Evaluation of ki-67 as independent risk factor and its role in the incidence of local recurrence/distant metastasis in luminal A and luminal B (her2 negative) breast cancer: a retrospective analysis from a single cancer center

Objective: We aimed to assess the relationship between the Ki-67 index and the risk of recur- rences and survival in patients with breast cancer (BC) that had positive estrogen receptor (ER), positive proges- terone receptor (PR), and negative human epidermal growth factor receptor (HER2). Patients and Methods: A total of 108 patients who visited the Clinical Oncology Department at Assuit Univer- sity Hospital between 2015 and 2018 were involved in the study. The level of Ki-67 was measured and patients were divided into low Ki-67 (n=62) and high Ki-67 (n=46) groups using 14% as the cut-off value. The Cox-regression hazard model was used for both Univariate and Multivariate analyses. Kaplan-Meier survival curves were used for the survival analysis. Results: Age, menopausal status, performance status (PS), pathological type, tumor stage (T), nodal stage (N), grade (G), and TNM stage were all analysed in relation to the Ki-67 index; the only statistically significant variable was the T stage (p=0.043). Patients with high Ki-67 level had a greater mortality rate than those with low levels (p=0.004). In comparison to low index groups, the mean disease free survival (DFS) and overall survival (OS) were lower in the high index groups (DFS: 48.41± 4.19 months vs. 64.53± 2.48 months and OS: 54.74± 3.59 months vs. 66.54± 1.99 months with p=0.001 and 0.002, respectively). When compared to the low index group, the high Ki-67 group had a significantly higher incidence of local recurrence (LR) and metastasis (p=0.001). Conclusions: In patients with positive ER/PR and HER2, negative HER2 BC, the level of Ki-67 strongly inversely correlates with LR/metastasis, DFS, and OS

Interferon-γ-producing immature myeloid cells confer protection against severe invasive group A Streptococcus infections

Cytokine-activated neutrophils are known to be essential for protection against group A Streptococcus infections. However, during severe invasive group A Streptococcus infections that are accompanied by neutropenia, it remains unclear which factors are protective against such infections, and which cell population is the source of them. Here we show that mice infected with severe invasive group A Streptococcus isolates, but not with non-invasive group A Streptococcus isolates, exhibit high concentrations of plasma interferon-γ during the early stage of infection. Interferon-γ is necessary to protect mice, and is produced by a novel population of granulocyte–macrophage colony-stimulating factor-dependent immature myeloid cells with ring-shaped nuclei. These interferon-γ-producing immature myeloid cells express monocyte and granulocyte markers, and also produce nitric oxide. The adoptive transfer of interferon-γ-producing immature myeloid cells ameliorates infection in wild-type and interferon-γ-deficient mice. Our results indicate that interferon-γ-producing immature myeloid cells have a protective role during the early stage of severe invasive group A Streptococcus infections

Host Genetics and Chlamydia Disease: Prediction and Validation of Disease Severity Mechanisms

Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention. We have previously demonstrated that polymorphisms in immunity-related GTPases (IRG) confer a significant difference in susceptibility to Chlamydia psittaci infection in BXD recombinant mice. Here we combine genetic mapping and network modeling to identify causal pathways underlying this association. We infected a large panel of BXD strains with C. psittaci and assessed host genotype, IRG protein polymorphisms, pathogen load, expression of 32 cytokines, inflammatory cell populations, and weight change. Proinflammatory cytokines correlated with each other and were controlled by a novel genetic locus on chromosome 1, but did not affect disease status, as quantified by weight change 6 days after infection In contrast, weight change correlated strongly with levels of inflammatory cell populations and pathogen load that were controlled by an IRG encoding genetic locus (Ctrq3) on chromosome 11. These data provided content to generate a predictive model of infection using a Bayesian framework incorporating genotypes, immune system parameters, and weight change as a measure of disease severity. Two predictions derived from the model were tested and confirmed in a second round of experiments. First, strains with the susceptible IRG haplotype lost weight as a function of pathogen load whereas strains with the resistant haplotype were almost completely unaffected over a very wide range of pathogen load. Second, we predicted that macrophage activation by Ctrq3 would be central in conferring pathogen tolerance. We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden. Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility

A deep learning framework for automated classification of histopathological kidney whole-slide images

Background: Renal cell carcinoma is the most common type of malignant kidney tumor and is responsible for 14,830 deaths per year in the United States. Among the four most common subtypes of renal cell carcinoma, clear cell renal cell carcinoma has the worst prognosis and clear cell papillary renal cell carcinoma appears to have no malignant potential. Distinction between these two subtypes can be difficult due to morphologic overlap on examination of histopathological preparation stained with hematoxylin and eosin. Ancillary techniques, such as immunohistochemistry, can be helpful, but they are not universally available. We propose and evaluate a new deep learning framework for tumor classification tasks to distinguish clear cell renal cell carcinoma from papillary renal cell carcinoma. Methods: Our deep learning framework is composed of three convolutional neural networks. We divided whole-slide kidney images into patches with three different sizes where each network processes a specific patch size. Our framework provides patchwise and pixelwise classification. The histopathological kidney data is composed of 64 image slides that belong to 4 categories: fat, parenchyma, clear cell renal cell carcinoma, and clear cell papillary renal cell carcinoma. The final output of our framework is an image map where each pixel is classified into one class. To maintain consistency, we processed the map with Gauss-Markov random field smoothing. Results: Our framework succeeded in classifying the four classes and showed superior performance compared to well-established state-of-the-art methods (pixel accuracy: 0.89 ResNet18, 0.92 proposed). Conclusions: Deep learning techniques have a significant potential for cancer diagnosis