Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis.
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ABSTRACT:
SUBMITTER: Nost TH
PROVIDER: S-EPMC8017014 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic-transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary-metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis.
| S-EPMC10115639 | biostudies-literature
Project description:OBJECTIVE:In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast cancer diagnosis. We estimate a penalized maximum likelihood logistic regression. We evaluate this in terms of calibration, concordance probability, and stability, all of which we estimate by the bootstrap. RESULTS:We identify a set of 108 candidate predictor genes that exhibit a fold change in average metastasized observation where there is none for the average non-metastasized observation.
| S-EPMC7238609 | biostudies-literature
Project description:The majority of lung cancer is caused by tobacco smoking, and lung cancer-relevant epigenetic markers have been identified in relation to smoking exposure. Still, smoking-related markers appear to mediate little of the effect of smoking on lung cancer. Thus in order to identify disease-relevant markers and enhance our understanding of pathways, a wide search is warranted. Through an epigenome-wide search within a case-control study (131 cases, 129 controls) nested in a Norwegian prospective cohort of women, we found 25 CpG sites associated with lung cancer. Twenty-three were classified as associated with smoking (LC-AwS), and two were classified as unassociated with smoking (LC-non-AwS), as they remained associated with lung cancer after stringent adjustment for smoking exposure using the comprehensive smoking index (CSI): cg10151248 (PC, CSI-adjusted odds ratio (OR)?=?0.34 [0.23-0.52] per standard deviation change in methylation) and cg13482620 (B3GNTL1, CSI-adjusted OR?=?0.33 [0.22-0.50]). Analysis among never smokers and a cohort of smoking-discordant twins confirmed the classification of the two LC-non-AwS CpG sites. Gene expression profiles demonstrated that the LC-AwS CpG sites had different enriched pathways than LC-non-AwS sites. In conclusion, using blood-derived DNA methylation and gene expression profiles from a prospective lung cancer case-control study in women, we identified 25 CpG lung cancer markers prior to diagnosis, two of which were LC-non-AwS markers and related to distinct pathways.
| S-EPMC6233189 | biostudies-literature
Project description:BackgroundLung cancer is a good example of the potential benefit of symptom-based diagnosis, as it is the commonest cancer worldwide, with the highest mortality from late diagnosis and poor symptom recognition. The diagnosis and risk assessment tools currently available have been shown to require further validation. In this study, we determine the symptoms associated with lung cancer prior to diagnosis and demonstrate that by separating prior risk based on factors such as smoking history and age, from presenting symptoms and combining them at the individual patient level, we can make greater use of this knowledge to create a practical framework for the symptomatic diagnosis of individual patients presenting in primary care.AimTo provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis.Design and settingSystematic review and meta-analysis of primary and secondary care data.MethodSeven databases were searched (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, Web of Science, British Nursing Index and Cochrane Library). Thirteen studies were selected based on predetermined eligibility and quality criteria for diagnostic assessment to establish the value of symptom-based diagnosis using diagnosistic odds ratio (DOR) and summary receiver operating characteristic (SROC) curve. In addition, routinely collated real-time data from primary care electronic health records (EHR), TransHis, was analysed to compare with our findings.ResultsHaemoptysis was found to have the greatest diagnostic value for lung cancer, diagnostic odds ratio (DOR) 6.39 (3.32-12.28), followed by dyspnoea 2.73 (1.54-4.85) then cough 2.64 (1.24-5.64) and lastly chest pain 2.02 (0.88-4.60). The use of symptom-based diagnosis to accurately diagnose lung cancer cases from non-cases was determined using the summary receiver operating characteristic (SROC) curve, the area under the curve (AUC) was consistently above 0.6 for each of the symptoms described, indicating reasonable discriminatory power. The positive predictive value (PPV) of diagnostic symptoms depends on an individual's prior risk of lung cancer, as well as their presenting symptom pattern. For at risk individuals we calculated prior risk using validated epidemiological models for risk factors such as age and smoking history, then combined with the calculated likelihood ratios for each symptom to establish posterior risk or positive predictive value (PPV).ConclusionOur findings show that there is diagnostic value in the clinical symptoms associated with lung cancer and the potential benefit of characterising these symptoms using routine data studies to identify high-risk patients.
| S-EPMC6248994 | biostudies-literature
Project description:AimTo examine patterns of recent pre-diagnostic fillings of antibiotics as an indicator of early symptoms of lung cancer.MethodsIndividuals diagnosed with lung cancer (cases) in 2009-2016 were identified in the Swedish National Lung Cancer Register, a population-based register, and randomly matched with up to five individuals free of lung cancer (controls) from the general population. Conditional logistic models were used to estimate odds ratios for the association between lung cancer and a recent history of filled antibiotic prescriptions.ResultsThe study included 27,017 cases and 129,355 controls. The likelihood of recent exposure was approximately two times higher in cases compared to controls. The magnitude of the effect size became more pronounced with proximity to the diagnosis of lung cancer and an increasing number of filled prescriptions. While the magnitude of the effect size did not differ by sex or educational level, it became attenuated with increasing age. There was no evidence supporting a trend in the magnitude of the effect size for the association between lung cancer and a history of repeated fillings by cancer stage.ConclusionLung cancer was associated with an increased likelihood of a recent history of filled antibiotic prescriptions. However, there was no evidence of an association between repeated fillings and a diagnostic delay, as reflected by stage. Our findings underscore the importance of clinical reassessment to rule out lung cancer following pneumonia treatment, especially for patients with multiple treatment cycles.
| S-EPMC8089077 | biostudies-literature
Project description:IntroductionAbnormal results in common blood tests may occur several months before lung cancer (LC) and colorectal cancer (CRC) diagnosis. Identifying early blood markers of cancer and distinct blood test signatures could support earlier diagnosis in general practice.MethodsUsing linked Australian primary care and hospital cancer registry data, we conducted a cohort study of 855 LC and 399 CRC patients diagnosed between 2001 and 2021. Requests and results from general practice blood tests (six acute phase reactants [APR] and six red blood cell indices [RBCI]) were examined in the 2 years before cancer diagnosis. Poisson regression models were used to estimate monthly incidence rates and examine pre-diagnostic trends in blood test use and abnormal results prior to cancer diagnosis, comparing patterns in LC and CRC patients.ResultsGeneral practice blood test requests increase from 7 months before CRC and 6 months before LC diagnosis. Abnormalities in many APR and RBCI tests increase several months before cancer diagnosis, often occur prior to or in the absence of anaemia (in 51% of CRC and 81% of LC patients with abnormalities), and are different in LC and CRC patients.ConclusionsThis study demonstrates an increase in diagnostic activity in Australian general practice several months before LC and CRC diagnosis, indicating potential opportunities for earlier diagnosis. It identifies blood test abnormalities and distinct signatures that are early markers of LC and CRC. If combined with other pre-diagnostic information, these blood tests have potential to support GPs in prioritising patients for cancer investigation of different sites to expedite diagnosis.
| S-EPMC11245636 | biostudies-literature
Project description:Lung cancer (LC) prognosis is closely linked to the stage of disease when diagnosed. We investigated the biomarker potential of serum RNAs for the early detection of LC in smokers at different prediagnostic time intervals and histological subtypes. In total, 1061 samples from 925 individuals were analyzed. RNA sequencing with an average of 18 million reads per sample was performed. We generated machine learning models using normalized serum RNA levels and found that smokers later diagnosed with LC in 10 years can be robustly separated from healthy controls regardless of histology with an average area under the ROC curve (AUC) of 0.76 (95% CI, 0.68-0.83). Furthermore, the strongest models that took both time to diagnosis and histology into account successfully predicted non-small cell LC (NSCLC) between 6 and 8 years, with an AUC of 0.82 (95% CI, 0.76-0.88), and SCLC between 2 and 5 years, with an AUC of 0.89 (95% CI, 0.77-1.0), before diagnosis. The most important separators were microRNAs, miscellaneous RNAs, isomiRs, and tRNA-derived fragments. We have shown that LC can be detected years before diagnosis and manifestation of disease symptoms independently of histological subtype. However, the highest AUCs were achieved for specific subtypes and time intervals before diagnosis. The collection of models may therefore also predict the severity of cancer development and its histology. Our study demonstrates that serum RNAs can be promising prediagnostic biomarkers in an LC screening setting, from early detection to risk assessment.
| S-EPMC8884722 | biostudies-literature
Project description:Lung cancer (LC) generally lacks unique core symptoms or signs. However, there are a multitude of bodily sensations that are often non-specific, not easily understood, and many times initially not recognized as indicative of LC by the affected person, which often leads to late diagnosis. In this international qualitative study, we inductively analyzed retrospective accounts of 61 people diagnosed with LC in Denmark, England and Sweden. Using the bodily sensations they most commonly spoke about (tiredness, breathlessness, pain, and cough), we constructed four sensation-based cases to understand the pre-diagnostic processes of reasoning and practice triggered by these key indicators of LC. We thereafter critically applied Hay's model of sensations to symptoms transformation, examining its central concepts of duration, disability and vulnerability, to support understanding of these processes. We found that while duration and disability are clearly relevant, vulnerability is more implicitly expressed in relation to perceived threat. Tiredness, even when of long duration and causing disability, was often related to normal aging, rather than a health threat. Regardless of duration, breathlessness was disturbing and threatening enough to lead to care-seeking. Pain varied by location, duration and degree of disability, and thus also varied in degree of threat perceived. Preconceived, but unmet expectations of what LC-related cough and pain would entail could cause delays by misleading participants; if cough lasted long enough, it could trigger health care contact. Duration, disability, and sense of threat, rather than vulnerability, were found to be relevant concepts for understanding the trajectory to diagnosis for LC among these participants. The process by which an individual, their family and health care providers legitimize sensations, allowing them to be seen as potential symptoms of disease, is also an essential, but varying part of the diagnostic processes described here.
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Project description:Triple-negative breast cancer (TNBC) is a highly metastatic subtype of breast cancer that has limited therapeutic options. Thus, developing novel treatments for metastatic TNBC is an urgent need. Here, we show that nanoparticle-mediated delivery of transforming growth factor-β1-activated kinase-1 (TAK1) inhibitor 5Z-7-Oxozeaenol can inhibit TNBC lung metastasis in most animals tested. P38 is a central signal downstream of TAK1 in TNBC cells in TAK1-mediated response to multiple cytokines. Following co-culturing with macrophages or fibroblasts, TNBC cells express interleukin-1 (IL1) or tumor necrosis factor-α (TNFα), respectively. Compared to TAK1 inhibition, suppressing IL1 signaling with recombinant IL1 receptor antagonist (IL1RA) is less efficient in reducing lung metastasis, possibly due to the additional TAK1 signals coming from distinct stromal cells. Together, these observations suggest that TAK1 may play a central role in promoting TNBC cell adaptation to the lung microenvironment by facilitating positive feedback signaling mediated by P38. Approaches targeting the key TAK1-P38 signal could offer a novel means for suppressing TNBC lung metastasis.
| S-EPMC5959931 | biostudies-literature
Project description: Not available
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