Role of patient characteristics in adherence to first-line treatment guidelines in breast, lung and prostate cancer: insights from the Nordic healthcare system.

OBJECTIVES: This study investigates the influence of socioeconomic status, health literacy, and numeracy on treatment decisions and the occurrence of adverse events in patients with breast, lung, and prostate cancer within a Nordic healthcare setting. DESIGN: A follow-up to a cross-sectional, mixed-methods, single-centre study. SETTING: A Nordic, tertiary cancer clinic. PARTICIPANTS: A total of 244 participants with breast, lung and prostate cancer were initially identified, of which 138 first-line treatment participants were eligible for this study. First-line treatment participants (n=138) surpassed the expected cases (n=108). INTERVENTIONS: Not applicable as this was an observational study. PRIMARY AND SECONDARY OUTCOME MEASURES: The study's primary endpoint was the rate of guideline adherence. The secondary endpoint involved assessing treatment toxicity in the form of adverse events. RESULTS: Guideline-adherent treatment was observed in 114 (82.6%) cases. First-line treatment selection appeared uninfluenced by participants' education, occupation, income or self-reported health literacy. A minority (3.6%) experienced difficulties following treatment instructions, primarily with oral cancer medications. CONCLUSIONS: The findings indicated lesser cancer health disparities regarding guideline adherence and treatment toxicity within the Nordic healthcare framework. A causal connection may not be established; however, the findings contribute to discourse on equitable cancer health provision.

Area-based disparities in non-small-cell lung cancer survival.

BACKGROUND: In the Nordic countries, universal healthcare access has been effective in reducing socioeconomic disparities in non-small-cell lung cancer (NSCLC) management. However, other factors, such as proximity to healthcare facilities, may still affect access to care. This study aimed at investigating the influence of residential area on NSCLC survival. METHODS: This population-based study utilized hospital records to identify NSCLC patients who underwent their initial treatment at Vaasa Central Hospital between January 1, 2016, and December 31, 2020. Patients were categorized based on their postal codes into urban areas (≤50 km from the hospital) and rural areas (>50 km from the hospital). Survival rates between these two groups were compared using Cox regression analysis. RESULTS: A total of 321 patients were included in the study. Patients residing in rural areas (n = 104) exhibited poorer 12-month survival rates compared to their urban counterparts (n = 217) (unadjusted Hazard Ratio [HR]: 1.38; 95% Confidence Interval [CI]: 1.01-1.89; p = 0.042). After adjusting for factors such as performance status, frailty, and stage at diagnosis in a multivariate Cox regression model, the adjusted HR increased to 1.47 (95% CI: 1.07-2.01; p = 0.017) for patients living in rural areas compared to those in urban areas. INTERPRETATION: The study findings indicate that the distance to the hospital is associated with increased lung cancer mortality. This suggests that geographical proximity may play a crucial role in the disparities observed in NSCLC survival rates. Addressing these disparities should involve strategies aimed at improving healthcare accessibility, particularly for patients residing in rural areas, to enhance NSCLC outcomes and reduce mortality.

Developments in predictive biomarker testing and targeted therapy in advanced stage non-small cell lung cancer and their application across European countries.

In the past two decades, the treatment of metastatic non-small cell lung cancer (NSCLC), has undergone significant changes due to the introduction of targeted therapies and immunotherapy. These advancements have led to the need for predictive molecular tests to identify patients eligible for targeted therapy. This review provides an overview of the development and current application of targeted therapies and predictive biomarker testing in European patients with advanced stage NSCLC. Using data from eleven European countries, we conclude that recommendations for predictive testing are incorporated in national guidelines across Europe, although there are differences in their comprehensiveness. Moreover, the availability of recently EMA-approved targeted therapies varies between European countries. Unfortunately, routine assessment of national/regional molecular testing rates is limited. As a result, it remains uncertain which proportion of patients with metastatic NSCLC in Europe receive adequate predictive biomarker testing. Lastly, Molecular Tumor Boards (MTBs) for discussion of molecular test results are widely implemented, but national guidelines for their composition and functioning are lacking. The establishment of MTB guidelines can provide a framework for interpreting rare or complex mutations, facilitating appropriate treatment decision-making, and ensuring quality control.

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.

BACKGROUND: Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. METHODS: Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. RESULTS: Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). CONCLUSIONS: Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies.

BACKGROUND: Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. METHODS: We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. RESULTS: Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10-8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. CONCLUSIONS: We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. IMPACT: Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility.

Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18 × 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis. SIGNIFICANCE: The causal pathway connecting CYP2A6 genetic variability and activity, cigarette consumption, and lung cancer susceptibility in smokers highlights the need for behavior modification interventions based on host susceptibility for cancer prevention.

Overall survival and treatment modalities in pancreatic adenocarcinoma: a retrospective analysis of a single centre in Western Australia.

Background: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of approximately 10.7% in Australia. It is becoming an increasingly common cause of cancer mortality. The therapeutic model for PDAC remains limited, especially for those with metastatic disease on presentation. Methods: We completed a retrospective cohort study including all patients with PDAC presenting between April 2008 and October 2021 to St. John of God Subiaco Hospital in Western Australia. Overall survival (OS) was calculated via Kaplan-Meier method. Results: We identified 251 patients treated for PDAC. Of these, 134 patients (53%) had resectable, borderline resectable or locally advanced (LA) disease at diagnosis and 117 patients (47%) had metastatic disease. The median age of all patients was 66 years (range, 25-87 years). OS in PDAC was 26 months [95% confidence interval (CI): 23-30]. In the non-metastatic group OS was 34 months (95% CI: 30-39). In the metastatic group OS was 19 months (95% CI: 14-22). Treatment modalities varied between patients. Overall 123 patients were treated with chemotherapy alone, 55 patients had chemoradiotherapy, 34 patients had chemotherapy and surgery and 37 had tri-modality treatment including chemotherapy, surgery and radiotherapy. Two patients received cyberknife radiation alone. Conclusions: This retrospective study shows a significant prolonged survival for PDAC patients. Further studies are needed to validate second- and third-line regimens in PDAC.

Resection of previously inoperable retroperitoneal liposarcoma.

We present a case of successful resection of a large right upper quadrant retroperitoneal dedifferentiated liposarcoma involving multiple adjacent organs, initially considered inoperable in a patient in his 40s. This case highlights the importance of extensive preoperative planning and a multidisciplinary approach in achieving a greater chance of curative resection. Preoperative optimisation included neoadjuvant chemotherapy, concurrent portal vein embolisation and hepatic vein embolisation. The patient then underwent en-bloc resection, including total pancreatectomy, hemihepatectomy and vena caval resection in conjunction with extracorporeal membrane oxygenation and percutaneous venovenous bypass.

Socioeconomic status and lifestyle patterns in the most common cancer types-community-based research.

INTRODUCTION: As the global burden of chronic cancer increases, its correlation to lifestyle, socioeconomic status (SES) and health equity becomes more important. The aim of the present study was to provide a snapshot of the socioeconomic and lifestyle patterns for different cancer types in patients at a Nordic tertiary cancer clinic. MATERIALS AND METHODS: In a descriptive observational study, questionnaires addressed highest-attained educational level, occupational level, economy, relationship status, exposures, and lifestyle habits. The questionnaire was distributed to all cancer patients attending the cancer clinic. Treating physicians added further information about the cancer disease, including primary origin, pathology report, TNM-classification and stage. RESULTS: Patients with lung cancer had the lowest SES, and patients with gastrointestinal (GI) cancer, other cancer types and prostate cancer had the second, third and fourth lowest SES, respectively. However, breast cancer patients had the highest SES. Lifestyle and exposure patterns differed among the major cancer types. Lung cancer patients reported the highest proportion of unfavourable lifestyle and exposure patterns, and patients with GI cancer, prostate cancer and other cancer types had the second, third and fourth highest proportion of unfavourable lifestyle and exposure patterns, respectively. The most favourable exposure and lifestyle patterns were observed in breast cancer patients. CONCLUSIONS: The present study indicated significant socioeconomic and lifestyle differences among cancer types at a Nordic cancer centre, with differences in lifestyle being more prominent than socioeconomic differences.

Candidate pathway analysis of surfactant proteins identifies CTSH and SFTA2 that influences lung cancer risk.

Pulmonary surfactant is a lipoprotein synthesized and secreted by alveolar type II cells in lung. We evaluated the associations between 200,139 single nucleotide polymorphisms (SNPs) of 40 surfactant-related genes and lung cancer risk using genotyped data from two independent lung cancer genome-wide association studies. Discovery data included 18,082 cases and 13,780 controls of European ancestry. Replication data included 1,914 cases and 3,065 controls of European descent. Using multivariate logistic regression, we found novel SNPs in surfactant-related genes CTSH [rs34577742 C > T, odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.89-0.93, P = 7.64 × 10-9] and SFTA2 (rs3095153 G > A, OR = 1.16, 95% CI = 1.10-1.21, P = 1.27 × 10-9) associated with overall lung cancer in the discovery data and validated in an independent replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.80-0.96, P = 5.76 × 10-3) and SFTA2 (rs3095153 G > A, OR = 1.14, 95% CI = 1.01-1.28, P = 3.25 × 10-2). Among ever smokers, we found SNPs in CTSH (rs34577742 C > T, OR = 0.89, 95% CI = 0.85-0.92, P = 1.94 × 10-7) and SFTA2 (rs3095152 G > A, OR = 1.20, 95% CI = 1.14-1.27, P = 4.25 × 10-11) associated with overall lung cancer in the discovery data and validated in the replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.79-0.97, P = 1.64 × 10-2) and SFTA2 (rs3095152 G > A, OR = 1.15, 95% CI = 1.01-1.30, P = 3.81 × 10-2). Subsequent transcriptome-wide association study using expression weights from a lung expression quantitative trait loci study revealed genes most strongly associated with lung cancer are CTSH (PTWAS = 2.44 × 10-4) and SFTA2 (PTWAS = 2.32 × 10-6).

An open-source human-in-the-loop BCI research framework: method and design.

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil).

KRAS G12C Mutant Non-Small Cell Lung Cancer Linked to Female Sex and High Risk of CNS Metastasis: Population-based Demographics and Survival Data From the National Swedish Lung Cancer Registry.

BACKGROUND: Real-world data on demographics related to KRAS mutation subtypes are crucial as targeted drugs against the p.G12C variant have been approved. METHOD: We identified 6183 NSCLC patients with reported NGS-based KRAS status in the Swedish national lung cancer registry between 2016 and 2019. Following exclusion of other targetable drivers, three cohorts were studied: KRAS-G12C (n = 848), KRAS-other (n = 1161), and driver negative KRAS-wild-type (wt) (n = 3349). RESULTS: The prevalence of KRAS mutations and the p.G12C variant respectively was 38%/16% in adenocarcinoma, 28%/13% in NSCLC-NOS and 6%/2% in squamous cell carcinoma. Women were enriched in the KRAS-G12C (65%) and KRAS-other (59%) cohorts versus KRAS-wt (48%). A high proportion of KRAS-G12C patients in stage IV (28%) presented with CNS metastasis (vs. KRAS-other [19%] and KRAS-wt [18%]). No difference in survival between the mutation cohorts was seen in stage I-IIIA. In stage IV, median overall survival (mOS) from date of diagnosis was shorter for KRAS-G12C and KRAS-other (5.8 months/5.2 months) vs. KRAS wt (6.4 months). Women had better outcome in the stage IV cohorts, except in KRAS-G12C subgroup where mOS was similar between men and women. Notably, CNS metastasis did not impact survival in stage IV KRAS-G12C, but was associated with poorer survival, as expected, in KRAS-other and KRAS-wt. CONCLUSION: The KRAS p.G12C variant is a prevalent targetable driver in Sweden and significantly associated with female sex and presence of CNS metastasis. We show novel survival effects linked to KRAS p.G12C mutations in these subgroups with implications for clinical practice.

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

BACKGROUND: We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test. METHODS: We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided. RESULTS: The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model. CONCLUSION: Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.

Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis.

BACKGROUND: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. METHODS: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. FINDINGS: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035). INTERPRETATION: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. FUNDING: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry.

Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units.

Predicting the origin-destination (OD) probability distribution of agent transfer is an important problem for managing complex systems. However, prediction accuracy of associated statistical estimators suffer from underdetermination. While specific techniques have been proposed to overcome this deficiency, there still lacks a general approach. Here, we propose a deep neural network framework with gated recurrent units (DNNGRU) to address this gap. Our DNNGRU is network-free, as it is trained by supervised learning with time-series data on the volume of agents passing through edges. We use it to investigate how network topologies affect OD prediction accuracy, where performance enhancement is observed to depend on the degree of overlap between paths taken by different ODs. By comparing against methods that give exact results, we demonstrate the near-optimal performance of our DNNGRU, which we found to consistently outperform existing methods and alternative neural network architectures, under diverse data generation scenarios.

Mosaic Chromosomal Alterations Are Associated With Increased Lung Cancer Risk: Insight From the INTEGRAL-ILCCO Cohort Analysis.

INTRODUCTION: Mosaic chromosomal alterations (mCAs) detected in white blood cells represent a type of clonal hematopoiesis (CH) that is understudied compared with CH-related somatic mutations. A few recent studies indicated their potential link with nonhematological cancers, especially lung cancer. METHODS: In this study, we investigated the association between mCAs and lung cancer using the high-density genotyping data from the OncoArray study of INTEGRAL-ILCCO, the largest single genetic study of lung cancer with 18,221 lung cancer cases and 14,825 cancer-free controls. RESULTS: We identified a comprehensive list of autosomal mCAs, ChrX mCAs, and mosaic ChrY (mChrY) losses from these samples. Autosomal mCAs were detected in 4.3% of subjects, in addition to ChrX mCAs in 3.6% of females and mChrY losses in 9.6% of males. Multivariable logistic regression analysis indicated that the presence of autosomal mCAs in white blood cells was associated with an increased lung cancer risk after adjusting for key confounding factors, including age, sex, smoking status, and race. This association was mainly driven by a specific type of mCAs: copy-neutral loss of heterozygosity on autosomal chromosomes. The association between autosome copy-neutral loss of heterozygosity and increased risk of lung cancer was further confirmed in two major histologic subtypes, lung adenocarcinoma and squamous cell carcinoma. In addition, we observed a significant increase of ChrX mCAs and mChrY losses in smokers compared with nonsmokers and racial differences in certain types of mCA events. CONCLUSIONS: Our study established a link between mCAs in white blood cells and increased risk of lung cancer.

HDF1, a novel flowering time regulator identified in a mutant suppressing sensitivity to red light reduced 1 early flowering.

Arabidopsis SENSITIVITY TO RED LIGHT REDUCED 1 (SRR1) delays the transition from vegetative to reproductive development in noninductive conditions. A second-site suppressor screen for novel genes that overcome early flowering of srr1-1 identified a range of suppressor of srr1-1 mutants flowering later than srr1-1 in short photoperiods. Here, we focus on mutants flowering with leaf numbers intermediate between srr1-1 and Col. Ssm67 overcomes srr1-1 early flowering independently of day-length and ambient temperature. Full-genome sequencing and linkage mapping identified a causative SNP in a gene encoding a Haloacid dehalogenase superfamily protein, named HAD-FAMILY REGULATOR OF DEVELOPMENT AND FLOWERING 1 (HDF1). Both, ssm67 and hdf1-1 show increased levels of FLC, indicating that HDF1 is a novel regulator of this floral repressor. HDF1 regulates flowering largely independent of SRR1, as the effect is visible in srr1-1 and in Col, but full activity on FLC may require SRR1. Furthermore, srr1-1 has a delayed leaf initiation rate that is dependent on HDF1, suggesting that SRR1 and HDF1 act together in leaf initiation. Another mutant flowering intermediate between srr1-1 and wt, ssm15, was identified as a new allele of ARABIDOPSIS SUMO PROTEASE 1, previously implicated in the regulation of FLC stability.

A cross-sectional study of inflammatory markers as determinants of circulating kynurenines in the Lung Cancer Cohort Consortium.

Circulating concentrations of metabolites (collectively called kynurenines) in the kynurenine pathway of tryptophan metabolism increase during inflammation, particularly in response to interferon-gamma (IFN-γ). Neopterin and the kynurenine/tryptophan ratio (KTR) are IFN-γ induced inflammatory markers, and together with C-reactive protein (CRP) and kynurenines they are associated with various diseases, but comprehensive data on the strength of associations of inflammatory markers with circulating concentrations of kynurenines are lacking. We measured circulating concentrations of neopterin, CRP, tryptophan and seven kynurenines in 5314 controls from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). The associations of neopterin, KTR and CRP with kynurenines were investigated using regression models. In mixed models, one standard deviation (SD) higher KTR was associated with a 0.46 SD higher quinolinic acid (QA), and 0.31 SD higher 3-hydroxykynurenine (HK). One SD higher neopterin was associated with 0.48, 0.44, 0.36 and 0.28 SD higher KTR, QA, kynurenine and HK, respectively. KTR and neopterin respectively explained 24.1% and 16.7% of the variation in QA, and 11.4% and 7.5% of HK. CRP was only weakly associated with kynurenines in regression models. In summary, QA was the metabolite that was most strongly associated with the inflammatory markers. In general, the inflammatory markers were most strongly related to metabolites located along the tryptophan-NAD axis, which may support suggestions of increased production of NAD from tryptophan during inflammation.