Cancer classification in the genomic era: five contemporary problems

Signal Amplification-Based Biosensors and Application in RNA Tumor Markers

Tumor markers are important substances for assessing cancer development. In recent years, RNA tumor markers have attracted significant attention, and studies have shown that their abnormal expression of post-transcriptional regulatory genes is associated with tumor progression. Therefore, RNA tumor markers are considered as potential targets in clinical diagnosis and prognosis. Many studies show that biosensors have good application prospects in the field of medical diagnosis. The application of biosensors in RNA tumor markers is developing rapidly. These sensors have the advantages of high sensitivity, excellent selectivity, and convenience. However, the detection abundance of RNA tumor markers is low. In order to improve the detection sensitivity, researchers have developed a variety of signal amplification strategies to enhance the detection signal. In this review, after a brief introduction of the sensing principles and designs of different biosensing platforms, we will summarize the latest research progress of electrochemical, photoelectrochemical, and fluorescent biosensors based on signal amplification strategies for detecting RNA tumor markers. This review provides a high sensitivity and good selectivity sensing platform for early-stage cancer research. It provides a new idea for the development of accurate, sensitive, and convenient biological analysis in the future, which can be used for the early diagnosis and monitoring of cancer and contribute to the reduction in the mortality rate.

Towards Development of a Standard Terminology of the World Health Organization Classification of Tumors of the Central Nervous System in the Turkish Language, and a Perspective on the Practical Implications of the WHO Classification for Low and Middle Income Countries

In our manuscript, we propose a common terminology in the Turkish language for the newly adopted WHO classification of the CNS tumors, also known as the WHO CNS 5th edition. We also comment on the applicability of this new scheme in low and middle income countries, and warn about further deepening disparities between the global north and the global south. This division, augmented by the recent COVID-19 pandemic, threatens our ability to coordinate efforts worldwide and may create significant disparities in the diagnosis and treatment of cancers between the "haves" and the "have nots".

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Electrochemical and Electrochemiluminescence Dendrimer-based Nanostructured Immunosensors for Tumor Marker Detection: A Review

The usage of dendrimers or cascade molecules in the biomedical area has recently attracted much attention worldwide. Furthermore, dendrimers are interesting in clinical and pre-clinical applications due to their unique characteristics. Cancer is one of the most widespread challenges and important diseases, which has the highest mortality rate. In this review, the recent advances and developments (from 2009 up to 2019) in the field of electrochemical and electroluminescence immunosensors for detection of the cancer markers are presented. Moreover, this review covers the basic fabrication principles and types of electrochemical and electrochemiluminescence dendrimer-based immunosensors. In this review, we have categorized the current dendrimer based-electrochemical/ electroluminescence immunosensors into five groups: dendrimer/ magnetic particles, dendrimer/ferrocene, dendrimer/metal nanoparticles, thiol-containing dendrimer, and dendrimer/quantum dots based-immunosensors.

Pharmacological properties of ginsenosides in inflammation-derived cancers

Ginseng is commonly used as an herbal medicine for improvement of life quality. It is also used as a supplemental medication with anti-cancer drugs to enhance chemotherapy efficacy and shows some beneficial effects. Ginsenosides, also known as saponins, are the major active pharmacological compounds found in ginseng and have been extensively using in treatment of not only cancers but also the other inflammatory diseases such as atherosclerosis, diabetes, acute lung injury, cardiovascular, and infectious diseases. The anti-cancer activities of ginsengs and ginsenosides in different types of cancers have been well studied experimentally and clinically. The major anti-cancer mechanisms of ginseng compounds include inhibition of angiogenesis and metastasis as well as induction of cell cycle arrest and apoptosis. Herein, we review and summarize the current knowledge on the pharmacological effects of ginsengs and ginseng-derived compounds in the treatment of cancers. Moreover, the molecular and cellular mechanism(s) by which ginsengs and ginsenosides modulate the immune response in cancer diseases as well as ginsengs-drugs interaction are also discussed.

The Application of Prussian Blue Nanoparticles in Tumor Diagnosis and Treatment

Prussian blue nanoparticles (PBNPs) have attracted increasing research interest in immunosensors, bioimaging, drug delivery, and application as therapeutic agents due to their large internal pore volume, tunable size, easy synthesis and surface modification, good thermal stability, and favorable biocompatibility. This review first outlines the effect of tumor markers using PBNPs-based immunosensors which have a sandwich-type architecture and competitive-type structure. Metal ion doped PBNPs which were used as T1-weight magnetic resonance and photoacoustic imaging agents to improve image quality and surface modified PBNPs which were used as drug carriers to decrease side effects via passive or active targeting to tumor sites are also summarized. Moreover, the PBNPs with high photothermal efficiency and excellent catalase-like activity were promising for photothermal therapy and O2 self-supplied photodynamic therapy of tumors. Hence, PBNPs-based multimodal imaging-guided combinational tumor therapies (such as chemo, photothermal, and photodynamic therapies) were finally reviewed. This review aims to inspire broad interest in the rational design and application of PBNPs for detecting and treating tumors in clinical research.

IDH clonal heterogeneity segregates a subgroup of non-1p/19q codeleted gliomas with unfavourable clinical outcome

AIMS

Diffuse gliomas (DGs) are classified into three major molecular subgroups following the revised World Health Organisation (WHO) classification criteria based on their IDH mutation and 1p/19q codeletion status. However, substantial biological heterogeneity and differences in the clinical course are apparent within each subgroup, which remain to be resolved. We sought to assess the clonal status of somatic mutations and explore whether additional molecular subgroups exist within DG.

METHODS

A computational framework that integrates the variant allele frequency, local copy number and tumour purity was used to infer the clonality of somatic mutations in 876 DGs from The Cancer Genome Atlas (TCGA). We performed an unsupervised cluster analysis to identify molecular subgroups and characterised their clinical and biological significance.

RESULTS

DGs showed widespread genetic intratumoural heterogeneity (ITH), with nearly all driver genes harbouring subclonal mutations, even for known glioma initiating event IDH1 (17.1%). Gliomas with subclonal IDH mutation and without 1p/19q codeletion showed shorter overall and disease-specific survival, higher ITH and exhibited differences in genomic patterns, transcript levels and proliferative potential, when compared with IDH clonal mutation and no 1p/19q codeletion gliomas. We defined a refined stratification system based on the current WHO glioma molecular classification, which showed close correlations with patients' clinical outcomes.

CONCLUSIONS

For the first time, we integrated the clonal status of somatic mutations into cancer genomic classification and highlighted the necessity of considering IDH clonal architectures in glioma precision stratification.

Exploring the classification of cancer cell lines from multiple omic views

BACKGROUND

Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.

METHODS

In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines.

RESULTS

Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines.

CONCLUSIONS

The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, sex and vital status of patients

Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients’ therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (sex, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

Electrochemical biosensors for the detection of lung cancer biomarkers: A review

Cancer is one of the most widespread challenges and important diseases, which has the highest mortality rate. Lung cancer is the most common type of cancer, so that about 25% of all cancer deaths are related to the lung cancer. The lung cancer is classified as two different types with different treatment methodology: the small cell lung carcinoma and nonsmall cell lung carcinoma are two categories of the lung cancer. Since the lung cancer is often in the latent period in its early stages, therefore, early diagnosis of lung cancer has many challenges. Hence, there is a need for sensitive and reliable tools for preclinical diagnosis of lung cancer. Therefore, many detection methods have been employed for early detection of lung cancer. As lung cancer tumors growth in the body, the cancerous cells release numerous DNA, proteins, and metabolites as special biomarkers of the lung cancer. The levels of these biomarkers show the stages of the lung cancer. Therefore, detection of the biomarkers can be used for screening and clinical diagnosis of the lung cancer. There are numerous biomarkers for the lung cancer such as EGFR, CEA, CYFRA 21-1, ENO1, NSE, CA 19-9, CA 125 and VEGF. Nowadays, electrochemical methods are very attractive and useful in the lung cancer detections. So, in this paper, the recent advances and improvements (2010-2018) in the electrochemical detection of the lung cancer biomarkers have been reviewed.

Influence of Pain and Analgesia on Cancer Research Studies

Mice and rats are valuable and commonly used as models for the study of cancer. The models and methods of experimentation have the potential to cause pain to some degree, and all charged with ensuring animal welfare must determine how to manage it. A commonly posed question, especially from investigators and IACUC, is whether the provision of analgesic agents will render the model invalid. Left untreated, pain is a stressor and has negative consequences, most notably immune system perturbations. In addition, analgesic agents in the opioid and NSAID drug classes exhibit immunomodulatory activity and influence processes such as cell proliferation, apoptosis, and angiogenesis that are important in cancer formation. Therefore, both pain and the agents used to alleviate it have the potential to act as confounding factors in a study. This review article presents data from both human medicine and work with animal models in an attempt to help inform discussions about the withholding of analgesic agents from animals used in cancer studies.

The Role of Centromere Defects in Cancer

The accurate segregation of chromosomes to daughter cells is essential for healthy development to occur. Imbalances in chromosome number have long been associated with cancers amongst other medical disorders. Little is known whether abnormal chromosome numbers are an early contributor to the cancer progression pathway. Centromere DNA and protein defects are known to impact on the fidelity of chromosome segregation in cell and model systems. In this chapter we discuss recent developments in understanding the contribution of centromere abnormalities at the protein and DNA level and their role in cancer in human and mouse systems.

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.

Female-specific association among I, J and K mitochondrial genetic haplogroups and cancer: A longitudinal cohort study

Recent studies highlighted the role of mitochondrial dysregulation in cancer, suggesting that the different mitochondrial haplogroups might play a role in tumorigenesis and risk of cancer development. Our aim is to investigate whether any mitochondrial haplogroups carried a significant higher risk of cancer development in a large prospective cohort of North American people. The haplogroup assignment was performed by a combination of sequencing and PCR-RFLP techniques. Our specific outcome of interest was the incidence of any cancer during follow-up period. Overall, 3222 participants were included in the analysis. Women having I, J, K haplogroup reported a significant higher incidence of cancer compared to people with other haplogroups (p < 0.0001), whilst in men non association was found. In the multivariate analysis, women having I, J, K mitochondrial haplogroup reported a 50% increased risk of cancer (HR = 1.50; 95%CI: 1.04-2.16; p = 0.03). This gender-linked association may be partly explained by the role of mitochondrial function in female-specific (e.g. BRCA-driven) oncogenesis, but further studies are needed to better understand this potential correlation. Our findings may have important implications for cancer epidemiology and prevention.

diceR: an R package for class discovery using an ensemble driven approach

BACKGROUND

Given a set of features, researchers are often interested in partitioning objects into homogeneous clusters. In health research, cancer research in particular, high-throughput data is collected with the aim of segmenting patients into sub-populations to aid in disease diagnosis, prognosis or response to therapy. Cluster analysis, a class of unsupervised learning techniques, is often used for class discovery. Cluster analysis suffers from some limitations, including the need to select up-front the algorithm to be used as well as the number of clusters to generate, in addition, there may exist several groupings consistent with the data, making it very difficult to validate a final solution. Ensemble clustering is a technique used to mitigate these limitations and facilitate the generalization and reproducibility of findings in new cohorts of patients.

RESULTS

We introduce diceR (diverse cluster ensemble in R), a software package available on CRAN: https://CRAN.R-project.org/package=diceR CONCLUSIONS: diceR is designed to provide a set of tools to guide researchers through a general cluster analysis process that relies on minimizing subjective decision-making. Although developed in a biological context, the tools in diceR are data-agnostic and thus can be applied in different contexts.

Informatics for cancer immunotherapy

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.

Cancer subtypes in aetiological research

Researchers often attempt to categorize tumors into more homogeneous subtypes to better predict prognosis or understand pathogenic mechanisms. In clinical research, typically the focus is on prognosis: the tumor subtypes are intended to be associated with specific responses to treatment and/or different clinical outcomes. In aetiological research, the focus is on identifying distinct pathogenic mechanisms, which may involve different risk factors. We used directed acyclic graphs to present a framework for considering potential biases arising in aetiological research of tumor subtypes, when there is incomplete correspondence between the identified subtypes and the underlying pathogenic mechanisms. We identified two main scenarios: (1) weak effect, when the tumor subtypes are identified through combinations of characteristics and some of these characteristics are affected by factors that are unrelated with the underlying pathogenic mechanisms; and (2) lack of causality, when the set of characteristics corresponds with a mechanism that is actually not a cause of the tumor of interest. Examples of the magnitude of bias that can be introduced in these situations are provided. Although categorization of tumors into homogenous subtypes may have important implications for aetiological research and identification of risk factors, the characteristics used to classify tumors into subtypes should be as close as possible to the actual pathogenic mechanisms to avoid interpretative biases. Whenever our knowledge of these mechanisms is limited, research into risk factors for tumor subtypes should first aim to causally link the characteristics to the pathogenic mechanisms.