Nature Communications<\/a>, investigators showed SIDISH successfully identified \u201chigh-risk\u201d cells across pancreatic, breast, and lung cancers using tumor samples collected from patients and analyzed in the lab.<\/p>\nHow the Tool Works<\/p>\n
SIDISH\u2019s key innovation is that it connects what happens inside individual cells with patient outcomes, a long-standing challenge in cancer research.<\/p>\n
\u201cSingle-cell data is very detailed, but it usually comes from only a few patients and rarely includes how those patients actually fared. Patient data, on the other hand, often at the bulk level, includes survival information but averages signals from millions of cells, hiding the rare but dangerous ones that drive disease,\u201d explained first author Yasmin Jolasun, a PhD student in McGill University\u2019s Department of Medicine.<\/p>\n
Existing computational tools have struggled to meaningfully bring these two types of data together.<\/p>\n
\u201cOur tool builds a bridge between both worlds. It can identify which cells are most strongly associated with faster disease progression and patient survival outcomes,\u201d said Ms. Jolasun.<\/p>\n
While SIDISH was tested first in cancers, the same approach could be applied to other complex diseases where cell-to-cell differences play a major role, she added.<\/p>\n
Beyond identifying the problem, SIDISH can also simulate how high-risk cells respond when specific genes are turned on or off, helping predict which genes might be promising drug targets.<\/p>\n
\u201cThis could ease a major bottleneck in drug development, where finding the right targets often requires years of trial-and-error testing,\u201d said senior author Jun Ding, PhD, Assistant Professor in McGill\u2019s Department of Medicine and a junior scientist at the Research Institute of the McGill University Health Centre.<\/p>\n
For example, he said, a patient\u2019s tumor could be analyzed with single cell-sequencing; SIDISH would then identify the cells driving that tumor and simulate how they respond to different drugs, generating a short list of treatments most likely to be effective.<\/p>\n
\u201cIn the short term, SIDISH could help us repurpose existing U.S. Food and Drug Administration\u2013approved drugs using public datasets. In the long term, it has the potential to fundamentally change how new drugs are discovered,\u201d said Dr. Ding.<\/p>\n
The work remains in development and is not yet used in clinical care. The research team is now applying SIDISH to additional diseases and collaborating with industry partners to further refine the approach.<\/p>\n
DISCLOSURE: For full disclosures of the study authors, visit nature.com<\/a>.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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