New method to uncover how cancer cells evolve and become drug-resistant
- Rafal Tekreeti
- 2 days ago
- 3 min read
By: Alexandra Pančíková, Ruben Cools, Marios Eftychiou, Margo Aertgeerts, Joris Vande Velde, Heidi Segers, Jan Cools, Luuk Harbers, Jonas Demeulemeester
Throughout recent decades, cancer therapies have become significantly more advanced. Indeed, our increasing understanding of cancer biology, coupled with rapid technological advancements, has led to more targeted and effective treatments. Despite this progress, patients still face many challenges during treatment, namely metastasis, relapse, and drug resistance.
These challenges have been linked to the evolution of tumours, a process through which genetic mutations confer growth advantages to different subpopulations of cancer cells. As a tumor grows and is subjected to different pressures (including cancer treatment), these subpopulations expand and cells accumulate new mutations.
As a result of this process, cancer cells may become resistant to certain drugs, which often leads to treatment failure. In this context, studying how tumours change throughout therapy and how they become resistant to drugs is incredibly important for the development of more effective, targeted treatments.
Current methods to study the mechanisms behind tumor evolution at high resolution have significant limitations, such as costly instruments, custom reagents, and low throughput. To address these challenges, our lab at the VIB-KU Leuven Center for Cancer Biology developed a new method called SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome).
By building on two widely accessible technologies – the 10X Genomics Multiome assay and Oxford Nanopore long read sequencing – SPLONGGET is uniquely capable of producing whole genome, open chromatin and single cell full-length transcriptome data (Figure 1). This method is very accessible and flexible, as it only requires common instruments and kits, and is backwards compatible with standard workflows.
To develop SPLONGGET, we first demonstrated that the resulting data was of high quality, and comparable to results from the original 10X Genomics Multiome assay. Afterwards, we were able to use SPLONGGET to study how tumours evolve to evade targeted treatment in a case of paediatric B-cell acute lymphoblastic leukaemia (during diagnosis and relapse).
“By leveraging the compute resources provided by the Flemish Supercomputer Center (VSC), we were able to perform our work much more efficiently.”
Acute lymphoblastic leukaemia is the most common childhood cancer, with B-cell acute lymphoblastic leukaemia making up the majority of cases. In this particular case study, the patient was treated with CAR-T cell therapy. In this therapy, the patient’s T cells are engineered to recognize and destroy cancer cells carrying a transmembrane protein called CD19. Throughout treatment, this patient had developed resistance to this particular therapy, and we wanted to understand why.
To do this, we first confirmed that CAR-T cell constructs, i.e. the synthetic receptors engineered into the patient's T cells to target cancer cells, were widely present in the T-cells at relapse. This meant that CAR-T cells should be able to identify tumour cells. Yet, we also noticed a decreased expression and several mutations affecting the CD19 gene in the tumour cells.
We saw that these mutations led to mRNA transcripts with early stop signals, hindering their translation into functional CD19 proteins. Without CD19 proteins on the surface of the tumor cells, the CAR-T cells could no longer recognise them, and tumor cells were therefore able to escape therapy. Together, these different discoveries showed us the mechanism behind the patient’s resistance to CAR-T cell therapy.
Developing data processing workflows and in-depth computational analysis were crucial parts of this project. Some of the steps require specialized compute resources such as GPUs, or large memory, which are not readily available on normal computers. Additionally, as this was a collaborative effort, working on the same compute cluster with a shared environment allowed us to collaborate more efficiently. By leveraging the computing resources provided by the Flemish Supercomputer Center (VSC), we were able to perform our work much more efficiently.
Read the full publication in bioRxiv here
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