新平台优化组合癌症疗法的选择
生物信息学方法可以预测临床前和临床研究中改善预后的组合
MD AndersonNews Release April 12, 2022
Abstract:LB119
Researchers at The University of TexasMD AndersonCancer Center have developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.
The findings were presented today at theAmerican Association for Cancer Research (AACR) Annual Meeting 2022by principal investigatorAnil Korkut, Ph.D., assistant professor of生物信息学和计算生物学。The study results also were published today inCancer Discovery。
The platform, calledREcurrent Features LEveraged for Combination Therapy(REFLECT), integrates machine learning and cancer informatics algorithms to analyze biological tumor features — including genetic mutations, copy number changes, gene expression and protein expression aberrations — and identify frequent co-occurring alterations that could be targeted by multiple drugs.
“Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit,” Korkut said. “We believe REFLECT may be one of the tools that can help overcome some of the current challenges in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors.”
Targeted therapies许多癌症患者的临床结局改善了，但是针对单个靶标的单疗法通常会导致耐药性。癌细胞经常依赖于同时发生的改变，例如两个信号通路中的突变，以驱动肿瘤进展。越来越多的证据表明，同时识别和靶向两种变化可能会增加耐用的反应。
在科尔科特（Korkut）和博士后Xubin Li博士的领导下，研究人员构建并使用了反射工具来开发一种系统性和无偏见的乐动体育LDsports中国方法，使其与最佳组合疗法的患者相匹配。
使用反射，他们分析了两者的Pan-Cancer数据集MD Anderson以及公开可用的来源，包括治疗前患者肿瘤样本，细胞系和患者衍生的异种移植物（PDXS），代表10,000多名患者和33种癌症类型。这产生了201个患者队列，每个人群都由单个治疗可行的生物标志物定义，例如EGFR突变或PD-L1过表达。
Within each cohort, the team generated REFLECT signatures of additional alterations that may be actionable therapeutic targets, thus pointing to sub-cohorts that may benefit from specific combination therapies. Across all cohorts, the researchers identified a total of 2,166 combinations, with at least one Food and Drug Administration-approved agent, matched to co-occurring alterations. In total, 45% of the patients included in the initial analysis were matched to at least one combination therapy.
The researchers validated the REFLECT approach through retrospective analysis of publicly available pre-clinical and clinical studies, comparing REFLECT-matched combinations used in those trials to combinations not matched by the tool.
In pre-clinical trials with PDX models, REFLECT-matched combinations had a 34.5% decrease in median tumor volume, while non-matched combinations had a 5.1% increase. Similarly, progression-free survival (PFS) was higher with matched combinations. The researchers also demonstrated a higher synergy score in REFLECT combinations relative to others, defined using the highest single agent (HSA) model.
The researchers also retrospectively validated the approach in the clinical setting through available data from the I-PREDICT trials, which evaluated many combination therapies across diverse cancer types. Patients in this trial that received combinations predicted by REFLECT to be most beneficial had significantly longer PFS and overall survival compared to other combinations.
在这项研究中，团队还开发了一张详细的致癌改变图，这些图与特定的免疫特征并存。该地图揭示了许多常见的变化，这些变化经常与免疫疗法反应标记相结合，例如DNA损伤修复的缺陷以及特定表观遗传调节剂水平的变化。研究结果表明，应进一步研究针对这些途径的疗法作为改进的选择immunotherapy回应。
“While REFLECT is still a concept that requires additional validation, we anticipate a great opportunity to translate this work into real clinical benefits,” Korkut said. “In the future, multi-omic profiles from pre-treatment patient samples could be loaded to the REFLECT pipeline to generate co-alteration signatures, allowing physicians to consider precision combination therapies tailored to molecular profiles of those patients.”
Korkut解释说，将来，这种方法将受益于改进的信息学资源，以更好地将疗法与RNA和蛋白质水平的改变相匹配。此外，研究人员计划扩大研究，以更好地乐动体育LDsports中国解决和预测匹配的药物组合的毒性。最后，未来的研究还将寻求解决肿瘤内的显着异质性，这可能会影响对靶向疗法的反应。
The research was supported by the National Institutes of Health/National Cancer Institute (P30 CA016672, U01 CA253472-01A1, 5UL1TR003167-02, 7R01CA206025-06), the Ovarian Cancer Research Alliance Collaborative Research Award, the Innovation in Cancer Informatics Fund, the Cancer Prevention and Research Institute of Texas (CPRIT) (RP170640),MD Anderson’s结直肠癌月亮射击®, the Department of Defense (W81WH-18-1-0678), the National Resource for Network Biology, and the National Institute of General Medical Sciences (GM103504)
In addition to Korkut and Li, additionalMD Anderson作者包括：Gonghong Yan，Ph.D.，Zeynep Dereli博士和Behnaz Bozorgui，Ph.D.，所有生物信息学和计算生物学；系统生物学的Parisa Imanirad博士；David Menter博士和胃肠道医学肿瘤学的M.D. Scott Kopetz博士；吉氏医学肿瘤学的M.D. Patrick G.Pilié；和Timothy Yap，M.B.B.S.，博士，研究性癌症治疗学。合作作家包括：休斯顿莱斯大学的Elisabeth Dowling博士；新奥尔良路易斯安那州立大学健康科学中心的雅各布·埃纳格加（Jacob Elnaggar）；波士顿达纳 - 法伯癌研究所的奥古斯丁·卢纳（Augustin Luna）博士；以及波士顿达纳 - 法伯癌症研究所和哈佛医学院的克里斯·桑德（Chris Sander）。完整的纸张可以找到完整的披露清单here。