32 results on '"Alcala, Karine"'
Search Results
2. Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program
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Robbins, Hilary A., Alcala, Karine, Moez, Elham Khodayari, Guida, Florence, Thomas, Sera, Zahed, Hana, Warkentin, Matthew T., Smith-Byrne, Karl, Brhane, Yonathan, Muller, David, Feng, Xiaoshuang, Albanes, Demetrius, Aldrich, Melinda C., Arslan, Alan A., Bassett, Julie, Berg, Christine D., Cai, Qiuyin, Chen, Chu, Davies, Michael P.A., Diergaarde, Brenda, Field, John K., Freedman, Neal D., Huang, Wen-Yi, Johansson, Mikael, Jones, Michael, Koh, Woon-Puay, Lam, Stephen, Lan, Qing, Langhammer, Arnulf, Liao, Linda M., Liu, Geoffrey, Malekzadeh, Reza, Milne, Roger L., Montuenga, Luis M., Rohan, Thomas, Sesso, Howard D., Severi, Gianluca, Sheikh, Mahdi, Sinha, Rashmi, Shu, Xiao-Ou, Stevens, Victoria L., Tammemägi, Martin C., Tinker, Lesley F., Visvanathan, Kala, Wang, Ying, Wang, Renwei, Weinstein, Stephanie J., White, Emily, Wilson, David, Yuan, Jian-Min, Zhang, Xuehong, Zheng, Wei, Amos, Christopher I., Brennan, Paul, Johansson, Mattias, and Hung, Rayjean J.
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- 2023
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3. Systemic inflammation markers and cancer incidence in the UK Biobank
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Nøst, Therese Haugdahl, Alcala, Karine, Urbarova, Ilona, Byrne, Karl Smith, Guida, Florence, Sandanger, Torkjel Manning, and Johansson, Mattias
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- 2021
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4. Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis
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Feng, Xiaoshuang, Goodley, Patrick, Alcala, Karine, Guida, Florence, Kaaks, Rudolf, Vermeulen, Roel, Downward, George S, Bonet, Catalina, Colorado-Yohar, Sandra M, Albanes, Demetrius, Weinstein, Stephanie J, Goldberg, Marcel, Zins, Marie, Relton, Caroline, Langhammer, Arnulf, Skogholt, Anne Heidi, Johansson, Mattias, and Robbins, Hilary A
- Abstract
Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.
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- 2024
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5. Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom
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Robbins, Hilary A., Alcala, Karine, Swerdlow, Anthony J., Schoemaker, Minouk J., Wareham, Nick, Travis, Ruth C., Crosbie, Philip A. J., Callister, Matthew, Baldwin, David R., Landy, Rebecca, and Johansson, Mattias
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- 2021
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6. Kidney Function and Risk of Renal Cell Carcinoma
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Alcala, Karine, primary, Zahed, Hana, additional, Cortez Cardoso Penha, Ricardo, additional, Alcala, Nicolas, additional, Robbins, Hilary A., additional, Smith-Byrne, Karl, additional, Martin, Richard M., additional, Muller, David C., additional, Brennan, Paul, additional, and Johansson, Mattias, additional
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- 2023
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7. Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools
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Feng, Xiaoshuang, primary, Wu, Wendy Yi-Ying, additional, Onwuka, Justina Ucheojor, additional, Haider, Zahra, additional, Alcala, Karine, additional, Smith-Byrne, Karl, additional, Zahed, Hana, additional, Guida, Florence, additional, Wang, Renwei, additional, Bassett, Julie K, additional, Stevens, Victoria, additional, Wang, Ying, additional, Weinstein, Stephanie, additional, Freedman, Neal D, additional, Chen, Chu, additional, Tinker, Lesley, additional, Nøst, Therese Haugdahl, additional, Koh, Woon-Puay, additional, Muller, David, additional, Colorado-Yohar, Sandra M, additional, Tumino, Rosario, additional, Hung, Rayjean J, additional, Amos, Christopher I, additional, Lin, Xihong, additional, Zhang, Xuehong, additional, Arslan, Alan A, additional, Sánchez, Maria-Jose, additional, Sørgjerd, Elin Pettersen, additional, Severi, Gianluca, additional, Hveem, Kristian, additional, Brennan, Paul, additional, Langhammer, Arnulf, additional, Milne, Roger L, additional, Yuan, Jian-Min, additional, Melin, Beatrice, additional, Johansson, Mikael, additional, Robbins, Hilary A, additional, and Johansson, Mattias, additional
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- 2023
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8. Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis
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Feng, Xiaoshuang, primary, Muller, David C., additional, Zahed, Hana, additional, Alcala, Karine, additional, Guida, Florence, additional, Smith-Byrne, Karl, additional, Yuan, Jian-Min, additional, Koh, Woon-Puay, additional, Wang, Renwei, additional, Milne, Roger L., additional, Bassett, Julie K., additional, Langhammer, Arnulf, additional, Hveem, Kristian, additional, Stevens, Victoria L., additional, Wang, Ying, additional, Johansson, Mikael, additional, Tjønneland, Anne, additional, Tumino, Rosario, additional, Sheikh, Mahdi, additional, Johansson, Mattias, additional, and Robbins, Hilary A., additional
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- 2023
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9. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium
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Guida, Florence, Tan, Vanessa Y., Corbin, Laura J., Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D., Butterworth, Adam S., Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano Exezarreta, Pilar, Bergmann, Manuela M., Bull, Caroline J., Dahm, Christina C., Gicquiau, Audrey, Giles, Graham G., Gunter, Marc J., Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L., Ljungberg, Börje, Metspalu, Andres, Milne, Roger L., Muller, David C., Nøst, Therese H., Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A., Scalbert, Augustin, Schmidt, Julie A., Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E., Waldenberger, Melanie, Timpson, Nicholas J., and Johansson, Mattias
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Kidney cancer -- Risk factors -- Development and progression -- Physiological aspects ,Metabolites -- Health aspects -- Measurement ,Body mass index ,Biological sciences - Abstract
Background Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). Methods and findings We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 x 10.sup.-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 x 10.sup.-5 ), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ß.sub.BMI ] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 x 10.sup.-5). BMI was also associated with increased levels of glutamate (ß.sub.BMI : 0.12, p = 1.5 x 10.sup.-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. Conclusions This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer., Author(s): Florence Guida 1, Vanessa Y. Tan 2,3, Laura J. Corbin 2,3, Karl Smith-Byrne 1, Karine Alcala 1, Claudia Langenberg 4, Isobel D. Stewart 4, Adam S. Butterworth 5,6,7,8, Praveen [...]
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- 2021
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10. Abstract 4202: Kidney function and risk of renal cell carcinoma
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Alcala, Karine, primary, Alcala, Nicolas, additional, Martin, Richard, additional, Brennan, Paul, additional, Muller, David, additional, Robbins, Hilary A., additional, and Johansson, Mattias, additional
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- 2023
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11. Abstract 1950: Socioeconomic status and lung cancer incidence: An analysis of data from 15 countries in the Lung Cancer Cohort Consortium
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Onwuka, Justina U., primary, Zahed, Hana, additional, Feng, Xiaoshuang, additional, Alcala, Karine, additional, Johansson, Mattias, additional, Robbins, Hilary A., additional, and Consortium, Lung Cancer Cohort, additional
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- 2023
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12. Correction: Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom
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Robbins, Hilary A., Alcala, Karine, Swerdlow, Anthony J., Schoemaker, Minouk J., Wareham, Nick, Travis, Ruth C., Crosbie, Philip A. J., Callister, Matthew, Baldwin, David R., Landy, Rebecca, and Johansson, Mattias
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- 2021
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13. Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools
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Feng, Xiaoshuang, Wu, Wendy Yi-Ying, Onwuka, Justina Ucheojor, Haider, Zahra, Alcala, Karine, Smith-Byrne, Karl, Zahed, Hana, Guida, Florence, Wang, Renwei, Bassett, Julie K., Stevens, Victoria, Wang, Ying, Weinstein, Stephanie, Freedman, Neal D., Chen, Chu, Tinker, Lesley, Nøst, Therese Haugdahl, Koh, Woon-Puay, Muller, David, Colorado-Yohar, Sandra M., Tumino, Rosario, Hung, Rayjean J., Amos, Christopher I., Lin, Xihong, Zhang, Xuehong, Arslan, Alan A., Sánchez, Maria-Jose, Sørgjerd, Elin Pettersen, Severi, Gianluca, Hveem, Kristian, Brennan, Paul, Langhammer, Arnulf, Milne, Roger L., Yuan, Jian-Min, Melin, Beatrice S., Johansson, Mikael, Robbins, Hilary A., Johansson, Mattias, Feng, Xiaoshuang, Wu, Wendy Yi-Ying, Onwuka, Justina Ucheojor, Haider, Zahra, Alcala, Karine, Smith-Byrne, Karl, Zahed, Hana, Guida, Florence, Wang, Renwei, Bassett, Julie K., Stevens, Victoria, Wang, Ying, Weinstein, Stephanie, Freedman, Neal D., Chen, Chu, Tinker, Lesley, Nøst, Therese Haugdahl, Koh, Woon-Puay, Muller, David, Colorado-Yohar, Sandra M., Tumino, Rosario, Hung, Rayjean J., Amos, Christopher I., Lin, Xihong, Zhang, Xuehong, Arslan, Alan A., Sánchez, Maria-Jose, Sørgjerd, Elin Pettersen, Severi, Gianluca, Hveem, Kristian, Brennan, Paul, Langhammer, Arnulf, Milne, Roger L., Yuan, Jian-Min, Melin, Beatrice S., Johansson, Mikael, Robbins, Hilary A., and Johansson, Mattias
- Abstract
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.
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- 2023
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14. Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis
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Feng, Xiaoshuang, Muller, David C., Zahed, Hana, Alcala, Karine, Guida, Florence, Smith-Byrne, Karl, Yuan, Jian-Min, Koh, Woon-Puay, Wang, Renwei, Milne, Roger L., Bassett, Julie K., Langhammer, Arnulf, Hveem, Kristian, Stevens, Victoria L., Wang, Ying, Johansson, Mikael, Tjønneland, Anne, Tumino, Rosario, Sheikh, Mahdi, Johansson, Mattias, Robbins, Hilary A., Feng, Xiaoshuang, Muller, David C., Zahed, Hana, Alcala, Karine, Guida, Florence, Smith-Byrne, Karl, Yuan, Jian-Min, Koh, Woon-Puay, Wang, Renwei, Milne, Roger L., Bassett, Julie K., Langhammer, Arnulf, Hveem, Kristian, Stevens, Victoria L., Wang, Ying, Johansson, Mikael, Tjønneland, Anne, Tumino, Rosario, Sheikh, Mahdi, Johansson, Mattias, and Robbins, Hilary A.
- Abstract
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.
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- 2023
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15. The relationship between blood pressure and risk of renal cell carcinoma
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Alcala, Karine, Mariosa, Daniela, Smith-Byrne, Karl, Nasrollahzadeh Nesheli, Dariush, Carreras-Torres, Robert, Ardanaz Aicua, Eva, Bondonno, Nicola P, Bonet, Catalina, Brunström, Mattias, Bueno-De-Mesquita, Bas, Chirlaque, María-Dolores, Christakoudi, Sofia, Heath, Alicia K, Kaaks, Rudolf, Katzke, Verena, Krogh, Vittorio, Ljungberg, Börje, Martin, Richard M, May, Anne, Melander, Olle, Palli, Domenico, Rodriguez-Barranco, Miguel, Sacerdote, Carlotta, Stocks, Tanja, Tjønneland, Anne, Travis, Ruth C., Vermeulen, Roel, Chanock, Stephen, Purdue, Mark, Weiderpass, Elisabete, Muller, David, Brennan, Paul, Johansson, Mattias, and IRAS OH Epidemiology Chemical Agents
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systolic blood pressure ,Mendelian randomization ,diastolic blood pressure ,kidney cancer ,RCC - Abstract
Background: The relation between blood pressure and kidney cancer risk is well established but complex and different study designs have reported discrepant findings on the relative importance of diastolic blood pressure (DBP) and systolic blood pressure (SBP). In this study, we sought to describe the temporal relation between diastolic and SBP with renal cell carcinoma (RCC) risk in detail. Methods: Our study involved two prospective cohorts: the European Prospective Investigation into Cancer and Nutrition study and UK Biobank, including >700 000 participants and 1692 incident RCC cases. Risk analyses were conducted using flexible parametric survival models for DBP and SBP both separately as well as with mutuality adjustment and then adjustment for extended risk factors. We also carried out univariable and multivariable Mendelian randomization (MR) analyses (DBP: ninstruments = 251, SBP: ninstruments = 213) to complement the analyses of measured DBP and SBP. Results: In the univariable analysis, we observed clear positive associations with RCC risk for both diastolic and SBP when measured ≥5 years before diagnosis and suggestive evidence for a stronger risk association in the year leading up to diagnosis. In mutually adjusted analysis, the long-term risk association of DBP remained, with a hazard ratio (HR) per standard deviation increment 10 years before diagnosis (HR10y) of 1.20 (95% CI: 1.10-1.30), whereas the association of SBP was attenuated (HR10y: 1.00, 95% CI: 0.91-1.10). In the complementary multivariable MR analysis, we observed an odds ratio for a 1-SD increment (ORsd) of 1.34 (95% CI: 1.08-1.67) for genetically predicted DBP and 0.70 (95% CI: 0.56-0.88) for genetically predicted SBP. Conclusion: The results of this observational and MR study are consistent with an important role of DBP in RCC aetiology. The relation between SBP and RCC risk was less clear but does not appear to be independent of DBP. © 2022 World Health Organization,. All rights reserved. The World Health Organization has granted the Publisher permission for the reproduction of this article.
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- 2022
16. The blood proteome of imminent lung cancer diagnosis.
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The Lung Cancer Cohort Consortium (LC3), Albanes, Demetrius, Alcala, Karine, Alcala, Nicolas, Amos, Christopher I., Arslan, Alan A., Bassett, Julie K., Brennan, Paul, Cai, Qiuyin, Chen, Chu, Feng, Xiaoshuang, Freedman, Neal D., Guida, Florence, Hung, Rayjean J., Hveem, Kristian, Johansson, Mikael, Johansson, Mattias, Koh, Woon-Puay, Langhammer, Arnulf, and Milne, Roger L.
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LUNG cancer ,CANCER diagnosis ,BLOOD proteins ,GROWTH factors ,EARLY detection of cancer - Abstract
Identification of risk biomarkers may enhance early detection of smoking-related lung cancer. We measured between 392 and 1,162 proteins in blood samples drawn at most three years before diagnosis in 731 smoking-matched case-control sets nested within six prospective cohorts from the US, Europe, Singapore, and Australia. We identify 36 proteins with independently reproducible associations with risk of imminent lung cancer diagnosis (all p < 4 × 10
−5 ). These include a few markers (e.g. CA-125/MUC-16 and CEACAM5/CEA) that have previously been reported in studies using pre-diagnostic blood samples for lung cancer. The 36 proteins include several growth factors (e.g. HGF, IGFBP-1, IGFP-2), tumor necrosis factor-receptors (e.g. TNFRSF6B, TNFRSF13B), and chemokines and cytokines (e.g. CXL17, GDF-15, SCF). The odds ratio per standard deviation range from 1.31 for IGFBP-1 (95% CI: 1.17–1.47) to 2.43 for CEACAM5 (95% CI: 2.04–2.89). We map the 36 proteins to the hallmarks of cancer and find that activation of invasion and metastasis, proliferative signaling, tumor-promoting inflammation, and angiogenesis are most frequently implicated. Lung cancer screening could enhance early diagnosis and treatment. Here, the authors used proteomic analysis of pre-diagnosis samples across 6 cohorts to identify 36 proteins associated with imminent lung cancer diagnosis. [ABSTRACT FROM AUTHOR]- Published
- 2023
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17. The relationship between blood pressure and risk of renal cell carcinoma
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IRAS OH Epidemiology Chemical Agents, Alcala, Karine, Mariosa, Daniela, Smith-Byrne, Karl, Nasrollahzadeh Nesheli, Dariush, Carreras-Torres, Robert, Ardanaz Aicua, Eva, Bondonno, Nicola P, Bonet, Catalina, Brunström, Mattias, Bueno-De-Mesquita, Bas, Chirlaque, María-Dolores, Christakoudi, Sofia, Heath, Alicia K, Kaaks, Rudolf, Katzke, Verena, Krogh, Vittorio, Ljungberg, Börje, Martin, Richard M, May, Anne, Melander, Olle, Palli, Domenico, Rodriguez-Barranco, Miguel, Sacerdote, Carlotta, Stocks, Tanja, Tjønneland, Anne, Travis, Ruth C., Vermeulen, Roel, Chanock, Stephen, Purdue, Mark, Weiderpass, Elisabete, Muller, David, Brennan, Paul, Johansson, Mattias, IRAS OH Epidemiology Chemical Agents, Alcala, Karine, Mariosa, Daniela, Smith-Byrne, Karl, Nasrollahzadeh Nesheli, Dariush, Carreras-Torres, Robert, Ardanaz Aicua, Eva, Bondonno, Nicola P, Bonet, Catalina, Brunström, Mattias, Bueno-De-Mesquita, Bas, Chirlaque, María-Dolores, Christakoudi, Sofia, Heath, Alicia K, Kaaks, Rudolf, Katzke, Verena, Krogh, Vittorio, Ljungberg, Börje, Martin, Richard M, May, Anne, Melander, Olle, Palli, Domenico, Rodriguez-Barranco, Miguel, Sacerdote, Carlotta, Stocks, Tanja, Tjønneland, Anne, Travis, Ruth C., Vermeulen, Roel, Chanock, Stephen, Purdue, Mark, Weiderpass, Elisabete, Muller, David, Brennan, Paul, and Johansson, Mattias
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- 2022
18. The relationship between blood pressure and risk of renal cell carcinoma
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Epi Kanker, Cancer, JC onderzoeksprogramma Kanker, Epidemiology & Health Economics, Alcala, Karine, Mariosa, Daniela, Smith-Byrne, Karl, Nesheli, Dariush Nasrollahzadeh, Carreras-Torres, Robert, Aicua, Eva Ardanaz, Bondonno, Nicola P., Bonet, Catalina, Brunstrom, Mattias, Bueno-de-Mesquita, Bas, Chirlaque, Maria-Dolores, Christakoudi, Sofia, Heath, Alicia K., Kaaks, Rudolf, Katzke, Verena, Krogh, Vittorio, Ljungberg, Borje, Martin, Richard M., May, Anne, Melander, Olle, Palli, Domenico, Rodriguez-Barranco, Miguel, Sacerdote, Carlotta, Stocks, Tanja, Tjonneland, Anne, Travis, Ruth C., Vermeulen, Roel, Chanock, Stephen, Purdue, Mark, Weiderpass, Elisabete, Muller, David, Brennan, Paul, Johansson, Mattias, Epi Kanker, Cancer, JC onderzoeksprogramma Kanker, Epidemiology & Health Economics, Alcala, Karine, Mariosa, Daniela, Smith-Byrne, Karl, Nesheli, Dariush Nasrollahzadeh, Carreras-Torres, Robert, Aicua, Eva Ardanaz, Bondonno, Nicola P., Bonet, Catalina, Brunstrom, Mattias, Bueno-de-Mesquita, Bas, Chirlaque, Maria-Dolores, Christakoudi, Sofia, Heath, Alicia K., Kaaks, Rudolf, Katzke, Verena, Krogh, Vittorio, Ljungberg, Borje, Martin, Richard M., May, Anne, Melander, Olle, Palli, Domenico, Rodriguez-Barranco, Miguel, Sacerdote, Carlotta, Stocks, Tanja, Tjonneland, Anne, Travis, Ruth C., Vermeulen, Roel, Chanock, Stephen, Purdue, Mark, Weiderpass, Elisabete, Muller, David, Brennan, Paul, and Johansson, Mattias
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- 2022
19. Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program
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Robbins, Hilary A, primary, Alcala, Karine, additional, Khodayari Moez, Elham, additional, Guida, Florence, additional, Thomas, Sera, additional, Zahed, Hana, additional, Warkentin, Matthew, additional, Smith-Byrne, Karl, additional, Brhane, Yonathan, additional, Muller, David, additional, Albanes, Demetrius, additional, Aldrich, Melinda C, additional, Arslan, Alan A, additional, Bassett, Julie, additional, Berg, Christine D, additional, Cai, Qiuyin, additional, Chen, Chu, additional, Davies, Michael PA, additional, Diergaarde, Brenda, additional, Field, John K, additional, Freedman, Neal D, additional, Huang, Wen-Yi, additional, Johansson, Mikael, additional, Jones, Michael, additional, Koh, Woon-Puay, additional, Lam, Stephen, additional, Lan, Qing, additional, Langhammer, Arnulf, additional, Liao, Linda M, additional, Liu, Geoffrey, additional, Malekzadeh, Reza, additional, Milne, Roger L, additional, Montuenga, Luis M, additional, Rohan, Thomas, additional, Sesso, Howard D, additional, Severi, Gianluca, additional, Sheikh, Mahdi, additional, Sinha, Rashmi, additional, Shu, Xiao-Ou, additional, Stevens, Victoria L, additional, Tammemägi, Martin C, additional, Tinker, Lesley F, additional, Visvanathan, Kala, additional, Wang, Ying, additional, Wang, Renwei, additional, Weinstein, Stephanie J, additional, White, Emily, additional, Wilson, David, additional, Yuan, Jian-Min, additional, Zhang, Xuehong, additional, Zheng, Wei, additional, Amos, Christopher I, additional, Brennan, Paul, additional, Johansson, Mattias, additional, and Hung, Rayjean J, additional
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- 2022
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20. Correction:Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom (British Journal of Cancer, (2021), 124, 12, (2026-2034), 10.1038/s41416-021-01278-0)
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Robbins, Hilary A., Alcala, Karine, Swerdlow, Anthony J., Schoemaker, Minouk J., Wareham, Nick, Travis, Ruth C., Crosbie, Philip A.J., Callister, Matthew, Baldwin, David R., Landy, Rebecca, and Johansson, Mattias
- Abstract
The article Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom, written by Hilary A. Robbins, Karine Alcala, Anthony J. Swerdlow, Minouk J. Schoemaker, Nick Wareham, Ruth C. Travis, Philip A. J. Crosbie, Matthew Callister, David R. Baldwin, Rebecca Landy and Mattias Johansson, was originally published electronically on the publisher’s internet portal on 12th April 2021 without open access.
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- 2021
21. IL-18 and Lower Risk for Lung Cancer: Triangulated Evidence from Germline Predictions, Pre-Diagnostic Measurements, and Tumor Expression
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Smith-Byrne, Karl, primary, Chen, Yan, additional, Kachuri, Linda, additional, Kapoor, Pooja M., additional, Guida, Florence, additional, Zahed, Hana, additional, Alcala, Karine, additional, Amos, Christopher, additional, Atkins, Joshua, additional, Bodinier, Barbara, additional, Ferreiro-Iglesia, Aida, additional, Hung, Rayjean J., additional, Johansson, Mikael, additional, Koh, Woon-Puay, additional, Kyrø, Cecilie, additional, Langhammer, Arnulf, additional, Pietzner, Maik, additional, Sala, Núria, additional, Sandanger, Torkjel M., additional, Travis, Ruth C., additional, Tsilidis, Kostas, additional, Yuan, Jian-Min, additional, Robbins, Hilary, additional, Brennan, Paul, additional, McKay, James, additional, Johansson, Mattias, additional, and Mälarstig, Anders, additional
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- 2021
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22. Circulating markers of cellular immune activation in prediagnostic blood sample and lung cancer risk in the Lung Cancer Cohort Consortium (LC3)
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Huang, Joyce Y., Larose, Tricia L., Luu, Hung N., Wang, Renwei, Fanidi, Anouar, Alcala, Karine, Stevens, Victoria L., Weinstein, Stephanie J., Albanes, Demetrius, Caporaso, Neil E., Purdue, Mark P., Ziegler, Regina G., Freedman, Neal D., Lan, Qing, Prentice, Ross L., Pettinger, Mary, Thomson, Cynthia A., Cai, Qiuyin, Wu, Jie, Blot, William J., Shu, Xiao-Ou, Zheng, Wei, Arslan, Alan A., Zeleniuch-Jacquotte, Anne, Le Marchand, Loic, Wilkens, Lynn R., Haiman, Christopher A., Zhang, Xuehong, Stampfer, Meir J., Giles, Graham G., Hodge, Allison M., Severi, Gianluca, Johansson, Mikael, Grankvist, Kjell, Langhammer, Arnulf, Hveem, Kristian, Xiang, Yong-Bing, Li, Hong-Lan, Gao, Yu-Tang, Visvanathan, Kala, Ueland, Per M., Midttun, Oivind, Ulvi, Arve, Buring, Julie E., Lee, I-Min, SeSS, Howard D., Gaziano, J. Michael, Manjer, Jonas, Relton, Caroline, Koh, Woon-Puay, Brennan, Paul, Johansson, Mattias, Yuan, Jian-Min, Han, Jiali, Huang, Joyce Y., Larose, Tricia L., Luu, Hung N., Wang, Renwei, Fanidi, Anouar, Alcala, Karine, Stevens, Victoria L., Weinstein, Stephanie J., Albanes, Demetrius, Caporaso, Neil E., Purdue, Mark P., Ziegler, Regina G., Freedman, Neal D., Lan, Qing, Prentice, Ross L., Pettinger, Mary, Thomson, Cynthia A., Cai, Qiuyin, Wu, Jie, Blot, William J., Shu, Xiao-Ou, Zheng, Wei, Arslan, Alan A., Zeleniuch-Jacquotte, Anne, Le Marchand, Loic, Wilkens, Lynn R., Haiman, Christopher A., Zhang, Xuehong, Stampfer, Meir J., Giles, Graham G., Hodge, Allison M., Severi, Gianluca, Johansson, Mikael, Grankvist, Kjell, Langhammer, Arnulf, Hveem, Kristian, Xiang, Yong-Bing, Li, Hong-Lan, Gao, Yu-Tang, Visvanathan, Kala, Ueland, Per M., Midttun, Oivind, Ulvi, Arve, Buring, Julie E., Lee, I-Min, SeSS, Howard D., Gaziano, J. Michael, Manjer, Jonas, Relton, Caroline, Koh, Woon-Puay, Brennan, Paul, Johansson, Mattias, Yuan, Jian-Min, and Han, Jiali
- Abstract
Cell-mediated immune suppression may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), quinolinic acid (QA) and neopterin as markers of immune regulation and inflammation with lung cancer risk in 5,364 smoking-matched case-control pairs from 20 prospective cohorts included in the international Lung Cancer Cohort Consortium. All biomarkers were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with individual biomarkers were calculated using conditional logistic regression with adjustment for circulating cotinine. Compared to the lowest quintile, the highest quintiles of kynurenine, KTR, QA and neopterin were associated with a 20-30% higher risk, and tryptophan with a 15% lower risk of lung cancer (all p(trend) < 0.05). The strongest associations were seen for current smokers, where the adjusted ORs (95% CIs) of lung cancer for the highest quintile of KTR, QA and neopterin were 1.42 (1.15-1.75), 1.42 (1.14-1.76) and 1.45 (1.13-1.86), respectively. A stronger association was also seen for KTR and QA with risk of lung squamous cell carcinoma followed by adenocarcinoma, and for lung cancer diagnosed within the first 2 years after blood draw. This study demonstrated that components of the tryptophan-kynurenine pathway with immunomodulatory effects are associated with risk of lung cancer overall, especially for current smokers. Further research is needed to evaluate the role of these biomarkers in lung carcinogenesis and progression., Grant numbers United States Department of Health & Human Services, National Institutes of Health (NIH) - USA, and NIH National Cancer Institute (NCI): P01CA87969, P30CA016087, R01CA092447, R01CA144034, U01CA155340, U01CA202979, UM1CA167552, UM1CA173640, UM1CA182876, UM1CA182910, UM1CA182934, UM1CA186107Grant numbers United States Department of Health & Human Services and National Institutes of Health (NIH) - USACA047988, CA097193, CA182913, CA34944, CA40360, HL043851, HL080467, HL099355, HL26490, HL34595, P30ES000260
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- 2020
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23. Circulating markers of cellular immune activation in pre-diagnostic human serum in relation to lung cancer risk in the Lung Cancer Cohort Consortium (LC3)
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Huang, Joyce Yongxu, Larose, Tricia L., Wang, Renwei, Fanidi, Anouar, Alcala, Karine, Stevens, Victoria L., Weinstein, Stephanie J., Albanes, Demetrius, Caporaso, Neil, Purdue, Mark, Zeigler, Regina, Freedman, Neal, Lan, Qin, Prentice, Ross, Pettinger, Mary, Thomsen, Cynthia A., Cai, Qiuyin, Wu, Jie, Blot, William J., Shu, Xiao-Ou, Zheng, Wei, Arslan, Alan A., Zeleniuch-Jacquotte, Anne, Le Marchand, Loïc, Wilkens, Lynn R., Haiman, Christopher A., Zhang, Xuehong, Stampfer, Meir, Smith-Warner, Stephanie, Han, Jiali, Giles, Graham G, Hodge, Allison M, Severi, Gianluca, Johansson, Mikael, Grankvist, Kjell, Langhammer, Arnulf, Hveem, Kristian, Xiang, Yong-Bing, Li, Hong-Lan, Gao, Yu-Tang, Visvanathan, Kala, Bolton, Judy Hoffman, Ueland, Per M, Midttun, Øivind, Ulvik, Arve, Buring, Julie E., Lee, I-Min, Sesso, Howard D., Gaziano, J. Michael, Manjer, Jonas, Relton, Caroline, Koh, Woon-Puay, Brennan, Paul, Johansson, Mattias, and Yuan, Jian-Min
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Adult ,Inflammation ,Male ,Lung Neoplasms ,Tryptophan ,Adenocarcinoma of Lung ,Middle Aged ,Prognosis ,Neopterin ,Small Cell Lung Carcinoma ,Article ,Risk Factors ,Case-Control Studies ,Biomarkers, Tumor ,Carcinoma, Squamous Cell ,Carcinoma, Large Cell ,Humans ,Female ,Prospective Studies ,Kynurenine ,Aged ,Follow-Up Studies - Abstract
Cell-mediated immunity may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), kynurenine metabolite—quinolinic acid (QA), and neopterin as markers of interferon-gamma-induced cellular immune activation with lung cancer risk in 5,364 cases and 5,364 individually matched control subjects from 20 prospective cohorts included the international Lung Cancer Cohort Consortium (LC3). Tryptophan, kynurenine, QA, and neopterin were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with different levels of these metabolites and KTR were calculated using conditional logistic regression with adjustment for matched smoking variables and circulating cotinine. Overall, the highest quintiles of circulating kynurenine, KTR, QA and neopterin were associated with a 20–30% higher risk of lung cancer, and tryptophan with a 15% lower risk compared with the lowest quintile (all P(trend)
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- 2019
24. Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions
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Alcala, Nicolas, primary, Mangiante, Lise, additional, Le-Stang, Nolwenn, additional, Gustafson, Corinne E., additional, Boyault, Sandrine, additional, Damiola, Francesca, additional, Alcala, Karine, additional, Brevet, Marie, additional, Thivolet-Bejui, Françoise, additional, Blanc-Fournier, Cécile, additional, Le Rochais, Jean-Philippe, additional, Planchard, Gaëtane, additional, Rousseau, Nathalie, additional, Damotte, Diane, additional, Pairon, Jean Claude, additional, Copin, Marie Christine, additional, Scherpereel, Arnaud, additional, Wasielewski, Eric, additional, Wicquart, Laurence, additional, Lacomme, Stéphanie, additional, Vignaud, Jean-Michel, additional, Ancelin, Gaspard, additional, Girard, Cécile, additional, Sagan, Christine, additional, Bonnetaud, Christelle, additional, Hofman, Véronique, additional, Hofman, Paul, additional, Mouroux, Jérôme, additional, Thomas de Montpreville, Vincent, additional, Clermont-Taranchon, Estelle, additional, Mazieres, Julien, additional, Rouquette, Isabelle, additional, Begueret, Hugues, additional, Blay, Jean-Yves, additional, Lantuejoul, Sylvie, additional, Bueno, Raphael, additional, Caux, Christophe, additional, Girard, Nicolas, additional, McKay, James D., additional, Foll, Matthieu, additional, Galateau-Salle, Françoise, additional, and Fernandez-Cuesta, Lynnette, additional
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- 2019
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25. Circulating markers of cellular immune activation in prediagnostic blood sample and lung cancer risk in the Lung Cancer Cohort Consortium (LC3)
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Huang, Joyce Y., primary, Larose, Tricia L., additional, Luu, Hung N., additional, Wang, Renwei, additional, Fanidi, Anouar, additional, Alcala, Karine, additional, Stevens, Victoria L., additional, Weinstein, Stephanie J., additional, Albanes, Demetrius, additional, Caporaso, Neil E., additional, Purdue, Mark P., additional, Ziegler, Regina G., additional, Freedman, Neal D., additional, Lan, Qing, additional, Prentice, Ross L., additional, Pettinger, Mary, additional, Thomson, Cynthia A., additional, Cai, Qiuyin, additional, Wu, Jie, additional, Blot, William J., additional, Shu, Xiao‐Ou, additional, Zheng, Wei, additional, Arslan, Alan A., additional, Zeleniuch‐Jacquotte, Anne, additional, Le Marchand, Loïc, additional, Wilkens, Lynn R., additional, Haiman, Christopher A., additional, Zhang, Xuehong, additional, Stampfer, Meir J., additional, Han, Jiali, additional, Giles, Graham G., additional, Hodge, Allison M., additional, Severi, Gianluca, additional, Johansson, Mikael, additional, Grankvist, Kjell, additional, Langhammer, Arnulf, additional, Hveem, Kristian, additional, Xiang, Yong‐Bing, additional, Li, Hong‐Lan, additional, Gao, Yu‐Tang, additional, Visvanathan, Kala, additional, Ueland, Per M., additional, Midttun, Øivind, additional, Ulvi, Arve, additional, Buring, Julie E., additional, Lee, I‐Min, additional, Sesso, Howard D., additional, Gaziano, J. Michael, additional, Manjer, Jonas, additional, Relton, Caroline, additional, Koh, Woon‐Puay, additional, Brennan, Paul, additional, Johansson, Mattias, additional, and Yuan, Jian‐Min, additional
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- 2019
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26. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium
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Guida, Florence, Tan, Vanessa Y, Corbin, Laura J, Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D, Butterworth, Adam S, Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano Exezarreta, Pilar, Bergmann, Manuela M, Bull, Caroline J, Dahm, Christina C, Gicquiau, Audrey, Giles, Graham G, Gunter, Marc J, Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L, Ljungberg, Börje, Metspalu, Andres, Milne, Roger L, Muller, David C, Nøst, Therese H, Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A, Scalbert, Augustin, Schmidt, Julie A, Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E, Waldenberger, Melanie, Timpson, Nicholas J, and Johansson, Mattias
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2. Zero hunger ,Male ,Victoria ,Incidence ,Mendelian Randomization Analysis ,Middle Aged ,Risk Assessment ,Kidney Neoplasms ,3. Good health ,Body Mass Index ,Europe ,Risk Factors ,Case-Control Studies ,Metabolome ,Humans ,Metabolomics ,Female ,Obesity ,Prospective Studies ,Biomarkers ,Aged - Abstract
BACKGROUND: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.
27. The blood metabolome of incident kidney cancer: A case–control study nested within the MetKid consortium
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Guida, Florence, Tan, Vanessa Y., Corbin, Laura J., Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D., Butterworth, Adam S., Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano, Pilar, Bergmann, Manuela M., Bull, Caroline J., Dahm, Christina C., Gicquiau, Audrey, Giles, Graham G., Gunter, Marc J., Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L., Ljungberg, Börje, Metspalu, Andres, Milne, Roger L., Muller, David C., Nøst, Therese H., Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A., Scalbert, Augustin, Schmidt, Julie A., Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E., Waldenberger, Melanie, Timpson, Nicholas J., and Johansson, Mattias
- Subjects
2. Zero hunger ,Medicine and health sciences ,Research and analysis methods ,Biology and life sciences ,16. Peace & justice ,3. Good health ,Research Article - Abstract
Background: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). Methods and findings: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case–control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10−8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10−5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some—but not all—metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., −0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10−5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10−3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. Conclusions: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI—the principal modifiable risk factor of kidney cancer.
28. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium
- Author
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Guida, Florence, Tan, Vanessa Y, Corbin, Laura J, Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D, Butterworth, Adam S, Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano, Pilar, Bergmann, Manuela M, Bull, Caroline J, Dahm, Christina C, Gicquiau, Audrey, Giles, Graham G, Gunter, Marc J, Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L, Ljungberg, Börje, Metspalu, Andres, Milne, Roger L, Muller, David C, Nøst, Therese H, Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A, Scalbert, Augustin, Schmidt, Julie A, Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E, Waldenberger, Melanie, Timpson, Nicholas J, and Johansson, Mattias
- Subjects
2. Zero hunger ,3. Good health - Abstract
BackgroundExcess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).Methods and findingsWe assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.ConclusionsThis study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.
29. Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom
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Ruth C. Travis, Philip A.J. Crosbie, Rebecca Landy, David R Baldwin, Karine Alcala, Anthony J. Swerdlow, Mattias Johansson, Matthew E.J. Callister, Minouk J. Schoemaker, Hilary A. Robbins, Nicholas J. Wareham, Robbins, Hilary A. [0000-0001-6041-6866], Alcala, Karine [0000-0003-2308-9880], Johansson, Mattias [0000-0002-3116-5081], Apollo - University of Cambridge Repository, and Robbins, Hilary A [0000-0001-6041-6866]
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Male ,Cancer Research ,Lung Neoplasms ,Epidemiology ,631/67/1612 ,Risk prediction models ,State Medicine ,Cohort Studies ,0302 clinical medicine ,Risk Factors ,030212 general & internal medicine ,692/308/174 ,Early Detection of Cancer ,United Kingdom/epidemiology ,article ,Middle Aged ,Biobank ,medicine.anatomical_structure ,Oncology ,030220 oncology & carcinogenesis ,Calibration ,Female ,Lung cancer ,Adult ,medicine.medical_specialty ,Lung Neoplasms/diagnosis ,MEDLINE ,631/67/2324 ,Risk Assessment ,Cancer screening ,03 medical and health sciences ,Cancer epidemiology ,692/699/67/1612 ,Predictive Value of Tests ,Internal medicine ,631/67/2322 ,medicine ,Humans ,Socioeconomic status ,Aged ,Lung ,Models, Statistical ,business.industry ,Patient Selection ,Correction ,medicine.disease ,National health service ,United Kingdom ,Social Class ,Early Detection of Cancer/methods ,business ,Lung cancer screening - Abstract
Background The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. Methods We analysed current and former smokers aged 40–80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). Results Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81–0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79–0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79–0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14–1.27) to 2.16 for LLPv2 (95% CI = 2.05–2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). Conclusion In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.
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- 2021
30. Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.
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Feng X, Wu WY, Onwuka JU, Haider Z, Alcala K, Smith-Byrne K, Zahed H, Guida F, Wang R, Bassett JK, Stevens V, Wang Y, Weinstein S, Freedman ND, Chen C, Tinker L, Nøst TH, Koh WP, Muller D, Colorado-Yohar SM, Tumino R, Hung RJ, Amos CI, Lin X, Zhang X, Arslan AA, Sánchez MJ, Sørgjerd EP, Severi G, Hveem K, Brennan P, Langhammer A, Milne RL, Yuan JM, Melin B, Johansson M, Robbins HA, and Johansson M
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- Humans, Risk Assessment, Case-Control Studies, Prospective Studies, Lung, Early Detection of Cancer, Proteomics, Lung Neoplasms diagnosis, Lung Neoplasms epidemiology
- Abstract
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., (© The Author(s) 2023. Published by Oxford University Press.)
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- 2023
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31. The relationship between blood pressure and risk of renal cell carcinoma.
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Alcala K, Mariosa D, Smith-Byrne K, Nasrollahzadeh Nesheli D, Carreras-Torres R, Ardanaz Aicua E, Bondonno NP, Bonet C, Brunström M, Bueno-de-Mesquita B, Chirlaque MD, Christakoudi S, Heath AK, Kaaks R, Katzke V, Krogh V, Ljungberg B, Martin RM, May A, Melander O, Palli D, Rodriguez-Barranco M, Sacerdote C, Stocks T, Tjønneland A, Travis RC, Vermeulen R, Chanock S, Purdue M, Weiderpass E, Muller D, Brennan P, and Johansson M
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- Blood Pressure, Humans, Prospective Studies, Risk Factors, Carcinoma, Renal Cell epidemiology, Hypertension epidemiology, Kidney Neoplasms epidemiology
- Abstract
Background: The relation between blood pressure and kidney cancer risk is well established but complex and different study designs have reported discrepant findings on the relative importance of diastolic blood pressure (DBP) and systolic blood pressure (SBP). In this study, we sought to describe the temporal relation between diastolic and SBP with renal cell carcinoma (RCC) risk in detail., Methods: Our study involved two prospective cohorts: the European Prospective Investigation into Cancer and Nutrition study and UK Biobank, including >700 000 participants and 1692 incident RCC cases. Risk analyses were conducted using flexible parametric survival models for DBP and SBP both separately as well as with mutuality adjustment and then adjustment for extended risk factors. We also carried out univariable and multivariable Mendelian randomization (MR) analyses (DBP: ninstruments = 251, SBP: ninstruments = 213) to complement the analyses of measured DBP and SBP., Results: In the univariable analysis, we observed clear positive associations with RCC risk for both diastolic and SBP when measured ≥5 years before diagnosis and suggestive evidence for a stronger risk association in the year leading up to diagnosis. In mutually adjusted analysis, the long-term risk association of DBP remained, with a hazard ratio (HR) per standard deviation increment 10 years before diagnosis (HR10y) of 1.20 (95% CI: 1.10-1.30), whereas the association of SBP was attenuated (HR10y: 1.00, 95% CI: 0.91-1.10). In the complementary multivariable MR analysis, we observed an odds ratio for a 1-SD increment (ORsd) of 1.34 (95% CI: 1.08-1.67) for genetically predicted DBP and 0.70 (95% CI: 0.56-0.88) for genetically predicted SBP., Conclusion: The results of this observational and MR study are consistent with an important role of DBP in RCC aetiology. The relation between SBP and RCC risk was less clear but does not appear to be independent of DBP., (© World Health Organization, 2022. All rights reserved. The World Health Organization has granted the Publisher permission for the reproduction of this article.)
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- 2022
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32. Circulating markers of cellular immune activation in prediagnostic blood sample and lung cancer risk in the Lung Cancer Cohort Consortium (LC3).
- Author
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Huang JY, Larose TL, Luu HN, Wang R, Fanidi A, Alcala K, Stevens VL, Weinstein SJ, Albanes D, Caporaso NE, Purdue MP, Ziegler RG, Freedman ND, Lan Q, Prentice RL, Pettinger M, Thomson CA, Cai Q, Wu J, Blot WJ, Shu XO, Zheng W, Arslan AA, Zeleniuch-Jacquotte A, Le Marchand L, Wilkens LR, Haiman CA, Zhang X, Stampfer MJ, Han J, Giles GG, Hodge AM, Severi G, Johansson M, Grankvist K, Langhammer A, Hveem K, Xiang YB, Li HL, Gao YT, Visvanathan K, Ueland PM, Midttun Ø, Ulvi A, Buring JE, Lee IM, Sesso HD, Gaziano JM, Manjer J, Relton C, Koh WP, Brennan P, Johansson M, and Yuan JM
- Subjects
- Adenocarcinoma of Lung blood, Adenocarcinoma of Lung etiology, Adult, Aged, Carcinoma, Large Cell blood, Carcinoma, Large Cell etiology, Carcinoma, Squamous Cell blood, Carcinoma, Squamous Cell etiology, Case-Control Studies, Female, Follow-Up Studies, Humans, Inflammation blood, Inflammation immunology, Kynurenine blood, Lung Neoplasms blood, Lung Neoplasms etiology, Male, Middle Aged, Neopterin blood, Prognosis, Prospective Studies, Risk Factors, Small Cell Lung Carcinoma blood, Small Cell Lung Carcinoma etiology, Tryptophan blood, Adenocarcinoma of Lung diagnosis, Biomarkers, Tumor blood, Carcinoma, Large Cell diagnosis, Carcinoma, Squamous Cell diagnosis, Inflammation complications, Lung Neoplasms diagnosis, Small Cell Lung Carcinoma diagnosis
- Abstract
Cell-mediated immune suppression may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), quinolinic acid (QA) and neopterin as markers of immune regulation and inflammation with lung cancer risk in 5,364 smoking-matched case-control pairs from 20 prospective cohorts included in the international Lung Cancer Cohort Consortium. All biomarkers were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with individual biomarkers were calculated using conditional logistic regression with adjustment for circulating cotinine. Compared to the lowest quintile, the highest quintiles of kynurenine, KTR, QA and neopterin were associated with a 20-30% higher risk, and tryptophan with a 15% lower risk of lung cancer (all p
trend < 0.05). The strongest associations were seen for current smokers, where the adjusted ORs (95% CIs) of lung cancer for the highest quintile of KTR, QA and neopterin were 1.42 (1.15-1.75), 1.42 (1.14-1.76) and 1.45 (1.13-1.86), respectively. A stronger association was also seen for KTR and QA with risk of lung squamous cell carcinoma followed by adenocarcinoma, and for lung cancer diagnosed within the first 2 years after blood draw. This study demonstrated that components of the tryptophan-kynurenine pathway with immunomodulatory effects are associated with risk of lung cancer overall, especially for current smokers. Further research is needed to evaluate the role of these biomarkers in lung carcinogenesis and progression., (© 2019 UICC.)- Published
- 2020
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