Biology subjects

Ha, G.

Publications and source records attributed to Ha, G..

17 recordsLinked to original sources

bioRxiv2026

Dysregulation of epithelial differentiation architecture underlies field cancerization in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) has exceptionally high recurrence rates, exceeding 50%, contributing to mortality and morbidity. This recurrence rate is proposed to be due to field cancerization, a phenomenon where tumor-adjacent "field" tissue that appears histologically normal harbors molecular changes that prime it for subsequent tumor growth. Current models attribute field cancerization to shared driver mutations between tumors and adjacent at-risk tissue, but this assumption has never been directly tested. To comprehensively characterize this phenomenon, we profiled patient-matched tumor, normal-adjacent field (NAF), contralateral normal, and blood samples from HPV-negative HNSCC patients using whole genome sequencing, targeted driver gene sequencing, single-nucleus multiome profiling, spatial transcriptomics and functional proliferation assays. While histologically normal NAF tissues did harbor expanded mutant clones, these mutations rarely overlapped with paired tumors and were not shared across patients, suggesting molecular features beyond mutations are altered in NAF. Single-cell multiomics revealed that NAF epithelium loses its normal organizational hierarchy: basal stem cell markers persist in differentiated cells, while terminal differentiation programs fail to activate, with reconfiguration of both gene regulatory networks and cell-cell interactions. Proliferating cells appeared in tissue layers normally restricted to post-mitotic cells, a pattern typically seen only in overt dysplasia. Within this disrupted hierarchy, a distinct epithelial state arose in NAF that expressed both normal epithelial and tumor-associated markers, which we localized to the parabasal layers using immunofluorescence and spatial transcriptomics. Together, our findings demonstrate that field cancerization operates through epithelial hierarchy disorganization that disrupts differentiation, generates cells with tumor-like features, and may create a permissive environment for the expansion of cancer-associated mutations, all within tissue that appears histologically normal.

cancer biology↗

bioRxiv2026

Minimally invasive monitoring of clonal evolution through integrated single cell and ctDNA analysis

Circulating cell-free DNA (cfDNA) offers a minimally invasive lens into tumor evolution. However, the accurate quantification of clonal composition from cfDNA remains challenging. Existing methods for clone tracking using liquid biopsies are constrained by issues such as their reliance on bulk tissue references, incomplete representations of clonal architecture or limited breadth of mutation panels. To address these limitations, we developed cfClone, a Bayesian framework that integrates single-cell whole-genome sequencing (scWGS) derived clonal structures with cfDNA whole-genome sequencing (cfDNA-WGS) data to enable high-resolution tissue-informed clonal tracking. By jointly modeling local copy-number alterations and haplotype-specific signals via Bayesian model selection and Markov chain Monte Carlo (MCMC) sampling, the algorithm yields uncertainty-aware estimates of clonal prevalence and tumour fraction (TF). Applied to longitudinal clinical cohorts, cfClone can be used to reconstruct evolutionary trajectories and uncovers clonal selection driving therapeutic resistance, including the de novo detection of emergent clonal populations. We demonstrate that cfClone achieves accurate TF estimates and circulating tumor DNA (ctDNA) detection in malignancies with varying degrees of copy-number variant (CNV) burden using semi-synthetic data. We then compare to the state of the art scWGS informed panel based approach, and demonstrate cfClone provides comparable accuracy while allowing for the tracking of more clones and detection of novel clones. Finally, we show how cfClone can be used to quantitatively track clonal dynamics in response to treatment in high grade serous ovarian cancer. Github link: https://github.com/Roth-Lab/cfclone

cancer biology↗

bioRxiv2026

Contributions of error correction and the spindle assembly checkpoint to mitotic timing and fidelity

Chromosome segregation is a tightly-regulated process that normally occurs with high fidelity. Errors in chromosome segregation are associated with aging, cancer, and infertility. Initially erroneously attached chromosomes are corrected over the course of mitosis, with the spindle assembly checkpoint preventing entry into anaphase until this error correction is complete. Despite extensive work on the molecular basis of error correction and the spindle assembly checkpoint, it is still unclear how disruption of these processes contribute to chromosome segregation errors. Here, we develop and experimentally test a coarse-grained model of error correction in the presence of a faulty spindle assembly checkpoint. We use the resulting model to disentangle the impact of various small molecule and genetic perturbations on both error correction and the spindle assembly checkpoint, and to compare chromosomally stable hTERT-RPE-1 cells and chromosomally unstable U2-OS cells. We find that the probability of error-free chromosome segregation is determined by the ratio of the checkpoint failure rate to the error correction rate, and validate a simple heuristic for understanding the source of chromosome segregation errors: perturbations which cause errors by disrupting the spindle assembly checkpoint decrease anaphase times, while those that disrupt error correction increase anaphase times. Taken together, this work provides a quantitative framework for understanding how error correction and the spindle assembly checkpoint contribute to mitotic timing and fidelity.

biophysics↗

bioRxiv2026

Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology

Tumor gene expression profiling provides crucial diagnostic information for guiding therapy, but standard tissue biopsies are invasive, spatially biased, and may inadequately sample metastatic disease. Cell-free DNA (cfDNA) provides a minimally invasive alternative for tumor genotyping, yet reconstructing robust, transcriptome-wide expression from standard-depth cfDNA whole-genome sequencing (WGS) remains a major challenge. We developed a deep learning framework comprising Triton, for comprehensive cfDNA feature extraction, and Proteus, a probabilistic model that infers single-gene expression from standard-depth cfDNA WGS. Proteus outperformed prior cfDNA approaches in reconstructing molecular phenotypes from matched tumor transcriptomes across multiple cancer types, including prostate, lung, and bladder cancer cohorts, with uncertainty-guided withholding improving model reliability. Proteus further enabled assessment of therapeutic target activity, prognostic transcriptional programs, and candidate treatment-emergent resistance states, establishing a generalizable framework for minimally invasive functional genomics in precision oncology.

bioinformatics↗

bioRxiv2025

Moving Beyond Binary Biomarkers: Machine Learning Model Resolves Concurrent and Molecularly Heterogeneous Mismatch Repair and Homologous Recombination Deficiencies in Prostate Cancer

Current DNA damage repair (DDR) biomarkers employ binary classifications that fail to capture the molecular complexity of tumors with concurrent repair deficiencies. We used genomics analysis to stratify 672 metastatic prostate cancer patients into 11 DDR subgroups, identifying 51 molecular signatures with weighted roles in class identity. We identified a tumor-mutational-burden very-high subset, characterized by 19 mutations/Mb or more, as a molecularly distinct group characterized by preserved genomic integrity and enhanced immunogenicity. Critically, 2.3 percent of tumors exhibited concurrent TMB-High and HRR mutant phenotypes, while 1.5 percent harbored MMR bi-allelic loss without MMRd (mismatch-repair-deficiency) signatures. Clinical validation in 130 patients demonstrated superior immunotherapy responses in tumors with very high TMB levels. We developed CHIMERA DDR, a probabilistic machine learning tool that integrates these 51 genomic features using a nested Random Forest architecture to infer seven clinically relevant DDR subgroups. After negating model overfit concerns, CHIMERA-DDR showed exceptional classification performance (AUCs 0.919-0.999) to accurately detect MMRd and HRR mutant molecular subtypes with or without concurrent DDR deficiencies, resolving admixed phenotypes to enable precision therapeutic stratification beyond binary methods.

genomics↗

bioRxiv2024

RCC1 depletion drives protein transport defects and rupture in micronuclei

Micronuclei (MN) are a commonly used marker of chromosome instability that form when missegregated chromatin recruits its own nuclear envelope (NE) after mitosis. MN frequently rupture, which results in genome instability, upregulation of metastatic genes, and increased immune signaling. MN rupture is linked to NE defects, but the cause of these defects is poorly understood. Previous work from our lab found that chromosome identity correlates with rupture timing for small MN, i.e. MN containing a short chromosome, with more euchromatic chromosomes forming more stable MN with fewer nuclear lamina gaps. Here we demonstrate that histone methylation promotes rupture and nuclear lamina defects in small MN. This correlates with increased MN size, and we go on to find that all MN have a constitutive nuclear export defect that drives MN growth and nuclear lamina gap expansion, making the MN susceptible to rupture. We demonstrate that these export defects arise from decreased RCC1 levels in MN and that additional loss of RCC1 caused by low histone methylation in small euchromatic MN results in additional import defects that suppress nuclear lamina gaps and MN rupture. Through analysis of mutational signatures associated with early and late rupturing chromosomes in the Pan-Cancer Analysis of Whole Genomes (PCAWG) dataset, we identify an enrichment of APOBEC and DNA polymerase E hypermutation signatures in chromothripsis events on early and mid rupturing chromosomes, respectively, suggesting that MN rupture timing could determine the landscape of structural variation in chromothripsis. Our study defines a new model of MN rupture where increased MN growth, caused by defects in protein export, drives gaps in nuclear lamina organization that make the MN susceptible to membrane rupture with long-lasting effects on genome architecture.

cell biology↗

bioRxiv2024

Molecular consequences of acute versus chronic CDK12 loss in prostate carcinoma nominates distinct therapeutic strategies

Genomic loss of the transcriptional kinase CDK12 occurs in [~]6% of metastatic castration-resistant prostate cancers (mCRPC) and correlates with poor patient outcomes. Prior studies demonstrate that acute CDK12 loss confers a homologous recombination (HR) deficiency (HRd) phenotype via premature intronic polyadenylation (IPA) of key HR pathway genes, including ATM. However, mCRPC patients have not demonstrated benefit from therapies that exploit HRd such as inhibitors of polyADP ribose polymerase (PARP). Based on this discordance, we sought to test the hypothesis that an HRd phenotype is primarily a consequence of acute CDK12 loss and the effect is greatly diminished in prostate cancers adapted to CDK12 loss. Analyses of whole genome sequences (WGS) and RNA sequences (RNAseq) of human mCRPCs determined that tumors with biallelic CDK12 alterations (CDK12BAL) lack genomic scar signatures indicative of HRd, despite carrying bi-allelic loss and the appearance of the hallmark tandem-duplicator phenotype (TDP). Experiments confirmed that acute CDK12 inhibition resulted in aberrant polyadenylation and downregulation of long genes (including BRCA1 and BRCA2) but such effects were modest or absent in tumors adapted to chronic CDK12BAL. One key exception was ATM, which did retain transcript shortening and reduced protein expression in the adapted CDK12BAL models. However, CDK12BALcells demonstrated intact HR as measured by RAD51 foci formation following irradiation. CDK12BAL cells showed a vulnerability to targeting of CDK13 by sgRNA or CDK12/13 inhibitors and in vivo treatment of prostate cancer xenograft lines showed that tumors with CDK12BALresponded to the CDK12/13 inhibitor SR4835, while CDK12-intact lines did not. Collectively, these studies show that aberrant polyadenylation and long HR gene downregulation is primarily a consequence of acute CDK12 deficiency, which is largely compensated for in cells that have adapted to CDK12 loss. These results provide an explanation for why PARPi monotherapy has thus far failed to consistently benefit patients with CDK12 alterations, though alternate therapies that target CDK13 or transcription are candidates for future research and testing.

cancer biology↗

bioRxiv2024

The hyperphosphorylation of SARS-CoV-2s nucleocapsid protein by GSK-3 involves a complex and redundant priming mechanism

Upon entry into host cells, the nucleocapsid (N) protein of coronavirus becomes heavily phosphorylated within its central Ser-Arg domain by glycogen synthase kinase-3 (GSK-3). Most substrates for GSK-3 need to be phosphorylated, or primed, by another Ser/Thr kinase four residues downstream of the GSK-3 phosphorylation site. It was widely assumed that the hyperphosphorylation of SARS-CoV-2s N protein requires independent priming at Ser-188 and Ser-206, which initiates sequential phosphorylation by GSK-3. Here we present biochemical and mass spectrometry data that contradicts this simple model, revealing instead a much more complex and highly cooperative mechanism where redundant priming, as well as exosite docking, enables N proteins efficient phosphorylation by GSK-3. The R203M and R203K/G204R mutations, found in the recent delta and omicron variants of concern, both hinder N hyperphosphorylation by GSK-3. The mechanistic insights generated in this study suggest a novel approach to treat COVID-19 by combining multiple classes of pharmacological agents to inhibit N hyperphosphorylation.

biochemistry↗

bioRxiv2024

Measuring and modeling the dynamics of mitotic error correction

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

cell biology↗

bioRxiv2024

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

bioinformatics↗

bioRxiv2023

Dual mechanism of kinetochore microtubule detachment

During eukaryotic cell division, microtubules connect to chromosomes by attaching to the kinetochore via the NDC80 complex (NDC80c). Improperly attached kinetochore microtubules (KMTs) are selective detached from the kinetochore. While the proper regulation of KMT detachments is crucial for accurate chromosome segregation, the manner by which KMT detachments are controlled by the biophysical properties of NDC80 and KMTs remains poorly understood. Here, we investigate the mechanism of KMT detachment by combining quantitative measurements of NCD80c-KMT binding and KMT detachments, with FLIM-FRET and photoconversion, and mathematical modeling. We find that individual NDC80c at kinetochores bind noncooperatively to KMTs, with a free energy difference between bound and unbound NDC80c of -1.0 {+/-} 0.1kBT, and we determine that each phosphorylated residue of NDC80c decreases the NDC80c-KMT binding energy by 0.35 {+/-} 0.03kBT. We show that the energetics of NDC80c binding can be related to KMT detachment by a dual detachment mechanism in which KMTs detach from kinetochores when either 1) all NDC80c spontaneously unbind from the KMT or 2) following KMT catastrophe. We find that the affinity of NDC80c for KMTs is reduced at low-tension, non-bioriented kinetochores due to centromere-localized Aurora B phosphorylating the NDC80c, resulting in an elevated detachment rate for the associated KMTs. Taken together, this work leads to a multiscale, bottom-up biophysical model for how the energetics of NDC80c-KMT interactions impact KMT detachments and provides an understanding of the molecular basis of their regulation during mitotic error correction. SIGNIFICANCE STATEMENTChromosome segregation errors can lead to aneuploidy, birth defects and cancer. Accurate cellular division, where one copy of each chromosome is segregated to each daughter cell, depends on regulation of attachments between chromosomes and microtubules in the mitotic spindle. While extensive work has identified proteins involved in suppressing mitotic errors, it is still unclear how incorrect chromosome-microtubule attachments which result in mitotic errors are selectively removed. Here, we employed a combination of quantitative live-cell fluorescence imaging, molecular perturbations, and mathematical modeling to elucidate the biophysical mechanism of how microtubules are selectively detached from misaligned mitotic chromosomes. This work leads to quantitative understanding of how chromosome attachments are regulated and mitotic errors are corrected.

biophysics↗

bioRxiv2023

Somatic mutation but not aneuploidy differentiates lung cancer in never-smokers and smokers.

Lung cancer in never-smokers disproportionately affects older women. To understand the mutational landscape of this cohort, we performed detailed genome characterization of 73 lung adenocarcinomas from participants of the Womens Health Initiative (WHI). We find enrichment of EGFR mutations in never-/light-smokers and KRAS mutations in heavy smokers as expected, but we also show that the specific variants of these genes differ by smoking status, with important therapeutic implications. Mutational signature analysis revealed signatures of clock, APOBEC, and DNA repair deficiency in never-/light-smokers; however, the mutational load of these signatures did not differ significantly from those found in smokers. Last, tumors from both smokers and never-/light-smokers shared copy number subtypes, with no significant differences in aneuploidy. Thus, the genomic landscape of lung cancer in never-/light-smokers and smokers is predominantly differentiated by somatic mutations and not copy number alterations.

genomics↗

bioRxiv2022

Nucleosome patterns in circulating tumor DNA reveal transcriptional regulation of advanced prostate cancer phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and NR3C1. Using these features, we designed a prediction model which distinguished NEPC from ARPC in patient plasma samples across three clinical cohorts with 97-100% sensitivity and 85-100% specificity. While phenotype classification is typically assessed by immunohistochemistry or transcriptome profiling, we demonstrate that ctDNA provides comparable results with numerous diagnostic advantages for precision oncology. STATEMENT OF SIGNIFICANCEThis study provides key insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. The new methods established for phenotype classification extend the utility of ctDNA beyond assessments of DNA alterations with important implications for molecular diagnostics and precision oncology.

cancer biology↗

bioRxiv2022

Patterns of Structural Variation Define Prostate Cancer Across Disease States

The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 278 localized and 143 metastatic prostate cancers profiled by whole genome and transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined nine subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.

genomics↗

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2021

DNA-based copy number analysis confirms genomic evolution of PDX models

We previously reported the genomic evolution of the copy number (CN) landscapes of patient-derived xenografts (PDXs) during their engraftment and passaging1. Woo et al. argue that the CN profiles of PDXs are highly conserved, and that the main conclusions of our paper are invalid due to our use of expression-based CN profiles2. Here, we reassess genomic evolution of PDXs using the DNA-based CN profiles reported by Woo et al. We find that the degree of genomic evolution in the DNA-based dataset of Woo et al. is similar to that which we had previously reported. While the overall Pearsons correlation of CN profiles between primary tumors (PTs) and their derived PDXs is high (as reported in our original paper as well), a median of ~10% of the genome is differentially altered between PTs and PDXs across cohorts (range, 0% to 73% across all models). In 24% of the matched PT-PDX samples, over a quarter of the genome is differentially affected by CN alterations. Moreover, in matched analyses of PTs and their derived PDXs at multiple passages, later-passage PDXs are significantly less similar to their parental PTs than earlier-passage PDXs, indicative of genomic divergence. We conclude that genomic evolution of PDX models during model generation and propagation should not be dismissed, and that the phenotypic consequences of this evolution ought to be assessed in order to optimize the application of these valuable cancer models.

cancer biology↗

bioRxiv2017

Patient-derived xenografts undergo murine-specific tumor evolution

Patient-derived xenografts (PDXs) have become a prominent model for studying human cancer in vivo. The underlying assumption is that PDXs faithfully represent the genomic features of primary tumors, retaining their molecular characteristics throughout propagation. However, the genomic stability of PDXs during passaging has not yet been evaluated systematically. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We found that new CNAs accumulated quickly, such that within four passages an average of 12% of the genome was affected by newly acquired CNAs. Selection for preexisting minor clones was a major contributor to these changes, leading to both gains and losses of CNAs. The rate of CNA acquisition in PDX models was correlated with the extent of both aneuploidy and genetic heterogeneity observed in primary tumors of the same tissue. However, the specific CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients, suggesting that PDX tumors are subjected to distinct selection pressures compared to those that exist in human hosts. Specifically, several recurrent CNAs observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Finally, we found that the genomic stability of PDX models also affected their responses to chemotherapy and targeted drugs. Our findings thus highlight the need to couple the timing of PDX molecular characterization to that of drug testing experiments. These results suggest that while PDX models are powerful tools, they should be used with caution.

cancer biology↗