Kinetic, telomere/telomerase, and mismatch repair features distinguish carcinomas from other malignancies

<p>Background: The main groups of malignancies (carcinomas, sarcomas, melanomas and lymphomas) are classified based on phenotypic features that frequently correlate with general genetic abnormalities. The kinetic and general biological features that explain the differences in these diagnostic groups have not been systematically analyzed in a set of neoplasms from common locations.</p> <p>Design: We selected carcinomas (90), sarcomas (25), malignant melanomas (15) and malignant lymphomas (25) from common locations that have appropriate archival material. Mitotic figure counting was performed screening at least 25 high-power fields and registering average and standard deviation. Representative samples were evaluated by standard immunohistochemistry for Ki67, telo- merase, mlh1, msh2, in situ end labeling of DNA fragments for apoptosis, and FISH-PNA of telomere. Positive cells were expressed as percentage of tumor cells. The results were statistically compared using non-parametric analysis of variance, and stepwise discriminant analysis/logistic regression (significant if P < 0.05). Cross validation was done only for those cases in the analysis, classifying each case by the functions derived from all cases other than that case.</p> <p>Results: The average results per variable are shown in the table. The stepwise discriminant analysis classified correctly 97% of carcinomas using Ki67 and MLH1, but failed to provide a reliable model for the other neoplasms.</p> <p>Conclusions: Biological variables employed in tumor pathology are useful for the diagnosis of carcinomas, but fail in the characterization of other malignancies. A sensible use of proliferation and mis- match repair markers improves the phenotypic classification and provides a biological support.</p

Biologic models of malignancies by patient age

<p>Background: There are controversial results on the influence of age on malignancy prognosis, being the patient age used as staging cri- teria (e.g., thyroid neoplasms), but no systematic analysis on the biological bases of tumor differences by age is available.</p> <p>Design: We selected carcinomas (90), sarcomas (25), malignant melanomas (15) and malignant lymphomas (25) from common locations that have appropriate archival material. Conventional morphological variables were evaluated, including nuclear features, growth pattern, stromal reaction, inflammatory response, mitotic figure counting. Representative samples were evaluated by standard immunohistochemistry for Ki67, telomerase, mlh1, msh2, In situ end labeling of DNA fragments for apoptosis, and FISH-PNA of telomere. Positive cells were expressed as percentage of tumor cells. Appropriate controls were run in each sample. Cases were stratified according to patient’s age in £50 years (group A, 31 cases), 50– 70 years (group B, 93 cases) and >70 years (group C, 31 cases). The results were statistically compared using chi-square, non-para- metric analysis of variance, and stepwise discriminant analysis/ logistic regression (significant if P < 0.05). Cross validation was done only for those cases in the analysis, classifying each case by the functions derived from all cases other than that case.</p> <p>Results: The average age in each group was 34 (group A), 59 (group B) and 78 years (group C). All neoplasms were revealed positive for mlh1 and msh2, regardless of the age group. Indepen- dent predicting variables of tumors in young patients were anisok- aryosis, desmoplastic stromal reaction, vascular invasion, and telomerase expression; while for old patients the independent variables were atypical mitoses, nuclear pleomorphism, presence of apoptosis, detectable telomeres, and irregular chromatin pattern. A logistic regression using these variables classified correctly 90% of cases analyzed.</p> <p>Conclusions: Neoplasms in young patients are defined by a combination of nuclear and growth-stromal features, while malignancies in old patients are essentially characterized by nuclear-proliferative variables.</p

Patient age modifies the power of histological variables used for tumor grading

<p>Background: Grading is one of the most powerful variable of tumor prognosis, but is mainly based on subjective features that frequently are site specific and do not consider the patient age. We aim to identify the variables that reliably predict grade, incorporating patient age as additional discriminator.</p> <p>Design: We selected carcinomas (90), sarcomas (25), malignant melanomas (15) and malignant lymphomas (25) from common locations that have appropriate archival material. Neoplasms were graded according to WHO criteria in their respective location, and conventional morphological variables were evaluated, including nuclear features, growth pattern, stromal reaction, inflammatory response, mitotic figure counting. Representative samples were eval- uated by standard immunohistochemistry for Ki67, telomerase, mlh1, msh2, In situ end labeling of DNA fragments for apoptosis, and FISH-PNA of telomere. The tests were assessed in the whole lesion and the positive cells expressed as percentage of tumor cells. Appropriate controls were run in each sample. Cases were stratified according to patient’s age in £50 years (group A, 31 cases), 50– 70 years (group B, 93 cases) and >70 years (group C, 31 cases). The results were statistically compared using chi-square, non-parametric analysis of variance, and stepwise discriminant analysis/ logistic regression (significant if P < 0.05). Cross validation was done only for those cases in the analysis, classifying each case by the functions derived from all cases other than that case.</p> <p>Results: The average age in each group was 34 (group A), 59 (group B) and 78 years (group C). All neoplasms were revealed positive for mlh1 and msh2, regardless of the age group. The variables which contributed most to the poorly differentiated neoplastic phenotype were the primary and secondary growth patterns, presence of confluent necrosis and hemorrhage, anisokaryosis, nucleo- lar prominence, and Ki67 index. The stepwise discriminant analysis correctly classified 97% of cases (well-moderate versus poorly differ- entiated, 96% after cross validation). The most important variables for grade prediction were anisokaryosis for tumors from young patients, and Ki67 index and the average mitotic count for tumors in old patients.</p> <p>Conclusions: A common grading system of malignancies must include a combined evaluation of growth pattern, confluent necro- sis, nuclear and proliferation features. The predictive power of these variables is age-dependent (anisokaryosis for young patient and proliferation features for old patients).</p

A multi-wavelength analysis of a collection of short-duration GRBs observed between 2012-2015

We investigate the prompt emission and the afterglow properties of short duration gamma-ray burst (sGRB) 130603B and another eight sGRB events during 2012-2015, observed by several multi-wavelength facilities including the GTC 10.4m telescope. Prompt emission high energy data of the events were obtained by INTEGRAL/SPI/ACS, Swift/BAT and Fermi/GBM satellites. The prompt emission data by INTEGRAL in the energy range of 0.1-10 MeV for sGRB 130603B, sGRB 140606A, sGRB 140930B, sGRB 141212A and sGRB 151228A do not show any signature of the extended emission or precursor activity and their spectral and temporal properties are similar to those seen in case of other short bursts. For sGRB130603B, our new afterglow photometric data constraints the pre jet-break temporal decay due to denser temporal coverage. For sGRB 130603B, the afterglow light curve, containing both our new as well as previously published photometric data is broadly consistent with the ISM afterglow model. Modeling of the host galaxies of sGRB 130603B and sGRB 141212A using the LePHARE software supports a scenario in which the environment of the burst is undergoing moderate star formation activity. From the inclusion of our late-time data for 8 other sGRBs we are able to; place tight constraints on the non-detection of the afterglow, host galaxy or any underlying kilonova emission. Our late-time afterglow observations of the sGRB 170817A/GW170817 are also discussed and compared with the sub-set of sGRBs