Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
A novel biophysical based marker with multilevel, multiparameter expression for early stage cancer detection.
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20673)
Author(s): Junjie Wu, Gengxi Jiang, Jiansong Ji, Xuedong Du, Yue Lin, Hongmei Tao, Xing Tang, Chris Chang Yu; Changhai Hospital, The Second Military Medical University, Shanghai, China; Department of Thoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai, China; Lishui Central Hospital, Lishui, China; Anpac Bio-Medical Science Co. Ltd., Shanghai, China; AnPac Bio-Medical Science and Technology Co., LTD, Shanghai, China; Anpac Bio-Medical Science Co Ltd, Shanghai, China
Background: Early stage cancer detection remains to be elusive despite of many years of efforts. In this work, a bio-physical based marker (named Cancer Differentiation Analysis (CDA)) with multi-level and multi-parameter expression features has been developed which has shown a number of clear advantages over the traditional approaches such as bio-chemistry based marker, circulating tumor cell (CTC) and circuiting DNA (ct-DNA). In stage I non-small cell lung cancer (NSCLC), sensitivity and specificity reached a record high of 85.2% and 93.0%, respectively. Methods: In this study, 832 NSCLC cancer samples with pathological information and 642 samples from healthy individuals were measured in a single blind test. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. The CDA data were first processed using an algorithm built from data base and subsequently analyzed using SPSS. The results were shown in the table. Results: The results indicated that CDA technology has a very good sensitivity and specificity even at stage I (85% and 93%, respectively), which is much better than those previously reported results by other methods. Conclusions: Initial results showed that CDA technology could effectively screen NSCLC patients from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA technology could be a potential candidate for early stage cancer screening.
|NSCLC||CDA Data Set||Gender|
|SD of CDA|
|Control||642||53||19 - 86||45||47||32.87||32.86||5.74||/||/||/|
|Stage ¢ñ||108||58||28 - 97||60||61||49.49||50.63||9.03||0.939||85.2%||93.0%|
|Stage ¢ò||90||84||45 - 78||61||60||52.38||53.66||7.21||0.967||93.3%||98.6%|
|Stage ¢ó||246||87||41 - 87||62||63||53.66||53.87||5.26||0.995||98.0%||98.1%|
|Stage ¢ô||388||71||36 - 90||60||59||52.45||52.96||6.11||0.987||94.1%||97.7%|