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Record W4283745047 · doi:10.1038/s41467-022-31430-0

The genetic heterogeneity and drug resistance mechanisms of relapsed refractory multiple myeloma

2022· article· en· W4283745047 on OpenAlexaff
Josh N. Vo, Yi‐Mi Wu, Jeanmarie Mishler, Sarah E. Hall, Rahul Mannan, Lisha Wang, Ning Yu, Jin Zhou, Alexander C. Hopkins, James C. Estill, Wallace Chan, Jennifer Yesil, Xuhong Cao, Arvind Rao, Alexander Tsodikov, Moshe Talpaz, Craig E. Cole, Jing C. Ye, Sikander Ailawadhi, Jesús G. Berdeja, Craig C. Hofmeister, Sundar Jagannath, Andrzej Jakubowiak, Amrita Krishnan, Shaji Kumar, Moshe Levy, Sagar Lonial, Gregory Orloff, David S. Siegel, Suzanne Trudel, Saad Z. Usmani, Ravi Vij, Jeffrey L. Wolf, Jeffrey A. Zonder, P. Leif Bergsagel, Daniel Auclair, Hearn Jay Cho, Dan R. Robinson, Arul M. Chinnaiyan

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
FundersMultiple Myeloma Research FoundationNational Cancer InstituteProstate Cancer FoundationHoward Hughes Medical Institute
KeywordsMultiple myelomaLenalidomideDrug resistanceDiseaseBiologyMedicineMalignancyOncologyBioinformaticsCancer researchImmunologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Multiple myeloma is the second most common hematological malignancy. Despite significant advances in treatment, relapse is common and carries a poor prognosis. Thus, it is critical to elucidate the genetic factors contributing to disease progression and drug resistance. Here, we carry out integrative clinical sequencing of 511 relapsed, refractory multiple myeloma (RRMM) patients to define the disease's molecular alterations landscape. The NF-κB and RAS/MAPK pathways are more commonly altered than previously reported, with a prevalence of 45-65% each. In the RAS/MAPK pathway, there is a long tail of variants associated with the RASopathies. By comparing our RRMM cases with untreated patients, we identify a diverse set of alterations conferring resistance to three main classes of targeted therapy in 22% of our cohort. Activating mutations in IL6ST are also enriched in RRMM. Taken together, our study serves as a resource for future investigations of RRMM biology and potentially informs clinical management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.315
Teacher spread0.291 · 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 teacher head, 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

Citations91
Published2022
Admission routes1
Has abstractyes

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