Molecular subtypes, histopathological grade and survival in a historic cohort of breast cancer patients

Molecular subtyping of breast cancer may provide additional prognostic information regarding patient outcome. However, its clinical significance remains to be established. In this study, the main aims were to discover whether reclassification of breast cancer into molecular subtypes provides more precise information regarding outcome compared to conventional histopathological grading and to study breast cancer-specific survival in the different molecular subtypes. Cases of breast cancer occurring in a cohort of women born between 1886 and 1928 with long-term follow-up were included in the study. Tissue microarrays were constructed from archival formalin-fixed, paraffin-embedded tissue from 909 cases. Using immunohistochemistry and in situ hybridisation as surrogates for gene expression analyses, all cases were reclassified into the following molecular subtypes: Luminal A; Luminal B (HER2−); Luminal B (HER2+); HER2 subtype; Basal phenotype; and five negative phenotype. Kaplan–Meier survival curves and Cox proportional hazards models were used in the analyses. During the first 5 years after diagnosis, there were significant differences in prognosis according to molecular subtypes with the best survival for the Luminal A subtype and the worst for HER2 and five negative phenotype. In this historic cohort of women with breast cancer, differences in breast cancer-specific survival according to subtype occur almost exclusively amongst the histopathological grade 2 tumours. From 5 years after time of diagnosis until the end of follow-up, there appears to be no difference in survival according to molecular subtype or histopathological grade.publishedVersio

Molecular Subtypes of Breast Cancer: Long-term Incidence Trends and Prognostic Differences

Background: Secular trends in incidence and prognosis of molecular breast cancer subtypes are poorly described. We studied long-term trends in a population of Norwegian women born 1886–1977. Methods: A total of 52,949 women were followed for breast cancer incidence, and 1,423 tumors were reclassified into molecular subtypes using IHC and in situ hybridization. We compared incidence rates among women born 1886–1928 and 1929–1977, estimated age-specific incidence rate ratios (IRR), and performed multiple imputations to account for unknown subtype. Prognosis was compared for women diagnosed before 1995 and in 1995 or later, estimating cumulative risk of death and HRs. Results: Between 50 and 69 years of age, incidence rates of Luminal A and Luminal B (HER2−) were higher among women born in 1929 or later, compared with before 1929 [IRRs 50–54 years; after imputations: 3.5; 95% confidence interval (CI), 1.8–6.9 and 2.5; 95% CI, 1.2–5.2, respectively], with no clear differences for other subtypes. Rates of death were lower in women diagnosed in 1995 or later, compared to before 1995, for Luminal A (HR 0.4; 95% CI, 0.3–0.5), Luminal B (HER2−; HR 0.5; 95% CI, 0.3–0.7), and Basal phenotype (HR 0.4; 95% CI, 0.2–0.9). Conclusions: We found a strong secular incidence increase restricted to Luminal A and Luminal B (HER2−) subtypes, combined with a markedly improved prognosis for these subtypes and for the Basal phenotype.acceptedVersio

Factors affecting quality of end-of-life hospital care - a qualitative analysis of free text comments from the i-CODE survey in Norway.

BackgroundThe ERANet-LAC CODE (Care Of the Dying Evaluation) international survey assessed quality of care for dying cancer patients in seven countries, by use of the i-CODE questionnaire completed by bereaved relatives. The aim of this sub study was to explore which factors improve or reduce quality of end-of-life (EOL) care from Norwegian relatives' point of view, as expressed in free text comments.Methods194 relatives of cancer patients dying in seven Norwegian hospitals completed the i-CODE questionnaire 6-8 weeks after bereavement; recruitment period 14 months; response rate 58%. Responders were similar to non-responders in terms of demographic details.104 participants (58% spouse/partner) added free text comments, which were analyzed by systematic text condensation.ResultsOf the 104 comments, 45% contained negative descriptions, 27% positive and 23% mixed. 78% described previous experiences, whereas 22% alluded to the last 2 days of life. 64% of the comments represented medical/surgical/oncological wards and 36% palliative care units. Four main categories were developed from the free text comments: 1) Participants described how attentive care towards the practical needs of patients and relatives promoted dignity at the end of life, which could easily be lost when this awareness was missing. 2) They experienced that lack of staff, care continuity, professional competence or healthcare service coordination caused uncertainty and poor symptom alleviation. 3) Inadequate information to patient and family members generated unpredictable and distressing final illness trajectories. 4) Availability and professional support from healthcare providers created safety and enhanced coping in a difficult situation.ConclusionsOur findings suggest that hospitals caring for cancer patients at the end of life and their relatives, should systematically identify and attend to practical needs, as well as address important organizational issues. Education of staff members ought to emphasize how professional conduct and communication fundamentally affect patient care and relatives' coping

Obesity, Metabolic Factors and Risk of Different Histological Types of Lung Cancer: A Mendelian Randomization Study

Background Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. Methods and findings We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01–1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15–2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79–1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84–0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25–2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. Conclusions Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior

Virological and serological surveillance for type A influenza in the black-legged kittiwake (Rissa tridactyla)

<p>Abstract</p> <p>Background</p> <p>The epidemiology of avian influenza viruses (AIVs) in gulls is only partially known. The role of the world's most numerous gull species, the black-legged kittiwake (<it>Rissa tridactyla</it>), as a potential AIV reservoir species has been unclear. The prevalence of AIV and humoral response against AIV were therefore studied in a colony of apparently healthy black-legged kittiwakes breeding in a nesting cliff in the South West Barents Region of Norway (70°22' N, 31°10' E), in 2008 and 2009.</p> <p>Results</p> <p>AIVs were detected from the oropharynx and cloaca in low amounts, with prevalences of 15% and 5%, in 2008 and 2009, respectively. Direct, partial sequencing of the hemagglutinin (HA) gene revealed that the H4 subtype was present. In 2009, antibodies to influenza A virus were detected in sera from 57 of 80 adult birds. In contrast, none of the three-week-old chicks (n = 18) tested seropositive. Hemagglutination inhibition (HI) assays demonstrated that the adult kittiwakes primarily had antibodies specific to the gull-associated H13 and H16 subtypes, with antibodies to H16 being most common.</p> <p>Conclusions</p> <p>These results support that the highly pelagic black-legged kittiwake is a reservoir of AIV. The serological findings suggest that H16 might be the main AIV subtype in the black-legged kittiwake. Further studies are needed to understand the ecology of AIV in the black-legged kittiwake and in gulls in general.</p

Lung Cancer Risk in Never-Smokers of European Descent is Associated With Genetic Variation in the 5(p)15.33 TERT-CLPTM1Ll Region

Introduction: Inherited susceptibility to lung cancer risk in never-smokers is poorly understood. The major reason for this gap in knowledge is that this disease is relatively uncommon (except in Asians), making it difficult to assemble an adequate study sample. In this study we conducted a genome-wide association study on the largest, to date, set of European-descent never-smokers with lung cancer. Methods: We conducted a two-phase (discovery and replication) genome-wide association study in never-smokers of European descent. We further augmented the sample by performing a meta-analysis with never-smokers from the recent OncoArray study, which resulted in a total of 3636 cases and 6295 controls. We also compare our findings with those in smokers with lung cancer. Results: We detected three genome-wide statistically significant single nucleotide polymorphisms rs31490 (odds ratio [OR]: 0.769, 95% confidence interval [CI]: 0.722-0.820; p value 5.31 x 10(-16)), rs380286 (OR: 0.770, 95% CI: 0.723-0.820; p value 4.32 x 10(-16)), and rs4975616 OR: 0.778, 95% CI: 0.730-0.829; p value 1.04 x 10(-14)). All three mapped to Chromosome 5 CLPTM1L-TERT region, previously shown to be associated with lung cancer risk in smokers and in never-smoker Asian women, and risk of other cancers including breast, ovarian, colorectal, and prostate. Conclusions: We found that genetic susceptibility to lung cancer in never-smokers is associated to genetic variants with pan-cancer risk effects. The comparison with smokers shows that top variants previously shown to be associated with lung cancer risk only confer risk in the presence of tobacco exposure, underscoring the importance of gene-environment interactions in the etiology of this disease. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Obesity, Metabolic Factors and Risk of Different Histological Types of Lung Cancer: A Mendelian Randomization Study

Background: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. Methods and findings: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01–1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15–2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79–1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84–0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25–2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. Conclusions: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior

Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study.

BACKGROUND: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. METHODS AND FINDINGS: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. CONCLUSIONS: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior