The retroviruses: classification and molecular biology.
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Animals↗
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[Use of multilocus enzyme electrophoresis for bacterial population genetics, classification and molecular epidemiology].
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Electrophoresis↗
Chromosomal aberrations in soft tissue tumors. Relevance to diagnosis, classification, and molecular mechanisms.
In recent years, significant progress has been made in identifying characteristic chromosomal rearrangements associated with several solid tumor types, notably sarcomas, a relatively rare subset of human cancer. Most sarcomas analyzed have been found to be characterized by recurrent chromosome translocations that are specific to histological types. We have reviewed published reports of chromosomal aberrations in benign and malignant soft tissue tumors and found an incidence of specific translocations in these neoplasms that ranged from 20% to 93% within histological tumor types. Identification of recurrent chromosomal abnormalities in benign tumors has resulted in a reappraisal of the general concept that benign tumors have a normal (diploid) chromosome constitution. The variety of recurrent changes present in the different tumor types attests to the cytogenetic diversity inherent in these tumors. The chromosomal rearrangements in each of the tumor types were unique and did not correspond to cancer-associated aberrations known from other solid or hematopoietic malignancies. Cytogenetics thus provides an essential adjunct to diagnostic surgical pathology in the case of malignant soft tissue tumors, which often present substantial diagnostic challenges. In addition, it represents another approach to determine the histogenetic origin of some tumors and identifies sites of gene deregulation for molecular analysis. Indeed, recent molecular analyses of several sarcoma-associated translocations have identified novel genes and novel mechanisms of their dysregulation.
Chromosome Aberrations↗
Molecular Phylogenetic classification of fungi.
Phylogenetic classification of fungi based on comparison of ribosomal RNA gene sequences is discussed with emphasis on (i) the place of fungi in all of life, (ii) the relationship of medically important fungi to other fungi including model organisms and, (iii) the integration of sexual and asexual fungi into one classification.
DNA, Ribosomal↗
Chromosomal imbalances in human lung cancer.
A wealth of cytogenetic data has demonstrated that numerous somatic genetic changes are involved in the pathogenesis of human lung cancer. Despite the complexity of the genomic changes observed in these neoplasms, recurrent chromosomal patterns have emerged. In this review, we summarize chromosomal alterations identified in small cell and non-small cell lung cancer, using classical and molecular cytogenetic techniques. These analyses have uncovered a set of chromosome regions implicated in lung cancer development and progression. However, many of the target genes remain unknown. Newer technology, such as array-CGH, when combined with cDNA microarrays and tissue microarrays, will facilitate the integration of genomic and gene expression data and pave the way toward a molecular classification of lung carcinomas. The molecular implications of consistent chromosome imbalances found in lung cancer to date are also discussed.
Carcinoma, Non-Small-Cell Lung↗
Multiclass cancer diagnosis using tumor gene expression signatures.
The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
Biomarkers, Tumor↗
Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.
Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.
Humans↗
Classification, pathogenesis and molecular pathology of primary CNS lymphomas.
Classification, pathogenesis and molecular pathology of primary central nervous system lymphomas (PCNSL) pose major clinico-pathologic problems. Application of histopathologic classification schemes developed for nodal lymphomas, i.e. the Kiel Classification and the Working Formulation, is not reliable and clinically not relevant for PCNSL. The more recent REAL Classification will simplify subtyping of PCNSL, but reliability and clinical significance remain to be determined. There are virtually no experimental data on whether PCNSL develop outside of the brain, or whether they arise from polyclonal lymphoproliferations within the brain. Mutations of oncogenes and tumor suppressor genes have been analyzed in only a few tumors, and their type and frequency is essentially unknown. In conclusion, compared with both neuroectodermal brain tumors and nodal lymphomas, and given the increasing incidence and clinical impact of PCNSL, there is a remarkable lack of histopathologic consensus as well as a surprising shortage of pathogenetic and molecular genetic information.
Brain Neoplasms↗
Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.
BACKGROUND: In patients with acute myeloid leukemia (AML), the presence or absence of recurrent cytogenetic aberrations is used to identify the appropriate therapy. However, the current classification system does not fully reflect the molecular heterogeneity of the disease, and treatment stratification is difficult, especially for patients with intermediate-risk AML with a normal karyotype. METHODS: We used complementary-DNA microarrays to determine the levels of gene expression in peripheral-blood samples or bone marrow samples from 116 adults with AML (including 45 with a normal karyotype). We used unsupervised hierarchical clustering analysis to identify molecular subgroups with distinct gene-expression signatures. Using a training set of samples from 59 patients, we applied a novel supervised learning algorithm to devise a gene-expression-based clinical-outcome predictor, which we then tested using an independent validation group comprising the 57 remaining patients. RESULTS: Unsupervised analysis identified new molecular subtypes of AML, including two prognostically relevant subgroups in AML with a normal karyotype. Using the supervised learning algorithm, we constructed an optimal 133-gene clinical-outcome predictor, which accurately predicted overall survival among patients in the independent validation group (P=0.006), including the subgroup of patients with AML with a normal karyotype (P=0.046). In multivariate analysis, the gene-expression predictor was a strong independent prognostic factor (odds ratio, 8.8; 95 percent confidence interval, 2.6 to 29.3; P<0.001). CONCLUSIONS: The use of gene-expression profiling improves the molecular classification of adult AML.
Acute Disease↗
Cross-Platform Concordance in DNA Methylation Based Classification of CNS Tumors.
DNA methylation profiling enables precise classification of pediatric central nervous system (CNS) tumors. Oxford Nanopore Technologies (ONT) offers same-day, single-sample methylation readouts, but its concordance with Illumina EPIC arrays in routine diagnostic tasks remains incompletely defined. We profiled 23 pediatric tumors (18 CNS, 5 non-CNS) by EPIC arrays and ONT. Methylation profiles from both platforms were classified with crossNN (brain model or pan-cancer model); ONT data were additionally classified with Rapid-CNS2 and Sturgeon. We compared (i) classifier agreement with integrated histology (w/o NGS) at family/class levels, (ii) pass-rate above platform-specific score cutoffs, (iii) cross-platform concordance of copy-number variation (CNV), and MGMT promoter methylation status. In CNS cases, ONT and EPIC methylation profiles demonstrated strong correlation, except for a single outlier (P2), which was excluded from further analysis. Comparative assessment of the two platforms showed that: (a) Molecular classification of CNS tumors using the crossNN classifier was consistent with histology (w/o NGS) at the family level in all cases. (b) Copy-number profiles showed high concordance between platforms. (c) MGMT promoter methylation status matched in 94% of cases (16/17). When comparing ONT-specific analysis pipelines using the ONT data, the Rapid-CNS2 pipeline yielded the most reliable class level assignments with 94% (16/17) concordance with the histopathological diagnosis, which marginally exceeded the crossNN and sturgeon classifiers. In non-CNS tumors, the pan-cancer model produced low-confidence outputs with poor agreement with histology (w/o NGS) (only 1/5 concordant), indicating limited readiness for these entities. In conclusion, ONT enables same-day, clinically reliable family-level CNS tumor classification with high concordance to arrays, while EPIC retains a modest class-level edge. A key limitation of ONT is its reliance on fresh-frozen DNA and on classifiers originally built around array-derived CpG sites, rather than on models developed natively from ONT data.
DNA methylation↗
The molecular basis of lung cancer: molecular abnormalities and therapeutic implications.
Lung cancer is the number one cause of cancer-related death in the western world. Its incidence is highly correlated with cigarette smoking, and about 10% of long-term smokers will eventually be diagnosed with lung cancer, underscoring the need for strengthened anti-tobacco policies. Among the 10% of patients who develop lung cancer without a smoking history, the environmental or inherited causes of lung cancer are usually unclear. There is no validated screening method for lung cancer even in high-risk populations and the overall five-year survival has not changed significantly in the last 20 years. However, major progress has been made in the understanding of the disease and we are beginning to see this knowledge translated into the clinic. In this review, we will summarize the current state of knowledge regarding the cascade of events associated with lung cancer development. From subclinical DNA damage to overt invasive disease, the mechanisms leading to clinically and molecularly heterogeneous tumors are being unraveled. These lesions allow cells to escape the normal regulation of cell division, apoptosis and invasion. While all subtypes of non-small cell lung cancer have historically been treated the same, stage-for-stage, recent technological advances have allowed a better understanding of the molecular classification of the disease and provide hypotheses for molecular early detection and targeted therapeutic strategies.
Biomarkers, Tumor↗
Robust classification of renal cell carcinoma based on gene expression data and predicted cytogenetic profiles.
Renal cell carcinoma (RCC) is a heterogeneous disease that includes several histologically distinct subtypes. The most common RCC subtypes are clear cell, papillary, and chromophobe, and recent gene expression profiling studies suggest that classification of RCC based on transcriptional signatures could be beneficial. Traditionally, however, patterns of chromosomal alterations have been used to assist in the molecular classification of RCC. The purpose of this study was to determine whether it was possible to develop a classification model for the three major RCC subtypes that utilizes gene expression profiles as the bases for both molecular genetic and cytogenetic classification. Gene expression profiles were first used to build an expression-based RCC classifier. The RCC gene expression profiles were then examined for the presence of regional gene expression biases. Regional expression biases are genetic intervals that contain a disproportionate number of genes that are coordinately up- or down-regulated. The presence of a regional gene expression bias often indicates the presence of a chromosomal abnormality. In this study, we demonstrate an expression-based classifier can distinguish between the three most common RCC subtypes in 99% of cases (n = 73). We also demonstrate that detection of regional expression biases accurately identifies cytogenetic features common to RCC. Additionally, the in silico-derived cytogenetic profiles could be used to classify 81% of cases. Taken together, these data demonstrate that it is possible to construct a robust classification model for RCC using both transcriptional and cytogenetic features derived from a gene expression profile.
Carcinoma, Renal Cell↗
[Hereditary ataxias and paraplegias: a clinicogenetic review].
Hereditary ataxias encompass a series of syndromes basically characterised by progressive cerebellar ataxia of slow clinical course (occasionally, periodic ataxia or spastic paraparesis) and primary spinocerebellar degeneration. The prevalence ratio of these syndromes in Spain is 20 cases per 100,000 inhabitants. Initially the ataxias were classified on the basis of clinicopathological criteria. Starting from the seminal papers by Harding published 20 years ago, a clinicogenetic classification was introduced that has given way to the present molecular classification. There have been localised about forty loci. In dominant ataxias the most frequent molecular defect is a dynamic CAG expansion responsible for abnormal polyglutamine tract transcription. The identification of such molecular defect has made it possible detection of gene carriers in clinical practice, this involving both presymptomatic and prenatal diagnosis; moreover, such molecular discoveries have contributed to develop a new pathogenetic era. A homozygous and intronic GAA expansion is the molecular basis of Friedreich's ataxia. This finding has also made it possible a molecular diagnosis in clinical practice. Molecular studies have demonstrated that hereditary spastic paraplegia is another heterogeneous genetic disorder.
Ataxia↗
Molecular abnormalities in lung carcinogenesis and their potential clinical implications.
Development of lung cancer is multistep and requires accumulation of multiple genetic and epigenetic alterations. Modern molecular technology has facilitated a rapid and effective identification of these genetic alterations as well as epigenetic alterations. The determination of molecular alterations in the early tumorigenic process of the lung will not only extend our understanding of the underlying biology but also provide molecular markers for cancer risk assessment, early detection, and molecular classification. In this article, I will discuss the common molecular abnormalities in lung cancer and how these abnormalities may be used as biomarkers in clinical practice.
Biomarkers, Tumor↗
Molecular and clinical classification of human prion disease.
While rare in humans, the prion diseases have become an area of intense clinical and scientific interest. The recognition that variant Creutzfeldt-Jakob disease is caused by the same prion strain as bovine spongiform encephalopathy in cattle has dramatically highlighted the need for a precise understanding of the molecular biology of human prion diseases. Detailed clinical, pathological and molecular data from a large number of human prion disease cases have shown that distinct abnormal isoforms of prion protein are associated with prion protein gene polymorphism and neuropathological phenotypes. A molecular classification of human prion diseases seems achievable through characterisation of structural differences of the infectious agent itself.
Adult↗
Genotypic heterogeneity of node based peripheral T-cell lymphoma.
PTCL represents a diverse group of histological entities that defy classification schemes based on normal T cell differentiation, differ in their clinical presentation and behave unpredictably. Genetic analyses of this phenotypically heterogeneous group have clearly shown that histologically defined PTCL may be subdivided on the basis of clonal gene rearrangements. The absence of clonal gene rearrangements in a significant proportion of PTCL cases has increased the complexity of classification. The data presented in this review suggest that a molecular classification would allow true reflection of PTCL aetiology, but carefully coordinated studies are required to evaluate the clinical usefulness of such a classification scheme.
Clone Cells↗
[Colorectal carcinogenesis: update].
Recent progresses in molecular biology have allowed us to identify at least two different molecular mechanisms implicated in colorectal carcinogenesis: chromosomal instability and genetic instability. These two molecular mechanisms are supported by two hereditary syndromes that predispose to colorectal cancers: familial adenomatous polyposis and hereditary non polyposis colorectal cancer syndrome. In spite of these two different mechanisms, the signalling pathways implicated the malignant transformation of colonic epithelial cells seem to be the same. They are essentially represented by APC/beta-catenin, TGFbeta, RAS and TP53 signalling pathways. This new molecular classification of colorectal cancers is important for the understanding of molecular alterations responsible for tumour development but also for the management of patients.
Adenomatous Polyposis Coli↗
Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of development.
BACKGROUND: The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis-spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance. METHODS AND FINDINGS: Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis. CONCLUSIONS: From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
Adenocarcinoma↗