Molecular genetic testing in the diagnosis of myeloid neoplasms

Recent advances in molecular technologies have led to an expanding catalogue of diagnostic, prognostic, and predictive biomarkers in myeloid neoplasms, contributing to the complexity of pathology classification. This article is intended as a guide to the molecular hematopathology of myeloid neoplasms for practicing pathologists and trainees. We review the relevance and limitations of common molecular technologies, and discuss the integration of molecular genetic biomarkers in the current (revised 4^th edition) WHO classification that was published in 2017. The topic of clonal hematopoiesis is also addressed.     

Molecular classification of breast carcinomas using tissue microarrays.

The histopathologic classification of breast cancer stratifies tumors based on tumor grade, stage, and type. Despite an overall correlation with survival, this classification is poorly predictive and tumors with identical grade and stage can have markedly contrasting outcomes. Recently, breast carcinomas have been classified by their gene expression profiles on frozen material. The validation of such a classification on formalin-fixed paraffin-embedded tumor archives linked to clinical information in a high-throughput fashion would have a major impact on clinical practice. The authors tested the ability of tumor tissue microarrays (TMAs) to sub-classify breast cancers using a TMA containing 107 breast cancers. The pattern of expression of 13 different protein biomarkers was assessed by immunohistochemistry and the multidimensional data was analyzed using an unsupervised two-dimensional clustering algorithm. This revealed distinct tumor clusters which divided into two main groups correlating with tumor grade (P<0.001) and nodal status (P = 0.04). None of the protein biomarkers tested could individually identify these groups. The biological significance of this classification is supported by its similarity with one derived from gene expression microarray analysis. Thus, molecular profiling of breast cancer using a limited number of protein biomarkers in TMAs can sub-classify tumors into clinically and biologically relevant subgroups.     

Biomarkers in tumors of the central nervous system – a review

Until recently, diagnostics of brain tumors were almost solely based on morphology and immunohistochemical stainings for relatively unspecific lineage markers. Although certain molecular markers have been known for longer than a decade (combined loss of chromosome 1p and 19q in oligodendrogliomas), molecular biomarkers were not included in the WHO scheme until 2016. Now, the classification of diffuse gliomas rests on an integration of morphology and molecular results. Also, for many other central nervous system tumor entities, specific diagnostic, prognostic and predictive biomarkers have been detected and continue to emerge. Previously, we considered brain tumors with similar histology to represent a single disease entity. We now realize that histologically identical tumors might show alterations in different molecular pathways, and often represent separate diseases with different natural history and response to treatment. Hence, knowledge about specific biomarkers is of great importance for individualized treatment and follow‐up. In this paper we review the biomarkers that we currently use in the diagnostic work‐up of brain tumors.     

Gastrointestinal stromal tumor: Recent advances in pathology and genetics

The discovery of KIT gene mutation in gastrointestinal stromal tumor (GIST) has provided a paradigm shift in the classification, diagnosis and molecular‐targeted therapy of gastrointestinal mesenchymal tumors. There is growing evidence of phenotype‐genotype (KIT, platelet‐derived growth factor receptor‐alpha, succinate dehydrogenase or other driver gene mutation) and genotype‐therapeutic (sensitivity to imatinib) correlations in GIST. Risk stratification based on mitotic counts, tumor size and rupture is useful for the prognostication and management of patients with GIST. Blood vessel invasion is a strong indicator of liver metastasis in GIST. In addition, novel biomarkers such as cell‐cycle regulators, microRNAs and their targets have been discovered by using high throughput molecular analyses. In contrast, leiomyosarcoma of the gastrointestinal tract has become a very rare entity in the ‘KIT’ era, and its molecular pathogenetic mechanism is unclear. Recent studies have revealed a wide spectrum of cytological atypia, mitotic counts and biological behavior of gastrointestinal smooth muscle tumors, suggesting the necessity of establishing the criteria for malignancy. Collectively, both classical histopathological procedures and modern molecular investigations are indispensable for the evolution of diagnosis and treatment of GIST and mimics.     

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms.     

Incorporation of molecular characteristics into endometrial cancer management

Histopathological evaluation including subtyping and grading is the current cornerstone for endometrial cancer (EC) classification. This provides clinicians with prognostic information and input for further treatment recommendations. Nonetheless, patients with histologically similar ECs may have very different outcomes, notably in patients with high-grade endometrial carcinomas. For endometrial cancer, four molecular subgroups have undergone extensive studies in recent years: POLE ultramutated (POLEmut), mismatch repair-deficient (MMRd), p53 mutant (p53abn) and those EC lacking any of these alterations, referred to as NSMP (non-specific molecular profile). Several large studies confirm the prognostic relevance of these molecular subgroups. However, this ‘histomolecular’ approach has so far not been implemented in clinical routine. The ongoing PORTEC4a trial is the first clinical setting in which the added value of integrating molecular parameters in adjuvant treatment decisions will be determined. For diagnostics, the incorporation of the molecular parameters in EC classification will add a level of objectivity which will yield biologically more homogeneous subclasses. Here we illustrate how the management of individual EC patients may be impacted when applying the molecular EC classification. We describe our current approach to the integrated diagnoses of EC with a focus on scenarios with conflicting morphological and molecular findings. We also address several pitfalls accompanying the diagnostic implementation of molecular EC classification and give practical suggestions for diagnostic scenarios.     

Benign and low-grade superficial endothelial cell neoplasms in the molecular era

Vascular tumors are the most common mesenchymal neoplasms of the skin and subcutis, and they encompass a heterogeneous group with diverse clinical, histological, and molecular features, as well as biological behavior. Over the past two decades, molecular studies have enabled the identification of pathogenic recurrent genetic alterations that can be used as additional data points to support the correct classification of these lesions. The purpose of this review is to summarize the available data related to superficially located benign and low-grade vascular neoplasms and to highlight recent molecular advances with the role of surrogate immunohistochemistry to target pathogenic proteins as diagnostic biomarkers.     

Conventional and novel “omics”‐based approaches to the study of carbon nanotubes pulmonary toxicity

The widespread application of carbon nanotubes (CNT) on industrial, biomedical, and consumer products can represent an emerging respiratory occupational hazard. Particularly, their similarity with the fiber‐like shape of asbestos have raised a strong concern about their carcinogenic potential. Several in vitro and in vivo studies have been supporting this view by pointing to immunotoxic, cytotoxic and genotoxic effects of some CNT that may conduct to pulmonary inflammation, fibrosis, and bronchioloalveolar hyperplasia in rodents. Recently, high throughput molecular methodologies have been applied to obtain more insightful information on CNT toxicity, through the identification of the affected biological and molecular pathways. Toxicogenomic approaches are expected to identify unique gene expression profiles that, besides providing mechanistic information and guiding new research, have also the potential to be used as biomarkers for biomonitoring purposes. In this review, the potential of genomic data analysis is illustrated by gene network and gene ontology enrichment analysis of a set of 41 differentially expressed genes selected from a literature search focused on studies of C57BL/6 mice exposed to the multiwalled CNT Mitsui‐7. The majority of the biological processes annotated in the network are regulatory processes and the molecular functions are related to receptor‐binding signalling. Accordingly, the network‐annotated pathways are cell receptor‐induced pathways. A single enriched molecular function and one biological process were identified. The relevance of specific epigenomic effects triggered by CNT exposure, for example, alteration of the miRNA expression profile is also discussed in light of its use as biomarkers in occupational health studies. Environ. Mol. Mutagen. 59:334–362, 2018. © 2018 Wiley Periodicals, Inc.     

Dermatopathology in the age of molecular biology

Proctor I, Stoeber K & Williams G H
(2010) Histopathology 57, 1–13
Biomarkers in bladder cancer Cancer biomarkers provide an opportunity to diagnose tumours earlier and with greater accuracy. They can also identify those patients most at risk of disease recurrence and predict which tumours will respond to different therapeutic approaches. Such biomarkers will be especially useful in the diagnosis and management of bladder cancer. At present, bladder tumours are diagnosed and followed‐up using a combination of cystoscopic examination, cytology and histology. These are not only expensive, but also highly subjective investigations and reveal little about the underlying molecular characteristics of the tumour. In recent years numerous diagnostic and prognostic biomarkers of bladder cancer have been identified. Two separate approaches to biomarker discovery have been employed. The first is hypothesis‐driven and focuses upon proteins involved in molecular pathways known to be implicated in tumorigenesis. An alternative approach has been to study the global expression of genes (so‐called ‘genomics’) looking for characteristic signatures associated with disease outcomes. In this review we summarize the current state of biomarker development in this field, and examine why so few have made the successful transition into the clinic. Finally, we introduce a novel approach to biomarker development utilizing components of the DNA replication licensing machinery.     

Method of regulatory network that can explore protein regulations for disease classification

ObjectiveTo develop regulatory network to explore and model the regulatory relationships of protein biomarkers and classify different disease groups.MethodsRegulatory network is constructed to be a hopfield-like network with nodes representing biomarkers and directional connections to be regulations in between. The input to the network is the measured expression levels of biomarkers, and the output is the summation of regulatory strengths from other biomarkers. The network is optimized towards minimizing the energy function that is defined as the measure of the disagreement between the input and output of the network. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network.ResultsTwo datasets have been used as test beds, one dataset includes patients of nasopharyngeal carcinoma with different responses to chemotherapy drug, and the other consists of patients of severe acute respiratory syndrome, influenza, and control normals. The regulatory networks among protein biomarkers were reconstructed for different disease conditions in each dataset. We demonstrated our methods have better classification capability when comparing with conventional methods including Fisher linear discriminant (FLD), K-nearest neighborhood (KNN), linear support vector machines (linSVM) and radial basis function based support vector machines (rbfSVM).ConclusionThe derived networks can effectively capture the unique regulatory patterns of protein markers associated with different patient groups and hence can be used for disease classification. The discovered regulation relationships can potentially provide insights to revealing the molecular signaling pathways.In this paper, a novel technique of regulatory network is proposed on purpose of modeling biomarker regulations and classifying different disease groups. The network is composed of a certain number of nodes that are directionally connected in between in which nodes denote predictors and connections to be the regulation relationship. The network is optimized towards minimizing its energy function with biomarker expression data acquired from a specific patient group, thus the optimized network can model the regulatory relationship of biomarkers under the same circumstance. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. The regulatory network can extract unique features of each disease condition, thus one immediate application of regulatory network is to classifying different diseases. We demonstrated that regulatory network is capable of performing disease classification through comparing with conventional methods including FLD, KNN, linSVM and rbfSVM on two protein datasets. We believe our method is promising in mining knowledge of protein regulations and be powerful for disease classification.     

The genotypic-phenotypic approach redefines the prognostic evaluation of lung neuroendocrine neoplasms

Lung neuroendocrine neoplasms are heterogeneous in terms of morphological features and clinical behavior. The four-tired WHO classification scheme, together with TNM stage, are currently the most effective prognostic indicators and, to date, they define the clinical management and therapeutic strategies in these neoplasms. However, in the last decade novel information on the phenotypical characteristics and molecular background of these tumors resulted in the proposal of novel biomarkers indicative of biological or clinical behavior. Although most of them are strongly histotype-dependent, some others have been proposed to be significantly associated to tumor characteristics also within individual tumor groups, and are therefore potential additional and complementary tools, with special reference to the carcinoid patients group whose prognostic prediction is still very uneffective. Indeed, these candidate biomarkers are still to be integrated in a multimodal approach and are in the vast majority of cases not validated in independent or prospective series and have been analyzed, with special reference to the molecular ones, on relatively small case series. Once the characterization of these tumors will be further refined, the clinical impact of these information will be strongly determined by their potentiality to be integrated with the current classification, and the tight collaboration between those who are active in this subject (diagnostic pathologists, molecular pathologists/biologists, clinicians) is necessary for a validation in the clinical practice.     

cIMPACT‐NOW update 7: advancing the molecular classification of ependymal tumors

Advances in our understanding of the biological basis and molecular characteristics of ependymal tumors since the latest iteration of the World Health Organization (WHO) classification of CNS tumors (2016) have prompted the cIMPACT‐NOW group to recommend a new classification. Separation of ependymal tumors by anatomic site is an important principle of the new classification and was prompted by methylome profiling data to indicate that molecular groups of ependymal tumors in the posterior fossa and supratentorial and spinal compartments are distinct. Common recurrent genetic or epigenetic alterations found in tumors belonging to the main molecular groups have been used to define tumor types at intracranial sites; C11orf95 and YAP1 fusion genes for supratentorial tumors and two types of posterior fossa ependymoma defined by methylation group, PFA and PFB. A recently described type of aggressive spinal ependymoma with MYCN amplification has also been included. Myxopapillary ependymoma and subependymoma have been retained as histopathologically defined tumor types, but the classification has dropped the distinction between classic and anaplastic ependymoma. While the cIMPACT‐NOW group considered that data to inform assignment of grade to molecularly defined ependymomas are insufficiently mature, it recommends assigning WHO grade 2 to myxopapillary ependymoma and allows grade 2 or grade 3 to be assigned to ependymomas not defined by molecular status.     

Development of proteomic patterns for detecting lung cancer

Lung cancer is at present the number one cause of cancer death and no biomarker is available to detect early lung cancer in serum samples so far. The objective of this study is to find specific biomarkers for detection of lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI) technology. In this study, serum samples from 30 lung cancer patients and 51 age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader, PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering and classification analyses were performed utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. Three protein peaks were automatically chosen for the system training and the development of a decision classification tree. The constructed model was then used to test an independent set of masked serum samples from 15 lung cancer patients and 31 healthy individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of 96.7%. These results suggest that the serum is a capable resource for detection of specific lung cancer biomarkers. SELDI technique combined with an artificial intelligence classification algorithm can both facilitate the discovery of better biomarkers for lung cancer and provide a useful tool for molecular diagnosis in future.     

The role of molecular analyses in the diagnosis and treatment of non-small-cell lung carcinomas

Non-small-cell lung cancer (NSCLC) subtyping has recently been a key factor in determining patient management with novel drugs. In addition, the identification of distinct oncogenic driver mutations frequently associated with NSCLC histotype and coupled to the clinical responses to targeted therapies have revolutionized the impact of histologic type and molecular biomarkers in lung cancer. Several molecular alterations involving different genes (EGFR, KRAS, ALK, BRAF, and HER2) seem to have a remarkable predilection for adenocarcinoma and specific inhibitors of EGFR and ALK are now available for patients with adenocarcinoma harboring the relevant gene alterations. The efficacy of histology-based and molecular-targeted therapies had a deep impact in (1) re-defining classification of lung cancer (particularly adenocarcinomas) and (2) routine clinical practice of pathologists involved in optimization of handling of tissue samples in order to guarantee NSCLC subtyping with the help of immunohistochemistry and adequately preserve tumor cells for molecular analysis. In agreement with the modern multidisciplinary approach to lung cancer, we reviewed here the diagnostic and predictive value of molecular biomarkers according to the clinical, pathologic, and molecular biologist viewpoints.     

A neural network-based biomarker association information extraction approach for cancer classification

A number of different approaches based on high-throughput data have been developed for cancer classification. However, these methods often ignore the underlying correlation between the expression levels of different biomarkers which are related to cancer. From a biological viewpoint, the modeling of these abnormal associations between biomarkers will play an important role in cancer classification. In this paper, we propose an approach based on the concept of Biomarker Association Networks (BAN) for cancer classification. The BAN is modeled as a neural network, which can capture the associations between the biomarkers by minimizing an energy function. Based on the BAN, a new cancer classification approach is developed. We validate the proposed approach on four publicly available biomarker expression datasets. The derived Biomarker Association Networks are observed to be significantly different for different cancer classes, which help reveal the underlying deviant biomarker association patterns responsible for different cancer types. Extensive comparisons show the superior performance of the BAN-based classification approach over several conventional classification methods.     

Potential biomarkers found by protein profiling may provide insight for the macrovascular pathogenesis of diabetes mellitus

Diabetes mellitus (DM) is an alarming threat to health of mankind, yet its pathogenesis is unclear. The purpose of this study was to find potential biomarkers to serve as indicators for the pathogenesis of DM in a time course manner. Based on our previous findings that oxidative stress occurred at week 8, aorta lysate and sera of 102 streptozotocin (STZ)-induced diabetic and 85 control male Sprague-Dawley rats were obtained at the 4th, 8th and 12th week after STZ injection. The protein profiles were studied employing surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology in attomole sensitivity range. In the aorta, a multiple biomarker panel was discovered at the 4th week. At the 8th week, 4 biomarkers were found, while at the 12th week, 3 biomarkers were identified. In the sera, a triplet of 3 peaks and 2 biomarkers were all discovered to have 100% classification accuracy rate to differentiate the DM and control groups at all time intervals. Besides, 2 biomarkers were also found to have high classification value at week 12. Comparing the aorta and sera from DM and non-DM rats, a bundle of potential biomarkers with significant changes in peak intensities and high classification values were found. Two of the serum biomarkers matched with islet amyloid polypeptide and resistin in the SWISS-PROT knowledgebase. Validation has been conducted using immunoassay kits. These potential biomarkers may provide valuable insight on the pathogenesis of DM and macrovascular complications.     

Lung cancer samples preserved in liquid medium: One step beyond cytology

Lung cancer is one of the most common cancer types in men and women worldwide with a high mortality rate. World Health Organization (WHO) classification has accepted biopsy as the primary sample for lung cancer diagnosis, pathological classification and molecular testing for management of patients, yet, the use of alternative sampling procedures is highly encouraged. Bronchial cytological samples require a less invasive collection technique and may be suitable for pathological and molecular analysis and storage in liquid medium. Furthermore, the molecular analysis of bronchial cytological samples allows the detection of molecular biomarkers, which may be useful for the selection of molecular targeted therapies. Thus, the purpose of this review is to describe the usefulness of bronchial cytological samples preserved in liquid medium from lung cancer patients for pathological diagnosis and molecular investigation.     

The evolving classification of soft tissue tumours – an update based on the new 2013 WHO classification

The new World Health Organization (WHO) classification of soft tissue tumours was published in early 2013, almost 11 years after the previous edition. While the number of newly recognized entities included for the first time is fewer than that in 2002, there have instead been substantial steps forward in molecular genetic and cytogenetic characterization of this family of tumours, leading to more reproducible diagnosis, a more meaningful classification scheme and providing new insights regarding pathogenesis, which previously has been obscure in most of these lesions. This brief overview summarizes changes in the classification in each of the broad categories of soft tissue tumour (adipocytic, fibroblastic, etc.) and also provides a short summary of newer genetic data which have been incorporated in the WHO classification.