Biology subjects

Dinh, K. N.

Publications and source records attributed to Dinh, K. N..

12 recordsLinked to original sources

bioRxiv2026

Correction of the cytosine deamination artifacts in FFPE-based sequencing experiments

Formalin-fixed, paraffin-embedded (FFPE) tissues remain an essential resource for molecular studies, yet formalin-induced cytosine deamination introduces characteristic C>T/G>A artifacts that compromise the accuracy of next-generation sequencing (NGS) analyses. Numerous computational methods and enzymatic DNA repair strategies have been proposed to reduce these artifacts, but no systematic comparison across tools and experimental conditions exists. Here, we evaluate the performance of seven computational approaches (SOBDetector, Ideafix, MicroSEC, FFPolish, DeepOmics FFPE/FFPE-PLUS, FFPErase) together with the NEBNext(R) FFPE DNA Repair Mix v2, a multi-enzyme repair system applied during DNA preparation. Using three independent datasets, one based on whole genome sequencing (CGCI-BL) and two on whole exome sequencing (TCGA-PC and SUT-LUAD, the latter containing enzymatically repaired samples), and matched fresh-frozen samples as the gold standard, we assess precision, sensitivity, and artifact reduction efficiency across all methods. We further examine the potential synergy between enzymatic repair and post-sequencing computational filtering. Our results provide practical guidelines for FFPE artifact correction and demonstrate that enzymatic treatment provides the best results, while among the computational methods, FFPErase offers the most robust reduction of cytosine deamination artifacts while maximizing the retention of true somatic variants. KEY MESSAGESO_LIFormalin fixation in FFPE samples introduces artifacts that can significantly affect the accuracy of NGS analyses. C_LIO_LIAmong the evaluated approaches, enzymatic repair using NEBNext(R) FFPE DNA Repair Mix v2 achieves the most effective reduction of sequencing artifacts. C_LIO_LIComputational methods vary in performance, with FFPErase showing the most robust balance between artifact removal and retention of true somatic variants. C_LIO_LICombining enzymatic repair with computational filtering did not lead to consistent improvements in performance across datasets. C_LI

bioinformatics↗

bioRxiv2026

Accurate detection of tumor clonality and ongoing expansion mode from genomic data

Recent evidence shows that despite considerable effort, currently available algorithms for estimating intratumor heterogeneity (ITH) remain limited. We developed DECODE (Deciphering Cancer Origin from DNA Evolution), a novel mutation clustering method that incorporates the impact of sample-specific sequencing coverage and mutation calling biases. On synthetic data, DECODE outperformed existing methods across multiple clonality metrics and accurately detected and characterized the neutral tail in the site frequency spectrum (SFS), which encodes the tumors ongoing expansion mode. In acute myeloid leukemia, accounting for the neutral tail enabled DECODE to yield more parsimonious clonal decompositions that align more closely with known subclonal dynamics that drive relapse. Applied to data from The Cancer Genome Atlas, DECODE not only detected a neutral SFS tail in most samples across tumor types but also uncovered a clinically meaningful link between ITH and survival in low-grade glioma. By jointly inferring clonality and expansion mode, DECODE provides two complementary and prognostically relevant readouts of tumor evolution from single tumor genomic samples.

bioinformatics↗

bioRxiv2026

BESTish: A Diffusion-Approximation Framework for Inferring Selection and Mutation in Clonal Hematopoiesis

Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSCs) gain a fitness advantage from somatic mutations and expand, resulting in an increase in variant allele frequency (VAF) over time. To analyze CH trajectories, we develop a state-dependent stochastic model of wild-type and mutant HSCs, in which an environmental parameter [isin] [0, 1] regulates death rates and interpolates between homeostatic (Moran-like, = 1) and growth-facilitating ( < 1) regimes. Using functional law of large numbers and central limit theorems, we derive explicit mean-field dynamics and a Gaussian-Markov approximation for VAF fluctuations. We show that the mean VAF trajectory has an explicit logistic form determined by selective advantage, while environmental effects affect only the variance and autocovariance structure. Building on these results, we introduce BESTish (Bayesian estimate for selection incorporating scaling-limit to detect mutant heterogeneity), a novel, efficient and accurate Bayesian inference method that can be applied to analyze both cohort-level and longitudinal VAF datasets. BESTish implements the closed-form finite-dimensional distributions that we derive to estimate mutation fitness, mutation rate, and environmental strength for individual CH drivers. When applied to existing CH datasets, BESTish produces consistent mutation fitness inferences across different studies, and estimates CH driver mutation rates in agreement with independent experimental studies. Furthermore, BESTish reveals patient-specific heterogeneity in the selective behavior of recurrent mutations, and identifies variants whose dynamics are compatible with non-homeostatic, growth-facilitating environments. BESTish provides a unified and mechanistic framework for quantifying CH evolution, with potential applications for other biological systems where clonal expansions can be measured.

bioinformatics↗

bioRxiv2025

Wavelet Based Whole Genome Doubling Aware Single Cell Copy Number Calling

Advances in single cell whole genome sequencing enable profiling of the copy number state of thousands of cells with minimal sequencing bias across the genome. The Direct Library Preparation + technique is an whole genome amplification-free single cell whole genome sequencing method that achieves high throughput by fragmenting each cells genome and ligating sequencing adapters using a modified Tn5 transposase, and sequencing to less than 0.1x coverage. Despite recent advances in experimental approaches, data analysis of single cell whole genome sequencing lags behind and the existing methods are not optimized for the analysis of frozen samples with variable DNA preservation. Furthermore, existing tools predominantly rely on read depth ratio in predefined genomic bins to call copy number, making whole genome duplication unidentifiable. To address this, we introduce Songbird, a single cell whole genome sequencing copy number caller that is whole genome duplication sensitive, and outperforms existing tools both in breakpoints identification and true copy number detection. We demonstrate that Songbird is robust down to extremely low coverage, adaptable to a variety of genome versions (hg19, hg38, hs.1), and is extensible to other single cell whole genome sequencing methods that rely on Tn5 tagmentation to fragment the genome.

bioinformatics↗

bioRxiv2025

Statistical and Evolutionary Analysis of Sequenced DNA from Breast Cancer FFPE Specimens

BackgroundDespite the introduction of instant freezing of tumor specimens, formalin-fixed paraffin-embedded (FFPE) blocks of tissue are still commonplace in clinical practice and constitute an important reference for genetic epidemiology of cancer. We carried out a study of a collection of breast tumors paired with lymph-node metastases and analyzed using advanced computational methods, to determine how much information can be obtained from mid-depth whole-exome bulk DNA sequencing. MethodsWe gathered 15 paired (primary and an involved lymph node) excised breast tumors of different molecular subtypes (HER2+, triple negative, luminal A and luminal B HER2-), from the National Research Institute of Oncology, Krakow (Poland) Branch. FFPE specimens contained typical artifacts, manifesting themselves in spurious DNA variant calls. We used several bioinformatics tools to remove the artifacts and analyzed the exomic data, using both commercial and original in-house computational techniques. ResultsWe used several of recent bioinformatics tools to remove the FFPE artifacts and found a serious dispersal of outcomes. After calibration, a series of analyses was performed, including copy number study, resulting in ploidy levels ranging from 1 to 5 (average of 2.5). Positive association was found between the frequency of oncogenes relative to tumor suppressor genes and DNA copy number. In addition, we carried out analyses of the clonal structure of the data using original computational methods based on evolutionary modeling. Interesting results concerning clonal structure, early tumor expansion, and interdependence of the primary tumor and lymph node metastases have been obtained. ConclusionsDespite the imperfections of the FFPE data, many important features of molecular evolution of tumor DNA can be recovered from routine clinical samples.

evolutionary biology↗

bioRxiv2024

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily active mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

cancer biology↗

bioRxiv2024

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

evolutionary biology↗

bioRxiv2024

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

genomics↗

bioRxiv2023

IL-2RαKO mice exhibit maternal microchimerism and reveal nuclear localization of IL-2Rα in lymphoid and non-lymphoid cells

IL-2R KO mice have been instrumental to discovering the immunoregulatory properties of IL-2R. While initially thought of only as a stimulatory cytokine, IL-2 and IL-2R knock out (KO) mice revealed that this cytokine-receptor system controls immune responses through restimulation-induced cell death and by promoting the survival of T regulatory cells. Although described mostly in the context of lymphocytes, recent studies by our laboratory showed that IL-2R is expressed in smooth muscle cells. Given this finding, we sought to use IL-2R knock mice to determine the function of this receptor in vascular smooth muscle cells. Surprisingly, we found that IL-2R knock out vascular smooth muscle cells had detectable IL-2R. Further studies suggested that the source of IL-2R protein was likely maternal heterozygous cells present in KO offspring due to maternal microchimerism. Because the KO was generated by using a neomycin resistance gene insert, we treated cells with G418 and were able to eliminate the majority of IL-2R expressing cells. This elimination revealed that IL-2R KO vascular smooth muscle cells exhibited increased proliferation, decreased size, and hypodiploid DNA content when compared to wildtype cells. Our findings suggest that the phenotype of complete IL-2R loss is more severe than demonstrated by IL-2R KO mice, and that IL-2R plays a here-to-fore unrecognized role in regulating cell proliferation in non-lymphoid cells.

immunology↗

bioRxiv2023

Comparison of Tug-of-War Models Assuming Moran versus Branching Process Population Dynamics

Mutations arising during cancer evolution are typically categorized as either drivers or passengers, depending on whether they increase the cell fitness. Recently, McFarland et al. introduced the Tug-of-War model for the joint effect of rare advantageous drivers and frequent but deleterious passengers. We examine this model under two common but distinct frameworks, the Moran model and the branching process. We show that frequently used statistics are similar between a version of the Moran model and the branching process conditioned on the final cell count, under different selection scenarios. We infer the selection coefficients for three breast cancer samples, resulting in good fits of the shape of their Site Frequency Spectra. All fitted values for the selective disadvantage of passenger mutations are nonzero, supporting the view that they exert deleterious selection during tumorigenesis that driver mutations must compensate.

cancer biology↗

bioRxiv2021

The Origin of Bladder Cancer from Mucosal Field Effects

We used whole-organ mapping to study loco-geographic molecular changes in evolution of human bladder cancer from mucosal field effects. The integrative multi-platform analyses based on genome-wide RNA sequencing, methylation, copy number variations, and whole exome sequencing identified over 100 dysregulated canonical pathways involving immunity, tissue differentiation and transformation as initiators of bladder carcinogenesis. Widespread dysregulation of interleukin signaling was the dominant change signifying the important role of inflammation and immunity in the incipient phases of urothelial carcinogenesis. The analyses of mutational patterns identified three types of mutations based on their geographic distribution and variant allele frequencies. The most common were low frequency subclonal mutations restricted to individual mucosal samples which were the progeny of their respective uroprogenitor cells. The two additional types of mutations were associated with clonal expansion and involved large areas of bladder mucosa. The first group referred to as mutations, showed a low mutational frequency across the mucosa. The second group referred to as {beta} mutations increased in their frequencies with disease progression and a large proportion of them represented mutated transcriptional regulators controlling proliferation. Time modeling revealed that bladder carcinogenesis is spanning 10-15 years and can be divided into dormant and progressive phases. The progressive phase lasted 1-2 years and was primarily driven by {beta} mutations with high proliferative advantage. This is the first detailed molecular characterization of mucosal field effects initiating bladder carcinogenesis on the whole-organ scale. It provides novel insights into incipient phases of bladder carcinogenesis and biomarkers for early detection of bladder cancer as well as targets for preventive therapies.

genomics↗

bioRxiv2019

Predicting Minimal Residual Disease in Acute Myeloid Leukemia through Stochastic Modeling of Clonality

Event-free and overall survival remains poor for acute myeloid leukemia (AML). Chemo-resistant clones contributing to relapse of the disease arise from minimal residual disease (MRD) rather than resulting from newly acquired mutations during or after chemotherapy. MRD is the presence of measurable leukemic cells using non-morphologic assays. It is considered a strong predictor of relapse. The dynamics of clones comprising MRD is poorly understood and is considered influenced by a form of Darwinian selection. We propose a stochastic model based on a multitype (multi-clone) age-dependent Markov branching process to study how random events in MRD contribute to the heterogeneity in response to treatment in a cohort of six patients from The Cancer Genome Atlas database with whole genome sequencing data at two time points. Our model offers a more accurate understanding of how relapse arises and which properties allow a leukemic clone to thrive in the Darwinian competition among leukemic and normal hematopoietic clones. The model suggests a quantitative relationship between MRD and time to relapse and therefore may aid clinicians in determining when and how to implement treatment changes to postpone or prevent the time to relapse.\n\nAuthor summaryRelapse affects about 50% of AML patients who achieved remission after treatment, and the prognosis of relapsed AML is poor. Current evidence has shown that in many patients, mutations giving rise to relapse are already present at diagnosis and remain in small numbers in remission, defined as the minimal residual disease (MRD). We propose a mathematical model to analyze how MRD develops into relapse, and how random events in MRD may affect the patients fate. This work may aid clinicians in predicting the range of outcomes of chemotherapy, given mutational data at diagnosis. This can help in choosing treatment strategies that reduce the risk of relapse.

cancer biology↗