Total loss of MHC class I is an independent indicator of good prognosis in breast cancer

Tumours can be recognised by CTL and NK cells. CTL recognition depends on expression of MHC Class I loaded with peptides from tumour antigens. In contrast, loss of MHC Class I results in NK activation. In our study a large set of samples from patients with primary operable invasive breast cancer was evaluated for the expression of MHC Class I heavy and light by immunohistochemical staining of 439 breast carcinomas in a tissue microarray. Forty-seven percent (206 of 439) of breast carcinomas were considered negative for HLA Class I heavy chain (HC10), whereas lack of anti-β2m-antibody staining was observed in 39% (167 of 424) of tumours, with only 3% of the β2m-negative tumours expressing detectable HLA Class I heavy chain. Correlation with patient outcome showed direct relationship between patient survival and HLA-negative phenotype (log rank = 0.004). A positive relationship was found between the intensity of expression of MHC Class I light and heavy chains expression and histological grade of invasive tumour (p < 0.001) and Nottingham Prognostic Index (p < 0.001). To investigate whether HLA Class I heavy and light chains expression had independent prognostic significance, Cox multivariate regression analysis, including the parameters of tumour size, lymph node stage, grade and intensity of HC10 and anti-β2m staining, was carried out. In our analysis, lymph node stage (p < 0.001), tumour grade (p = 0.005) and intensity of MHC Class I light and heavy chains expression were shown to be independent prognostic factors predictive of overall survival (p-values HC10 = 0.047 and β2m = 0.018)

Lymphatic expression of CLEVER-1 in breast cancer and its relationship with lymph node metastasis

BACKGROUND Mechanisms regulating breast cancer lymph node metastasis are unclear. Staining of CLEVER-1 (common lymphatic endothelial and vascular endothelial receptor-1) in human breast tumors was used, along with in vitro techniques, to assess involvement in the metastatic process. METHODS 148 sections of primary invasive breast cancers, with 10 yr follow-up, were stained with anti-CLEVER-1. Leukocyte infiltration was assessed, along with involvement of specific subpopulations by staining with CD83 (mature dendritic cells, mDC), CD209 (immature DC, iDC) and CD68 (macrophage, Mϕ). In vitro expression of CLEVER-1 on lymphatic (LEC) and blood endothelial cells (BEC) was examined by flow cytometry. RESULTS In vitro results showed that although both endothelial cell types express CLEVER-1, surface expression was only evident on LEC. In tumour sections CLEVER-1 was expressed in blood vessels (BV, 61.4% of samples), lymphatic vessels (LV, 18.2% of samples) and in Mϕ/DCs (82.4% of samples). However, only CLEVER-1 expression in LV was associated with LN metastasis (p = 0.027) and with Mϕ indices (p = 0.021). Although LV CLEVER-1 was associated with LN positivity there was no significant correlation with recurrence or overall survival, BV CLEVER-1 expression was, however, associated with increased risk of recurrence (p = 0.049). The density of inflammatory infiltrate correlated with CLEVER-1 expression in BV (p &lt; 0.001) and LV (p = 0.004). CONCLUSIONS The associations between CLEVER-1 expression on endothelial vessels and macrophage/leukocyte infiltration is suggestive of its regulation by inflammatory conditions in breast cancer, most likely by macrophage-associated cytokines. Its upregulation on LV, related surface expression, and association with LN metastasis suggest that it may be an important mediator of tumor cell metastasis to LN

A comparison of three different methods for classification of breast cancer data

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classification of a novel dataset and perform a methodological comparison of these. We used the C4.5 tree classifier, a Multilayer Perceptron and a naïve Bayes classifier over a large set of tumour markers. We found good performance of the Multilayer Perceptron even when we reduced the number of features to be classified. We found naive Bayes achieved a competitive performance even though the assumption of normality of the data is strongly violated

A "non-parametric" version of the naive Bayes classifier

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence and a Normal distribution of the variables) are perhaps violated. In previous work, we applied naive Bayes and other standard algorithms to a breast cancer database from Nottingham City Hospital in which the variables are highly non-Normal and found that the algorithm performed well when predicting a class that had been derived from the same data. However, when we then applied naive Bayes to predict an alternative clinical variable, it performed much worse than other techniques. This motivated us to propose an alternative method, based on naive Bayes, which removes the requirement for the variables to be Normally distributed, but retains the essential structure and other underlying assumptions of the method. We tested our novel algorithm on our breast cancer data and on three UCI datasets which also exhibited strong violations of Normality. We found our algorithm outperformed naive Bayes in all four cases and outperformed multinomial logistic regression (MLR) in two cases. We conclude that our method offers a competitive alternative to MLR and naive Bayes when dealing with data sets in which non-Normal distributions are observed

Evidence that the p53 negative / Bcl-2 positive phenotype is an independent indicator of good prognosis in colorectal cancer: A tissue microarray study of 460 patients

BACKGROUND: Advances in our understanding of the molecular biology of colorectal cancer have fuelled the search for novel molecular prognostic markers to complement existing staging systems. Markers assessed in combination may perform better than those considered individually. Using high-throughput tissue microarray technology, we describe the prognostic value of combined p53 / Bcl-2 status in colorectal cancer. PATIENTS AND METHODS: Tumour samples from 462 patients who underwent elective surgery to resect a primary colorectal cancer between 1994 and 2000 (mean follow-up of 75 months) were assembled in tissue microarray format. Clinico-pathological data including tumour grade, stage, vascular invasion status along with disease specific survival data has been collected prospectively. Immunohistochemical analysis of p53 and Bcl-2 expression was performed using antibodies DO-7 (p53) and 124 (Bcl-2), and results correlated with known clinico-pathological variables and outcomes. RESULTS: Abnormal nuclear p53 accumulation and Bcl-2 overexpression were detected in 221/445 (49.6%) and199/437 (45.5%) tumours respectively, with a significant inverse correlation between the two markers (p = 0.023). On univariate analysis no correlations were found between either marker and standard clinico-pathological variables, however nuclear p53 expression was associated with a significantly reduced survival (p = 0.024). Combined analysis of the two markers indicated that 112/432 (24.2%) cases displayed a p53(-)/Bcl-2(+) phenotype, this occurring more frequently in earlier stage tumours. Kaplan-Meier analysis revealed a significant survival advantage in these p53(-)/Bcl-2(+) tumours compared with the remaining cases (p = 0.0032). On multivariate analysis using the Cox proportional hazards model, neither p53 expression nor Bcl-2 expression alone were of independent prognostic significance, however the combined p53(-)/Bcl-2(+) phenotype was significantly associated with a good prognosis in this series (HR 0.659, 95%CI 0.452–0.959, p = 0.029). CONCLUSION: Patient stratification by combined p53 / Bcl-2 phenotype provides stage-independent prognostic information in colorectal cancer. Specifically, that up to a quarter of patients display a good prognosis p53(-)/Bcl-2(+) phenotype. This may indicate a more clinically indolent phenotype and a subset of patients for whom less aggressive adjuvant treatment appropriate

Delay in diagnosis of PD-1 Inhibitor induced Secondary adrenal Insufficiency

Introduction: Immune checkpoint inhibitors including PD-1 inhibitors, were initially approved for the treatment of metastatic melanoma but are now increasingly being used for different types of solid organ malignancies. Despite the important clinical benefits, they are associated with immune-related adverse events. The most critical endocrinopathy associated with PD -1 inhibitor is adrenal insufficiency (AI), which requires prompt diagnosis and management to avoid fatality. Case presentation: We present the case of a 78-year-old woman with colon adenocarcinoma treated with Nivolumab (PD-1 inhibitor) after her pulmonary metastases progressed on chemotherapy. She presented to the hospital with progressive generalized weakness, fatigue, headache, lightheadedness, nausea, myalgia, reduced oral intake. She had 2 prior hospitalizations on account of similar symptoms with workup negative for cancer progression or gastrointestinal obstruction. Her laboratory values showed Na 128mmol/L, K 3.4mmol/L, Cr 0.52mg/dL and blood sugar 42mg/dL. Morning cortisol was low at 2.2µg/dL and ACTH stimulation test was positive. She was diagnosed with AI secondary to Nivolumab use and was started on Hydrocortisone while Nivolumab was discontinued. Conclusion: Immune checkpoint inhibitors have a unique side effect profile of immune-related adverse events, the most critical of which is AI. However, the non-specific manifestations of AI can lead to misdiagnosis or delay in diagnosis. Therefore, it is important for physicians to have high index suspicion for AI in acutely ill patients on PD-1 inhibitors for prompt recognition, diagnosis and treatment of AI which is important to prevent life-threatening adrenal crisis

An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

A feature selection method was used in an analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in estrogen receptor-negative breast cancer, showing that it is a heterogeneous disease with at least four main subtypes