Biology subjects

Kimmel, M.

Publications and source records attributed to Kimmel, M..

17 recordsLinked to original sources

bioRxiv2026

Microhomology-Driven Genomic Alterations in Cancer Genomes: Patterns, Prevalence, and Clinical Implications.

Homologous recombination deficiency (HRD) can force cancer cells to rely on alternative DNA repair pathways, including microhomology-mediated end joining (MMEJ), an error-prone mechanism that can generate deletions with microhomology at repair junctions. Because such patterns may reflect DNA repair defects with clinical relevance, this study aimed to assess the biological and prognostic significance of microhomology-associated deletions and optimize their detection parameters to improve patient stratification and inform targeted therapeutic strategies, including PARP inhibition. We optimized the microhomology length threshold (Mlt parameter) using signal-to-noise ratios and survival models. We evaluated the utility of whole-exome (WES) versus whole-genome sequencing (WGS). We then investigated how Loss of Function (LoF) alterations affect microhomology-associated deletion burden and assessed their prognostic significance across an ovarian cancer (OV) cohort and a TCGA Pan-Cancer dataset. Applying an Mlt = 2 threshold maximized the precision and statistical reliability of microhomology-associated deletion identification. WGS yielded higher reproducibility, whereas WES proved insufficient for accurate estimation. Inactivation of tumor suppressor genes, including RB1 and CCDC122, was associated with increased microhomology-associated deletion burden. Importantly, higher burden correlated with extended overall survival in ovarian cancer. Furthermore, while baseline microhomology-associated deletion burden varies by tumor type, Pan-Cancer analysis identified candidate gene-level alterations, including FDX1 and PDE8B, whose LoF increased microhomology-associated deletion burden across tumors. Precise parameterization combined with WGS resolution highlights the extent to which specific gene losses affect deletion burden consistent with MMEJ-mediated repair. Identifying these genetic vulnerabilities and the resulting deletion burden may provide a candidate prognostic biomarker and foundation for personalized therapies.

bioinformatics↗

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

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

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Intra-tumor heterogeneity is characterized by a diverse population of tumor clones and subclones which are important drivers of tumor evolution and therapeutic response. However, accurate subclonal reconstruction at scale remains challenging. We developed a machine learning tool, CliPP, and surveyed 9,972 tumors from 32 cancer types. We found that high subclonal mutation load (sML), the fraction of subclonal single nucleotide variants (SNVs) to all SNVs in the coding region, was prognostic of survival (progression free survival or overall survival) in 18 cancer types. In 14 cancers with low to moderate tumor mutation burden (TMB), high sML was associated with better prognosis. In immunotherapy trials for 42 metastatic prostate cancer (mCRPC), high sML was predictive of favorable response to ipilimumab and associated with increased CD8+ T-cell infiltration and decreased macrophage population. A validation using 613 whole-genomes of esophageal adenocarcinoma confirms the favorable effect of high sML and the observed tumor-associated macrophage. Our study identifies sML as a key feature of cancer, suggesting a biphasic relationship between evolutionary dynamics and differential immune environments. Finally, sML may serve as an orthogonal approach to identify likely responders of immune checkpoint blockade in low to moderate TMB tumors.

genomics↗

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↗

bioRxiv2022

Mutation patterns in SARS-COV-2 Alpha and Beta variants indicate non-neutral evolution

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Two early emergent strains, Alpha and Beta, characterized by a significant number of missense mutations, provide natural testing samples. In this study we are exploring the history of each of the segregating sites present in Alpha and Beta variants of concern, to address the question whether defining mutations were accumulating gradually leading to the formation of sequence characteristic of these variants. Our analysis exposes data features that suggest other than neutral evolution of SARS-CoV-2 genomes, leading to emergence of variants of concern. We observe only small number of possible combinations of mutations indicating rapid evolution of genomes. In addtion, mutation patterns observed in whole genome samples of Alpha and Beta variants also indicate presence of stronger selection than in remaining genome samples.

evolutionary biology↗

bioRxiv2022

Evolutionary analysis of genomes of SARS-CoV-2-related bat viruses suggests old roots, constant effective population size, and possible increase of fitness

It is of vital practical interest to understand the co-evolution of bat {beta}-coronaviruses with their hosts, since a number of these most likely crossed the species boundaries and infected humans. Complete sequences of 47 consensus genomes are available for bat {beta}-coronaviruses related to the SARS-CoV-2 human virus. We carried out several types of evolutionary analyses using these data. First, using the publicly available BEAST 2 software, we generated phylogenetic trees and skyline plots. The roots of the trees, both for the entire sequences and subsequences coding for the E and S proteins as well as the 5 and 3 UTR regions, are estimated to be located from several decades to more than a thousand years ago, while the effective population sizes remained largely constant. Motivated by this, we developed a simple estimator of the effective population size in a Moran model with constant population, which, under the model is equal to the expected age of the MRCA measured in generations. Comparisons of these estimates to those produced by BEAST 2 shows qualitative agreement. We also compared the site frequency spectra (SFS) of the bat genomes to those provided by the Moran Tug-of-War model. Comparison does not exclude the possibility that overall fitness of the bat {beta}-coronaviruses was increasing over time as a result of directional selection. Stability of interactions of bats and their viruses was considered likely on the basis of specific manner in which bat immunity is tuned, and it seems consistent with our analysis.

evolutionary biology↗

bioRxiv2021

Moran Process Version of the Tug-of-War Model: Complex Behavior Revealed by Mathematical Analysis and Simulation Studies

AO_SCPLOWBSTRACTC_SCPLOWIn a series of publications McFarland and co-authors introduced the tug-of-war model of evolution of cancer cell populations. The model is explaining the joint effect of rare advantageous and frequent slightly deleterious mutations, which may be identifiable with driver and passenger mutations in cancer. In this paper, we put the Tug-of-War model in the framework of a denumerable-type Moran process and use mathematics and simulations to understand its behavior. The model is associated with a time-continuous Markov Chain (MC), with a generator that can be split into a sum of the drift and selection process part and of the mutation process part. Operator semigroup theory is then employed to prove that the MC does not explode, as well as to characterize a strong-drift limit version of the MC which displays "instant fixation" effect, which was an assumption in the original McFarlands model. Mathematical results are fully confirmed by simulations of the complete and limit versions. They also visualize complex stochastic transients and genealogies of clones arising in the model.

evolutionary biology↗

bioRxiv2021

Detection and characterization of replication origins defined by DNA polymerase epsilon

BackgroundAlthough the process of DNA replication is highly conserved the location of origins of replication (ORI) may vary from one tissue to the next or one round of replication to the next in eukaryotes, suggesting flexibility in the choice of locations to initiate replication. Lists of human ORI therefore vary widely in number and location and there are no methods available to compare them. ResultsHere we report the genome-wide localization of ORI in POLE-mutated human tumors using whole genome sequencing data. Mutations accumulated after many rounds of replication of unsynchronized dividing cell populations in tumors allow to identify constitutive origins, which we show are shared with high-fidelity between individuals and tumor types. Using a Smith-Waterman-like dynamic programming approach, we compared replication origin positions obtained from multiple different methods. The comparison allowed us to define a consensus set of replication origins, identified consistently by multiple ORI detection methods. ConclusionsMany DNA features co-localized with the consensus set of ORI, including chromatin loop anchors, G-quadruplexes, S/MARs and CpGs. Among all features, the H2A.Z histone exhibited the most significant association. Our results show that mutation-based detection of replication origins is a viable approach to determining their location and associated sequence features.

genomics↗

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↗

bioRxiv2021

Heat Shock Factor 1 (HSF1) as a new tethering factor for ESR1 supporting its action in breast cancer.

Heat shock factor 1 (HSF1), a key regulator of transcriptional responses to proteotoxic stress, was recently linked to estrogen (E2) signaling through ESR1. We found that an HSF1 deficiency could lead to the inhibition of the mitogenic action of E2 in breast cancer cells. The stimulatory effect of E2 on the transcriptome is weaker in HSF1-deficient cells, in part due to the higher basal expression of E2-dependent genes, which correlates with the enhanced binding of unliganded ESR1 to chromatin. HSF1 and ESR1 can cooperate directly in E2-stimulated regulation of transcription, and HSF1 potentiates the action of ESR1 through a mechanism involving chromatin reorganization. Analyses of data from the TCGA database indicate that HSF1 increases the transcriptome diversity in ER-positive breast cancer and can enhance the genomic action of ESR1. Moreover, ESR1 and HSF1 are only prognostic when analyzed together (the worst prognosis for ER-/HSF1high cancers).

cancer biology↗

bioRxiv2021

CliP: subclonal architecture reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model

Tumor subclonal architecture shapes cancer evolution, yet subclonal reconstruction from bulk sequencing remains difficult to scale due to computational cost and model complexity. We present CliPP, a penalized-likelihood framework that jointly estimates cellular prevalence with pairwise fusion penalties, automatically identifying subclones without requiring extensive priors. Across simulations and 2,778 whole-genome tumors with external consensus reconstructions, CliPP achieves consistently good performances when compared to state-of-the-art approaches while providing substantial runtime reductions. Applied to 7,000+ tumors across >30 cancer types, CliPP quantifies pervasive subclonality and delineates cohort-level subclone landscapes. CliPP enables fast, reproducible large-scale subclonal analysis and is freely available to the community through GitHub and a shiny app.

bioinformatics↗

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↗

bioRxiv2019

Heat shock response pathways regulate stimulus-specificity and sensitivity of NF-κB signalling to temperature stress

Ability to adapt to temperature changes trough the Heat Shock Response (HSR) pathways is one of the most fundamental and clinically relevant cellular response systems. Here we report that Heat Shock (HS) induces a temporally-coordinated and stimulus-specific adaptation of the signalling and gene expression responses of the Nuclear Factor {kappa}B (NF-{kappa}B) transcription factor. We show that exposure of MCF7 breast adenocarcinoma cells to 43{degrees}C 1h HS inhibits the immediate signalling response to pro-inflammatory Interleukin 1{beta} (IL1{beta}) and Tumour Necrosis Factor (TNF) cytokines. Within 4h after HS treatment IL1{beta}-induced responses return to normal levels, but the recovery of the TNF-induced responses is delayed. Using siRNA knock-down of Heat Shock Factor 1 and mathematical modelling we show that the stimulus-specificity is conferred via the Inhibitory {kappa}B kinase signalosome, with HSR differentially controlling individual cytokine transduction pathways. Finally, using a novel mathematical model we predict and experimentally validate that the HSR cross-talk confers differential cytokine sensitivity of the NF-{kappa}B system to a range of physiological and clinically-relevant temperatures. This quantitative understanding of NF-{kappa}B and HSR cross-talk mechanisms is fundamentally important for the potential improvement of current hyperthermia protocols.

cell biology↗

bioRxiv2019

Statistical inference for the evolutionary history of cancer genomes

Recent years have produced a large amount of work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the linear birth-death process (lbdp), a binary fission Markov age-dependent branching process. A genealogical view of such models is also available. The two approaches lead to similar but not identical results. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a sequencing experiment with a known number of sequences, we can estimate for each site at which a novel somatic mutation has arisen, the number of cells that carry that mutation. These numbers are then grouped into sites which have the same number of copies of the mutant. SFS can be computed from the statistics of mutations in a sample of cells, in which DNA has been sequenced. In this paper, examine how the SFS based on birth-death processes differ from those based on the coalescent model. This may stem from the different sampling mechanisms in the two approaches. However, we also show mathematically and computationally that despite this, they can be made quantitatively comparable at least for the range of parameters typical for tumor cell populations. We also present a model of tumor evolution with selective sweeps, based on coalescence, and demonstrate how it may help in understanding the past history of tumor as well the influence of data pre-processing. We illustrate the theory with applications to several examples of The Cancer Genome Atlas tumors.

cancer biology↗