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Atul Khajuria,
Ashish Kumar,
- Dean, 1Dean, University School of Allied and Health Care Sciences Rayat Bahra Professional University VPO Bohan, Tehsil & Distt. Hoshiarpur, Punjab, India
- Assistant Professor, University School of Medical Laboratory Sciences Rayat Bahra Professional University VPO Bohan, Tehsil &Dist. Hoshiarpur, Punjab, India
Abstract
Mass spectrometry-based phosphoproteomics has emerged as a powerful tool for predicting kinase inhibitor responses in solid tumors, offering direct functional insights into signaling pathways that surpass traditional genomic profiling by capturing dynamic kinase activities and adaptive resistance mechanisms. Technological breakthroughs, including data-independent acquisition (DIA), trapped ion mobility spectrometry (timsTOF), and efficient enrichment methods like TiO2 or IMAC, now enable comprehensive profiling of over 40,000 phosphorylation sites from limited clinical biopsies, FFPE tissues, or extracellular vesicles (EVs), with high reproducibility even in heterogeneous samples. Tumor-specific phosphosignatures reveal critical patterns: in NSCLC, EGFR downstream sites like pERK and pAKT predict TKI efficacy; BRAF-mutant melanoma shows RTK rebound (e.g., pEGFR) upon BRAF/MEK blockade; and CRC patient-derived xenografts highlight MAPK/PI3K feedback loops tied to progression-free survival (PFS). Integrated phosphomarker panels, refined by machine learning and kinase activity inference tools like KSEA, deliver 80-95% predictive accuracy for PFS across NSCLC, CRC, melanoma, and breast cancer cohorts, outperforming mutation-based predictors with lower error rates in drug sensitivity forecasting. Prospective clinical trials demonstrate 30-50% outcome improvements through phosphoguided therapy selection, as seen in multi-omic strategies matching inhibitors to active signaling nodes. Emerging liquid biopsy applications using plasma EVs for non-invasive phosphomonitoring, combined with AI-driven multi-omic integration, promise standardized precision oncology by detecting resistance early and personalizing combinations for multikinase inhibitors targeting EGFR, BRAF, MEK, and beyond.
Keywords: Mass spectrometry, BRAF/MEK inhibitors, clinical translation, EGFR inhibitors, kinase inhibitors, liquid biopsy, machine learning, multi-omic integration, phosphoproteomic biomarkers, phosphoproteomics, precision oncology, predictive biomarkers, resistance mechanisms, signal-transduction pathways, solid tumors
Atul Khajuria, Ashish Kumar. Mass Spectrometry–Based Phosphoproteomic Markers to Predict Kinase Inhibitor Response in Solid Tumors. International Journal of Cell Biology and Cellular Functions. 2026; 04(02):-.
Atul Khajuria, Ashish Kumar. Mass Spectrometry–Based Phosphoproteomic Markers to Predict Kinase Inhibitor Response in Solid Tumors. International Journal of Cell Biology and Cellular Functions. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijcbcf/article=2026/view=246106
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International Journal of Cell Biology and Cellular Functions
| Volume | 04 |
| 02 | |
| Received | 21/03/2026 |
| Accepted | 27/03/2026 |
| Published | 07/04/2026 |
| Publication Time | 17 Days |
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