Mutational heterogeneity in human cancers: origin and consequences

Tumor heterogeneity in retinoblastoma: a literature review

Tumor heterogeneity, characterized by the presence of diverse cell populations within a tumor, is a key feature of the complex nature of cancer. This diversity arises from the emergence of cells with varying genomic, epigenetic, transcriptomic, and phenotypic profiles over the course of the disease. Host factors and the tumor microenvironment play crucial roles in driving both inter-patient and intra-patient heterogeneity. These diverse cell populations can exhibit different behaviors, such as varying rates of proliferation, responses to treatment, and potential for metastasis. Both inter-patient heterogeneity and intra-patient heterogeneity pose significant challenges to cancer therapeutics and management. In retinoblastoma, while heterogeneity at the clinical presentation level has been recognized for some time, recent attention has shifted towards understanding the underlying cellular heterogeneity. This review primarily focuses on retinoblastoma heterogeneity and its implications for therapeutic strategies and disease management, emphasizing the need for further research and exploration in this complex and challenging area.

CAR T-cell therapy in cancer: Integrating nursing perspectives for enhanced patient care

Chimeric antigen receptor (CAR) T-cell therapy represents a significant advancement in cancer treatment, particularly for hematologic malignancies. Various cancer immunotherapy strategies are presently being explored, including cytokines, cancer vaccines, immune checkpoint inhibitors, immunomodulators monoclonal antibodies, etc. The therapy has shown impressive efficacy in treating conditions such as acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), and multiple myeloma, often leading to complete remission in refractory cases. However, the clinical application of CAR T-cell therapy is accompanied by challenges, notably severe side effects. Effective management of these adverse effects requires meticulous monitoring and prompt intervention, highlighting the critical role of nursing in this therapeutic process. Nurses play a crucial role in patient education, monitoring, symptom management, care coordination, and psychosocial support, ensuring safe and effective treatment. As research advances and new CAR T-cell therapies are developed, the role of nursing professionals remains pivotal in optimizing patient outcomes. The continued evolution of CAR T-cell therapy promises improved outcomes, with nursing professionals integral to its success.

BCL2A1 neoepitope-elicited cytotoxic T lymphocytes are a promising individualized immunotherapy of pancreatic cancer

Conventional treatments have shown a limited efficacy for pancreatic cancer, and immunotherapy is an emerging option for treatment of this highly fatal malignancy. Neoantigen is critical to improving the efficacy of tumor-specific immunotherapy. The cancer and peripheral blood specimens from an HLA-A0201-positive pancreatic cancer patient were subjected to next-generation sequencing, and bioinformatics analyses were performed to screen high-affinity and highly stable neoepitopes. The activation of cytotoxic T lymphocytes (CTLs) by dendritic cells (DCs) loaded with mutBCL2A111-20 neoepitope targeting a BCL2A1 mutant epitope was investigated, and the cytotoxicity of mutBCL2A111-20 neoepitope-specific CTLs to pancreatic cancer cells was evaluated. The mutBCL2A111-20 neoepitope was found to present a high immunogenicity and induce CTLs activation and proliferation, and these CTLs were cytotoxic to mutBCL2A111-20 neoepitope-loaded T2 cells and pancreatic cancer PANC-1-Neo and A2-BxPC-3-Neo cells that overexpressed mutBCL2A111-20 neoepitopes, appearing to be a targeting neoepitope specificity. In addition, high BCL2A1 expression correlated with a low 5-yr progression-free interval among pancreatic cancer patients. Our findings provide experimental supports to individualized T cell therapy targeting mutBCL2A111-20 neoepitopes, and provide an option of immunotherapy for pancreatic cancer.

Multifactorial drug carrier system bringing both chemical and physical therapeutics to the treatment of tumor heterogeneity

Tumor heterogeneity and drug resistance have been invincible features of cancer for its complete cure. Despite the advent of immunotherapy, the expansion and diversification of cancer cells evolved even in the absence or presence of drug treatment discourage additional therapeutic interventions. For the eradication of cancer cells, therefore, an 'all-at-once' strategy is required, which exploits both target-selective chemotherapy and non-selective physicotherapy. Multifactorial microcapsules comprising gold nanoparticles (AuNPs) and a self-assembly protein of α-synuclein (αS) were fabricated, in which hydrophobic and hydrophilic drugs could be separately encapsulated by employing lipid-based inverted micelles (IMs). Their combined physico-chemical therapeutic effects were examined since they also contained both membrane-disrupting IMs and heat-generating AuNPs upon irradiation as physicotherapeutic agents. For the optimal enclosure of IMs containing hydrophilic drugs, a porous inner skeleton made of poly(lactic-co-glycolic acid) was introduced, which would play the roles of not only compartmentalizing the internal space but also enhancing proteolytic disintegration of the microcapsules to discharge and stabilize IMs to the outside. In fact, hydrophobic paclitaxel and hydrophilic doxorubicin showed markedly enhanced drug efficacy when delivered in the IM-containing microcapsules exhibiting the 'quantal' release of both drugs into the cells whose integrity could be also affected by the IMs. In addition, the remnants of αS-AuNP microcapsules produced via proteolysis also caused cell death through photothermal effect. The multifactorial microcapsules are therefore considered as a promising anti-cancer drug carrier capable of performing combinatorial selective and non-selective chemical and physical therapies to overcome tumor heterogeneity and drug resistance.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Y-Box Binding Protein 1: Unraveling the Multifaceted Role in Cancer Development and Therapeutic Potential

Y-box binding protein 1 (YBX1), a member of the Cold Shock Domain protein family, is overexpressed in various human cancers and is recognized as an oncogenic gene associated with poor prognosis. YBX1's functional diversity arises from its capacity to interact with a broad range of DNA and RNA molecules, implicating its involvement in diverse cellular processes. Independent investigations have unveiled specific facets of YBX1's contribution to cancer development. This comprehensive review elucidates YBX1's multifaceted role in cancer across cancer hallmarks, both in cancer cell itself and the tumor microenvironment. Based on this, we proposed YBX1 as a potential target for cancer treatment. Notably, ongoing clinical trials addressing YBX1 as a target in breast cancer and lung cancer have showcased its promise for cancer therapy. The ramp up in in vitro research on targeting YBX1 compounds also underscores its growing appeal. Moreover, the emerging role of YBX1 as a neural input is also proposed where the high level of YBX1 was strongly associated with nerve cancer and neurodegenerative diseases. This review also summarized the up-to-date advanced research on the involvement of YBX1 in pancreatic cancer.

A Clonal Evolution Simulator for Planning Somatic Evolution Studies

Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regard to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions. Versatile simulation tools are needed to explore and optimize potential study designs if researchers are to deploy multiomic technologies most effectively. In this study, we present a simulator allowing for the generation of synthetic data from a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. Users can input various properties of the somatic evolutionary system, mutation classes, and biotechnology options, and then generate samples of synthetic sequence reads and their corresponding ground truth parameters for a given study design. We demonstrate the utility of the simulator for testing and optimizing study designs for various experimental queries.

The Emerging Role of Liquid Biopsies in Revolutionising Cancer Diagnosis and Therapy

A potential non-invasive technique for identifying and tracking cancer is a liquid biopsy. This review article provides a comprehensive overview of the principles, applications, and challenges associated with liquid biopsies. The circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), exosomes, and microRNAs are just a few of the biomarkers we cover in this article that are discovered in liquid biopsies. The clinical application of liquid biopsies in many stages of cancer management, including early cancer identification, therapy selection and response monitoring, and minimum residual illness, is also investigated. The technical advancements in liquid biopsy techniques, including digital polymerase chain reaction (dPCR) and next-generation sequencing (NGS), have improved the sensitivity and specificity of biomarker identification. Liquid biopsies require assistance with cost-effectiveness, sensitivity, and standardisation despite the potential benefits. We talk about these restrictions and potential solutions. In conclusion, liquid biopsies revolutionise personalised therapies and cancer diagnostics by providing a real-time, non-invasive tool for characterising and monitoring tumours. It will be possible to expand the use of liquid biopsies in clinical practises by having a better understanding of their current state and predicted future developments.

Improving Yields in Multi-analyte Extractions by Utilizing Post-homogenized Tissue Debris

In multi-analyte extractions, tissue is typically homogenized in a lysis buffer, and then DNA, RNA, and protein are purified from the supernatant. However, yields are typically lower than in dedicated, single-analyte extractions. In a two-part experiment, we assessed whether yields could be improved by revisiting the normally discarded, post-homogenized tissue debris. We initially performed additional homogenizations, each followed by a simultaneous extraction. These yielded no additional RNA, 13% additional DNA (which became progressively more degraded), and 161.7% additional protein (which changed in proteome when analyzed using SDS-PAGE). We then digested post-homogenized tissue debris from a simultaneous extraction using proteinase K and extracted DNA using silica spin columns or alcohol precipitation. An average additional DNA yield of 27.1% (silica spin columns) or 203.9% (alcohol precipitation) was obtained with/without compromising DNA integrity (assessment by long-range PCR, DNA Integrity Numbers, and size at peak fluorescence of electropherogram). Validation using a cohort of 65 tissue blocks returned an average additional DNA yield of 31.6% (silica columns) and 54.8% (alcohol precipitation). Users can therefore refreeze the homogenized remnants of tissue blocks rather than disposing of them and then perform additional DNA extractions if yields in the initial multi-analyte extractions were low.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

BACKGROUND

Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed.

RESULTS

In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets.

CONCLUSIONS

From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

Pan-cancer genomic analysis shows hemizygous PTEN loss tumors are associated with immune evasion and poor outcome

In tumors, somatic mutations of the PTEN suppressor gene are associated with advanced disease, chemotherapy resistance, and poor survival. PTEN loss of function may occur by inactivating mutation, by deletion, either affecting one copy (hemizygous loss) leading to reduced gene expression or loss of both copies (homozygous) with expression absent. Various murine models have shown that minor reductions in PTEN protein levels strongly influence tumorigenesis. Most PTEN biomarker assays dichotomize PTEN (i.e. presence vs. absence) ignoring the role of one copy loss. We performed a PTEN copy number analysis of 9793 TCGA cases from 30 different tumor types. There were 419 (4.28%) homozygous and 2484 (25.37%) hemizygous PTEN losses. Hemizygous deletions led to reduced PTEN gene expression, accompanied by increased levels of instability and aneuploidy across tumor genomes. Outcome analysis of the pan-cancer cohort showed that losing one copy of PTEN reduced survival to comparable levels as complete loss, and was associated with transcriptomic changes controlling immune response and the tumor microenvironment. Immune cell abundances were significantly altered for PTEN loss, with changes in head and neck, cervix, stomach, prostate, brain, and colon more evident in hemizygous loss tumors. These data suggest that reduced expression of PTEN in tumors with hemizygous loss leads to tumor progression and influences anticancer immune response pathways.

CAR T Cell Therapy: A Versatile Living Drug

After seeing a dramatic increase in the development and use of immunotherapy and precision medicine over the past few decades, oncological care now embraces the start of the adoptive cell therapy (ACT) era. This impulse towards a new treatment paradigm has been led by chimeric antigen receptor (CAR) T cells, the only type of ACT medicinal product to be commercialized so far. Brought about by an ever-growing understanding of cellular engineering, CAR T cells are T lymphocytes genetically modified with an appropriate DNA construct, which endows them with expression of a CAR, a fusion protein between a ligand-specific recognition domain, often an antibody-like structure, and the activating signaling domain of the T cell receptor. Through this genetic enhancement, CAR T cells are engineered from a cancer patient’s own lymphocytes to better target and kill their cancer cells, and the current amassed data on clinical outcomes point to a stream of bright developments in the near future. Herein, from concept design and present-day manufacturing techniques to pressing hurdles and bright discoveries around the corner, we review and thoroughly describe the state of the art in CAR T cell therapy.

Differential expression of DNA repair genes and treatment outcome of chemoradiotherapy (CRT) in cervical cancer

Cervical cancer (CaCx) is the malignancy of uterine cervix which induce by human papillomavirus (HPV) infections. HPV infection starts with the induction of double-stranded breaks by increasing oxidative stress and modulation of DNA repair pathways. Deficiency in DNA repair pathways and accumulation of DNA damage increases mutation rates resulting in genomic instability and cancer development. Patients with HPV-associated CaCx display increased sensitivity to cisplatin-based chemoradiotherapy (CRT) and improved survival rates. However, the cellular mechanisms responsible for this characteristic difference are unclear. Here, we have evaluated expression of DNA repair genes in peripheral blood cells and correlated them with treatment outcomes. A total of 211 study subjects includes in the study comprised 103 CaCx patients and 108 healthy controls. All the study subjects were analyzed for the expression profile of DNA repair genes by using real-time PCR (RT-PCR). The differentially expressed DNA repair gene was correlated with the treatment outcome of CRT. OGG1, XRCC2, XRCC3, XRCC4 and XRCC6 genes were found to be significant (P=0.001) down-regulated as compared to controls. While XRCC5 and RAD51 showed significant up-regulated (P=0.024 and 0.041) in CaCx patients. XRCC6 was associated (P=0.033) with poor vital while up-regulated RAD51 showed slight association (P=0.075) with better vital with an increased 2.96- and 2.33-fold risk in the study population. In the case of overall survival, down-regulated XRCC4 was associated (P=0.042) with poor survival (27 months) with the least hazard ratio (0.56 HR). Down-regulated OGG1 involved BER, XRCC2 and XRCC3 in homologous recombination and XRCC4, XRCC5 and XRCC6 in Non-homologous end-joining repair, which showed a deficiency of DNA repair capacity resulting caused of an accumulation of DNA damage and genome instability. Impaired DNA repair gene expression is responsible for poor prognosis and survival in CaCx. Therefore, these gene expressions can be considered a potential prognostic, diagnostic and therapeutic biomarker for CaCx.

Fragment length profiles of cancer mutations enhance detection of circulating tumor DNA in patients with early-stage hepatocellular carcinoma

BACKGROUND

Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity.

METHODS

Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC.

RESULTS

Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%.

CONCLUSIONS

Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Biochemistry-Not Oncogenes-May Demystify and Defeat Cancer

The presence of mutated genes strongly correlates with the incidence of cancer. Decades of research, however, has not yielded any specific causative gene or set of genes for the vast majority of cancers. The Cancer Genome Atlas program was supposed to provide clarity, but it only gave much more data without any accompanying insight into how the disease begins and progresses. It may be time to notice that epidemiological studies consistently show that the environment, not genes, has the principal role in causing cancer. Since carcinogenic chemicals in our food, drink, air, and water are the primary culprits, we need to look at the biochemistry of cancer, with a focus on enzymes that invariably facilitate transformations in a cell. In particular, attention should be paid to the rate-limiting enzyme in DNA synthesis, ribonucleotide reductase (RnR), whose activity is tightly linked to tumor growth. Besides circumstantial evidence that cancer is induced at this enzyme's vulnerable free-radical-containing active site by various carcinogens, its role in initiating retinoblastoma and human papillomavirus (HPV)-related cervical cancers has been well documented in recent years. Blocking the activity of malignant RnR is a certain way to arrest cancer.

From the Catastrophic Objective Irreproducibility of Cancer Research and Unavoidable Failures of Molecular Targeted Therapies to the Sparkling Hope of Supramolecular Targeted Strategies

The unprecedented non-reproducibility of the results published in the field of cancer research has recently come under the spotlight. In this short review, we try to highlight some general principles in the organization and evolution of cancerous tumors, which objectively lead to their enormous variability and, consequently, the irreproducibility of the results of their investigation. This heterogeneity is also extremely unfavorable for the effective use of molecularly targeted medicine. Against the seemingly comprehensive background of this heterogeneity, we single out two supramolecular characteristics common to all tumors: the clustered nature of tumor interactions with their microenvironment and the formation of biomolecular condensates with tumor-specific distinctive features. We suggest that these features can form the basis of strategies for tumor-specific supramolecular targeted therapies.

Evolution of small cell lung cancer tumor mutation: from molecular mechanisms to novel viewpoints

Small cell lung cancer (SCLC) is a clinically common malignant tumor originating from the lung neuroendocrine stem cells, which has a poor prognosis and accounts for approximately 15% of all lung cancer cases. However, research on its treatment has been slow, and the 5-year survival rate of patients with SCLC has been < 5% for many years. In recent years, the development and popularization of gene sequencing technology have facilitated the understanding of the gene mutation landscape and tumor evolution of SCLC, thereby leading to a more accurate prediction of the prognosis of SCLC and the development of individualized treatment. In this review, we aimed to discuss the mutation evolution of SCLC from the perspective of a tumor evolution theory and described the sequence of mutation evolution in the occurrence and development of SCLC. In addition, we summarized the existing whole-exome sequencing (WES) data of SCLC cases at our center along with relevant publications on sequencing. Thereafter, we discuss the role of different mutated pathways in the occurrence of SCLC to predict its prognosis more accurately and summarized individualized treatment strategies.

HCV and tumor-initiating stem-like cells

Neoplasms contain tumor-initiating stem-like cells (TICs) that are characterized by increased drug resistance. The incidence of many cancer types have trended downward except for few cancer types, including hepatocellular carcinoma (HCC). Therefore mechanism of HCC development and therapy resistance needs to be understood. These multiple hits by hepatitis C virus (HCV) eventually promotes transformation and TIC genesis, leading to HCC development. This review article describes links between HCV-associated HCC and TICs. This review discusses 1) how HCV promotes genesis of TICs and HCC development; 2) how this process avails itself as a novel therapeutic target for HCC treatment; and 3) ten hall marks of TIC oncogenesis and HCC development as targets for novel therapeutic modalities.

The prognostic value of 18F-FDG PET/CT intra-tumoural metabolic heterogeneity in pretreatment neuroblastoma patients

BACKGROUND

Neuroblastoma (NB) is the most common tumour in children younger than 5 years old and notable for highly heterogeneous. Our aim was to quantify the intra-tumoural metabolic heterogeneity of primary tumour lesions by using 18F-FDG PET/CT and evaluate the prognostic value of intra-tumoural metabolic heterogeneity in NB patients.

METHODS

We retrospectively enrolled 38 pretreatment NB patients in our study. 18F-FDG PET/CT images were reviewed and analyzed using 3D slicer software. The semi-quantitative metabolic parameters of primary tumour were measured, including the maximum standard uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion glycolysis (TLG). The areas under the curve of cumulative SUV-volume histogram index (AUC-CSH index) was used to quantify intra-tumoural metabolic heterogeneity. The median follow-up was 21.3 months (range 3.6 - 33.4 months). The outcome endpoint was event-free survival (EFS), including progression-free survival and overall survival. Survival analysis was performed using Cox regression models and Kaplan Meier survival plots.

RESULTS

In all 38 newly diagnosed NB patients, 2 patients died, and 17 patients experienced a relapse. The AUC-CSHtotal (r=0.630, P<0.001) showed moderate correlation with the AUC-CSH40%. In univariate analysis, chromosome 11q deletion (P=0.033), Children's Oncology Group (COG) risk grouping (P=0.009), bone marrow involvement (BMI, P=0.015), and AUC-CSHtotal (P=0.007) were associated with EFS. The AUC-CSHtotal (P=0.036) and BMI (P=0.045) remained significant in multivariate analysis. The Kaplan Meier survival analyses demonstrated that patients with higher intra-tumoural metabolic heterogeneity and BMI had worse outcomes (log-rank P=0.002).

CONCLUSION

The intra-tumoural metabolic heterogeneity of primary lesions in NB was an independent prognostic factor for EFS. The combined predictive effect of intra-tumoural metabolic heterogeneity and BMI provided prognostic survival information in NB patients.

Optimal control model of tumor treatment in the context of cancer stem cell

We consider cancer cytotoxic drugs as an optimal control problem to stabilize a heterogeneous tumor by attacking not the most abundant cancer cells, but those that are crucial in the tumor ecosystem. We propose a mathematical cancer stem cell model that translates the hierarchy and heterogeneity of cancer cell types by including highly structured tumorigenic cancer stem cells that yield low differentiated cancer cells. With respect to the optimal control problem, under a certain admissibility hypothesis, the optimal controls of our problem are bang-bang controls. These control treatments can retain the entire tumor in the neighborhood of an equilibrium. We simulate the bang-bang control numerically and demonstrate that the optimal drug scheduling should be administered continuously over long periods with short rest periods. Moreover, our simulations indicate that combining multidrug therapies and monotherapies is more efficient for heterogeneous tumors than using each one separately.

Current Status of the Use of Multifunctional Enzymes as Anti-Cancer Drug Targets

Fighting cancer is one of the major challenges of the 21st century. Among recently proposed treatments, molecular-targeted therapies are attracting particular attention. The potential targets of such therapies include a group of enzymes that possess the capability to catalyze at least two different reactions, so-called multifunctional enzymes. The features of such enzymes can be used to good advantage in the development of potent selective inhibitors. This review discusses the potential of multifunctional enzymes as anti-cancer drug targets along with the current status of research into four enzymes which by their inhibition have already demonstrated promising anti-cancer effects in vivo, in vitro, or both. These are PFK-2/FBPase-2 (involved in glucose homeostasis), ATIC (involved in purine biosynthesis), LTA₄H (involved in the inflammation process) and Jmjd6 (involved in histone and non-histone posttranslational modifications). Currently, only LTA₄H and PFK-2/FBPase-2 have inhibitors in active clinical development. However, there are several studies proposing potential inhibitors targeting these four enzymes that, when used alone or in association with other drugs, may provide new alternatives for preventing cancer cell growth and proliferation and increasing the life expectancy of patients.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.

Analysis of KRAS Mutation Subtype in Tissue DNA and Cell-Free DNA Using Droplet Digital PCR and the Function of Cell-Free DNA as a Recurrence Predictive Marker in Pancreatic Cancer

KRAS mutation is a major regulator in the tumor progression of pancreatic cancer. Here, we compared the frequency and mutation burden of KRAS mutation subtypes with paired tumor tissue and blood in patients and examined their clinical significance. DNA from tumor tissues and cell-free DNA (cfDNA) from preoperative blood were obtained from 70 patients with pancreatic cancer. Subtypes and mutation burdens of KRAS G12D and G12V mutations were evaluated using droplet digital PCR. Comparing the presence of mutations in tissue, accumulative and simultaneous mutations of G12D or G12V were identified of 67 (95.7%), and 48 patients (68.6%). Conversely, in blood, they were only identified in 18 (25.7%) and four (5.7%) patients; respectively. Next, comparing the mutation burden in tissue, the mutation burden varied from less than 0.1 to more than five, whereas that of cfDNA in blood was mostly between one and five, as cases with a mutation burden lower than 0.1 and higher than five were rare. Finally, the presence of the G12V mutation alone in cfDNA and the combination of the G12V mutation with elevated CA 19-9 levels were associated with poor recurrence-free survival. These fundamental data on the KRAS mutation subtypes and their clinical significance could support their potential as predictive markers for postoperative recurrence.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Comprehensively Exploring the Mutational Landscape and Patterns of Genomic Evolution in Hypermutated Cancers

Simple Summary

To identify potential genetic markers for evaluating hypermutated cancers, we investigated driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution in the genetic variants of 533 cancer patients with six different cancer types. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Cancer driver mutations and mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational predictions, we identified two tumor-associated neoantigens. Sequential mutations were used in a logistic model to predict hypermutated cancers according to genomic evolution. The sequential mutation order and coexisting genetic mutations were found to influence the hypermutation phenotype. Based on our observations, we developed a new concept for hypermutated cancers, whereby sequential mutations are significant for hypermutated cancers, which are mutationally heterogeneous. Through the comprehensive assessments of cancer gene panels, mutational pattern analysis was conducted as a basis for providing recommendations regarding therapeutic strategies for hypermutated cancer patients.

Abstract

Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients with six different cancer types using deep targeted sequencing and gene expression data from 78 colorectal cancer patients, whereby driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution were investigated. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Most hypermutated endometrial and pancreatic cancer patients carry genetic mutations in EGFR, FBXW7, and PIK3CA that are linked to immunotherapy resistance, while hypermutated head and neck cancer patients carry genetic mutations associated with better treatment responses, such as ATM and BRRCA2 mutations. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and DNA repair defects are mutational drivers that are signatures for hypermutated cancer. Cancer driver mutations and other mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational prediction, we identified NF1 p.T700I and NOTCH1 p.V2153M as tumor-associated neoantigens, representing potential therapeutic targets for immunotherapy. Sequential mutations were used to predict hypermutated cancers based on genomic evolution. Using a logistic model, we achieved an area under the curve (AUC) = 0.93, accuracy = 0.93, and sensitivity = 0.81 in the testing set. The sequential patterns were distinct among the six cancer types, and the sequential mutation order of MSH2 and the coexisting BRAF genetic mutations influenced the hypermutated phenotype. The TP53~MLH1 and NOTCH1~TET2 sequential mutations impacted colorectal cancer survival (p-value = 0.027 and 0.0001, respectively) by reducing the expression of PTPRCAP (p-value = 1.06 × 10⁻⁶) and NOS2 (p-value = 7.57 × 10⁻⁷) in immunity. Sequential mutations are significant for hypermutated cancers, which are characterized by mutational heterogeneity. In addition to driver mutations and mutational signatures, sequential mutations in cancer evolution can impact hypermutated cancers. They characterize potential responses or predictive markers for hypermutated cancers. These data can also be used to develop hypermutation-associated drug targets and elucidate the evolutionary biology of cancer survival. In this study, we conducted a comprehensive analysis of mutational patterns, including sequential mutations, and identified useful markers and therapeutic targets in hypermutated cancer patients.

Elucidating the anticancer activities of guanidinium-functionalized amphiphilic random copolymers by varying the structure and composition in the hydrophobic monomer

Rationale: Use of traditional anticancer chemotherapeutics has been hindered by the multifactorial nature of multi-drug resistance (MDR) development and metastasis. Recently, cationic polycarbonates were reported as novel unconventional anticancer agents that mitigated MDR and inhibited metastasis. The aim of this study is to explore structure-anticancer activity relationship. Specifically, a series of cationic guanidinium-based random copolymers of varying hydrophobicity was synthesized with a narrow polydispersity (Ð = 1.12-1.27) via organocatalytic ring-opening polymerization (OROP) of functional cyclic carbonate monomers, and evaluated for anticancer activity, killing kinetics, degradability and functional mechanism. Methods: Linear, branched and aromatic hydrophobic side chain units, such as ethyl, benzyl, butyl, isobutyl and hexyl moieties were explored as comonomer units for modulating anticancer activity. As hydrophobicity/hydrophilicity balance of the polymers determines their anticancer efficacy, the feed ratio between the two monomers was varied to tune their hydrophobicity. Results: Notably, incorporating the hexyl moiety greatly enhanced anticancer efficiency and killing kinetics on cancer cells. Degradation studies showed that the polymers degraded completely within 4-6 days. Flow cytometry and lactate dehydrogenase (LDH) release analyses demonstrated that anticancer mechanism of the copolymers containing a hydrophobic co-monomer was concentration dependent, apoptosis at IC50, and both apoptosis and necrosis at 2 × IC50. In contrast, the homopolymer without a hydrophobic comonomer killed cancer cells predominantly via apoptotic mechanism. Conclusion: The hydrophobicity of the polymers played an important role in anticancer efficacy, killing kinetics and anticancer mechanism. This study provides valuable insights into designing novel anticancer agents utilizing polymers.

Randomness in Cancer Breakpoint Prediction

Cancer genomes are susceptible to multiple rearrangements by deleting, inserting, and translocating genomic regions. Recently, the problem of finding determinants of breakpoint formations was approached with machine learning methods; however, unlike cancer point mutations, breakpoint prediction appeared to be a more difficult task, and various machine learning models did not achieve high prediction power often slightly exceeding the threshold of random guessing. This raised the question of whether the breakpoints are random noise in cancer mutagenesis or there exist determinants in structural mutagenesis. In the present study, we investigated randomness in cancer breakpoint genome distributions through the power of machine learning models to predict breakpoint hot spots. We divided all cancer types into three groups by degree of randomness in their breakpoint formation. We tested different density thresholds and explored the bias in hot spot definition. We also compared prediction of hot spots versus individual breakpoints. We found that hot spots are considerably better predicted than individual breakpoints; however, some individual breakpoints can also be predicted with a satisfactory power, and thus, it is not proper to filter them from analyses. We demonstrated that positive-unlabeled learning can provide insights into insufficiency of cancer data sets, which are not always reflected by data set sizes. Overall, the present results support the view that cancer breakpoint landscape can be represented by predictable dense breakpoint regions and scattered individual breakpoints, which are not all random noise, but some are generated by detectable mechanism.

Genomic alterations in gynecological malignancies: histotype-associated driver mutations, molecular subtyping schemes, and tumorigenic mechanisms

There are numerous histological subtypes (histotypes) of gynecological malignancies, with each histotype considered to largely reflect a feature of the "cell of origin," and to be tightly linked with the clinical behavior and biological phenotype of the tumor. The recent advances in massive parallel sequencing technologies have provided a more complete picture of the range of the genomic alterations that can persist within individual tumors, and have highlighted the types and frequencies of driver-gene mutations and molecular subtypes often associated with these histotypes. Several large-scale genomic cohorts, including the Cancer Genome Atlas (TCGA), have been used to characterize the genomic features of a range of gynecological malignancies, including high-grade serous ovarian carcinoma, uterine corpus endometrial carcinoma, uterine cervical carcinoma, and uterine carcinosarcoma. These datasets have also been pivotal in identifying clinically relevant molecular targets and biomarkers, and in the construction of molecular subtyping schemes. In addition, the recent widespread use of clinical sequencing for the more ubiquitous types of gynecological cancer has manifested in a series of large genomic datasets that have allowed the characterization of the genomes, driver mutations, and histotypes of even rare cancer types, with sufficient statistical power. Here, we review the field of gynecological cancer, and seek to describe the genomic features by histotype. We also will demonstrate how these are linked with clinicopathological attributes and highlight the potential tumorigenic mechanisms.

Diet-Dependent Metabolic Regulation of DNA Double-Strand Break Repair in Cancer: More Choices on the Menu

Despite several epidemiologic and preclinical studies supporting the role of diet in cancer progression, the complexity of the diet-cancer link makes it challenging to deconvolute the underlying mechanisms, which remain scantly elucidated. This review focuses on genomic instability as one of the cancer hallmarks affected by diet-dependent metabolic alterations. We discuss how altered dietary intake of metabolites of the one-carbon metabolism, including methionine, folate, and vitamins B and C, can impact the methylation processes and thereby tumorigenesis. We present the concept that the protumorigenic effect of certain diets, such as the Western diet, is in part due to a diet-induced erosion of the DNA repair capacity caused by altered epigenetic and epitranscriptomic landscapes, while the protective effect of other dietary patterns, such as the Mediterranean diet, can be partly explained by their ability to sustain a proficient DNA repair. In particular, considering that diet-dependent alterations of the one-carbon metabolism can impact the rate of methylation processes, changes in dietary patterns can affect the activity of writers and erasers of histone and RNA methyl marks and consequently impair their role in ensuring a proficient DNA damage repair.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Full-coverage TP53 deep sequencing of recurrent head and neck squamous cell carcinoma facilitates prognostic assessment after recurrence

OBJECTIVES

This study aims to evaluate whether the accumulation of TP53 mutations is associated with clinical outcome by comparing full-coverage TP53 deep sequencing of the initial and recurrent head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Medical records and surgical specimens of 400 patients with HNSCC surgically treated with curative intent, of which 95 patients developed local or locoregional recurrence, were reviewed. Of these patients, 63 were eligible for genomic analysis. Full-coverage TP53 deep sequencing of 126 paired initial and recurrent tumor samples was examined using next-generation sequencing (NGS). Temporal changes in the mutation status, molecular characterization, and clinical outcome were compared. Fisher's exact test, Kaplan-Meier method, log-rank test, and Cox regression models were used for statistical analysis.

RESULTS

Of the recurrent tumors, 22% harbored accumulation of TP53 mutations, and 16% lost the original mutation. The accumulation of TP53 mutations was significantly more frequent in oral cancer than in pharyngeal or laryngeal cancer (33% vs. 7%, p = 0.016). Two-year post-recurrence survival (PRS) was associated with TP53 status for recurrent tumors, but not for initial tumors. The TP53 status for recurrent tumors was an independent risk factor in multivariate analysis (hazard ratio, 5.76; 95% confidence interval, 1.86-17.8; p = 0.0023).

CONCLUSION

Approximately one-third of the recurrent HNSCC cases showed a different TP53 status from the initial tumor. Temporal changes in the mutation status differed by primary site. Full-coverage TP53 deep sequencing of recurrent tumors was useful in predicting post-recurrence prognosis.

Intra-tumoral heterogeneity and immune responses predicts prognosis of gastric cancer

Chemotherapy resistance eventually develops in patients with gastric cancer (GC). Intra-tumoral heterogeneity (ITH) refers to the intercellular genetic variations and phenotypic diversity that affect responses to drug therapy. We measured ITH using mutant-allele tumor heterogeneity (MATH) derived from whole-exome sequencing data of patients with GC in The Cancer Genome Atlas (TCGA) database. The study included 385 patients from the TCGA database with available data regarding gastrectomy, survival, and whole-exome sequencing. Further analysis was performed in 171 GC patients with available data regarding adjuvant chemotherapy. Multiple factor analysis showed that MATH was an independent predictor of OS (hazard ratio [HR], 1.432; 95% confidence interval [CI], 1.073-1.913; P = 0.015) in patients with GC. Moreover, MATH was also an independent predictor of OS among the 171 GC patients who received adjuvant chemotherapy (HR, 2.016; 95% CI, 1.236-3.289; P = 0.005). Pathway enrichment and immune cell analyses revealed significantly higher infiltration by 20 types of immune cells in the low/intermediate group, compared to the group with high MATH scores. In conclusion, low/intermediate MATH scores predicted longer OS, when compared to those with high MATH scores. The immune response was obviously upregulated in patients with GC and low/intermediate MATH scores.

Immunotherapy for Metastatic Prostate Cancer: Current and Emerging Treatment Options

The advent of immunotherapy has revolutionized cancer treatment. Prostate cancer has an immunosuppressive microenvironment and a low tumor mutation burden, resulting in low neoantigen expression. The consensus was that immunotherapy would be less effective in prostate cancer. However, recent studies have reported that prostate cancer does have a high number of DNA damage and repair gene defects. Immunotherapies that have been tested in prostate cancer so far have been mainly vaccines and checkpoint inhibitors. A combination of genomically targeted therapies, with approaches to alleviate immune response and thereby make the tumor microenvironment immunologically hot, is promising.

Application of simultaneous selective pressures slows adaptation

Beneficial mutations that arise in an evolving asexual population may compete or interact in ways that alter the overall rate of adaptation through mechanisms such as clonal or functional interference. The application of multiple selective pressures simultaneously may allow for a greater number of adaptive mutations, increasing the opportunities for competition between selectively advantageous alterations, and thereby reducing the rate of adaptation. We evolved a strain of Saccharomyces cerevisiae that could not produce its own histidine or uracil for ~500 generations under one or three selective pressures: limitation of the concentration of glucose, histidine, and/or uracil in the media. The rate of adaptation was obtained by measuring evolved relative fitness using competition assays. Populations evolved under a single selective pressure showed a statistically significant increase in fitness on those pressures relative to the ancestral strain, but the populations evolved on all three pressures did not show a statistically significant increase in fitness over the ancestral strain on any single pressure. Simultaneously limiting three essential nutrients for a population of S. cerevisiae effectively slows the rate of evolution on any one of the three selective pressures applied, relative to the single selective pressure cases. We identify possible mechanisms for fitness changes seen between populations evolved on one or three limiting nutrient pressures by high-throughput sequencing. Adding multiple selective pressures to evolving disease like cancer and infectious diseases could reduce the rate of adaptation and thereby may slow disease progression, prolong drug efficacy and prevent deaths.

Roniciclib down-regulates stemness and inhibits cell growth by inducing nucleolar stress in neuroblastoma

Neuroblastoma, an embryonic tumor arising from neuronal crest progenitor cells, has been shown to contain a population of undifferentiated stem cells responsible for the malignant state and the unfavorable prognosis. Although many previous studies have analyzed neuroblastoma stem cells and their therapeutic targeting, this topic appears still open to novel investigations. Here we found that neurospheres derived from neuroblastoma stem-like cells showed a homogeneous staining for several key nucleolar proteins, such as Nucleolin, Nucleophosmin-1, Glypican-2 and PES-1. We investigated the effects of Roniciclib (BAY 1000394), an anticancer stem cells agent, on neurospheres and on an orthotopic neuroblastoma mouse model, discovering an impressive inhibition of tumor growth and indicating good chances for the use of Roniciclib in vivo. We demonstrated that Roniciclib is not only a Wnt/β-catenin signaling inhibitor, but also a nucleolar stress inducer, revealing a possible novel mechanism underlying Roniciclib-mediated repression of cell proliferation. Furthermore, we found that high expression of Nucleophosmin-1 correlates with patients’ short survival. The co-expression of several stem cell surface antigens such as CD44v6 and CD114, together with the nucleolar markers here described, extends new possibilities to isolate undifferentiated subpopulations from neuroblastoma and identify new targets for the treatment of this childhood malignancy.

Why Formalin-fixed, Paraffin-embedded Biospecimens Must Be Used in Genomic Medicine: An Evidence-based Review and Conclusion

Fresh-frozen tissue is the “gold standard” biospecimen type for next-generation sequencing (NGS). However, collecting frozen tissue is usually not feasible because clinical workflows deliver formalin-fixed, paraffin-embedded (FFPE) tissue blocks. Some clinicians and researchers are reticent to embrace the use of FFPE tissue for NGS because FFPE tissue can yield low quantities of degraded DNA, containing formalin-induced mutations. We describe the process by which formalin-induced deamination can lead to artifactual cytosine (C) to thymine (T) and guanine (G) to adenine (A) (C:G > T:A) mutation calls and perform a literature review of 17 publications that compare NGS data from patient-matched fresh-frozen and FFPE tissue blocks. We conclude that although it is indeed true that sequencing data from FFPE tissue can be poorer than those from frozen tissue, any differences occur at an inconsequential magnitude, and FFPE biospecimens can be used in genomic medicine with confidence:

Klotho and the Treatment of Human Malignancies

Klotho was first discovered as an anti-ageing protein linked to a number of age-related disease processes, including cardiovascular, renal, musculoskeletal, and neurodegenerative conditions. Emerging research has also demonstrated a potential therapeutic role for Klotho in cancer biology, which is perhaps unsurprising given that cancer and ageing share similar molecular hallmarks. In addition to functioning as a tumour suppressor in numerous solid tumours and haematological malignancies, Klotho represents a candidate therapeutic target for patients with these diseases, the majority of whom have limited treatment options. Here, we examine contemporary evidence evaluating the anti-neoplastic effects of Klotho and describe the modulation of downstream oncogenic signalling pathways, including Wnt/β-catenin, FGF, IGF1, PIK3K/AKT, TGFβ, and the Unfolded Protein Response. We also discuss possible approaches to developing therapeutic Klotho and consider technological advances that may facilitate the delivery of Klotho through gene therapy.

Profiling DNA mutation patterns by SERS fingerprinting for supervised cancer classification

Profiling DNA mutation patterns for cancer classification plays an essential role in precision and personalized medicine. Conventional PCR-based mutation assay is limited by the extensive labour on target amplification. We herein create an amplification-free surface enhanced Raman spectroscopy (SERS) biochip which enables direct and simultaneous identification of multiple point mutations in tumor cells. Without pre-amplifying the target sequences, the SERS assay reads out the presence of cellular mutations through the interpretation of Raman fingerprints. The SERS sensor is integrated into a microfluidic chip, achieving one-step multiplex analysis within 40 min. Importantly, by combining SERS spectra encoding technique with supervised learning algorithm, a panel of nucleotide mixtures can be well distinguished according to their mutation profiles. We initially demonstrate an excellent levels of classification in samples from colorectal cancer and melanoma cell lines. For final clinical validation, the system performance is verified by classifying cancer patient samples, which shows an accuracy above 90%. Due to the simplicity and rapidness, the SERS biosensor is expected to become a promising tool for clinical point-of-care diagnosis towards precision medicine.

RECQ1 Helicase in Genomic Stability and Cancer

RECQ1 (also known as RECQL or RECQL1) belongs to the RecQ family of DNA helicases, members of which are linked with rare genetic diseases of cancer predisposition in humans. RECQ1 is implicated in several cellular processes, including DNA repair, cell cycle and growth, telomere maintenance, and transcription. Earlier studies have demonstrated a unique requirement of RECQ1 in ensuring chromosomal stability and suggested its potential involvement in tumorigenesis. Recent reports have suggested that RECQ1 is a potential breast cancer susceptibility gene, and missense mutations in this gene contribute to familial breast cancer development. Here, we provide a framework for understanding how the genetic or functional loss of RECQ1 might contribute to genomic instability and cancer.

APLNR is involved in ATRA-induced growth inhibition of nasopharyngeal carcinoma and may suppress EMT through PI3K-Akt-mTOR signaling

The apelin receptor (APLNR) is a GPCR involved in many pathophysiological processes; however, the correlation between APLNR expression and nasopharyngeal carcinoma (NPC) has not been reported. In this study, we used cDNA microarray data to determine APLNR expression levels in NPC tissues. We found that APLNR expression was reduced in NPC tissues compared with noncancerous nasopharyngeal epithelial tissues. Subsequently, a large-scale sample of 1015 tissues was used to validate this discovery and explore the relationship between APLNR expression and prognosis of NPC. Expression levels of APLNR in NPC tissues were indeed down-regulated. Furthermore, positive expression of APLNR in NPC predicted a better prognosis (disease-free survival: P = 0.001; overall survival: P < 0.001). Moreover, ingenuity pathway analysis revealed that an indirect interaction existed between APLNR and retinoic acid (RA) in the cancer regulatory network. Consistently, after treatment with all-trans-RA (ATRA), we found that APLNR was significantly up-regulated in NPC cell lines (5-8F and HNE1), and proliferation of NPC cells was inhibited. Cell cycle arrest occurred in the G0/G1 phase. In contrast, knockdown of APLNR diminished ATRA-induced growth inhibition of NPC cells. In addition, we surprisingly found that APLNR also played an important role in migration and invasion of NPC. Wound-healing and Transwell assays revealed that APLNR overexpression led to reduced migratory and invasive properties in 2 NPC cell lines. Western blot results revealed that hallmarks of epithelial-mesenchymal transition (EMT) were altered as well, suggesting that APLNR was capable of inhibiting EMT in NPC cells. Our study further demonstrated that low expression of APLNR promoted EMT in NPC cells by activating the PI3K-protein kinase B-mammalian target of rapamycin signaling pathway. Taken together, our data suggest that APLNR could potentially predict prognosis for patients with NPC and inhibit proliferation, migration, invasion, and EMT in nasopharyngeal cancer cells.-Liu, Y., Liu, Q., Chen, S., Liu, Y., Huang, Y., Chen, P., Li, X., Gao, G., Xu, K., Fan, S., Zeng, Z., Xiong, W., Tan, M., Li, G., Zhang, W. APLNR is involved in ATRA-induced growth inhibition of nasopharyngeal carcinoma and may suppress EMT through PI3K-Akt-mTOR signaling.

Treating tumors with minimally invasive therapy: A review

With high level of morbidity and mortality, tumor is one of the deadliest diseases worldwide. Aiming to tackle tumor, researchers have developed a lot of strategies. Among these strategies, the minimally invasive therapy (MIT) is very promising, for its capability of targeting tumor cells and resulting in a small incision or no incisions. In this review, we will first illustrate some mechanisms and characteristics of tumor metastasis from the primary tumor to the secondary tumor foci. Then, we will briefly introduce the history, characteristics, and advantages of some of the MITs. Finally, emphasis will be, respectively, focused on an overview of the state-of-the-art of the HIFU-, PDT-, PTT-and SDT-based anti-tumor strategies on each stage of tumor metastasis.

Clinical relevance of mutant-allele tumor heterogeneity and lung adenocarcinoma

Background

Intra-tumor heterogeneity (ITH) plays an important role in the progressing of lung adenocarcinoma (LUAD). We used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and the correlation between ITH and clinical parameters and its prognosis.

Methods

We assessed 230 LUAD patients from The Cancer Genome Atlas (TCGA). We calculated the MATH values from whole-exome sequencing data and further investigated their correlation with clinical characteristics.

Results

The patients were divided into low and high MATH groups. People with high MATH were more likely to be female, smoking and EGFR mutation. And a high MATH may predict a poor prognosis.

Conclusions

MATH is a new method for describing the internal heterogeneity of tumors in lung cancer. We used TCGA data to demonstrate that groups with higher MATH values are more likely to be female, smoker and EGFR mutations, and may have a poor prognosis.

DNA damage in aging, the stem cell perspective

DNA damage is one of the most consistent cellular process proposed to contribute to aging. The maintenance of genomic and epigenomic integrity is critical for proper function of cells and tissues throughout life, and this homeostasis is under constant strain from both extrinsic and intrinsic insults. Considering the relationship between lifespan and genotoxic burden, it is plausible that the longest-lived cellular populations would face an accumulation of DNA damage over time. Tissue-specific stem cells are multipotent populations residing in localized niches and are responsible for maintaining all lineages of their resident tissue/system throughout life. However, many of these stem cells are impacted by genotoxic stress. Several factors may dictate the specific stem cell population response to DNA damage, including the niche location, life history, and fate decisions after damage accrual. This leads to differential handling of DNA damage in different stem cell compartments. Given the importance of adult stem cells in preserving normal tissue function during an individual's lifetime, DNA damage sensitivity and accumulation in these compartments could have crucial implications for aging. Despite this, more support for direct functional effects driven by accumulated DNA damage in adult stem cell compartments is needed. This review will present current evidence for the accumulation and potential influence of DNA damage in adult tissue-specific stem cells and propose inquiry directions that could benefit individual healthspan.

SREBP1-dependent de novo fatty acid synthesis gene expression is elevated in malignant melanoma and represents a cellular survival trait

de novo fatty acid biosynthesis (DNFA) is a hallmark adaptation of many cancers that supports survival, proliferation, and metastasis. Here we elucidate previously unexplored aspects of transcription regulation and clinical relevance of DNFA in cancers. We show that elevated expression of DNFA genes is characteristic of many tumor types and correlates with poor prognosis, especially in melanomas. Elevated DNFA gene expression depends on the SREBP1 transcription factor in multiple melanoma cell lines. SREBP1 predominantly binds to the transcription start sites of DNFA genes, regulating their expression by recruiting RNA polymerase II to promoters for productive transcription elongation. We find that SREBP1-regulated DNFA represents a survival trait in melanoma cells, regardless of proliferative state and oncogenic mutation status. Indeed, malignant melanoma cells exhibit elevated DNFA gene expression after the BRAF/MEK signaling pathway is blocked (e.g. by BRAF inhibitors), and DNFA expression remains higher in melanoma cells resistant to vemurafenib treatment than in untreated cells. Accordingly, DNFA pathway inhibition, whether by direct targeting of SREBP1 with antisense oligonucleotides, or through combinatorial effects of multiple DNFA enzyme inhibitors, exerts potent cytotoxic effects on both BRAFi-sensitive and -resistant melanoma cells. Altogether, these results implicate SREBP1 and DNFA enzymes as enticing therapeutic targets in melanomas.

mTOR Signaling Upregulates CDC6 via Suppressing miR-3178 and Promotes the Loading of DNA Replication Helicase

mTOR signaling pathway is deregulated in most cancers and uncontrolled cell cycle progression is a hallmark of cancer cell. However, the precise molecular mechanisms of the regulation of DNA replication and chromatin metabolism by mTOR signaling are largely unknown. We herein report that mTOR signaling promotes the loading of MCM2-7 helicase onto chromatin and upregulates DNA replication licensing factor CDC6. Pharmacological inhibition of mTOR kinase resulted in CHK1 checkpoint activation and decreased MCM2-7 replication helicase and PCNA associated with chromatins. Further pharmacological and genetic studies demonstrated CDC6 is positively controlled by mTORC1-S6K1 and mTORC2 signaling. miRNA screening revealed mTOR signaling suppresses miR-3178 thereby upregulating CDC6. Analysis of TCGA data found that CDC6 is overexpressed in most cancers and associates with the poor survival of cancer patients. Our findings suggest that mTOR signaling may control DNA replication origin licensing and replisome stability thereby cell cycle progression through CDC6 regulation.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Modeling dynamics for oncogenesis encompassing mutations and genetic instability

Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: self-sufficiency on growth signals, insensibility to anti-growth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.