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The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample <3 years prior to diagnosis. Conditional logistic regression was used to estimate the association between EarlyCDT-Lung test results and lung cancer risk. Sensitivity and specificity of the EarlyCDT-Lung test were calculated in all subjects and subgroups based on age, smoking history, lung cancer stage, sample collection time before diagnosis and year of sample collection. The overall lung cancer odds ratios were 0.89 (95% CI: 0.34-2.30) for a moderate risk EarlyCDT-Lung test result and 1.09 (95% CI: 0.48-2.47) for a high-risk test result compared to no significant test result. The overall sensitivity was 8.4% (95% CI: 4.6-14) and overall specificity was 92% (95% CI: 87-96) when considering a high-risk result as positive. Stratified analysis indicated higher sensitivity (17%, 95% CI: 7.2-32.1) in subjects with blood drawn up to 1 year prior to diagnosis. In conclusion, our study does not support a role of the EarlyCDT-Lung test in identifying the high-risk subjects in ever-smokers for lung cancer screening in the EPIC and NSHDS cohorts.

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.

Purpose: The development and refinement of breast imaging modalities offer a wealth of diagnostic information such as imaging biomarkers, which are primarily the mammographic appearance of the various breast cancer subtypes. These are readily available preoperatively at the time of diagnosis and can enhance the prognostic value of currently used molecular biomarkers. In this study, we investigated the relative utility of the molecular and imaging biomarkers, both jointly and independently, when predicting long-term patient outcome according to the site of tumour origin. Methods: We evaluated the association of imaging biomarkers and conventional molecular biomarkers, (ER, PR, HER-2, Ki67), separately and combined, with long-term patient outcome in all breast cancer cases having complete data on both imaging and molecular biomarkers (n = 2236) diagnosed in our Institute during the period 2008–2019. Large format histopathology technique was used to document intra- and intertumoural heterogeneity and select the appropriate foci for evaluating molecular biomarkers. Results: The breast cancer imaging biomarkers were strongly predictive of long-term patient outcome. The molecular biomarkers were predictive of outcome only for unifocal acinar adenocarcinoma of the breast (AAB), but less reliable in the multifocal AAB cases due to variability of molecular biomarkers in the individual tumour foci. In breast cancer of mesenchymal origin (BCMO), conventionally termed classic invasive lobular carcinoma, and in cancers originating from the major lactiferous ducts (ductal adenocarcinoma of the breast, DAB), the molecular biomarkers misleadingly indicated favourable prognosis, whereas the imaging biomarkers in BCMO and DAB reliably indicated the high risk of breast cancer death. Among the 2236 breast cancer cases, BCMO and DAB comprised 21% of the breast cancer cases, but accounted for 45% of the breast cancer deaths. Conclusions: Integration of imaging biomarkers into the diagnos

Physicians treating breast cancer patients often wonder why this dreaded disease is still fatal in some women despite our best diagnostic and therapeutic efforts. Our own studies on prospectively documented cases spanning several decades have given us new insights for approaching this problem. By using imaging biomarkers to classify breast cancer subtypes according to their apparent site of origin, we found that a majority of breast cancer deaths (71%) occur in a minority of breast cancers (45%). Breast cancer deaths are significantly more likely to occur in women with multifocal acinar adenocarcinoma of the breast, AAB (13.1%), diffusely invasive breast cancers of ductal origin, DAB (24 %) and breast malignancies of mesenchymal hybrid cell origin, BCMO (33.7%) compared with women having unifocal invasive breast cancers (6.1%). Preventing more of these fatal events will require a re-evaluation of the current imperfect histopathologic terminology of breast cancer with special attention to the diffuse breast cancer subtypes, intensification of multimodality imaging and multidisciplinary management, as well as application of image guided large format histopathology.

The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample <3 years prior to diagnosis. Conditional logistic regression was used to estimate the association between EarlyCDT-Lung test results and lung cancer risk. Sensitivity and specificity of the EarlyCDT-Lung test were calculated in all subjects and subgroups based on age, smoking history, lung cancer stage, sample collection time before diagnosis and year of sample collection. The overall lung cancer odds ratios were 0.89 (95% CI: 0.34-2.30) for a moderate risk EarlyCDT-Lung test result and 1.09 (95% CI: 0.48-2.47) for a high-risk test result compared to no significant test result. The overall sensitivity was 8.4% (95% CI: 4.6-14) and overall specificity was 92% (95% CI: 87-96) when considering a high-risk result as positive. Stratified analysis indicated higher sensitivity (17%, 95% CI: 7.2-32.1) in subjects with blood drawn up to 1 year prior to diagnosis. In conclusion, our study does not support a role of the EarlyCDT-Lung test in identifying the high-risk subjects in ever-smokers for lung cancer screening in the EPIC and NSHDS cohorts, The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample

Background: Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis. Methods: Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors. Results: Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer. Conclusions: We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

Background: Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm. Methods: We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model. Results: ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30. Conclusions: IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals. Impact: Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers.

Objectives: To explain apparent differences among mammography screening services in Sweden using individual data on participation in screening and with breast cancer-specific survival as an outcome. Methods: We analysed breast cancer survival data from the Swedish Cancer Register on breast cancer cases from nine Swedish counties diagnosed in women eligible for screening. Data were available on 38,278 breast cancers diagnosed and 4312 breast cancer deaths. Survival to death from breast cancer was estimated using the Kaplan-Meier estimate, for all cases in each county, and separately for cases of women participating and not participating in their last invitation to screening. Formal statistical comparisons of survival were made using proportional hazards regression. Results: All counties showed a reduction in the hazard of breast cancer death with participation in screening, but the reductions for individual counties varied substantially, ranging from 51% (95% confidence interval 46-55%) to 81% (95% confidence interval 74-85%). Survival rates in nonparticipating women ranged from 53% (95% confidence interval 40-65%) to 74% (95% confidence interval 72-77%), while the corresponding survival in women participating in screening varied from 80% (95% confidence interval 77-84%) to 86% (95% confidence interval 83-88%), a considerably narrower range. Conclusions: Differences among counties in the effect of screening on breast cancer outcomes were mainly due to variation in survival in women not participating in screening. Screening conferred similarly high survival rates in all counties. This indicates that the performance of screening services was similar across counties and that detection and treatment of breast cancer in early-stage reduces inequalities in breast cancer outcome.

Overdiagnosis is a harmful consequence of screening which is particularly challenging to estimate. An unbiased setting to measure overdiagnosis in breast cancer screening requires comparative data from a screened and an unscreened cohort for at least 30 years. Such randomised data will not become available, leaving us with observational data over shorter time periods and outcomes of modelling. This collaborative effort of the International Cancer Screening Network quantified the variation in estimated breast cancer overdiagnosis in organised programmes with evaluation of both observed and simulated data, and presented examples of how modelling can provide additional insights. Reliable observational data, analysed with study design accounting for methodological pitfalls, and modelling studies with different approaches, indicate that overdiagnosis accounts for less than 10% of invasive breast cancer cases in a screening target population of women aged 50 to 69. Estimates above this level are likely to derive from inaccuracies in study design. The widely discrepant estimates of overdiagnosis reported from observational data could substantially be reduced by use of a cohort study design with at least 10 years of follow-up after screening stops. In contexts where concomitant opportunistic screening or gradual implementation of screening occurs, and data on valid comparison groups are not readily available, modelling of screening intervention becomes an advantageous option to obtain reliable estimates of breast cancer overdiagnosis.

Background It is of paramount importance to evaluate the impact of participation in organized mammography service screening independently from changes in breast cancer treatment. This can be done by measuring the incidence of fatal breast cancer, which is based on the date of diagnosis and not on the date of death. Methods Among 549,091 women, covering approximately 30% of the Swedish screening-eligible population, the authors calculated the incidence rates of 2473 breast cancers that were fatal within 10 years after diagnosis and the incidence rates of 9737 advanced breast cancers. Data regarding each breast cancer diagnosis and the cause and date of death of each breast cancer case were gathered from national Swedish registries. Tumor characteristics were collected from regional cancer centers. Aggregated data concerning invitation and participation were provided by Sectra Medical Systems AB. Incidence rates were analyzed using Poisson regression. Results Women who participated in mammography screening had a statistically significant 41% reduction in their risk of dying of breast cancer within 10 years (relative risk, 0.59; 95% CI, 0.51-0.68 [P < .001]) and a 25% reduction in the rate of advanced breast cancers (relative risk, 0.75; 95% CI, 0.66-0.84 [P < .001]). Conclusions Substantial reductions in the incidence rate of breast cancers that were fatal within 10 years after diagnosis and in the advanced breast cancer rate were found in this contemporaneous comparison of women participating versus those not participating in screening. These benefits appeared to be independent of recent changes in treatment regimens.

The aim was to evaluate how the inter-screening interval affected the performance of screening by mammographic appearances. This was a Swedish retrospective screening cohort study with information on screening history and mammography features in two periods (1977-1985 and 1996-2010). The pre-clinical incidence and the mean sojourn time (MST) for small breast cancer allowing for sensitivity by mammographic appearances were estimated. The percentage of interval cancer against background incidence (I/E ratio) was used to assess the performance of mammography screening by different inter-screening intervals. The sensitivity-adjusted MSTs (in years) were heterogeneous with mammographic features, being longer for powdery and crushed stone-like calcifications (4.26, (95% CI, 3.50-5.26)) and stellate masses (3.76, (95% CI, 3.15-4.53)) but shorter for circular masses (2.65, (95% CI, 2.06-3.55)) in 1996-2010. The similar trends, albeit longer MSTs, were also noted in 1977-1985. The I/E ratios for the stellate type were 23% and 32% for biennial and triennial screening, respectively. The corresponding figures were 32% and 43% for the circular type and 21% and 29% for powdery and crushed stone-like calcifications, respectively. Mammography-featured progressions of small invasive breast cancer provides a new insight into personalized quality assurance, surveillance, treatment and therapy of early-detected breast cancer.

Objectives: To explain apparent differences among mammography screening services in Sweden using individual data on participation in screening and with breast cancer-specific survival as an outcome. Methods: We analysed breast cancer survival data from the Swedish Cancer Register on breast cancer cases from nine Swedish counties diagnosed in women eligible for screening. Data were available on 38,278 breast cancers diagnosed and 4312 breast cancer deaths. Survival to death from breast cancer was estimated using the Kaplan-Meier estimate, for all cases in each county, and separately for cases of women participating and not participating in their last invitation to screening. Formal statistical comparisons of survival were made using proportional hazards regression. Results: All counties showed a reduction in the hazard of breast cancer death with participation in screening, but the reductions for individual counties varied substantially, ranging from 51% (95% confidence interval 46-55%) to 81% (95% confidence interval 74-85%). Survival rates in nonparticipating women ranged from 53% (95% confidence interval 40-65%) to 74% (95% confidence interval 72-77%), while the corresponding survival in women participating in screening varied from 80% (95% confidence interval 77-84%) to 86% (95% confidence interval 83-88%), a considerably narrower range. Conclusions: Differences among counties in the effect of screening on breast cancer outcomes were mainly due to variation in survival in women not participating in screening. Screening conferred similarly high survival rates in all counties. This indicates that the performance of screening services was similar across counties and that detection and treatment of breast cancer in early-stage reduces inequalities in breast cancer outcome.

Genome-wide association studies have identified 25 germline genetic loci that increase the risk of glioma. The somatic tumor molecular alterations, including IDH-mutation status and 1p/19q co-deletion, have been included into the WHO 2016 classification system for glioma. To investigate how the germline genetic risk variants correlate with the somatic molecular subtypes put forward by WHO, we performed a meta-analysis that combined findings from 330 Swedish cases and 876 controls with two other recent studies. In total, 5,103 cases and 10,915 controls were included. Three categories of associations were found. First, variants in TERT and TP53 were associated with increased risk of all glioma subtypes. Second, variants in CDKN2B-AS1, EGFR, and RTEL1 were associated with IDH-wildtype glioma. Third, variants in CCDC26 (the 8q24 locus), C2orf80 (close to IDH), LRIG1, PHLDB1, ETFA, MAML2 and ZBTB16 were associated with IDH-mutant glioma. We therefore propose three etiopathological pathways in gliomagenesis based on germline variants for future guidance of diagnosis and potential functional targets for therapies. Future prospective clinical trials of patients with suspicion of glioma diagnoses, using the genetic variants as biomarkers, are necessary to disentangle how strongly they can predict glioma diagnosis.

Background. To differentiate the risk of breast cancer death in a longitudinal cohort using imaging biomarkers of tumor extent and biology, specifically, the mammographic appearance, basal phenotype, histologic tumor distribution, and conventional tumor attributes. Methods. Using a prospective cohort study design, 498 invasive breast cancer patients diagnosed between 1996 and 1998 were used as the test cohort to assess the independent effects of the imaging biomarkers and other predictors on the risk of breast cancer death. External validation was performed with a cohort of 848 patients diagnosed between 2006 and 2010. Results. Mammographic tumor appearance was an independent predictor of risk of breast cancer death (P=0.0003) when conventional tumor attributes and treatment modalities were controlled. The casting type calcifications and architectural distortion were associated with 3.13-fold and 3.19-fold risks of breast cancer death, respectively. The basal phenotype independently conferred a 2.68-fold risk compared with nonbasal phenotype. The observed deaths did not differ significantly from expected deaths in the validation cohort. The application of imaging biomarkers together with other predictors classified twelve categories of risk for breast cancer death. Conclusion. Combining imaging biomarkers such as the mammographic appearance of the tumor with the histopathologic distribution and basal phenotype, accurately predicted long-term risk of breast cancer death. The information may be relevant for determining the need for molecular testing, planning treatment, and determining the most appropriate clinical surveillance schedule for breast cancer patients.

Background: The Mono-amine oxidase-A (MAO-A) enzyme is involved in the degradation and regulation of catecholamines such as serotonin, dopamine, epinephrine and nor-epinephrine. Preclinical studies suggest that this enzyme may contribute to an environment favorable for growth of malignant glioma. The MAO-A gene is located on the X-chromosome and has at least one functional genetic polymorphism. The aim of the present study was to explore possible effects of MAO-A genotype on development of glioblastoma in males. Methods: Genotypes for 437 glioma cases and 876 population-based controls from the Swedish Glioma International Case–Control study (GICC) were compared. We analyzed the germline DNA using the Illumina Oncoarray. We selected seven single nucleotide polymorphisms (SNPs) located in the MAO-A gene, and imputed genotypes based on data from the 1000 genomes project. We used 1579 male glioblastoma cases and 1875 controls comprising the whole GICC cohort for subsequent validation of findings. Results: The rs144551722 SNP was a significant predictor of development of glioblastoma in males (p-value = 0.0056) but not in females even after correction for multiple testing. We conducted haplotype analysis to confirm an association between MAO-A gene and risk of glioblastoma (p-value = 0.016). We found similar results in the validation sample. Conclusions: These results suggest the possibility of a role for the MAO-A enzyme and the MAO-A gene in the development of glioblastoma in males.

Background: Women and their health care providers need a reliable answer to this important question: If a woman chooses to participate in regular mammography screening, then how much will this choice improve her chances of avoiding a death from breast cancer compared with women who choose not to participate? Methods: To answer this question, we used comprehensive registries for population, screening history, breast cancer incidence, and disease-specific death data in a defined population in Dalarna County, Sweden. The annual incidence of breast cancer was calculated along with the annual incidence of breast cancers that were fatal within 10 and within 11 to 20 years of diagnosis among women aged 40 to 69 years who either did or did not participate in mammography screening during a 39-year period (1977-2015). For an additional comparison, corresponding data are presented from 19 years of the prescreening period (1958-1976). All patients received stage-specific therapy according to the latest national guidelines, irrespective of the mode of detection. Results: The benefit for women who chose to participate in an organized breast cancer screening program was a 60% lower risk of dying from breast cancer within 10 years after diagnosis (relative risk, 0.40; 95% confidence interval, 0.34-0.48) and a 47% lower risk of dying from breast cancer within 20 years after diagnosis (relative risk, 0.53; 95% confidence interval, 0.44-0.63) compared with the corresponding risks for nonparticipants. Conclusions: Although all patients with breast cancer stand to benefit from advances in breast cancer therapy, the current results demonstrate that women who have participated in mammography screening obtain a significantly greater benefit from the therapy available at the time of diagnosis than do those who have not participated., First published: 08 November 2018

Background: It has been asserted that mammography screening preferentially benefits those with less aggressive cancers, with lesser or no impact on more rapidly progressing and therefore more life-threatening tumors. Methods: We utilized data from the Swedish Two-County Trial, which randomized 77,080 women ages 40 to 74 to invitation to screening and 55,985 for usual care. We tabulated cancers by histologic grade and then compared mortality from cancers specific to histologic grade between the invited and control group using Poisson regression, with specific interest in the effect on mortality from grade 3 cancers. We used incidence-based mortality from tumors diagnosed within the screening phase of the trial. Finally, we cross-tabulated grade with tumor size and node status, to assess downstaging within tumor grades. Results: There was a major reduction in mortality from grade 3 tumors (RR = 0.65; 95% CI, 0.53-0.80; P < 0.001), and more deaths prevented from grade 3 tumors (n = 95) than grade 1 and 2 tumors combined (n = 48) in the invited group. The proportions of tumors >= 15 mm or larger and node-positive tumors were substantially reduced in the grade 3 tumors in the invited group. Conclusions: The combination of prevention of tumors progressing to grade 3 and detection at smaller sizes and lesser rates of lymph node metastases within grade 3 tumors results in a substantial number of deaths from grade 3 cancers being prevented by invitation to mammographic screening. Impact: Mammography screening prevents deaths from aggressive cancers.

Objective: The possibility that a subset of persons who are obese may be metabolically healthy—referred to as the ‘metabolically healthy obese’ (MHO) phenotype—has attracted attention recently. However, few studies have followed individuals with MHO or other obesity phenotypes over time to assess change in their metabolic profiles. The aim of the present study was to examine transitions over a 6-year period among different states defined simultaneously by body mass index (BMI) and the presence/absence of the metabolic syndrome (MetS). Methods: We used repeated measurements available for a subcohort of participants enrolled in the Women’s Health Initiative (N=3512) and followed for an average of 6 years to examine the frequency of different metabolic obesity phenotypes at baseline, the 6-year transition probabilities to other states and predictors of the risk of different transitions. Six phenotypes were defined by cross-tabulating BMI (18.5–<25.0, 25.0–<30.0, 30.0 kg m−2) by MetS (yes, no). A continuous-time Markov model was used to estimate 6-year transition probabilities from one state to another. Results: Over the 6 years of follow-up, one-third of women with the healthy obese phenotype transitioned to the metabolically unhealthy obese (MUO) phenotype. Overall, there was a marked tendency toward increased metabolic deterioration with increasing BMI and toward metabolic improvement with lower BMI. Among MHO women, the 6-year probability of becoming MUO was 34%, whereas among unhealthy normal-weight women, the probability of ‘regressing’ to the metabolically healthy normal-weight phenotype was 52%. Conclusions: The present study demonstrated substantial change in metabolic obesity phenotypes over a 6-year period. There was a marked tendency toward metabolic deterioration with greater BMI and toward metabolic improvement with lower BMI.