Personalized medicine research offers fast, cost-effective method to match cancer drugs to patients

In a major leap toward more effective cancer treatments, researchers at the University of Oulu have developed an innovative pipeline designed to directly analyze drug responses in primary patient tumor samples.

This method addresses a crucial gap in cancer treatment—the difficulty in identifying the right drug, or combination of drugs, for each patient. The new discovery represents a huge step towards personalized medicine, as it enables the analysis of how individual cancers behave in response to different therapies.

The pipeline, which utilizes live-cell barcoding, enables the simultaneous screening of 96 drug treatments at single-cell resolution.

The study was focused on high-grade serous ovarian cancer (HGSOC), and the pipeline revealed the complex transcriptional landscape of tumors treated with 45 different drugs, representing 13 distinct classes of mechanism of action.

By integrating advanced single-cell RNA-sequencing, researchers can now map the gene regulatory dynamics driving cancer drug resistance and sensitivity in real time, directly from a patient's tumor. The work is published in the journal Nature Chemical Biology.

Experimental approaches using traditional cell line models often oversimplify the biology of real tumors, making it challenging to predict how a patient will respond to therapy. Working with primary patient samples not only improves the accuracy of these predictions but also opens the door to building a large-scale, data-driven "omics" database of drug responses.

"Our ability to directly study drug responses at single-cell levels in primary tumor samples from patients in a multiplexed way represents a huge step toward personalized medicine," said Daniela Ungureanu, Associate Professor at the University of Oulu.

"This approach enables us to explore how individual cancers behave in response to different therapies, which could help overcome the unpredictability of using cell lines or animal models. With this new data, we can create a powerful resource to guide treatment decisions in the clinic."

The establishment of a comprehensive drug response database using primary samples will significantly enhance the ability to match patients with the therapies most likely to be effective, ultimately improving outcomes in personalized cancer treatment.

By leveraging data from a wide array of patient tumors, clinicians can pinpoint specific gene regulatory responses that drive resistance or sensitivity to treatments, creating a more tailored, data-driven approach to care.

"Our findings offer a promising framework for enhancing drug repurposing and improving patient selection strategies, especially for those battling cancers with poor prognosis and limited treatment options," said Alice Dini, University of Oulu, the first author of the study.

"By harnessing advanced sequencing technologies, we can pave the way for more effective and personalized treatment approaches in HGSOC."