The molecular pathology of cancer: from pan‐genomics to post‐genomics

Huntsman, David G.; Ladanyi, Marc

doi:10.1002/path.5057

Cited by 25 publications

(19 citation statements)

References 21 publications

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“…Despite the late transition from pan-genomics to the post-genomics era, BC still remains one of the main causes of cancer related deaths [ 30 ]. TNBC is the most aggressive subtype of BC and it presents the worse clinical outcome among BC cases [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

Aberrant miRNAs expressed in HER-2 negative breast cancers patient

Braicu

Ráduly

Morar-Bolba

et al. 2018

J Exp Clin Cancer Res

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BackgroundBreast cancer is a highly heterogeneous pathology, exhibiting a number of subtypes commonly associated with a poor outcome. Due to their high stability, microRNAs are often regarded as non-invasive cancer biomarkers, having an expression pattern specific for their ‘cell of origin’.MethodTriple negative breast cancer (TNBC: ER-, PR-, Her-2-) and double positive breast cancer (DPBC: ER+, PR+, Her-2) miRNA expression patterns were obtained by analysis of the TCGA (The Cancer Genome Atlas) data, followed by PCR-array analysis on plasma samples from 20 TNBC patients, 14 DPBC patients and 11 controls.ResultsThree downregulated and nine upregulated miRNAs were obtained from the TNBC analysis. Five overexpressed miRNAs were identified in the DPBC group. Four of the dysregulated miRNAs (miR-10a, miR-125b, miR-210 and miR-489) were common for both groups. The cluster miR-17-92 (miR-17, miR-20a, miR-20b, and miR-93), along with miR-130, miR-22 and miR-29a/c, were found to differentiate between TNBC and DPBC. A panel of five transcripts (miR-10a, miR-125, miR-193b, miR-200b and miR-489) was validated in a new set of plasma samples. The overlapping of TCGA and plasma profiling data revealed miR-200b, miR-200c, miR-210 and miR-29c as common signature. MiR-200b was validated on additional normal and tumor tissue samples. The expression level of this transcript from the TCGA data was correlated with lung and bone metastatic genes.ConclusionThe miR-200b presents a great potential for the future advancements in the diagnostic/prognostic and therapeutic approach of TNBC, along with other coding or non-coding transcripts. However, this needs to be further integrated in a regulatory network that acts in conjunction with other markers that affect the patients’ prognosis or response to therapy.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0920-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Aberrant miRNAs expressed in HER-2 negative breast cancers patient

Braicu

Ráduly

Morar-Bolba

et al. 2018

J Exp Clin Cancer Res

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“…More recently, studies have shifted to better understanding the association between TIME and tumorigenesis. Genomic analysis is a standard approach for studying the structure, function, evolution, and effects of genomes on organisms (5). Several methods have been established to act as a bridge between gene expression and immune cell components.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study of Tumor Immune Microenvironment and Relevant Genes in Hepatocellular Carcinoma Identifies Potential Prognostic Significance

Chen

Zhang

et al. 2020

Front. Oncol.

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Background: The tumor immune microenvironment (TIME) is an external immune system that regulates tumorigenesis. However, cellular interactions involving the TIME in hepatocellular carcinoma (HCC) are poorly characterized. Methods: In this study, we used multidimensional bioinformatic methods to comprehensively analyze cellular TIME characteristics in 735 HCC patients. Additionally, we explored associations involving TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature. Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1-3) and two gene clusters (Gene G1-2), respectively. Further analysis revealed that Gene G1 was associated with immune activation and surveillance and included CD8 + T cells, natural killer cell activation, and activated CD4 + memory T cells. In contrast, Gene G2 was characterized by increased M0 macrophage and regulatory T cell levels. After calculation of principal component algorithms, a TIME score (TS) model, including 78 differentially expressed genes, was constructed based on TIME phenotypes and gene clusters. Furthermore, we observed that the Gene G2 cluster was characterized by high TS, and Gene G1 was characterized by low TS, which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive association with several clinicopathologic signatures [such as grade, stage, tumor (T), and node (N)] and known somatic gene mutations (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets. Conclusion: We constructed a TS model based on differentially expressed genes and involving immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.

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“…A tumor is the final stage of a multistep genetic process that involves "cancer genes" [8] and the inhibitory and stimulatory signals that they produce [9]. In hematopoietic and lymphoid tissue, the definition of neoplasia relies on molecular tools to distinguish monoclonal from polyclonal cell proliferation.…”

Section: Impact Of Molecular-genetic Data On Tumor Classification Andmentioning

confidence: 99%

Cancer Classification at the Crossroads

Carbone

2020

Cancers

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Internationally accepted classifications of malignant tumors, developed by the World Health Organization (WHO) and the Union for International Cancer Control (UICC), are based on the histotype, site of origin, morphologic grade, and spread of cancer throughout the body. The WHO classifications are the foundation of cancer diagnosis and the starting point for cancer management. Starting in 2000, the WHO classifications began to include biologic and molecular–genetic features. These developments are having a strong impact on cancer diagnosis and treatment, and this impact is amplifying, given the advances in cancer genomics. Molecular–genetic profiling can be used to refine existing classifications of tumors and, for a small but increasing number of cancers, even determine the treatment irrespective of histotype. Here I discuss how cancer classifications may change in the era of cancer genomics.

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The molecular pathology of cancer: from pan‐genomics to post‐genomics

Cited by 25 publications

References 21 publications

Aberrant miRNAs expressed in HER-2 negative breast cancers patient

Aberrant miRNAs expressed in HER-2 negative breast cancers patient

Comprehensive Study of Tumor Immune Microenvironment and Relevant Genes in Hepatocellular Carcinoma Identifies Potential Prognostic Significance

Cancer Classification at the Crossroads

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