Biomarkers of Imatinib Resistance in Chronic Myeloid Leukemia
With oncology pivotal trials averaging $32 million and a 95% drug failure rate, there is a growing interest in leveraging biomarkers for treatment optimization. Notably, drug resistance biomarkers are key in identifying treatment sensitivity, adjusting treatment protocols, and improving survival rates.
Leveraging Biomarkers for Treatment Optimization
With oncology pivotal trials averaging $32 million¹ and a 95% drug failure rate,² there is a growing interest in leveraging biomarkers for treatment optimization. Notably, drug resistance biomarkers are key in identifying treatment sensitivity, adjusting treatment protocols, and improving survival rates.³
In our latest Spotlight Report, we utilized Causaly Cloud to identify and prioritize drug resistance biomarkers of Chronic Myeloid Leukemia (CML), zeroing in on signaling proteins and transcription factors associated with imatinib treatment sensitivity.
Drug Resistance Biomarkers
Characterized by the over-production of white blood cells, CML affects up to 2.8 people per 100,000 and accounts for 15-20% leukemia cases in adults.⁴ To date, there is no cure for CML. Although, tyrosine kinase inhibitors (TKIs) – which inhibit BCR-ABL – have markedly improved prognosis.⁵ However, TKI resistance is a major challenge, necessitating combination therapies which target alternate pathways.⁶
Identifying drug resistance biomarkers is crucial in addressing TKI resistance and enhancing treatment effectiveness in CML. These biomarkers guide therapeutic interventions towards more pertinent resistance mechanisms, enabling improved patient stratification for clinical trials and the development of more targeted therapeutics.
Here, we used Causaly to identify resistance biomarkers of imatinib, the mainstay drug for CML, zeroing in on signaling proteins and transcription factors which play key roles in resistance.
Biomarker Identification and Prioritization
Biomarkers for CML were categorized by target class using Causaly’s advanced filtering capabilities. The majority were enzymes, followed by non-coding RNA, as shown in Figure 1.
With 90% of chemotherapeutic failures during invasion and metastasis attributable to drug resistance,¹⁰ biomarkers are critical for improving patient outcomes. Such biomarkers can identify novel and evolving resistance mechanisms, aiding more informed clinical decisions and optimization of treatment strategies.
Identifying relevant biomarkers from 38,000+ PubMed papers on CML is time-consuming and subject to bias, presenting limited opportunity for hypothesis generation. With human-centric AI, Causaly can expedite biomarker discovery, extracting relevant insights while reducing bias, enabling projects to transition into clinical programs with confidence.
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