Exploring the genomes of cancer cells: progress and promise

TP53 somatic evolution in the normal endometrium of Black and White individuals

BACKGROUND

TP53 mutations are the main drivers of aggressive, high-risk endometrial carcinomas commonly diagnosed in Black individuals. However, TP53 mutations have also been identified in benign, non-cancerous tissues. We sought to understand the TP53 mutational landscape in benign endometrium throughout the lifespan of Black and White individuals, accounting for structural socioeconomic context.

METHODS

Ultra-sensitive TP53 mutation detection was performed with high-depth duplex sequencing (∼13,000×) in DNA extracted from histologically normal endometrium collected at autopsy (69 % of cases) or surgery (31 % of cases) from 83 individuals ages 0 to 81 (31 Black and 52 White, median age 35 years) without endometrial cancer. Histologically normal endometrium was also collected from 10 White individuals with endometrial cancer.

RESULTS

We identified 266 coding TP53 mutations in the normal endometrium of individuals without endometrial cancer, 57 % of which were pathogenic. The number, pathogenicity, and size of TP53 mutant clones in normal endometrium increased with age. Multivariable models showed no significant association between race or socioeconomic metrics and TP53 mutation frequency in normal endometrium. An exploratory analysis on the histologically normal endometrium of White individuals with endometrial cancer identified the tumor mutations at low levels in the normal biopsy of 5 out of 6 cases.

CONCLUSIONS

Our study revealed prevalent TP53 somatic evolution in benign endometrium across human lifespan and no racial differences in this cohort of predominantly younger individuals. Future studies should consider the analysis of larger cohorts with older individuals to detect potential effects of racial disparities on TP53 somatic evolution later in life.

The Warburg hypothesis and the emergence of the mitochondrial metabolic theory of cancer

Otto Warburg originally proposed that cancer arose from a two-step process. The first step involved a chronic insufficiency of mitochondrial oxidative phosphorylation (OxPhos), while the second step involved a protracted compensatory energy synthesis through lactic acid fermentation. His extensive findings showed that oxygen consumption was lower while lactate production was higher in cancerous tissues than in non-cancerous tissues. Warburg considered both oxygen consumption and extracellular lactate as accurate markers for ATP production through OxPhos and glycolysis, respectively. Warburg's hypothesis was challenged from findings showing that oxygen consumption remained high in some cancer cells despite the elevated production of lactate suggesting that OxPhos was largely unimpaired. New information indicates that neither oxygen consumption nor lactate production are accurate surrogates for quantification of ATP production in cancer cells. Warburg also did not know that a significant amount of ATP could come from glutamine-driven mitochondrial substrate level phosphorylation in the glutaminolysis pathway with succinate produced as end product, thus confounding the linkage of oxygen consumption to the origin of ATP production within mitochondria. Moreover, new information shows that cytoplasmic lipid droplets and elevated aerobic lactic acid fermentation are both biomarkers for OxPhos insufficiency. Warburg's original hypothesis can now be linked to a more complete understanding of how OxPhos insufficiency underlies dysregulated cancer cell growth. These findings can also address several questionable assumptions regarding the origin of cancer thus allowing the field to advance with more effective therapeutic strategies for a less toxic metabolic management and prevention of cancer.

Reduced occludin expression is related to unfavorable tumor phenotype and poor prognosis in many different tumor types: A tissue microarray study on 16,870 tumors

Occludin is a key component of tight junctions. Reduced occludin expression has been linked to cancer progression in individual tumor types, but a comprehensive and standardized analysis across human tumor types is lacking. To study the prevalence and clinical relevance of occludin expression in cancer, a tissue microarray containing 16,870 samples from 148 different tumor types and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. Occludin immunostaining was observed in 10,746 (76.6%) of 14,017 analyzable tumors, including 18.9% with weak, 16.2% with moderate, and 41.6% with strong staining intensity. Occludin positivity was found in 134 of 148 tumor categories and was most frequent in adenocarcinomas (37.5-100%) and neuroendocrine neoplasms (67.9-100%), less common in squamous cell carcinomas (23.8-93%) and in malignant mesotheliomas (up to 48.1%), and rare in Non-Hodgkin's lymphomas (1-2%) and most mesenchymal tumors. Reduced occludin staining was linked to adverse tumor features in several tumor types, including colorectal adenocarcinoma (advanced pT stage, p < 0.0001; L1 status, p = 0.0384; absence of microsatellite instability, p < 0.0001), pancreatic adenocarcinoma (advanced pT stage, p = 0.005), clear cell renal cell carcinoma (high ISUP grade, p < 0.0001; advanced pT stage, p < 0.0001; high UICC stage, p < 0.0001; distant metastasis, p = 0.0422; shortened overall or recurrence-free survival, p ≤ 0.0116), papillary renal cell carcinoma (high pT stage, p < 0.0001; high UICC stage, p = 0.0228; distant metastasis, p = 0.0338; shortened recurrence-free survival, p = 0.006), and serous high-grade ovarian cancer (advanced pT stage, p = 0.0133). Occludin staining was unrelated to parameters of tumor aggressiveness in breast, gastric, endometrial, and thyroidal cancer. Our data demonstrate significant levels of occludin expression in many different tumor entities and identify reduced occludin expression as a potentially useful prognostic feature in several tumor entities.

Tumor microenvironment: Nurturing cancer cells for immunoevasion and druggable vulnerabilities for cancer immunotherapy

The tumor microenvironment (TME) is an intricate ecosystem where cancer cells thrive, encompassing a wide array of cellular and non-cellular components. The TME co-evolves with tumor progression in a spatially and temporally dynamic manner, which endows cancer cells with the adaptive capability of evading immune surveillance. To this end, diverse cancer-intrinsic mechanisms were exploited to dampen host immune system, such as upregulating immune checkpoints, impairing antigens presentation and competing for nutrients. In this review, we discuss how cancer immunoevasion is tightly regulated by hypoxia, one of the hallmark biochemical features of the TME. Moreover, we comprehensively summarize how immune evasiveness of cancer cells is facilitated by the extracellular matrix, as well as soluble components of TME, including inflammatory factors, lactate, nutrients and extracellular vesicles. Given their important roles in dictating cancer immunoevasion, various strategies to target TME components are proposed, which holds promising translational potential in developing novel therapeutics to sensitize anti-cancer immunotherapy such as immune checkpoint blockade.

Optimizing ABA-based chemically induced proximity for enhanced intracellular transcriptional activation and modification response to ABA

Abscisic acid (ABA)-based chemically induced proximity (CIP) is primarily mediated by the interaction of the ABA receptor pyrabactin resistance 1-like 1 (PYL1) and the 2C-type protein phosphatase ABI1, which confers ABA-induced proximity to their fusion proteins, and offers precise temporal control of a wide array of biological processes. However, broad application of ABA-based CIP has been limited by ABA response intensity. In this study, we demonstrated that ABA-induced interaction between another ABA receptor pyrabactin resistance 1 (PYR1) and ABI1 exhibited higher ABA response intensity than that between PYL1 and ABI1 in HEK293T cells. We engineered PYR1-ABI1 and PYL1-ABI1 into ABA-induced transcriptional activation tools in mammalian cells by integration with CRISPR/dCas9 and found that the tool based on PYR1-ABI1 demonstrated better ABA response intensity than that based on PYL1-ABI1 for both exogenous and endogenous genes in mammalian cells. We further achieved ABA-induced RNA m6A modification installation and erasure by combining ABA-induced PYR1-ABI1 interaction with CRISPR/dCas13, successfully inhibiting tumor cell proliferation. We subsequently improved the interaction of PYR1-ABI1 through phage-assisted continuous evolution (PACE), successfully generating a PYR1 mutant (PYR1m) whose interaction with ABI1 exhibited a higher ABA response intensity than that of the wild-type. In addition, we tested the transcriptional activation tool based on PYRm-ABI1 and found that it also showed a higher ABA response intensity than that of the wild type. These results demonstrate that we have developed a novel ABA-based CIP and further improved upon it using PACE, providing a new approach for the modification of other CIP systems.

RB1 Mutations Induce Smoking-Related Bladder Cancer by Modulating the Cytochrome P450 Pathway

Cigarette smoking causes multiple cancers by directly influencing mutation burden of driver mutations. However, the mechanism between somatic mutation caused by cigarette smoking and bladder tumorigenesis remains elusive. Smoking-related mutation profile of bladder cancer was characterized by The Cancer Genome Atlas cohort. Integraticve OncoGenomics database was utilized to detect the smoking-related driver genes, and its biological mechanism predictions were interpreted based on bulk transcriptome and single-cell transcriptome, as well as cell experiments. Cigarette smoking was associated with an increased tumor mutational burden under 65 years old (p = 0.031), and generated specific mutational signatures in smokers. RB1 was identified as a differentially mutated driver gene between smokers and nonsmokers, and the mutation rate of RB1 increased twofold after smoking (p = 0.008). RB1 mutations and the 4-aminobiphenyl interference could significantly decrease the RB1 expression level and thus promote the proliferation, invasion, and migration ability of bladder cancer cells. Enrichment analysis and real-time quantitative PCR (RT-qPCR) data showed that RB1 mutations inhibited cytochrome P450 pathway by reducing expression levels of UGT1A6 and AKR1C2. In addition, we also observed that the component of immunological cells was regulated by RB1 mutations through the stronger cell-to-cell interactions between epithelial scissor+ cells and immune cells in smokers. This study highlighted that RB1 mutations could drive smoking-related bladder tumorigenesis through inhibiting cytochrome P450 pathway and regulating tumor immune microenvironment.

Carbon quantum dots in breast cancer modulate cellular migration via cytoskeletal and nuclear structure

Carbon nanomaterials have been widely applied for cutting edge therapeutic applications as they offer tunable physio-chemical properties with economic scale-up options. Nuclear delivery of cancer drugs has been of prime focus since it controls important cellular signaling functions leading to greater anti-cancer drug efficacies. Better cellular drug uptake per unit drug injection drastically reduces severe side-effects of cancer therapies. Similarly, carbon dots (CDs) uptaken by the nucleus can also be used to set-up cutting edge nano delivery systems. In an earlier paper, we showed the cellular uptake and plasma membrane impact of combustion generated yellow luminescing CDs produced by our group from fuel rich combustion reactors in a one-step tunable production. In this paper, we aim to specifically study the nucleus by establishing the uptake kinetics of these combustion-generated yellow luminescing CDs. At sub-lethal doses, after crossing the plasma membrane, they impact the actin and microtubule mesh, affecting cell adhesion and migration; enter nucleus by diffusion processes; modify the overall appearance of the nucleus in terms of morphology; and alter chromatin condensation. We thus establish how this one-step produced, cost and bulk production friendly carbon dots from fuel rich combustion flames can be innovatively repurposed as potential nano delivery agents in cancer cells.

Topography of mutational signatures in non-small cell lung cancer: emerging concepts, clinical applications, and limitations

The genome of a cell is continuously battered by a plethora of exogenous and endogenous processes that can lead to damaged DNA. Repair mechanisms correct this damage most of the time, but failure to do so leaves mutations. Mutations do not occur in random manner, but rather typically follow a more or less specific pattern due to known or imputed mutational processes. Mutational signature analysis is the process by which the predominant mutational process can be inferred for a cancer and can be used in several contexts to study both the genesis of cancer and its response to therapy. Recent pan-cancer genomic efforts such as "The Cancer Genome Atlas" have identified numerous mutational signatures that can be categorized into single base substitutions, doublet base substitutions, or small insertions/deletions. Understanding these mutational signatures as they occur in non-small lung cancer could improve efforts at prevention, predict treatment response to personalized treatments, and guide the development of therapies targeting tumor evolution. For non-small cell lung cancer, several mutational signatures have been identified that correlate with exposures such as tobacco smoking and radon and can also reflect endogenous processes such as aging, APOBEC activity, and loss of mismatch repair. Herein, we provide an overview of the current knowledge of mutational signatures in non-small lung cancer.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk

OBJECTIVE

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

METHODS

Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.

RESULTS

The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.

CONCLUSION

We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

MYEOV with High Frequencies of Mutations in Head and Neck Cancers Facilitates Cancer Cell Malignant Behaviors

Cancer driver genes (CDGs) and the driver mutations disrupt the homeostasis of numerous critical cell activities, thereby playing a critical role in tumor initiation and progression. In this study, integrative bioinformatics analyses were performed based on a series of online databases, aiming to identify driver genes with high frequencies of mutations in head and neck cancers. Higher myeloma overexpressed (MYEOV) genetic variation frequency and expression level were connected to a poorer prognosis in head and neck cancer patients. MYEOV was dramatically upregulated within head and neck tumor samples and cells. Consistently, MYEOV overexpression remarkably enhanced the aggressiveness of head and neck cancer cells by promoting colony formation, cell invasion, and cell migration. Conversely, MYEOV knockdown attenuated cancer cell aggressiveness and inhibited tumor growth and metastasis in the oral orthotopic tumor model. In conclusion, MYEOV is overexpressed in head and neck cancer, with greater mutation frequencies correlating to a poorer prognosis in head and neck cancer patients. MYEOV serves as an oncogene in head and neck cancer through the promotion of tumor cell colony formation, invasion, and migration, as well as promoting tumor growth and metastasis in the oral orthotopic tumor model.

DNA modifications: Biomarkers for the exposome?

The concept of the exposome is the encompassing of all the environmental exposures, both exogenous and endogenous, across the life course. Many, if not all, of these exposures can result in the generation of reactive species, and/or the modulation of cellular processes, that can lead to a breadth of modifications of DNA, the nature of which may be used to infer their origin. Because of their role in cell function, such modifications have been associated with various major human diseases, including cancer, and so their assessment is crucial. Historically, most methods have been able to only measure one or a few DNA modifications at a time, limiting the information available. With the development of DNA adductomics, which aims to determine the totality of DNA modifications, a far more comprehensive picture of the DNA adduct burden can be gained. Importantly, DNA adductomics can facilitate a "top-down" investigative approach whereby patterns of adducts may be used to trace and identify the originating exposure source. This, together with other 'omic approaches, represents a major tool for unraveling the complexities of the exposome and hence allow a better a understanding of the environmental origins of disease.

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis.

METHODS

Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays.

RESULTS

Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression.

CONCLUSION

In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.

Microsatellite instability in mismatch repair proficient colorectal cancer: clinical features and underlying molecular mechanisms

BACKGROUND

Both defects in mismatch repair (dMMR) and high microsatellite instability (MSI-H) have been recognised as crucial biomarkers that guide treatment strategies and disease management in colorectal cancer (CRC). As MMR and MSI tests are being widely conducted, an increasing number of MSI-H tumours have been identified in CRCs with mismatch repair proficiency (pMMR). The objective of this study was to assess the clinical features of patients with pMMR/MSI-H CRC and elucidate the underlying molecular mechanism in these cases.

METHODS

From January 2015 to December 2018, 1684 cases of pMMR and 401 dMMR CRCs were enrolled. Of those patients, 93 pMMR/MSI-H were identified. The clinical phenotypes and prognosis were analysed. Frozen and paraffin-embedded tissue were available in 35 patients with pMMR/MSI-H, for which comprehensive genomic and transcriptomic analyses were performed.

FINDINGS

In comparison to pMMR/MSS CRCs, pMMR/MSI-H CRCs exhibited significantly less tumour progression and better long-term prognosis. The pMMR/MSI-H cohorts displayed a higher presence of CD8+ T cells and NK cells when compared to the pMMR/MSS group. Mutational signature analysis revealed that nearly all samples exhibited deficiencies in MMR genes, and we also identified deleterious mutations in MSH3-K383fs.

INTERPRETATION

This study revealed pMMR/MSI-H CRC as a distinct subgroup within CRC, which manifests diverse clinicopathological features and long-term prognostic outcomes. Distinct features in the tumour immune-microenvironment were observed in pMMR/MSI-H CRCs. Pathogenic deleterious mutations in MSH3-K383fs were frequently detected, suggesting another potential biomarker for identifying MSI-H.

FUNDING

This work was supported by the Science and Technology Commission of Shanghai Municipality (20DZ1100101).

The power and the promise of synthetic lethality for clinical application in cancer treatment

Synthetic lethality is a phenomenon wherein the simultaneous deficiency of two or more genes results in cell death, while the deficiency of any individual gene does not lead to cell death. In recent years, synthetic lethality has emerged as a significant topic in the field of targeted cancer therapy, with certain drugs based on this concept exhibiting promising outcomes in clinical trials. Nevertheless, the presence of tumor heterogeneity and the intricate DNA repair mechanisms pose challenges to the effective implementation of synthetic lethality. This review aims to explore the concepts, development, and ethical quandaries surrounding synthetic lethality. Additionally, it will provide an in-depth analysis of the clinical application and underlying mechanism of synthetic lethality.

Cell fate regulation governed by p53: Friends or reversible foes in cancer therapy

Cancer is a leading cause of death worldwide. Targeted therapies aimed at key oncogenic driver mutations in combination with chemotherapy and radiotherapy as well as immunotherapy have benefited cancer patients considerably. Tumor protein p53 (TP53), a crucial tumor suppressor gene encoding p53, regulates numerous downstream genes and cellular phenotypes in response to various stressors. The affected genes are involved in diverse processes, including cell cycle arrest, DNA repair, cellular senescence, metabolic homeostasis, apoptosis, and autophagy. However, accumulating recent studies have continued to reveal novel and unexpected functions of p53 in governing the fate of tumors, for example, functions in ferroptosis, immunity, the tumor microenvironment and microbiome metabolism. Among the possibilities, the evolutionary plasticity of p53 is the most controversial, partially due to the dizzying array of biological functions that have been attributed to different regulatory mechanisms of p53 signaling. Nearly 40 years after its discovery, this key tumor suppressor remains somewhat enigmatic. The intricate and diverse functions of p53 in regulating cell fate during cancer treatment are only the tip of the iceberg with respect to its equally complicated structural biology, which has been painstakingly revealed. Additionally, TP53 mutation is one of the most significant genetic alterations in cancer, contributing to rapid cancer cell growth and tumor progression. Here, we summarized recent advances that implicate altered p53 in modulating the response to various cancer therapies, including chemotherapy, radiotherapy, and immunotherapy. Furthermore, we also discussed potential strategies for targeting p53 as a therapeutic option for cancer.

Cell competition and cancer from Drosophila to mammals

Throughout an individual's life, somatic cells acquire cancer-associated mutations. A fraction of these mutations trigger tumour formation, a phenomenon partly driven by the interplay of mutant and wild-type cell clones competing for dominance; conversely, other mutations function against tumour initiation. This mechanism of 'cell competition', can shift clone dynamics by evaluating the relative status of clonal populations, promoting 'winners' and eliminating 'losers'. This review examines the role of cell competition in the context of tumorigenesis, tumour progression and therapeutic intervention.

Regulating Tumorigenicity and Cancer Metastasis through TRKA Signaling

Tropomyosin receptor kinase (TRK) A, TRKA, is a specific binding receptor of nerve growth factor (NGF), which plays an essential role in the occurrence and progression of human cancers. TRKA overexpression has been proven to be a powerful carcinogenic driver and has been verified in many tumors. The TRKA receptor kinase domain is over-activated in an NGF-dependent manner, accompanied by activation of downstream signal pathways, such as RAS-MAPK, PI3K-AKT, JAK2-STAT3 pathway, PLC γ pathway, and Hippo pathway, which participate in tumor cell proliferation, invasion, epithelial-mesenchymal transition (EMT), perineural invasion (PNI), drug resistance, and cancer pain. In addition, chimeric oncogenes produced by the fusion of NTRK1 and other genes are also the direct cause of tumorigenesis and cancer development. The newly developed TRK inhibitors can improve symptoms and tumor regression in cancer patients with overexpression of TRKA or NTRK1 fusion gene. With the emergence of drug resistance, next generation of TRK inhibitors can still maintain strong clinical efficacy in the case of TRK kinase domain mutations, and these inhibitors are in clinical trials. This review summarizes the characteristics and research progress of TRKA, focusing on the regulatory role of the TRKA signal pathway in different tumors. In addition, we have summarized the clinical significance of TRKA and the TRK inhibitors. This review may provide a new reference for the study of the mechanism of TRKA in different tumors, and also provide a new perspective for the in-depth understanding of the role of TRKA as a biomarker and therapeutic target in human cancer.

WNT Signalling Promotes NF-κB Activation and Drug Resistance in KRAS-Mutant Colorectal Cancer

Approximately 40% of colorectal cancer (CRC) cases are characterized by KRAS mutations, rendering them insensitive to most CRC therapies. While the reasons for this resistance remain incompletely understood, one key aspect is genetic complexity: in CRC, oncogenic KRAS is most commonly paired with mutations that alter WNT and P53 activities ("RAP"). Here, we demonstrate that elevated WNT activity upregulates canonical (NF-κB) signalling in both Drosophila and human RAS mutant tumours. This upregulation required Toll-1 and Toll-9 and resulted in reduced efficacy of RAS pathway targeted drugs such as the MEK inhibitor trametinib. Inhibiting WNT activity pharmacologically significantly suppressed trametinib resistance in RAP tumours and more genetically complex RAP-containing 'patient avatar' models. WNT/MEK drug inhibitor combinations were further improved by targeting brm, shg, ago, rhoGAPp190 and upf1, highlighting these genes as candidate biomarkers for patients sensitive to this duel approach. These findings shed light on how genetic complexity impacts drug resistance and proposes a therapeutic strategy to reverse this resistance.

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at the single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours.

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

BACKGROUND

Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures.

RESULTS

Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access.

CONCLUSION

signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Association of Germline Variation in Driver Genes with Breast Cancer Risk in Chilean Population

Cancer is a genomic disease, with driver mutations contributing to tumorigenesis. These potentially heritable variants influence risk and underlie familial breast cancer (BC). This study evaluated associations between BC risk and 13 SNPs in driver genes MAP3K1, SF3B1, SMAD4, ARID2, ATR, KMT2C, MAP3K13, NCOR1, and TBX3, in BRCA1/2-negative Chilean families. SNPs were genotyped using TaqMan Assay in 492 cases and 1285 controls. There were no associations between rs75704921:C>T (ARID2); rs2229032:A>C (ATR); rs3735156:C>G (KMT2C); rs2276738:G>C, rs2293906:C>T, rs4075943T:>A, rs13091808:C>T (MAP3K13); rs178831:G>A (NCOR1); or rs3759173:C>A (TBX3) and risk. The MAP3K1 rs832583 A allele (C/A+A/A) showed a protective effect in families with moderate BC history (OR = 0.7 [95% CI 0.5-0.9] p = 0.01). SF3B1 rs16865677-T (G/T+T/T) increased risk in sporadic early-onset BC (OR = 1.4 [95% CI 1.0-2.0] p = 0.01). SMAD4 rs3819122-C (A/C+C/C) increased risk in cases with moderate family history (OR = 2.0 [95% CI 1.3-2.9] p ≤ 0.0001) and sporadic cases diagnosed ≤50 years (OR = 1.6 [95% CI 1.1-2.2] p = 0.006). SMAD4 rs12456284:A>G increased BC risk in G-allele carriers (A/G + G/G) in cases with ≥2 BC/OC cases and early-onset cases (OR = 1.2 [95% CI 1.0-1.6] p = 0.04 and OR = 1.4 [95% CI 1.0-1.9] p = 0.03, respectively). Our study suggests that specific germline variants in driver genes MAP3K1, SF3B1, and SMAD4 contribute to BC risk in Chilean population.

Establishment and validation of an immune infiltration predictive model for ovarian cancer

BACKGROUND

The most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease.

METHODS

We investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the IPM on the immunological microenvironment.

RESULTS

TP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group.

CONCLUSIONS

The IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

Functional Distinctions of Endometrial Cancer-Associated Mutations in the Fibroblast Growth Factor Receptor 2 Gene

Functional analysis of somatic mutations in tumorigenesis facilitates the development and optimization of personalized therapy for cancer patients. The fibroblast growth factor receptor 2 (FGFR2) gene is frequently mutated in endometrial cancer (EC), but the functional implications of FGFR2 mutations in cancer development remain largely unexplored. In this study, we introduced a reliable and readily deployable screening method to investigate the effects of FGFR2 mutations. We demonstrated that distinct mutations in FGFR2 can lead to differential downstream consequences, specifically affecting a disintegrin- and metalloprotease 17 (ADAM17)-dependent shedding of the epidermal growth factor receptor (EGFR) ligand heparin-binding EGF-like growth factor (HB-EGF) and phosphorylation of mitogen-activated protein kinases (MAPKs). Furthermore, we showed that the distribution of mutations within the FGFR2 gene can influence their oncogenic effects. Together, these findings provide important insights into the complex nature of FGFR2 mutations and their potential implications for EC. By unraveling the distinct effects of different mutations, our study contributes to the identification of personalized treatment strategies for patients with FGFR2-mutated cancers. This knowledge has the potential to guide the development of targeted therapies that specifically address the underlying molecular alterations associated with FGFR2 mutations, ultimately improving patient outcomes in EC and potentially other cancer types characterized by FGFR2 mutations.

Recent advances in mRNA cancer vaccines: meeting challenges and embracing opportunities

Since the successful application of messenger RNA (mRNA) vaccines in preventing COVID-19, researchers have been striving to develop mRNA vaccines for clinical use, including those exploited for anti-tumor therapy. mRNA cancer vaccines have emerged as a promising novel approach to cancer immunotherapy, offering high specificity, better efficacy, and fewer side effects compared to traditional treatments. Multiple therapeutic mRNA cancer vaccines are being evaluated in preclinical and clinical trials, with promising early-phase results. However, the development of these vaccines faces various challenges, such as tumor heterogeneity, an immunosuppressive tumor microenvironment, and practical obstacles like vaccine administration methods and evaluation systems for clinical application. To address these challenges, we highlight recent advances from preclinical studies and clinical trials that provide insight into identifying obstacles associated with mRNA cancer vaccines and discuss potential strategies to overcome them. In the future, it is crucial to approach the development of mRNA cancer vaccines with caution and diligence while promoting innovation to overcome existing barriers. A delicate balance between opportunities and challenges will help guide the progress of this promising field towards its full potential.

Microbiome and MicroRNA or Long Non-Coding RNA-Two Modern Approaches to Understanding Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of humans' most common and fatal neoplasms. Nowadays, a number of PDAC studies are being conducted in two different fields: non-coding RNA (especially microRNA and long non-coding RNA) and microbiota. It has been recently discovered that not only does miRNA affect particular bacteria in the gut microbiome that can promote carcinogenesis in the pancreas, but the microbiome also has a visible impact on the miRNA. This suggests that it is possible to use the combined impact of the microbiome and noncoding RNA to suppress the development of PDAC. Nevertheless, insufficient research has focused on bounding both approaches to the diagnosis, treatment, and prevention of pancreatic ductal adenocarcinoma. In this article, we summarize the recent literature on the molecular basis of carcinogenesis in the pancreas, the two-sided impact of particular types of non-coding RNA and the pancreatic cancer microbiome, and possible medical implications of the discovered phenomenon.

Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes.

Methods

We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR.

Results

In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue.

Conclusions

In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.

A spindle cell neoplasm with MYH9::EGFR fusion and co-expression of S100 and CD34, further expanding the family of kinase fusion positive spindle cell neoplasms

Soft tissue neoplasms displaying CD34 and S100 positivity with immunohistochemistry are rare with a wide morphological range and frequent neurotrophic tyrosine receptor kinase (NTRK) alterations. Recent reports describe fusions in other kinases besides NTRK in these tumors. In the present article, we report a case of a young male suffering from a soft tissue neoplasm in the lumbar region. At microscopic examination, it was a CD34 and S100-positive soft tissue tumor showing a multilobulated growth pattern composed of cells with pale cytoplasm and abundant normal smooth muscle stroma. The genetic profile showed two alterations affecting EGFR gene represented by a novel MYH9::EGFR fusion transcript and a p.K714N mutation.

Drosophila as Model System to Study Ras-Mediated Oncogenesis: The Case of the Tensin Family of Proteins

Oncogenic mutations in the small GTPase Ras contribute to ~30% of human cancers. However, tissue growth induced by oncogenic Ras is restrained by the induction of cellular senescence, and additional mutations are required to induce tumor progression. Therefore, identifying cooperating cancer genes is of paramount importance. Recently, the tensin family of focal adhesion proteins, TNS1-4, have emerged as regulators of carcinogenesis, yet their role in cancer appears somewhat controversial. Around 90% of human cancers are of epithelial origin. We have used the Drosophila wing imaginal disc epithelium as a model system to gain insight into the roles of two orthologs of human TNS2 and 4, blistery (by) and PVRAP, in epithelial cancer progression. We have generated null mutations in PVRAP and found that, as is the case for by and mammalian tensins, PVRAP mutants are viable. We have also found that elimination of either PVRAP or by potentiates Ras^V12-mediated wing disc hyperplasia. Furthermore, our results have unraveled a mechanism by which tensins may limit Ras oncogenic capacity, the regulation of cell shape and growth. These results demonstrate that Drosophila tensins behave as suppressors of Ras-driven tissue hyperplasia, suggesting that the roles of tensins as modulators of cancer progression might be evolutionarily conserved.

Overexpression of POLA2 in hepatocellular carcinoma is involved in immune infiltration and predicts a poor prognosis

BACKGROUND

Hepatocellular carcinoma (HCC) is the second malignancy worldwide. POLA2 initiates DNA replication, regulates cell cycle and gene repair that promote tumorigenesis and disease progression. However, the prognostic and biological function roles of POLA2 in HCC had not been conclusively determined.

METHODS

The expression levels and prognosis role of POLA1 and POLA2 in HCC were analyzed based on TCGA-LIHC database and recruited 24 HCC patients. Gene mutations were analyzed using "maftools" package. POLA2 and immune cells correlations were analyzed by TIMER. POLA2 co-expressed genes functional enrichment were evaluated using Metascape. The mRNA and protein level of POLA2 was detected in HCC cells and tissues. Cell migration, invasion, proliferation, cell cycle and HCC cell lines derived xenograft model were performed to investigate POLA2 biological function.

RESULTS

POLA2 was significantly high expressed in HCC than in normal liver tissue in both TCGA-LIHC and our collected HCC samples. In validation cohort, POLA2 significantly related to tumor differentiation, tumor size and Ki-67 (p < 0.05). In TCGA-LIHC cohort, overexpression of POLA2 predicted a low OS and associated with different clinical stages. Multivariate Cox regression showed overexpression of POLA2 effectively distinguished the prognosis at different T, N, M, stages and grades of HCC. POLA2 expression correlated with mutation burden, immune cells infiltration and immune-associated genes expression of HCC. Functional enrichment revealed that POLA2 co-expressed genes were linked to cellular activity, plasma membrane protein complex and leukocyte activity, immune response-regulated cell surface receptor signaling pathway, and immune response-regulated signaling pathway. Moreover, POLA2 was also positively co-expressed with some immune checkpoints (CD274, CTL-4, HAVCR2, PDCD1, PDCD1LG2, TIGIT, and LAG3) (p < 0.001). Gene knockdown revealed that POLA2 promoted proliferation, migration, invasion, and cell cycle of SMMC-7721 and HepG2. The HCC xenograft tumor model also demonstrated remarkably tumor size inhibition, tumor proliferation inhibtion and tumor necrosis promotion when POLA2 knockdown.

CONCLUSIONS

POLA2 influenced immune microenvironment and tumor progression of HCC indicated that it might be a potential molecular marker for prognostic evaluation or a therapeutic target for HCC.

Inhibition of Cancer Development by Natural Plant Polyphenols: Molecular Mechanisms

Malignant tumors remain one of the main sources of morbidity and mortality around the world. A chemotherapeutic approach to cancer treatment poses a multitude of challenges, primarily due to the low selectivity and genotoxicity of the majority of chemotherapeutic drugs currently used in the clinical practice, often leading to treatment-induced tumors formation. Highly selective antitumor drugs can largely resolve this issue, but their high selectivity leads to significant drawbacks due to the intrinsic tumor heterogeneity. In contrast, plant polyphenols can simultaneously affect many processes that are involved in the acquiring and maintaining of hallmark properties of malignant cells, and their toxic dose is typically much higher than the therapeutic one. In the present work we describe the mechanisms of the action of polyphenols on cancer cells, including their effects on genetic and epigenetic instability, tumor-promoting inflammation, and altered microbiota.

Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.

Cytokeratin 13 (CK13) expression in cancer: a tissue microarray study on 10,439 tumors

Cytokeratin 13 (CK13) is a type I acidic low molecular weight cytokeratin, which is mainly expressed in urothelium and in the squamous epithelium of various sites of origin. Loss of CK13 has been implicated in the development and progression of squamous epithelial neoplasms. To comprehensively determine CK13 expression in normal and neoplastic tissues, a tissue microarray containing 10,439 samples from 131 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. CK13 immunostaining was detectable in 42 (32.1%) of the 131 tumor categories including 24 (18.3%) tumor types with at least one strongly positive case. The highest rate of positive staining was found in various urothelial neoplasms (52.1-92.3%) including Brenner tumor of the ovary (86.8%) and in squamous cell carcinomas from various sites of origin (39.1-77.6%), Warthin tumors of parotid glands (66.7%), adenosquamous carcinomas of the cervix (33.3%), thymomas (16.0%), and endometroid carcinomas of the ovary (15.3%). Twenty other epithelial or germ cell neoplasms showed - a usually weak - CK13 positivity in less than 15% of the cases. In bladder cancer, reduced CK13 expression was linked to high grade and advanced stage (p < 0.0001 each). In squamous cell carcinoma of the cervix, reduced CK13 immunostaining was related to high grade (p = 0.0295) and shortened recurrence-free (p = 0.0094) and overall survival (p = 0.0274). In a combined analysis of 1,151 squamous cell carcinomas from 11 different sites of origin, reduced CK13 staining was linked to high grade (p = 0.0050). Our data provide a comprehensive overview on CK13 expression in normal and neoplastic human tissues. CK13 expression predominates in urothelial neoplasms and in squamous cell carcinomas of different organs, and a loss of CK13 expression is associated with aggressive disease in these tumors.

Updates in neuroendocrine neoplasms: From mechanisms to the clinic

Scientific advances constantly improve our understanding of the mechanisms underlying tumorigenesis, allowing us now to analyze cancer in a more precise manner and to identify at an earlier stage the tumors that have greater risk of aggressive behavior. Understanding neuroendocrine neoplasms at molecular level has enabled increasingly targeted treatments, with safety and efficacy validated in large randomized trials. Moreover, the first studies of targeted therapies after molecular profiling of neuroendocrine neoplasms have shown encouraging results, allowing us to foresee ever more personalized medical treatments in the future. This literature review aims to summarize recent advances in the study of neuroendocrine neoplasms and to show how identification of new mechanisms underlying tumorigenesis can be of benefit in clinical practice.

DeteX: A highly accurate software for detecting SNV and InDel in single and paired NGS data in cancer research

Background: Genetic testing is becoming more and more accepted in the auxiliary diagnosis and treatment of tumors. Due to the different performance of the existing bioinformatics software and the different analysis results, the needs of clinical diagnosis and treatment cannot be met. To this end, we combined Bayesian classification model (BC) and fisher exact test (FET), and develop an efficient software DeteX to detect SNV and InDel mutations. It can detect the somatic mutations in tumor-normal paired samples as well as mutations in a single sample. Methods: Combination of Bayesian classification model (BC) and fisher exact test (FET). Results: We detected SNVs and InDels in 11 TCGA glioma samples, 28 clinically targeted capture samples and 2 NCCL-EQA standard samples with DeteX, VarDict, Mutect, VarScan and GatkSNV. The results show that, among the three groups of samples, DeteX has higher sensitivity and precision whether it detects SNVs or InDels than other callers and the F1 value of DeteX is the highest. Especially in the detection of substitution and complex mutations, only DeteX can accurately detect these two kinds of mutations. In terms of single-sample mutation detection, DeteX is much more sensitive than the HaplotypeCaller program in Gatk. In addition, although DeteX has higher mutation detection capabilities, its running time is only .609 of VarDict, which is .704 and .343 longer than VarScan and MuTect, respectively. Conclusion: In this study, we developed DeteX to detect SNV and InDel mutations in single and paired samples. DeteX has high sensitivity and precision especially in the detection of substitution and complex mutations. In summary, DeteX from NGS data is a good SNV and InDel caller.

Single-Cell and Transcriptome-Based Immune Cell-Related Prognostic Model in Clear Cell Renal Cell Carcinoma

Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

Mainstreaming of genomics in oncology: a nationwide survey of the genomics training needs of UK oncologists

OBJECTIVE

Genomics is rapidly changing treatment paradigms for cancers, obligating oncologists to have good genomics knowledge. Through this survey, we aimed to assess the current understanding of cancer genomics among UK oncologists.

METHODS

We conducted a web-based nation-wide self-assessment survey of the cancer genomics knowledge of UK clinical and medical oncology trainees and consultants.

RESULTS

In total, 150 oncologists (81 consultants and 69 trainees) responded, representing 10% of UK oncologists.Formal training in genomics had not been received by 38.7% of oncologists and 92.7% identified a need for additional genomics training.In total, 71.3% self-reported to have good knowledge of defining somatic and germline mutations, falling to 35.3% for understanding principles of gene expression and regulation. Knowledge of cancer-predisposing syndromes was highest for Lynch syndrome (40.7% good knowledge) and lowest for multiple endocrine neoplasia (14.0% good knowledge).Overall, 49.0% of respondents had consented patients for germline testing, but 80.7% reported a lack of training in genetic counselling.

CONCLUSION

Large knowledge gaps have been identified through this survey, highlighting the need for incorporation of improved formal training in cancer genomics for consultants and trainees, with an aim to equip oncologists for advances in clinical practice and to take up genetic mainstreaming confidently.

PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology

MOTIVATION

Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer.

RESULTS

We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Construction and analysis of sample-specific driver modules for breast cancer

BACKGROUND

It is important to understand the functional impact of somatic mutation and methylation aberration at an individual level to implement precision medicine. Recent studies have demonstrated that the perturbation of gene interaction networks can provide a fundamental link between genotype (or epigenotype) and phenotype. However, it is unclear how individual mutations affect the function of biological networks, especially for individual methylation aberration. To solve this, we provided a sample-specific driver module construction method using the 2-order network theory and hub-gene theory to identify individual perturbation networks driven by mutations or methylation aberrations.

RESULTS

Our method integrated multi-omics of breast cancer, including genomics, transcriptomics, epigenomics and interactomics, and provided new insight into the synergistic collaboration between methylation and mutation at an individual level. A common driver pattern of breast cancer was identified from a novel perspective of a driver module, which is correlated to the occurrence and development of breast cancer. The constructed driver module reflects the survival prognosis and degree of malignancy among different subtypes of breast cancer. Additionally, subtype-specific driver modules were identified.

CONCLUSIONS

This study explores the driver module of individual cancer, and contributes to a better understanding of the mechanism of breast cancer driven by the mutations and methylation variations from the point of view of the driver network. This work will help identify new therapeutic combinations of gene mutations and drugs in humans.

Therapeutic and prognostic potential of GPCRs in prostate cancer from multi-omics landscape

Prostate cancer (PRAD) is a common and fatal malignancy. It is difficult to manage clinically due to drug resistance and poor prognosis, thus creating an urgent need for novel therapeutic targets and prognostic biomarkers. Although G protein-coupled receptors (GPCRs) have been most attractive for drug development, there have been lack of an exhaustive assessment on GPCRs in PRAD like their molecular features, prognostic and therapeutic values. To close this gap, we herein systematically investigate multi-omics profiling for GPCRs in the primary PRAD by analyzing somatic mutations, somatic copy-number alterations (SCNAs), DNA methylation and mRNA expression. GPCRs exhibit low expression levels and mutation frequencies while SCNAs are more prevalent. 46 and 255 disease-related GPCRs are identified by the mRNA expression and DNA methylation analysis, respectively, complementing information lack in the genome analysis. In addition, the genomic alterations do not exhibit an observable correlation with the GPCR expression, reflecting the complex regulatory processes from DNA to RNA. Conversely, a tight association is observed between the DNA methylation and mRNA expression. The virtual screening and molecular dynamics simulation further identify four potential drugs in repositioning to PRAD. The combination of 3 clinical characteristics and 26 GPCR molecular features revealed by the transcriptome and genome exhibit good performance in predicting progression-free survival in patients with the primary PRAD, providing candidates as new biomarkers. These observations from the multi-omics analysis on GPCRs provide new insights into the underlying mechanism of primary PRAD and potential of GPCRs in developing therapeutic strategies on PRAD.

Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

Background: Hepatocellular carcinoma (HCC), which has high rates of recurrence and metastasis and is the main reason and the most common tumor for cancer mortality worldwide, has an unfavorable prognosis. N7-methylguanosine (m7G) modification can affect the formation and development of tumors by affecting gene expression and other biological processes. In addition, many previous studies have confirmed the unique function of long noncoding RNAs (lncRNAs) in tumor progression; however, studies exploring the functions of m7G-related lncRNAs in HCC patients has been limited.

Methods: Relevant RNA expression information was acquired from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), and m7G-related lncRNAs were identified via gene coexpression analysis. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate regression analyses were implemented to construct an ideal risk model whose validity was verified using Kaplan–Meier survival, principal component, receiver operating characteristic (ROC) curve, and nomogram analyses. In addition, the potential functions of lncRNAs in the novel signature were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses and gene set enrichment analysis (GSEA). At last, in both risk groups and subtypes classified based on the expression of the risk-related lncRNAs, we analyzed the immune characteristics and drug sensitivity of patients.

Results: After rigorous screening processes, we built a model based on 11 m7G-related lncRNAs for predicting patient overall survival (OS). The results suggested that the survival status of patients with high-risk scores was lower than that of patients with low-risk scores, and a high-risk score was related to malignant clinical features. Cox regression analysis showed that the m7G risk score was an independent prognostic parameter. Moreover, immune cell infiltration and immunotherapy sensitivity differed between the risk groups.

Conclusion: The m7G risk score model constructed based on 11 m7G-related lncRNAs can effectively assess the OS of HCC patients and may offer support for making individualized treatment and immunotherapy decisions for HCC patients.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

Intracellular accumulation and DNA damage caused by methylmercury in glial cells

Mercury is widely used in industrial and extractive processes, and the improper disposal of waste or products containing this metal produces a significant impact on ecosystems, causing adverse effects on living organisms, including humans. Exposure to methylmercury, a highly toxic organic compound, causes important neurological and developmental impairments. Recently, the genotoxicity of mercurial compounds has gained prominence as one of the possible mechanisms associated with the neurological effects of mercury, mostly by disturbing the mitotic spindle and causing chromosome loss. In this sense, it is important to investigate if these compounds can also cause direct damage to DNA, such as single and double-strand breaks. Thus, the aim of this study was to investigate the cytotoxic and genotoxic potential of methylmercury in cell lines derived from neurons (B103) and glia (C6), exposed to methylmercury (MeHg) for 24 h, by analyzing cell viability, metabolic activity, and damage to DNA and chromosomes. We found that in comparison to the neuronal cell line, glial cells showed higher tolerance to MeHg, and therefore a higher LC₅₀ and consequent higher intracellular accumulation of Hg, which led to the occurrence of several genotoxic effects, as evidenced by the presence of micronuclei, bridges, sprouts, and chromosomal aberrations.

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph

Background

Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive.

Results

To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data.

Conclusion

DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04788-7.

A Prognostic Gene Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma is the third most common cause of cancer-related deaths in China and immune-based therapy can improve patient outcomes. In this study, we investigated the relationship between immunity-associated genes and hepatocellular carcinoma from the prognostic perspective. The data downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and the Gene Expression Omnibus (GEO) was screened for gene mutation frequency using the maftools package. Immunity-associated eight-gene signature with strong prognostic ability was constructed and proved as an independent predictor of the patient outcome in LIHC. Seven genes in the immune-related eight-gene signature were strongly associated with the infiltration of M0 macrophages, resting mast cells, and regulatory T cells. Our research may provide clinicians with a quantitative method to predict the prognosis of patients with liver cancer, which can assist in the selection of the optimal treatment plan.

A practical framework RNMF for exploring the association between mutational signatures and genes using gene cumulative contribution abundance

Background

Mutational signatures are somatic mutation patterns enriching operational mutational processes, which can provide abundant information about the mechanism of cancer. However, understanding of the pathogenic biological processes is still limited, such as the association between mutational signatures and genes.

Methods

We developed a simple and practical R package called RNMF (https://github.com/zhenzhang‐li/RNMF) for mutational signature analysis, including a key model of cumulative contribution abundance (CCA), which was designed to highlight the association between mutational signatures and genes and then applying it to a meta‐analysis of 1073 individuals with esophageal squamous cell carcinoma (ESCC).

Results

We revealed a number of known and previously undescribed SBS or ID signatures, and we found that APOBEC signatures (SBS2* and SBS13*) were closely associated with PIK3CA mutation, especially the E545k mutation. Furthermore, we found that age signature is closely related to the frequent mutation of TP53, of which R342* is highlighted due to strongly linked to age signature. In addition, the CCA matrix image data of genes in the signatures New, SBS3*, and SBS17b* were helpful for the preliminary evaluation of shortened survival outcome. These results can be extended to estimate the distribution of mutations or features, and study the potential impact of clinical factors.

Conclusions

In a word, RNMF can successfully achieve the correlation analysis of mutational signatures and genes, proving a strong theoretical basis for the study of mutational processes during tumor development.

Evidence for reduced BRCA2 functional activity in Homo sapiens after divergence from the chimpanzee-human last common ancestor

We performed a comparative analysis of human and 12 non-human primates to identify sequence variations in known cancer genes. We identified 395 human-specific fixed non-silent substitutions that emerged during evolution of human. Using bioinformatics analyses for functional consequences, we identified a number of substitutions that are predicted to alter protein function; one of these mutations is located at the most evolutionarily conserved domain of human BRCA2.

Antihyperglycemic effect of an anthocyanin, cyanidin-3-O-glucoside, is achieved by regulating GLUT-1 via the Wnt/β-catenin-WISP1 signaling pathway

Cyanidin-3-O-glucoside (C3G), an essential representative of anthocyanins, has been proved to possess a myriad of biological activities. However, the effects of C3G on glucose metabolism and its underlying molecular mechanisms remain elusive. The aim of the present study was to investigate the metabolic impact of C3G on db/db mice and to determine whether its consequent anti-diabetic effects were related to glucose transporter-1 (GLUT-1) by in vivo and in vitro studies. As a result, through diabetic db/db mice, C3G treatment was found to significantly reduce the fasting blood glucose level and increase glycogen synthesis, which were associated with upregulation of GLUT-1 expression in the liver of the mice. In addition, in liver cells of the HepG2 and L02 lines, we further discovered that C3G could effectively promote glucose consumption by regulating the Wnt/β-catenin-WISP1 signaling pathway. Nevertheless, such effects would be restricted when the expression of GLUT-1 was blocked by the inhibitor IWR-1. Meanwhile, molecular docking technology was applied to simulate the possible action sites of C3G at the molecular level, and the results indicated that C3G might bind to β-catenin. In conclusion, our study provided evidence of the antihyperglycemic effect of C3G in vivo and in vitro via regulating GLUT-1 expression and the related signaling pathways.