Spatiotemporal strategies to identify aggressive biology in precancerous breast biopsies

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

From space to biomedicine: Enabling biomarker data science in the cloud

NASA’s Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute’s major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as “LabCAS”. As the data increases in size, it is critical that automated approaches be developed to “plug” laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.

Facilitating cancer systems epidemiology research

Systems epidemiology offers a more comprehensive and holistic approach to studies of cancer in populations by considering high dimensionality measures from multiple domains, assessing the inter-relationships among risk factors, and considering changes over time. These approaches offer a framework to account for the complexity of cancer and contribute to a broader understanding of the disease. Therefore, NCI sponsored a workshop in February 2019 to facilitate discussion about the opportunities and challenges of the application of systems epidemiology approaches for cancer research. Eight key themes emerged from the discussion: transdisciplinary collaboration and a problem-based approach; methods and modeling considerations; interpretation, validation, and evaluation of models; data needs and opportunities; sharing of data and models; enhanced training practices; dissemination of systems models; and building a systems epidemiology community. This manuscript summarizes these themes, highlights opportunities for cancer systems epidemiology research, outlines ways to foster this research area, and introduces a collection of papers, "Cancer System Epidemiology Insights and Future Opportunities" that highlight findings based on systems epidemiology approaches.