Genome-wide analyses have identified thousands of long noncoding RNAs (lncRNAs). Malat1 (metastasis-associated lung adenocarcinoma transcript 1) is among the most abundant lncRNAs whose expression is ...altered in numerous cancers. Here we report that genetic loss or systemic knockdown of Malat1 using antisense oligonucleotides (ASOs) in the MMTV (mouse mammary tumor virus)-PyMT mouse mammary carcinoma model results in slower tumor growth accompanied by significant differentiation into cystic tumors and a reduction in metastasis. Furthermore, Malat1 loss results in a reduction of branching morphogenesis in MMTV-PyMT- and Her2/neu-amplified tumor organoids, increased cell adhesion, and loss of migration. At the molecular level, Malat1 knockdown results in alterations in gene expression and changes in splicing patterns of genes involved in differentiation and protumorigenic signaling pathways. Together, these data demonstrate for the first time a functional role of Malat1 in regulating critical processes in mammary cancer pathogenesis. Thus, Malat1 represents an exciting therapeutic target, and Malat1 ASOs represent a potential therapy for inhibiting breast cancer progression.
Background
Gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are a complex family of tumors of widely variable clinical behavior. The World Health Organization (WHO) 2010 classification ...provided a valuable tool to stratify neuroendocrine neoplasms (NENs) in three prognostic subgroups based on the proliferation index. However, substantial heterogeneity remains within these subgroups, and simplicity sometimes entails an ambiguous and imprecise prognostic stratification. The purpose of our study was to evaluate the prognostic impact of histological differentiation within the WHO 2010 grade (G) 1/G2/G3 categories, and explore additional Ki‐67 cutoff values in GEP‐NENs.
Subjects, Materials, and Methods
A total of 2,813 patients from the Spanish National Tumor Registry (RGETNE) were analyzed. Cases were classified by histological differentiation as NETs (neuroendocrine tumors well differentiated) or NECs (neuroendocrine carcinomas poorly differentiated), and by Ki‐67 index as G1 (Ki‐67 <2%), G2 (Ki‐67 3%–20%), or G3 (Ki‐67 >20%). Patients were stratified into five cohorts: NET‐G1, NET‐G2, NET‐G3, NEC‐G2, and NEC‐G3.
Results
Five‐year survival was 72%. Age, gender, tumor site, grade, differentiation, and stage were all independent prognostic factors for survival. Further subdivision of the WHO 2010 grading improved prognostic stratification, both within G2 (5‐year survival: 81% Ki‐67 3%–5%, 72% Ki‐67 6%–10%, 52% Ki‐67 11%–20%) and G3 NENs (5‐year survival: 35% Ki‐67 21%–50%, 22% Ki‐67 51%–100%). Five‐year survival was significantly greater for NET‐G2 versus NEC‐G2 (75.5% vs. 58.2%) and NET‐G3 versus NEC‐G3 (43.7% vs. 25.4%).
Conclusion
Substantial clinical heterogeneity is observed within G2 and G3 GEP‐NENs. The WHO 2010 classification can be improved by including the additive effect of histological differentiation and the proliferation index.
Implications for Practice
Gastroenteropancreatic neuroendocrine neoplasms are tumors of widely variable clinical behavior, roughly stratified by the World Health Organization (WHO) 2010 classification into three subgroups based on proliferation index. Real‐world data from 2,813 patients of the Spanish Registry RGETNE demonstrated substantial clinical heterogeneity within grade (G) 2 and G3 neuroendocrine neoplasms. Tumor morphology and further subdivision of grading substantially improves prognostic stratification of these patients and may help individualize therapy. This combined, additive effect shall be considered in future classifications of neuroendocrine tumors and incorporated for stratification purposes in clinical trials.
This article evaluates the prognostic impact of histological differentiation within the WHO 2010 grading classifications and explores additional Ki‐67 cut‐off values for gastroenteropancreatic neuroendocrine neoplasms.
To investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT).
For this ...retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated.
Among 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm
was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level.
Tumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.
Current guidelines recommend that clinically staged T1N0 esophageal cancers are to be referred to surgery or endoscopic resection. Using the National Cancer Database, we identified 733 individuals ...with clinically staged T1N0 esophageal carcinoma, who underwent upfront surgery and did not receive any prior treatment. We assessed upstaging, which was defined as ≥ T2 disease or positive lymph nodes. Poorly differentiated adenocarcinomas were associated with upstaging, whereas squamous cell carcinomas were not. Specifically, the percentage of upstaging among individuals with clinically staged T1b and poorly differentiated tumor was 33.8%. Therefore, clinically staged T1bN0 poorly differentiated esophageal adenocarcinomas are at high risk for upstaging following surgery.
Abstract
Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the ...relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.
It has been reported that one of the neurotrophin receptors, tropomyosin receptor kinase B (TRKB), is frequently overexpressed in various tumor tissues including oral squamous cell carcinoma (OSCC), ...and that its upregulation promotes tumor progression in human cancers. However, the correlation between TRKB overexpression and clinicopathological characteristics is not fully elucidated. Here, we present the correlation between the expression levels of TRKB and/or its secreted ligand, brain-derived neurotrophic factor (BDNF), and clinicopathological characteristics, especially regarding tumor differentiation, tissue invasion, and disease-free survival in patients with OSCC. The results obtained through immunohistochemical analysis of human OSCC tumor specimens showed that the expression levels of TRKB and/or BDNF, were significantly higher in moderately and poorly differentiated OSCC (MD/PD-OSCC) tumor cells than in well differentiated cells (WD-OSCC). Moreover, the OSCC tumors highly expressing TRKB and/or BDNF exhibited promotion in tissue invasion and reduction in disease-free survival in the patients. In an orthotopic transplantation mouse model of human OSCC cell lines, administration of a TRKB-specific inhibitor significantly suppressed the tumor growth and invasion in PD-OSCC-derived tumor cells, but not in WD-OSCC-derived tumor cells. Moreover, the TRKB inhibitor selectively blocked BDNF-induced tumor cell proliferation and migration accompanied with the suppression of TRKB phosphorylation in PD-OSCC but not in WD-OSCC
. Taken together, these data suggest that the BDNF/TRKB signaling pathway may regulate tumor progression in poorly differentiated OSCC. Expression levels of signal molecules may be an accurate prognosis marker for tumor aggressiveness, and the molecules may be an attractive target for new OSCC therapies.
Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used ...biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.
Recent observations indicate that, in several types of human cancer, only a phenotypic subset of cancer cells within each tumor is capable of initiating tumor growth. This functional subset of cancer ...cells is operationally defined as the "cancer stem cell" (CSC) subset. Here we developed a CSC model for the study of human colorectal cancer (CRC). Solid CRC tissues, either primary tissues collected from surgical specimens or xenografts established in nonobese diabetic/severe combined immunodeficient (NOD/SCID) mice, were disaggregated into single-cell suspensions and analyzed by flow cytometry. Surface markers that displayed intratumor heterogeneous expression among epithelial cancer cells were selected for cell sorting and tumorigenicity experiments. Individual phenotypic cancer cell subsets were purified, and their tumorinitiating properties were investigated by injection in NOD/SCID mice. Our observations indicate that, in six of six human CRC tested, the ability to engraft in vivo in immunodeficient mice was restricted to a minority subpopulation of epithelial cell adhesion molecule$({\rm EpCAM})^{\text{high}}/{\rm CD}44^{+}$epithelial cells. Tumors originated from${\rm EpCAM}^{\text{high}}/{\rm CD}44^{+}$cells maintained a differentiated phenotype and reproduced the full morphologic and phenotypic heterogeneity of their parental lesions. Analysis of the surface molecule repertoire of${\rm EpCAM}^{\text{high}}/{\rm CD}44^{+}$cells led to the identification of CD166 as an additional differentially expressed marker, useful for CSC isolation in three of three CRC tested. These results validate the stem cell working model in human CRC and provide a highly robust surface marker profile for CRC stem cell isolation.
Conventional tumor grading systems based on the degree of tumor differentiation may not always be optimal because of difficulty in objective assessment and insufficient prognostic value for decision ...making in colorectal cancer (CRC) treatment. This study aimed to determine the importance of assessing the number of poorly differentiated clusters as the primary criterion for histologic grading of CRC. Five hundred consecutive patients with curatively resected stage II and III CRCs (2000 to 2005) were pathologically reviewed. Cancer clusters of ≥5 cancer cells and lacking a gland-like structure were counted under a ×20 objective lens in a field containing the highest number of clusters. Tumors with <5, 5 to 9, and ≥10 clusters were classified as grade (G)1, G2, and G3, respectively (n=156, 198, and 146 tumors, respectively). Five-year disease-free survival rates were 96%, 85%, and 59% for G1, G2, and G3, respectively (P<0.0001). Poorly differentiated clusters affected survival outcome independent of T and N stages and could help in more effective stratification of patients by survival outcome compared with tumor staging (Akaike information criterion, 1086.7 vs. 1117.0; Harrell concordance index, 0.73 vs. 0.67). The poorly differentiated cluster-based grading system showed a higher weighted κ coefficient for interobserver variability (5 observers) compared with conventional grading systems (mean, 0.66 vs. 0.52; range, 0.55 to 0.73 vs. 0.39 to 0.68). Our novel histologic grading system is expected to be less subjective and more informative for prognostic prediction compared with conventional tumor grading systems and TNM staging. It could be valuable in determining individualized postoperative CRC treatment.
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