scholarly journals Breast Cancer Detection and Treatment Monitoring Using a Noninvasive Prenatal Testing Platform: Utility in Pregnant and Nonpregnant Populations

2020 ◽  
Vol 66 (11) ◽  
pp. 1414-1423
Author(s):  
Liesbeth Lenaerts ◽  
Huiwen Che ◽  
Nathalie Brison ◽  
Maria Neofytou ◽  
Tatjana Jatsenko ◽  
...  

Abstract Background Numerous publications have reported the incidental detection of occult malignancies upon routine noninvasive prenatal testing (NIPT). However, these studies were not designed to evaluate the NIPT performance for cancer detection. Methods We investigated the sensitivity of a genome-wide NIPT pipeline, called GIPSeq, for detecting cancer-specific copy number alterations (CNAs) in plasma tumor DNA (ctDNA) of patients with breast cancer. To assess whether a pregnancy itself, with fetal cell-free DNA (cfDNA) in the maternal circulation, might influence the detection of ctDNA, results were compared in pregnant (n = 25) and nonpregnant (n = 25) cancer patients. Furthermore, the ability of GIPSeq to monitor treatment response was assessed. Results Overall GIPSeq sensitivity for detecting cancer-specific CNAs in plasma cfDNA was 26%. Fifteen percent of detected cases were asymptomatic at the time of blood sampling. GIPSeq sensitivity mainly depended on the tumor stage. Also, triple negative breast cancers (TNBC) were more frequently identified compared to hormone-positive or HER2-enriched tumors. This might be due to the presence of high-level gains and losses of cfDNA or high ctDNA loads in plasma of TNBC. Although higher GIPSeq sensitivity was noted in pregnant (36%) than in nonpregnant women (16%), the limited sample size prohibits a definite conclusion. Finally, GIPSeq profiling of cfDNA during therapy allowed monitoring of early treatment response. Conclusions The results underscore the potential of NIPT-based tests, analyzing CNAs in plasma cfDNA in a genome-wide and unbiased fashion for breast cancer detection, cancer subtyping and treatment monitoring in a pregnant and nonpregnant target population.

2012 ◽  
Vol 413 (13-14) ◽  
pp. 1058-1065 ◽  
Author(s):  
Qian Wu ◽  
Chao Wang ◽  
Zuhong Lu ◽  
Li Guo ◽  
Qinyu Ge

2008 ◽  
Vol 47 (04) ◽  
pp. 322-327 ◽  
Author(s):  
D. Blokh ◽  
N. Zurgil ◽  
I. Stambler ◽  
E. Afrimzon ◽  
Y. Shafran ◽  
...  

Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.


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