Discover and read the best of Twitter Threads about #pcawg

Most recents (6)

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
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.
Read 25 tweets
Delighted to share our new study… 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.
For many cancers & nearly all currently available techniques, the impressive KM plots of #ctDNA #MRD immediately after definitive Rx w/ curative-intent unfortunately still miss ~50% of all events which occur in the MRD-negative subset, thus having modest NPV.
Read 21 tweets
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…
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 (
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.
Read 26 tweets
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 (...)…
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. Image
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.
Read 9 tweets
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
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.

Applying these methods on 3107 WGS primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures.
Spot the workflow 👇
Read 15 tweets
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 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
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👇
Read 9 tweets

Related hashtags

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3.00/month or $30.00/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!