The aim of this article is to propose guidelines and recommendations in problematic areas in pathologic reporting of endometrial carcinoma (EC) regarding special techniques and ancillary studies. An organizing committee designed a comprehensive survey with different questions related to pathologic features, diagnosis, and prognosis of EC that was sent to all members of the International Society of Gynecological Pathologists. The special techniques/ancillary studies group received 4 different questions to be addressed. Five members of the group reviewed the literature and came up with recommendations and an accompanying text which were discussed and agreed upon by all members of the group. Twelve different recommendations are made. They address the value of immunohistochemistry, ploidy, and molecular analysis for assessing prognosis in EC, the value of steroid hormone receptor analysis to predict response to hormone therapy, and parameters regarding applying immunohistochemistry and molecular tests for assessing mismatch deficiency in EC.

暂无摘要。

BACKGROUND: Bioinformatics tools, such as assemblers and aligners, are expected to produce more accurate results when given better quality sequence data as their starting point. This expectation has led to the development of stand-alone tools whose sole purpose is to detect and remove sequencing errors. A good error-correcting tool would be a transparent component in a bioinformatics pipeline, simply taking sequence data in any of the standard formats and producing a higher quality version of the same data containing far fewer errors. It should not only be able to correct all of the types of errors found in real sequence data (substitutions, insertions, deletions and uncalled bases), but it has to be both fast enough and scalable enough to be usable on the large datasets being produced by current sequencing technologies, and work on data derived from both haploid and diploid organisms.
RESULTS: This article presents Blue, an error-correction algorithm based on k-mer consensus and context. Blue can correct substitution, deletion and insertion errors, as well as uncalled bases. It accepts both FASTQ and FASTA formats, and corrects quality scores for corrected bases. Blue also maintains the pairing of reads, both within a file and between pairs of files, making it compatible with downstream tools that depend on read pairing. Blue is memory efficient, scalable and faster than other published tools, and usable on large sequencing datasets. On the tests undertaken, Blue also proved to be generally more accurate than other published algorithms, resulting in more accurately aligned reads and the assembly of longer contigs containing fewer errors. One significant feature of Blue is that its k-mer consensus table does not have to be derived from the set of reads being corrected. This decoupling makes it possible to correct one dataset, such as small set of 454 mate-pair reads, with the consensus derived from another dataset, such as Illumina reads derived from the same DNA sample. Such cross-correction can greatly improve the quality of small (and expensive) sets of long reads, leading to even better assemblies and higher quality finished genomes.
METHODS: The code for Blue and its related tools are available from http://www.bioinformatics.csiro.au/Blue. These programs are written in C# and run natively under Windows and under Mono on Linux.

A combination of karyotyping and aneuploidy analysis by interphase fluorescent in situ hybridization is a sensitive method for evaluation of genetic stability of stem cell cultures. The methodology and specific features of preparing and analyzing the cytogenetic preparations are described as exemplified by human multipotent mesenchymal stromal cells.

Neuroblastoma serves as a paradigm for utilising tumour genomic data for determining patient prognosis and treatment allocation. However, before the establishment of the International Neuroblastoma Risk Group (INRG) Task Force in 2004, international consensus on markers, methodology, and data interpretation did not exist, compromising the reliability of decisive genetic markers and inhibiting translational research efforts. The objectives of the INRG Biology Committee were to identify highly prognostic genetic aberrations to be included in the new INRG risk classification schema and to develop precise definitions, decisive biomarkers, and technique standardisation. The review of the INRG database (n=8800 patients) by the INRG Task Force finally enabled the identification of the most significant neuroblastoma biomarkers. In addition, the Biology Committee compared the standard operating procedures of different cooperative groups to arrive at international consensus for methodology, nomenclature, and future directions. Consensus was reached to include MYCN status, 11q23 allelic status, and ploidy in the INRG classification system on the basis of an evidence-based review of the INRG database. Standardised operating procedures for analysing these genetic factors were adopted, and criteria for proper nomenclature were developed. Neuroblastoma treatment planning is highly dependant on tumour cell genomic features, and it is likely that a comprehensive panel of DNA-based biomarkers will be used in future risk assignment algorithms applying genome-wide techniques. Consensus on methodology and interpretation is essential for uniform INRG classification and will greatly facilitate international and cooperative clinical and translational research studies.

BACKGROUND: Lack of generalized guidelines for DNA flow cytometric analysis (FCM) may be the main reason for its limited use in the clinical management of breast cancer.
METHODS: After an initial interlaboratory reproducibility study (Round I), we concluded that it was the evaluation of the DNA histograms rather than the technical performance of the analysis that was the main reason for discordant results between laboratories. Guidelines for the interpretation of DNA histograms were therefore drawn up. We present here data from a new reproducibility study (Round II) using these guidelines.
RESULTS: For 10 laboratories also participating in Round I, use of the guidelines increased the concordance in DNA ploidy status from 89% to 100% for the 46 samples used in both rounds. The concordance rate for SPF also increased; mean r(s)-value increased from 0.81 to 0.88, and mean kappa value (lower two-thirds versus upper third versus not reported) increased from 0.55 to 0.71. Five new laboratories, participating only in Round II, also agreed with the 10 original laboratories regarding DNA ploidy status. With the inclusion of all 15 laboratories, we obtained a mean r(s)-value of 0.81 and a mean kappa value of 0.72 for SPF.
CONCLUSIONS: Generalized guidelines for DNA FCM increase interlaboratory agreement, which is highly important in clinical routines and in multicenter studies. Furthermore, inexperienced FCM laboratories using generalized guidelines can produce and interpret DNA FCM data equally as well as experienced laboratories.

BACKGROUND: Under the auspices of the College of American Pathologists, a multidisciplinary group of clinicians, pathologists, and statisticians considered prognostic and predictive factors in prostate cancer and stratified them into categories reflecting the strength of published evidence and taking into account the expert opinions of the Prostate Working Group members.
METHODS: Factors were ranked according to the previous College of American Pathologists categorical rankings: category I, factors proven to be of prognostic importance and useful in clinical patient management; category II, factors that have been extensively studied biologically and clinically but whose importance remains to be validated in statistically robust studies; and category III, all other factors not sufficiently studied to demonstrate their prognostic value. Factors in categories I and II were considered with respect to variations in methods of analysis, interpretation of findings, reporting of data, and statistical evaluation. For each factor, detailed recommendations for improvement were made. Recommendations were based on the following aims: (1) increasing uniformity and completeness of pathologic evaluation of tumor specimens, (2) enhancing the quality of data collected pertaining to existing prognostic factors, and (3) improving patient care.
CONCLUSIONS: Factors ranked in category I included preoperative serum prostate-specific antigen level, TNM stage grouping, histologic grade as Gleason score, and surgical margin status. Category II factors included tumor volume, histologic type, and DNA ploidy. Factors in category III included perineural invasion, neuroendocrine differentiation, microvessel density, nuclear roundness, chromatin texture, other karyometric factors, proliferation markers, prostate-specific antigen derivatives, and other factors (oncogenes, tumor suppressor genes, apoptosis genes, etc).

BACKGROUND: Under the auspices of the College of American Pathologists, a multidisciplinary group of clinicians, pathologists, and statisticians considered prognostic and predictive factors in breast cancer and stratified them into categories reflecting the strength of published evidence.
METHODS: Factors were ranked according to previously established College of American Pathologists categorical rankings: category I, factors proven to be of prognostic import and useful in clinical patient management; category II, factors that had been extensively studied biologically and clinically, but whose import remains to be validated in statistically robust studies; and category III, all other factors not sufficiently studied to demonstrate their prognostic value. Factors in categories I and II were considered with respect to variations in methods of analysis, interpretation of findings, reporting of data, and statistical evaluation. For each factor, detailed recommendations for improvement were made. Recommendations were based on the following aims: (1) increasing uniformity and completeness of pathologic evaluation of tumor specimens, (2) enhancing the quality of data collected about existing prognostic factors, and (3) improving patient care.
CONCLUSIONS: Factors ranked in category I included TNM staging information, histologic grade, histologic type, mitotic figure counts, and hormone receptor status. Category II factors included c-erbB-2 (Her2-neu), proliferation markers, lymphatic and vascular channel invasion, and p53. Factors in category III included DNA ploidy analysis, microvessel density, epidermal growth factor receptor, transforming growth factor-alpha, bcl-2, pS2, and cathepsin D. This report constitutes a detailed outline of the findings and recommendations of the consensus conference group, organized according to structural guidelines as defined.

From 1990-1996, 1,485 previously untreated invasive breast carcinomas were sampled by a pathologist for flow cytometric DNA analysis. The aim of the present work was to study the variations of flow cytometric DNA ploidy and S-phase evaluation according to the conditions of DNA histogram interpretation. Results obtained with the American Consensus guidelines of 1993 and the François Baclesse Department of Pathology\'s own guidelines are presented. According to the percentage of events taken into account to identify a DNA aneuploid peak, the proportion of DNA diploid cases can change from 35-39%. For S-phase evaluation, although the two guidelines were quite different, the results of S-phase cutoff were identical. Whichever guidelines were used, there was a strong relationship between DNA ploidy and/or S-phase and classical clinicopathological factors (T, N, histological type, grade, receptor status, or lymphatic invasion), with the exception of age, whose correlation was discrepant with S phase according to the set of guidelines. Whichever guidelines were used, ploidy and S phase correlated strongly with survival (overall, metastasis-free, or recurrence-free). Hence we recommend the use of the American consensus guidelines, despite minor imperfections, because they are now well-known, allow a high yield in the ratio of assessable S phases, and permit standardization in the technical processing and reporting of S phases, thanks to the use of terciles.

Guidelines are given to assist the standardisation of DNA flow cytometry in clinical pathology. They have been agreed by a group of twelve scientists from nine European countries.