Incidental Detection of Maternal Neoplasia in Noninvasive Prenatal Testing.

BACKGROUND: Noninvasive prenatal testing (NIPT) uses cell-free DNA (cfDNA) as an analyte to detect copy-number alterations in the fetal genome. Because maternal and fetal cfDNA contributions are comingled, changes in the maternal genome can manifest as abnormal NIPT results. Circulating tumor DNA (ctDNA) present in cases of maternal neoplasia has the potential to distort the NIPT readout to a degree that prevents interpretation, resulting in a nonreportable test result for fetal aneuploidy. METHODS: NIPT cases that showed a distortion from normal euploid genomic representation were communicated to the caregiving physician as nonreportable for fetal aneuploidy. Follow-up information was subsequently collected for these cases. More than 450000 pregnant patients who submitted samples for clinical laboratory testing >3 years are summarized. Additionally, in-depth analysis was performed for >79000 research-consented samples. RESULTS: In total, 55 nonreportable NIPT cases with altered genomic profiles were cataloged. Of these, 43 had additional information available to enable follow-up. A maternal neoplasm was confirmed in 40 of these cases: 18 malignant, 20 benign uterine fibroids, and 2 with radiological confirmation but without pathological classification. CONCLUSIONS: In a population of pregnant women who submitted a blood sample for cfDNA testing, an abnormal genomic profile not consistent with fetal abnormalities was detected in about 10 out of 100000 cases. A subset of these observations (18 of 43; 41.9%) was attributed to maternal malignant neoplasms. These observational results suggest the need for a controlled trial to evaluate the potential of using cfDNA as an early biomarker of cancer.

Genomically amplified Akt3 activates DNA repair pathway and promotes glioma progression.

Akt is a robust oncogene that plays key roles in the development and progression of many cancers, including glioma. We evaluated the differential propensities of the Akt isoforms toward progression in the well-characterized RCAS/Ntv-a mouse model of PDGFB-driven low grade glioma. A constitutively active myristoylated form of Akt1 did not induce high-grade glioma (HGG). In stark contrast, Akt2 and Akt3 showed strong progression potential with 78% and 97% of tumors diagnosed as HGG, respectively. We further revealed that significant variations in polarity and hydropathy values among the Akt isoforms in both the pleckstrin homology domain (P domain) and regulatory domain (R domain) were critical in mediating glioma progression. Gene expression profiles from representative Akt-derived tumors indicated dominant and distinct roles for Akt3, consisting primarily of DNA repair pathways. TCGA data from human GBM closely reflected the DNA repair function, as Akt3 was significantly correlated with a 76-gene signature DNA repair panel. Consistently, compared with Akt1 and Akt2 overexpression models, Akt3-expressing human GBM cells had enhanced activation of DNA repair proteins, leading to increased DNA repair and subsequent resistance to radiation and temozolomide. Given the wide range of Akt3-amplified cancers, Akt3 may represent a key resistance factor.

Using Targeted Sequencing of Paralogous Sequences for Noninvasive Detection of Selected Fetal Aneuploidies.

BACKGROUND: Current methods for noninvasive prenatal testing (NIPT) ascertain fetal aneuploidies using either direct counting measures of DNA fragments from specific genomic regions or relative measures of single nucleotide polymorphism frequencies. Alternatively, the ratios of paralogous sequence pairs were predicted to reflect fetal aneuploidy. We developed a NIPT assay that uses paralog sequences to enable noninvasive detection of fetal trisomy 21 (T21) and trisomy 18 (T18) using cell-free DNA (cfDNA) from maternal plasma. METHODS: A total of 1060 primer pairs were designed to determine fetal aneuploidy status, fetal sex, and fetal fraction. Each library was prepared from cfDNA by coamplifying all 1060 target pairs together in a single reaction well. Products were measured using massively parallel sequencing and deviations from expected paralog ratios were determined based on the read depth from each paralog. RESULTS: We evaluated this assay in a blinded set of 480 cfDNA samples with fetal aneuploidy status determined by the MaterniT21® PLUS assay. Samples were sequenced (mean = 2.3 million reads) with 432 samples returning a result. Using the MaterniT21 PLUS assay for paired plasma aliquots from the same individuals as a reference, all 385 euploid samples, all 31 T21 samples, and 14 of 16 T18 samples were detected with no false positive results observed. CONCLUSIONS: This study introduces a novel NIPT aneuploidy detection approach using targeted sequencing of paralog motifs and establishes proof-of-concept for a potentially low-cost, highly scalable method for the identification of selected fetal aneuploidies with performance and nonreportable rate similar to other published methods.

BPDA2d--a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass spectrometry.

MOTIVATION: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. RESULTS: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy. AVAILABILITY: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/.

A systematic model of the LC-MS proteomics pipeline.

MOTIVATION: Mass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view. RESULTS: In this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.

BPDA - a Bayesian peptide detection algorithm for mass spectrometry.

BACKGROUND: Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides. RESULTS: We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean squared error of the inferred spectrum to the observed spectrum. In contrast to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software, such as flexAnalysis, OpenMS, and Decon2LS, according to sensitivity and detection accuracy. CONCLUSION: Unlike previous detection methods, which only employ isotopic distributions and work at each single charge state alone, BPDA takes into account the charge state distribution as well, thus lending information to better identify weak peptide signals and produce more robust results. The proposed approach is based on a rigorous statistical framework, which avoids problems generally encountered in algorithms based on template matching. Our experiments indicate that BPDA performs well on both simulated data and real data, and compares very favorably with commonly used commercial and open-source software. The BPDA software can be downloaded from http://gsp.tamu.edu/Publications/supplementary/sun10a/bpda.

Impact of missing value imputation on classification for DNA microarray gene expression data--a model-based study.

Many missing-value (MV) imputation methods have been developed for microarray data, but only a few studies have investigated the relationship between MV imputation and classification accuracy. Furthermore, these studies are problematic in fundamental steps such as MV generation and classifier error estimation. In this work, we carry out a model-based study that addresses some of the issues in previous studies. Six popular imputation algorithms, two feature selection methods, and three classification rules are considered. The results suggest that it is beneficial to apply MV imputation when the noise level is high, variance is small, or gene-cluster correlation is strong, under small to moderate MV rates. In these cases, if data quality metrics are available, then it may be helpful to consider the data point with poor quality as missing and apply one of the most robust imputation algorithms to estimate the true signal based on the available high-quality data points. However, at large MV rates, we conclude that imputation methods are not recommended. Regarding the MV rate, our results indicate the presence of a peaking phenomenon: performance of imputation methods actually improves initially as the MV rate increases, but after an optimum point, performance quickly deteriorates with increasing MV rates.