About the Cancer Biomarkers Research Group

The Cancer Biomarkers Research Group promotes research to identify, develop, and validate biological markers for early cancer detection and cancer risk assessment. Activities include development and validation of promising cancer biomarkers, collaborative databases and informatics systems, and new technologies or the refinement of existing technologies.

NCI DCP News Note

Posted: 09/11/2015

Consortium on Imaging and Biomarkers (CIB) Created: Eight Grants Awarded to Improve Accuracy of Cancer Screening, Detection, and Diagnosis
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Posted: 10/14/2015

Consortium for Molecular Characterization of Screen-Detected Lesions Created: Eight Grants Awarded
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Early Detection Research Network

A major program of the group is the Early Detection Research Network (EDRN). EDRN is a collaborative network that maintains comprehensive infrastructure and resources critical to the discovery, development and validation of biomarkers for cancer risk and early detection. The program comprises a public/private sector consortium to accelerate the development of biomarkers that will change medical practice, ensure data reproducibility, and adapt to the changing landscape of biomarker science.

Alliance of Glycobiologists for Cancer Research

The group administers the Alliance of Glycobiologists for Cancer Research, a consortium of seven Tumor Glycomics Laboratories working to reveal cancer-related dynamics of complex carbohydrates in order to develop new, validated clinical biomarkers for early detection. Studying important biologic mechanisms, Alliance investigators focus their efforts on specific classes of glycan markers that are likely to play important roles in cancer development.

Translational Liver Cancer (TLC) Consortium

The Translational Liver Cancer (TLC) Consortium was created to advance translational research focused on early detection of liver cancer. The consortium goals are to conduct studies to improve the surveillance of liver cancer in high-risk populations, increase the fraction of liver cancer detected at an early stage, and better stratify patients at risk of developing liver cancer.

Molecular Characterization Laboratories Consortium

The NCI has awarded eight grants to create the Consortium for Molecular Characterization of Screen-Detected Lesions. The consortium has seven molecular characterization laboratories (MCLs) and a coordinating center, and is supported by the Division of Cancer Prevention and the Division of Cancer Biology.

The consortium focuses on a critical area in cancer science – the need to characterize molecular and cellular fe atures of screening-detected pre-cancers and early cancers, including within the tumor microenvironment. The resulting information will help to distinguish between a pre-cancer or cancer that is indolent (non-growing) versus an aggressive cancer; and to find minimally invasive methods to address the questions of how to treat a cancer found through a screening test.

Innovative Molecular Analysis Technologies (IMAT) Program

The group’s work in the Innovative Molecular Analysis Technologies (IMAT) Program encompasses an array of 12 closely related Funding Opportunity Announcements (FOAs), each of which is segregated based on program and type of funding mechanism. The IMAT Program, aimed at the development and integration of novel and emerging technologies in support of cancer research, diagnosis, and treatment, comprises three related thematic components: innovative technologies for the molecular analysis of cancer, applications of emerging technologies for cancer research, and innovations in cancer cell preparations.

Cancer Biomarker Aggregator (CBAG)

In October 2018, DCP initiated a one-year FNLCR contract to test the feasibility of a Cancer Biomarker Aggregator (CBAG), an artificial intelligence (AI) empowered platform for the development of image-based AI algorithms for risk assessment and early detection of cancer. Specific objectives of this proof-of-concept (PoC) project were to (i) landscape analysis to prioritize biomarker datasets with potential clinical utility for risk assessment, early detection, or diagnosis of cancer; (ii) stakeholder engagement to assess needs and requirements; (iii) evaluation of existing AI platforms, repositories, and semantic resources; and (iv) recommendations.

Through multiple channels, it was established that an imaging-focused CBAG platform (CBAG-I) could significantly accelerate the development of image-based AI algorithms for risk assessment and early detection of cancer. The proposed CBAG-I platform should enable aggregation of high-quality image collections of precancer and early-stage cancer with sufficient metadata for the development of clinically relevant classification and regression algorithms.

The performance of algorithms could be tested and evaluated using challenge-based competitions or similar mechanisms. These AI-based algorithms and models should improve existing practices through (i) more accurate identification of benign, indolent, and aggressive lesions in at risk-individuals and (ii) provide more precise recommendations for their follow up care.

Additionally, the PoC project identified pancreatic cancer as a top use case for a pilot CBAG-I. To assess needs and requirements of the research community, the project engaged the Alliance of Pancreatic Cancer Consortia (APaCC), a broad-based “umbrella” consortium, which aims to coordinate existing ‘pancreatic’ consortia and partnerships as well as the much-needed infrastructure and standards for biomarker data collection and curation. Since its formation in 2018, APaCC has represented four NCI consortia: The Pancreatic Cancer Detection Consortium (PCDC), Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC), Early Detection Research Network (EDRN), and Consortium for Molecular Characterization of Screen-Detected Lesions Created (MCL), as well as industry, non-profit, and advocacy groups.

The final recommendation was to ensure that interoperability of CBAG-I with the NCI Cancer Research Data Commons infrastructure by incorporating existing standards, data models, and vocabularies.