A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-11-19 DOI:10.1002/cam4.70401

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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Abstract

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.

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快速预测急性髓细胞性白血病治疗反应的组合功能精准医学平台。

背景：尽管基于生物标志物的靶向治疗在急性髓性白血病（AML）治疗方面取得了进展，但缓解期往往很短，且随之而来的是复发和获得性耐药。事实证明，功能精准医学（FPM）通过整合全面的生物数据，为个体治疗量身定制，从而改善了治疗选择指导。然而，如何有效管理复杂的生物数据，同时确保将可操作的见解快速转化为临床实用性仍是一项挑战：我们评估了二次表型优化平台（QPOP）的临床适用性，以预测急性髓细胞白血病患者对联合疗法的临床反应，并揭示以患者为中心的联合疗法敏感性。在这项前瞻性研究中，QPOP对51例新诊断（ND）或难治/复发（R/R）急性髓细胞白血病患者的原始样本进行了体内外药物测试评估：结果：55/63（87.3%）份患者样本生成了个性化药物敏感性报告，从样本采集到报告生成的中位周转时间为 5（4-10）天。为了评估临床可行性，QPOP 预测反应与临床治疗结果进行了比较，结果显示两者一致，灵敏度为 83.3%，特异度为 90.9%，总体准确率为 86.2%。在FLT3突变患者样本中进行的QPOP序列分析表明，FLT3抑制剂（FLT3i）的敏感性下降，这与FLT3等位基因负荷增加和耐药性发展是一致的。随后发现叉头盒M1（FOXM1）-AKT信号转导导致了对FLT3i的耐药性：总之，这项研究证明了应用 QPOP 作为功能性组合精准医疗平台来预测急性髓细胞性白血病治疗敏感性的可行性，并为评估活体指导联合疗法的前瞻性临床试验奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

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.