Biology subjects

Thakurta, A.

Publications and source records attributed to Thakurta, A..

7 recordsLinked to original sources

bioRxiv2026

A platform for deep topographic multiomic mapping of the bone marrow environment in multiple myeloma

Multiple myeloma is a genetically complex plasma cell malignancy in which profound intratumoral heterogeneity shapes disease progression and therapeutic resistance. Genomic subclones dynamically emerge and evolve under treatment pressure, yet their spatial organization within intact bone marrow remains poorly defined. We hypothesized that genetically distinct myeloma subclones occupy spatially discrete ecological niches associated with unique molecular and microenvironmental states. To test this, we here demonstrate a proof-of-concept spatial tri-omics workflow combining laser capture microdissection-based genomics and deep LC-MS/MS proteomics with single-cell spatial transcriptomics, integrated by digital co-registration of adjacent tissue sections. Applied to a myeloma bone marrow trephine, this approach enabled coordinated profiling of matched anatomical regions demonstrating that genomic, transcriptomic and proteomic profiles converged to define clone-specific microenvironmental programmes. We show that genetically distinct myeloma subclones occupy mutually exclusive regions of the same bone marrow biopsy. Remarkably, an aggressive del(17p) clone and a gain(12q) clone occupied defined anatomical territories within the same trephine-biopsied section, each clone exhibiting distinct genomic, transcriptomic, proteomic and cellular ecosystems. The del(17p) clone localized to a fibrotic, osteolytic and angiogenic niche enriched for activated stromal cells, osteoblasts, osteoclasts and immunoregulatory myeloid populations, whereas the gain(12q) clone occupied a comparatively quiescent marrow niche with limited stromal remodelling. Together, these findings demonstrate that genetically distinct myeloma subclones establish spatially and biologically discrete ecological niches through coordinated remodelling of stromal, immune and bone compartments, providing a framework for understanding clonal evolution within the bone marrow microenvironment.

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↗

bioRxiv2026

Early plasma proteomic alterations precede amyloidosis diagnosis, reflecting cardiac and immune dysregulation

Systemic amyloidosis is typically diagnosed only after irreversible organ damage has occurred, limiting the effectiveness of available therapies. Whether the disease is preceded by detectable molecular changes long before clinical presentation has remained unclear. Here, we leveraged population-scale plasma proteomics and longitudinal follow-up from the UK Biobank to investigate early circulating protein signatures associated with future diagnosis of amyloidosis. Among approximately 53,000 participants with proteomic profiling, we identified 61 individuals who developed amyloidosis up to 14 years after protein assessment. Differential expression and correlation analyses identified a seven-protein panel, including MYL3, MYBPC1, NT-proBNP, NPPB, FCRLB, IGFBP1, and FABP1, consistent with early cardiac stress and immune dysregulation. Time-to-event modelling demonstrated robust stratification of amyloidosis risk and timing. Importantly, a parsimonious subset of these proteins retained strong predictive performance, indicating that a reduced set of biologically informative markers is sufficient for risk stratification. Furthermore, these proteomic signals were not explained by pre-existing cardiac disease, clonal haematopoiesis, or related plasma cell disorders, indicating that they capture disease-specific biological processes preceding clinical diagnosis. Together, these findings show that amyloidosis is preceded by persistent plasma proteomic alterations, providing a framework for early risk stratification and insight into the preclinical biology of this under-recognised disease.

bioinformatics↗

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↗

bioRxiv2021

Highly accurate barcode and UMI error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing

Droplet-based single-cell sequencing techniques have provided unprecedented insight into cellular heterogeneities within tissues. However, these approaches only allow for the measurement of the distal parts of a transcript following short-read sequencing. Therefore, splicing and sequence diversity information is lost for the majority of the transcript. The application of long-read Nanopore sequencing to droplet-based methods is challenging because of the low base-calling accuracy currently associated with Nanopore sequencing. Although several approaches that use additional short-read sequencing to error-correct the barcode and UMI sequences have been developed, these techniques are limited by the requirement to sequence a library using both short- and long-read sequencing. Here we introduce a novel approach termed single-cell Barcode UMI Correction sequencing (scBUC-seq) to efficiently error-correct barcode and UMI oligonucleotide sequences synthesized by using blocks of dimeric nucleotides. The method can be applied to correct either short-read or long-read sequencing, thereby allowing users to recover more reads per cell and permits direct single-cell Nanopore sequencing for the first time. We illustrate our method by using species-mixing experiments to evaluate barcode assignment accuracy and evaluate differential isoform usage and fusion transcripts using myeloma and sarcoma cell line models.

genomics↗

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↗

bioRxiv2019

Microhomology-mediated end joining drives complex rearrangements and over expression of MYC and PVT1 in multiple myeloma

MYC is a widely acting transcription factor and its deregulation is a crucial event in many human cancers. MYC is important biologically and clinically in multiple myeloma, but the mechanisms underlying its dysregulation are poorly understood. We show that MYC rearrangements are present in 36.0% of newly diagnosed myeloma patients, as detected in the largest set of next generation sequencing data to date (n=1267). Rearrangements were complex and associated with increased expression of MYC and PVT1, but not other genes at 8q24. The highest effect on gene expression was detected in cases where the MYC locus is juxtaposed next to super-enhancers associated with genes such as IGH, IGK, IGL, TXNDC5/BMP6, FAM46C and FOXO3. We identified three hotspots of recombination at 8q24, one of which is enriched for IGH-MYC translocations. Breakpoint analysis indicates primary myeloma rearrangements involving the IGH locus occur through non-homologous end joining, whereas secondary MYC rearrangements occur through microhomology-mediated end joining. This mechanism is different to lymphomas, where non-homologous end joining generates MYC rearrangements. Rearrangements resulted in over-expression of key genes and ChIP-seq identified that HK2, a member of the glucose metabolism pathway, is directly over-expressed through binding of MYC at its promoter.

cancer biology↗