ISMB 2026 – iRNA COSI Meeting

Alternative RNA splicing exponentially expands the functional proteome, yet its systematic dysregulation across cancer and aging remains incompletely understood. Our work integrates multi-modal genomic approaches to decode how splicing programs are remodeled during aging, malignant transformation, and tumor immune evasion. Using long-read RNA-sequencing of human breast and lung tumors, we have mapped the landscape of full-length cancer isoforms, uncovering thousands of novel transcripts that are often missed by short-read approaches. Building on this isoform atlas, we reveal that aging drives broad splicing remodeling in mammary epithelial cells, and that select tumor-associated isoform signatures are in fact age-driven, positioning splicing alterations as early preneoplastic events. We further define the regulatory architecture controlling these splicing programs, demonstrating that post-transcriptional autoregulatory mechanisms within splicing factors are selectively disrupted in tumors, representing novel intervention points. Finally, we show that specific splicing events have direct clinical relevance: intron retention in immune-regulatory genes predicts patient response to checkpoint inhibitor immunotherapy, and splice-switching oligonucleotides that correct tumor-associated isoforms exert selective anti-proliferative effects across cancer models. Together, this work defines splicing dysregulation as a molecular hallmark of aging and cancer, and identifies novel isoforms and splicing regulators as actionable biomarkers and therapeutic targets.

The overarching vision of our research program is to construct predictive models that explain how cells determine their protein abundance. Achieving this goal involves two major components: (1) higher-resolution and higher-precision measurements of gene expression modalities, and (2) computational and theoretical advancements capable of integrating these quantitative measurements into cohesive, predictive frameworks. Towards these goals, we have compiled measurements of translation from more than 3,500 experiments and introduced the concept of translation efficiency covariation (TEC), revealing that transcripts associated with shared biological functions and those that are members of the same protein complexes exhibit TEC. We then leveraged our expansive compendium of translation efficiency measurements to develop a deep neural network model, called RiboNN, capable of predicting mRNA translation rates across numerous cell types based solely on the full-length mRNA sequence. RiboNN can evaluate the impact of genetic variants in the human population, providing insight into diseases driven by abnormal mRNA translation. This approach has implications for bioengineering applications, genetic diagnostics as well as the design and optimization of mRNA therapies.

In vitro evolution has greatly expanded the functional repertoire of RNA and highlights opportunities to explore beyond natural natural nucleic acid chemistry. Here, I will present our efforts to extend RNA biology using non-natural nucleic acids (XNAs). We focused on an XNA-SELEX platform that enables in vitro evolution of XNAs with programmable binding and catalytic activities. This approach has yielded XNA molecules capable of precise RNA targeting and protein modulation in complex biological contexts. Our work further demonstrates how alternative backbone chemistries can access functional properties not readily available to canonical RNA. Together, these results establish XNAs as a versatile molecular toolkit for probing and modulating RNA and protein function, and provide a general framework for extending nucleic acid structures and functions beyond natural systems.

Abstract to be announced.