Biomarkers of Tumor Heterogeneity in Glioblastoma Multiforme Cohort of TCGA.

Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because "age" is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab's Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan-Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers.

Influence of Simulated Microgravity on Mammary Epithelial Cells Grown as 2D and 3D Cultures.

During space travel, astronauts will experience a unique environment that includes continuous exposure to microgravity and stressful living conditions. Physiological adaptation to this is a challenge and the effect of microgravity on organ development, architecture, and function is not well understood. How microgravity may impact the growth and development of an organ is an important issue, especially as space flight becomes more commonplace. In this work, we sought to address fundamental questions regarding microgravity using mouse mammary epithelial cells in 2D and 3D tissue cultures exposed to simulated microgravity. Mouse mammary HC11 cells contain a higher proportion of stem cells and were also used to investigate how simulated microgravity may impact mammary stem cell populations. In these studies, we exposed mouse mammary epithelial cells to simulated microgravity in 2D and then assayed for changes in cellular characteristics and damage levels. The microgravity treated cells were also cultured in 3D to form acini structures to define if simulated microgravity affects the cells' ability to organize correctly, a quality that is of key importance for mammary organ development. These studies identify changes occurring during exposure to microgravity that impact cellular characteristics such as cell size, cell cycle profiles, and levels of DNA damage. In addition, changes in the percentage of cells revealing various stem cell profiles were observed following simulated microgravity exposure. In summary, this work suggests microgravity may cause aberrant changes in mammary epithelial cells that lead to an increase in cancer risk.

CD36+ Fibroblasts Secrete Protein Ligands That Growth-Suppress Triple-Negative Breast Cancer Cells While Elevating Adipogenic Markers for a Model of Cancer-Associated Fibroblast.

Tumor and stroma coevolve to facilitate tumor growth. Hence, effective tumor therapeutics would not only induce growth suppression of tumor cells but also revert pro-tumor stroma into anti-tumoral type. Previously, we showed that coculturing triple-negative or luminal A breast cancer cells with CD36+ fibroblasts (FBs) in a three-dimensional extracellular matrix induced their growth suppression or phenotypic reversion, respectively. Then, we identified SLIT3, FBLN-1, and PENK as active protein ligands secreted from CD36+ FBs that induced growth suppression of MDA-MB-231 breast cancer cells and determined their minimum effective concentrations. Here, we have expanded our analyses to include additional triple-negative cancer cell lines, BT549 and Hs578T, as well as HCC1937 carrying a BRCA1 mutation. We show that the ectopic addition of each of the three ligands to cancer-associated fibroblasts (CAFs) elevates the expression of CD36, as well as the adipogenic marker FABP4. Lastly, we show that an agonist antibody for one of the PENK receptors induces growth suppression of all cancer cell lines tested but not for non-transformed MCF10A cells. These results clearly suggest that proteins secreted from CD36+ FBs induce not only growth suppression of tumor cells through binding the cognate receptors but also increasing adipogenic markers of CAFs to reprogram tumor stroma.

Protein Ligands in the Secretome of CD36+ Fibroblasts Induce Growth Suppression in a Subset of Breast Cancer Cell Lines.

Reprogramming the tumor stroma is an emerging approach to circumventing the challenges of conventional cancer therapies. This strategy, however, is hampered by the lack of a specific molecular target. We previously reported that stromal fibroblasts (FBs) with high expression of CD36 could be utilized for this purpose. These studies are now expanded to identify the secreted factors responsible for tumor suppression. Methodologies included 3D colonies, fluorescent microscopy coupled with quantitative techniques, proteomics profiling, and bioinformatics analysis. The results indicated that the conditioned medium (CM) of the CD36+ FBs caused growth suppression via apoptosis in the triple-negative cell lines of MDA-MB-231, BT549, and Hs578T, but not in the ERBB2+ SKBR3. Following the proteomics and bioinformatic analysis of the CM of CD36+ versus CD36- FBs, we determined KLF10 as one of the transcription factors responsible for growth suppression. We also identified FBLN1, SLIT3, and PENK as active ligands, where their minimum effective concentrations were determined. Finally, in MDA-MB-231, we showed that a mixture of FBLN1, SLIT3, and PENK could induce an amount of growth suppression similar to the CM of CD36+ FBs. In conclusion, our findings suggest that these ligands, secreted by CD36+ FBs, can be targeted for breast cancer treatment.

An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

MOTIVATION: Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to stress or therapeutic targets. In fact, differentiating malignant and normal cells is often difficult in monolayer cultures. But in 3D culture, colony organization can provide a clear set of indices for differentiating malignant and normal cells. The limiting factors are delineating each cell in a 3D colony in the presence of perceptual boundaries between adjacent cells and heterogeneity associated with cells being at different cell cycles. RESULTS: In a previous paper, we defined a potential field for delineating adjacent nuclei, with perceptual boundaries, in 2D histology images by coupling three deep networks. This concept is now extended to 3D and simplified by an enhanced cost function that replaces three deep networks with one. Validation includes four cell lines with diverse mutations, and a comparative analysis with the UNet models of microscopy indicates an improved performance with the F1-score of 0.83. AVAILABILITY AND IMPLEMENTATION: All software and annotated images are available through GitHub and Bioinformatics online. The software includes the proposed method, UNet for microscopy that was extended to 3D and report generation for profiling colony organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rapid identification of a subset of foodborne bacteria in live-cell assays.

The detection and identification of microbial pathogens in meat and fresh produce play an essential role in food safety for reducing foodborne illnesses every year. A new approach based on targeting a specific sequence of the 16S rRNA region for each bacterium is proposed and validated. The probe complex consists of a C60, a conjugated RNA detector which targets a specific 16S rRNA sequence, and a complementary fluorescent reporter. The RNA detectors were designed by integrating NIH nucleotide and Vienna RNA Webservice databases, and their specificities were validated by the RDP database. Probe complexes were synthesized for identifying E. coli K12, E. coli O157: H7, S. enterica, Y. enterocolitica, C. perfringens, and L. monocytogenes. First, under controlled conditions of known bacterial mixtures, the efficiency and crosstalk for identifying the foodborne bacteria were quantified to be above 94% and below 5%, respectively. Second, experiments were designed by inoculating meat products by known numbers of bacteria and measuring the limit of detection. In one experiment, 225 g of autoclaved ground chicken was inoculated with 9 E. coli O157:H7, where 6.8 ± 1.2 bacteria with 95% confidence interval were recovered. Third, by positionally printing probe complexes in microwells, specific microorganisms were identified with only one fluorophore. The proposed protocol is a cell-based system, can identify live bacteria in 15 min, requires no amplification, and has the potential to open new surveillance opportunities.Key points• The identification of foodborne bacteria is enabled in live-cell assays.• The limit of detection for 100 g of fresh chicken breast inoculated with 4 bacteria is 2.7 ± 1.4 with 95% confidence interval.• The identification of five bacteria in a coded microwell chip is enabled with only one fluorophore.

Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines.

Human breast tumors are not fully autonomous. They are dependent on nutrients and growth-promoting signals provided by the supporting stromal cells. Within the tumor microenvironment, one of the secreted macromolecules by tumor cells is activin A, where we show to downregulate CD36 in fibroblasts. Downregulation of CD36 in fibroblasts also increases the secretion of activin A by fibroblasts. We hypothesize that overexpression of CD36 in fibroblasts inhibits the formation of solid tumors in subtypes of breast cancer models. For the first time, we show that co-culturing organoid models of breast cancer cell lines of MDA-MB-231 (e.g., a triple-negative line) or MCF7 (e.g., a luminal-A line) with CD36+ fibroblasts inhibit the growth and normalizes basal and lateral polarities, respectively. In the long-term anchorage-independent growth assay, the rate of colony formation is also reduced for MDA-MB-231. These observations are consistent with the mechanism of tumor suppression involving the downregulation of pSMAD2/3 and YY1 expression levels. Our integrated analytical methods leverage and extend quantitative assays at cell- and colony-scales in both short- and long-term cultures using brightfield or immunofluorescent microscopy and robust image analysis. Conditioned media are profiled with the ELISA assay.

Organoid model of mammographic density displays a higher frequency of aberrant colony formations with radiation exposure.

MOTIVATION: Aberrant three-dimensional (3D) colony organization of premalignant human mammary epithelial cells (HMECs) is one of the indices of dysplasia. An experiment has been designed where the stiffness of the microenvironment, in 3D culture, has been set at either low or high level of mammographic density (MD) and the organoid models are exposed to 50 cGy X-ray radiation. This study utilizes published bioinformatics tools to quantify the frequency of aberrant colony formations by the combined stressors of stiffness and X-ray exposure. One of the goals is to develop a quantitative assay for evaluating the risk factors associated with women with high MD exposed to X-ray radiation. RESULTS: Analysis of 3D colony formations indicate that high stiffness, within the range of high MD, and X-ray radiation have an approximately additive effect on increasing the frequency of aberrant colony formations. Since both stiffness and X-ray radiation are DNA-damaging stressors, the additive effect of these stressors is also independently validated by profiling activin A-secreted protein. Secretion of activin A is known to be higher in tissues with a high MD as well as tumor cells. In addition, we show that increased stiffness of the microenvironment also induces phosphorylation of γH2AX-positive foci. The study uses two HMECs derived from a diseased tissue (e.g. MCF10A) and reduction mammoplasty of normal breast tissue (e.g. 184A1) to further demonstrate similar traits in the frequency of aberrant colony organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

YY1 is a cis-regulator in the organoid models of high mammographic density.

MOTIVATION: Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines. RESULTS: 3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep fusion of contextual and object-based representations for delineation of multiple nuclear phenotypes.

MOTIVATION: Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g. fixation, staining) that originate as a result of sample preparation; (ii) biological heterogeneity (e.g. vesicular versus high chromatin phenotypes, nuclear atypia) and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology or integration with genome-wide molecular data. RESULTS: The method has been evaluated on two independent datasets. One dataset originates from our lab and includes H&E stained sections of brain and breast samples. The second dataset is publicly available through IEEE with a focus on gland-based tissue architecture. We report an approximate AJI of 0.592 and an F1-score 0.93 on both datasets. AVAILABILITY AND IMPLEMENTATION: The code-base, modified dataset and results are publicly available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections.

Aberration in tissue architecture is an essential index for cancer diagnosis and tumor grading. Therefore, extracting features of aberrant phenotypes and classification of the histology tissue can provide a model for computer-aided pathology (CAP). As a case study, we investigate the application of convolutional neural networks (CNN)s for tumor grading and decomposing tumor architecture from hematoxylin and eosin (H&E) stained histology sections of kidney. The former and latter contribute to CAP and the role of the tumor architecture on the outcome (e.g., survival), respectively. A training set is constructed and sample images are classified into six categories of normal, fat, blood, stroma, low-grade granular tumor, and high-grade clear cell carcinoma. We have compared the performances of a deep versus shallow networks, and shown that the deeper model outperforms the shallow network.

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.

BACKGROUND: Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., regions, boundaries) contributes to improved nuclear segmentation. RESULTS: We have demonstrated that training of deep encoder-decoder convolutional networks overcomes complexities associated with multiple nuclear phenotypes, where we evaluate alternative architecture of deep learning for an improved performance against the simplicity of the design. In addition, improved nuclear segmentation is achieved by color decomposition and combining region- and boundary-based features through a fusion network. The trained models have been evaluated against approximately 19,000 manually annotated nuclei, and object-level Precision, Recall, F1-score and Standard Error are reported with the best F1-score being 0.91. Raw training images, annotated images, processed images, and source codes are released as a part of the Additional file 1. CONCLUSIONS: There are two intrinsic barriers in nuclear segmentation to H&E stained images, which correspond to the diversity of nuclear phenotypes and perceptual boundaries between adjacent cells. We demonstrate that (i) the encoder-decoder architecture can learn complex phenotypes that include the vesicular type; (ii) delineation of overlapping nuclei is enhanced by fusion of region- and edge-based networks; (iii) fusion of ENets produces an improved result over the fusion of UNets; and (iv) fusion of networks is better than multitask learning. We suggest that our protocol enables processing a large cohort of whole slide images for applications in precision medicine.

High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.

Aging is associated with tissue-level changes in cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that change with age in a manner consistent with increased susceptibility to breast cancer.

Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network.

Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.

Buckyballs conjugated with nucleic acid sequences identifies microorganisms in live cell assays.

BACKGROUND: Rapid identification of bacteria can play an important role at the point of care, evaluating the health of the ecosystem, and discovering spatiotemporal distributions of a bacterial community. We introduce a method for rapid identification of bacteria in live cell assays based on cargo delivery of a nucleic acid sequence and demonstrate how a mixed culture can be differentiated using a simple microfluidic system. METHODS: C60 Buckyballs are functionalized with nucleic acid sequences and a fluorescent reporter to show that a diversity of microorganisms can be detected and identified in live cell assays. The nucleic acid complexes include an RNA detector, targeting a species-specific sequence in the 16S rRNA, and a complementary DNA with an attached fluorescent reporter. As a result, each bacterium can be detected and visualized at a specific emission frequency through fluorescence microscopy. RESULTS: The C60 probe complexes can detect and identify a diversity of microorganisms that include gram-position and negative bacteria, yeast, and fungi. More specifically, nucleic-acid probes are designed to identify mixed cultures of Bacillus subtilis and Streptococcus sanguinis, or Bacillus subtilis and Pseudomonas aeruginosa. The efficiency, cross talk, and accuracy for the C60 probe complexes are reported. Finally, to demonstrate that mixed cultures can be separated, a microfluidic system is designed that connects a single source-well to multiple sinks wells, where chemo-attractants are placed in the sink wells. The microfluidic system allows for differentiating a mixed culture. CONCLUSIONS: The technology allows profiling of bacteria composition, at a very low cost, for field studies and point of care.

Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks.

Detection of nuclei is an important step in phenotypic profiling of histology sections that are usually imaged in bright field. However, nuclei can have multiple phenotypes, which are difficult to model. It is shown that convolutional neural networks (CNN)s can learn different phenotypic signatures for nuclear detection, and that the performance is improved with the feature-based representation of the original image. The feature-based representation utilizes Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Several combinations of input data representations are evaluated to show that by LoG representation, detection of nuclei is advanced. In addition, the efficacy of CNN for vesicular and hyperchromatic nuclei is evaluated. In particular, the frequency of detection of nuclei with the vesicular and apoptotic phenotypes is increased. The overall system has been evaluated against manually annotated nuclei and the F-Scores for alternative representations have been reported.

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma.

Integrative analysis based on quantitative representation of whole slide images (WSIs) in a large histology cohort may provide predictive models of clinical outcome. On one hand, the efficiency and effectiveness of such representation is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. On the other hand, perceptual interpretation/validation of important multivariate phenotypic signatures are often difficult due to the loss of visual information during feature transformation in hyperspace. To address these issues, we propose a novel approach for integrative analysis based on cellular morphometric context, which is a robust representation of WSI, with the emphasis on tumor architecture and tumor heterogeneity, built upon cellular level morphometric features within the spatial pyramid matching (SPM) framework. The proposed approach is applied to The Cancer Genome Atlas (TCGA) lower grade glioma (LGG) cohort, where experimental results (i) reveal several clinically relevant cellular morphometric types, which enables both perceptual interpretation/validation and further investigation through gene set enrichment analysis; and (ii) indicate the significantly increased survival rates in one of the cellular morphometric context subtypes derived from the cellular morphometric context.

Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

The effects of the stiffness of the microenvironment on the molecular response of 3D colony organization, at the maximum level of mammographic density (MD), are investigated. Phenotypic profiling reveals that 3D colony formation is heterogeneous and increased stiffness of the microenvironment, within the range of the MD, correlates with the increased frequency of aberrant 3D colony formation. Further integrative analysis of the genome-wide transcriptome and phenotypic profiling hypothesizes overexpression of ERBB2 in the premalignant MCF10A cell lines at a stiffness value that corresponds to the collagen component at high mammographic density. Subsequently, ERBB2 overexpression has been validated in the same cell line. Similar experiments with a more genetically stable cell line of 184A1 also revealed an increased frequency of aberrant colony formation with the increased stiffness; however, 184A1 did not demonstrate overexpression of ERBB2 at the same stiffness value of the high MD. These results suggest that stiffness exacerbates premalignant cell line of MCF10A.