Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells

© 2014 Tafavogh et al.; licensee BioMed Central Ltd. Background: Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity; and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points.Results: We compare the cell counting results of our system with those of a pathologist as the ground-truth. We also compare the system with three state-of-the-art methods, and the results of statistical tests show a significant improvement in the performance of our system compared to state-of-the-art methods. The F-measure obtained by our system is 88.70%. To evaluate the generalizability of our algorithm, we apply it to images of follicular lymphoma, which has similar histological regions to NT. Of the algorithms tested, our algorithm obtains the highest F-measure of 92.79%.Conclusion: We develop a novel overlapping cell splitting algorithm to enhance the cellular quantitative analysis of infant neuroblastoma. The performance of the proposed algorithm promises a reliable automated cell counting system for pathology laboratories. Moreover, the high performance obtained by our algorithm for images of follicular lymphoma demonstrates the generalization of the proposed algorithm for cancers with similar histological regions and histological structures

Automated quantitative and qualitative analysis of neuroblastoma cancer tissue

University of Technology, Sydney. Faculty of Engineering and Information Technology.The goal of this thesis is to develop an innovative Computer Aided Diagnosis (CAD) system for the common deadly infant cancer of Neuroblastoma. Neuroblastoma accounts for more than 15% of childhood cancer deaths, and it has the lowest survival rate among the paediatric cancers in Australia. In quantitative analysis the total number of different regions of interest are counted, and qualitative analysis determines abnormalities within the tumour. Quantitative and qualitative analysis of tumor samples under the microscope is one of the key markers used by pathologists to determine the aggressiveness of the cancer, and consequently its therapy. Because of the variety of the histological region types and histological structures in the tissue, analyzing them under the microscope is a tedious and error-prone task for pathologists. The negative effects of inaccurate quantitative and qualitative analysis have led to an urgent call from pathologists for accurate, consistent and automated approaches. Computer Aided Diagnosis (CAD) is an automated cancer diagnostic and prognostic system which enhances the ability of pathologists in the quantitative and qualitative analysis of tumor tissues. However, there are four main issues with developing a CAD system for pathology labs: First is the fluctuating quality of the histological images. Second is a wide range of different types of histological regions and histological structures with complex morphology each adopting a specific algorithm. Third is overlapping cells which decrease the accuracy of quantitative analysis. Fourth is a lack of utility for pathology labs when they do not follow an appropriate clinical prognosis scheme. Moreover, most of the proposed CAD systems perform either quantitative or quantitative analysis and only very few of them manipulate both types of analysis on the cancerous tumor tissue. This thesis aims to address the issues raised by developing an innovative CAD system that assists pathologists in determining a more appropriate prognosis for the leading infant cancer of Neuroblastoma. The CAD will automatically perform quantitative and qualitative analysis on images of tumor tissue to extract specific histological regions and histological structures which are used for determining the prognosis for Neuroblastoma. This thesis has four main contributions. Contribution 1 develops novel algorithms to enhance the quality of histological images by reducing the wide range of intensity variations. Contribution 2 proposes a series of segmentation algorithms for extracting different types of histological regions and histological structures. Contribution 3 addresses the issue of overlapping cells by developing algorithms for splitting them into single cells. Contribution 4 grades the aggressiveness level of neuroblastoma tumor by developing a prognosis decision engine. The main outcomes of the proposed CAD system in this thesis are a series of novel algorithms for enhancing the quality of the histological images and for segmenting histological regions and histological structures of interests, introducing a prognosis decision engine for grading a neuroblastoma tumor based on a well established histopathological scheme, facilitating the process of prognosis and tumor classification by performing accurate and consistent quantitative and qualitative tissue analysis, and enhancing digital pathology by incorporating a digital and automated system in the work flow of pathologists. The performance of all the developed algorithms in this thesis in terms of correctly extracting histological regions, histological structures and grading the level of tumor aggressiveness, is evaluated by a pathologist from the department of histopathology in the Children's Hospital at Westmead, Sydney. Moreover, all the results are compared with state of the art methods. The results indicate that the algorithms proposed in this thesis outperform state of the art quantitative and qualitative methods of analysis

The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research

Can Archival Tissue Reveal Answers to Modern Research Questions?: Computer-Aided Histological Assessment of Neuroblastoma Tumours Collected over 60 Years.

Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain consistent over time, could prove useful to research of rare disease types. We applied immunohistochemistry to investigate whether long term storage caused any changes to antigens used diagnostically for neuroblastoma. We constructed and quantitatively assessed a tissue microarray containing neuroblastoma archival material dating between 1950 and 2007. A total of 119 neuroblastoma tissue cores were included spanning 6 decades. Fourteen antibodies were screened across the tissue microarray (TMA). These included seven positive neuroblastoma diagnosis markers (NB84, Chromogranin A, NSE, Ki-67, INI1, Neurofilament Protein, Synaptophysin), two anticipated to be negative (S100A, CD99), and five research antibodies (IL-7, IL-7R, JAK1, JAK3, STAT5). The staining of these antibodies was evaluated using Aperio ImageScope software along with novel pattern recognition and quantification algorithms. This analysis demonstrated that marker signal intensity did not decrease over time and that storage for 60 years had little effect on antigenicity. The construction and assessment of this neuroblastoma TMA has demonstrated the feasibility of using archival samples for research

Identification Of Neuroblastoma And Its Prognostic Markers Using Raman Spectroscopy

Introduction: Neuroblastoma is the most common cancer of infancy. It is one of several peripheral nervous system tumors, including ganglioneuroma, peripheral nerve sheath tumor, and pheochromocytoma. It is commonly situated on the adrenal gland. It displays similar histology to other small round blue cell tumors, including non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma. One method of judging neuroblastoma aggressiveness uses tumor histology factors, including mitosis-karyorrhexis index, Schwannian stromal development, degree of differentiation, and patient age. Tumor aggressiveness can also be judged based on the amplification of certain genes, including MYCN. Raman spectroscopy is a physics-based method which identifies the biochemical fingerprint of a sample. It has recently been applied to disease classification, specifically in adult cancers. Methods: To identify neuroblastoma from adrenal gland, peripheral nervous system tumors, and small round blue cell tumors, and to identify tumor histology, fresh and frozen samples were collected from the operating room and tested with Raman spectroscopy. Tissues were assigned a `gold standard\u27 diagnosis by experienced pediatric pathologists. Tumor histology was further evaluated with blinded tissues provided by the Children\u27s Oncology Group. To test identification of gene amplification, the tet-on MYCN-3 cell line was cultured in the presence and absence of tetracycline to induce or repress MYCN gene expression. Cells were harvested and tested with Raman spectroscopy and polymerase chain reaction. All Raman spectra were preprocessed and classified with discriminant function analysis. Results: Raman spectroscopy identified neuroblastoma from healthy adrenal gland, peripheral nervous system tumors, and small round blue cell tumors with 100% sensitivity and specificity. It identified favorable, unfavorable, and treated neuroblastoma with high accuracy. Neuroblastoma cells with and without MYCN amplification were identified with 100% sensitivity and specificity. Conclusions: This is the first study applying Raman spectroscopy to identify pediatric tumors, and the first blinded Raman spectroscopy study performed in collaboration with the Children\u27s Oncology Group, a national tumor bank. It provides the first in-depth examination of specific markers of aggressiveness, including tumor favorability and MYCN gene amplification. Raman spectroscopy has the potential to revolutionize the field of cancer diagnostics. It can provide a detailed, accurate diagnosis in minutes instead of days

Mitotic cell detection in H&E stained meningioma histopathology slides

Indiana University-Purdue University Indianapolis (IUPUI)Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science

TP63 and TP73 in cancer, an unresolved “family” puzzle of complexity, redundancy and hierarchy

AbstractTP53 belongs to a small gene family that includes, in mammals, two additional paralogs, TP63 and TP73. The p63 and p73 proteins are structurally and functionally similar to p53 and their activity as transcription factors is regulated by a wide repertoire of shared and unique post-translational modifications and interactions with regulatory cofactors. p63 and p73 have important functions in embryonic development and differentiation but are also involved in tumor suppression. The biology of p63 and p73 is complex since both TP63 and TP73 genes are transcribed into a variety of different isoforms that give rise to proteins with antagonistic properties, the TA-isoforms that act as tumor-suppressors and DN-isoforms that behave as proto-oncogenes. The p53 family as a whole behaves as a signaling “network” that integrates developmental, metabolic and stress signals to control cell metabolism, differentiation, longevity, proliferation and death. Despite the progress of our knowledge, the unresolved puzzle of complexity, redundancy and hierarchy in the p53 family continues to represent a formidable challenge

Computational methods to analyze image-based siRNA knockdown screens

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. Thus, we performed a study to find genes that cause mitosis linked cell death upon inhibition in neuroblastoma cells. We investigated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed image-based time-lapse screening of gene knockdowns in neuroblastoma cells. We developed a classifier to classify images into cellular phenotypes, using SVM, performing manual evaluation and automatic corrections. This classifier yielded better predictions of cellular phenotypes than the standard classification protocol. We further developed an elaborated analysis pipeline based on the phenotype kinetics from the gene knockdown screening to identify genes with vital role in mitosis to identify therapeutic targets for neuroblastoma. We developed two methods (1) to generate clusters of genes with similar phenotype profiles and (2) to track the sequence of phenotype events, particularly mitosis-linked-celldeath. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) that cause mitosis-linked-cell-death upon knockdown in both of the neuroblastoma cell lines tested (SH-EP and SK-N-BE(2)-C). Gene expression analysis of neuroblastoma patients show that these genes are up-regulated in aggressive tumors and they show good prediction performance for overall survival. Four of these hits (DLGAP5, DSCC1, SSBP1, UBE2C) are directly involved in cell cycle and one (SMO) indirectly which is involved in cell cycle regulation. Functional association and gene-expression analysis of these hits indicated that monitoring cell cycle dynamics enabled finding promising drug targets for neuroblastoma cells. In summary, we present a bioinformatics pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells