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Record W4401702221 · doi:10.1038/s41588-024-01853-0

Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes

2024· article· en· W4401702221 on OpenAlexaff
Sheri Skerget, Daniel Peñaherrera, Ajai Chari, Sundar Jagannath, David S. Siegel, Ravi Vij, Gregory Orloff, Andrzej Jakubowiak, Rubén Niesvizky, Darla Liles, Jesús G. Berdeja, Moshe Levy, Jeffrey L. Wolf, Saad Z. Usmani, Robert M. Rifkin, Kenneth R. Meehan, Don Benson, Jeffrey A. Zonder, João L. Ascensão, Cristina Gasparetto, Miguel‐Teodoro Hernández, Suzanne Trudel, Shaker R. Dakhil, Nizar J. Bahlis, Juan Vazquez Paganini, Pablo Rios, Antònia Sampol, Siva Mannem, Rebecca Silbermann, Matthew A. Lunning, Michael P. Chu, Carter Milner, Allyson Harroff, Mark E. Graham, Spencer H. Shao, Jyothi Dodlapati, Carlos Fernández de Larrea, Leonard Klein, Charles Kuzma, Rafaël Fonseca, Gemma Azaceta, Miquel Granell, Carmen Martínez‐Chamorro, Rama Balaraman, Carlos Fernandes da Silva, Anabelle Chinea, Caitlin Costello, Suman Kambhampati, DeQuincy Andrew Lewis, Michael L. Grossbard, Kathleen J. Yost, Robert Robles, Michaël Sébag, Wayne Harris, Justinian R. Ngaiza, Michael Bär, Marie Shieh, Fredrick Min, Adedayo A. Onitilo, Fabio Volterra, William Wachsman, Madhuri Yalamachili, Eugènia Abella, Larry J. Anderson, Joan Bargay, Hani Hassoun, Gerald C Hsu, Hakan Kaya, Alex R. Menter, Dilip Patel, Donald Richards, William B. Solomon, Robert F. Anderson, Sumeet Chandra, Miguel Á. Conde, Saulias Girnius, May Matkiwsky, Isabel Krsnik, Shaji Kumar, Albert Oriol, Paula Rodríguez, Vivek Roy, Shanti Srinivas, Ronald G. Steis, Austin Christofferson, Sara Nasser, Jessica L. Aldrich, Christophe Legendre, Brooks A. Benard, C. S. Miller, Bryce Turner, Ahmet Kurdoglu, Megan Washington, Venkata D. Yellapantula, Jonathan Adkins, Lori Cuyugan, Martin Boateng, Adrienne Helland, Shari Kyman, Jackie McDonald, Rebecca Reiman, Kristi Stephenson, Erica E. Tassone, Alex Blanski, Brianne Livermore, Meghan Kirchhoff, Daniel C. Rohrer, Mattia D’Agostino, Kimberly Collison, Jennifer Stumph, Pam Kidd, Andrea Donnelly, Barbara Zaugg, Maureen Toone, Kyle McBride, Mary DeRome, Jennifer Rogers, David W. Craig, Winnie S. Liang, Norma C. Gutiérrez, Scott D. Jewell, John D. Carpten, Kenneth C. Anderson, Hearn Jay Cho, Daniel Auclair, Sagar Lonial, Jonathan J. Keats

Bibliographic record

VenueNature Genetics · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcGill UniversityAlberta Cancer FoundationRoyal Victoria HospitalPrincess Margaret Cancer Centre
FundersPrincipia BiopharmaLegend BiotechPharmacyclicsKite PharmaGenentechEMD SeronoMorphoSysSeagenBeiGeneSkylineDxModernaPfizerIncyteAstex PharmaceuticalsBaxaltaTG TherapeuticsDaewoong Pharmaceutical CompanyRegeneron PharmaceuticalsEli Lilly and CompanyAstraZenecaCSL BehringSwedish Orphan BiovitrumNovo NordiskTeva Pharmaceutical IndustriesArray BioPharmaNational Cancer InstituteGilead SciencesSanofiAmgenVifor PharmaMultiple Myeloma Research Foundation
KeywordsBiologyMultiple myelomaGene expression profilingComputational biologyProfiling (computer programming)Expression (computer science)GeneticsGene expressionGeneImmunologyComputer science

Abstract

fetched live from OpenAlex

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundation’s Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study ( NCT01454297 ) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option. Longitudinal genomic and transcriptomic profiling of 1,143 patients with multiple myeloma by the Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study yields an improved copy number and gene expression subtype scheme, most notably a high-risk proliferative subtype associated with complete loss of RB1 or MAX.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.460

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.329
Teacher spread0.312 · 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 designBench or experimental
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

Citations75
Published2024
Admission routes1
Has abstractyes

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