---
title: "Multiomic predictors of minimal residual disease after Isa-KRd induction in newly diagnosed multip"
id: "nature-1-molecular-predictors-of-minimal-residual-disease-in-newly-diagnosed-multiple"
canonical_url: "https://medichelpline.com/clinical-feed/nature-1-molecular-predictors-of-minimal-residual-disease-in-newly-diagnosed-multiple"
content_type: "clinical_feed_article"
specialty: "Hematology"
source_name: "Nature Medicine"
source_url: "https://www.nature.com/articles/s41591-026-04692-0"
published_at: "2026-10-19T10:18:12.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Multiomic predictors of minimal residual disease after Isa-KRd induction in newly diagnosed multip
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/nature-1-molecular-predictors-of-minimal-residual-disease-in-newly-diagnosed-multiple
- **Specialty:** [Hematology](https://medichelpline.com/clinical-feed/hematology.md)
- **Primary Source:** Nature Medicine
- **Source URL:** [Original Journal Publication](https://www.nature.com/articles/s41591-026-04692-0)
- **Published At:** 2026-10-19T10:18:12.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Investigators profiled 423 tumor samples from the MIDAS cohort using integrated genomic, transcriptomic and epigenomic assays to study response to quadruplet induction with **Isa-KRd**. - Postinduction **minimal residual disease** (MRD) negativity varied by gene-expression subtype; HP1/HP3 and MS subgroups showed higher MRD-negativity than MF and CD2 subtypes. - Transcriptomic programs linked to proliferation and **MYC** activity associated with MRD negativity, while B cell differentiation signatures associated with MRD positivity. - The study team validated transcriptomic MRD associations in the external CASSIOPEIA cohort. - Whole-genome sequencing exposed known mutational processes such as **APOBEC** mutagenesis and **chromothripsis**, plus newly described genomic instability phenotypes validated in the MMRF cohort. - New driver alterations were identified and independently validated across three external datasets (MMRF, MANHATTAN, CASSIOPEIA; 1,451 cases). - Driver genes and instability features — including alterations involving **CCND1** and **MYC** — correlated with different MRD outcomes. - DNA methylation profiling delineated nine MRD-linked epigenetic subgroups defined by hypomethylation in quiescent chromatin and cis/trans signatures of driver translocations. - A multimodal random forest model that combined genomic, transcriptomic and epigenomic inputs predicted MRD status with an area under the curve of 0.77, outperforming models that used only clinical or genomic features. - ClinicalTrials.gov identifier for the cohort: NCT04934475. - The authors note a prior absence of large-scale, prospectively collected integrative multiomic analyses in newly diagnosed multiple myeloma treated with contemporary quadruplet regimens, a gap this work addresses.
## Clinical Analysis & Structured Key Points
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[articles](https://www.nature.com/nm/articles?type=article) 4. article Molecular predictors of minimal residual disease in newly diagnosed multiple myeloma treated with quadruplet therapy [ Download PDF ](https://www.nature.com/articles/s41591-026-04692-0.pdf) [ Download PDF ](https://www.nature.com/articles/s41591-026-04692-0.pdf) * Article * [Open access](https://www.springernature.com/gp/open-science/about/the-fundamentals-of-open-access-and-open-research) * Published: 19 October 2026 # Molecular predictors of minimal residual disease in newly diagnosed multiple myeloma treated with quadruplet therapy * [Florence Magrangeas](https://www.nature.com/articles/s41591-026-04692-0#auth-Florence-Magrangeas-Aff1-Aff2) [ORCID: orcid.org/0000-0002-5031-3748](https://orcid.org/0000-0002-5031-3748)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[2](https://www.nature.com/articles/s41591-026-04692-0#Aff2), * [Mia Cherkaoui](https://www.nature.com/articles/s41591-026-04692-0#auth-Mia-Cherkaoui-Aff1) [ORCID: orcid.org/0000-0001-5488-2238](https://orcid.org/0000-0001-5488-2238)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1), * [Victor Bessonneau-Gaborit](https://www.nature.com/articles/s41591-026-04692-0#auth-Victor-Bessonneau_Gaborit-Aff1)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1), * [Jennifer Derrien](https://www.nature.com/articles/s41591-026-04692-0#auth-Jennifer-Derrien-Aff1) [ORCID: orcid.org/0000-0002-3135-2055](https://orcid.org/0000-0002-3135-2055)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1), * [Céline Chevalier](https://www.nature.com/articles/s41591-026-04692-0#auth-C_line-Chevalier-Aff1-Aff2) [ORCID: orcid.org/0000-0002-7411-089X](https://orcid.org/0000-0002-7411-089X)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[2](https://www.nature.com/articles/s41591-026-04692-0#Aff2), * [Catherine Guérin-Charbonnel](https://www.nature.com/articles/s41591-026-04692-0#auth-Catherine-Gu_rin_Charbonnel-Aff1-Aff3)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[3](https://www.nature.com/articles/s41591-026-04692-0#Aff3), * [Benjamin Diamond](https://www.nature.com/articles/s41591-026-04692-0#auth-Benjamin-Diamond-Aff4) [ORCID: orcid.org/0000-0002-8638-9365](https://orcid.org/0000-0002-8638-9365)[4](https://www.nature.com/articles/s41591-026-04692-0#Aff4), * [Mehmet Kemal Samur](https://www.nature.com/articles/s41591-026-04692-0#auth-Mehmet_Kemal-Samur-Aff5-Aff6) [ORCID: orcid.org/0000-0002-9978-5682](https://orcid.org/0000-0002-9978-5682)[5](https://www.nature.com/articles/s41591-026-04692-0#Aff5),[6](https://www.nature.com/articles/s41591-026-04692-0#Aff6), * [Intergroupe Francophone du Myélome (IFM)](https://www.nature.com/articles/s41591-026-04692-0#group-1), * [Cyrille Hulin](https://www.nature.com/articles/s41591-026-04692-0#auth-Cyrille-Hulin-Aff7)[7](https://www.nature.com/articles/s41591-026-04692-0#Aff7), * [Denis Caillot](https://www.nature.com/articles/s41591-026-04692-0#auth-Denis-Caillot-Aff8)[8](https://www.nature.com/articles/s41591-026-04692-0#Aff8), * [Lionel Karlin](https://www.nature.com/articles/s41591-026-04692-0#auth-Lionel-Karlin-Aff9)[9](https://www.nature.com/articles/s41591-026-04692-0#Aff9), * [Bertrand Arnulf](https://www.nature.com/articles/s41591-026-04692-0#auth-Bertrand-Arnulf-Aff10)[10](https://www.nature.com/articles/s41591-026-04692-0#Aff10), * [Ola Landgren](https://www.nature.com/articles/s41591-026-04692-0#auth-Ola-Landgren-Aff4) [ORCID: orcid.org/0000-0001-6485-4839](https://orcid.org/0000-0001-6485-4839)[4](https://www.nature.com/articles/s41591-026-04692-0#Aff4), * [Nikhil C. Munshi](https://www.nature.com/articles/s41591-026-04692-0#auth-Nikhil_C_-Munshi-Aff11-Aff12) [ORCID: orcid.org/0000-0002-7344-9795](https://orcid.org/0000-0002-7344-9795)[11](https://www.nature.com/articles/s41591-026-04692-0#Aff11),[12](https://www.nature.com/articles/s41591-026-04692-0#Aff12), * [Hervé Avet-Loiseau](https://www.nature.com/articles/s41591-026-04692-0#auth-Herv_-Avet_Loiseau-Aff13-Aff14)[13](https://www.nature.com/articles/s41591-026-04692-0#Aff13),[14](https://www.nature.com/articles/s41591-026-04692-0#Aff14), * [Ludovic Martinet](https://www.nature.com/articles/s41591-026-04692-0#auth-Ludovic-Martinet-Aff13-Aff14) [ORCID: orcid.org/0000-0002-0867-7724](https://orcid.org/0000-0002-0867-7724)[13](https://www.nature.com/articles/s41591-026-04692-0#Aff13),[14](https://www.nature.com/articles/s41591-026-04692-0#Aff14), * [Jill Corre](https://www.nature.com/articles/s41591-026-04692-0#auth-Jill-Corre-Aff13-Aff14) [ORCID: orcid.org/0000-0003-1580-6106](https://orcid.org/0000-0003-1580-6106)[13](https://www.nature.com/articles/s41591-026-04692-0#Aff13),[14](https://www.nature.com/articles/s41591-026-04692-0#Aff14), * [Aurore Perrot](https://www.nature.com/articles/s41591-026-04692-0#auth-Aurore-Perrot-Aff15) [ORCID: orcid.org/0000-0003-0131-8689](https://orcid.org/0000-0003-0131-8689)[15](https://www.nature.com/articles/s41591-026-04692-0#Aff15), * [Philippe Moreau](https://www.nature.com/articles/s41591-026-04692-0#auth-Philippe-Moreau-Aff1-Aff16)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[16](https://www.nature.com/articles/s41591-026-04692-0#Aff16), * [Cyrille Touzeau](https://www.nature.com/articles/s41591-026-04692-0#auth-Cyrille-Touzeau-Aff1-Aff16)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[16](https://www.nature.com/articles/s41591-026-04692-0#Aff16), * [Stéphane Minvielle](https://www.nature.com/articles/s41591-026-04692-0#auth-St_phane-Minvielle-Aff1-Aff2) [ORCID: orcid.org/0000-0003-1389-312X](https://orcid.org/0000-0003-1389-312X)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[2](https://www.nature.com/articles/s41591-026-04692-0#Aff2) [na2](https://www.nature.com/articles/s41591-026-04692-0#na2) & * … * [Eric Letouzé](https://www.nature.com/articles/s41591-026-04692-0#auth-Eric-Letouz_-Aff1-Aff2) [ORCID: orcid.org/0000-0002-6369-2839](https://orcid.org/0000-0002-6369-2839)[1](https://www.nature.com/articles/s41591-026-04692-0#Aff1),[2](https://www.nature.com/articles/s41591-026-04692-0#Aff2) [na2](https://www.nature.com/articles/s41591-026-04692-0#na2) Show authors [_Nature Medicine_](https://www.nature.com/nm) (2026) [Cite this article](https://www.nature.com/articles/s41591-026-04692-0#citeas) Save article [ View saved research ](https://www.nature.com/saved-research) ## Abstract Quadruplet therapy, combining anti-CD38 antibodies, immunomodulatory drugs, proteasome inhibitors and dexamethasone, is now the standard induction treatment for newly diagnosed multiple myeloma (NDMM). However, treatment response is heterogeneous and molecular predictors are needed to guide early therapeutic decisions. We performed integrated genomic, transcriptomic and epigenomic profiling of 423 tumor samples from patients with NDMM from the MIDAS cohort to characterize molecular heterogeneity and its association with measurable residual disease (MRD) after quadruplet induction with isatuximab, carfilzomib, lenalidomide and dexamethasone (Isa-KRd). Postinduction MRD-negativity rates varied significantly across gene expression subtypes, with HP1/HP3 and MS groups showing superior responses compared to the MF and CD2 subtypes. B cell differentiation was associated with MRD positivity, while proliferation and MYC programs were associated with MRD negativity. These MRD-associated transcriptomic features were independently validated in the CASSIOPEIA cohort. Whole-genome sequencing revealed both known (APOBEC mutagenesis, chromothripsis) and new genomic instability phenotypes validated in the MMRF cohort. New driver alterations were identified with independent validation across three external cohorts with multiple myeloma (MMRF, MANHATTAN and CASSIOPEIA; 1,451 cases). Genomic instability features and driver genes such as _CCND1_ and _MYC_ correlated with differential MRD outcomes. DNA methylation profiling identified nine MRD-linked subgroups distinguished by the extent of hypomethylation in quiescent chromatin and _cis_ /_trans_ signatures of driver translocations. A multimodal random forest model integrating genomic, transcriptomic and epigenomic features predicted MRD status after induction, with an area under the curve of 0.77, outperforming clinical and genomic models, while providing comparable discrimination and improved calibration relative to the transcriptomic and epigenomic models. These findings define a multiomic classification of NDMM linked to Isa-KRd response. ClinicalTrials.gov identifier: [NCT04934475](https://clinicaltrials.gov/study/NCT04934475). ### Explore related subjects Discover the latest articles and news in related subjects. * [Cancer genomics](https://www.nature.com/subjects/cancer-genomics) * [Myeloma](https://www.nature.com/subjects/myeloma) * [Tumour biomarkers](https://www.nature.com/subjects/tumour-biomarkers) * [Multiple Myeloma Treatment Strategies](https://www.nature.com/subjects/multiple-myeloma-treatment-strategies) ## Main Multiple myeloma (MM) is a genetically and clinically heterogeneous plasma-cell malignancy. Large-scale genomic studies have delineated the landscape of driver mutations, translocations and copy number alterations (CNAs) in MM, several of which correlate with clinical outcome[1](https://www.nature.com/articles/s41591-026-04692-0#ref-CR1 "Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101 \(2014\)."),[2](https://www.nature.com/articles/s41591-026-04692-0#ref-CR2 "Walker, B. A. et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood 132, 587–597 \(2018\)."),[3](https://www.nature.com/articles/s41591-026-04692-0#ref-CR3 "Bolli, N. et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia 32, 2604–2616 \(2018\)."),[4](https://www.nature.com/articles/s41591-026-04692-0#ref-CR4 "Shah, V. et al. Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients. Leukemia 32, 102–110 \(2018\)."),[5](https://www.nature.com/articles/s41591-026-04692-0#ref-CR5 "Barwick, B. G. et al. Multiple myeloma immunoglobulin lambda translocations portend poor prognosis. Nat. Commun. 10, 1911 \(2019\)."),[6](https://www.nature.com/articles/s41591-026-04692-0#ref-CR6 "Samur, M. K. et al. Genome-wide somatic alterations in multiple myeloma reveal a superior outcome group. J. Clin. Oncol. 38, 3107–3118 \(2020\)."),[7](https://www.nature.com/articles/s41591-026-04692-0#ref-CR7 "Maura, F. et al. Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens. Nat. Cancer 4, 1660–1674 \(2023\)."),[8](https://www.nature.com/articles/s41591-026-04692-0#ref-CR8 "Skerget, S. et al. Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nat. Genet. 56, 1878–1889 \(2024\)."). Mutational signature analyses have further uncovered key processes underlying genomic instability in MM, including APOBEC-mediated mutagenesis and chromothripsis, which are enriched in high-risk disease[9](https://www.nature.com/articles/s41591-026-04692-0#ref-CR9 "Magrangeas, F., Avet-Loiseau, H., Munshi, N. C. & Minvielle, S. Chromothripsis identifies a rare and aggressive entity among newly diagnosed multiple myeloma patients. Blood 118, 675–678 \(2011\)."),[10](https://www.nature.com/articles/s41591-026-04692-0#ref-CR10 "Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5, 2997 \(2014\)."),[11](https://www.nature.com/articles/s41591-026-04692-0#ref-CR11 "Walker, B. A. et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat. Commun. 6, 6997 \(2015\)."),[12](https://www.nature.com/articles/s41591-026-04692-0#ref-CR12 "Maura, F. et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat. Commun. 10, 3835 \(2019\)."). Transcriptomic profiling has defined reproducible gene expression subgroups[8](https://www.nature.com/articles/s41591-026-04692-0#ref-CR8 "Skerget, S. et al. Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nat. Genet. 56, 1878–1889 \(2024\)."),[13](https://www.nature.com/articles/s41591-026-04692-0#ref-CR13 "Zhan, F. et al. The molecular classification of multiple myeloma. Blood 108, 2020–2028 \(2006\)."),[14](https://www.nature.com/articles/s41591-026-04692-0#ref-CR14 "Broyl, A. et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood 116, 2543–2553 \(2010\).") that reflect initiating genetic events such as _IGH_ translocations and hyperdiploidy, while also capturing refined functional signatures associated with high risk[15](https://www.nature.com/articles/s41591-026-04692-0#ref-CR15 "Shaughnessy, J. D. et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 109, 2276–2284 \(2007\)."),[16](https://www.nature.com/articles/s41591-026-04692-0#ref-CR16 "Decaux, O. et al. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myélome. J. Clin. Oncol. 26, 4798–4805 \(2008\)."). In parallel, genome-wide DNA methylation studies have demonstrated extensive and heterogeneous epigenetic remodeling in MM, contributing to transcriptional plasticity and associated with clinical outcome[17](https://www.nature.com/articles/s41591-026-04692-0#ref-CR17 "Agirre, X. et al. Whole-epigenome analysis in multiple myeloma reveals DNA hypermethylation of B cell-specific enhancers. Genome Res. 25, 478–487 \(2015\)."),[18](https://www.nature.com/articles/s41591-026-04692-0#ref-CR18 "Derrien, J. et al. The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability. Genome Med. 13, 127 \(2021\)."). Together, the genome, transcriptome and methylome represent complementary molecular dimensions that jointly inform MM pathogenesis and patient stratification. Despite these advances, integrative analyses that encompass all three molecular layers within large, prospectively collected clinical trial cohorts are lacking. This gap is particularly relevant given the rapid evolution of the therapeutic landscape for newly diagnosed MM (NDMM). Quadruplet regimens combining an anti-CD38 antibody, a proteasome inhibitor, an immunomodulatory drug and dexamethasone have become the new standard induction therapy for NDMM[19](https://www.nature.com/articles/s41591-026-04692-0#ref-CR19 "Dimopoulos, M. A. et al. EHA-EMN evidence-based guidelines for diagnosis, treatment and follow-up of patients with multiple myeloma. Nat. Rev. Clin. Oncol. 2
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