Study of creep cavitation in a stainless steel weldment

A study of creep cavities near reheat cracking in AISI Type 316H austenitic stainless steel headers, removed from long-time high temperature operation in nuclear power plants, is reported. It is shown how application of scanning electron microscopy (SEM), cryogenic fractography and small angle neutron scattering (SANS) can be applied, in a complementary way, to observe and quantify creep cavitation damage. Creep cavities in the vicinity of the crack are found to be mainly surrounding inter-granular carbides. Trends in the size and area fraction of creep cavities relative to the crack path are quantified based on an optimised metallographic and image analysis procedure. Brittle fracture, with clear facets, of material remote from crack is achieved using a cryogenic procedure, and is compared with fractured facets of material within 8 mm of the crack which show evidence of large numbers of cavities. The SANS technique is found to be a very suitable method of quantifying creep cavitation averaged over a larger gauge volume. The distribution of cavities within the size range of 100 to 400 nm is quantified using SANS from positions normal to the crack line. It is shown that the cavity size distribution peaks at about 200 nm and this correlates closely with the quantitative SEM observations

Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients

Multiscale correlative tomography: an investigation of creep cavitation in 316 stainless steel

Creep cavitation in an ex-service nuclear steam header Type 316 stainless steel sample is investigated through a multiscale tomography workflow spanning eight orders of magnitude, combining X-ray computed tomography (CT), plasma focused ion beam (FIB) scanning electron microscope (SEM) imaging and scanning transmission electron microscope (STEM) tomography. Guided by microscale X-ray CT, nanoscale X-ray CT is used to investigate the size and morphology of cavities at a triple point of grain boundaries. In order to understand the factors affecting the extent of cavitation, the orientation and crystallographic misorientation of each boundary is characterised using electron backscatter diffraction (EBSD). Additionally, in order to better understand boundary phase growth, the chemistry of a single boundary and its associated secondary phase precipitates is probed through STEM energy dispersive X-ray (EDX) tomography. The difference in cavitation of the three grain boundaries investigated suggests that the orientation of grain boundaries with respect to the direction of principal stress is important in the promotion of cavity formation

Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer

BACKGROUND: Drug resistance in breast cancer is a major obstacle to successful chemotherapy. In this study we used cDNA microarray technology to examine gene expression profiles obtained from fine needle aspiration (FNA) of primary breast tumors before and after systemic chemotherapy. Our goal was to determine the feasibility of obtaining representative expression array profiles from limited amounts of tissue and to identify those expression profiles that correlate with treatment response. METHODS: Repeat presurgical FNA samples were taken from six patients who were to undergo primary surgical treatment. Additionally, a group of 10 patients who were to receive neoadjuvant chemotherapy underwent two FNAs before chemotherapy (adriamycin 60 mg/m(2) and cyclophosphamide 600 mg/m(2)) followed by another FNA on day 21 after the first cycle. Total RNA was amplified with T7 Eberwine's procedure and labeled cDNA was hybridized onto a 7600-feature glass cDNA microarray. RESULTS: We identified candidate gene expression profiles that might distinguish tumors with complete response to chemotherapy from tumors that do not respond, and found that the number of genes that change after one cycle of chemotherapy was 10 times greater in the responding group than in the non-responding group. CONCLUSION: This study supports the suitability of FNA-derived cDNA microarray expression profiling of breast cancers as a comprehensive genomic approach for studying the mechanisms of drug resistance. Our findings also demonstrate the potential of monitoring post-chemotherapy changes in expression profiles as a measure of pharmacodynamic effect and suggests that these approaches might yield useful results when validated by larger studies

Frequent loss of endothelin-3 (EDN3) expression due to epigenetic inactivation in human breast cancer

Introduction: Endothelin (EDN) signalling plays a crucial role in cell differentiation, proliferation and migration processes. There is compelling evidence that altered EDN signalling is involved in carcinogenesis by modulating cell survival and promoting invasiveness. To date, most reports have focused on the oncogenic potential of EDN1 and EDN2, both of which are overexpressed in various tumour entities. Here, we aimed at a first comprehensive analysis on EDN3 expression and its implication in human breast cancer. Methods: EDN3 mRNA expression was assessed by Northern blotting in normal human tissues (n = 9) as well as in matched pairs of normal and tumourous tissues from breast specimens (n = 50). EDN3 mRNA expression in breast cancer was further validated by real-time polymerase chain reaction (PCR) (n = 77). A tissue microarray was used to study EDN3 protein expression in breast carcinoma (n = 150) and normal breast epithelium (n = 44). EDN3 promoter methylation was analysed by methylation-specific PCR in breast cell lines (n = 6) before and after demethylating treatment, normal breast tissues (n = 17) and primary breast carcinomas (n = 128). EDN3 expression and methylation data were statistically correlated with clinical patient characteristics and patient outcome. Results: Loss of EDN3 mRNA expression in breast cancer, as initially detected by array-based expression profiling, could be confirmed by Northern blot analysis (> 2-fold loss in 96%) and real-time PCR (> 2-fold loss in 78%). Attenuated EDN3 expression in breast carcinoma was also evident at the protein level (45%) in association with adverse patient outcome in univariate (P = 0.022) and multivariate (hazard ratio 2.0; P = 0.025) analyses. Hypermethylation of the EDN3 promoter could be identified as the predominant mechanism leading to gene silencing. Reversion of the epigenetic lock by 5-aza-2'-deoxycytidine and trichostatin A resulted in EDN3 mRNA reexpression in vitro. Furthermore, EDN3 promoter hypermethylation was detected in 70% of primary breast carcinomas with significant association to loss of EDN3 mRNA expression (P = 0.005), whilst normal matched breast tissues revealed no EDN3 promoter methylation. Conclusions EDN3 is a frequent target of epigenetic inactivation in human breast cancer, potentially contributing to imbalanced EDN signalling commonly found in this disease. The clinical implication supports the view that EDN3, in contrast to EDN1 and EDN2, may act as natural tumour suppressor in the human mammary gland

Expression profiling identifies genes involved in neoplastic transformation of serous ovarian cancer

Background: The malignant potential of serous ovarian tumors, the most common ovarian tumor subtype, varies from benign to low malignant potential (LMP) tumors to frankly invasive cancers. Given the uncertainty about the relationship between these different forms, we compared their patterns of gene expression. Methods: Expression profiling was carried out on samples of 7 benign, 7 LMP and 28 invasive (moderate and poorly differentiated) serous tumors and four whole normal ovaries using oligonucleotide microarrays representing over 21,000 genes. Results: We identified 311 transcripts that distinguished invasive from benign tumors, and 20 transcripts that were significantly differentially expressed between invasive and LMP tumors at p < 0.01 (with multiple testing correction). Five genes that were differentially expressed between invasive and either benign or normal tissues were validated by real time PCR in an independent panel of 46 serous tumors (4 benign, 7 LMP, 35 invasive). Overexpression of SLPI and WNT7A and down-regulation of C6orf31, PDGFRA and GLTSCR2 were measured in invasive and LMP compared with benign and normal tissues. Over-expression of WNT7A in an ovarian cancer cell line led to increased migration and invasive capacity. Conclusion: These results highlight several genes that may play an important role across the spectrum of serous ovarian tumorigenesis

HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures.

Approximately 1-5% of breast cancers are attributed to inherited mutations in BRCA1 or BRCA2 and are selectively sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors. In other cancer types, germline and/or somatic mutations in BRCA1 and/or BRCA2 (BRCA1/BRCA2) also confer selective sensitivity to PARP inhibitors. Thus, assays to detect BRCA1/BRCA2-deficient tumors have been sought. Recently, somatic substitution, insertion/deletion and rearrangement patterns, or 'mutational signatures', were associated with BRCA1/BRCA2 dysfunction. Herein we used a lasso logistic regression model to identify six distinguishing mutational signatures predictive of BRCA1/BRCA2 deficiency. A weighted model called HRDetect was developed to accurately detect BRCA1/BRCA2-deficient samples. HRDetect identifies BRCA1/BRCA2-deficient tumors with 98.7% sensitivity (area under the curve (AUC) = 0.98). Application of this model in a cohort of 560 individuals with breast cancer, of whom 22 were known to carry a germline BRCA1 or BRCA2 mutation, allowed us to identify an additional 22 tumors with somatic loss of BRCA1 or BRCA2 and 47 tumors with functional BRCA1/BRCA2 deficiency where no mutation was detected. We validated HRDetect on independent cohorts of breast, ovarian and pancreatic cancers and demonstrated its efficacy in alternative sequencing strategies. Integrating all of the classes of mutational signatures thus reveals a larger proportion of individuals with breast cancer harboring BRCA1/BRCA2 deficiency (up to 22%) than hitherto appreciated (∼1-5%) who could have selective therapeutic sensitivity to PARP inhibition