PO.CL01.18 · 临床研究

多组学肺癌筛查血液检测的开发与性能

Development and performance of a multiomics lung cancer screening blood test

海报缩略图:多组学肺癌筛查血液检测的开发与性能
编号 1107 展板 17 时间 4/19 02:00–05:00 区域 Section 43 主讲 Ofer Shapira, PhD
分会场 Early Detection Biomarkers 1
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作者与单位 Authors & Affiliations

Ofer Shapira, Alexander F. Lovejoy, Álvaro González, Urvee Desai, Thomas Royce, Gurnit Atwal, Ian Bast, Eric Beraut, Alexandra Buckley, Austin Cauwels, Peter Combs, Nicholas Eisele, G Parker Flowers, Lourdes Gomez, Rebecca Gupte, Johnnie Hahm, Teng-Kuei Hsu, Saiful Islam, Poorval Joshi, Amanda Kahn-Kirby, Phuong Thuy Menchavez, Erene Mina, Jinesh Niroula, Cameron Pospisil, Sodany Son, Rashmi Sriram, Peter Ulz, Russell Williams, Rui Yang, Wilson Zhang, Marian Navratil, C Jimmy Lin, Tanya Moreno, Richard Bourgon

Freenome Holdings, Inc., South San Francisco, CA

摘要 Abstract

中文摘要
肺癌是美国癌症死亡的首要原因。早期检测对提高生存率至关重要,然而只有18%符合条件的高风险成人(年龄50-80岁且有≥20包年吸烟史)能够按时接受推荐的年度低剂量计算机断层扫描筛查。无创血液检测可提高筛查参与率。在此,我们评估了一种在预期使用人群(IUP)患者中检测肺癌的多组学方法。我们的检测使用如前所述的循环游离DNA(cfDNA)碱基分辨率甲基化测序和血浆蛋白免疫检测。人工智能/机器学习分类器在组织(n=136)和血浆(n=6,716)样本上进行训练。在包含来自IUP的肺癌病例(n=363)和癌症阴性对照(n=310)的673份血浆样本队列中评估准确性。该队列涵盖所有癌症分期和三种主要亚型——腺癌、鳞状细胞癌和小细胞肺癌(SCLC)——其年龄和包年吸烟史分布反映了IUP。考虑了两种特异性:50%(优先敏感性)和75%(优先特异性)。所报告的敏感性经过分期和亚型校正,以解决评估队列与文献报道的IUP分布之间的差异。(腺癌和鳞状细胞数据按分期和亚型加权;由于样本量小,SCLC数据仅按亚型加权。)95%置信区间(CI)通过Wilson方法计算。多组学检测在50%特异性下IUP校正敏感性为90.7%(CI:86.8-93.7%),在75%特异性下为80.4%(75.4-84.7%)。仅甲基化检测的结果分别为85.8%(81.3-89.5%)和78.2%(73.1-82.8%)。多组学检测在两种特异性下均显示更高的敏感性,但差异无统计学显著性。在50%特异性下,多组学检测对I至IV期的校正敏感性分别为77.7%(73.3-89.0%)、95.2%(85.0-98.1%)、98.8%(93.6-99.7%)和97.1%(90.7-98.9%)。在75%特异性下的相应结果为63.5%(51.9-71.6%)、89.8%(76.6-94.1%)、90.9%(85.0-96.3%)和95.9%(89.8-98.6%)。在50%特异性下,多组学检测对腺癌和鳞状细胞癌的校正敏感性分别为88.7%(81.4-92.0%)和91.1%(84.3-96.1%);对SCLC的名义敏感性为97.1%(85.1-99.5%)。在75%特异性下的相应结果为73.0%(63.1-77.6%)、88.6%(81.6-94.6%)和91.2%(77.0-97.0%)。我们的多组学平台在基于血液的肺癌检测中展现出有前景的初步性能。cfDNA甲基化和血浆蛋白的互补性可能相对于仅甲基化方法提升性能,这将在未来一项采用先前未见评估队列的研究中进一步评估。
查看英文原文 English abstract
Lung cancer is the leading cause of cancer death in the US. Early detection is crucial for improving survival rates, yet only 18% of eligible high-risk adults (aged 50-80 with ≥20 pack-years of cigarette smoking history) are up to date with recommended annual low-dose computed tomography screening. Non-invasive blood tests could increase screening participation. Here, we evaluate a multiomics approach for lung cancer detection in patients in the intended use population (IUP). Our test uses base-resolution methylation sequencing of circulating cell-free DNA (cfDNA), as previously described, and plasma protein immunoassays. An artificial intelligence/machine learning classifier was trained on tissue (n = 136) and plasma (n = 6,716) samples. Accuracy was evaluated in a cohort of 673 plasma samples, including lung cancer cases (n = 363) and cancer-negative controls (n = 310) from the IUP. This cohort encompassed all cancer stages and three major subtypes - adenocarcinoma, squamous cell carcinoma and small-cell lung cancer (SCLC) - and its age and pack-year smoking history distributions reflected the IUP. Two specificities were considered: 50% (prioritizing sensitivity) and 75% (prioritizing specificity). Reported sensitivities were adjusted for stage and subtype to address differences between the evaluation cohort and literature-reported distributions for the IUP. (Adenocarcinoma and squamous data were weighted per stage and subtype; due to small sample size, SCLC data were weighted only per subtype.) 95% confidence intervals (CIs) were computed via Wilson's method. The multiomics test had an IUP-adjusted sensitivity of 90.7% (CI: 86.8 - 93.7%) at 50% specificity and 80.4% (75.4 - 84.7%) at 75% specificity. Results for a methylation-only test were 85.8% (81.3 - 89.5%) and 78.2% (73.1 - 82.8%), respectively. The multiomics test showed higher sensitivity at both specificities but the differences were not statistically significant. At 50% specificity, the multiomics test had adjusted sensitivity for stages I through IV of 77.7% (73.3 - 89.0%), 95.2% (85.0 - 98.1%), 98.8% (93.6 - 99.7%) and 97.1% (90.7 - 98.9%), respectively. Corresponding results at 75% specificity were 63.5% (51.9 - 71.6%), 89.8% (76.6 - 94.1%), 90.9% (85.0 - 96.3%) and 95.9% (89.8 - 98.6%). At 50% specificity, the multiomics test's adjusted sensitivity for adenocarcinoma and squamous cell carcinoma was 88.7% (81.4 - 92.0%) and 91.1% (84.3 - 96.1%), respectively; nominal sensitivity for SCLC was 97.1% (85.1 - 99.5%). Corresponding results at 75% specificity were 73.0% (63.1 - 77.6%), 88.6% (81.6 - 94.6%) and 91.2% (77.0 - 97.0%). Our multiomics platform demonstrated promising initial performance for blood-based lung cancer detection. The complementary nature of cfDNA methylation and plasma protein may enhance performance relative to a methylation-only approach, and this will be further assessed in a future study with a previously unseen evaluation cohort.
利益披露 Disclosure
O. Shapira, Freenome Holdings, Inc. Employment, Stock. A. F. Lovejoy, Freenome Holdings, Inc. Employment, Stock. Á. González, Freenome Holdings, Inc. Employment, Stock. U. Desai, Freenome Holdings, Inc. Employment, Stock. T. Royce, Freenome Holdings, Inc. Employment, Stock. G. Atwal, Freenome Holdings, Inc. Employment, Stock. I. Bast, Freenome Holdings, Inc. Employment, Stock. E. Beraut, Freenome Holdings, Inc. Employment, Stock. A. Buckley, Freenome Holdings, Inc. Employment, Stock. A. Cauwels, Freenome Holdings, Inc. Employment, Stock. P. Combs, Freenome Holdings, Inc. Employment, Stock. N. Eisele, Freenome Holdings, Inc. Employment, Stock. G. Flowers, Freenome Holdings, Inc. Employment, Stock. L. Gomez, Freenome Holdings, Inc. Employment, Stock. R. Gupte, Freenome Holdings, Inc. Employment, Stock. J. Hahm, Freenome Holdings, Inc. Employment, Stock. T. Hsu, Freenome Holdings, Inc. Employment, Stock. S. Islam, Freenome Holdings, Inc. Employment, Stock. P. Joshi, Freenome Holdings, Inc. Employment, Stock. A. Kahn-Kirby, Freenome Holdings, Inc. Employment, Stock. P. Menchavez, Freenome Holdings, Inc. Employment, Stock. E. Mina, Freenome Holdings, Inc. Employment, Stock. J. Niroula, Freenome Holdings, Inc. Employment, Stock. C. Pospisil, Freenome Holdings, Inc. Employment, Stock. S. Son, Freenome Holdings, Inc. Employment, Stock. R. Sriram, Freenome Holdings, Inc. Employment, Stock. P. Ulz, Freenome Holdings, Inc. Employment, Stock. R. Williams, Freenome Holdings, Inc. Employment, Stock. R. Yang, Freenome Holdings, Inc. Employment, Stock. W. Zhang, Freenome Holdings, Inc. Employment, Stock. M. Navratil, Freenome Holdings, Inc. Employment, Stock. C. Lin, Freenome Holdings, Inc. Employment, Stock. T. Moreno, Freenome Holdings, Inc. Employment, Stock. R. Bourgon, Freenome Holdings, Inc. Employment, Stock.

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