Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel.
Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel.
Adv Healthc Mater. 2022 Sep;11(17):e2200356. doi: 10.1002/adhm.202200356. Epub 2022 Jul 14.
Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.
癌症在早期通常没有症状。然而,早期发现可以极大地改善预后。液体活检在早期检测方面具有很大的潜力,但它仍然存在许多缺点,主要是寻找特定的癌症生物标志物。在这里,提出了一种新的液体活检方法,基于血液顶空的挥发性有机化合物 (VOC) 模式。开发了一种基于多种化学敏感纳米结构薄膜的人工智能纳米阵列,并用于检测和分期癌症。作为概念验证,测试了三种在人群中具有高发病率和死亡率的癌症模型:乳腺癌、卵巢癌和胰腺癌。该纳米阵列对早期检测的准确率>84%、灵敏度>81%、特异性>80%,对转移检测的准确率>97%、灵敏度 100%、特异性>88%。补充的质谱分析验证了这些结果。使用人工智能纳米阵列分析血液等复杂生物液的能力,同时考虑到许多 VOC 的数据,增加了预测模型的灵敏度,并为癌症的有效早期诊断和疾病监测工具提供了潜力。