Genome-wide normalized score: a novel algorithm to detect fetal trisomy 21 during non-invasive prenatal testing.

OBJECTIVES: Non-invasive prenatal testing for fetal trisomy 21 (T21) by massively parallel shotgun sequencing (MPSS) is available for clinical use but its efficacy is limited by several factors, e.g. the proportion of cell-free fetal DNA in maternal plasma and sequencing depth. Existing algorithms discard DNA reads from the chromosomes for which testing is not being performed (i.e. those other than chromosome 21) and are thus more susceptible to diluted fetal DNA and limited sequencing depth. We aimed to describe and evaluate a novel algorithm for aneuploidy detection (genome-wide normalized score (GWNS)), which normalizes read counts by the proportions of DNA fragments from chromosome 21 in normal controls. METHODS: We assessed the GWNS approach by comparison with two existing algorithms, i.e. Z-score and normalized chromosome value (NCV), using theoretical approximations and computer simulations in a set of 86 cases (64 euploid and 22 T21 cases). We then validated GWNS by studying an expanded set of clinical samples (n = 208). Finally, dilution experiments were undertaken to compare performance of the three algorithms (Z-score, NCV, GWNS) when fetal DNA concentration was low. RESULTS: At fixed levels of significance and power, GWNS required a smaller fetal DNA proportion and fewer total MPSS reads compared to Z-score or NCV. In dilution experiments, GWNS also outperformed the other two methods by reaching the correct diagnosis with the lowest range of fetal DNA concentrations (GWNS, 3.83-4.75%; Z-score, 4.75-5.22%; NCV, 6.47-8.58%). CONCLUSION: Our results demonstrate that GWNS is comparable to Z-score and NCV methods regarding the performance of detecting fetal T21. Dilution experiments suggest that GWNS may perform better than the other methods when fetal fraction is low.

Inferring disease-related pathways using a probabilistic epistasis model.

MOTIVATION: We present a probabilistic model called a Joint Intervention Network (JIN) for inferring interactions among a chosen set of regulator genes. The input to the method are expression changes of downstream indicator genes observed under the knock-out of the regulators. JIN can use any number of perturbation combinations for model inference (e.g. single, double, and triple knock-outs). RESUITS/CONCLUSIONS: We applied JIN to a Vibrio cholerae regulatory network to uncover mechanisms critical to its environmental persistence. V. cholerae is a facultative human pathogen that causes cholera in humans and responsible for seven pandemics. We analyzed the expression response of 17 V. cholerae biofilm indicator genes under various single and multiple knock-outs of three known biofilm regulators. Using the inferred network, we were able to identify new genes involved in biofilm formation more accurately than clustering expression profiles.

Multiclass cancer diagnosis using tumor gene expression signatures.

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

Molecular classification of multiple tumor types.

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.