RNA Multi-Omics to Reveal New Targets in Mounting DLBCL and B-ALL Drug Resistance

Drug resistance remains a major challenge in treating Diffuse Large B-cell Lymphoma (DLBCL) and B-cell Acute Lymphoblastic Leukemia (B-ALL). These highly prevalent blood cancers have distinct subtypes, which often associate with different chemotherapeutic prognoses. In DLBCL, the main identified subtypes are Activated B Cell-like (ABC; usually better prognosis) and Germinal Centre B cell-like (GCB; usually worse prognosis). In B-ALL, subtypes are often associated with specific gene fusions, such as ETV6-RUNX1 (usually better prognosis) and KMT2A-MLLT1 (usually worse prognosis).

While many of the terminal effectors of drug resistance are well-studied and their mechanism of action is often established, fully evolved resistant cells are difficult to target with alternative treatment strategies. Thus, it is important to understand mechanisms of the onset of resistance and subtype differences that contribute to the resistance development, and outline pathways and signatures of early identification of the resistance onset, as well as targets for suppressing the specific route towards drug resistance.

Here, we reasoned that the dynamic gene expression regulation offered by RNA transcription and splicing, translation and turnover, can contribute to the early drug response, initial survival of the cancer cells and the subsequent clonal selection of survived cells resulting in the resistance onset. To address this, we have controllably evolved prototypical DLBCL cell lines representing ABC and GCB types (SU-DHL-5 and SU-DHL-8, respectively) and B-ALL cell types representing ETV6-RUNX1 and KMT2A-MLLT1 fusions (REH and KOPN8, respectively) to vincristine resistance using progressive treatment rounds in a format mimicking standard chemotherapeutic regimens such as CHOP.

We then employed a multi-omic RNA framework to comprehensively interrogate the underlying RNA-level features and control mechanisms, and compared the resistant and naive cells. Based on the direct RNA sequencing (DRS)-derived long reads we established accurate transcriptomes of the naive and resistant cells, quantified their isoforms and isoform switching, and then used our in-house developed software INDEGRA¹ to assess differential stability and turnover rates of the messenger(m)RNA. We further employed our machine-learning-based RNA modification basecallers CHEUI/SWARM^2,3 and revealed the subtype-specific patterning of the main RNA modifications N(6)-methyladenosine (m6A), 5-methylcytosine (m5C), N4-acetylcytosine (ac4C), and Pseudouridine (pU) in the drug resistance onset.

Importantly, applying our enhanced translation complex profiling method eTCP-seq^4,5 that uniquely highlights the positions of scanning small ribosomal subunits (SSUs) and identifies putative stalling sites by revealing stable disome (DS) locations over mRNA, we reveal mechanistic details of the alteration of translational control in the resistant cells. Using the high temporal resolution of eTCP-seq, we for the first time reveal rapid (30 minutes) translation-level adaptation to the vincrisitine exposure across ABC and GCB DLBCL, and identify conserved as well as divergent mechanisms of the response.

Overall, our work provides the first deep data combining accurate and quantitative isoform-resolved transcriptomics, RNA modification quantification, and assessment of RNA stability and translational control at the same time during the onset of chemotherapeutic drug resistance, and in the initial adaptive response of the cancer cells. These results reveal many new subtype-specific pathways associated with the drug resistance potential and will aid in the development of complementary therapeutic approaches to suppress drug resistance onset in blood cancers.

1. Cleynen, A. et al. INtegrity and DEGradation of RNA Analysis. https://github.com/Arnaroo/INDEGRA.

2. Acera Mateos, P. et al. Prediction of m6A and m5C at single-molecule resolution ... Nat Commun15, 1-17 (2024).

3. Cleynen, A. et al. Single-molecule Workflow for Analysing RNA Modifications. https://github.com/comprna/SWARM.

4. Horvath, A. et al. Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress. Nucleic Acids Research gkae365 (2024)

5. Janapala, Y. et al. Rapid In Vivo Fixation and Isolation of Translational Complexes from Eukaryotic Cells. JoVE e62639 (2021)

Talaulikar:Antengene: Honoraria; Roche: Research Funding; Janssen: Research Funding; Beigene: Speakers Bureau; Immutep: Current equity holder in publicly-traded company.