PO.CL07.01 · 临床研究
组合式功能性精准平台Optim.AI™在血液系统恶性肿瘤中的真实世界临床表现
Real-world clinical performance of a combinatorial functional precision platform, Optim.AI™, in hematological malignancies
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：
体外药物敏感性检测作为个体化治疗的策略已被探索近五十年，尤其适用于没有可靶向生物标志物或已用尽标准治疗选择的患者。历史上，其应用一直受到诸多挑战的限制，例如肿瘤样本稀缺、难以重现特定适应证的微环境，以及与临床结局的相关性不一致。功能性精准医学的进展如今使更可靠的转化成为可能。Optim.AI™作为一种组合式功能性精准医学平台，已在血液系统恶性肿瘤和肉瘤中展现出前瞻性临床效用。在此，我们报告其在一家临床认证实验室中用于血液系统癌症的真实世界表现数据。
方法：
从实体组织、外周血或骨髓抽吸物中分离肿瘤细胞，并以组合形式暴露于12种FDA批准的化疗和靶向药物。对治疗后细胞活力进行定量，以生成可操作治疗组合的Optim.AI™排序。对随后接受Optim.AI™指导治疗的患者的临床结局进行回顾性评估。
结果：
在154份血液系统样本中，91%产生了足够用于检测的细胞，其中94%生成了成功的报告。与既往耐药模式的一致性很高：88%的病例显示预测的标准化细胞活力（NCV）>0.6，与临床观察到的耐药性一致。对5例根据Optim.AI™最高排序推荐接受治疗的急性髓系白血病（AML）患者进行回顾性分析显示，该平台在全部5例病例中均具有临床效用。3例患者对Optim.AI™指导的治疗产生反应，包括2例完全缓解——其中1例成功桥接至移植。对于2例未产生反应的患者，Optim.AI™谱准确预测了无反应（NCV>0.6），并支持及时决定限制进一步的无效治疗，包括转向姑息治疗。在这些病例中，NCV<0.3有效地区分了反应者与无反应者。
结论：
与既往前瞻性研究一致，这些真实世界结果验证了Optim.AI™预测血液系统恶性肿瘤治疗反应的能力。重要的是，该平台在所有接受评估的患者中均展现出临床效用——在有可用选择时识别有效的治疗方案，并在进一步强化治疗不太可能有益时引导医生采取适当的姑息方法。跨多种适应证的更大规模前瞻性研究将进一步巩固其迈向更广泛临床应用的道路。
查看英文原文 English abstract
Background:
Ex vivo drug sensitivity testing has been explored for nearly five decades as a strategy to individualize treatment, especially for patients without actionable biomarkers or who have exhausted standard options. Historically, adoption has been limited by challenges such as scarce tumor samples, difficulty recreating indication-specific microenvironments, and inconsistent correlation with clinical outcomes. Advances in functional precision medicine now enable more reliable translation. Optim.AI™, a combinatorial functional precision medicine platform, has shown prospective clinical utility in hematologic malignancies and sarcoma. Here, we report real-world performance data from its use in a clinical-certified laboratory for hematological cancers.
Methods:
Tumor cells from solid tissue, peripheral blood, or bone marrow aspirates were isolated and exposed to 12 FDA-approved chemotherapy and targeted agents in combinatorial formats. Post-treatment cell viability was quantified to generate Optim.AI™ rankings of actionable treatment combinations. Clinical outcomes were retrospectively assessed for patients who subsequently received Optim.AI™-guided therapies.
Results:
Among 154 hematological samples, 91% yielded sufficient cells for testing and 94% of these produced successful reports. Concordance with prior resistance patterns was high: 88% of cases demonstrated predicted normalized cell viability (NCV) > 0.6, consistent with clinically observed resistance. Retrospective analysis of five acute myeloid leukemia (AML) patients treated according to top-ranked Optim.AI™ recommendations showed that the platform was clinically useful in all five cases. Three patients responded to Optim.AI™-guided therapy, including two complete remissions-one successfully bridged to transplant. For the two patients who did not respond, the Optim.AI™ profiles accurately predicted non-response (NCV > 0.6) and supported timely decisions to limit further futile therapy, including transition to palliative care. Across these cases, NCV < 0.3 effectively stratified responders from non-responders.
Conclusion:
Consistent with prior prospective studies, these real-world results validate Optim.AI™'s ability to predict treatment responses in hematological malignancies. Importantly, the platform demonstrated clinical utility in all evaluated patients-by identifying effective therapeutic options when available and by guiding physicians toward appropriate palliative approaches when further intensive therapy was unlikely to help. Larger prospective studies across diverse indications will strengthen its path toward broader clinical adoption.
利益披露 Disclosure
M. Rashid,
KYAN Technologies Pte Ltd Employment.