Cancer Molecular Analysis Project: weaving a rich cancer research tapestry

Promotion of apoptosis in melanoma cells by taxifolin through the PI3K/AKT signaling pathway: Screening of natural products using WGCNA and CMAP platforms

Melanoma is a skin cancer originating from melanocytes. The global incidence rate of melanoma is rapidly increasing, posing significant public health challenges. Identifying effective therapeutic agents is crucial in addressing this growing problem. Natural products have demonstrated promising anti-tumor activity. In this study, a plant flavonoid, taxifolin, was screened using Weighted Correlation Network Analysis (WGCNA) in combination with the Connectivity Map (CMAP) platform. Taxifolin was confirmed to inhibit the proliferation, migration, and invasion ability of melanoma A375 and MV-3 cells by promoting apoptosis. Additionally, it suppressed the Epithelial-Mesenchymal Transition (EMT) process of melanoma cells. Cyber pharmacological analysis revealed that taxifolin exerts its inhibitory effect on melanoma through the PI3K/AKT signaling pathway, specifically by downregulating the protein expression of p-PI3K and p-AKT. Notably, the addition of SC-79, an activator of the PI3K/AKT signaling pathway, reversed the effects of taxifolin on cell migration and apoptosis. Furthermore, in vivo experiments demonstrated that taxifolin treatment slowed tumor growth in mice without significant toxic effects. Based on these findings, taxifolin holds promise as a potential drug for melanoma treatment.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

Is the Oncotype DX Assay Necessary in Strongly Estrogen Receptor-Positive Breast Cancers?

The 21-gene Oncotype DX recurrence score (RS) assay quantifies risk of distant recurrence and predicts benefit from chemotherapy in tamoxifen-treated estrogen receptor (ER)-positive, node-negative breast cancer. Although clinically useful, the assay costs roughly $4650. Because the assay is weighted heavily towards expression of ER, our objective was to determine its clinical utility in strongly ER-positive tumors. This was a retrospective study of Huntington Hospital patients undergoing an Oncotype DX assay between 2007 and 2010. Data collected included patient age, expression of ER, progesterone receptor (PR), HER2/neu, ki67, and p53, tumor size, node status, lymphovascular invasion, and nuclear grade. Of 133 total patients, 84 (63.2%) had strongly ER-positive tumors (≥90% expression). Only seven of 84 patients (8.3%) had a high risk RS (>30), indicating statistically significant predicted benefit from chemotherapy. All seven had intermediate to high ki67 expression (>20%) and lower PR expression (≤50%). Our study demonstrates that the clinical utility of the Oncotype DX assay in these patients is limited as most patients with strongly ER-positive tumors will have a low or intermediate RS. Future studies are needed to identify additional predictive factors in these patients with otherwise good prognosis.

Detecting cancer gene networks characterized by recurrent genomic alterations in a population

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.

Identification of key processes underlying cancer phenotypes using biologic pathway analysis

Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically. Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes. Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways – Trka Receptor, Apoptosis response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin – whose differences robustly distinguish diverse tumor types from corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state. Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome. This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.

caCORE version 3: Implementation of a model driven, service-oriented architecture for semantic interoperability

One of the requirements for a federated information system is interoperability, the ability of one computer system to access and use the resources of another system. This feature is particularly important in biomedical research systems, which need to coordinate a variety of disparate types of data. In order to meet this need, the National Cancer Institute Center for Bioinformatics (NCICB) has created the cancer Common Ontologic Representation Environment (caCORE), an interoperability infrastructure based on Model Driven Architecture. The caCORE infrastructure provides a mechanism to create interoperable biomedical information systems. Systems built using the caCORE paradigm address both aspects of interoperability: the ability to access data (syntactic interoperability) and understand the data once retrieved (semantic interoperability). This infrastructure consists of an integrated set of three major components: a controlled terminology service (Enterprise Vocabulary Services), a standards-based metadata repository (the cancer Data Standards Repository) and an information system with an Application Programming Interface (API) based on Domain Model Driven Architecture. This infrastructure is being leveraged to create a Semantic Service-Oriented Architecture (SSOA) for cancer research by the National Cancer Institute's cancer Biomedical Informatics Grid (caBIG).

The caCORE Software Development Kit: streamlining construction of interoperable biomedical information services

Background

Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems.

Results

The caCORE SDK requires a Unified Modeling Language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enabling technology for caBIG.

Conclusion

The caCORE SDK substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment. It has gained acceptance among developers in the caBIG program, and is expected to provide a common mechanism for creating data service nodes on the data grid that is under development.

WEBSAGE: a web tool for visual analysis of differentially expressed human SAGE tags

The serial analysis of gene expression (SAGE) is a powerful method to compare gene expression of mRNA populations. To provide quantitative expression levels on a genome-wide scale, the Cancer Genome Anatomy Project (CGAP) uses SAGE. Over 7 million SAGE tags, from 171 human cell types have been assembled. The growing number of laboratories involved in SAGE research necessitates the use of software that provides statistical analysis of raw data, allowing the rapid visualization and interpretation of results. We have created the first simple tool that performs statistical analysis on SAGE data, identifies the tags differentially expressed and shows the results in a scatter plot. It is freely available and accessible at .

A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer

BACKGROUND

The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor-positive tumors is poorly defined by clinical and histopathological measures.

METHODS

We tested whether the results of a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancer-related genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient.

RESULTS

Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan-Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients.

CONCLUSIONS

The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer.

Sequence-based cancer genomics: progress, lessons and opportunities

Technologies that provide a genome-wide view offer an unprecedented opportunity to scrutinize the molecular biology of the cancer cell. The information that is derived from these technologies is well suited to the development of public databases of alterations in the cancer genome and its expression. Here, we describe the synergistic efforts of research programmes in Brazil, the United Kingdom and the United States towards building integrated databases that are widely accessible to the research community, to enable basic and applied applications in cancer research.