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In eukaryotes, cellular differentiation is often orchestrated by programmed arrays of activation and repression of genes underlying the specific phenotypes of cell-types. To complete its life cycle, the single-celled Apicomplexan parasite Plasmodium falciparum, the most deadly of the human malaria parasites, must repeatedly differentiate and convert into unique cell types that can exploit niches within their human and mosquito hosts. One of these key developmental transitions occurs in the human host and includes sexual-stage commitment, followed by gametocyte development, which results in male and female gametocytes that can infect the female anopheline mosquito once taken up during the blood meal. The following gamete stage undergoes fusion and meiosis, followed by morphological changes as they invade the midgut wall to form the oocyst. The sexual stages are responsible for malaria transmission and the spread of antimalarial resistance, making it an important target for malaria eradication. Previous studies have established the understanding of a few mechanisms involved in sexual differentiation; the transcription factor, AP2-G, is a master regulator of sexual commitment. Uncovering genes and regulators underlying P. falciparum sexual stage development, including host-parasite interactions are the subjects of this thesis. In paper I, we describe the temporal landscape of differential transcriptional programs underlying the P. falciparum male and female gametocyte development and highlight a bifurcation point for the onset of differential male and female transcriptional programs. Furthermore, we predict novel candidate driver genes underlying sexual cell fate determination in P. falciparum. Finally, we traced the potential sex-specific regulatory mechanisms among the ApiAP2 family of transcription factors including prediction of target genes based on co-expression and the presence of upstream binding sites, rendering a motif-driven gene regulatory network. In paper II, we study the P. falciparum development in the mosquito midgut, from unfertilized female gamete through the first 20 hours of development, including the zygote and ookinete stages. Using pseudotime analysis, we demonstrate that the transcriptional trajectory of the parasite is controlled by three main transcriptional programs including five unique transcriptional stages. Further, we employ structural-based functional predictions to predict intrinsically disordered proteins encoding upregulated genes through the mid to late ookinete with antigenic properties, which may serve as suitable targets for transmission suppression strategies. In paper III, we utilize single-cell RNA sequencing combined with functional assays to demonstrate an increased complexity of the Anopheles mosquito immune cells, previously defined as hemocytes, Based on panels of genetic markers and morphological traits. We define additional marker genes that enable deeper characterization of Anopheles immune cell subtypes and uncover transcriptional programs involved in immune cell differentiation and maturation. In summary, this thesis aims to uncover previously unknown aspects of eukaryotic cell fate determination and life cycle stage differentiation with a focus on P. falciparum sexual stage development. Furthermore, we aim to enable deeper characterization of the Anopheles mosquito immune cell subtypes, their differentiation, development, and fate during steady-state and upon immune activation.

Lung cancer is the leading cause of cancer death all over the world. The majority (80-85 %) of lung cancer cases are classified as non-small cell lung cancer (NSCLC). Within NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the most often recognized. The histological and immunohistochemical examination of NSCLC is a basic diagnostic tool, but insufficient for comprehensive therapeutic decisions. In some NSCLC patients, mainly adenocarcinoma, molecular alterations in driver genes, like EGFR, KRAS, HER2, ALK, MET, BRAF, RET, ROS1, and NTRK are recognized. The frequency of some of those changes is different depending on race, and between smokers and non-smokers. The molecular diagnostics of NSCLC using modern methods, like next-generation sequencing, is essential in estimating targeted, personalized therapy. In recent years, a breakthrough in understanding the importance of molecular studies for the precise treatment of NSCLC has been observed. Many new drugs were approved, including tyrosine kinase and immune checkpoint inhibitors. Clinical trials testing novel molecules like miRNAs and trials with CAR-T cells (chimeric antigen receptor – T cells) dedicated to NSCLC patients are ongoing., EXCLI Journal; 22:Doc415; ISSN 1611-2156

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network’s central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Data for the case studies reported in the publication: https://www.biorxiv.org/content/10.1101/2022.11.18.517066v1. The data should be use together with the code deposited in GitHub to reproduce the case study: https://github.com/ELELAB/Moonlight2_DMA_basal_like and using Moonlight2 https://github.com/ELELAB/Moonlight2R

Background Molecular testing for alterations in oncogenic driver genes and targeted therapies have become standard procedures for non‐small cell lung cancer (NSCLC) patients. However, little evidence has shed light on the pattern of co‐existence of driver genes in NSCLC, and whether they may have different tumor features affecting immunotherapy is still unclarified. Methods Genomic alterations in 14 lung cancer‐related genes were conducted in 3440 Chinese NSCLC patients using next‐generation sequencing. Meanwhile, tumor mutational burden and immunotherapy dataset from the Memorial sloan kettering cancer center (MSKCC) and lung adenocarcinoma dataset from The Cancer Genome Atlas (TCGA) were utilized for analyzing the impact of the co‐occurring alterations on patients’ survival following immunotherapy. Results In this cohort, 90.17% of patients had at least one somatic alteration in the 14 genes, including 51% of co‐occurring alterations. TP53 and epidermal growth factor receptor (EGFR) were the most prevalent genes (54.74% and 53.55%, respectively), followed by KRAS, ERBB2, ALK, PIK3CA, ROS1, RET, MET, BRAF, KIT, FGFR1, PDGFRA, and NRAS. The prevalence of TP53, EGFR, and ERBB2 in our cohort were significantly higher than that from the TCGA database, whereas KRAS, BRAF, and PDGFRA were significantly lower than the latter. Furthermore, the patients who harbored multiple alterations (8.86%, 31/350) in eight driver genes survived longer and have a higher tumor mutation burden compared to the patients with a single alteration. Similar result was found between the patients with co‐occurring alteration of EGFR and other driver genes and the patients with single EGFR alteration. Meanwhile, we found a distinct immune cell infiltration feature between patients with single and multiple driver gene alterations, as well as between patients with only EGFR alteration and co‐occurring groups. Conclusion This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of EGFR and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated NSCLC patients and merit additional investigation., This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of epidermal growth factor receptor (EGFR) and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated non‐small cell lung cancer patients and merit additional investigation.

Background As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

Development of primary mediastinal B-cell lymphoma (PMBL) is driven by cumulative genomic aberrations. We discovered a unique copy-neutral loss of heterozygosity (CN-LOH) landscape of PMBL which distinguishes this tumor from other B-cell malignancies, including the biologically related diffuse large B-cell lymphoma. Using single nucleotide polymorphism array analysis we identified large-scale CN-LOH lesions in 91% (30/33) of diagnostic PMBLs and both investigated PMBL-derived cell lines. Altogether, the cohort showed 157 extra-large (25.3-248.4 Mb) CN-LOH lesions affecting up to 14 chromosomes per case (mean of 4.4) and resulting in a reduction of heterozygosity an average of 9.9% (range 1.3-51%) of the genome. Predominant involvement of terminal chromosomal segments suggests the implication of B-cell specific crossover events in the pathogenesis of PMBL. Notably, CN-LOH stretches non-randomly clustered on 6p (60%), 15 (37.2%), and 17q (40%), and frequently co-occurred with homozygous mutations in the MHC I (6p21), B2M (15q15), and GNA13 (17q23) genes, respectively, as shown by preliminary whole-exome/genome sequencing data. Altogether, our findings implicate CN-LOH as a novel and distinct mutational process contributing to the molecular pathogenesis of PMBL. The aberration acting as "second hit" in the Knudson hypothesis, ranks as the major mechanism converting to homozygosity the PMBL-related driver genes. Screening of the cohort of 199 B cell leukemia/lymphoma whole-genomes revealed significant differences in the CN-LOH landscape of PMBL and other B-cell malignancies, including the biologically related diffuse large B-cell lymphoma.

Graphical abstract, Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

Background Malignant melanoma of the female genital tract is relatively uncommon and accounts for 3–7% of all melanoma localizations. This study aimed to identify driver genes in melanoma of the female genital tract with the purpose of enhancing understanding of disease pathogenesis and identifying potential new therapeutic targets to develop effective therapies. Methods KIT (CD117) and BRAF expression were detected immunohistochemically. Polymerase Chain Reaction (PCR) and Sanger sequencing techniques were performed to identify the mutational status of BRAF, NRAS, KRAS, NF1, KIT, PDGFRA and SF3B1 on 19 melanomas of the female genital tract, paired with 25 cutaneous melanomas, 18 acral melanomas and 11 melanomas of nasal cavity. Results Somatic variant analysis identified KRAS (6/19; 32%) as the most commonly mutated gene, followed by KIT (4/19; 21%), SF3B1 (3/19; 16%) and NRAS (1/19; 5%). None of the cases were found to harbor BRAF, NF1 and PDGFRA mutations in melanomas of the female genital tract. However, none of the cases were found to harbor SF3B1 and KIT mutations in cutaneous melanomas, acral melanomas and melanomas of nasal cavity. Recurrent KIT mutations, as well as mutations in the less frequently mutated genes NRAS and SF3B1, were exclusively detected in vulvovaginal melanomas, but not in tumors arising in the cervix. However, recurrent KRAS mutations were detected in similar frequencies in tumors of the vulva, vagina, and cervix. Additionally, recurrent KRAS and KIT mutations occurred predominantly in polygonal and epithelioid cell types of melanoma in the female genital tract. Immunohistochemistry revealed moderate or strong cytoplasmic CD117 expression in 6 of the 19 cases (31.6%). Conclusions We observed that gynecologic melanoma harbored distinct mutation rates in the KIT, BRAF, SF3B1, KRAS, and NRAS genes. Our findings support the notion that gynecologic melanoma is a distinct entity from non-gynecologic melanoma, and these findings offer insights into future therapeutic options for these patients.

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver

As one of few viral-positive cancers, nasopharyngeal carcinoma (NPC) is extremely rare across the world but very frequent in several regions of the world, including Southern China (known as the Cantonese cancer). Even though several genomic studies have been conducted for NPC, their sample sizes are relatively small and systematic comparison with other cancer types has not been explored. In this study, we collected four-hundred-thirty-one samples from six previous studies and provided the first integrative analysis of NPC genomes. Combining several statistical methods for detecting driver genes, we identified 25 novel drivers for NPC, including ATG14 and NLRC5. Many of these novel drivers are enriched in several important pathways, such as autophagy and immunity. By comparing NPC with many other cancer types, we found NPC is a unique cancer type in which a high proportion of patients (45.2%) do not have any known driver mutations (termed as “missing driver events”) but have a preponderance of deletion events, including chromosome 3p deletion. Through signature analysis, we identified many known and novel signatures, including single-base signatures (n = 12), double-base signatures (n = 1), indel signatures (n = 9) and copy number signatures (n = 8). Many of these new signatures are involved in DNA repair and have unknown etiology and genome instability, implying an unprecedented dynamic mutational process possibly driven by complex interactions between viral and host genomes. By combining clinical, molecular and intra-tumor heterogeneity features, we constructed the first integrative survival model for NPC, providing a strong basis for patient prognosis and stratification. Taken together, we have performed one of the first integrative analyses of NPC genomes and brought unique genomic insights into tumorigenesis of a viral-driven cancer.

Background: Aberrant DNA methylation occurs commonly during carcinogenesis and is of clinical value in human cancers. However, knowledge of the impact of DNA methylation changes on lung carcinogenesis and progression remains limited. Methods: Genome-wide DNA methylation profiles were surveyed in 18 pairs of tumors and adjacent normal tissues from non-small cell lung cancer (NSCLC) patients using Reduced Representation Bisulfite Sequencing (RRBS). An integrated epigenomic-transcriptomic landscape of lung cancer was depicted using the multi-omics data integration method. Results: We discovered a large number of hypermethylation events pre-marked by poised promoter in embryonic stem cells, being a hallmark of lung cancer. These hypermethylation events showed a high conservation across cancer types. Eight novel driver genes with aberrant methylation (e.g., PCDH17 and IRX1) were identified by integrated analysis of DNA methylome and transcriptome data. Methylation level of the eight genes measured by pyrosequencing can distinguish NSCLC patients from lung tissues with high sensitivity and specificity in an independent cohort. Their tumor-suppressive roles were further experimentally validated in lung cancer cells, which depend on promoter hypermethylation. Similarly, 13 methylation-driven ncRNAs (including 8 lncRNAs and 5 miRNAs) were identified, some of which were co-regulated with their host genes by the same promoter hypermethylation. Finally, by analyzing the transcription factor (TF) binding motifs, we uncovered sets of TFs driving the expression of epigenetically regulated genes and highlighted the epigenetic regulation of gene expression of TCF21 through DNA methylation of EGR1 binding motifs. Conclusions: We discovered several novel methylation driver genes of diagnostic and therapeutic relevance in lung cancer. Our findings revealed that DNA methylation in TF binding motifs regulates target gene expression by affecting the binding ability of TFs. Our study also provides a valuable epigenetic resource for identifying DNA methylation-based diagnostic biomarkers, developing cancer drugs for epigenetic therapy and studying cancer pathogenesis.

Background A retrospective analysis verified the role of gene mutations in brain metastasis in patients with non‐small cell lung cancer (NSCLC). Methods Data from 552 patients with advanced NSCLC treated from January 2015 to June 2017 in the Affiliated Cancer Hospital of Zhengzhou University were retrospectively analyzed. Next‐generation sequencing was used to detect mutations in eight reported driver genes and various risk factors were evaluated. Results Of the 552 patients with advanced NSCLC, 153 (27.7%) had brain metastases. The univariate analysis showed that age (P = .008), gender (P = .016), smoking history (P = .010), lymph node metastasis (P = .003), and three driver genes, positive epidermal growth factor receptor (EGFR) mutation (P = .001), positive anaplastic lymphoma kinase (ALK) gene fusion (P = .021), and positive rearranged during transfection (RET) gene fusion (P = .003), were the factors influencing the incidence of brain metastasis. Logistic multivariate regression analysis revealed that positive EGFR mutation (P = .012), positive ALK gene fusion (P = .015), positive RET gene fusion (P = .003), pathological type (P = .009), lymph node N2‐3 metastasis (P, The incidence of brain metastasis in non‐small cell lung cancer (NSCLC) patients is 30%‐40%. Brain metastasis (BM) in NSCLC correlated with age, pathological type and lymph node N2‐3 metastasis. Epidermal growth factor receptor, anaplastic lymphoma kinase, and rearranged during transfection (RET) mutations were associated with BM in NSCLC patients.

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Background Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. Methods We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. Results In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. Conclusions These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.

Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

Significance Recently, investigators have shown that only a few driver gene mutational events appear to be needed for cancer to occur. However, the reason that some mutational events precede others in the same cancer and the explanation for tissue-specific differences in this timing, remain mysterious. We here combine mathematical modeling with epidemiologic studies and sequencing data to address these questions. We suggest that the first driver event in cancers generally occurred at early ages and provide estimates for the fitness of different types of drivers during tumor evolution, showing how they vary with the tissue of origin., Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Methods Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). Results This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. Conclusion The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA–miRNA–mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer’s disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA–miRNA–mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA–miRNA–mRNA regulatory mechanisms in cancers.

In this study, we mined out hepatocellular carcinoma (HCC) driver genes from MEDLINE literatures by bioinformatics methods of pathway crosstalk and protein interaction network. Furthermore, the relationship between driver genes and their clinicopathological characteristics, as well as classification effectiveness was verified in the public databases. We identified 560 human genes reported to be associated with HCC in 1074 published articles. Functional analysis revealed that biological processes and biochemical pathways relating to tumor pathogenesis, cancer disease, tumor cell molecule, and hepatic disease were enriched in these genes. Pathway crosstalk analysis indicated that significant pathways could be divided into three modules: cancer disease, virus infection, and tumor signaling pathway. The HCC-related protein-protein interaction network comprised 10,212 nodes, and 56,400 edges were mined out to identify 18 modules corresponding to 14 driver genes. We verified that these 14 driver genes have high classification effectiveness to distinguish cancer samples from normal samples and the classification effectiveness was better than that of randomly selected genes. Present study provided pathway crosstalk and protein interaction network for understanding potential tumorigenesis genes underlying HCC. The 14 driver genes identified from this study are of great translational value in HCC diagnosis and treatment, as well as in clinical study on the pathogenesis of HCC.

Niewiadomski Paweł, Mikołajczyk Klaudia, Rauhut Sebastian, Stryjak Krzysztof, Lazarek Maciej, Olszewski Bogusz, SochańAgata, Kędziora - Kornatowska Kornelia. Selected genetic factors increasing risk of neoplasia. Journal of Education, Health and Sport. 2019;9(8):843-856. eISSN 2391-8306. DOI http://dx.doi.org/10.5281/zenodo.3370434 http://ojs.ukw.edu.pl/index.php/johs/article/view/7254 The journal has had 7 points in Ministry of Science and Higher Education parametric evaluation. Part B item 1223 (26/01/2017). 1223 Journal of Education, Health and Sport eISSN 2391-8306 7 © The Authors 2019; This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 05.07.2019. Revised: 25.07.2019. Accepted: 18.08.2019. Selected genetic factors increasing risk of neoplasia Paweł Niewiadomski1, Klaudia Mikołajczyk1, Sebastian Rauhut1, Krzysztof Stryjak1, Maciej Lazarek1, Bogusz Olszewski1, Agata Sochań1, Kornelia Kędziora - Kornatowska1 1. Faculty of Health Sciences, Department and Clinic of Geriatrics, Nicolaus Copernicus University, Bydgoszcz Abstract Introduction: Neoplasia is defined as abnormal and excessive growth of selected tissue. Several factors, such as mutations in selected genes, can increase the risk of cancer expansion in human body. The aim of the article is to review the selected genetic factors which increase the risk of neoplasia and results of their presence in the human body. Material and methods: Articles in the Google Scholar, Pub Med database have been analysed using keywords: cancer, neoplasia, genetic factors, driver genes, cancer transformation, older people. Results: The aim of several genes is repairing damaged or dysfunctional DNA and preventing cells from abnormal or excessive. Mutations in selected genes cause inhibited production of the gene protein product or a change in its function, which increase the risk of neoplasia. The presence of mutated genes results in the initiation of the process leading to expansion of cancer cells in selected tissue. Moreover, a genetic mutation can lead to syndrome of tumors occurring in several organs. However, the increasement of cancer risk is related to numerous mutations, whereas the minority of carcinomas occur because of congenital gene defect and the majority is caused by environmental factors which contribute to creating various mutations. Conclusions: Thanks to the development of genetics in the field of medicine and introduction of genetic tests, the process of diagnosis of several tumors and syndromes is more efficient than in the past. The occurrence of mutation in genes, such as BRCA1, BRCA2, VHL, MSH2 and MLH1 and many more, relates to presence of selected tumors. By the introduction of pharmacogenetics, dozens of molecular-targeted drugs are used in the treatment of several types of cancers leading the achievement of therapeutic success. Nevertheless, the genetic background of many types of cancers is unknown and needs further study, as well as drugs targeting at selected genes mutations requires more development and guidelines in the treatment process. Keywords: cancer; neoplasia; genetic factors; driver genes; cancer transformation; older people

Between 2006 and 2015, the U.S. Food and Drug Administration’s (FDA) overall likelihood of approval (LOA) from Phase I clinical trials for all therapeutic candidates was 9.6%, with the highest LOA in haematology (26.1%) and the lowest in oncology (5.1%). Two critical features attributed to the success of advancing trials were i) targeting driver genes responsible for disease, and ii) use of human disease-relevant animal models during preclinical studies. For decades, conventional wisdom has been that growth factors are the drivers of wound healing, but few have either advanced to clinical applications or proven effective. The purpose of this paper is to explore heat shock protein 90-alpha (Hsp90α)’s role as a potential driver of wound healing and as a possible future therapeutic entity through a review of recent literature, including studies with human disease-relevant animal models. Of the approximately 7,000 gene products generated by a given mammalian cell type, the Hsp90 family of proteins (Hsp90α and Hsp90β) accounts for 2–3% of them. Hsp90β fulfils the role of an intracellular chaperone, but Hsp90α’s intracellular function is surprisingly dispensable. Instead, the abundancy of Hsp90α appears to have been prepared for extracellular purposes. When secreted via exosomes by cells under environmental stress, such as injury, Hsp90α protects cells from hypoxia-induced cell death, reduces local inflammation, and subsequently promotes cell migration to repair the injured tissue. Unlike conventional growth factors, secreted Hsp90α stimulates all major cell types involved in wound healing equally, resists microenvironmental inhibitors like TGFβ and hyperglycaemia, and is highly stable. Inhibition of exosome-mediated Hsp90α secretion, neutralisation of Hsp90α’s ATPase-independent extracellular functions, or interruption of Hsp90α-LRP-1 signalling blocks wound closure in vivo. Topical application of Hsp90α’s therapeutic entity, F-5 (a 115-amino acid peptide), has shown great promise for healing acute burn and diabetic wounds in mice and pigs.

Objective To screen the core-driving pathogenic genes in the occurrence and development of ependymoma (EPN), explore the involved signal pathways of its pathogenesis, and to clarify the relationship of prognosis with expression of these driver genes. Methods The differentially expressed gene (DEGs) were identified after comparing between gene expression profiles of the EPN tissues and normal tissues based on gene chip, microarray and bioinformatics. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analyses were conducted to find out the enrichment functions, pathways and hub genes. After hub genes were identified, the survival analysis of 325 patients, which data obtained from Chinese Glioma Genome Atlas (CCGA), were performed to clarify the relationship between prognosis and the expression levels of the hub genes. Results Genes TP53, TOP2A, CDK1, PCNA and ACTA2 were found as core driver genes, and their aberrant expression promoted the occurrence and development of EPN; Hedgehog signaling pathway, Notch signal pathway and mismatch repair signal pathway were the high-risk signal pathways for the development of EPN. And the results of survival analysis showed that the patients with lower expression of TOP2A, CDK1, PCNA and ACTA2 had favorable prognosis and longer progression-free survival (PFS) and overall survival (OS) (P < 0.05). Conclusion In this study, TP53, TOP2A, CDK1, PCNA and ACTA2 were selected as the core driver genes of ependymoma. At the same time, we explored the high-risk signaling pathways that cause EPN, and provided new targets for clinical diagnosis and treatment of EPN, which is of great significance.

It is rare that stage IV colon cancer is cured with chemotherapy. Here we report the long-term survival of a patient who presented with highly advanced disease characterized by a papillary architecture as well as porta hepatis lymph nodes but responded extremely well to FOLFIRI/bevacizumab. His original tumor underwent comprehensive genomic testing that included whole genome DNA sequencing, targeted sequencing, and RNA sequencing. These genetic results suggest the patient’s tumor harbored mutations in APC, KRAS, and TP53 as well as in PIK3CB. Moreover, the RNA-seq data suggested that the tumor belonged to the consensus molecular subtype 4, the “inflamed, immune phenotype,” with increased angiogenesis. Deep sequencing of highly responsive cancers may yield molecular insights into mechanisms underpinning a remarkable response.

Circulating tumor DNA (ctDNA) is a promising biomarker for accurate monitoring and less invasive assessment of tumor burden and treatment response. Here, targeted next-generation sequencing (NGS) with a designed gene panel of 176 cancer-relevant genes was used to assess mutations in 90 ctDNA samples from 90 patients with multiple types of liver disease and 10 healthy donor samples for control. Using our ctDNA detection panel, we identified mutations in 98.89% (89/90) of patient plasma biopsy samples, and 19 coding variants located in 10 cancer-related genes [ACVR2A, PCLO, TBCK, adhesion G protein-coupled receptor (ADGRV1), COL1A1, GABBR1, MUC16, MAGEC1, FASLG, and JAK1] were identified in 96.7% of patients (87/90). The 10 top mutated genes were tumor protein p53 (TP53), ACVR2A, ADGRV1, MUC16, TBCK, PCLO, COL11A1, titin (TTN), DNAH9, and GABBR1. TTN and TP53 and TTN and DNAH9 mutations tended to occur together in hepatocellular carcinoma samples. Most importantly, we found that most of those variants were insertions (frameshift insertions) and deletions (frameshift deletions and in-frame deletions), such as insertion variants in ACVR2A, PCLO, and TBCK; such mutations were detected in almost 95% of patients. Our study demonstrated that the targeted NGS-based ctDNA mutation profiling was a useful tool for hepatocellular carcinoma (HCC) monitoring and could potentially be used to guide treatment decisions in HCC.

The incidence of Human-papillomavirus-positive (HPV+) tonsillar and base-of-tongue squamous cell carcinoma (TSCC and BOTSCC, respectively) is increasing epidemically, but they have better prognosis than equivalent HPV-negative (HPV−) cancers, with roughly 80% vs. 50% 3-year disease-free survival, respectively. The majority of HPV+ TSCC and BOTSCC patients therefore most likely do not require the intensified chemoradiotherapy given today to head and neck cancer patients and would with de-escalated therapy avoid several severe side effects. Moreover, for those with poor prognosis, survival has not improved, so better-tailored alternatives are urgently needed. In line with refined personalized medicine, recent studies have focused on identifying predictive markers and driver cancer genes useful for better stratifying patient treatment as well as for targeted therapy. This review presents some of these endeavors and briefly describes some recent experimental progress and some clinical trials with targeted therapy.

Background: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention, however, no comprehensive data exists elucidating common gene expression between the two diseases. The objective of this study was to (1) demonstrate the practicability and feasibility of utilizing a systems biology approach called network-based identification of common driver genes (NICD) to identify common genomic features between two associated diseases and (2) utilize this approach to identify genes associated with both OSA and dementia. Methods: This study utilized 2 public databases (PCNet, DisGeNET) and a permutation assay in order to identify common genes between two co-morbid but mutually exclusive diseases. These genes were then linked to their mechanistic pathways through Enrichr, producing a list of genes that were common between the two different diseases. Results: 42 common genes were identified between OSA and dementia which were primarily linked to the G-coupled protein receptor (GPCR) and olfactory pathways. No single nucleotide polymorphisms (SNPs) were identified. Conclusions: This study demonstrates the viability of using publicly available databases and permutation assays along with canonical pathway linkage to identify common gene drivers as potential mechanistic targets for comorbid diseases.

Gastric cancer (GC) pathogenesis is complex and heterogeneous, reflecting morphological, molecular and genetic diversity. Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the major histological types. GC may be sporadic or hereditary; sporadic GC is related to environmental and genetic low‐risk factors and hereditary GC is caused by inherited high‐risk mutations, so far identified only for the diffuse histotype. DGC phenotypic heterogeneity challenges the current understanding of molecular mechanisms underlying carcinogenesis. The definition of a DGC‐specific mutational profile remains controversial, possibly reflecting the heterogeneity of DGC‐related histological subtypes [signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS)]. Indeed, DGC and DGC‐related subtypes may present specific mutational profiles underlying the particularly aggressive behaviour and dismal prognosis of DGC vs IGC and PCC‐NOS vs SRCC. In this systematic review, we revised the histological presentations, molecular classifications and approved therapies for gastric cancer, with a focus on DGC. We then analysed results from the most relevant studies, reporting mutational analysis data specifying mutational frequencies, and their relationship with DGC and IGC histological types, and with specific DGC subtypes (SRCC and PCC‐NOS). We aimed at identifying histology‐associated mutational profiles with an emphasis in DGC and its subtypes (DGC vs IGC; sporadic vs hereditary DGC; and SRCC vs PCC‐NOS). We further used these mutational profiles to identify the most commonly affected molecular pathways and biological functions, and explored the clinical trials directed specifically to patients with DGC. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing., The main diffuse gastric cancer (DGC) histological subtypes are signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS). Clinical trials, targeted therapies and predictive markers of therapy response directed to any of these entities are residual. This systematic review exposes the lack of integrated knowledge in these clinical entities and uses their mutational profiles to rethink therapeutic intervention.

Tumor development is based on mutations of genes involved in cell growth (e.g. transcription factors, growth receptors or intracellular signal molecules) or in suppressor genes (e.g. p53). During tumor growth cell clones are selected, which contain driver genes, leading to uncontrolled growth of these cell clones. During all phases of tumor development (immunosurveillance, equilibrium phase, escape of the tumor from the immune system) the interaction between the immune system and the tumor cells and the development of a chronic inflammation in the tumor microenvironment play a crucial role. The aim of cancer immunotherapy is to activate the immune system. A promising immunotherapy is based on antibodies that activate immune cells, inhibit tumor growth or lead to destruction of tumor cells. Applied are recombinant IgG antibodies or genetically engineered antibody fragments against tumor-associated antigens (TAA’s). They are applied singularly or in combination with chemo- or radiotherapy. Promising are checkpoint antibodies, which abrogate blocking of cytotoxic CD8+ T cells and CD4+ T cells by tumor cells and/or dendritic cells. Other successfully applied antibodies are bispecific antibodies (recognize T‑cell and tumor cell), chimeric antigen receptors (CARs) for T cell therapy, immunocytokines (cytokines fused to antibodies) and immunotoxins (toxins fused to antibodies). In addition intracellular antibodies successfully tested in xenograft tumor mouse models have promising therapeutic potential.

Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the Gene Ontology terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Son yıllardaki kanser genom çalışmaları yüksek sayıda kanser genomu için detaylı moleküler veri üretmiştir. Ortaya çıkan önemli bir problem bu verileri kullanarak kanser sürücü genleri tespit etmektir. Bu tezde, genomik veriyi protein-protein etkileşim ağlarıyla birleştirerek kanser sürücü genleri tespit eden BetweenNet isimli işlemsel bir yöntem önerilmektedir. BetweenNet, hastaya özgü oluşturulmuş ağları arasındalık merkeziliği tabanlı bir metrik ile inceleyerek etkinliği değişmiş aykırı genleri bulmaktadır. Mutasyona uğramış genlerle etkinliği değişmiş genlerin arasındaki ilişkileri iki parçalı bir çizgede tanımlayıp, bu çizgede rastgele yürüyüş algoritması uygulayarak mutasyona uğramış genleri sürücülük potansiyeline göre sıralamaktadır. BetweenNet yöntemi varolan benzer yöntemlerle akciğer, meme ve pan-kanser verileri kullanılarak karşılaştırılmıştır. Değerlendirmelerimiz BetweenNet'in bilinen kanser genlerini bulmada daha başarılı olduğunu göstermektedir. Ayrıca, bilinen kanser genleriyle BetweenNet tarafından sıralanan genlerin Gene Ontology terimleri ve referans ağlar bakımından birbiriyle önemli derecede örtüştüğü tespit edilmiştir. No sponsor

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.

High-throughput sequencing technologies have identified millions of genetic mutations in multiple human diseases. However, the interpretation of the pathogenesis of these mutations and the discovery of driver genes that dominate disease progression is still a major challenge. Combining functional features such as protein post-translational modification (PTM) with genetic mutations is an effective way to predict such alterations. Here, we present PTMsnp, a web server that implements a Bayesian hierarchical model to identify driver genetic mutations targeting PTM sites. PTMsnp accepts genetic mutations in a standard variant call format or tabular format as input and outputs several interactive charts of PTM-related mutations that potentially affect PTMs. Additional functional annotations are performed to evaluate the impact of PTM-related mutations on protein structure and function, as well as to classify variants relevant to Mendelian disease. A total of 4,11,574 modification sites from 33 different types of PTMs and 1,776,848 somatic mutations from TCGA across 33 different cancer types are integrated into the web server, enabling identification of candidate cancer driver genes based on PTM. Applications of PTMsnp to the cancer cohorts and a GWAS dataset of type 2 diabetes identified a set of potential drivers together with several known disease-related genes, indicating its reliability in distinguishing disease-related mutations and providing potential molecular targets for new therapeutic strategies. PTMsnp is freely available at: http://ptmsnp.renlab.org.

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC. Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan–Meier survival curves. Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value. Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.

Tumor heterogeneity is one of the characteristics of malignant tumors, which can cause differences in tumor growth rate, invasion, migration, drug sensitivity and prognosis. Discoveries and development upon on tumor-driver genes and targeted therapy paved the way on dealing with cancer diagnosis and treatment. However, the existence of tumor heterogeneity makes malignant tumor more hardly to overcome. It is generally present and far more complicated during the process of cancer recurrence, development and evolution. Thus, it has becoming key areas in precision medicine regarding to designing optimal therapeutic approaches targeting the mechanisms and phenotypes of tumor heterogeneity, based on novel detecting techniques and new concepts and theory. This review summarized the current references on tumor heterogeneity, in order to better understand the function and mechanisms, and eventually manage tumor heterogeneity through various methods.

Background Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge. Results We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008). Conclusions Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy. Reviewers This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca. Electronic supplementary material The online version of this article (10.1186/s13062-018-0218-5) contains supplementary material, which is available to authorized users.

A meeting report on the Third European Association for Cancer Research Conference on Cancer Genomics, held at Churchill College, Cambridge, UK, 25–28 June 2017.

// Tiejun Zhang 1 and Di Zhang 2 1 GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong 511436, China 2 School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China Correspondence to: Di Zhang, email: zhangdi@ahu.edu.cn Keywords: driver genes, protein interaction network, integrative data Received: April 19, 2017 Accepted: June 19, 2017 Published: July 22, 2017 ABSTRACT Although numerous approaches have been proposed to discern driver from passenger, identification of driver genes remains a critical challenge in the cancer genomics field. Driver genes with low mutated frequency tend to be filtered in cancer research. In addition, the accumulation of different omics data necessitates the development of algorithmic frameworks for nominating putative driver genes. In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers ( PTPN6 in THCA, SRC , GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC).

// Qing-Ge Zhu 1, * , Shi-Ming Zhang 1, * , Xiao-Xiao Ding 1, * , Bing He 1 and Hu-Qin Zhang 1 1 The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China * These authors contributed equally to the work Correspondence to: Hu-Qin Zhang, email: huqzhang@mail.xjtu.edu.cn Bing He, email: hebinghb@mail.xjtu.edu.cn Keywords: driver genes, non-small cell lung cancer, characteristics, detection methods, targeted therapies Received: February 23, 2017 Accepted: March 30, 2017 Published: April 10, 2017 ABSTRACT Lung cancer is one of the most common causes of cancer-related death in the world. The large number of lung cancer cases is non-small cell lung cancer (NSCLC), which approximately accounting for 75% of lung cancer. Over the past years, our comprehensive knowledge about the molecular biology of NSCLC has been rapidly enriching, which has promoted the discovery of driver genes in NSCLC and directed FDA-approved targeted therapies. Of course, the targeted therapies based on driver genes provide a more exact option for advanced non-small cell lung cancer, improving the survival rate of patients. Now, we will review the landscape of driver genes in NSCLC including the characteristics, detection methods, the application of target therapy and challenges.

Prostate cancer is one of the most common uro-oncological disease in men and is globally leading cause of cancer related deaths in males. The somatic mutation has a strong link in the occurrence of cancer. Mutation in the oncogenes and tumor suppressor genes that alter key cellular functions can lead to prostate cancer initiation and progression. Whole genome sequencing has identified numerous genetic alternations and further provided a detail view of the mutations in genes that drive progression of prostate cancer. TP53, SPOP, PTEN, ATM, AR, CTNNB1, FOXA1, KMT2D, BRACA2 and APC were found as frequently mutated genes in prostate cancer. Using data from cBioPortal and PubMed, this review summarizes the status and possible impact of mutations in these driver genes on survival, progression, and metastasis of prostate cancer. This study will contribute a better understanding of biological basis for clinical variability in prostate cancer patients and may provide new genetic diagnostic markers and drug targets.

Background Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. Results Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. Conclusions The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. Availability The source code can be obtained at https://github.com/weiba/Subdyquency. Electronic supplementary material The online version of this article (10.1186/s12859-019-2847-9) contains supplementary material, which is available to authorized users.

Tijekom života pacijenta, genom stanice raka postupno nakuplja somatske mutacije uslijed djelovanja raznih endogenih i egzogenih mutacijskih procesa. Genomske otiske ovih procesa moguće je razlučiti iz podataka o somatskim mutacijama tumora te ih nazivamo mutacijskim potpisima. Cilj ovog diplomskog rada bio je proučiti povezanost opsežnog skupa somatskih mutacija koje pripadaju 721 odabranom genu sa 30 mutacijskih potpisa i zamjenskih varijabli na 33 različita tipa raka. Dvostrano neparametarsko testiranje primijenjeno je na razini svih tipova raka te pojedinih tipova raka u svrhu testiranja pojedinih ponavljajućih somatskih mutacija te testiranja tereta mutacija u ponavljajuće mutiranim genima na osnovi podatkovnog seta TCGA MC3 koji sadrži ~10,000 uzoraka. Analize su podijeljene po statusu hipermutiranosti uzoraka te predviđenom dobroćudnom ili zloćudnom tipu mutacija. Rezultati su u svrhu ispravka višestrukog testiranja hipoteza podvrgnuti metodi genomske kontrole te Benjamini-Hochbergovoj metodi i filtrirani po apsolutnoj vrijednosti veličine učinka. Testiranjem pojedinih mutacija otkrivene su 62 značajne poveznice, dok su testiranjem tereta mutacija otkrivene 162 značajne poveznice na razini pojedinih tipova raka i svim podskupovima podataka. Većina otkrivenih poveznica predstavljaju nova otkrića i moguće vrijedne ciljeve budućih istraživanja mutacijskih procesa aktivnih u stanicama raka. Throughout the lifetime of a patient, the genome of a cancer cell gradually accumulates somatic mutations due to a variety of endogenous and exogenous mutational processes. The genomic fingerprints of those processes can be deconvoluted from tumor somatic mutation data and are termed mutational signatures. The goal of this master thesis was to assess the relationships of a comprehensive set of somatic mutations belonging to a list of 721 selected genes with the 30 mutational signatures and their proxies across 33 different cancer types. Two-sided nonparametric testing was employed on both pan-cancer and per-cancer type levels to individually test recurrent somatic mutations and to perform burden testing of recurrently mutated genes based on the TCGA MC3 dataset comprising ~10,000 samples. Analyses were stratified by sample hypermutation status and predicted benign or pathogenic mutation character. Results were submitted to genomic control and Benjamini-Hochberg multiple testing correction procedures and filtered using absolute effect sizes. Individual mutation testing identified 62 significant associations, while burden testing identified 162 significant associations on the per-cancer type level across data subsets. The majority of identified associations were novel findings and constitute potentially valuable targets for future research into the mutational processes operative in cancer cells.

Background Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage. Results In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study. Conclusion The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer. Electronic supplementary material The online version of this article (10.1186/s12920-018-0452-9) contains supplementary material, which is available to authorized users.

Despite the progress in precision oncology, development of cancer therapies is limited by the dearth of suitable drug targets1. Novel candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. We developed SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We applied SLIdR to Project DRIVE data2 and found both established and novel pan-cancer and cancer type-specific SL pairs. We identified and experimentally validated a novel SL interaction between AXIN1 and URI1 in hepatocellular carcinoma, thus corroborating the potential of SLIdR to identify new SL-based drug targets., bioRxiv

Uveal melanoma (UM) is the most common malignant eye tumor in adults affecting ~7,000 individuals per year worldwide. UM is a rare subtype of melanoma with distinct clinical and molecular features as compared to other melanoma subtypes. UMs lack the most typical cutaneous melanoma-associated mutations (BRAF, NRAS, and NF1) and are instead characterized by a different set of genes with oncogenic or loss-of-function mutations. By next-generation sequencing efforts on UM tumors, several driver genes have been detected. The most frequent ones are BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. In many cases, mutations in these genes appear in a mutually exclusive manner, have different risk of metastasis, and are consequently of prognostic importance. The majority of UM cases are sporadic but a few percentage of the cases occurs in families with an inherited predisposition for this malignancy. In recent years, germline mutations in the BAP1 gene have been found to segregate in an autosomal dominant pattern with numerous different cancer types including UM in cancer-prone families. This cancer syndrome has been denoted as the tumor predisposition syndrome.

Objective: This study aims to establish a method for highly parallel multiplexed detection of genetic mutations in Chinese lung cancer samples through Agena iPLEX chemistry and matrix-assisted laser desorption ionization time-of-flight analysis on MassARRAY mass spectrometry platform. Objective: We reviewed the related literature and data on lung cancer treatments. We also identified 99 mutation hot spots in 13 target genes closely related to the pathogenesis, drug resistance, and metastasis of lung cancer. A total of 297 primers, composed of 99 paired forward and reverse amplification primers and 99 matched extension primers, were designed using Assay Design software. The detection method was established by analyzing eight cell lines and six lung cancer specimens. The proposed method was then validated through comparisons by using a LungCartaTM kit. The sensitivity and specificity of the proposed method were evaluated by directly sequencing EGFR and KRAS genes in 100 lung cancer cases. Results: The proposed method was able to detect multiplex genetic mutations in lung cancer cell lines. This finding was consistent with the observations on previously reported mutations. The proposed method can also detect such mutations in clinical lung cancer specimens. This result was consistent with the observations with LungCartaTM kit. However, an FGFR2 mutation was detected only through the proposed method. The measured sensitivity and specificity were 100% and 96.3%, respectively. Conclusions: The proposed MassARRAY technology-based multiplex method can detect genetic mutations in Chinese lung cancer patients. Therefore, the proposed method can be applied to detect mutations in other cancer tissues.