P10.26.A UTILIZING HIGH-THROUGHPUT SCREENING PLATFORMS FOR PROFILING DRUG SENSITIVITY ACROSS MULTIPLE CANCER CELL LINES TO UNLOCK NEW TARGETED THERAPIES IN GLIOBLASTOMA
Authors: Morelli M , Carli F , Lessi F , Bisceglia L , Franceschi S , Aretini P , Pasqualetti F , Gambacciani C , Pieri F , Grimod G , Santonocito O , Di Stefano A , Raimondi F , Mazzanti C 
Published: October 2024

BACKGROUND

Glioblastoma (GBM) presents a significant challenge in oncology, characterized by its aggressive nature and bleak prognosis. Despite advancements in medical science, such as the introduction of Temozolomide as the standard treatment since 2005, current therapies’ effectiveness remains limited. The critical need to discover novel drugs for glioblastoma is underscored by the urgency to enhance patient outcomes and prolong survival rates. Recognizing this imperative, our study aims to employ cutting-edge methodologies to explore new avenues in glioblastoma therapeutics.

MATERIAL AND METHODS

Our investigation relies on extensive datasets from high-throughput screening platforms, evaluating drug sensitivity across a wide array of cancer cell lines. Central to our objective is the utilization of predictive models on drug sensitivity in cancer cells, constructed with machine learning algorithms trained on cell line transcriptomics data. The model forecasted the growth inhibitory potential of a diverse range of drugs on 64 glioblastoma patient samples analyzed via whole transcriptome analysis.

RESULTS

We obtained sensitivity scores for 280 antineoplastic drugs. For each drug, the model highlights the main genes involved in the targeted pathway. The 64 samples were clustered based on their drug sensitivity profiles, which also correlated well with the histological type of primary or recurrent tumors. To validate the predictions obtained from the model, we identified representative samples with specific drug sensitivity predictions. We selected one drug with varying efficacy on two different samples, as well as two drugs with differing efficacy on the same sample. Predictions were experimentally validated using matched patient-derived primary tumor cell lines. This experimental validation not only bolsters the reliability and robustness of the prediction model but also underscores its potential clinical translatability.

CONCLUSION

The model we employed represents a paradigm shift in glioblastoma research, offering a potent tool for future exploitation and exploration. Based on these results and a deeper study of the most common pathway targeted by the most efficient drugs, we can compile a list of potential new drugs that have been overlooked thus far. These can be tested in vitro on patient-derived glioblastoma primary cells and translated to the clinic. Leveraging these high-throughput platforms, along with the ability to facilitate inference using larger drug sets on diverse patient cohorts, our results hold promise for transformative advancements in glioblastoma treatment. By addressing the longstanding clinical need for effective therapies in this challenging disease, our study paves the way for advancements in glioblastoma therapeutics, offering renewed hope to patients and clinicians alike.