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SUMMARY:Deciphering Shared Intratumor Transcriptional Heterogeneity of Hum
 an Tumors - Valentina Boeva\, Swiss Institute of Bioinformatics
DTSTART:20220620T130000Z
DTEND:20220620T140000Z
UID:TALK167231@talks.cam.ac.uk
CONTACT:105893
DESCRIPTION:Human tumors are highly heterogeneous in their cell compositio
 n\; specifically\, they exhibit heterogeneity in transcriptional states of
  malignant cells\, as has been recently discovered through single-cell RNA
  sequencing (scRNA-seq). Distinct states of malignant cells have been link
 ed to variability in tumorigenic properties and resistance to anti-cancer 
 treatment. Despite the fact that scRNA-seq data contain all necessary info
 rmation to uncover shared transcriptional states of malignant cells in tum
 ors\, jointly analyzing cells from multiple cancer patients comes with its
  set of challenges including batch correction and accounting for patient-s
 pecific genetic background driving differences between gene expression vec
 tors. We propose CanSig\, an easy-to-use approach designed to discover kno
 wn and de novo shared signatures in cancer single cells. CanSig preprocess
 es\, integrates and analyzes scRNA-seq data to provide new signatures of s
 hared transcriptional states and links these states to known pathways. \n\
 nWe show that CanSig successfully rediscovers ground truth pathways determ
 ining shared transcriptional states in two simulated and three experimenta
 l datasets\; the latter spanning 135 patients and 72\,000 cells. We then i
 llustrate CanSig’s investigative potential by discovering novel signatur
 es in esophageal squamous cell carcinoma possibly linked to targeted patie
 nt treatment\; we also point out a de novo signature in breast cancer pred
 ictive of patients’ survival. In the cancer types studied\, we juxtapose
  copy number variation with discovered shared transcriptional states and u
 ncover a genetic component predisposing cancer cells to activation of spec
 ific transcriptional programs. \n\nIn sum\, CanSig\, specifically develope
 d to analyze shared transcriptional heterogeneity of malignant cells of di
 fferent genetic backgrounds\, can greatly facilitate the exploratory analy
 sis of scRNA-seq cancer data and efficiently identify novel transcriptiona
 l signatures linked to known biological pathways.\n\n\nhttps://zoom.us/j/9
 3461389816?pwd=NmNHbTljVXo0aG9lS09JVXNIeFpKUT09\n
LOCATION:CRUK CI Lecture Theatre
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