ architecture. Here, we investigated how two critical proliferative pathways, Myc and E2f, are integrated to control cell cycles in normal and Rb-deficient cells using a murine intestinal model. We show that Myc and E2f1-3 have little impact on normal G1-S transitions. Instead, they synergistically control an S-G2 transcriptional program required for normal cell divisions and maintaining crypt-villus integrity. Surprisingly, Rb deficiency results in the Myc-dependent accumulation of E2f3 protein and chromatin repositioning of both Myc and E2f3, leading to the 'super activation' of a G1-S transcriptional program, ectopic S phase entry and rampant cell proliferation. These findings reveal that Rb-deficient cells hijack and redeploy Myc and E2f3 from an S-G2 program essential for normal cell cycles to a G1-S program that re-engages ectopic cell cycles, exposing an unanticipated addiction of Rb-null cells on Myc.

iGPSe: a visual analytic system for integrative genomic based cancer patient stratification.

BACKGROUND: Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. RESULTS: In this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction. CONCLUSIONS: Visualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.

Breast cancer patient stratification using a molecular regularized consensus clustering method.

Breast cancers are highly heterogeneous with different subtypes that lead to different clinical outcomes including prognosis, response to treatment and chances of recurrence and metastasis. An important task in personalized medicine is to determine the subtype for a breast cancer patient in order to provide the most effective treatment. In order to achieve this goal, integrative genomics approach has been developed recently with multiple modalities of large datasets ranging from genotypes to multiple levels of phenotypes. A major challenge in integrative genomics is how to effectively integrate multiple modalities of data to stratify the breast cancer patients. Consensus clustering algorithms have often been adopted for this purpose. However, existing consensus clustering algorithms are not suitable for the situation of integrating clustering results obtained from a mixture of numerical data and categorical data. In this work, we present a mathematical formulation for integrative clustering of multiple-source data including both numerical and categorical data to resolve the above issue. Specifically, we formulate the problem as a novel consensus clustering method called Molecular Regularized Consensus Patient Stratification (MRCPS) based on an optimization process with regularization. Unlike the traditional consensus clustering methods, MRCPS can automatically and spontaneously cluster both numerical and categorical data with any option of similarity metrics. We apply this new method by applying it on the TCGA breast cancer datasets and evaluate using both statistical criteria and clinical relevance on predicting prognosis. The result demonstrates the superiority of this method in terms of effectiveness of aggregation and differentiating patient outcomes. Our method, while motivated by the breast cancer research, is nevertheless universal for integrative genomics studies.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.

BACKGROUND: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences. METHODS: We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets. RESULTS: Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes. CONCLUSIONS: This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

BACKGROUND AND OBJECTIVE: Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we propose a workflow for analyzing histopathological images and integrate them with genomic data for discovering biomarkers for TNBC. MATERIALS AND METHODS: We developed an image analysis workflow for extracting a large collection of morphological features and deployed the same on histological images from The Cancer Genome Atlas (TCGA) TNBC samples during the discovery phase (n=44). Strong correlations between salient morphological features and gene expression profiles from the same patients were identified. We then evaluated the same morphological features in predicting survival using a local TNBC cohort (n=143). We further tested the predictive power on patient prognosis of correlated gene clusters using two other public gene expression datasets. RESULTS AND CONCLUSION: Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.

Identify condition-specific gene co-expression networks.

Since co-expressed genes often are co-regulated by a group of transcription factors, different conditions (e.g. disease versus normal) may lead to different transcription factor activities and therefore different co-expression networks. We propose a method for identifying condition-specific co-expression networks by combining our recently developed network quasi-clique mining algorithm and the expected conditional F-statistic. We apply this method to compare the transcriptional programmes between the non-basal and basal types of breast cancers. The results provide a new perspective for studying gene interaction dynamics in cancers and assessing the effects of perturbation on key genes such as transcription factors. Our work is a way for dynamically characterising the gene interaction networks.

Canonical and atypical E2Fs regulate the mammalian endocycle.

The endocycle is a variant cell cycle consisting of successive DNA synthesis and gap phases that yield highly polyploid cells. Although essential for metazoan development, relatively little is known about its control or physiologic role in mammals. Using lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical transcription activation (E2F1, E2F2 and E2F3) and the other by atypical repression (E2F7 and E2F8), that converge on the regulation of endocycles in vivo. Ablation of canonical activators in the two endocycling tissues of mammals, trophoblast giant cells in the placenta and hepatocytes in the liver, augmented genome ploidy, whereas ablation of atypical repressors diminished ploidy. These two antagonistic arms coordinate the expression of a unique G2/M transcriptional program that is critical for mitosis, karyokinesis and cytokinesis. These results provide in vivo evidence for a direct role of E2F family members in regulating non-traditional cell cycles in mammals.

A signal processing approach for enriched region detection in RNA polymerase II ChIP-seq data.

BACKGROUND: RNA polymerase II (PolII) is essential in gene transcription and ChIP-seq experiments have been used to study PolII binding patterns over the entire genome. However, since PolII enriched regions in the genome can be very long, existing peak finding algorithms for ChIP-seq data are not adequate for identifying such long regions. METHODS: Here we propose an enriched region detection method for ChIP-seq data to identify long enriched regions by combining a signal denoising algorithm with a false discovery rate (FDR) approach. The binned ChIP-seq data for PolII are first processed using a non-local means (NL-means) algorithm for purposes of denoising. Then, a FDR approach is developed to determine the threshold for marking enriched regions in the binned histogram. RESULTS: We first test our method using a public PolII ChIP-seq dataset and compare our results with published results obtained using the published algorithm HPeak. Our results show a high consistency with the published results (80-100%). Then, we apply our proposed method on PolII ChIP-seq data generated in our own study on the effects of hormone on the breast cancer cell line MCF7. The results demonstrate that our method can effectively identify long enriched regions in ChIP-seq datasets. Specifically, pertaining to MCF7 control samples we identified 5,911 segments with length of at least 4 Kbp (maximum 233,000 bp); and in MCF7 treated with E2 samples, we identified 6,200 such segments (maximum 325,000 bp). CONCLUSIONS: We demonstrated the effectiveness of this method in studying binding patterns of PolII in cancer cells which enables further deep analysis in transcription regulation and epigenetics. Our method complements existing peak detection algorithms for ChIP-seq experiments.

Non-parametric population analysis of cellular phenotypes.

Methods to quantify cellular-level phenotypic differences between genetic groups are a key tool in genomics research. In disease processes such as cancer, phenotypic changes at the cellular level frequently manifest in the modification of cell population profiles. These changes are hard to detect due the ambiguity in identifying distinct cell phenotypes within a population. We present a methodology which enables the detection of such changes by generating a phenotypic signature of cell populations in a data-derived feature-space. Further, this signature is used to estimate a model for the redistribution of phenotypes that was induced by the genetic change. Results are presented on an experiment involving deletion of a tumor-suppressor gene dominant in breast cancer, where the methodology is used to detect changes in nuclear morphology between control and knockout groups.

Identifying nuclear phenotypes using semi-supervised metric learning.

In systems-based approaches for studying processes such as cancer and development, identifying and characterizing individual cells within a tissue is the first step towards understanding the large-scale effects that emerge from the interactions between cells. To this end, nuclear morphology is an important phenotype to characterize the physiological and differentiated state of a cell. This study focuses on using nuclear morphology to identify cellular phenotypes in thick tissue sections imaged using 3D fluorescence microscopy. The limited label information, heterogeneous feature set describing a nucleus, and existence of subpopulations within cell-types makes this a difficult learning problem. To address these issues, a technique is presented to learn a distance metric from labeled data which is locally adaptive to account for heterogeneity in the data. Additionally, a label propagation technique is used to improve the quality of the learned metric by expanding the training set using unlabeled data. Results are presented on images of tumor stroma in breast cancer, where the framework is used to identify fibroblasts, macrophages and endothelial cells--three major stromal cells involved in carcinogenesis.