Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors’ complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer. In this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly. The limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Anorexia nervosa (AN) is a psychiatric disorder affected by psychological, environmental, and biological factors. Individuals with AN avoid high-fat, high-calorie diets and have shown abnormal metabolism of fatty acids (FAs), which are essential for brain and cognitive/neuropsychiatric health. To clarify the relationship between FAs and AN, fasting and postprandial plasma FAs in AN patients and age-matched control women were analyzed via mass-spectrometry. Clinical phenotypes were assessed using Becker Anxiety Inventory and Becker Depression Inventory. AN patients and controls exhibited different FA signatures at both fasting and postprandial timepoints. Lauric acid, eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and alpha-linoleic acid (ALA) were higher in AN than in controls (lauric acid: 15,081.6 ± 14,970.2 vs. 8257.4 ± 4740.2 pmol/mL; ALA at fasting: 2217.7 ± 1587.6 vs. 1087.9 ± 821.2 pmol/mL; ALA at postprandial: 1830.9 ± 1115.6 vs. 1159.4 ± 664.7 pmol/mL. EPA: 33,788.3 ± 17,487.5 vs. 22,860.6 ± 12,642.4 pmol/mL; DPA: 32,664.8 ± 16,215.0 vs. 20,969.0 ± 12,350.0 pmol/mL. FDR-adjusted p-values < 0.05). Food intake and AN status modified the correlations of FAs with body mass index (BMI), depression, and anxiety. Desaturases SCD-18 and D6D showed lower activities in AN compared to controls. Altered FA signature, specifically correlations between elevated n-3 FAs and worsened symptoms, illustrate metabolic underpinnings in AN. Future studies should investigate the mechanisms by which FA dysregulation, specifically elevated n-3 FAs, affects AN risk and outcome.

Background: In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cell could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets ( TOP1, TOP2A, DCK, WDR33, SLCO3A1) . In addition, some lines carried structural variants that encompassed additional known resistance genes ( ABCB1, WWOX and RRM1) . Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

Tumor infiltrating lymphocytes (TILs) have been associated with favorable prognosis in multiple tumor types. The Cancer Genome Atlas (TCGA) represents the largest collection of cancer molecular data, but lacks detailed information about the immune environment. Here, we show that exome reads mapping to the complementarity-determining-region 3 (CDR3) of mature T-cell receptor beta ( TCRB ) can be used as an immune DNA (iDNA) signature. Specifically, we propose a method to identify CDR3 reads in a breast tumor exome and validate it using deep TCRB sequencing. In 1,078 TCGA breast cancer exomes, the fraction of CDR3 reads was associated with TILs fraction, tumor purity, adaptive immunity gene expression signatures and improved survival in Her2+ patients. Only 2/839 TCRB clonotypes were shared between patients and none associated with a specific HLA allele or somatic driver mutations. The iDNA biomarker enriches the comprehensive dataset collected through TCGA, revealing associations with other molecular features and clinical outcomes.