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Record W2547612211 · doi:10.1182/blood.v104.11.783.783

Sequencing the Multiple Myeloma Kinome: Absence of Mutation in Known Malignancy-Associated Kinases.

2004· article· en· W2547612211 on OpenAlexaff
Jaime O. Claudio, Razi Khaja, Lihua Zhuang, Meenakshi Bali, Kamalanayani Sivananthan, Constantine Christopoulos, Rafaël Fonseca, P. Leif Bergsagel, Stephen W. Scherer, A. Keith Stewart

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsKinomeBiologyReceptor tyrosine kinaseGeneticsCancer researchTyrosine kinaseKinaseMutationCDKN2AExonBruton's tyrosine kinaseGeneSignal transduction

Abstract

fetched live from OpenAlex

Abstract In approximately 50% of Multiple Myeloma (MM), translocation of non random genes into the IgH locus is believed to be the seminal event in the pathogenesis of the disease. Another 50% of cases are hyperdiploid and trisomic to certain autosomes, but do not harbor any translocation and thus are believed to have genetic alterations in unidentified loci. These observations, together with the finding of somatic mutations in FGFR3, N- and K-RAS, MYC, TP53 and CDKN2C/p18INK4C during the later stages of MM indicate that defective signaling pathways likely play a role in the progression of this malignancy. Of relevance then, in recent years recurrent mutations in kinases have frequently been implicated in malignancies including notably colon cancer and melanoma. We have therefore begun a comprehensive effort to sequence the tyrosine kinome for mutations and genetic polymorphisms in MM. Of particular interest are 90 receptor tyrosine kinases, 43 receptor tyrosine kinase-like, 5 receptor guanylate cyclase, and a lipid kinase. We report here results from our pilot high throughput exon scanning in 32 human MM cell lines which initially focused on 13 kinases known to be somatically mutated in human cancers. To date we have expanded this effort to assess 30 genes with sequence obtained which currently spans 80% coverage of the kinase domains of these genes. A total of 1.9 million bp have been sequenced across 235 exons. No recurrent mutations have been identified in the kinase domains of the cancer-associated genes: SRC, ILK1, KIT, GUCY2F, PDGFRA; in the genetic disorder-associated kinases: BTK, EPHA4, LAMA2, EPHB6, ACVR2; and in the mutation hot spots of frequently mutated cancer gene PIK3CA. A novel missense mutation is however identified upstream of the kinase domain of FGFR3 changing a Ser residue to Arg at codon 433. This residue, which is conserved across species and in FGFR1 and FGFR2, has not been reported in myeloma and in thanatophoric dysplasia, but the biological significance of this mutation is unknown. Several single nucleotide polymorphisms were identified in the coding regions of some of these kinases. Notably, synonymous polymorphisms in the kinase domains of EPHA4, PDGFRA3, KIT, MLK1, ILK1, NTRK3, FLT3, ABL1, FES, MLK4, and EGFR1 were identified that changed a codon but not the amino acid. More importantly, we identified non-synonymous amino acid variations in the kinase domains of EPHA4, GUCY2F, PTK2, and PIK3CA genes that are more likely to effect variability in the activity of these kinases. In summary, no recurrent kinase mutations of significance in Myeloma development or progression have yet been identified. Sequencing of the known cancer associated kinases in MGUS and hyperdiploid MM patients is now underway and our data set is being expanded to include all 139 kinases.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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
Published2004
Admission routes1
Has abstractyes

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