Noor Rashidha Binte Meera Sahib, Jameelah Sheik Mohamed, Masturah Bte Mohd Abdul Rashid, Jayalakshmi, Yihao Clement Lin, Yen Lin Chee, Bingwen Eugene Fan, Sanjay De Mel, Melissa Gaik Ming Ooi, Wei-Ying Jen, Edward Kai-Hua Chow
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However, effectively managing complex biological data, while also ensuring rapid conversion of actionable insights into clinical utility remains challenging.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We have evaluated the clinical applicability of quadratic phenotypic optimization platform (QPOP), to predict clinical response to combination therapies in AML and reveal patient-centric insights into combination therapy sensitivities. In this prospective study, 51 primary samples from newly diagnosed (ND) or refractory/relapsed (R/R) AML patients were evaluated by QPOP following ex vivo drug testing.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Individualized drug sensitivity reports were generated in 55/63 (87.3%) patient samples with a median turnaround time of 5 (4–10) days from sample collection to report generation. To evaluate clinical feasibility, QPOP-predicted response was compared to clinical treatment outcomes and indicated concordant results with 83.3% sensitivity and 90.9% specificity and an overall 86.2% accuracy. Serial QPOP analysis in a FLT3-mutant patient sample indicated decreased FLT3 inhibitor (FLT3i) sensitivity, which is concordant with increasing FLT3 allelic burden and drug resistance development. Forkhead box M1 (FOXM1)—AKT signaling was subsequently identified to contribute to resistance to FLT3i.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Overall, this study demonstrates the feasibility of applying QPOP as a functional combinatorial precision medicine platform to predict therapeutic sensitivities in AML and provides the basis for prospective clinical trials evaluating ex vivo-guided combination therapy.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"13 22","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70401","citationCount":"0","resultStr":"{\"title\":\"A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML\",\"authors\":\"Noor Rashidha Binte Meera Sahib, Jameelah Sheik Mohamed, Masturah Bte Mohd Abdul Rashid, Jayalakshmi, Yihao Clement Lin, Yen Lin Chee, Bingwen Eugene Fan, Sanjay De Mel, Melissa Gaik Ming Ooi, Wei-Ying Jen, Edward Kai-Hua Chow\",\"doi\":\"10.1002/cam4.70401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Despite advances made in targeted biomarker-based therapy for acute myeloid leukemia (AML) treatment, remission is often short and followed by relapse and acquired resistance. Functional precision medicine (FPM) efforts have been shown to improve therapy selection guidance by incorporating comprehensive biological data to tailor individual treatment. However, effectively managing complex biological data, while also ensuring rapid conversion of actionable insights into clinical utility remains challenging.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We have evaluated the clinical applicability of quadratic phenotypic optimization platform (QPOP), to predict clinical response to combination therapies in AML and reveal patient-centric insights into combination therapy sensitivities. 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A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML
Background
Despite advances made in targeted biomarker-based therapy for acute myeloid leukemia (AML) treatment, remission is often short and followed by relapse and acquired resistance. Functional precision medicine (FPM) efforts have been shown to improve therapy selection guidance by incorporating comprehensive biological data to tailor individual treatment. However, effectively managing complex biological data, while also ensuring rapid conversion of actionable insights into clinical utility remains challenging.
Methods
We have evaluated the clinical applicability of quadratic phenotypic optimization platform (QPOP), to predict clinical response to combination therapies in AML and reveal patient-centric insights into combination therapy sensitivities. In this prospective study, 51 primary samples from newly diagnosed (ND) or refractory/relapsed (R/R) AML patients were evaluated by QPOP following ex vivo drug testing.
Results
Individualized drug sensitivity reports were generated in 55/63 (87.3%) patient samples with a median turnaround time of 5 (4–10) days from sample collection to report generation. To evaluate clinical feasibility, QPOP-predicted response was compared to clinical treatment outcomes and indicated concordant results with 83.3% sensitivity and 90.9% specificity and an overall 86.2% accuracy. Serial QPOP analysis in a FLT3-mutant patient sample indicated decreased FLT3 inhibitor (FLT3i) sensitivity, which is concordant with increasing FLT3 allelic burden and drug resistance development. Forkhead box M1 (FOXM1)—AKT signaling was subsequently identified to contribute to resistance to FLT3i.
Conclusion
Overall, this study demonstrates the feasibility of applying QPOP as a functional combinatorial precision medicine platform to predict therapeutic sensitivities in AML and provides the basis for prospective clinical trials evaluating ex vivo-guided combination therapy.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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