Background: Limitations of Single Modality Analysis and the Bottleneck of Linking Splicing-Driver Mutations in Omics Data for Solid Tumor R&D

Existing simple, linear transcriptome profiling and static analysis guidelines fail to capture the complexity of resistance feedback loops and result in discrepancies between cell line and patient tissue behavior due to noise caused by the loss of cellular heterogeneity during cell dissociation. Consequently, they fail to predict in silico the effective concentrations for engraftment and inhibition, creating a critical barrier to achieving clinical response rates. Current genomic baseline analysis assumes only the independent occurrence of single gene mutations, failing to computationally control the evolutionary cascading selection mechanisms induced within multidimensional genetic gradients, such as the reciprocal rescue effect between RNA splicing factors like U2AF1 and KRAS mutations. This has led to a critical data bottleneck, generating a large number of false-positive candidates in anticancer drug screening, and severely hindering the productivity of the development process for targeted anticancer drugs for complex solid tumors.

Discovery: Implementation of a Splicing Mechanism Prediction Algorithm and Demonstration of Cell Resolution-Independent Variable Tensor Synchronization

This computational platform architecture calculates in silico the thermodynamic free energy map by which U2AF1 mutations functionally rescue (rescue) intron retention and aberrant splicing defects associated with specific KRAS hotspot mutations. Advanced machine learning models refine technical noise and batch effects from multi-omics matrices, and differential equation-based rate constants are derived to restore cancer cell evolutionary mechanisms. By synchronizing single-cell resolution transcriptome independent variables into multidimensional tensors, we confirmed the biological mechanism by which U2AF1 mutants upregulate or downregulate specific oncogene-induced transcriptional stress to maximize cancer cell survival. This is a disruptive achievement compared to existing simple models, successfully demonstrating the principle of molecular biological integrity by clearly defining the topological variation curve of the downstream transcriptome network.

Establishment of a KRAS-U2AF1 Co-Evolutionary Pathway Regulatory and Reversible Homeostatic Precision Stratification Model

Using an intelligent computational backbone that integrates multi-omics sequencing datasets and protein structure free energy profiles, we have established a precision stratification technique that precisely segments patient-specific molecular phenotypes. Through precise tuning of rate-limiting step constants, we have completed dynamic modeling to elucidate how tumor cells maintain reversible homeostasis and survive under aberrant microenvironmental stress. In particular, by extracting specific splicing junction genetic gradient data rescued by U2AF1 mutations, we can virtually pre-filter potential immunogenic side effects or resistance feedback that specific drug candidates may exhibit during target docking. This provides a solid foundation for establishing targeted therapeutic strategies that can proactively block the reversible homeostatic recovery ability of cancer cells based on patient lineage and genetic background, ultimately maximizing personalized anticancer therapy design.

Prospects: Establishment of a Programmable Computational Biology Standard and Launch of a Next-Generation IND Digital Governance

This computational framework redefines the core governance of the R&D pipeline, shifting it from a static, post-hoc observation approach to a programmable computational biology infrastructure. By linking the genetic mutation correction coefficients validated in the U2AF1 rescue mechanism to a high-throughput screening database, we can build a computational barrier that eliminates batch-to-batch variation in next-generation KRAS inhibitors and splicing modulator combination therapies. Furthermore, by incorporating a precision stratification algorithm validation module that meets the core standards of digital healthcare, specifically the companion diagnostic (CDx) co-development process, it will function as a disruptive master digital asset that drastically shortens the data integrity requirements for Investigational New Drug (IND) clinical trial submissions to global regulatory agencies. This will dramatically improve patient selection success rates in future large-scale clinical trial designs and ensure consistency validation at the cGMP manufacturing approval stage.