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Tuberculosis (TB) is an underestimated cause of death in children (1); it is accurately diagnosed and reported in only 45% of children with the disease (2). When accounting for underdetection, [...]

Background: Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1-negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1-negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1-negative LUAD.Methods: Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1-negative and 4 TTF-1-positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided.Results: SRGN was markedly overexpressed at mRNA and protein levels in TTF-1-negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1-positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase-mediated impairment of methionine metabolism.Conclusions: Our findings suggest that SRGN plays a pivotal role in tumor-stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1-negative LUAD. [ABSTRACT FROM AUTHOR]

Background: CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone.Methods: Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n = 187), benign pancreatic disease (n = 93), and healthy controls (n = 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided.Results: Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] = 0.917 to 0.981) and 0.887 (95% CI = 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P = .008, test set).Conclusion: The addition of TIMP1 and LRG1 immunoassays to CA19-9 statistically significantly improves the detection of early-stage PDAC. [ABSTRACT FROM AUTHOR]

Background: Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1-negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1-negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1-negative LUAD., Methods: Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1-negative and 4 TTF-1-positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided., Results: SRGN was markedly overexpressed at mRNA and protein levels in TTF-1-negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1-positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase-mediated impairment of methionine metabolism., Conclusions: Our findings suggest that SRGN plays a pivotal role in tumor-stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1-negative LUAD., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases., Objective: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria., Design, Setting, and Participants: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS)., Main Outcomes and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity)., Results: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model., Conclusions and Relevance: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.

Background: CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone., Methods: Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n = 187), benign pancreatic disease (n = 93), and healthy controls (n = 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided., Results: Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] = 0.917 to 0.981) and 0.887 (95% CI = 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P = .008, test set)., Conclusion: The addition of TIMP1 and LRG1 immunoassays to CA19-9 statistically significantly improves the detection of early-stage PDAC., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)