Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Weill Cornell Medicine, Cornell University, New York, NY, USA.
Nat Biomed Eng. 2022 Mar;6(3):267-275. doi: 10.1038/s41551-022-00860-y. Epub 2022 Mar 17.
Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In ovarian cancer, the few established serum biomarkers are highly specific, yet insufficiently sensitive to detect early-stage disease and to impact the mortality rates of patients with this cancer. Here we show that a 'disease fingerprint' acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best clinical screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography). We used 269 serum samples to train and validate several machine-learning classifiers for the discrimination of patients with ovarian cancer from those with other diseases and from healthy individuals. The predictive values of the best classifier could not be attained via known protein biomarkers, suggesting that the array of nanotube sensors responds to unidentified serum biomarkers.
血清生物标志物通常不够敏感或特异,无法用于癌症筛查或诊断检测。在卵巢癌中,少数已确立的血清生物标志物具有很高的特异性,但不够敏感,无法检测早期疾病,也无法降低患有这种癌症的患者的死亡率。在这里,我们通过机器学习从一系列功能化量子点缺陷的碳纳米管的近红外荧光发射光谱中获得的“疾病指纹”,在有症状个体的血清样本中检测到高级别浆液性卵巢癌,其敏感性为 87%,特异性为 98%(与目前使用癌症抗原 125 和经阴道超声测量的最佳临床筛查测试相比,敏感性为 84%,特异性为 98%)。我们使用 269 份血清样本来训练和验证几种用于区分卵巢癌患者、其他疾病患者和健康个体的机器学习分类器。通过已知的蛋白质生物标志物无法达到最佳分类器的预测值,这表明纳米管传感器阵列对未识别的血清生物标志物有反应。