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Biology subjects

Carter, P.

Publications and source records attributed to Carter, P..

6 recordsLinked to original sources

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

BackgroundProfiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies. MethodsHere, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing. ResultsDirect comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses. Conclusions These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

cancer biology↗

Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

Emerging data on small cell lung cancer (SCLC), an aggressive malignancy with exceptionally poor prognosis, support subtypes driven by distinct transcription regulators, which engender unique therapeutic vulnerabilities. However, the translational potential of these observations is limited by access to tumor biopsies. Here, we leverage chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) on 442 plasma samples from individuals with advanced SCLC, neuroendocrine carcinomas (NEC), non-SCLC cancers, and healthy adults. Beyond providing reliable estimates of SCLC circulating free DNA tumor fraction, cfChIP-seq captures the unique epigenetic states of SCLC tissue- and cell-of-origin. Comparison of cfChIP-seq signals to matched tumor transcriptomes reveals genome-wide concordance, establishing a direct link between gene expression in the tumor and plasma cell-free nucleosomes. Exploiting this link, we develop a classifier that discriminates between SCLC lineage-defining transcription factor subtypes based on cfChIP-seq data. This work sets the stage to non-invasively profile SCLC transcriptomes using plasma cfDNA histone modifications.

cancer biology↗

Body size and composition and site-specific cancers in UK Biobank: a Mendelian randomisation study

ObjectivesTo investigate the casual role of body mass index, body fat composition and height in cancer. DesignTwo stage mendelian randomisation study SettingPrevious genome wide association studies and the UK Biobank ParticipantsGenetic instrumental variables for body mass index (BMI), fat mass index (FMI), fat free mass index (FFMI) and height from previous genome wide association studies and UK Biobank. Cancer outcomes from 367 586 participants of European descent from the UK Biobank. Main outcome measuresOverall cancer risk and 22 site-specific cancers risk for genetic instrumental variables for BMI, FMI, FFMI and height. ResultsGenetically predicted BMI (per 1 kg/m2) was not associated with overall cancer risk (OR 0.99; 95% confidence interval (CI) 0-98-1.00, p=0.105). Elevated BMI was associated with increased risk of stomach cancer (OR 1.15, 95% (CI) 1.05-1.26; p=0.003) and melanoma (OR 0.96, 95% CI 0.92-1.00; p=0.044). For sex-specific cancers, BMI was positively associated with uterine cancer (OR 1.08, 95% CI 1.01-1.14; p=0.015) but inversely associated with breast (OR 0.95, 95% CI 0.92-0.98; p=0.001), prostate (OR 0.95, 95% CI 0.92-0.99; p=0.007) and testicular cancer (OR 0.89, 95% CI 0.81-0.98; p=0.017). Elevated FMI (per 1 kg/m2) was associated with gastrointestinal cancer (stomach cancer OR 4.23, 95% CI 1.18-15.13, p=0.027; colorectal cancer OR 1.94, 95% CI 1.23-3.07; p=0.004). Increased height (per 1 standard deviation, approximately 6.5cm) was associated with increased risk of overall cancer (OR 1.06; 95% 1.04-1.09; p = 2.97x10-8) and most site-specific cancers with the strongest estimates for kidney, non-Hodgkin lymphoma, colorectal, lung, melanoma and breast cancer. ConclusionsThere is little evidence for BMI as a casual risk factor for cancer. BMI may have a causal role for sex-specific cancers, although with inconsistent directions of effect, and FMI for gastrointestinal malignancies. Elevated height is a risk factor for overall cancer and multiple site cancers.

genetics↗

Global kinome silencing combined with 3D invasion screening of the tumor microenvironment identifies fibroblast-expressed PIK3Cδ involvement in triple-negative breast cancer progression

As there is growing evidence for the tumor microenvironments (TME) role in tumorigenesis, we sought to investigate the role of fibroblast-expressed kinases in triple negative breast cancer (TNBC). Using a high-throughput kinome screen combined with 3D invasion assays, we identified fibroblast-expressed PIK3C{delta} (f-PIK3C{delta}) as a key regulator of progression. Although PIK3C{delta} has been mainly described in leucocytes, we detected high expression in primary fibroblasts derived from TNBC patients, while PIK3C{delta} was undetectable in cancer epithelial cell lines. Genetic and pharmacologic gain- and loss-of functions experiments verified the contribution of f-PIK3C{delta} in TNBC cell invasion. By employing an integrated secretomics and transcriptomics analysis, we revealed a paracrine mechanism via which f-PIK3C{delta} confers its pro-tumorigenic effects. Inhibition of f-PIK3C{delta} promoted the secretion of factors, including PLGF and BDNF, which subsequently led to upregulation of NR4A1 in TNBC cells where it acts as a tumor suppressor. Inhibition of PIK3C{delta} in an orthotopic BC mouse model reduced tumor growth only after inoculation with fibroblasts, indicating a role of f-PIK3C{delta} in cancer progression. Similar results were observed in the MMTV-PyMT transgenic BC mouse model, in addition to a decrease on tumor metastasis emphasizing the potential immune-independent effects of PIK3C{delta} inhibition. Finally, analysis of BC patient cohorts and TCGA datasets identified f-PIK3C{delta} (protein and mRNA levels) as an independent prognostic factor for overall and disease free survival, highlighting it as a therapeutic target for TNBC.

cancer biology↗

Genetic determinants of lipids and cardiovascular disease outcomes: a wide-angled Mendelian randomization investigation

AimsTo systematically investigate causal relationships between circulating lipids and cardiovascular outcomes, using a Mendelian randomization approach.\n\nMethods and ResultsIn the primary analysis, we performed two-sample multivariable Mendelian randomization using data from participants of European ancestry. We also conducted univariable analyses using inverse-variance weighted and robust methods, and gene-specific analyses using variants that can be considered as proxies for specific lipid-lowering medications. We obtained associations with lipid fractions from the Global Lipids Genetics Consortium, a meta-analysis of 188,577 participants, and genetic associations with cardiovascular outcomes from 367,703 participants in UK Biobank.\n\nFor LDL-cholesterol, in addition to the expected positive associations with coronary artery disease (CAD) risk (odds ratio per 1 standard deviation increase [OR], 1.45; 95% confidence interval [95%CI] 1.35-1.57) and other atheromatous outcomes (ischemic cerebrovascular disease and peripheral vascular disease), we found independent associations of genetically-predicted LDL-cholesterol with abdominal aortic aneurysm (OR 1.75; 95%CI 1.40-2.17), and aortic valve stenosis (OR 1.46; 95%CI 1.25-1.70). Genetically-predicted triglyceride levels were positively associated with CAD (OR 1.25; 95%CI 1.12-1.40), aortic valve stenosis (OR 1.29; 95%CI 1.04-1.61), and hypertension (OR 1.17; 95%CI 1.07-1.27), but inversely associated with venous thromboembolism (OR 0.79; 95%CI 0.67-0.93). The positive associations of genetically-predicted LDL-cholesterol and triglycerides with heart failure and aortic stenosis appeared to be mediated by CAD.\n\nConclusionLowering LDL-cholesterol is likely to prevent abdominal aortic aneurysm and aortic stenosis, in addition to CAD and other atheromatous cardiovascular outcomes. Lowering triglycerides is likely to prevent CAD and aortic valve stenosis, but may increase risk of thromboembolism.

epidemiology↗

Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Ancestral state reconstruction models use genetic data to characterize a group of organisms common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host populations effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

epidemiology↗