Our goal is to understand mechanisms of drug sensitivity and resistance in individual cancers and through this knowledge identify new effective cancer precision medicine strategies.
Our goal is to understand mechanisms of drug sensitivity and resistance in individual cancers and through this knowledge identify new effective cancer precision medicine strategies. Our research focus is on acute myeloid leukemia (AML) and high-grade serous ovarian carcinoma. In both these diseases, there are existing therapies that are generally effective at treating the diseases at diagnosis. However, relapses occur in a large number of cases and at that point, current therapies are typically ineffective. There is therefore a great need to identify new therapeutic strategies that overcome the mechanisms of drug resistance in these cancers. Using primary cancer cell models, we perform drug response profiling and genetic screens to build functional understanding of each tested cancer and thereby identify potential new treatments that can overcome drug resistance. This information is further related to the molecular characteristics of the individual cancers with the goal of gaining further understanding of the molecular mechanisms behind the drug vulnerabilities and for identifying molecular biomarkers that are predictive of drug response.
Establishment of cancer cell ex vivo drug response profiling methods to identify personalized cancer therapies.
- Developed drug response profiling platforms for real-time discoveries of personalized therapies
- Established analytical methods for optimal analyses of high throughput drug response profiling
- Primary culturing methods allow for identifying and repurposing drugs that can eradicate individual and subgroups of cancers
- Repurposing of drugs for new cancer types and for new molecular targeting
Combining in vitro drug screens with drug target information and molecular profiles to identify molecular vulnerabilities and effective drug combinations.
- Collaborative establishment of the largest available curated drug-molecular target interaction resource (Drug target commons)
- Combining drug response profiling, drug target information and molecular profiling has allowed us computationally predict and validate new drug combination strategies for individual cancers and cancer subtypes and to build
Targeting RAF kinases in acute myeloid leukemia.
- RAF kinase inhibition is selectively inducing apoptosis in a subset of AMLs. This is different than inhibition of the downstream kinases MEK and ERK, which only cause temporary cytostasis.
- RAF inhibition leads to loss of translation of the anti-apoptotic protein MCL-1, thereby inducing apoptosis.
- RAF inhibition can overcome BCL-2 inhibitor resistance in AML
Discoveries of selective drug combinations overcoming drug resistance development in mature NK cell cancers.
- Mature NK cell cancers are rare cancers but very aggressive cancers with a poor prognosis
- These cancers are driven by JAK/STAT signaling, but blocking this signal only slows down the cancer cells and is insufficient to eradicate them
- Through series of short- to long-term drug response profiling assays, several clinically relevant JAK inhibitor containing combinations with capacity to completely eradicate mature NK cell cancer models were identified, including combinations targeting TP53-mutant disease.
Discovery of personalized AML stem cell targeting therapies:
Using high throughput flow cytometry, we are aiming at understanding differentiation hierarchies in individual AML patient samples and how drugs can selectively target different leukemic subpopulations, including the leukemic stem cells.
Overcoming drug resistance to BCL-2 inhibition in AML:
Studying samples from AML patients that are responding or relapsed/refractory to the BCL-2 inhibitor venetoclax treatment with chemical screening and molecular profiling, we are exploring venetoclax resistance-associated vulnerabilities and new therapeutic strategies to overcome the resistance.
Reactivation of the immune system against AML cells:
Exploring mechanisms by which the endogenous immune system can be reactivated to target and eradicate the AML cells.
Overcoming drug resistance to chemotherapy in high-grade ovarian cancer:
Using primary ovarian cancer cell organoid cultures, we use chemical and genetic screens to trace and understand mechanisms of drug resistance as well as identifying therapeutic strategies to overcome it.
In the drug response profiling of primary cancer cells, we use laboratory automation systems in the BRIC High Throughput Cell-based Screens/High Content CRISPR Screening core facility, including high content microscopy and arrayed CRISPR screening, and the automated systems for high throughput flow cytometry in the High Throughput Translational Hematology lab under the Program for Translational Hematology.
Parri E, Kuusanmäki H, Bulanova D, Mustjoki S, Wennerberg K. Selective drug combination vulnerabilities in STAT3- and TP53-mutant malignant NK cells. Blood Adv. 2021 Apr 13;5(7):1862-1875. doi: 10.1182/bloodadvances.2020003300. PMID: 33792631; PMCID: PMC8045497.
Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P, Vähä- Koskela M, Turunen L, Linnavirta N, Kuusanmäki H, Kontro M, Porkka K, Heckman CA, Mattila P, Wennerberg K, Giri AK, Aittokallio T. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Sci Adv. 2021 Feb 19;7(8):eabe4038. doi: 10.1126/sciadv.abe4038. PMID: 33608276; PMCID: PMC7895436.
Tambe M, Karjalainen E, Vähä-Koskela M, Bulanova D, Gjertsen BT, Kontro M, Porkka K, Heckman CA, Wennerberg K. Pan-RAF inhibition induces apoptosis in acute myeloid leukemia cells and synergizes with BCL2 inhibition. Leukemia. 2020 Dec;34(12):3186-3196. doi: 10.1038/s41375-020-0972-0. Epub 2020 Jul 10. PMID: 32651543.
Akimov Y, Bulanova D, Timonen S, Wennerberg K, Aittokallio T. Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics. Mol Syst Biol. 2020 Mar;16(3):e9195. doi: 10.15252/msb.20199195. PMID: 32187448; PMCID: PMC7080434.
Gautam P, Jaiswal A, Aittokallio T, Al-Ali H, Wennerberg K. Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets. Cell Chem Biol. 2019 Jul 18;26(7):970-979.e4. doi: 10.1016/j.chembiol.2019.03.011. Epub 2019 May 2. PMID: 31056464; PMCID: PMC6642004.
Pemovska T, Johnson E, Kontro M, Repasky GA, Chen J, Wells P, Cronin CN, McTigue M, Kallioniemi O, Porkka K, Murray BW, Wennerberg K. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature. 2015 Mar 5;519(7541):102-5. doi: 10.1038/nature14119. Epub 2015 Feb 9. PMID: 25686603.