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The reviews express strong support for the study's comprehensive analysis, with posts discussing the complexity of integrating multiple tools or visual approaches for identifying cancer driver genes. The tone demonstrates enthusiasm and recognition of the research’s potential to influence future clinical applications, reflecting deep engagement and perceived high impact.
Most discussions acknowledge and support the significance of the research, with some praising the comprehensive approach and its implications for cancer treatment.
Participants show high engagement with the topic, referencing detailed aspects like computational tools, gene identification, and visualization methods.
Several posts go beyond surface-level reactions, discussing methodologies, validation, and clinical relevance, indicating active participation.
The consensus emphasizes the importance of the findings for understanding cancer genetics and developing future therapies, underlining its high impact.
@AbbiesArmy Please invite the TCGA consortium. As of 2018 #DIPG is not one of the 33 cancers studied, nor is #H3K27M included in its "Comprehensive Characterization of Cancer Driver Genes and Mutations." https://t.co/PBaHSK7xnD Perhaps pediatric & adult neuro scientists don't communicate
@jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size is completely justified. TCGA WES identifies many driver mutations (https://t.co/zVPyejjJzR), while ICGC WGS found only ~<10% role for non-coding (https://t.co/SDK3vp86hX).
RT @ctokheim: @ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller A couple of visual ways: 1) lollipop diagrams display the linear seque…
RT @ctokheim: @jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size i…
@anshulkundaje @hjpimentel @arjunrajlab @michaelhoffman I agree that the ideal setup is to integrate multiple tools. Actually, we've done that before for driver genes (https://t.co/zVPyejjJzR). But unfortunately integrating multiple can often be complex and not always practical for everyone's analysis.
@ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller Sources for which genes are tumor suppressor genes include: 1) Cancer Genome Landscape review (https://t.co/mp5E1qjeEC); 2) Per cancer type annotation of the TCGA (Table S1, see https://t.co/zVPyejjJzR); 3) OncoKB (https://t.co/ewzr1DgoYF); 4) Cancer Gene Census.
This video explores a comprehensive study analyzing cancer driver genes and mutations across 33 cancer types, highlighting the clinical relevance of over 300 tumors with actionable genetic alterations for personalized…
This video discusses a comprehensive study analyzing cancer driver genes and mutations, highlighting the clinical relevance of certain mutations and their association with better immunotherapy responses in hypermutated…
In this video we explore a comprehensive study analyzing tumor exomes from The Cancer Genome Atlas, identifying hundreds of cancer driver genes and mutations, their clinical relevance, and implications for precision…
This video explores a comprehensive analysis of cancer driver genes and mutations from the 2018 Cell publication. It highlights the identification of key oncogenic pathways, mutation frequencies, clinical relevance, and…
In this video we explore a foundational paper in the field of cancer genomics. The study analyzed 9,423 tumor exomes across TCGA, identifying 299 driver genes and over 3,400 mutations, many with clinical relevance…
This video explores a significant paper in cancer genomics analyzing 9,423 tumor exomes from TCGA, identifying 299 driver genes and over 3,400 mutations. The study advances understanding of oncogenic pathways and…
@DMarzese They were derived from this paper: https://t.co/9M5kmFSUtL
@anshulkundaje @hjpimentel @arjunrajlab @michaelhoffman I agree that the ideal setup is to integrate multiple tools. Actually, we've done that before for driver genes (https://t.co/zVPyejjJzR). But unfortunately integrating multiple can often be complex and not always practical for everyone's analysis.
RT @ctokheim: @jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size i…
@jgschraiber @cj_battey In cancer for somatic variant interpretation, the conclusion of coding variants as high effect size is completely justified. TCGA WES identifies many driver mutations (https://t.co/zVPyejjJzR), while ICGC WGS found only ~<10% role for non-coding (https://t.co/SDK3vp86hX).
@ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller Sources for which genes are tumor suppressor genes include: 1) Cancer Genome Landscape review (https://t.co/mp5E1qjeEC); 2) Per cancer type annotation of the TCGA (Table S1, see https://t.co/zVPyejjJzR); 3) OncoKB (https://t.co/ewzr1DgoYF); 4) Cancer Gene Census.
RT @ctokheim: @ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller A couple of visual ways: 1) lollipop diagrams display the linear seque…
@ChiragNepal @tangming2005 @notSoJunkDNA @chrisamiller A couple of visual ways: 1) lollipop diagrams display the linear sequence (usually protein) of your mutation with all mutations in a cohort. 2) Closeness in 3D protein structure is a much better predictor of drivers (see https://t.co/zVPyejjJzR). OpenCRAVAT provides both.
@AbbiesArmy Please invite the TCGA consortium. As of 2018 #DIPG is not one of the 33 cancers studied, nor is #H3K27M included in its "Comprehensive Characterization of Cancer Driver Genes and Mutations." https://t.co/PBaHSK7xnD Perhaps pediatric & adult neuro scientists don't communicate
Comprehensive Characterization of Cancer Driver Genes and Mutations https://t.co/YpDCHo2tTr