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Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti-PD-1 Therapy.

Kun WangSushant PatkarJoo Sang LeeEdward Michael GertzWelles RobinsonFiorella SchischlikDavid R CrawfordAlejandro A SchäfferEytan Ruppin
Published in: Cancer discovery (2022)
This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type-specific gene expression profiles and identify cell type-specific ligand-receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873.
Keyphrases
  • gene expression
  • genome wide
  • copy number
  • dna methylation
  • machine learning
  • deep learning
  • genome wide identification
  • cell therapy
  • transcription factor
  • smoking cessation
  • genome wide analysis