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Quantitative Diagnosis of Malignant Pleural Effusions by Single-Cell Mechanophenotyping

Authors :
Daniel R. Gossett
Henry T. K. Tse
Yo Sup Moon
Kimberly Mislick
Yong Ying
Marie Sohsman
Mahdokht Masaeli
Jianyu Rao
Dino Di Carlo
Ryan P. Adams
Source :
Science Translational Medicine. 5
Publication Year :
2013
Publisher :
American Association for the Advancement of Science (AAAS), 2013.

Abstract

Biophysical characteristics of cells are attractive as potential diagnostic markers for cancer. Transformation of cell state or phenotype and the accompanying epigenetic, nuclear, and cytoplasmic modifications lead to measureable changes in cellular architecture. We recently introduced a technique called deformability cytometry (DC) that enables rapid mechanophenotyping of single cells in suspension at rates of 1000 cells/s-a throughput that is comparable to traditional flow cytometry. We applied this technique to diagnose malignant pleural effusions, in which disseminated tumor cells can be difficult to accurately identify by traditional cytology. An algorithmic diagnostic scoring system was developed on the basis of quantitative features of two-dimensional distributions of single-cell mechanophenotypes from 119 samples. The DC scoring system classified 63% of the samples into two high-confidence regimes with 100% positive predictive value or 100% negative predictive value, and achieved an area under the curve of 0.86. This performance is suitable for a prescreening role to focus cytopathologist analysis time on a smaller fraction of difficult samples. Diagnosis of samples that present a challenge to cytology was also improved. Samples labeled as "atypical cells," which require additional time and follow-up, were classified in high-confidence regimes in 8 of 15 cases. Further, 10 of 17 cytology-negative samples corresponding to patients with concurrent cancer were correctly classified as malignant or negative, in agreement with 6-month outcomes. This study lays the groundwork for broader validation of label-free quantitative biophysical markers for clinical diagnoses of cancer and inflammation, which could help to reduce laboratory workload and improve clinical decision-making.

Details

ISSN :
19466242 and 19466234
Volume :
5
Database :
OpenAIRE
Journal :
Science Translational Medicine
Accession number :
edsair.doi.dedup.....bb25af827bf6f8644086890e86bc3e7d
Full Text :
https://doi.org/10.1126/scitranslmed.3006559