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Carbon quantum dots (CQDs) are a new carbon-based nanomaterial that has attracted tremendous attention due to their excellent fluorescent properties, chemical stability, water solubility, and biocompatibility features. Here, fluorescent CQDs synthesized by a green nanoarchitectonic method using Cinchona Pubescens Vahl extract were evaluated as drug nanocarriers for carboplatin (CBP) delivery. The characterization methods showed CQDs with semispherical shapes and sizes around 5 nm, temperature- and pH-dependent functional groups that interact with the CBP molecule adding specificity to the drug-delivery system. Based on the load efficiency results, it seems that the CQDs can carry almost 100 μg of carboplatin for every 1 mg of CQDs. This is possible due to the self-assembly process that takes place through the interaction between the protonation/deprotonation functional groups of CQDs and the hydrolyzed CBP molecule. Through this process, it is created spherical nanoparticles with an average size of 77.44 nm. The CQDs-CBP nanoparticles release the drug through a diffusion-controlled release mechanism where the acidic media is preferred, and the EPR effect also plays a helpful role. Besides, the viability test shows that the CQDs have almost null cytotoxicity suggesting that they could be used as a promising cancer treatment, improving the efficiency of cell internalization and significantly increasing their drug delivery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Comprehensive sequencing of patient tumors reveals genomic mutations across tumor types that enable tumorigenesis and progression. A subset of oncogenic driver mutations results in neomorphic activity where the mutant protein mediates functions not engaged by the parental molecule. Here, we identify prevalent variant-enabled neomorph-protein-protein interactions (neoPPI) with a quantitative high-throughput differential screening (qHT-dS) platform. The coupling of highly sensitive BRET biosensors with miniaturized coexpression in an ultra-HTS format allows large-scale monitoring of the interactions of wild-type and mutant variant counterparts with a library of cancer-associated proteins in live cells. The screening of 17,792 interactions with 2,172,864 data points revealed a landscape of gain of interactions encompassing both oncogenic and tumor suppressor mutations. For example, the recurrent BRAF V600E lesion mediates KEAP1 neoPPI, rewiring a BRAF V600E /KEAP1 signaling axis and creating collateral vulnerability to NQO1 substrates, offering a combination therapeutic strategy. Thus, cancer genomic alterations can create neo-interactions, informing variant-directed therapeutic approaches for precision medicine., Competing Interests: Declaration of interests H.F. is scientific founder of PiVista Therapeutics., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8 + T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration., Competing Interests: Declaration of Interests E.M.V.A. serves in an advisory/consulting role for the following corporations: Tango Therapeutics, Genome Medical, Invitae, Illumina, Ervaxx, and Janssen. He receives research support from Novartis and Bristol-Myers Squibb. He owns equity in Tango Therapeutics, Genome Medical, Syapse, Microsoft, and Ervaxx. He has received travel reimbursement from Roche/Genentech. He holds institutional patents filed on ERCC2 mutations and chemotherapy response, chromatin mutations and immunotherapy response, and methods for clinical interpretation., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Motivation: The analysis of high-dimensional 'omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an 'omics dataset and are topologically close (e.g. connected) on an interaction network., Results: We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different 'omics datasets., Availability and Implementation: http://github.com/raphael-group/hierarchical-hotnet., Supplementary Information: Supplementary material are available at Bioinformatics online.

Motivation: The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision., Results: We formulate a weighted exact test for assessing the significance of mutual exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute P-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches., Availability and Implementation: See http://compbio.cs.brown.edu/projects/wext for software., Contact: braphael@cs.brown.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)