192 results on '"Laajala, Teemu D"'
Search Results
2. A harmonized resource of integrated prostate cancer clinical, -omic, and signature features
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Laajala, Teemu D., Sreekanth, Varsha, Soupir, Alex C., Creed, Jordan H., Halkola, Anni S., Calboli, Federico C. F., Singaravelu, Kalaimathy, Orman, Michael V., Colin-Leitzinger, Christelle, Gerke, Travis, Fridley, Brooke L., Tyekucheva, Svitlana, and Costello, James C.
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- 2023
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3. Antibiotic Treatment is an Independent Poor Risk Factor in NSCLC But Not in Melanoma Patients Who had Received Anti-PD-1/L1 Monotherapy
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Vihinen, Hannes, Jokinen, Artturi, Laajala, Teemu D., Wahid, Nesna, Peltola, Lotta, Kettunen, Tiia, Rönkä, Aino, Tiainen, Leena, Skyttä, Tanja, Kohtamäki, Laura, Tulokas, Sanni, Karhapää, Hanna, Hernberg, Micaela, Silvoniemi, Maria, and Mattila, Kalle E.
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- 2023
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4. High intratumoral dihydrotestosterone is associated with antiandrogen resistance in VCaP prostate cancer xenografts in castrated mice
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Huhtaniemi, Riikka, Sipilä, Petra, Junnila, Arttu, Oksala, Riikka, Knuuttila, Matias, Mehmood, Arfa, Aho, Eija, Laajala, Teemu D., Aittokallio, Tero, Laiho, Asta, Elo, Laura, Ohlsson, Claes, Thulin, Malin Hagberg, Kallio, Pekka, Mäkelä, Sari, Mustonen, Mika V.J., and Poutanen, Matti
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- 2022
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5. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer
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Mason, Mike, Lapuente-Santana, Óscar, Halkola, Anni S., Wang, Wenyu, Mall, Raghvendra, Xiao, Xu, Kaufman, Jacob, Fu, Jingxin, Pfeil, Jacob, Banerjee, Jineta, Chung, Verena, Chang, Han, Chasalow, Scott D., Lin, Hung Ying, Chai, Rongrong, Yu, Thomas, Finotello, Francesca, Mirtti, Tuomas, Mäyränpää, Mikko I., Bao, Jie, Verschuren, Emmy W., Ahmed, Eiman I., Ceccarelli, Michele, Miller, Lance D., Monaco, Gianni, Hendrickx, Wouter R.L., Sherif, Shimaa, Yang, Lin, Tang, Ming, Gu, Shengqing Stan, Zhang, Wubing, Zhang, Yi, Zeng, Zexian, Das Sahu, Avinash, Liu, Yang, Yang, Wenxian, Bedognetti, Davide, Tang, Jing, Eduati, Federica, Laajala, Teemu D., Geese, William J., Guinney, Justin, Szustakowski, Joseph D., Vincent, Benjamin G., Carbone, David P., Mason, Mike, Lapuente-Santana, Óscar, Halkola, Anni S., Wang, Wenyu, Mall, Raghvendra, Xiao, Xu, Kaufman, Jacob, Fu, Jingxin, Pfeil, Jacob, Banerjee, Jineta, Chung, Verena, Chang, Han, Chasalow, Scott D., Lin, Hung Ying, Chai, Rongrong, Yu, Thomas, Finotello, Francesca, Mirtti, Tuomas, Mäyränpää, Mikko I., Bao, Jie, Verschuren, Emmy W., Ahmed, Eiman I., Ceccarelli, Michele, Miller, Lance D., Monaco, Gianni, Hendrickx, Wouter R.L., Sherif, Shimaa, Yang, Lin, Tang, Ming, Gu, Shengqing Stan, Zhang, Wubing, Zhang, Yi, Zeng, Zexian, Das Sahu, Avinash, Liu, Yang, Yang, Wenxian, Bedognetti, Davide, Tang, Jing, Eduati, Federica, Laajala, Teemu D., Geese, William J., Guinney, Justin, Szustakowski, Joseph D., Vincent, Benjamin G., and Carbone, David P.
- Abstract
BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of
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- 2024
6. ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer.
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Orman, Michael V., Sreekanth, Varsha, Laajala, Teemu D., Cramer, Scott D., and Costello, James C.
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PROSTATE cancer ,CANCER invasiveness ,BIOINFORMATICS software ,ANTIGEN presentation ,BIOINFORMATICS ,METASTASIS - Abstract
Background: Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multigene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment. Methods: We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression. Results: Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific coalterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes. Conclusion: ProstaMine is a method to systematically identify novel subtypespecific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtypespecific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https:// bioinformatics.cuanschutz.edu/prostamine. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets
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Murtojärvi, Mika, Halkola, Anni S., Airola, Antti, Laajala, Teemu D., Mirtti, Tuomas, Aittokallio, Tero, and Pahikkala, Tapio
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- 2020
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8. A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
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Mattila, Kalle E., Laajala, Teemu D., Tornberg, Sara V., Kilpeläinen, Tuomas P., Vainio, Paula, Ettala, Otto, Boström, Peter J., Nisen, Harry, Elo, Laura L., and Jaakkola, Panu M.
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- 2021
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9. Modeling genetic heterogeneity of drug response and resistance in cancer
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Laajala, Teemu D., Gerke, Travis, Tyekucheva, Svitlana, and Costello, James C.
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- 2019
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10. Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts
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Huhtaniemi, Riikka, Oksala, Riikka, Knuuttila, Matias, Mehmood, Arfa, Aho, Eija, Laajala, Teemu D., Nicorici, Daniel, Aittokallio, Tero, Laiho, Asta, Elo, Laura, Ohlsson, Claes, Kallio, Pekka, Mäkelä, Sari, Mustonen, Mika V.J., Sipilä, Petra, and Poutanen, Matti
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- 2018
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11. Low nuclear expression of HIF‐hydroxylases PHD2/EGLN1 and PHD3/EGLN3 are associated with poor recurrence‐free survival in clear cell renal cell carcinoma.
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Luomala, Lassi, Mattila, Kalle, Vainio, Paula, Nisén, Harry, Pellinen, Teijo, Lohi, Jouni, Laajala, Teemu D., Järvinen, Petrus, Koskenniemi, Anna‐Riina, Jaakkola, Panu, and Mirtti, Tuomas
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RENAL cell carcinoma ,CELL survival ,IMMUNOSTAINING ,PROGNOSIS ,ELECTRONIC health records - Abstract
Background: Hypoxia inducible factors, HIF‐1α and HIF‐2α, and their main regulators, the prolyl hydroxylase domain proteins (PHDs), mediate cellular response to hypoxia and contribute to tumor progression in clear cell renal cell carcinoma (ccRCC). These biomarkers may improve the value of traditional histopathological features in predicting disease progression after nephrectomy for localized ccRCC and guide patient selection for adjuvant treatments. Patients and Methods: In this study, we analyzed the associations of PHD2 and PHD3 with histopathological tumor features and recurrence‐free survival (RFS) in a retrospective cohort of 173 patients who had undergone surgery for localized ccRCC at Helsinki University Hospital (HUH), Finland. An external validation cohort of 191 patients was obtained from Turku University Hospital (TUH), Finland. Tissue‐microarrays (TMA) were constructed using the primary tumor samples. Clinical parameters and follow‐up information from 2006 to 2019 were obtained from electronic medical records. The cytoplasmic and nuclear expression of PHD2, and PHD3 were scored based on immunohistochemical staining and their associations with histopathological features and RFS were evaluated. Results: Nuclear PHD2 and PHD3 expression in cancer cells were associated with lower pT‐stage and Fuhrman grade compared with negative nuclei. Patients with positive nuclear expression of PHD2 and PHD3 in cancer cells had favorable RFS compared with patients having negative tumors. The nuclear expression of PHD2 was independently associated with a decreased risk of disease recurrence or death from RCC in multivariable analysis. These results were observed in both cohorts. Conclusions: The absence of nuclear PHD2 and PHD3 expression in ccRCC was associated with poor RFS and the nuclear expression of PHD2 predicted RFS regardless of other known histopathological prognostic factors. Nuclear PHD2 and PHD3 are potential prognostic biomarkers in patients with localized ccRCC and should be further investigated and validated in prospective studies. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Cumulative Cancer Locations is a Novel Metric for Predicting Active Surveillance Outcomes: A Multicenter Study
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Erickson, Andrew M., Luzzago, Stefano, Semjonow, Axel, Vasarainen, Hanna, Laajala, Teemu D., Musi, Gennaro, de Cobelli, Ottavio, Mirtti, Tuomas, and Rannikko, Antti
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- 2018
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13. Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma
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Huvila, Jutta, Laajala, Teemu D., Edqvist, Per-Henrik, Mardinoglu, Adil, Talve, Lauri, Pontén, Fredrik, Grénman, Seija, Carpén, Olli, Aittokallio, Tero, and Auranen, Annika
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- 2018
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14. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
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Abdallah, Kald, Aittokallio, Tero, Airola, Antti, Anghe, Catalina, Azima, Helia, Baertsch, Robert, Ballester, Pedro J, Bare, Chris, Bhandari, Vinayak, Bot, Brian M, Dang, Cuong C, Dunbar, Maria Bekker-Nielsen, Buchardt, Ann-Sophie, Buturovic, Ljubomir, Cao, Da, Chalise, Prabhakar, Cho, Junwoo, Chu, Tzu-Ming, Coley, R Yates, Conjeti, Sailesh, Correia, Sara, Costello, James C, Dai, Ziwei, Dai, Junqiang, Dargatz, Philip, Delavarkhan, Sam, Deng, Detian, Dhanik, Ankur, Du, Yu, Elangovan, Aparna, Ellis, Shellie, Elo, Laura L, Espiritu, Shadrielle M, Fan, Fan, Farshi, Ashkan B, Freitas, Ana, Fridley, Brooke, Friend, Stephen, Fuchs, Christiane, Gofer, Eyal, Peddinti, Gopalacharyulu, Graw, Stefan, Greiner, Russ, Guan, Yuanfang, Guinney, Justin, Guo, Jing, Gupta, Pankaj, Guyer, Anna I, Han, Jiawei, Hansen, Niels R, Chang, Billy HW, Hirvonen, Outi, Huang, Barbara, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph G, Jayaswa, Vivek, Jeon, Jouhyun, Ji, Zhicheng, Juvvadi, Deekshith, Jyrkkiö, Sirkku, Kanigel-Winner, Kimberly, Katouzian, Amin, Kazanov, Marat D, Khan, Suleiman A, Khayyer, Shahin, Kim, Dalho, Golinska, Agnieszka K, Koestler, Devin, Kokowicz, Fernanda, Kondofersky, Ivan, Krautenbacher, Norbert, Krstajic, Damjan, Kumar, Luke, Kurz, Christoph, Kyan, Matthew, Laajala, Teemu D, Laimighofer, Michael, Lee, Eunjee, Lesinski, Wojciech, Li, Miaozhu, Li, Ye, Lian, Qiuyu, Liang, Xiaotao, Lim, Minseong, Lin, Henry, Lin, Xihui, Lu, Jing, Mahmoudian, Mehrad, Manshaei, Roozbeh, Meier, Richard, Miljkovic, Dejan, Mirtti, Tuomas, Mnich, Krzysztof, Navab, Nassir, Neto, Elias C, Newton, Yulia, Norman, Thea, Pahikkala, Tapio, Pal, Subhabrata, Park, Byeongju, Patel, Jaykumar, Pathak, Swetabh, Pattin, Alejandrina, Ankerst, Donna P, Peng, Jian, Petersen, Anne H, Philip, Robin, Piccolo, Stephen R, Pölsterl, Sebastian, Polewko-Klim, Aneta, Rao, Karthik, Ren, Xiang, Rocha, Miguel, Rudnicki, Witold R., Ryan, Charles J, Ryu, Hyunnam, Sartor, Oliver, Scherb, Hagen, Sehgal, Raghav, Seyednasrollah, Fatemeh, Shang, Jingbo, Shao, Bin, Shen, Liji, Sher, Howard, Shiga, Motoki, Sokolov, Artem, Söllner, Julia F, Song, Lei, Soule, Howard, Stolovitzky, Gustavo, Stuart, Josh, Sun, Ren, Sweeney, Christopher J, Tahmasebi, Nazanin, Tan, Kar-Tong, Tomaziu, Lisbeth, Usset, Joseph, Vang, Yeeleng S, Vega, Roberto, Vieira, Vitor, Wang, David, Wang, Difei, Wang, Junmei, Wang, Lichao, Wang, Sheng, Wang, Tao, Wang, Yue, Wolfinger, Russ, Wong, Chris, Wu, Zhenke, Xiao, Jinfeng, Xie, Xiaohui, Xie, Yang, Xin, Doris, Yang, Hojin, Yu, Nancy, Yu, Thomas, Yu, Xiang, Zahedi, Sulmaz, Zanin, Massimiliano, Zhang, Chihao, Zhang, Jingwen, Zhang, Shihua, Zhang, Yanchun, Zhou, Fang Liz, Zhu, Hongtu, Zhu, Shanfeng, Zhu, Yuxin, Winner, Kimberly Kanigel, Bare, J Christopher, Neto, Elias Chaibub, Peddinti, Gopal, and Scher, Howard I
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- 2017
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15. Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse
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Vehmas, Anni P., Adam, Marion, Laajala, Teemu D., Kastenmüller, Gabi, Prehn, Cornelia, Rozman, Jan, Ohlsson, Claes, Fuchs, Helmut, Hrabě de Angelis, Martin, Gailus-Durner, Valérie, Elo, Laura L., Aittokallio, Tero, Adamski, Jerzy, Corthals, Garry, Poutanen, Matti, and Strauss, Leena
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- 2016
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16. Publisher Correction: Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy
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Haikala, Heidi M., Anttila, Johanna M., Marques, Elsa, Raatikainen, Tiina, Ilander, Mette, Hakanen, Henna, Ala-Hongisto, Hanna, Savelius, Mariel, Balboa, Diego, Von Eyss, Bjoern, Eskelinen, Vilja, Munne, Pauliina, Nieminen, Anni I., Otonkoski, Timo, Schüler, Julia, Laajala, Teemu D., Aittokallio, Tero, Sihto, Harri, Mattson, Johanna, Heikkilä, Päivi, Leidenius, Marjut, Joensuu, Heikki, Mustjoki, Satu, Kovanen, Panu, Eilers, Martin, Leverson, Joel D., and Klefström, Juha
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- 2019
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17. Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy
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Haikala, Heidi M., Anttila, Johanna M., Marques, Elsa, Raatikainen, Tiina, Ilander, Mette, Hakanen, Henna, Ala-Hongisto, Hanna, Savelius, Mariel, Balboa, Diego, Von Eyss, Bjoern, Eskelinen, Vilja, Munne, Pauliina, Nieminen, Anni I., Otonkoski, Timo, Schüler, Julia, Laajala, Teemu D., Aittokallio, Tero, Sihto, Harri, Mattson, Johanna, Heikkilä, Päivi, Leidenius, Marjut, Joensuu, Heikki, Mustjoki, Satu, Kovanen, Panu, Eilers, Martin, Leverson, Joel D., and Klefström, Juha
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- 2019
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18. The Effect of a Fish Oil and/or Probiotic Intervention from Early Pregnancy Onwards on Colostrum Immune Mediators: A Randomized, Placebo‐Controlled, Double‐Blinded Clinical Trial in Overweight/Obese Mothers.
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Soukka, Jenni, Polari, Lauri, Kalliomäki, Marko, Saros, Lotta, Laajala, Teemu D., Vahlberg, Tero, Toivola, Diana M., and Laitinen, Kirsi
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- 2023
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19. Castration Induces Up-Regulation of Intratumoral Androgen Biosynthesis and Androgen Receptor Expression in an Orthotopic VCaP Human Prostate Cancer Xenograft Model
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Knuuttila, Matias, Yatkin, Emrah, Kallio, Jenny, Savolainen, Saija, Laajala, Teemu D., Aittokallio, Tero, Oksala, Riikka, Häkkinen, Merja, Keski-Rahkonen, Pekka, Auriola, Seppo, Poutanen, Matti, and Mäkelä, Sari
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- 2014
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20. Non–muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical Bacillus Calmette-Guérin.
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de Jong, Florus C., Laajala, Teemu D., Hoedemaeker, Robert F., Jordan, Kimberley R., van der Made, Angelique C. J., Boevé, Egbert R., van der Schoot, Deric K. E., Nieuwkamer, Bart, Janssen, Emiel A. M., Mahmoudi, Tokameh, Boormans, Joost L., Theodorescu, Dan, Costello, James C., and Zuiverloon, Tahlita C. M.
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NON-muscle invasive bladder cancer ,CANCER invasiveness ,BLADDER cancer ,INTRAVESICAL administration ,EPITHELIAL-mesenchymal transition - Abstract
The recommended treatment for patients with high-risk non–muscle-invasive bladder cancer (HR-NMIBC) is tumor resection followed by adjuvant Bacillus Calmette-Guérin (BCG) bladder instillations. However, only 50% of patients benefit from this therapy. If progression to advanced disease occurs, then patients must undergo a radical cystectomy with risks of substantial morbidity and poor clinical outcome. Identifying tumors unlikely to respond to BCG can translate into alternative treatments, such as early radical cystectomy, targeted therapies, or immunotherapies. Here, we conducted molecular profiling of 132 patients with BCG-naive HR-NMIBC and 44 patients with recurrences after BCG (34 matched), which uncovered three distinct BCG response subtypes (BRS1, 2 and BRS3). Patients with BRS3 tumors had a reduced recurrence-free and progression-free survival compared with BRS1/2. BRS3 tumors expressed high epithelial-to-mesenchymal transition and basal markers and had an immunosuppressive profile, which was confirmed with spatial proteomics. Tumors that recurred after BCG were enriched for BRS3. BRS stratification was validated in a second cohort of 151 BCG-naive patients with HR-NMIBC, and the molecular subtypes outperformed guideline-recommended risk stratification based on clinicopathological variables. For clinical application, we confirmed that a commercially approved assay was able to predict BRS3 tumors with an area under the curve of 0.87. These BCG response subtypes will allow for improved identification of patients with HR-NMIBC at the highest risk of progression and have the potential to be used to select more appropriate treatments for patients unlikely to respond to BCG. Editor's summary: Intravesical administration of Bacillus Calmette-Guérin (BCG) is used as adjuvant treatment for high-risk non-muscle invasive bladder cancer (HR-NMIBC). However, about half of treated patients will experience recurrence, and predicting non-response could lead to earlier use of alternative treatments and potentially improved survival. Here, de Jong and colleagues performed molecular profiling on two cohorts of patients with HR-NMIBC. They identified three BCG response subtypes; BRS3 tumors were more aggressive and were associated with immune suppressive features and poorer progression-free survival. The BCG response subtypes performed better than current risk stratification approaches, and a commercially available qPCR-based assay could identify patients with BRS3 NR-NMIBC, highlighting the utility of these molecular subtypes in identifying those at highest risk of progression. —Melissa Norton [ABSTRACT FROM AUTHOR]
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- 2023
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21. OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer.
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Halkola, Anni S., Joki, Kaisa, Mirtti, Tuomas, Mäkelä, Marko M., Aittokallio, Tero, and Laajala, Teemu D.
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PROGNOSTIC models ,FEATURE selection ,PROSTATE cancer ,SUBSET selection ,PROSTATE cancer patients ,ELECTRONIC health records ,PATHOLOGICAL laboratories - Abstract
In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L
0 -pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data. Author summary: Feature subset selection has become a crucial part of building biomedical models, due to the abundance of available predictors in many applications, yet there remains an uncertainty of their importance and generalization ability. Regularized regression methods have become popular approaches to tackle this challenge by balancing the model goodness-of-fit against the increasing complexity of the model in terms of coefficients that deviate from zero. Regularization norms are pivotal in formulating the model complexity, and currently L1 -norm (LASSO), L2 -norm (Ridge Regression) and their hybrid (Elastic Net) dominate the field. In this paper, we present a novel methodology that is based on the L0 -pseudonorm, also known as the best subset selection, which has largely gone overlooked due to its challenging discrete nature. Our methodology makes use of a continuous transformation of the discrete optimization problem, and provides effective solvers implemented in a user friendly R software package. We exemplify the use of oscar-package in the context of prostate cancer prognostic prediction using both real-world hospital registry and clinical cohort data. By benchmarking the methodology against existing regularization methods, we illustrate the advantages of the L0 -pseudonorm for better clinical applicability, selection of grouped features, and demonstrate its applicability in high-dimensional transcriptomics datasets. [ABSTRACT FROM AUTHOR]- Published
- 2023
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22. Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility
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Kallionpää, Henna, Elo, Laura L., Laajala, Essi, Mykkänen, Juha, Ricaño-Ponce, Isis, Vaarma, Matti, Laajala, Teemu D., Hyöty, Heikki, Ilonen, Jorma, Veijola, Riitta, Simell, Tuula, Wijmenga, Cisca, Knip, Mikael, Lähdesmäki, Harri, Simell, Olli, and Lahesmaa, Riitta
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- 2014
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23. Optimized detection of transcription factor-binding sites in ChIP-seq experiments
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Elo, Laura L., Kallio, Aleksi, Laajala, Teemu D., Hawkins, R. David, Korpelainen, Eija, and Aittokallio, Tero
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- 2012
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24. ePCR : an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts
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Laajala, Teemu D, Murtojärvi, Mika, Virkki, Arho, Aittokallio, Tero, Institute for Molecular Medicine Finland, University of Helsinki, Tero Aittokallio / Principal Investigator, and Bioinformatics
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Male ,3122 Cancers ,Humans ,Prostatic Neoplasms ,1182 Biochemistry, cell and molecular biology ,3111 Biomedicine ,Data and Text Mining ,Prognosis ,Applications Notes ,Polymerase Chain Reaction ,Software ,PROGNOSTIC MODEL - Abstract
Motivation Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. Results We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. Availability and implementation The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. Supplementary information Supplementary data are available at Bioinformatics online.
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- 2018
25. A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments
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Lahesmaa Riitta, Tuomela Soile, Raghav Sunil, Laajala Teemu D, Aittokallio Tero, and Elo Laura L
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Biotechnology ,TP248.13-248.65 ,Genetics ,QH426-470 - Abstract
Abstract Background Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications. Results The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments. Conclusions In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.
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- 2009
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26. Exploratory Analysis of CA125-MGL and --STn Glycoforms in the Differential Diagnostics of Pelvic Masses.
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Salminen, Liina, Nadeem, Nimrah, Rolfsen, Anne Lone, Dørum, Anne, Laajala, Teemu D., Grènman, Seija, Hietanen, Sakari, Heinosalo, Taija, Perheentupa, Antti, Poutanen, Matti, Bolstad, Nils, Carpén, Olli, Lamminmäki, Urpo, Pettersson, Kim, Gidwani, Kamlesh, Hynninen, Johanna, and Huhtinen, Kaisa
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CA 125 test ,IMMUNOASSAY ,OVARIAN epithelial cancer ,BIOMARKERS ,POSTMENOPAUSE - Abstract
Background: The cancer antigen 125 (CA125) immunoassay (IA) does not distinguish epithelial ovarian cancer (EOC) from benign disease with the sensitivity needed in clinical practice. In recent studies, glycoforms of CA125 have shown potential as biomarkers in EOC. Here, we assessed the diagnostic abilities of two recently developed CA125 glycoform assays for patients with a pelvic mass. Detailed analysis was further conducted for postmenopausal patients with marginally elevated conventionally measured CA125 levels, as this subgroup presents a diagnostic challenge in the clinical setting. Methods: Our study population contained 549 patients diagnosed with EOC, benign ovarian tumors, and endometriosis. Of these, 288 patients were postmenopausal, and 98 of them presented with marginally elevated serum levels of conventionally measured CA125 at diagnosis. Preoperative serum levels of conventionally measured CA125 and its glycoforms (CA125-MGL and CA125-STn) were determined. Results: The CA125-STn assay identified EOC significantly better than the conventional CA125-IA in postmenopausal patients (85% vs. 74% sensitivity at a fixed specificity of 90%, P¼0.0009). Further, both glycoform assays had superior AUCs compared to the conventional CA125-IA in postmenopausal patients with marginally elevated CA125. Importantly, the glycoform assays reduced the false positive rate of the conventional CA125-IA. Conclusions: The results indicate that the CA125 glycoform assays markedly improve the performance of the conventional CA125-IA in the differential diagnosis of pelvic masses. This result is especially valuable when CA125 is marginally elevated. [ABSTRACT FROM AUTHOR]
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- 2020
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27. Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths.
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Ruuth, Maija, Nguyen, Su Duy, Vihervaara, Terhi, Hilvo, Mika, Laajala, Teemu D, Kondadi, Pradeep Kumar, Gisterå, Anton, Lähteenmäki, Hanna, Kittilä, Tiia, and Huusko, Jenni
- Abstract
Aims Low-density lipoprotein (LDL) particles cause atherosclerotic cardiovascular disease (ASCVD) through their retention, modification, and accumulation within the arterial intima. High plasma concentrations of LDL drive this disease, but LDL quality may also contribute. Here, we focused on the intrinsic propensity of LDL to aggregate upon modification. We examined whether inter-individual differences in this quality are linked with LDL lipid composition and coronary artery disease (CAD) death, and basic mechanisms for plaque growth and destabilization. Methods and results We developed a novel, reproducible method to assess the susceptibility of LDL particles to aggregate during lipolysis induced ex vivo by human recombinant secretory sphingomyelinase. Among patients with an established CAD, we found that the presence of aggregation-prone LDL was predictive of future cardiovascular deaths, independently of conventional risk factors. Aggregation-prone LDL contained more sphingolipids and less phosphatidylcholines than did aggregation-resistant LDL. Three interventions in animal models to rationally alter LDL composition lowered its susceptibility to aggregate and slowed atherosclerosis. Similar compositional changes induced in humans by PCSK9 inhibition or healthy diet also lowered LDL aggregation susceptibility. Aggregated LDL in vitro activated macrophages and T cells, two key cell types involved in plaque progression and rupture. Conclusion Our results identify the susceptibility of LDL to aggregate as a novel measurable and modifiable factor in the progression of human ASCVD. View large Download slide View large Download slide [ABSTRACT FROM AUTHOR]
- Published
- 2018
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28. Novel Lignan and Stilbenoid Mixture Shows Anticarcinogenic Efficacy in Preclinical PC-3M-luc2 Prostate Cancer Model
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University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Yatkin, Emrah, Polari, Lauri, Laajala, Teemu D., Smeds, Annika, Eckerman, Christer, Holmbom, Bjarne, Saarinen, Niina M., Aittokallio, Tero, Makela, Sari I., University of Helsinki, Institute for Molecular Medicine Finland (FIMM), Yatkin, Emrah, Polari, Lauri, Laajala, Teemu D., Smeds, Annika, Eckerman, Christer, Holmbom, Bjarne, Saarinen, Niina M., Aittokallio, Tero, and Makela, Sari I.
- Published
- 2014
29. Orphan G protein-coupled receptor GPRC5A modulates integrin β 1-mediated epithelial cell adhesion.
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Bulanova, Daria R., Akimov, Yevhen A., Rokka, Anne, Laajala, Teemu D., Aittokallio, Tero, Kouvonen, Petri, Pellinen, Teijo, and Kuznetsov, Sergey G.
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- 2017
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30. Antiandrogens Reduce Intratumoral Androgen Concentrations and Induce Androgen Receptor Expression in Castration-Resistant Prostate Cancer Xenografts
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Knuuttila, Matias, Mehmood, Arfa, Huhtaniemi, Riikka, Yatkin, Emrah, Häkkinen, Merja R., Oksala, Riikka, Laajala, Teemu D., Ryberg, Henrik, Handelsman, David J., Aittokallio, Tero, Auriola, Seppo, Ohlsson, Claes, Laiho, Asta, Elo, Laura L., Sipilä, Petra, Mäkelä, Sari I., and Poutanen, Matti
- Abstract
The development of castration-resistant prostate cancer (CRPC) is associated with the activation of intratumoral androgen biosynthesis and an increase in androgen receptor (AR) expression. We recently demonstrated that, similarly to the clinical CRPC, orthotopically grown castration-resistant VCaP (CR-VCaP) xenografts express high levels of AR and retain intratumoral androgen concentrations similar to tumors grown in intact mice. Herein, we show that antiandrogen treatment (enzalutamide or ARN-509) significantly reduced (10-fold, P < 0.01) intratumoral testosterone and dihydrotestosterone concentrations in the CR-VCaP tumors, indicating that the reduction in intratumoral androgens is a novel mechanism by which antiandrogens mediate their effects in CRPC. Antiandrogen treatment also altered the expression of multiple enzymes potentially involved in steroid metabolism. Identical to clinical CRPC, the expression levels of the full-length AR (twofold, P < 0.05) and the AR splice variants 1 (threefold, P < 0.05) and 7 (threefold, P < 0.01) were further increased in the antiandrogen-treated tumors. Nonsignificant effects were observed in the expression of certain classic androgen-regulated genes, such as TMPRSS2and KLK3, despite the low levels of testosterone and dihydrotestosterone. However, other genes recently identified to be highly sensitive to androgen-regulated AR action, such as NOVand ST6GalNAc1, were markedly altered, which indicated reduced androgen action. Taken together, the data indicate that, besides blocking AR, antiandrogens modify androgen signaling in CR-VCaP xenografts at multiple levels.
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- 2018
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31. Orphan G protein-coupled receptor GPRC5A modulates integrin β1-mediated epithelial cell adhesion
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Bulanova, Daria R., Akimov, Yevhen A., Rokka, Anne, Laajala, Teemu D., Aittokallio, Tero, Kouvonen, Petri, Pellinen, Teijo, and Kuznetsov, Sergey G.
- Abstract
ABSTRACTG-Protein Coupled Receptor (GPCR), Class C, Group 5, Member A (GPRC5A) has been implicated in several malignancies. The underlying mechanisms, however, remain poorly understood. Using a panel of human cell lines, we demonstrate that CRISPR/Cas9-mediated knockout and RNAi-mediated depletion of GPRC5A impairs cell adhesion to integrin substrates: collagens I and IV, fibronectin, as well as to extracellular matrix proteins derived from the Engelbreth-Holm-Swarm (EHS) mouse sarcoma (Matrigel). Consistent with the phenotype, knock-out of GPRC5A correlated with a reduced integrin β1 (ITGB1) protein expression, impaired phosphorylation of the focal adhesion kinase (FAK), and lower activity of small GTPases RhoA and Rac1. Furthermore, we provide the first evidence for a direct interaction between GPRC5A and a receptor tyrosine kinase EphA2, an upstream regulator of FAK, although its contribution to the observed adhesion phenotype is unclear. Our findings reveal an unprecedented role for GPRC5A in regulation of the ITGB1-mediated cell adhesion and it's downstream signaling, thus indicating a potential novel role for GPRC5A in human epithelial cancers.
- Published
- 2017
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32. The Hydroxysteroid (17β) Dehydrogenase Family Gene HSD17B12 Is Involved in the Prostaglandin Synthesis Pathway, the Ovarian Function, and Regulation of Fertility
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Kemiläinen, Heidi, Adam, Marion, Mäki-Jouppila, Jenni, Damdimopoulou, Pauliina, Damdimopoulos, Anastasios E., Kere, Juha, Hovatta, Outi, Laajala, Teemu D., Aittokallio, Tero, Adamski, Jerzy, Ryberg, Henrik, Ohlsson, Claes, Strauss, Leena, and Poutanen, Matti
- Abstract
The hydroxysteroid (17beta) dehydrogenase (HSD17B)12 gene belongs to the hydroxysteroid (17β) dehydrogenase superfamily, and it has been implicated in the conversion of estrone to estradiol as well as in the synthesis of arachidonic acid (AA). AA is a precursor of prostaglandins, which are involved in the regulation of female reproduction, prompting us to study the role of HSD17B12 enzyme in the ovarian function. We found a broad expression of HSD17B12 enzyme in both human and mouse ovaries. The enzyme was localized in the theca interna, corpus luteum, granulosa cells, oocytes, and surface epithelium. Interestingly, haploinsufficiency of the HSD17B12 gene in female mice resulted in subfertility, indicating an important role for HSD17B12 enzyme in the ovarian function. In line with significantly increased length of the diestrous phase, the HSD17B+/−females gave birth less frequently than wild-type females, and the litter size of HSD17B12+/−females was significantly reduced. Interestingly, we observed meiotic spindle formation in immature follicles, suggesting defective meiotic arrest in HSD17B12+/−ovaries. The finding was further supported by transcriptome analysis showing differential expression of several genes related to the meiosis. In addition, polyovular follicles and oocytes trapped inside the corpus luteum were observed, indicating a failure in the oogenesis and ovulation, respectively. Intraovarian concentrations of steroid hormones were normal in HSD17B12+/−females, whereas the levels of AA and its metabolites (6-keto prostaglandin F1alpha, prostaglandin D2, prostaglandin E2, prostaglandin F2α, and thromboxane B2) were decreased. In conclusion, our study demonstrates that HSD17B12 enzyme plays an important role in female fertility through its role in AA metabolism.
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- 2016
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33. Novel Lignan and Stilbenoid Mixture Shows Anticarcinogenic Efficacy in Preclinical PC-3M-luc2 Prostate Cancer Model.
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Yatkin, Emrah, Polari, Lauri, Laajala, Teemu D., Smeds, Annika, Eckerman, Christer, Holmbom, Bjarne, Saarinen, Niina M., Aittokallio, Tero, and Mäkelä, Sari I.
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PROSTATE cancer treatment ,PROSTATE cancer risk factors ,PHENOLS ,XENOGRAFTS ,SCOTS pine ,LABORATORY mice - Abstract
Prostate cancer is the most common cancer of men in the Western world, and novel approaches for prostate cancer risk reduction are needed. Plant-derived phenolic compounds attenuate prostate cancer growth in preclinical models by several mechanisms, which is in line with epidemiological findings suggesting that consumption of plant-based diets is associated with low risk of prostate cancer. The objective of this study was to assess the effects of a novel lignan-stilbenoid mixture in PC-3M-luc2 human prostate cancer cells in vitro and in orthotopic xenografts. Lignan and stilbenoid –rich extract was obtained from Scots pine (Pinus sylvestris) knots. Pine knot extract as well as stilbenoids (methyl pinosylvin and pinosylvin), and lignans (matairesinol and nortrachelogenin) present in pine knot extract showed antiproliferative and proapoptotic efficacy at ≥40 μM concentration in vitro. Furthermore, pine knot extract derived stilbenoids enhanced tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induced apoptosis already at ≥10 μM concentrations. In orthotopic PC-3M-luc2 xenograft bearing immunocompromized mice, three-week peroral exposure to pine knot extract (52 mg of lignans and stilbenoids per kg of body weight) was well tolerated and showed anti-tumorigenic efficacy, demonstrated by multivariate analysis combining essential markers of tumor growth (i.e. tumor volume, vascularization, and cell proliferation). Methyl pinosylvin, pinosylvin, matairesinol, nortrachelogenin, as well as resveratrol, a metabolite of pinosylvin, were detected in serum at total concentration of 7−73 μM, confirming the bioavailability of pine knot extract derived lignans and stilbenoids. In summary, our data indicates that pine knot extract is a novel and cost-effective source of resveratrol, methyl pinosylvin and other bioactive lignans and stilbenoids. Pine knot extract shows anticarcinogenic efficacy in preclinical prostate cancer model, and our in vitro data suggests that compounds derived from the extract may have potential as novel chemosensitizers to TRAIL. These findings promote further research on health-related applications of wood biochemicals. [ABSTRACT FROM AUTHOR]
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- 2014
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34. Ovarian Endometriosis Signatures Established through Discovery and Directed Mass Spectrometry Analysis
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Vehmas, Anni P., Muth-Pawlak, Dorota, Huhtinen, Kaisa, Saloniemi-Heinonen, Taija, Jaakkola, Kimmo, Laajala, Teemu D., Kaprio, Heidi, Suvitie, Pia A., Aittokallio, Tero, Siitari, Harri, Perheentupa, Antti, Poutanen, Matti, and Corthals, Garry L.
- Abstract
New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silicostudies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.
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- 2014
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35. A practical comparison of methods for detecting transcriptionfactor binding sites in ChIP-seq experiments.
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Laajala, Teemu D., Raghav, Sunil, Tuomela, Soile, Lahesmaa, Riitta, Aittokallio, Tero, and Elo, Laura L.
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- *
CHROMATIN , *TRANSCRIPTION factors , *BINDING sites , *NUCLEOPROTEINS , *ALGORITHMS - Abstract
Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications. Results: The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments. Conclusions: In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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36. Androgen Receptor Signaling in Prostate Cancer Genomic Subtypes.
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Jillson, Lauren K., Yette, Gabriel A., Laajala, Teemu D., Tilley, Wayne D., Costello, James C., and Cramer, Scott D.
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GENETICS ,GENETIC mutation ,CELL receptors ,CELLULAR signal transduction ,CELL survival ,GENOMICS ,GENE expression profiling ,PROSTATE tumors ,DRUG resistance in cancer cells - Abstract
Simple Summary: Approximately 1 in 8 men will be diagnosed with prostate cancer (PCa) at some point in their lifetime. Five-year survival rate for patients with local or regionally spread disease is near 100%, but this drops to 30% when the cancer has spread to distant sites. Androgen receptor (AR) signaling plays a pivotal role in normal prostate development and PCa cell survival. Therapies targeting the AR pathway are mainline treatments, but resistance is a major clinical problem. From the literature and our own meta-analysis of PCa databases, we aimed to review the connections between PCa genetic alterations and the AR signaling axis at the primary stage. Assessing how combinations of PCa genetic drivers that arise in patients affect AR signaling will aid in stratifying patients who will likely respond to AR-directed therapies, and those who will require other therapeutic agents upfront in order to prevent disease progression. While many prostate cancer (PCa) cases remain indolent and treatable, others are aggressive and progress to the metastatic stage where there are limited curative therapies. Androgen receptor (AR) signaling remains an important pathway for proliferative and survival programs in PCa, making disruption of AR signaling a viable therapy option. However, most patients develop resistance to AR-targeted therapies or inherently never respond. The field has turned to PCa genomics to aid in stratifying high risk patients, and to better understand the mechanisms driving aggressive PCa and therapy resistance. While alterations to the AR gene itself occur at later stages, genomic changes at the primary stage can affect the AR axis and impact response to AR-directed therapies. Here, we review common genomic alterations in primary PCa and their influence on AR function and activity. Through a meta-analysis of multiple independent primary PCa databases, we also identified subtypes of significantly co-occurring alterations and examined their combinatorial effects on the AR axis. Further, we discussed the subsequent implications for response to AR-targeted therapies and other treatments. We identified multiple primary PCa genomic subtypes, and given their differing effects on AR activity, patient tumor genetics may be an important stratifying factor for AR therapy resistance. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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37. Erratum: Longitudinal modeling of ultrasensitive and traditional prostate-specific antigen and prediction of biochemical recurrence after radical prostatectomy.
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Laajala, Teemu D., Seikkula, Heikki, Seyednasrollah, Fatemeh, Mirtti, Tuomas, Boström, Peter J., and Elo, Laura L.
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- 2017
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38. Longitudinal modeling of ultrasensitive and traditional prostate-specific antigen and prediction of biochemical recurrence after radical prostatectomy.
- Author
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Laajala, Teemu D., Seikkula, Heikki, sadat, Fatemeh Seyednasrollah, Mirtti, Tuomas, Boström, Peter J., and Elo, Laura L.
- Abstract
Ultrasensitive prostate-specific antigen (u-PSA) remains controversial for follow-up after radical prostatectomy (RP). The aim of this study was to model PSA doubling times (PSADT) for predicting biochemical recurrence (BCR) and to capture possible discrepancies between u-PSA and traditional PSA (t-PSA) by utilizing advanced statistical modeling. 555 RP patients without neoadjuvant/adjuvant androgen deprivation from the Turku University Hospital were included in the study. BCR was defined as two consecutive PSA values >0.2 ng/mL and the PSA measurements were log2-transformed. One third of the data was reserved for independent validation. Models were first fitted to the post-surgery PSA measurements using cross-validation. Major trends were then captured using linear mixed-effect models and a predictive generalized linear model effectively identified early trends connected to BCR. The model generalized for BCR prediction to the validation set with ROC-AUC of 83.6% and 95.1% for the 1 and 3 year follow-up censoring, respectively. A web-based tool was developed to facilitate its use. Longitudinal trends of u-PSA did not display major discrepancies from those of t-PSA. The results support that u-PSA provides useful information for predicting BCR after RP. This can be beneficial to avoid unnecessary adjuvant treatments or to start them earlier for selected patients. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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39. Optimized design and analysis of preclinical intervention studies in vivo.
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Laajala, Teemu D., Jumppanen, Mikael, Huhtaniemi, Riikka, Fey, Vidal, Kaur, Amanpreet, Knuuttila, Matias, Aho, Eija, Oksala, Riikka, Westermarck, Jukka, Mäkelä, Sari, Poutanen, Matti, and Aittokallio, Tero
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- 2016
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40. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.
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Guinney, Justin, Wang, Tao, Laajala, Teemu D, Winner, Kimberly Kanigel, Bare, J Christopher, Neto, Elias Chaibub, Khan, Suleiman A, Peddinti, Gopal, Airola, Antti, Pahikkala, Tapio, Mirtti, Tuomas, Yu, Thomas, Bot, Brian M, Shen, Liji, Abdallah, Kald, Norman, Thea, Friend, Stephen, Stolovitzky, Gustavo, Soule, Howard, and Sweeney, Christopher J
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PROSTATE cancer prognosis , *PROSTATE cancer treatment , *DOCETAXEL , *CASTRATION , *CROWDSOURCING , *PLACEBOS , *CLINICAL trials , *ANTINEOPLASTIC agents , *COMPARATIVE studies , *HYDROCARBONS , *LONGITUDINAL method , *RESEARCH methodology , *MEDICAL cooperation , *META-analysis , *PREDNISONE , *PROBABILITY theory , *PROGNOSIS , *PROSTATE tumors , *RESEARCH , *RESEARCH funding , *SURVIVAL , *TUMOR classification , *EVALUATION research , *STATISTICAL models - Abstract
Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease.Methods: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone.Findings: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker.Interpretation: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Funding: Sanofi US Services, Project Data Sphere. [ABSTRACT FROM AUTHOR]- Published
- 2017
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41. NPEPPS Is a Druggable Driver of Platinum Resistance.
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Jones RT, Scholtes M, Goodspeed A, Akbarzadeh M, Mohapatra S, Feldman LE, Vekony H, Jean A, Tilton CB, Orman MV, Romal S, Deiter C, Kan TW, Xander N, Araki SP, Joshi M, Javaid M, Clambey ET, Layer R, Laajala TD, Parker SJ, Mahmoudi T, Zuiverloon TCM, Theodorescu D, and Costello JC
- Subjects
- Humans, Animals, Mice, Cell Line, Tumor, Aminopeptidases genetics, Aminopeptidases metabolism, Xenograft Model Antitumor Assays, Antineoplastic Agents pharmacology, Organoids drug effects, Organoids metabolism, Drug Resistance, Neoplasm, Cisplatin pharmacology, Urinary Bladder Neoplasms drug therapy, Urinary Bladder Neoplasms genetics, Urinary Bladder Neoplasms pathology, Urinary Bladder Neoplasms metabolism
- Abstract
There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations., Significance: Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities., (©2024 The Authors; Published by the American Association for Cancer Research.)
- Published
- 2024
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42. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.
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Mason M, Lapuente-Santana Ó, Halkola AS, Wang W, Mall R, Xiao X, Kaufman J, Fu J, Pfeil J, Banerjee J, Chung V, Chang H, Chasalow SD, Lin HY, Chai R, Yu T, Finotello F, Mirtti T, Mäyränpää MI, Bao J, Verschuren EW, Ahmed EI, Ceccarelli M, Miller LD, Monaco G, Hendrickx WRL, Sherif S, Yang L, Tang M, Gu SS, Zhang W, Zhang Y, Zeng Z, Das Sahu A, Liu Y, Yang W, Bedognetti D, Tang J, Eduati F, Laajala TD, Geese WJ, Guinney J, Szustakowski JD, Vincent BG, and Carbone DP
- Subjects
- Humans, Immune Checkpoint Inhibitors therapeutic use, B7-H1 Antigen, Biomarkers, Tumor, Carcinoma, Non-Small-Cell Lung drug therapy, Carcinoma, Non-Small-Cell Lung genetics, Lung Neoplasms pathology
- Abstract
Background: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC., Methods: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials., Results: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1., Conclusions: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy., Trial Registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015., (© 2024. The Author(s).)
- Published
- 2024
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43. Microbiome-based risk prediction in incident heart failure: a community challenge.
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Erawijantari PP, Kartal E, Liñares-Blanco J, Laajala TD, Feldman LE, Carmona-Saez P, Shigdel R, Claesson MJ, Bertelsen RJ, Gomez-Cabrero D, Minot S, Albrecht J, Chung V, Inouye M, Jousilahti P, Schultz JH, Friederich HC, Knight R, Salomaa V, Niiranen T, Havulinna AS, Saez-Rodriguez J, Levinson RT, and Lahti L
- Abstract
Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF., Competing Interests: Conflict of Interest Illumina, Inc., and Janssen Pharmaceutica provided additional support by sponsoring the Center for Microbiome Innovation at the University of California San Diego. T.N. has received honoraria for speaking engagements from Servier and AstraZeneca. V.S. has had research collaboration with Bayer AG, unrelated to this study. J.S.-R. has received funding from GSK, Pfizer and Sanofi, and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer and Grunenthal. M.I. is a trustee of the Public Health Genomics (PHG) Foundation, a member of the Scientific Advisory Board of Open Targets, and has a research collaboration with AstraZeneca unrelated to this study. R.K. is a cofounder of Micronoma and Biota, holding stock for Gencirq, Cybele, Biomesense, Micronoma, and Biota, serve as a member of the Scientific Advisory Board in Gencirq, DayTwo, Biomesense, and Micronoma and serve as consultant for DayTwo, Cybele, and Biomesense.
- Published
- 2023
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44. curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.
- Author
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Laajala TD, Sreekanth V, Soupir A, Creed J, Calboli FC, Singaravelu K, Orman M, Colin-Leitzinger C, Gerke T, Fidley BL, Tyekucheva S, and Costello JC
- Abstract
Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility., Competing Interests: COMPETING INTERESTS The authors declare the following competing interests: J.C.C. is co-founder of PrecisionProfile and OncoRX Insights. All other authors declare no competing interests.
- Published
- 2023
- Full Text
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45. Ovarian Cancers with Low CIP2A Tumor Expression Constitute an APR-246-Sensitive Disease Subtype.
- Author
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Cvrljevic AN, Butt U, Huhtinen K, Grönroos TJ, Böckelman C, Lassus H, Butzow R, Haglund C, Kaipio K, Arsiola T, Laajala TD, Connolly DC, Ristimäki A, Carpen O, Pouwels J, and Westermarck J
- Subjects
- Animals, Autoantigens genetics, Carcinoma, Ovarian Epithelial, Cell Line, Tumor, Female, Humans, Mice, Quinuclidines, NF-kappa B, Ovarian Neoplasms drug therapy, Ovarian Neoplasms genetics, Ovarian Neoplasms metabolism
- Abstract
Identification of ovarian cancer patient subpopulations with increased sensitivity to targeted therapies could offer significant clinical benefit. We report that 22% of the high-grade ovarian cancer tumors at diagnosis express CIP2A oncoprotein at low levels. Furthermore, regardless of their significantly lower likelihood of disease relapse after standard chemotherapy, a portion of relapsed tumors retain their CIP2A-deficient phenotype. Through a screen for therapeutics that would preferentially kill CIP2A-deficient ovarian cancer cells, we identified reactive oxygen species inducer APR-246, tested previously in ovarian cancer clinical trials. Consistent with CIP2A-deficient ovarian cancer subtype in humans, CIP2A is dispensable for development of MISIIR-Tag-driven mouse ovarian cancer tumors. Nevertheless, CIP2A-null ovarian cancer tumor cells from MISIIR-Tag mice displayed APR-246 hypersensitivity both in vitro and in vivo. Mechanistically, the lack of CIP2A expression hypersensitizes the ovarian cancer cells to APR-246 by inhibition of NF-κB activity. Accordingly, combination of APR-246 and NF-κB inhibitor compounds strongly synergized in killing of CIP2A-positive ovarian cancer cells. Collectively, the results warrant consideration of clinical testing of APR-246 for CIP2A-deficient ovarian cancer tumor subtype patients. Results also reveal CIP2A as a candidate APR-246 combination therapy target for ovarian cancer., (©2022 American Association for Cancer Research.)
- Published
- 2022
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46. Community mining of open clinical trial data.
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Laajala TD, Guinney J, and Costello JC
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- 2017
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47. Improved statistical modeling of tumor growth and treatment effect in preclinical animal studies with highly heterogeneous responses in vivo.
- Author
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Laajala TD, Corander J, Saarinen NM, Mäkelä K, Savolainen S, Suominen MI, Alhoniemi E, Mäkelä S, Poutanen M, and Aittokallio T
- Subjects
- Algorithms, Animals, Antineoplastic Agents administration & dosage, Antineoplastic Agents pharmacology, Cell Line, Tumor, Computer Simulation, Disease Models, Animal, Humans, MCF-7 Cells, Neoplasms drug therapy, Neoplasms pathology, Tumor Burden drug effects, Xenograft Model Antitumor Assays, Models, Statistical
- Abstract
Purpose: Preclinical tumor growth experiments often result in heterogeneous datasets that include growing, regressing, or stable growth profiles in the treatment and control groups. Such confounding intertumor variability may mask the true treatment effects especially when less aggressive treatment alternatives are being evaluated., Experimental Design: We developed a statistical modeling approach in which the growing and poorly growing tumor categories were automatically detected by means of an expectation-maximization algorithm coupled within a mixed-effects modeling framework. The framework is implemented and distributed as an R package, which enables model estimation and statistical inference, as well as statistical power and precision analyses., Results: When applied to four tumor growth experiments, the modeling framework was shown to (i) improve the detection of subtle treatment effects in the presence of high within-group tumor variability; (ii) reveal hidden tumor subgroups associated with established or novel biomarkers, such as ERβ expression in a MCF-7 breast cancer model, which remained undetected with standard statistical analysis; (iii) provide guidance on the selection of sufficient sample sizes and most informative treatment periods; and (iv) offer flexibility to various cancer models, experimental designs, and treatment options. Model-based testing of treatment effect on the tumor growth rate (or slope) was shown as particularly informative in the preclinical assessment of treatment alternatives based on dietary interventions., Conclusions: In general, the modeling framework enables identification of such biologically significant differences in tumor growth profiles that would have gone undetected or had required considerably higher number of animals when using traditional statistical methods.
- Published
- 2012
- Full Text
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