Discover and read the best of Twitter Threads about #PCAWG
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I am thrilled to announce that our work on In silico saturation mutagenesis of cancer genes is finally out @Nature. We propose a method inspired in evo biology to identify driver mutations in cancer genes. Here’s #tweetorial to sketch our results rdcu.be/cqsM1
Although we have a good knowledge of the genes that cause cancer upon mutations, most mutations in such genes are of uncertain significance. Classifying these variants in cancer genes is key to understand the mechanisms of tumorigenesis and advance precision medicine of cancer. 
One approach to bridge this gap is saturation mutagenesis (aka saturation genome editing), an amazing endeavour, at the moment done for a few cancer genes (e.g. TP53, PIK3CA, PTEN, BRCA1, Ras domain) in specific experimental systems.
Delighted to share our new study nature.com/articles/s4158… introducing PhasED-Seq out today @NatureBiotech.
A fantastic collaboration from @StanfordMedicine led by Dave_Kurtz & Joanne Soo with @max_diehn to help transform #cancer interception & monitoring by improving #LiquidBiopsy #ctDNA detection of #MRD.
We are really excited to share our @biorxivpreprint on "In silico saturation mutagenesis of cancer genes". Here’s a #tweetorial to briefly summarize our findings doi.org/10.1101/2020.0…
Tumors are known to follow an evolutionary process whereby somatic variants can contribute to cancer development. The study of the traces of positive selection of tumorigenic mutations at the level of genes has yielded a compendium of cancer driver genes (intogen.org)
But not all mutations reported in cancer genes are tumorigenic. Our knowledge of how selection acts at the level of individual mutations is still limited, so we aimed to explore ways for identifying all potential driver mutations in driver genes, even if we did not observe them.
Here's a short (?) thread on a recent #PCAWG paper focused on #cancer, long-reads, tumor mutational burden, and a very hard-to-pronounce word called chromothripsis.
'chromo-' = chromosome ; '-thripsis' = shattering into pieces
TL;DR at end (...)
nature.com/articles/s4158…
'chromo-' = chromosome ; '-thripsis' = shattering into pieces
TL;DR at end (...)
nature.com/articles/s4158…
The researchers' used short-read (~40X) whole-genome #sequencing on ~2,700 tumors across 38 cancer types (most tumors were advanced).
The goal was to study the frequency of a mutational phenomenon (chromothripsis), which previously was though to exist in only 2-3% of tumors.
The goal was to study the frequency of a mutational phenomenon (chromothripsis), which previously was though to exist in only 2-3% of tumors.
The prevailing view on tumor formation is that somatic mutations gradually accumulate over time, eventually overwhelming a cell's DNA repair machinery. Conversely, chromothripsis (above) is a single, catastrophic event defined by hundreds of structural rearrangements all at once.
Our paper is out! 😊
Showcasing the complexity of #mutationalsignatures
A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies @NatureCancer
👇
1/ nature.com/articles/s4301…
Showcasing the complexity of #mutationalsignatures
A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies @NatureCancer
👇
1/ nature.com/articles/s4301…
We compare and contrast components of analytical steps in signatures analysis and suggest a practical framework for seeking Mutational signatures in tens to hundreds of tumour samples.
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Applying these methods on 3107 WGS primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures.
Spot the workflow 👇
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Spot the workflow 👇
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Very happy to announce a major IntOGen update. This new version contains a comprehensive and reliable compendium of cancer genes and their mutational features across 61 tumour types, obtained by analysing 26,955 cancer genomes intogen.org. Thread 👇
How does intOGen identify cancer genes?
Briefly, intOGen runs six complementary driver discovery methods that analyze the pattern of mutations per gene and cohort and combines their output to produce a compendium of driver genes and a repository of their mutational features
Briefly, intOGen runs six complementary driver discovery methods that analyze the pattern of mutations per gene and cohort and combines their output to produce a compendium of driver genes and a repository of their mutational features
How did you collect genomic data from >26,000 tumors?
We manually downloaded, processed and annotated tumor genomic data from different sources, such as @cbioportal #PedcBioPortal @icgc_dcc #TCGA #PCAWG @HartwigMedical #TARGET @StJude and others. 190 cohorts in total👇
We manually downloaded, processed and annotated tumor genomic data from different sources, such as @cbioportal #PedcBioPortal @icgc_dcc #TCGA #PCAWG @HartwigMedical #TARGET @StJude and others. 190 cohorts in total👇
