Biology subjects

Samur, M.

Publications and source records attributed to Samur, M..

5 recordsLinked to original sources

bioRxiv2026

Synthetic lethality of p97 inhibition and PSMC2-overexpressionovercomes bortezomib resistance in multiple myeloma

The aim of the present study was to analyze genomic profiles obtained from multiple myeloma (MM) patient tumors to identify prognostic classifiers of clinical response and survival following bortezomib monotherapy and then target key actionable classifiers that underly bortezomib resistance. We discovered higher levels of proteasome PSMC2 messenger RNA in myeloma cells from patients that did not respond to bortezomib and that higher PSMC2 expression correlated with inferior progression-free and overall survival. Pharmacologic screening revealed that CB-5339, an ATP-competitive inhibitor of the molecular chaperone p97/VCP that shuttles ubiquitin-conjugated substrates to proteasomes, was synthetic lethal in MM cells engineered to overexpress PSMC2. p97 co-immunoprecipitated with 26S proteasomes affinity-purified from PSMC2-overexpressing cells which exhibited greater activity and reduced bortezomib sensitivity relative to PSMC2-KO cells. Bortezomib treatment followed by in-situ cross-linking and western blotting revealed higher levels of K48- and K63-linkage-specific ubiquitin chains covalently bound to 26S proteasomes isolated from PSMC2-KO cells compared to proteasomes affinity-purified from PSMC2-overexpressing cells. Western blotting demonstrated higher levels of 19S RPs, 26S proteasomes, and a PSMC2-PSMC1-PSMD2 subcomplex in PSMC2-overexpressing cells relative to PSMC2-KO cells. CB-5339 and bortezomib co-treatment synergistically targeted tumor cells obtained from MM patients previously exposed to bortezomib therapy and reduced the in vivo growth of PSMC2-overexpressing myeloma xenografts in mice. Taken together, we have discovered a genetic vulnerability that provokes proteasome-intrinsic bortezomib resistance and validated the potential of synthetic lethality to guide therapeutic interventions. Our study reaffirms that transcriptomic heterogeneity in MM is a primary driver of prognostic variability.

cancer biology↗

bioRxiv2026

Sequential acquisition of 1p31.1-p12 LOH and 1q Gain is a common double-hit event in relapsed/refractory myeloma

Therapy-driven genomic changes in multiple myeloma (MM) remain poorly defined. We analyzed whole-genome sequencing (WGS) data from relapsed/refractory MM (rrMM, N=386) and identified regional 1p31.1-p12 (hereafter 1pCEN, a region proximal to the centromere) loss-of-heterozygosity (LOH) as the only enriched aberration showing strong therapy-associated clonal selection (clonal timing rank fold-change = 3.7, P<2.2x10-16). This event showed enriched co-occurrence with 1qGain (OR = 2.3 (1.5-3.8), P=2x10-4) forming a recurrent "double-hit" in rrMM. To validate the clonal selection process, we examined three longitudinal cohorts (180 patients, 390 samples) and confirmed clonal expansion of 1pCEN and consistent prevalence of the 1pCEN+1q double-hit (20-24%). Survival analyses demonstrated significantly reduced progression-free survival in rrMM patients with this double-hit compared with those without. Comparison with a large newly diagnosed MM (ndMM) cohort confirmed previously-described 1p32 LOH is the prognostic locus at baseline, whereas 1pCEN is therapy-selected and largely independent of the 1p32 locus. Thus, 1pCEN+1q represents a recurrent double-hit event that clonally emerges in rrMM, conferring selective advantage under drug exposure and is distinct from the ndMM high-risk markers defined by current consensus guidelines. These findings nominate 1pCEN as a new genomic biomarker in rrMM and 1pCEN+1q may help patient stratification for therapeutic monitoring. Key PointsA therapy-driven common genomic double-hit (1p31.1-p12 LOH with 1q gain) clonally emerges in relapsed/refractory myeloma.

cancer biology↗

bioRxiv2025

Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma

Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naive B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e-4). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naive B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of SignificanceLongitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8 T cells limited memory formation post-transplant, while naive B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape.

cancer biology↗

bioRxiv2021

Integrative multi-omics identifies high risk Multiple Myeloma segment associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Despite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval<1x10-6)], which uniquely presents a broad genomic loss (>9% of entire genome, t.test pval<1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Statement of SignificanceUsing multi-omics unsupervised clustering we discovered a new high-risk multiple myeloma patient segment. We linked its diverse genetic markers (previously known, and new including genomic loss) to transcriptional dysregulation (cell cycle, DNA repair and DNA damage) and identified master regulators that control these key biological pathways.

cancer biology↗

bioRxiv2019

Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier, PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.\n\nKey pointsO_LIMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.\nC_LIO_LIIdentification of PHF19 as the expression based biomarker most strongly associated with rapid progression in MM patients.\nC_LI

cancer biology↗