PO_I_P: Pathology and Oncology I. Posters
Kovács Szonja Anna1, Balajti Máté2, Prof. Dr. Győrffy Balázs1
1 Semmelweis Egyetem Általános Orvostudományi Kar Bioinformatika Tanszék, Budapest
2Eötvös Loránd Kutatási Hálózat Természettudományi Kutatóközpont Enzimológiai Intézet, Budapest
Introduction: Utilization of immunotherapy has resulted in a paradigm shift in the field of oncology in the last few years. Yet, there are some factors which prevents its further spread (e.g. unpleasant side effects, high cost and low response rate). Biomarkers used in clinical practice are not strong or good enough to predict patient response. Thus, investigation of biomarkers is required to maximize the clinical benefit of cancer immunotherapy.
Aims: Our goal was to find predictive biomarkers and develop an online web platform. The online analysis tool can help clinicians to decide which immunotherapeutic agent may be benefitial in a certain tumor type based on patients’ transcriptome.
Methods: We searched for publicly available transcriptomic and clinical response data gained from patients treated with anti-PD-1, anti-PD-L1 or anti-CTLA-4 immune checkpoint inhibitors. 154 datasets (containing 3006 samples) were used for further screening.
Result: 17 datasets (1850 samples from 1366 patients) were used for database building and statistical analysis conducted in R. We plan to validate our results using cell cultures and human tissues (fresh frozen, FFPE).
Conclusion: We screened >3000 clinical sample data. Those samples which passed the eligibility criteria were used for statistical analysis. We used transcriptomic data to discover differentially expressed genes in response to immunotherapy. Hopefully, our findings may contribute to a more specialized, more precise immunotherapy application for patients suffering from cancer.
Funding: 2020-4.1.1.-TKP2020, 2018-2.1.17-TET-KR-00001, KDP-2020
Semmelweis University, Doctoral School of Pathological Sciences