Biology subjects

Yong, K.

Publications and source records attributed to Yong, K..

5 recordsLinked to original sources

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

Conserved programs and specificities of T cells targeting hematological malignancies

T cell-mediated immune surveillance is critical for cancer control, yet its endogenous effectiveness in hematological malignancies remains limited and poorly understood. Here, we integrate single-cell T cell receptor (TCR) profiling, HLA immunopeptidomics and functional antigen mapping to dissect the specificity landscape of bone marrow lymphocytes (BMLs) in multiple myeloma (MM) and acute myeloid leukemia (AML). We identify a rare subset of tumor-reactive T cells that exhibit a stereotyped transcriptional state distinct from bystander and virus-specific populations. Across both malignancies, immunopeptidomic profiling uncovers a partially conserved antigen repertoire enriched for noncanonical peptides, including products of novel or unannotated open reading frames (nuORFs), pseudogenes, and clonotypic immunoglobulin sequences. Several of these epitopes are recurrently presented and associated with convergent TCR responses across individuals. Based on this immune architecture, we develop a TCR-intrinsic fitness model that infers BML tumor specificity from transcriptional cues and stratifies immunotherapy response across three independent patient cohorts. Together, these findings map the latent potential of endogenous anti-tumor immunity in two biologically distinct diseases and provide a framework for decoding and restoring productive immune surveillance of hematological malignancies. HighlightsO_LISingle-cell resolved TCR profiling maps rare tumor-reactive T cells in the bone marrow of multiple myeloma (MM) and acute myeloid leukemia (AML) reveals conserved transcriptional programs C_LIO_LIA shared immunopeptidome across MM and AML includes noncanonical epitopes from nuORFs and idiotype sequences C_LIO_LIConserved tumor antigens elicit convergent T cell responses across patients C_LI O_LIA TCR fitness model predicts tumor specificity in bone marrow lymphocytes and stratifies immunotherapy response in both hematological malignancies C_LI

immunology↗

bioRxiv2025

Single-cell profiling of human bone marrow reveals multiple myeloma progression is accompanied by an increase in CD56bright bone marrow resident NK cells.

Natural killer (NK) cells play a key role in the innate immune response against tumour progression. While the immune microenvironment in multiple myeloma (MM) becomes increasingly dysfunctional during disease evolution, little is known about changes in the NK cell compartment. Using primary samples from clinical trial patients, we performed detailed phenotypic analyses of bone marrow mononuclear cells from MGUS, SMM, and newly diagnosed MM patients. We found that disease progression is associated with an increase in CD56bright NK cells with a dynamic positive association between this NK cell subset and local tumour burden. We generated a large single-cell RNA sequencing dataset of >100,000 NK cells from healthy donor individuals and plasma cell disorder patients and identify a bone marrow specific CD56bright-like NK cell population (BM-NK) that is enriched in the marrow of MM patients. These findings highlight the evolution of the NK-cell compartment in MM and suggest a role for BM-resident CD56bright NK cells with impaired cytotoxicity in promoting immune evasion.

cancer biology↗

bioRxiv2021

Genetic Subtypes of Smoldering Multiple Myeloma are associated with Distinct Pathogenic Phenotypes and Clinical Outcomes

Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrated 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identified six distinct genetic subtypes. These subtypes were differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three subtypes were associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification model.

cancer biology↗

bioRxiv2017

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique - Subtype and Stage Inference (SuStaIn) - able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimers disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10--4) or temporal stage (p=3.96x10-5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

neuroscience↗