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1,371 results on '"Grönberg, Henrik"'

1. Cluster effect for SNP–SNP interaction pairs for predicting complex traits

Author

Lin, Hui-Yi, Mazumder, Harun, Sarkar, Indrani, Huang, Po-Yu, Eeles, Rosalind A., Kote-Jarai, Zsofia, Muir, Kenneth R., Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Neal, David E., Nielsen, Sune F., Nordestgaard, Børge G., Grönberg, Henrik, Wiklund, Fredrik, MacInnis, Robert J., Haiman, Christopher A., Travis, Ruth C., Stanford, Janet L., Kibel, Adam S., Cybulski, Cezary, Khaw, Kay-Tee, Maier, Christiane, Thibodeau, Stephen N., Teixeira, Manuel R., Cannon-Albright, Lisa, Brenner, Hermann, Kaneva, Radka, Pandha, Hardev, and Park, Jong Y.

Published
2024
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2. Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

Author

Fonseca, Nicolette M., Maurice-Dror, Corinne, Herberts, Cameron, Tu, Wilson, Fan, William, Murtha, Andrew J., Kollmannsberger, Catarina, Kwan, Edmond M., Parekh, Karan, Schönlau, Elena, Bernales, Cecily Q., Donnellan, Gráinne, Ng, Sarah W. S., Sumiyoshi, Takayuki, Vergidis, Joanna, Noonan, Krista, Finch, Daygen L., Zulfiqar, Muhammad, Miller, Stacy, Parimi, Sunil, Lavoie, Jean-Michel, Hardy, Edward, Soleimani, Maryam, Nappi, Lucia, Eigl, Bernhard J., Kollmannsberger, Christian, Taavitsainen, Sinja, Nykter, Matti, Tolmeijer, Sofie H., Boerrigter, Emmy, Mehra, Niven, van Erp, Nielka P., De Laere, Bram, Lindberg, Johan, Grönberg, Henrik, Khalaf, Daniel J., Annala, Matti, Chi, Kim N., and Wyatt, Alexander W.

Published
2024
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3. Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Asona, Lui, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth R, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Blot, William J, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Wiklund, Fredrik, Penney, Kathryn L, Huff, Chad D, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Ost, Piet, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Abraham, Aswin, Claessens, Frank, Castelao, Jose Esteban, Townsend, Paul A, Crawford, Dana C, Petrovics, Gyorgy, van Schaik, Ron HN, Parent, Marie-Élise, Hu, Jennifer J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Urologic Diseases, Prevention, Genetics, Good Health and Well Being, Male, Humans, Prostate-Specific Antigen, Prostatic Neoplasms, Early Detection of Cancer, Polymorphism, Single Nucleotide, Risk Factors, Risk Assessment, Genetic Predisposition to Disease, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Published
2022

5. Prostate Cancer–specific and All-cause Mortality After Robot-assisted Radical Prostatectomy: 20 Years’ Report from the European Association of Urology Robotic Urology Section Scientific Working Group

Author

Falagario, Ugo Giovanni, Knipper, Sophie, Pellegrino, Francesco, Martini, Alberto, Akre, Olof, Egevad, Lars, Grönberg, Henrik, Moschovas, Marcio Covas, Bravi, Carlo Andrea, Tran, Joshua, Heiniger, Yasmin, von Kempis, Antonius, Schaffar, Robin, Carrieri, Giuseppe, Rochat, Charles-Henry, Mottrie, Alexandre, Ahlering, Thomas E., John, Hubert, Patel, Vipul, Graefen, Markus, and Wiklund, Peter

Published
2024
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6. Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

Author

Liu, Bojing, Wang, Yinxi, Weitz, Philippe, Lindberg, Johan, Hartman, Johan, Egevad, Lars, Grönberg, Henrik, Eklund, Martin, and Rantalainen, Mattias

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volume of the prostate, and tumour lesions in regions between biopsy cores can be missed, leading to a well-known low sensitivity to detect clinically relevant cancer. As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer. Methods: This study included 14,354 hematoxylin and eosin stained whole slide images from benign prostate biopsies from 1,508 men in two groups: men without an established prostate cancer (PCa) diagnosis and men with at least one core biopsy diagnosed with PCa. 80% of the participants were assigned as training data and used for model optimization (1,211 men), and the remaining 20% (297 men) as a held-out test set used to evaluate model performance. An ensemble of 10 deep convolutional neural network models was optimized for classification of biopsies from men with and without established cancer. Hyperparameter optimization and model selection was performed by cross-validation in the training data . Results: Area under the receiver operating characteristic curve (ROC-AUC) was estimated as 0.727 (bootstrap 95% CI: 0.708-0.745) on biopsy level and 0.738 (bootstrap 95% CI: 0.682 - 0.796) on man level. At a specificity of 0.9 the model had an estimated sensitivity of 0.348. Conclusion: The developed model has the ability to detect men with risk of missed PCa due to under-sampling of the prostate. The proposed model has the potential to reduce the number of false negative cases in routine systematic prostate biopsies and to indicate men who could benefit from MRI-guided re-biopsy., Comment: 13 pages, 3 figures

Published
2021

10. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Author

Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F, van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B, Evans, Andrew J, van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S, Peng, Lily, Mermel, Craig H, Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Urologic Diseases, Prostate Cancer, Algorithms, Biopsy, Cohort Studies, Humans, Male, Neoplasm Grading, Prostatic Neoplasms, Reproducibility of Results, PANDA challenge consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

Published
2022

11. Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks

Author

Weitz, Philippe, Wang, Yinxi, Kartasalo, Kimmo, Egevad, Lars, Lindberg, Johan, Grönberg, Henrik, Eklund, Martin, and Rantalainen, Mattias

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource intense to implement, and is just starting to be introduced into clinical diagnostics. Molecular changes, including genetic alterations and gene expression changes, occuring in tumors cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes directly from routine haematoxylin and eosin (H&E) stained whole slide images (WSIs) using deep convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach for disease specific modelling of relationships between morphology and gene expression, and we conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs of H&E stained tissue. The work is based on the TCGA PRAD study and includes both WSIs and RNA-seq data for 370 patients. Out of 15586 protein coding and sufficiently frequently expressed transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted p-value < 1*10-4) in a cross-validation. 5419 (81.9%) of these were subsequently validated in a held-out test set. We also demonstrate the ability to predict a prostate cancer specific cell cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.

Published
2021
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12. Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

Author

Landi, Maria Teresa, Stevens, Victoria, Wang, Ying, Albanes, Demetrios, Caporaso, Neil, Brennan, Paul, Amos, Christopher I., Shete, Sanjay, Hung, Rayjean J., Bickeböller, Heike, Risch, Angela, Houlston, Richard, Lam, Stephen, Tardon, Adonina, Chen, Chu, Bojesen, Stig E., Johansson, Mattias, Wichmann, H-Erich, Christiani, David, Rennert, Gadi, Arnold, Susanne, Field, John K., Le Marchand, Loic, Melander, Olle, Brunnström, Hans, Liu, Geoffrey, Andrew, Angeline, Kiemeney, Lambertus A., Shen, Hongbing, Zienolddiny, Shan, Grankvist, Kjell, Johansson, Mikael, Teare, M. Dawn, Hong, Yun-Chul, Yuan, Jian-Min, Lazarus, Philip, Schabath, Matthew B., Aldrich, Melinda C., Eeles, Rosalind A., Haiman, Christopher A., Kote-Jarai, Zsofia, Schumacher, Fredrick R., Benlloch, Sara, Al Olama, Ali Amin, Muir, Kenneth R., Berndt, Sonja I., Conti, David V., Wiklund, Fredrik, Chanock, Stephen, Tangen, Catherine M., Batra, Jyotsna, Clements, Judith A., Grönberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Albanes, Demetrius, Weinstein, Stephanie J., Wolk, Alicja, West, Catharine M.L., Mucci, Lorelei A., Cancel-Tassin, Géraldine, Koutros, Stella, Sørensen, Karina Dalsgaard, Grindedal, Eli Marie, Neal, David E., Hamdy, Freddie C., Donovan, Jenny L., Travis, Ruth C., Hamilton, Robert J., Ingles, Sue Ann, Rosenstein, Barry S., Lu, Yong-Jie, Giles, Graham G., MacInnis, Robert J., Kibel, Adam S., Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L., Park, Jong Y., Stanfrod, Janet L., Cybulski, Cezary, Nordestgaard, Børge G., Nielsen, Sune F., Brenner, Hermann, Maier, Christiane, Logothetis, Christopher J., John, Esther M., Teixeira, Manuel R., Neuhausen, Susan L., De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F., Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A., Castelao, Jose Esteban, Roobol, Monique J., Menegaux, Florence, Khaw, Kay-Tee, Cannon-Albright, Lisa, Pandha, Hardev, Thibodeau, Stephen N., Hunter, David J., Kraft, Peter, Blot, William J., Riboli, Elio, Yarmolinsky, James, Robinson, Jamie W., Mariosa, Daniela, Karhunen, Ville, Huang, Jian, Dimou, Niki, Murphy, Neil, Burrows, Kimberley, Bouras, Emmanouil, Smith-Byrne, Karl, Lewis, Sarah J., Galesloot, Tessel E., Vermeulen, Sita, Martin, Paul, Hou, Lifang, Newcomb, Polly A., White, Emily, Wu, Anna H., Le Marchand, Loïc, Phipps, Amanda I., Buchanan, Daniel D., Zhao, Sizheng Steven, Gill, Dipender, Chanock, Stephen J., Purdue, Mark P., Davey Smith, George, Herzig, Karl-Heinz, Järvelin, Marjo-Riitta, Amos, Chris I., Dehghan, Abbas, Gunter, Marc J., Tsilidis, Kostas K., and Martin, Richard M.

Published
2024
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13. Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks

Author

Ström, Peter, Kartasalo, Kimmo, Ruusuvuori, Pekka, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Egevad, Lars, and Eklund, Martin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task. Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm. Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55). Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest., Comment: 20 pages, 5 figures

Published
2020

14. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, UKGPCS collaborators, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, APCB BioResource (Australian Prostate Cancer BioResource), Grönberg, Henrik, Walsh, Eleanor I, Turner, Emma L, Lane, Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Wiklund, Fredrik, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Vega, Ana, IMPACT Study Steering Committee and Collaborators, Kogevinas, Manolis, Penney, Kathryn L, Teixeira, Manuel R, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, Razack, Azad, Newcomb, Lisa F, Canary PASS Investigators, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Roobol, Monique J, Zheng, Wei, Profile Study Steering Committee, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and PRACTICAL Consortium

Subjects
UKGPCS collaborators, APCB BioResource, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Prevention, Urologic Diseases, Cancer, Prostate Cancer, Aging, Urology & Nephrology, Oncology and Carcinogenesis
Abstract

BackgroundPolygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.Results166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.ConclusionsIncorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Published
2021

15. Polygenic hazard score is associated with prostate cancer in multi-ethnic populations.

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Nielsen, Sune F, Nordestgaard, Børge G, Wiklund, Fredrik, Tangen, Catherine M, Giles, Graham G, Wolk, Alicja, Albanes, Demetrius, Travis, Ruth C, Blot, William J, Zheng, Wei, Sanderson, Maureen, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Khaw, Kay-Tee, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Thibodeau, Stephen N, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L, Huff, Chad, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Cannon-Albright, Lisa, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Pandha, Hardev, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Bush, William S, Roobol, Monique J, Parent, Marie-Élise, Hu, Jennifer J, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, UKGPCS collaborators, APCB (Australian Prostate Cancer BioResource), NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, and PRACTICAL Consortium

Subjects
UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Humans, Prostatic Neoplasms, Neoplasm Invasiveness, Multivariate Analysis, Multifactorial Inheritance, Aged, Middle Aged, Ethnic Groups, Male, Self Report, Aging, Urologic Diseases, Cancer, Prostate Cancer
Abstract

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p

Published
2021

16. Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Author

Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M., Bostwick, David G., Evans, Andrew J., Grignon, David J., Humphrey, Peter A., Iczkowski, Kenneth A., Kench, James G., Kristiansen, Glen, van der Kwast, Theodorus H., Leite, Katia R. M., McKenney, Jesse K., Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John R., Takahashi, Hiroyuki, Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Lindberg, Johan, Bergström, Cecilia, Ruusuvuori, Pekka, Wählby, Carolina, Grönberg, Henrik, Rantalainen, Mattias, Egevad, Lars, and Eklund, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73)., Comment: 45 pages, 11 figures

Published
2019

17. A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Walsh, Eleanor I, Turner, Emma L, Lane, J Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, Zsofia, Al Olama, Ali Amin, Garcia, Sara Benlloch, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo LJ, Nordestgaard, Børge Grønne, Key, Timothy J, Travis, Ruth C, Pharoah, Paul DP, Pashayan, Nora, Khaw, Kay-Tee, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa A, Brenner, Hermann, Schöttker, Ben, Holleczek, Bernd, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar Kroumov, Kaneva, Radka P, Mitev, Vanio I, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda B, BioResource, for the Australian Prostate Cancer, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and Consortium, for the PRACTICAL

Subjects
Biomedical and Clinical Sciences, Health Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Prevention, Urologic Diseases, Good Health and Well Being, Aged, Early Detection of Cancer, Humans, Male, Middle Aged, Neoplasm Grading, Population Control, Prostatic Neoplasms, Australian Prostate Cancer BioResource, PRACTICAL Consortium, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundA polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.MethodsUnited Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.ResultsThe expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.ConclusionsPHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.ImpactPersonalized genetic risk assessments could inform prostate cancer screening decisions.

Published
2020

25. Prostate cancer incidence and mortality in men exposed to α1-adrenergic receptor antagonists.

Author

Björnebo, Lars, Razdan, Shirin, Discacciati, Andrea, Palsdottir, Thorgerdur, Aly, Markus, Nordström, Tobias, Eklund, Martin, Lundon, Dara, Grönberg, Henrik, Tewari, Ash, Wiklund, Peter, Kyprianou, Natasha, and Lantz, Anna

Subjects
PROSTATE-specific antigen, STATISTICAL models, PROPORTIONAL hazards models, BENIGN prostatic hyperplasia, PROSTATE cancer patients, PROSTATE cancer
Abstract

Background α1-Adrenergic receptor antagonists are commonly used to treat benign prostatic hyperplasia. Preclinical studies suggest that they induce cell death and inhibit tumor growth. This study evaluated the risk of prostate cancer death in men using α1-adrenergic receptor antagonists. Methods A population-based cohort study in Stockholm, Sweden (January 1, 2007, to December 31, 2019) included 451 779 men with a prostate-specific antigen test result. Study entry was 1 year after the first prostate-specific antigen test. Men were considered exposed at their second filled prescription. The primary outcome was prostate cancer mortality. Secondary outcomes were all-cause mortality and prostate cancer incidence. Cox proportional hazards regression models were used to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all outcomes. Inverse-probability weighting with marginal structural models accounted for time-dependent confounders. Results Of 351 297 men in the final cohort, 39 856 (11.3%) were exposed to α1-adrenergic receptor antagonists. Median (interquartile range) follow-up for prostate cancer mortality was 8.9 (5.1-10.9) years; median (interquartile range) exposure time to α1-adrenergic receptor antagonists was 4.4 (2.0-7.6) years. There was no evidence of an association between α1-adrenergic receptor antagonist use and prostate cancer mortality, all-cause mortality, or high-grade prostate cancer. α1-Adrenergic receptor antagonist use was associated with an increased risk of prostate cancer (HR = 1.11, 95% CI = 1.06 to 1.17) and low-grade prostate cancer (HR = 1.22, 95% CI = 1.11 to 1.33). Men whose prostate cancer was treated with α1-adrenergic receptor antagonists underwent more frequent prostate-specific antigen testing. Conclusions Our findings show no significant association between α1-adrenergic receptor adrenoceptor antagonist exposure and prostate cancer mortality or high-grade prostate cancer. Although the preclinical evidence indicates a potential chemopreventive effect, this study's findings do not support it. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts.

Author

Seibert, Tyler M, Fan, Chun Chieh, Wang, Yunpeng, Zuber, Verena, Karunamuni, Roshan, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, ZSofia, Al Olama, Ali Amin, Garcia, Sara Benlloch, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo Lj, Nordestgaard, Børge G, Nielsen, Sune F, Weischer, Maren, Bisbjerg, Rasmus, Røder, M Andreas, Iversen, Peter, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Cuk, Katarina, Saum, Kai-Uwe, Park, Jong Y, Sellers, Thomas A, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej, Karow, David S, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and PRACTICAL Consortium*

Subjects
PRACTICAL Consortium*, Humans, Prostatic Neoplasms, Kallikreins, Prostate-Specific Antigen, Disease-Free Survival, Risk Assessment, Survival Analysis, Cohort Studies, Predictive Value of Tests, Age of Onset, Genotype, Polymorphism, Single Nucleotide, Aged, Middle Aged, European Continental Ancestry Group, Male, Early Detection of Cancer, Outcome Assessment, Health Care, Polymorphism, Single Nucleotide, Outcome Assessment, Health Care, Aging, Urologic Diseases, Cancer, Genetic Testing, Prevention, Prostate Cancer, Genetics, 2.1 Biological and endogenous factors, General & Internal Medicine, Public Health and Health Services, Clinical Sciences
Abstract

ObjectivesTo develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.DesignAnalysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.SettingMultiple institutions that were members of international PRACTICAL consortium.ParticipantsAll consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.Main outcome measuresPrediction with hazard score of age of onset of aggressive cancer in validation set.ResultsIn the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.ConclusionsPolygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Published
2018

27. Prostate Cancer Incidence and Mortality in Men Exposed to α1-Adrenoceptor Antagonists

Author

Björnebo, Lars, primary, Razdan, Shirin, additional, Discacciati, Andrea, additional, Palsdottir, Thorgerdur, additional, Aly, Markus, additional, Nordström, Tobias, additional, Eklund, Martin, additional, Lundon, Dara, additional, Grönberg, Henrik, additional, Tewari, Ash, additional, Wiklund, Peter, additional, Kyprianou, Natasha, additional, and Lantz, Anna, additional

Published
2024
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28. PD29-12 THE IMPACT OF THE TIME ELAPSED BETWEEN PRIMARY TREATMENT AND BCR ON CANCER SPECIFIC MORTALITY IN PATIENTS WHO EXPERIENCED BCR AFTER PRIMARY TREATMENT

Author

Pellegrino, Francesco, primary, Falagario, Ugo G., additional, Abbadi, Ahmad, additional, Björnebo, Lars, additional, Valdman, Alexander, additional, Carrieri, Giuseppe, additional, Briganti, Alberto, additional, Montorsi, Francesco, additional, Akre, Olof, additional, Aly, Markus, additional, Eklund, Martin, additional, Nordström, Tobias, additional, Grönberg, Henrik, additional, Lantz, Anna, additional, and Wiklund, Peter, additional

Published
2024
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29. Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non-inferiority trial

Author

Cavalli-Björkman, Carin, Björklund, Astrid, Hune, Britt-Marie, Clements, Mark, Hao, Shuang, Discacciati, Andrea, Grönberg, Henrik, Eklund, Martin, Nordström, Tobias, Carlsson, Stefan, Aly, Markus, Walldén, Mats, Steinberg, Ola, Andersson, Karl, Jäderling, Fredrik, Wimmercranz, Fredrik, Meurling, Edward, Gleassgen, Axel, Majeed, Nada, Awadelkarim, Rihab, Fyhr, Ing-Marie, Sandström, Dag, Waage, Linda, Imamov, Otabek, Lantz, Rafael, Thorstensson, Andreas, Stiernstedt, Carl, Wande, Dushaid, Trygg, Gunnar, Söderbäck, Harald, Michajlowski, Jerzy, Leykamm, Lars, Svedberg, Nils-Erik, Bergman, Tommy, Sabockis, Raimundas, Akrawi, Sirvan, Bergman, Martin, Annerstedt, Magnus, and Glaessgen, Axel

Published
2021
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30. Rare Germline Variants in ATM Predispose to Prostate Cancer: A PRACTICAL Consortium Study

Author

Karlsson, Questa, Brook, Mark N., Dadaev, Tokhir, Wakerell, Sarah, Saunders, Edward J., Muir, Kenneth, Neal, David E., Giles, Graham G., MacInnis, Robert J., Thibodeau, Stephen N., McDonnell, Shannon K., Cannon-Albright, Lisa, Teixeira, Manuel R., Paulo, Paula, Cardoso, Marta, Huff, Chad, Li, Donghui, Yao, Yu, Scheet, Paul, Permuth, Jennifer B., Stanford, Janet L., Dai, James Y., Ostrander, Elaine A., Cussenot, Olivier, Cancel-Tassin, Géraldine, Hoegel, Josef, Herkommer, Kathleen, Schleutker, Johanna, Tammela, Teuvo L.J., Rathinakannan, Venkat, Sipeky, Csilla, Wiklund, Fredrik, Grönberg, Henrik, Aly, Markus, Isaacs, William B., Dickinson, Jo L., FitzGerald, Liesel M., Chua, Melvin L.K., Nguyen-Dumont, Tu, Schaid, Daniel J., Southey, Melissa C., Eeles, Rosalind A., and Kote-Jarai, Zsofia

Published
2021
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36. Observational and genetic associations between cardiorespiratory fitness and cancer : a UK Biobank and international consortia study

Author

Watts, Eleanor L., Gonzales, Tomas I., Strain, Tessa, Saint-Maurice, Pedro F., Bishop, D. Timothy, Chanock, Stephen J., Johansson, Mattias, Keku, Temitope O., Le Marchand, Loic, Moreno, Victor, Newcomb, Polly A., Newton, Christina C., Pai, Rish K., Purdue, Mark P., Ulrich, Cornelia M., Smith-Byrne, Karl, van Guelpen, Bethany, Eeles, Rosalind A., Haiman, Christopher A., Kote-Jarai, Zsofia, Schumacher, Fredrick R., Benlloch, Sara, Olama, Ali Amin Al, Muir, Kenneth R., Berndt, Sonja I., Conti, David V., Wiklund, Fredrik, Wang, Ying, Tangen, Catherine M., Batra, Jyotsna, Clements, Judith A., Grönberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Albanes, Demetrius, Weinstein, Stephanie J., Wolk, Alicja, West, Catharine M. L., Mucci, Lorelei A., Cancel-Tassin, Géraldine, Koutros, Stella, Sørensen, Karina Dalsgaard, Grindedal, Eli Marie, Neal, David E., Hamdy, Freddie C., Donovan, Jenny L., Travis, Ruth C., Hamilton, Robert J., Ingles, Sue Ann, Rosenstein, Barry S., Lu, Yong-Jie, Giles, Graham G., MacInnis, Robert J., Kibel, Adam S., Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L., Park, Jong Y., Stanford, Janet L., Cybulski, Cezary, Nordestgaard, Børge G., Nielsen, Sune F., Brenner, Hermann, Maier, Christiane, Kim, Jeri, John, Esther M., Teixeira, Manuel R., Neuhausen, Susan L., De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F., Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A., Castelao, Jose Esteban, Roobol, Monique J., Menegaux, Florence, Khaw, Kay-Tee, Cannon-Albright, Lisa, Pandha, Hardev, Thibodeau, Stephen N., Hunter, David J., Kraft, Peter, Blot, William J., Riboli, Elio, Day, Felix R., Wijndaele, Katrien, Wareham, Nicholas J., Matthews, Charles E., Moore, Steven C., Brage, Soren, Watts, Eleanor L., Gonzales, Tomas I., Strain, Tessa, Saint-Maurice, Pedro F., Bishop, D. Timothy, Chanock, Stephen J., Johansson, Mattias, Keku, Temitope O., Le Marchand, Loic, Moreno, Victor, Newcomb, Polly A., Newton, Christina C., Pai, Rish K., Purdue, Mark P., Ulrich, Cornelia M., Smith-Byrne, Karl, van Guelpen, Bethany, Eeles, Rosalind A., Haiman, Christopher A., Kote-Jarai, Zsofia, Schumacher, Fredrick R., Benlloch, Sara, Olama, Ali Amin Al, Muir, Kenneth R., Berndt, Sonja I., Conti, David V., Wiklund, Fredrik, Wang, Ying, Tangen, Catherine M., Batra, Jyotsna, Clements, Judith A., Grönberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Albanes, Demetrius, Weinstein, Stephanie J., Wolk, Alicja, West, Catharine M. L., Mucci, Lorelei A., Cancel-Tassin, Géraldine, Koutros, Stella, Sørensen, Karina Dalsgaard, Grindedal, Eli Marie, Neal, David E., Hamdy, Freddie C., Donovan, Jenny L., Travis, Ruth C., Hamilton, Robert J., Ingles, Sue Ann, Rosenstein, Barry S., Lu, Yong-Jie, Giles, Graham G., MacInnis, Robert J., Kibel, Adam S., Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L., Park, Jong Y., Stanford, Janet L., Cybulski, Cezary, Nordestgaard, Børge G., Nielsen, Sune F., Brenner, Hermann, Maier, Christiane, Kim, Jeri, John, Esther M., Teixeira, Manuel R., Neuhausen, Susan L., De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F., Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A., Castelao, Jose Esteban, Roobol, Monique J., Menegaux, Florence, Khaw, Kay-Tee, Cannon-Albright, Lisa, Pandha, Hardev, Thibodeau, Stephen N., Hunter, David J., Kraft, Peter, Blot, William J., Riboli, Elio, Day, Felix R., Wijndaele, Katrien, Wareham, Nicholas J., Matthews, Charles E., Moore, Steven C., and Brage, Soren

Abstract

Background: The association of fitness with cancer risk is not clear. Methods: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method. Results: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min−1⋅kg−1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73–0.89), colorectal (0.94, 0.90–0.99), and breast cancer (0.96, 0.92–0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min−1⋅kg−1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86–0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. Discussion: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.

Published
2024
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37. Observational and genetic associations between cardiorespiratory fitness and cancer:a UK Biobank and international consortia study

Author

Watts, Eleanor L., Gonzales, Tomas I., Strain, Tessa, Saint-Maurice, Pedro F., Bishop, D. Timothy, Chanock, Stephen J., Johansson, Mattias, Keku, Temitope O., Le Marchand, Loic, Moreno, Victor, Newcomb, Polly A., Newton, Christina C., Pai, Rish K., Purdue, Mark P., Ulrich, Cornelia M., Smith-Byrne, Karl, Van Guelpen, Bethany, Eeles, Rosalind A., Haiman, Christopher A., Kote-Jarai, Zsofia, Schumacher, Fredrick R., Benlloch, Sara, Olama, Ali Amin Al, Muir, Kenneth R., Berndt, Sonja I., Conti, David V., Wiklund, Fredrik, Wang, Ying, Tangen, Catherine M., Batra, Jyotsna, Clements, Judith A., Grönberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Albanes, Demetrius, Weinstein, Stephanie J., Wolk, Alicja, West, Catharine M.L., Mucci, Lorelei A., Cancel-Tassin, Géraldine, Koutros, Stella, Sørensen, Karina Dalsgaard, Grindedal, Eli Marie, Neal, David E., Hamdy, Freddie C., Donovan, Jenny L., Travis, Ruth C., Hamilton, Robert J., Ingles, Sue Ann, Rosenstein, Barry S., Lu, Yong Jie, Giles, Graham G., MacInnis, Robert J., Kibel, Adam S., Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L., Park, Jong Y., Stanford, Janet L., Cybulski, Cezary, Nordestgaard, Børge G., Nielsen, Sune F., Brenner, Hermann, Maier, Christiane, Kim, Jeri, John, Esther M., Teixeira, Manuel R., Neuhausen, Susan L., De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F., Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A., Castelao, Jose Esteban, Roobol, Monique J., Menegaux, Florence, Khaw, Kay Tee, Cannon-Albright, Lisa, Pandha, Hardev, Thibodeau, Stephen N., Hunter, David J., Kraft, Peter, Blot, William J., Riboli, Elio, Day, Felix R., Wijndaele, Katrien, Wareham, Nicholas J., Matthews, Charles E., Moore, Steven C., Brage, Soren, Watts, Eleanor L., Gonzales, Tomas I., Strain, Tessa, Saint-Maurice, Pedro F., Bishop, D. Timothy, Chanock, Stephen J., Johansson, Mattias, Keku, Temitope O., Le Marchand, Loic, Moreno, Victor, Newcomb, Polly A., Newton, Christina C., Pai, Rish K., Purdue, Mark P., Ulrich, Cornelia M., Smith-Byrne, Karl, Van Guelpen, Bethany, Eeles, Rosalind A., Haiman, Christopher A., Kote-Jarai, Zsofia, Schumacher, Fredrick R., Benlloch, Sara, Olama, Ali Amin Al, Muir, Kenneth R., Berndt, Sonja I., Conti, David V., Wiklund, Fredrik, Wang, Ying, Tangen, Catherine M., Batra, Jyotsna, Clements, Judith A., Grönberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Albanes, Demetrius, Weinstein, Stephanie J., Wolk, Alicja, West, Catharine M.L., Mucci, Lorelei A., Cancel-Tassin, Géraldine, Koutros, Stella, Sørensen, Karina Dalsgaard, Grindedal, Eli Marie, Neal, David E., Hamdy, Freddie C., Donovan, Jenny L., Travis, Ruth C., Hamilton, Robert J., Ingles, Sue Ann, Rosenstein, Barry S., Lu, Yong Jie, Giles, Graham G., MacInnis, Robert J., Kibel, Adam S., Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L., Park, Jong Y., Stanford, Janet L., Cybulski, Cezary, Nordestgaard, Børge G., Nielsen, Sune F., Brenner, Hermann, Maier, Christiane, Kim, Jeri, John, Esther M., Teixeira, Manuel R., Neuhausen, Susan L., De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F., Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A., Castelao, Jose Esteban, Roobol, Monique J., Menegaux, Florence, Khaw, Kay Tee, Cannon-Albright, Lisa, Pandha, Hardev, Thibodeau, Stephen N., Hunter, David J., Kraft, Peter, Blot, William J., Riboli, Elio, Day, Felix R., Wijndaele, Katrien, Wareham, Nicholas J., Matthews, Charles E., Moore, Steven C., and Brage, Soren

Abstract

Background: The association of fitness with cancer risk is not clear. Methods: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method.Results: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min−1⋅kg−1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73–0.89), colorectal (0.94, 0.90–0.99), and breast cancer (0.96, 0.92–0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min−1⋅kg−1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86–0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. Discussion: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.

Published
2024

39. Identification and Validation of Leucine-rich α-2-glycoprotein 1 as a Noninvasive Biomarker for Improved Precision in Prostate Cancer Risk Stratification

Author

Guldvik, Ingrid J., Zuber, Verena, Braadland, Peder R., Grytli, Helene H., Ramberg, Håkon, Lilleby, Wolfgang, Thiede, Bernd, Zucknick, Manuela, Saatcioglu, Fahri, Gislefoss, Randi, Kvåle, Rune, George, Anne, Grönberg, Henrik, Wiklund, Fredrik, Neal, David E., Gnanapragasam, Vincent J., Taskén, Kristin A., and Mills, Ian G.

Published
2020
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41. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

Author

Ström, Peter, Kartasalo, Kimmo, Olsson, Henrik, Solorzano, Leslie, Delahunt, Brett, Berney, Daniel M, Bostwick, David G, Evans, Andrew J, Grignon, David J, Humphrey, Peter A, Iczkowski, Kenneth A, Kench, James G, Kristiansen, Glen, van der Kwast, Theodorus H, Leite, Katia R M, McKenney, Jesse K, Oxley, Jon, Pan, Chin-Chen, Samaratunga, Hemamali, Srigley, John R, Takahashi, Hiroyuki, Tsuzuki, Toyonori, Varma, Murali, Zhou, Ming, Lindberg, Johan, Lindskog, Cecilia, Ruusuvuori, Pekka, Wählby, Carolina, Grönberg, Henrik, Rantalainen, Mattias, Egevad, Lars, and Eklund, Martin

Published
2020
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43. Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

Author

Yarmolinsky, James, primary, Robinson, Jamie W., additional, Mariosa, Daniela, additional, Karhunen, Ville, additional, Huang, Jian, additional, Dimou, Niki, additional, Murphy, Neil, additional, Burrows, Kimberley, additional, Bouras, Emmanouil, additional, Smith-Byrne, Karl, additional, Lewis, Sarah J., additional, Galesloot, Tessel E., additional, Kiemeney, Lambertus A., additional, Vermeulen, Sita, additional, Martin, Paul, additional, Albanes, Demetrius, additional, Hou, Lifang, additional, Newcomb, Polly A., additional, White, Emily, additional, Wolk, Alicja, additional, Wu, Anna H., additional, Le Marchand, Loïc, additional, Phipps, Amanda I., additional, Buchanan, Daniel D., additional, Zhao, Sizheng Steven, additional, Gill, Dipender, additional, Chanock, Stephen J., additional, Purdue, Mark P., additional, Davey Smith, George, additional, Brennan, Paul, additional, Herzig, Karl-Heinz, additional, Järvelin, Marjo-Riitta, additional, Amos, Chris I., additional, Hung, Rayjean J., additional, Dehghan, Abbas, additional, Johansson, Mattias, additional, Gunter, Marc J., additional, Tsilidis, Kostas K., additional, Martin, Richard M., additional, Landi, Maria Teresa, additional, Stevens, Victoria, additional, Wang, Ying, additional, Albanes, Demetrios, additional, Caporaso, Neil, additional, Amos, Christopher I., additional, Shete, Sanjay, additional, Bickeböller, Heike, additional, Risch, Angela, additional, Houlston, Richard, additional, Lam, Stephen, additional, Tardon, Adonina, additional, Chen, Chu, additional, Bojesen, Stig E., additional, Wichmann, H-Erich, additional, Christiani, David, additional, Rennert, Gadi, additional, Arnold, Susanne, additional, Field, John K., additional, Le Marchand, Loic, additional, Melander, Olle, additional, Brunnström, Hans, additional, Liu, Geoffrey, additional, Andrew, Angeline, additional, Shen, Hongbing, additional, Zienolddiny, Shan, additional, Grankvist, Kjell, additional, Johansson, Mikael, additional, Teare, M. Dawn, additional, Hong, Yun-Chul, additional, Yuan, Jian-Min, additional, Lazarus, Philip, additional, Schabath, Matthew B., additional, Aldrich, Melinda C., additional, Eeles, Rosalind A., additional, Haiman, Christopher A., additional, Kote-Jarai, Zsofia, additional, Schumacher, Fredrick R., additional, Benlloch, Sara, additional, Al Olama, Ali Amin, additional, Muir, Kenneth R., additional, Berndt, Sonja I., additional, Conti, David V., additional, Wiklund, Fredrik, additional, Chanock, Stephen, additional, Tangen, Catherine M., additional, Batra, Jyotsna, additional, Clements, Judith A., additional, Grönberg, Henrik, additional, Pashayan, Nora, additional, Schleutker, Johanna, additional, Weinstein, Stephanie J., additional, West, Catharine M.L., additional, Mucci, Lorelei A., additional, Cancel-Tassin, Géraldine, additional, Koutros, Stella, additional, Sørensen, Karina Dalsgaard, additional, Grindedal, Eli Marie, additional, Neal, David E., additional, Hamdy, Freddie C., additional, Donovan, Jenny L., additional, Travis, Ruth C., additional, Hamilton, Robert J., additional, Ingles, Sue Ann, additional, Rosenstein, Barry S., additional, Lu, Yong-Jie, additional, Giles, Graham G., additional, MacInnis, Robert J., additional, Kibel, Adam S., additional, Vega, Ana, additional, Kogevinas, Manolis, additional, Penney, Kathryn L., additional, Park, Jong Y., additional, Stanfrod, Janet L., additional, Cybulski, Cezary, additional, Nordestgaard, Børge G., additional, Nielsen, Sune F., additional, Brenner, Hermann, additional, Maier, Christiane, additional, Logothetis, Christopher J., additional, John, Esther M., additional, Teixeira, Manuel R., additional, Neuhausen, Susan L., additional, De Ruyck, Kim, additional, Razack, Azad, additional, Newcomb, Lisa F., additional, Lessel, Davor, additional, Kaneva, Radka, additional, Usmani, Nawaid, additional, Claessens, Frank, additional, Townsend, Paul A., additional, Castelao, Jose Esteban, additional, Roobol, Monique J., additional, Menegaux, Florence, additional, Khaw, Kay-Tee, additional, Cannon-Albright, Lisa, additional, Pandha, Hardev, additional, Thibodeau, Stephen N., additional, Hunter, David J., additional, Kraft, Peter, additional, Blot, William J., additional, and Riboli, Elio, additional

Published
2024
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44. Two susceptibility loci identified for prostate cancer aggressiveness.

Author

Berndt, Sonja I, Wang, Zhaoming, Yeager, Meredith, Alavanja, Michael C, Albanes, Demetrius, Amundadottir, Laufey, Andriole, Gerald, Beane Freeman, Laura, Campa, Daniele, Cancel-Tassin, Geraldine, Canzian, Federico, Cornu, Jean-Nicolas, Cussenot, Olivier, Diver, W Ryan, Gapstur, Susan M, Grönberg, Henrik, Haiman, Christopher A, Henderson, Brian, Hutchinson, Amy, Hunter, David J, Key, Timothy J, Kolb, Suzanne, Koutros, Stella, Kraft, Peter, Le Marchand, Loic, Lindström, Sara, Machiela, Mitchell J, Ostrander, Elaine A, Riboli, Elio, Schumacher, Fred, Siddiq, Afshan, Stanford, Janet L, Stevens, Victoria L, Travis, Ruth C, Tsilidis, Konstantinos K, Virtamo, Jarmo, Weinstein, Stephanie, Wilkund, Fredrik, Xu, Jianfeng, Lilly Zheng, S, Yu, Kai, Wheeler, William, Zhang, Han, African Ancestry Prostate Cancer GWAS Consortium, Sampson, Joshua, Black, Amanda, Jacobs, Kevin, Hoover, Robert N, Tucker, Margaret, and Chanock, Stephen J

Subjects
African Ancestry Prostate Cancer GWAS Consortium, Humans, Prostatic Neoplasms, Neoplasm Invasiveness, Genetic Predisposition to Disease, Case-Control Studies, Male, Genetic Loci, Neoplasm Grading
Abstract

Most men diagnosed with prostate cancer will experience indolent disease; hence, discovering genetic variants that distinguish aggressive from nonaggressive prostate cancer is of critical clinical importance for disease prevention and treatment. In a multistage, case-only genome-wide association study of 12,518 prostate cancer cases, we identify two loci associated with Gleason score, a pathological measure of disease aggressiveness: rs35148638 at 5q14.3 (RASA1, P=6.49 × 10(-9)) and rs78943174 at 3q26.31 (NAALADL2, P=4.18 × 10(-8)). In a stratified case-control analysis, the SNP at 5q14.3 appears specific for aggressive prostate cancer (P=8.85 × 10(-5)) with no association for nonaggressive prostate cancer compared with controls (P=0.57). The proximity of these loci to genes involved in vascular disease suggests potential biological mechanisms worthy of further investigation.

Published
2015

48. Biochemical Recurrence and Risk of Mortality Following Radiotherapy or Radical Prostatectomy

Author

Falagario, Ugo Giovanni, primary, Abbadi, Ahmad, additional, Remmers, Sebastiaan, additional, Björnebo, Lars, additional, Bogdanovic, Darko, additional, Martini, Alberto, additional, Valdman, Alexander, additional, Carrieri, Giuseppe, additional, Menon, Mani, additional, Akre, Olof, additional, Eklund, Martin, additional, Nordström, Tobias, additional, Grönberg, Henrik, additional, Lantz, Anna, additional, and Wiklund, Peter, additional

Published
2023
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49. Prostate Cancer–specific and All-cause Mortality After Robot-assisted Radical Prostatectomy: 20 Years’ Report from the European Association of Urology Robotic Urology Section Scientific Working Group

Author

Falagario, Ugo Giovanni, primary, Knipper, Sophie, additional, Pellegrino, Francesco, additional, Martini, Alberto, additional, Akre, Olof, additional, Egevad, Lars, additional, Grönberg, Henrik, additional, Moschovas, Marcio Covas, additional, Bravi, Carlo Andrea, additional, Tran, Joshua, additional, Heiniger, Yasmin, additional, von Kempis, Antonius, additional, Schaffar, Robin, additional, Carrieri, Giuseppe, additional, Rochat, Charles-Henry, additional, Mottrie, Alexandre, additional, Ahlering, Thomas E., additional, John, Hubert, additional, Patel, Vipul, additional, Graefen, Markus, additional, and Wiklund, Peter, additional

Published
2023
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