2022-09-26 カリフォルニア大学サンディエゴ校(UCSD)
アレクサンドロフの研究室が開発した次世代の機械学習ツールでタバコのシグネチャーを発見しました。チームは、大量の遺伝子データから直接突然変異シグネチャを抽出するための最も先進的で自動化されたバイオインフォマティクスツールであると言います。
膀胱がんのDNAに、タバコの煙と関連する変異シグネチャーが見つかりました。この発見は、肺がんではタバコの喫煙による変異シグネチャーが検出されているが、膀胱がんではまだ検出されていないため、重要なものである。
膀胱がんとタバコを結びつける強力な疫学的証拠がある。口、食道、肺など、タバコの発癌物質に直接さらされる他の組織でも、特定の変異シグネチャーが見られる。
<関連情報>
- https://today.ucsd.edu/story/mutational-signature-linking-bladder-cancer-and-tobacco-smoking-found-with-new-ai-tool
- https://www.sciencedirect.com/science/article/pii/S2666979X22001240
「SigProfilerExtractor」を用いた「de novo抽出」による新規変異シグネチャーの発見 Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
S.M. AshiqulIslam,Marcos Díaz-Gay,Yang Wu,Mark Barnes,Raviteja Vangara,Erik N.Bergstrom,YudouHe,Mike Vella,Jingwei Wang,Jon W.Teague,Peter Clapham,Sarah Moody,Sergey Senkin,Yun RoseLi,Laura Riva,Tongwu Zhang,Andreas J.Gruber,Christopher D.Steele,Burçak Otlu,Azhar Khandekar,Ammal Abbasi,Laura Humphreys,Natalia Syulyukina,Samuel W.Brady,Boian S.Alexandrov,Nischalan Pillay,Jinghui Zhang,David J.Adams,Iñigo Martincorena,David C.Wedge,Maria Teresa Landi,Paul Brennan,Michael R.Stratton,Steven G.Rozen,Ludmil B.Alexandrov
Cell Genomics Published: September 23, 2022
DOI:https://doi.org/10.1016/j.xgen.2022.100179
Highlights
- •Most advanced bioinformatics tool for de novo extraction of mutational signatures
- •Comprehensive benchmarking of 14 de novo extraction tools with and without noise
- •Analysis of 23,827 sequenced cancers revealing four novel mutational signatures
- •Novel signature attributed to direct tobacco smoking mutagenesis in bladder tissues
Summary
Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.
