MétaCan
Menu
Back to cohort
Record W2101035019 · doi:10.1038/nature09837

Initial genome sequencing and analysis of multiple myeloma

2011· article· en· W2101035019 on OpenAlexaff
Michael A. Chapman, Michael S. Lawrence, Jonathan J. Keats, Kristian Cibulskis, Carrie Sougnez, Anna C. Schinzel, Christina L. Harview, Jean-Philippe Brunet, Gregory Ahmann, Mazhar Adli, Kenneth C. Anderson, Kristin Ardlie, Daniel Auclair, Angela Baker, P. Leif Bergsagel, B Bernstein, Yotam Drier, Rafaël Fonseca, Stacey Gabriel, Craig C. Hofmeister, Sundar Jagannath, Andrzej Jakubowiak, Amrita Krishnan, Joan Levy, Ted Liefeld, Sagar Lonial, Scott Mahan, Bunmi Mfuko, Stefano Monti, Louise M. Perkins, Robb Onofrio, Trevor J. Pugh, S. Vincent Rajkumar, Alex H. Ramos, David S. Siegel, Andrey Sivachenko, A. Keith Stewart, Suzanne Trudel, Ravi Vij, Douglas Voet, Wendy Winckler, Todd M. Zimmerman, John D. Carpten, J.M. Trent, William C. Hahn, Levi A. Garraway, Matthew Meyerson, Eric S. Lander, Gad Getz, Todd R. Golub

Bibliographic record

VenueNature · 2011
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer InstituteMultiple Myeloma Research FoundationNational Human Genome Research InstituteNational Institute on AgingLeukaemia and Lymphoma ResearchBroad Institute
KeywordsBiologyMultiple myelomaGeneGenomeGeneticsMassive parallel sequencingMutationCancerSomatic cellDNA sequencingGermline mutationCancer researchCancer genome sequencingComputational biologyWhole genome sequencingImmunology

Abstract

fetched live from OpenAlex

Multiple myeloma is an incurable malignancy of plasma cells, and its pathogenesis is poorly understood. Here we report the massively parallel sequencing of 38 tumour genomes and their comparison to matched normal DNAs. Several new and unexpected oncogenic mechanisms were suggested by the pattern of somatic mutation across the data set. These include the mutation of genes involved in protein translation (seen in nearly half of the patients), genes involved in histone methylation, and genes involved in blood coagulation. In addition, a broader than anticipated role of NF-κB signalling was indicated by mutations in 11 members of the NF-κB pathway. Of potential immediate clinical relevance, activating mutations of the kinase BRAF were observed in 4% of patients, suggesting the evaluation of BRAF inhibitors in multiple myeloma clinical trials. These results indicate that cancer genome sequencing of large collections of samples will yield new insights into cancer not anticipated by existing knowledge. Multiple myeloma, a malignancy of plasma cells, remains incurable and is poorly understood. Chapman et al. have used next-generation sequencing to compare 38 multiple myeloma genomes with those of normal cells from the same patients. The disease involves mutations of genes with roles in protein translation, histone methylation and blood coagulation. In terms of clinically relevant findings, unexpected activating mutations were found in the kinase BRAF, inhibitors of which have recently shown dramatic clinical activity. This suggests that BRAF inhibitors should be evaluated in patients with BRAF-mutated multiple myeloma. Multiple myeloma, a malignancy of plasma cells, remains incurable and is poorly understood. Using next-generation sequencing of several multiple myeloma genomes reveals that this disease involves mutations of genes involved in protein translation, histone methylation and blood coagulation. The study suggests that BRAF inhibitors should be evaluated in multiple myeloma clinical trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.326
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1,439
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueNatureSame topicMultiple Myeloma Research and TreatmentsFrench-language works237,207