Research Roundup: June 27, 2022

Heart failure at the single-cell level, deep learning reveals new cancer mutations, and multiple myeloma progression predictors

Susanna M. Hamilton
Credit: Susanna M. Hamilton

Welcome to the June 27, 2022 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.

A single-cell look at heart failure

Heart failure is one of the most common causes of hospitalization in the United States. Most forms are treated the same way despite clinical variability, in part because their underlying molecular mechanisms remain poorly understood. Mark Chaffin, institute member and director of the Cardiovascular Disease Initiative director Patrick Ellinor, and colleagues used single-nuclei RNA sequencing to study dilated and hypertrophic cardiomyopathy, two causes of heart failure. They found a shared transcriptional profile at the cell- and tissue-level in both conditions that differs from that of healthy heart cells. They also observed a population of activated fibroblasts unique to failing hearts that could be a therapeutic target. Read more in Nature, Patrick’s tweetorial, and a Broad news story.

Digging for genetic clues to cancer

Cancer cells often harbor thousands of genomic mutations, but telling which ones actually drive disease can be hard. Max Sherman, Adam Yaari, Oliver Priebe, Felix Dietlein, associate member Po-Ru Loh of the Program in Medical and Population Genetics, and associate member Bonnie Berger of the Cell Circuits Program built a deep learning model that can screen any part of the genome for possibly cancerous mutations. Across 37 screened cancers, their model, called Dig, was able to find additional mutations that could contribute to up to 10 percent of tumors depending on the cancer type. They say Dig could be used to find new targets for cancer drugs. Read more in Nature Biotechnology, stories from MIT News and GenomeWeb, and a tweetorial by Max.

Predicting multiple myeloma progression

Multiple myeloma cancer is often preceded by an asymptomatic stage called "smoldering" multiple myeloma (SMM). Understanding the progression from one stage to the next and predicting which SMM patients are at the highest risk for developing full-blown disease is necessary to design treatment interventions. In Nature Communications, Shankara Anand, Mark Bustoros (DFCI), institute member Gad Getz, associate member Irene Ghobrial of the Cancer Program, and colleagues describe six subtypes of SMM with distinct DNA alterations, transcriptional profiles, and dysregulated molecular pathways. These subtypes had distinct disease progression outcomes, indicating they could potentially help predict high- and low-risk patients in the clinic and guide precision future medicine efforts.

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