Cancer spread, COVID spread, better tissue maps, and more
Research Roundup: December 11, 2020
Welcome to the December 11, 2020 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.
A better understanding of inherited risk factors for lung cancer could help identify populations who’d benefit from increased screening. Jian Carrot-Zhang, Oscar Arrieta (Instituto Nacional de Cancerologia), Andres Cardona (FICMAC), Alexander Gusev, and institute member Matthew Meyerson in the Cancer Program and of Dana-Farber Cancer Institute led a genomic and ancestry analysis of 1,153 lung cancers from Latin America. The effort revealed that Native American ancestry was correlated with somatic driver alterations, including targetable EGFR and KRAS mutations. Described in Cancer Discovery and a press release, the study highlights the importance of providing somatic genetic testing for Latin American lung cancer patients with admixed ancestries, particularly those who are non-smokers.
To sift cancer-related somatic mutations out of whole genome data, researchers need high-quality benchmarking datasets. But the available benchmarks largely rely on synthetic or germline data, or require expensive validation work in order to reveal real variants. Megan Shand, Jose Soto, Lee Lichtenstein, David Benjamin, Yossi Farjoun, Yehuda Brody, Yosef Maruvka, core institute member Paul Blainey, and institute scientist and Data Sciences Platform senior director Eric Banks have developed a new somatic mutation benchmarking set called Lineage derived Somatic Truth (LinST). In Communications Biology, they validate LinST in a colon cancer cell line, and note their methods for constructing the set could be used to generate somatic truth data for many types of cancer.
Charting richer maps of cells and tissues
The Slide-seq technique uses spatially barcoded RNA sequencing to generate three-dimensional maps of cellular relationships in tissues. It offers high-resolution views of the cellular structure and gene expression directly in tissues, giving researchers rich maps of tissue function. The team that built it has now developed Slide-seqV2, with almost 10-fold greater efficiency and detection sensitivity than the original Slide-seq. Reporting in Nature Biotechnology, Robert Stickels, Evan Murray, institute member Evan Macosko in the Stanley Center for Psychiatric Research, core institute member Fei Chen, and colleagues explain how they applied Slide-seqV2 to gain insight into biological problems where higher capture sensitivity is important, such as understanding complex neuronal development trajectories in the mouse brain.
MetMap shows whether a cancer spreads and where
Metastasis is a major cause of cancer death, but determining if a cancer will metastasize has been all but impossible. Research scientist Xin Jin, Todd Golub, Broad’s Chief Scientific Officer and Director of Broad’s Cancer Program, and colleagues show that it is possible to predict metastasis of human cancer cells in animal models. They pulled together genetic and clinical factors associated with whether a cancer spreads, how well it spreads, and to which organ, for 500 human cancer cell lines representing 21 different cancers. Their Metastasis Map, or MetMap, is the first ever map of how different cancers metastasize. Learn more in Nature, a Broad story, and an interactive graph.
Tracking the spread of SARS-CoV-2 with genomic epidemiology
A team including Jacob Lemieux, Katie Siddle, institute member Pardis Sabeti, viral computational genomics group leader Daniel Park, director of pathogen genomic surveillance Bronwyn MacInnis, and colleagues has sequenced and analyzed 772 complete SARS-CoV-2 genomes to describe the virus's introduction and spread in the greater Boston area across the first wave of the pandemic. The study provides a unique window into the amplification of transmission in an urban setting — including the local, national, and global impact of “superspreading” events. Read about the work in Science and check out a new Broad video explainer for more about genomic epidemiology.