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1. MT53 Remote Symptom Monitoring for Lung Cancer Patients: Lessons Learned from the Lung Aid Study

Author

Schmalz, O., primary, Santorelli, M., additional, Burke, T., additional, Norquist, J., additional, Sidduri, N., additional, Gruber, N., additional, Barthuber, L., additional, Saum, K.U., additional, Scheuringer, M., additional, Yakut, E., additional, Vainio, J., additional, Kataja, V., additional, Heger, M., additional, Bohnet, S., additional, and Kenny, C.N., additional

Published
2023
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2. Evidence of Gene-Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors

Author

Nickels, S, Truong, T, Hein, R, Stevens, K, Buck, K, Behrens, S, Eilber, U, Schmidt, M, Häberle, L, Vrieling, A, Gaudet, M, Figueroa, J, Schoof, N, Spurdle, AB, Rudolph, A, Fasching, PA, Hopper, JL, Makalic, E, Schmidt, DF, Southey, MC, Beckmann, MW, Ekici, AB, Fletcher, O, Gibson, L, dos Santos Silva, I, Peto, J, Humphreys, MK, Wang, J, Cordina-Duverger, E, Menegaux, F, Nordestgaard, BG, Bojesen, SE, Lanng, C, Anton-Culver, H, Ziogas, A, Bernstein, L, Clarke, CA, Brenner, H, Müller, H, Arndt, V, Stegmaier, C, Brauch, H, Brüning, T, Harth, V, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Lambrechts, D, Smeets, D, Neven, P, Paridaens, R, Flesch-Janys, D, Obi, N, Wang-Gohrke, S, Couch, FJ, Olson, JE, Vachon, CM, Giles, GG, Severi, G, Baglietto, L, Offit, K, John, EM, Miron, A, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Chanock, SJ, Lissowska, J, Liu, J, Cox, A, Cramp, H, Connley, D, Balasubramanian, S, Dunning, AM, Shah, M, Trentham-Dietz, A, Newcomb, P, Titus, L, Egan, K, Cahoon, EK, and Rajaraman, P

Abstract

Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction= 2.4×10-6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction= 3.1×10-4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01-1.16) in nulliparous women and ranged from 1.03 (0.96-1.10) in parous women with one birth to 1.26 (1.16-1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85-0.98) in those with an alcohol intake of

Published
2013

3. Evaluation of variation in the phosphoinositide-3-kinase catalytic subunit alpha oncogene and breast cancer risk

Author

Stevens, KN, Garcia-Closas, M, Fredericksen, Z, Kosel, M, Pankratz, VS, Hopper, JL, Dite, GS, Apicella, C, Southey, MC, Schmidt, MK, Broeks, A, Van ‘t Veer, LJ, Tollenaar, RAEM, Fasching, PA, Beckmann, MW, Hein, A, Ekici, AB, Johnson, N, Peto, J, dos Santos Silva, I, Gibson, L, Sawyer, E, Tomlinson, I, Kerin, MJ, Chanock, S, Lissowska, J, Hunter, DJ, Hoover, RN, Thomas, GD, Milne, RL, Pérez, JI Arias, González-Neira, A, Benítez, J, Burwinkel, B, Meindl, A, Schmutzler, RK, Bartrar, CR, Hamann, U, Ko, YD, Brüning, T, Chang-Claude, J, Hein, R, Wang-Gohrke, S, Dörk, T, Schürmann, P, Bremer, M, Hillemanns, P, Bogdanova, N, Zalutsky, JV, Rogov, YI, Antonenkova, N, Lindblom, A, Margolin, S, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, J, Chenevix-Trench, G, Chen, X, Peterlongo, P, Bonanni, B, Bernard, L, Manoukian, S, Wang, X, Cerhan, J, Vachon, CM, Olson, J, Giles, GG, Baglietto, L, McLean, CA, Severi, G, John, EM, Miron, A, Winqvist, R, Pylkäs, K, Jukkola-Vuorinen, A, Grip, M, Andrulis, I, Knight, JA, Glendon, G, Mulligan, AM, Cox, A, Brock, IW, Elliott, G, Cross, SS, Pharoah, PP, Dunning, AM, Pooley, KA, Humphreys, MK, Wang, J, Kang, D, Yoo, K-Y, Noh, D-Y, Sangrajrang, S, Gabrieau, V, Brennan, P, McKay, J, Anton-Culver, H, Ziogas, A, and Couch, FJ

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Prevention, Genetics, Cancer, Breast Cancer, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Breast Neoplasms, Case-Control Studies, Class I Phosphatidylinositol 3-Kinases, Female, Genetic Predisposition to Disease, Genetic Variation, Humans, Phosphatidylinositol 3-Kinases, genetic susceptibility, neoplasms, association study, GENICA Network, kConFab Investigators, Australian Ovarian Cancer Study Group, Public Health and Health Services, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundSomatic mutations in phosphoinositide-3-kinase catalytic subunit alpha (PIK3CA) are frequent in breast tumours and have been associated with oestrogen receptor (ER) expression, human epidermal growth factor receptor-2 overexpression, lymph node metastasis and poor survival. The goal of this study was to evaluate the association between inherited variation in this oncogene and risk of breast cancer.MethodsA single-nucleotide polymorphism from the PIK3CA locus that was associated with breast cancer in a study of Caucasian breast cancer cases and controls from the Mayo Clinic (MCBCS) was genotyped in 5436 cases and 5280 controls from the Cancer Genetic Markers of Susceptibility (CGEMS) study and in 30 949 cases and 29 788 controls from the Breast Cancer Association Consortium (BCAC).ResultsRs1607237 was significantly associated with a decreased risk of breast cancer in MCBCS, CGEMS and all studies of white Europeans combined (odds ratio (OR)=0.97, 95% confidence interval (CI) 0.95-0.99, P=4.6 × 10(-3)), but did not reach significance in the BCAC replication study alone (OR=0.98, 95% CI 0.96-1.01, P=0.139).ConclusionCommon germline variation in PIK3CA does not have a strong influence on the risk of breast cancer.

Published
2011

4. Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: A combined case-control study

Author

Milne, RL, Gaudet, MM, Spurdle, AB, Fasching, PA, Couch, FJ, Benítez, J, Arias Pérez, JI, Zamora, MP, Malats, N, dos Santos Silva, I, Gibson, LJ, Fletcher, O, Johnson, N, Anton-Culver, H, Ziogas, A, Figueroa, J, Brinton, L, Sherman, ME, Lissowska, J, Hopper, JL, Dite, GS, Apicella, C, Southey, MC, Sigurdson, AJ, Linet, MS, Schonfeld, SJ, Freedman, DM, Mannermaa, A, Kosma, VM, Kataja, V, Auvinen, P, Andrulis, IL, Glendon, G, Knight, JA, Weerasooriya, N, Cox, A, Reed, MWR, Cross, SS, Dunning, AM, Ahmed, S, Shah, M, Brauch, H, Ko, YD, Brüning, T, Lambrechts, D, Reumers, J, Smeets, A, Wang-Gohrke, S, Hall, P, Czene, K, Liu, J, Irwanto, AK, Chenevix-Trench, G, Holland, H, Fab, KC, Giles, GG, Baglietto, L, Severi, G, Bojensen, SE, Nordestgaard, BG, Flyger, H, John, EM, West, DW, Whittemore, AS, Vachon, C, Olson, JE, Fredericksen, Z, Kosel, M, Hein, R, Vrieling, A, Flesch-Janys, D, Heinz, J, Beckmann, MW, Heusinger, K, Ekici, AB, Haeberle, L, Humphreys, MK, Morrison, J, Easton, DF, and Pharoah, PD

Abstract

Introduction: Several common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium.Methods: We evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects.Results: These analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar.Conclusions: The relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified. © 2010 Milne et al.; licensee BioMed Central Ltd.

Published
2010

5. ePRO symptom follow-up of colorectal cancer patients receiving oxaliplatin-based adjuvant chemotherapy is feasible and enhances the quality of patient care:a prospective multicenter study

Author

Iivanainen, S. (Sanna), Ravichandra, R. (Ravi), Jekunen, A. (Antti), Arokoski, R. (Reetta), Mentu, S. (Santeri), Lang, L. (Laura), Ekström, J. (Jussi), Virtanen, H. (Henri), Kataja, V. (Vesa), Koivunen, J. P. (Jussi P.), Iivanainen, S. (Sanna), Ravichandra, R. (Ravi), Jekunen, A. (Antti), Arokoski, R. (Reetta), Mentu, S. (Santeri), Lang, L. (Laura), Ekström, J. (Jussi), Virtanen, H. (Henri), Kataja, V. (Vesa), and Koivunen, J. P. (Jussi P.)

Abstract

Purpose: Electronic (e) patient-reported outcomes (PROs) have been shown to improve the quality of life and survival in chemotherapy treated advanced cancer patients. We hypothesized that multidimensional ePRO centered approach could improve symptom management, streamline patient flow, and optimize the use of healthcare resources. Methods: In this multicenter trial (NCT04081558), colorectal cancer (CRC) patients receiving oxaliplatin-based chemotherapy as adjuvant or in the first- or second-line setting in advanced disease were included in the prospective ePRO cohort, while a comparative retrospective cohort was collected from the same institutes. The investigated tool consisted of a weekly e-symptom questionnaire integrated to an urgency algorithm and laboratory value interface, which generated semi-automated decision support for chemotherapy cycle prescription and individualized symptom management. Results: Recruitment to the ePRO cohort occurred 1/2019–1/2021 (n = 43). The comparator group (n = 194) consisted of patients treated in the same institutes 1–7/2017. The analysis was limited to adjuvant treated (n = 36 and n = 35). The feasibility of the ePRO follow-up was good with 98% reporting easy usage and 86% improved care, while health care personnel valued the easy use and logical workflow. In the ePRO cohort, 42% needed a phone call before planned chemotherapy cycles, while this was 100% in the retrospective cohort (p = 1.4e−8). Peripheral sensory neuropathy was detected significantly earlier with ePRO followed (p = 1e−5) but did not translate to earlier dose reduction, delays, or unplanned therapy termination compared to the retrospective cohort. Conclusion: The results suggest that the investigated approach is feasible and streamlines workflow. Earlier symptom detection may improve the quality in cancer care.

Published
2023

10. Prostate cancer: ESMO Consensus Conference Guidelines 2012

Author

Parker, Chris, Dr., Bellmunt, Joaquim, Berthold, Dominik, Bill-Axelson, Anna, Carlsson, Sigrid, Daugaard, Gedske, De Meerleer, Gert, de Reijke, Theo, Dearnaley, David, Fizazi, Karim, Fonteyne, Valérie, Gillessen, Silke, Heinrich, Daniel, Horwich, Alan, Hugosson, Jonas, Kataja, Vesa, Kwiatkowski, Maciej, Nilsson, Sten, Padhani, Anwar, Papandreou, Christos, Parker, Chris, Roobol, Monique, Sella, Avishay, Valdagni, Riccardo, Van der Kwast, Theo, Verhagen, Paul, Wiegel, Thomas, Horwich, A., Hugosson, J., de Reijke, T., Wiegel, T., Fizazi, K., and Kataja, V.

Published
2013
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12. Predicting Objective Response Rate (ORR) in Immune Checkpoint Inhibitor (ICI) Therapies with Machine Learning (ML) by combining clinical and patient-reported data

Author

Iivanainen, S. (Sanna), Ekström, J. (Jussi), Virtanen, H. (Henri), Kataja, V. V. (Vesa V.), Koivunen, J. P. (Jussi P.), Iivanainen, S. (Sanna), Ekström, J. (Jussi), Virtanen, H. (Henri), Kataja, V. V. (Vesa V.), and Koivunen, J. P. (Jussi P.)

Abstract

ICIs are a standard of care in several malignancies; however, according to overall response rate (ORR), only a subset of eligible patients benefits from ICIs. Thus, an ability to predict ORR could enable more rational use. In this study a ML-based ORR prediction model was built, with patient-reported symptom data and other clinical data as inputs, using the extreme gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), and the performance was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew’s correlation coefficient). The ORR prediction model had a promising LOOCV performance with all four metrics: accuracy (75%), AUC (0.71), F1 score (0.58), and MCC (0.4). A rather good sensitivity (0.58) and high specificity (0.82) of the model were seen in the confusion matrix for all 63 LOOCV ORR predictions. The two most important symptoms for predicting the ORR were itching and fatigue. The results show that it is possible to predict ORR for patients with multiple advanced cancers undergoing ICI therapies with a ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort.

Published
2022

20. Safety and tolerability of long-term treatment with darolutamide in patients with metastatic castration-resistant prostate cancer

Author

Vjaters, E., primary, Fizazi, K., additional, James, N.D., additional, Tammela, T.L., additional, Matsubara, N., additional, Priou, F., additional, Beuzeboc, P., additional, Lesimple, T., additional, Bono, P., additional, Kataja, V., additional, Garcia, J.A., additional, Protheroe, A., additional, Aspegren, J., additional, Joensuu, H., additional, Kuss, I., additional, Thiele, S., additional, Fiala-Buskies, S., additional, and Jones, R.H., additional

Published
2022
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32. Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

Author

Iivanainen, S. (Sanna), Ekstrom, J. (Jussi), Virtanen, H. (Henri), Kataja, V. V. (Vesa V.), Koivunen, J. P. (Jussi P.), Iivanainen, S. (Sanna), Ekstrom, J. (Jussi), Virtanen, H. (Henri), Kataja, V. V. (Vesa V.), and Koivunen, J. P. (Jussi P.)

Abstract

Background: Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods: The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results: The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion: The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration: Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019.

Published
2021

33. Prostate cancer: ESMO Consensus Conference Guidelines 2012

Author

Horwich, A., Hugosson, J., de Reijke, T., Wiegel, T., Fizazi, K., Kataja, V., Parker, Chris, Dr., Bellmunt, Joaquim, Berthold, Dominik, Bill-Axelson, Anna, Carlsson, Sigrid, Daugaard, Gedske, De Meerleer, Gert, de Reijke, Theo, Dearnaley, David, Fizazi, Karim, Fonteyne, Valérie, Gillessen, Silke, Heinrich, Daniel, Horwich, Alan, Hugosson, Jonas, Kataja, Vesa, Kwiatkowski, Maciej, Nilsson, Sten, Padhani, Anwar, Papandreou, Christos, Parker, Chris, Roobol, Monique, Sella, Avishay, Valdagni, Riccardo, Van der Kwast, Theo, Verhagen, Paul, and Wiegel, Thomas

Published
2013
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