Biology subjects

Baughn, L. B.

Publications and source records attributed to Baughn, L. B..

3 recordsLinked to original sources

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↗

bioRxiv2024

Unified somatic calling and machine learning-based classification enhance the discovery of clonal hematopoiesis of indeterminate potential

Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline and artifact. In whole-exome data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes, predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had <5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies.

bioinformatics↗

bioRxiv2023

Machine Learning Investigation Of Gene Expression Datasets Reveals TP53 Mutant-like AML With Wild Type TP53 And Poor Prognosis

Acute myeloid leukemia (AML) with TP53 mutations (TP53Mut) has poor clinical outcomes with 1-year survival rates of less than 10%. We investigated whether this AML subtype harbors a distinct gene expression profiling (GEP), what this GEP reveals about TP53Mut AML pathophysiology, and whether this GEP is prognostic in TP53 wild type (TP53WT) AML. We applied a supervised machine-learning approach to assess whether a unique TP53Mut GEP could be detected. Using the BEAT-AML dataset, we randomly divided the samples into training and testing datasets, while the TCGA dataset was reserved as a validation dataset. We trained a ridge regression machine learning model to classify TP53Mut and TP53WT cases. This model was highly accurate in distinguishing TP53Mut versus TP53WT cases in both the test and validation data sets. Additionally, we noted a cohort of TP53WT samples with high ridge regression scores and poor overall survival, suggesting share clinical and GEP features with TP53Mut AML. We defined these TP53WT samples as TP53 mutant-like (TP53Mut-like) AMLs. We trained a second ridge regression model to specifically detect TP53Mut-like samples in the BEAT AML dataset and found that TCGA data also harbors TP53Mut-like samples. The TP53Mut-like samples in the TCGA also have a worse OS rate than TP53WT cases. Using drug sensitivity data from 122 small molecules in the BEAT AML dataset, we found TP53Mut-like AMLs have distinct drug sensitivity patterns compared to TP53WT. Finally, we identified a 25 gene signature that can identify TP53Mut-like cases. This signature could be used clinically to identify this novel subset of poor-prognosis AML.

cancer biology↗