September 26^th at 12:00
Onsite event
in Arje Scheinin auditorium, Dentalia
Autumn 2024 program

Prof. James C. Costello, University of Colorado, USA
A novel mechanism of chemotherapy resistance by the regulation of volume regulated anion channels
Host: Teemu Daniel Laajala (teelaa@utu.fi)

Coffee and sandwich at 11:45, first come first served!

Six PhD researchers and early-career postdocs are welcome to have a lunch and discuss with Prof. Costello after the seminar. This is a great possibility to learn hosting skills in friendly environment and create connections for future. Everyone is welcome to join, BioCity Turku will offer the lunch.

If you got interested, please send an email to biocityturku@bioscience.fi

Today, we have the ability to take a snapshot of the transcriptional activity of genes, identify mutations, and quantify the protein and metabolite content of cells.  All of these measurements can be made at a systems-wide level. These data present the potential to greatly improve our ability to characterize and treat disease; however, the rate of data production is far outpacing our ability to analyze, interpret, and ultimately build predictive tools in medicine.  Costello lab takes a complementary dry and wet lab approach to close the gap between raw data and biological understanding. Their dry lab research focuses on developing and implementing computational tools that distill this large pool of genome-scale data into actionable hypothesis. Costello wet lab research brings the computational modeling to the bench where the aim is to characterize the genomic components that contribute to drug mode of action in cancer biology.

Selected publications

Hughes CJ, Fields KM, Danis EP, Hsu JY, Neelakantan D, Vincent MY, Gustafson AL, Oliphant MJ, Sreekanth V, Zaberezhnyy V, Costello JC, Jedlicka P, Ford HL. 2023. SIX1 and EWS/FLI1 co-regulate an anti-metastatic gene network in Ewing Sarcoma. Nat Commun. 2023 14(1):4357. doi: 10.1038/s41467-023-39945-w.

de Jong FC, Laajala TD, Hoedemaeker RF, Jordan KR, van der Made ACJ, Boevé ER, van der Schoot DKE, Nieuwkamer B, Janssen EAM, Mahmoudi T, Boormans JL, Theodorescu D, Costello JC, Zuiverloon TCM. 2023. Non-muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical Bacillus Calmette-Guérin. Sci Transl Med. 15(697):eabn4118. doi: 10.1126/scitranslmed.abn4118.

Sun D, Nguyen TM, Allaway RJ, Wang J, Chung V, Yu TV, Mason M, Dimitrovsky I, Ericson L, Li H, Guan Y, Israel A, Olar A, Pataki BA, Stolovitzky G, Guinney J, Gulko PS, Frazier MB, Chen JY, Costello JC, Bridges SL Jr; RA2-DREAM Challenge Community. 2022. A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis. JAMA Netw Open. 25(8):e2227423. doi: 10.1001/jamanetworkopen.2022.27423.

Tarca AL, Pataki BÁ, Romero R, Sirota M, Guan Y, Kutum R, Gomez-Lopez N, Done B, Bhatti G, Yu T, Andreoletti G, Chaiworapongsa T; DREAM Preterm Birth Prediction Challenge Consortium; Hassan SS, Hsu CD, Aghaeepour N, Stolovitzky G, Csabai I, Costello JC. 2021. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep Med. 2(6):100323. doi: 10.1016/j.xcrm.2021.100323.

Greene CS, Costello JC. 2020. Biologically Informed Neural Networks Predict Drug Responses. Cancer Cell. 38(5):613-615. doi: 10.1016/j.ccell.2020.10.014.

General information

• You can download and save all the autumn 2024 FoS-seminars to your calendar from here: https://seafile.utu.fi/d/44a70f80ac1e46a9ad0a/
• Registration is not needed, participation list is circulated in the audience
• If you are a student and later wish to get a certificate of attendance from the Frontier of Science seminars, print out the seminar diary and after the seminar ask the BioCity coordinator to sign it https://seafile.utu.fi/d/44a70f80ac1e46a9ad0a/
• Please note that any audio or video recording of the seminars is strictly forbidden.
• Autumn 2024 FoS image credits to Mia Åstrand: Details of the Bacteroides fragilis VgrG protein (PDB ID: 8GRA). Image created in the PyMOL Molecular Graphics System, Version 2.5 Schrödinger, LLC.