Biology subjects

Curtius, K.

Publications and source records attributed to Curtius, K..

9 recordsLinked to original sources

bioRxiv2025

Community assembly modeling of microbial evolution within Barrett's esophagus and esophageal adenocarcinoma

Mathematical modeling of somatic evolution, a process impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barretts esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE and EAC have focused on quantifying static microbial abundance differences rather than evolutionary dynamics. Using whole genome sequencing data from esophageal tissues, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of microbial evolution to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a case-control study of BE patients who progressed to EAC cancer outcomes (CO) versus those who had non-cancer outcomes (NCO) during follow-up (mean=10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, simulations incorporating selection recapitulated non-neutral behaviors observed in the datasets. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.

cancer biology↗

bioRxiv2023

Estimating single cell clonal dynamics in human blood using coalescent theory

While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. We derive methods based on coalescent theory for estimating the net growth rate of clones from either reconstructed phylogenies or the number of shared mutations. Using single-cell datasets from blood, we apply and validate our analytical methods for estimating the net growth rate of hematopoietic clones, eliminating the need for complex simulations. We show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6 x 10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026).

evolutionary biology↗

bioRxiv2022

CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. The application of CNETML to real data from Barretts esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations.

bioinformatics↗

bioRxiv2022

AI-assisted Discovery of an Ethnicity-influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas

Although Barretts metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of the metaplasia[->]dysplasia[->]neoplasia cascade in the esophagus remains incompletely understood. Using an AI-guided network transcriptomics approach, in which EAC initiation and progression is modeled as networks to simplify complex multi-cellular processes, we first predict cellular continuum states and disease driving processes with an unprecedented degree of precision. Key AI-guided predictions are subsequently validated in a human organoid model and patient-derived biopsies of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. We find that all EACs must originate from BE, pinpoint a CXCL8/IL8{leftrightarrow}neutrophil immune microenvironment as a driver of cellular transformation in both EACs and gastroesophageal junction-ACs. This driver is prominent in Caucasians (Cau), but notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with ethnic changes in ANC modify that risk. Thus, findings define a racially influenced immunological basis for cell transformation and suggest that benign ethnic neutropenia in AAs may serve as a deterrent to BE[->]EAC progression. BRIEF SUMMARYEsophageal adenocarcinoma (EAC) is a highly lethal cancer among Caucasians, while African Americans are somewhat protected; what factors drive transformation with racial disparity remain unknown. AI-enabled creation of the first computational map of neoplastic progression in the esophagus built and validated using transcriptomic datasets from diverse cohorts of human samples pinpointed CXCL8{leftrightarrow}neutrophil tumor immune-microenvironment as a racially influenced driver of EACs and GEJ-ACs. Computational tools pinpoint a racially influenced driver of cell transformation during BE[->]EAC progression; in doing so, it reveals new novel biology, informs disease modeling, therapeutic strategies, and biomarkers. LAY SUMMARYBy modeling diseases as networks, this work unravels a fundamental race-influenced immunologic driver of cell transformation in adenocarcinomas of the esophagus and the gastroesophageal junction.

cancer biology↗

bioRxiv2021

Autoimmune Alleles at the Major Histocompatibility Locus Modify Melanoma Susceptibility

Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T-cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo and psoriasis predisposing MHC-I alleles conferred a melanoma protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (N = 451) and an independent validation cohort (N = 586), MHC-I autoimmune allele carrier status was significantly associated with a later age of melanoma diagnosis. Furthermore, MHC-I autoimmune allele carriers were significantly associated with decreased risk of developing melanoma in the Million Veterans Program cohort (OR = 0.962, p = 0.024). Existing melanoma polygenic risk scores (PRS) did not predict autoimmune allele carrier status, suggesting these alleles provide new risk-relevant information. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles (population frequency > 1%). However, autoimmune alleles showed higher affinity relative to common alleles for particular windows of melanocyte conserved antigens suggesting a potential relationship between antigen processing, binding, and cell-surface presentation. Overall, this study presents evidence that MHC-I autoimmune risk alleles modulate melanoma risk unaccounted for by current PRS.

cancer biology↗

bioRxiv2021

The mutational signatures of formalin fixation on the human genome

BackgroundFormalin fixation and paraffin embedding (FFPE) of patient material remains standard practice in clinical pathology labs around the world. Clinical archives of patient material near-exclusively consist of FFPE blocks. The ability to perform high quality genome sequencing on FFPE-derived DNA would accelerate a broad spectrum of medical research. However, formalin is a recognised mutagen and sequencing of DNA derived from FFPE material is known to be riddled with artefactual mutations. ResultsHere we derive genome-wide mutational signatures caused by formalin fixation, and provide a computational method to correct mutational profiles for these formalin-induced artefacts. We show that the FFPE-signature is dominated by C>T transitions caused by cytosine deamination, and has very high similarity to COSMIC signature SBS30 (base excision repair deficiency due to inactivation mutations in NTHL1). Further, we demonstrate that chemical repair of formalin-induced DNA lesions, a process that is routinely performed as part of sequencing library preparation, leads to a signature highly similar to COSMIC signature SBS1 (spontaneous deamination of methylated cytosine). Next, we design FFPEsig, a computational method to remove the formalin-induced artefacts from mutational counts. We prove the efficacy of this method by generating synthetic FFPE samples using 2,780 cancer genomes from the Pan-Cancer Analysis of Whole Genome (PCAWG) project, and via analysis of FFPE-derived genome sequencing data from colorectal cancers. ConclusionsFormalin fixation leaves a predictable mutational footprint across the genome. The application of our FFPEsig software corrects the mutational profile for the influence of formalin, enabling robust mutational signature analysis in FFPE-derived patient material.

bioinformatics↗

bioRxiv2020

Barrett's esophagus is the precursor of all esophageal adenocarcinomas

ObjectiveBarretts esophagus (BE) is a known precursor to esophageal adenocarcinoma (EAC) but current clinical data have not been consolidated to address whether BE is the origin of all incident EAC, which would reinforce evidence for BE screening efforts. We aimed to answer whether all expected prevalent BE, diagnosed and undiagnosed, could account for all incident EACs in the US cancer registry data. DesignWe used a multi-scale computational model of EAC that includes the evolutionary process from normal esophagus through BE in individuals from the US population. The model was previously calibrated to fit SEER cancer incidence curves. Here we also utilized age- and sex-specific US census data for numbers at-risk. The primary outcome for model validation was the expected number of EAC cases for a given calendar year. Secondary outcomes included the comparisons of resulting model-predicted prevalence of BE and BE-to-EAC progression to the observed prevalence and progression rates. ResultsThe model estimated the total number of EAC cases in 2010 was 9,970 (95% CI 9,140 - 11,980), which recapitulates all EAC cases from population data. The model simultaneously predicted 8-9% BE prevalence in high-risk males age 45-55, and 0.1-0.2% non-dysplastic BE-to-EAC annual progression in males, consistent with clinical studies. ConclusionThere are no additional EAC cases that plausibly arise in the US population outside the BE pathway. Effective screening of high-risk patients could capture the majority of population destined for EAC progression and decrease mortality through early detection and curative removal of small (pre)cancers during surveillance. Summary BoxWhat is already known about this subject? O_LIBarretts esophagus (BE) patients have a 40 to 50-fold higher risk of developing esophageal adenocarcinoma (EAC) than the general population yet many remain undiagnosed. C_LIO_LIIdentified BE patients receiving surveillance can have early cancers discovered endoscopically, which decreases the high overall EAC-associated mortality. C_LIO_LICurrently around 90% of patients who develop EAC were never part of a BE surveillance program, and those BE patients on surveillance have a low annual progression rate of 0.1 - 0.3% to develop EAC. C_LI What are the new findings?O_LIBy applying a model that incorporates the evolution from normal cells to BE to EAC in patients, we found that the numbers add up - the expected number of EAC cases in the US population are explained by the published rates of BE described above. C_LIO_LIWe cohesively examined the published estimates to determine that all EAC likely arises from both identified BE and occult, undiagnosed BE in the population. C_LI How might it impact on clinical practice in the foreseeable future?O_LIBased on current best estimates, our findings suggest there is no public health need to seek cases of a non-BE alternative pathway to EAC. C_LIO_LIIncreasing efforts for effective, sensitive screening and surveillance of the true BE population will decrease EAC mortality in the coming years. C_LI

cancer biology↗

bioRxiv2020

Optimal timing for cancer screening and adaptive surveillance using mathematical modeling

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality but many improvements are needed. Although current medical practice is mostly informed by epidemiological studies, the decisions for guidelines are ultimately made ad hoc. We propose that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and to optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain "window of opportunity" for intervention when early cancer development may be observable. Alternative to a strictly empirical approach, or microsimulations of a multitude of possible scenarios, biologically-based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barretts esophagus (BE), we applied our methods for a model of esophageal adenocarcinoma (EAC) that was previously calibrated to US cancer registry data. We found optimal screening ages for patients with symptomatic gastroesophageal reflux disease to be older (58 for men, 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-effective range to start screening and were independently validated by data used in current guidelines. Our framework captures critical aspects of cancer evolution within BE patients for a more personalized screening design. SignificanceOur study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes. Surveillance regimes could also be improved if they were based on these models. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/927475v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@6f9324org.highwire.dtl.DTLVardef@1254188org.highwire.dtl.DTLVardef@f1777eorg.highwire.dtl.DTLVardef@dc0e66_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

Mechanisms of Chromosomal Instability in High-grade Serous Ovarian Carcinoma.

Chromosomal instability (CIN), the continual gain and loss of chromosomes or parts of chromosomes, occurs in the majority of cancers and confers poor prognosis. Mechanisms driving CIN remain unknown in most cancer types due to a scarcity of functional studies. High-grade serous ovarian carcinoma (HGSC), the most common subtype of ovarian cancer, is the major cause of death due to gynaecological malignancy in the Western world with chemotherapy resistance developing in almost all patients. HGSC exhibits high rates of chromosome aberrations and knowledge of causative mechanisms is likely to represent an important step towards combating the poor prognosis of this disease. However, very little is known about the nature of chromosomal instability exhibited by this cancer type in particular due to a historical lack of appropriate cell line models. Here we perform the first in-depth functional characterisation of mechanisms driving CIN in HGSC by analysing eight cell lines that accurately recapitulate HGSC genetics as defined by recent studies. We show, using a range of established functional CIN assays combined with live cell imaging and single molecule DNA fibre analysis, that multiple mechanisms co-exist to drive CIN in HGSC. These include supernumerary centrosomes, elevated microtubule dynamics and DNA replication stress. By contrast, the spindle assembly checkpoint was intact. These findings are relevant for developing therapeutic approaches to manipulating CIN in ovarian cancer, and suggests that such approaches may need to be multimodal to combat multiple co-existing CIN drivers.

cancer biology↗