CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

Abstract High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs

A Comprehensive Patient-Derived Xenograft Collection Representing the Heterogeneity of Melanoma

Therapy of advanced melanoma is changing dramatically. Following mutational and biological subclassification of this heterogeneous cancer, several targeted and immune therapies were approved and increased survival significantly. To facilitate further advancements through pre-clinical in vivo modeling, we have established 459 patient-derived xenografts (PDX) and live tissue samples from 384 patients representing the full spectrum of clinical, therapeutic, mutational, and biological heterogeneity of melanoma. PDX have been characterized using targeted sequencing and protein arrays and are clinically annotated. This exhaustive live tissue resource includes PDX from 57 samples resistant to targeted therapy, 61 samples from responders and non-responders to immune checkpoint blockade, and 31 samples from brain metastasis. Uveal, mucosal, and acral subtypes are represented as well. We show examples of pre-clinical trials that highlight how the PDX collection can be used to develop and optimize precision therapies, biomarkers of response, and the targeting of rare genetic subgroups

Chitin Adsorbents for Toxic Metals: A Review

Wastewater treatment is still a critical issue all over the world. Among examined methods for the decontamination of wastewaters, adsorption is a promising, cheap, environmentally friendly and efficient procedure. There are various types of adsorbents that have been used to remove different pollutants such as agricultural waste, compost, nanomaterials, algae, etc., Chitin (poly-β-(1,4)-N-acetyl-d-glucosamine) is the second most abundant natural biopolymer and it has attracted scientific attention as an inexpensive adsorbent for toxic metals. This review article provides information about the use of chitin as an adsorbent. A list of chitin adsorbents with maximum adsorption capacity and the best isotherm and kinetic fitting models are provided. Moreover, thermodynamic studies, regeneration studies, the mechanism of adsorption and the experimental conditions are also discussed in depth

Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600

In the original publication [...

Using Transcriptional Signatures to Find Cancer Drivers with LURE.

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure

Using Transcriptional Signatures to Find Cancer Drivers with LURE.

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure

BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers

Complete loss of BRCA1 or BRCA2 function is associated with sensitivity to DNA damaging agents. However, not all BRCA1 and BRCA2 germline mutation-associated tumors respond. Herein we report analyses of 160 BRCA1 and BRCA2 germline mutation-associated breast and ovarian tumors. Retention of the normal BRCA1 or BRCA2 allele (absence of locus-specific loss of heterozygosity (LOH)) is observed in 7% of BRCA1 ovarian, 16% of BRCA2 ovarian, 10% of BRCA1 breast, and 46% of BRCA2 breast tumors. These tumors have equivalent homologous recombination deficiency scores to sporadic tumors, significantly lower than scores in tumors with locus-specific LOH (ovarian, P = 0.0004; breast P < 0.0001, two-tailed Student's t-test). Absence of locus-specific LOH is associated with decreased overall survival in ovarian cancer patients treated with platinum chemotherapy (P = 0.01, log-rank test). Locus-specific LOH may be a clinically useful biomarker to predict primary resistance to DNA damaging agents in patients with germline BRCA1 and BRCA2 mutations.Most tumours associated with germline BRCA1/BRCA2 loss of function mutations respond to DNA damaging agents, however, some do not. Herein, the authors identify that a subset of breast/ovarian tumors retain a normal allele, which is associated with decreased overall survival after DNA damage-inducing platinum chemotherapy