Poorly differentiated clusters (PDC) in colorectal cancer: what is and ought to be known.

The counting of poorly differentiated clusters of 5 or more cancer cells lacking a gland-like structure in a tumor mass has recently been identified among the histological features predictive of poor prognosis in colorectal cancer. Poorly differentiated clusters can easily be recognized in the histological sections of colorectal cancer routinely stained with haematoxylin and eosin. Despite some limitations related to specimen fragmentation, counting can also be assessed in endoscopic biopsies. Based on the number of poorly differentiated clusters that appear under a microscopic field of a ×20 objective lens (i.e., a microscopic field with a major axis of 1 mm), colorectal cancer can be graded into malignancies as follows: tumors with <5 clusters as grade 1, tumors with 5 to 9 clusters as grade 2, and tumors with ≥10 clusters as grade 3. High poorly differentiated cluster counts are significantly associated with peri-neural and lympho-vascular invasion, the presence of nodal metastases or micrometastases, as well as shorter overall and progression free survival to colorectal cancer. The morphological aspects and clinical relevance of poorly differentiated clusters counting in colorectal cancer are discussed in this review

Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.

Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the `GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, `leave-one-out' cross-validation method. Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P <= 0.01, false discovery rate <= 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100\% sensitivity and specificity (95\% confidence interval: 67.9-99.2\%). Supervised prediction using `leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212). Conclusions: Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Evaluating the miR-302b and miR-145 expression in formalin-fixed paraffin-embedded samples of esophageal squamous cell carcinoma

Background: MicroRNAs are involved in key cellular processes regulating, and their misregulation is linked to cancer. The miR-302-367 cluster is exclusively expressed in embryonic stem and carcinoma cells. This cluster also promotes cell reprogramming and stemness process. In contrast, miR-145 is mostly regarded as a tumor suppressor, where it regulates cellular functions such as cell division, differentiation, and apoptosis. By suppressing the main pluripotency factors (OCT4, SOX2, MYC and KLF4), miR-145 silences the self-renewal program in ESCs. Therefore, the main aim of this study is to find a potential link between the expression level of hsa-miR-302b and hsa-miR-145 with tumor vs. non-tumor as well as high-grade vs. low-grade states of the esophageal tissue samples. Methods: A total number of 40 formalin-fixed, paraffin-embedded (FFPE) samples of esophageal squamous-cell carcinoma (ESCC) were obtained, and the tumor and marginal non-tumor areas delineated and punched off by an expert pathologist. Total RNA was extracted with Trizol, and cDNA synthesized using the miRCURY LNA™ Universal RT microRNA PCR Kit. Real-time reverse transcription polymerase chain reaction (RT-PCR) assays were performed using specific LNA-primers and SYBR Green master mix. Results: The expression level of miR-302b failed to show any significant difference, neither between tumor and their non-tumor counterparts, nor among tumors with different grades of malignancies (P > 0.05). In contrast, miR-145 was significantly down regulated in all grades of tumor samples (P 0.05). CONCLUSION: Our data revealed a significant down-regulation of miR-145 in ESCC tissue samples. Based on our ROC curve analysis data (AUC = 0.74, P < 0.001) miR-145 could be regarded as a potential tumor marker for diagnosis of esophageal cancer. © 2015, Academy of Medical Sciences of I.R. Iran. All rights reserved

Loss of histone macroH2A1 in hepatocellular carcinoma cells promotes paracrine-mediated chemoresistance and CD4+CD25+FoxP3+ regulatory T cells activation

Rationale: Loss of histone macroH2A1 induces appearance of cancer stem cells (CSCs)-like cells in hepatocellular carcinoma (HCC). How CSCs interact with the tumor microenvironment and the adaptive immune system is unclear. Methods: We screened aggressive human HCC for macroH2A1 and CD44 CSC marker expression. We also knocked down (KD) macroH2A1 in HCC cells, and performed integrated transcriptomic and secretomic analyses. Results: Human HCC showed low macroH2A1 and high CD44 expression compared to control tissues. MacroH2A1 KD CSC-like cells transferred paracrinally their chemoresistant properties to parental HCC cells. MacroH2A1 KD conditioned media transcriptionally reprogrammed parental HCC cells activated regulatory CD4+/CD25+/FoxP3+ T cells (Tregs). Conclusions: Loss of macroH2A1 in HCC cells drives cancer stem-cell propagation and evasion from immune surveillance

Solution structure of human MBD1 CXXC1

No abstract available

Post-translational deregulation of YAP1 is genetically controlled in rat liver cancer and determines the fate and stem-like behavior of the human disease

Previous studies showed that YAP1 is over-expressed in hepatocellular carcinoma (HCC). Here we observed higher expression of Yap1/Ctgf axis in dysplastic nodules and HCC chemically-induced in F344 rats, genetically susceptible to hepatocarcinogenesis, than in lesions induced in resistant BN rats. In BN rats, highest increase in Yap1-tyr357, p73 phosphorylation and Caspase 3 cleavage occurred. In human HCCs with poorer prognosis ( 3 years survival; HCCB). In the latter, higher levels of phosphorylated YAP1-ser127, YAP1-tyr357 and p73, YAP1 ubiquitination, and Caspase 3 cleavage occurred. Expression of stemness markers NANOG, OCT-3/4, and CD133 were highest in HCCP and correlated with YAP1 and YAP1-TEAD levels. In HepG2, Huh7, and Hep3B cells, forced YAP1 over-expression led to stem cell markers expression and increased cell viability, whereas inhibition of YAP1 expression by specific siRNA, or transfection of mutant YAP1 which does not bind to TEAD, induced opposite alterations. These changes were associated, in Huh7 cells transfected with YAP1 or YAP1 siRNA, with stimulation or inhibition of cell migration and invasivity, respectively. Furthermore, transcriptome analysis showed that YAP1 transfection in Huh7 cells induces over-expression of genes involved in tumor stemness. In conclusion, Yap1 post-translational modifications favoring its ubiquitination and apoptosis characterize HCC with better prognosis, whereas conditions favoring the formation of YAP1-TEAD complexes are associated with aggressiveness and acquisition of stemness features by HCC cells