Integrating Gene Mutation Mutual Exclusion Information Into Radiomics Algorithms to Improve Gene Mutation Prediction in Non-small Cell Lung Cancer

Purpose Gene mutations are mutually exclusive in non-small cell lung cancer (NSCLC). Using EGFR and KRAS as examples, this study aims to assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.Methods We retrospectively analyzed 161 NSCLC patients with 18F-FDG PET/CT scans and EGFR and KRAS gene mutation data. Patients were randomly divided into training and testing cohorts. The Pyradiomics toolkit was used for radiomics feature extraction. Based on these features, radiomics score (RS) models were developed for predicting KRAS mutations using the gradient boosting decision tree (GBDT) algorithm. Furthermore, to investigate the value of adding mutation mutual exclusion information, a composite model combining PET/CT RS and EGFR mutation status was developed using logistic regression. The area under the curve (AUC), specificity, sensitivity, and accuracy were calculated for model performance evaluation in the training and test cohorts. To test the generalizability of this optimization method, models for predicting EGFR mutation were established in parallel, with or without adding KRAS gene mutation information.Results Compared with CT, the PET/CT based RS model exhibited higher AUC (KRAS: 0.792 vs 0.426; EGFR: 0.786 vs 0.644). By integrating EGFR mutation information into the PET/CT RS model, the AUC, accuracy, and specificity for predicting KRAS mutations were all elevated in the test cohort (0.928, 0.857, 0.897 vs 0.792, 0.755, 0.769). Conversely, the composite model for predicting EGFR mutations could also be optimized by adding KRAS gene mutation information (AUC, accuracy, and specificity: 0.877, 0.776, 0.700 vs 0.786, 0.694, 0.567). By adding EGFR and KRAS exclusive mutation information, respectively, the composite model corrected 55.4% and 30.7% false positive cases produced by the PET/CT RS model in the test cohort, without sacrificing sensitivity.Conclusion Integrating the mutation status of a known gene is a potential method to optimize radiomics models for predicting another gene mutation. This method may help predict unconventional gene mutations when the second biopsy is clinically difficult to carry out.

Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data

: Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.

MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer

Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs and vulnerabilities in software. A key challenge of CGF is how to select conducive seeds and allocate accurate energy. To address this problem, we propose a novel many-objective optimization solution, MooFuzz, which can identify different states of the seed pool and continuously gather different information about seeds to guide seed schedule and energy allocation. First, MooFuzz conducts risk marking in dangerous positions of the source code. Second, it can automatically update the collected information, including the path risk, the path frequency, and the mutation information. Next, MooFuzz classifies seed pool into three states and adopts different objectives to select seeds. Finally, we design an energy recovery mechanism to monitor energy usage in the fuzzing process and reduce energy consumption. We implement our fuzzing framework and evaluate it on seven real-world programs. The experimental results show that MooFuzz outperforms other state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, and PerfFuzz, in terms of path discovery and bug detection.

Observing Variations of Differences on COVID-19 in Varous Regions Extracting Type and Mutation Information

Covid-19 genomes were collected from three regions: Shanghai-China, Tbilisi-Georgia and Sydney-Australia. Five similar genomes were selected from each region for research in this paper. Applying the “datum gene sequence” method proposed, our results show that variation is immense in the Sydney-Australia region, followed by variation in the Tbilisi-Georgia region, which has a minimal value in the Shanghai-China region.

Observing Variations of Differences on COVID-19 in Varous Regions Extracting Type and Mutation Information

Covid-19 genomes were collected from three regions: Shanghai-China, Tbilisi-Georgia and Sydney-Australia. Five similar genomes were selected from each region for research in this paper. Applying the "datum gene sequence" method proposed, our results show that variation is immense in the Sydney-Australia region, followed by variation in the Tbilisi-Georgia region, which has a minimal value in the Shanghai-China region.

Delineation of the Germline and Somatic Mutation Interaction Landscape in Triple-Negative and Non-Triple-Negative Breast Cancer

Background. Breast cancer development and progression involve both germline and somatic mutations. High-throughput genotyping and next-generation sequencing technologies have enabled discovery of genetic risk variants and acquired somatic mutations driving the disease. However, the possible oncogenic interactions between germline genetic risk variants and somatic mutations in triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) have not been characterized. Here, we delineated the possible oncogenic interactions between genes containing germline and somatic mutations in TNBC and non-TNBC and investigated whether there are differences in gene expression and mutation burden between the two types of breast cancer. Methods. We addressed this problem by integrating germline mutation information from genome-wide association studies with somatic mutation information from next-generation sequencing using gene expression data as the intermediated phenotype. We performed network and pathway analyses to discover molecular networks and signalling pathways enriched for germline and somatic mutations. Results. The investigation revealed signatures of differentially expressed and differentially somatic mutated genes between TNBC and non-TNBC. Network and pathway analyses revealed functionally related genes interacting in gene regulatory networks and multiple signalling pathways enriched for germline and somatic mutations for each type of breast cancer. Among the signalling pathways discovered included the DNA repair and Androgen and ATM signalling pathways for TNBC and the DNA damage response, molecular mechanisms of cancer, and ATM and GP6 signalling pathways for non-TNBC. Conclusions. The results show that integrative genomics is a powerful approach for delineating oncogenic interactions between genes containing germline and genes containing somatic mutations in TNBC and non-TNBC and establishes putative functional bridges between genetic and somatic alterations and the pathways they control in the two types of breast cancer.

Mapping the Germline and Somatic Mutation Interaction Landscape in Indolent and Aggressive Prostate Cancers

Background. A majority of prostate cancers (PCas) are indolent and cause no harm even without treatment. However, a significant proportion of patients with PCa have aggressive tumors that progress rapidly to metastatic disease and are often lethal. PCa develops through somatic mutagenesis, but emerging evidence suggests that germline genetic variation can markedly contribute to tumorigenesis. However, the causal association between genetic susceptibility and tumorigenesis has not been well characterized. The objective of this study was to map the germline and somatic mutation interaction landscape in indolent and aggressive tumors and to discover signatures of mutated genes associated with each type and distinguishing the two types of PCa. Materials and Methods. We integrated germline mutation information from genome-wide association studies (GWAS) with somatic mutation information from The Cancer Genome Atlas (TCGA) using gene expression data from TCGA on indolent and aggressive PCas as the intermediate phenotypes. Germline and somatic mutated genes associated with each type of PCa were functionally characterized using network and pathway analysis. Results. We discovered gene signatures containing germline and somatic mutations associated with each type and distinguishing the two types of PCa. We discovered multiple gene regulatory networks and signaling pathways enriched with germline and somatic mutations including axon guidance, RAR, WINT, MSP-RON, STAT3, PI3K, TR/RxR, and molecular mechanisms of cancer, NF-kB, prostate cancer, GP6, androgen, and VEGF signaling pathways for indolent PCa and MSP-RON, axon guidance, RAR, adipogenesis, and molecular mechanisms of cancer and NF-kB signaling pathways for aggressive PCa. Conclusion. The investigation revealed germline and somatic mutated genes associated with indolent and aggressive PCas and distinguishing the two types of PCa. The study revealed multiple gene regulatory networks and signaling pathways dysregulated by germline and somatic alterations. Integrative analysis combining germline and somatic mutations is a powerful approach to mapping germline and somatic mutation interaction landscape.