Cross-platform comparison of independent datasets identifies an immune signature associated with improved survival in metastatic melanoma

Platform and study differences in prognostic signatures from metastatic melanoma (MM) gene expression reports often hinder consensus arrival. We performed survival/outcome-based pairwise comparisons of three independent MM gene expression profiles using the threshold-free algorithm rank-rank hypergeometric overlap analysis (RRHO). We found statistically significant overlap for genes overexpressed in favorable outcome (FO) groups, but no overlap for poor outcome (PO) groups. This "favorable outcome signature" (FOS) of 228 genes coinciding on all three overlapping gene lists showed immune function predominated in FO MM. Surprisingly, specific cell signature-enrichment analysis showed B cell-associated genes enriched in FO MM, along with T cell-associated genes. Higher levels of B and T cells (p<0.05) and their relative proximity (p<0.05) were detected in FO-to-PO tumor comparisons from an independent MM patients cohort. Finally, expression of FOS in two independent Stage III MM tumor datasets correctly predicted clinical outcome in 12/14 and 44/70 patients using a weighted gene voting classifier (area under the curve values 0.96 and 0.75, respectively). This RRHO-based, cross-study analysis emphasizes the RRHO approach power, confirms T cells relevance for prolonged MM survival, supports a favorable role for B cells in anti-melanoma immunity, and suggests B cells potential as means of intervention in melanoma treatment.Fil: Lardone, Ricardo Dante. The John Wayne Cancer Institute; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Plaisier, Seema. The John Wayne Cancer Institute; Estados UnidosFil: Navarrete, Marian S.. The John Wayne Cancer Institute; Estados UnidosFil: Shamonki, Jaime M.. California Cryobank; Estados UnidosFil: Jalas, John R.. Providence Saint John’s Health Center; Estados UnidosFil: Sieling, Peter A. The John Wayne Cancer Institute; Estados UnidosFil: Lee, Delphine J.. The John Wayne Cancer Institute; Estados Unido

Microbial Dysbiosis Is Associated with Human Breast Cancer

<div><p>Breast cancer affects one in eight women in their lifetime. Though diet, age and genetic predisposition are established risk factors, the majority of breast cancers have unknown etiology. The human microbiota refers to the collection of microbes inhabiting the human body. Imbalance in microbial communities, or microbial dysbiosis, has been implicated in various human diseases including obesity, diabetes, and colon cancer. Therefore, we investigated the potential role of microbiota in breast cancer by next-generation sequencing using breast tumor tissue and paired normal adjacent tissue from the same patient. In a qualitative survey of the breast microbiota DNA, we found that the bacterium <i>Methylobacterium radiotolerans</i> is relatively enriched in tumor tissue, while the bacterium <i>Sphingomonas yanoikuyae</i> is relatively enriched in paired normal tissue. The relative abundances of these two bacterial species were inversely correlated in paired normal breast tissue but not in tumor tissue, indicating that dysbiosis is associated with breast cancer. Furthermore, the total bacterial DNA load was reduced in tumor versus paired normal and healthy breast tissue as determined by quantitative PCR. Interestingly, bacterial DNA load correlated inversely with advanced disease, a finding that could have broad implications in diagnosis and staging of breast cancer. Lastly, we observed lower basal levels of antibacterial response gene expression in tumor versus healthy breast tissue. Taken together, these data indicate that microbial DNA is present in the breast and that bacteria or their components may influence the local immune microenvironment. Our findings suggest a previously unrecognized link between dysbiosis and breast cancer which has potential diagnostic and therapeutic implications.</p></div

Quantification of bacterial load in tissue from healthy and breast cancer patients.

<p><b>A</b>) Copy numbers of the bacterial 16S gene were compared among healthy (age-matched) (n = 23), paired normal (n = 39) and tumor tissue (n = 39). Healthy specimens were obtained from patients undergoing reduction mammoplasty, with no evidence of breast cancer. Statistical analysis was performed using Kruskal-Wallis nonparametric ANOVA with Dunn’s Multiple Comparison post-test. <b>B</b>) Bacterial load in tissue according to clinical staging of the tumor specimen. Statistical analysis was performed using Cuzick’s Trend test. All statistical analyses were considered significant when P<0.05. Data represent the average of duplicate values. Error bars represent mean ± s.e.m.</p

Survey of microbial communities residing in breast tissue from breast cancer patients.

<p><b>A</b>) Phylum level distribution of microbial communities comparing paired normal adjacent (“paired normal”) and breast cancer tissue from 20 patients with ER-positive breast cancer (n = 20). Each bar represents 100% of the bacteria detected in a given sample. <b>B</b>) Combined distribution at the phylum level in paired normal and breast tumor tissue (n = 20). <b>C</b>) Number of OTUs found in each community (n = 20). <b>D</b>) Analysis of OTUs with differential abundance between paired normal and tumor tissue (n = 20). <b>E</b>) Correlation of relative abundances of <i>M. radiotolerans</i> and <i>S. yanoikuyae</i> (n = 20). <b>F</b>) Relative abundances of commonly found skin bacteria (n = 20). p-values from Student’s paired t-test are shown, with P<0.05 considered significant. Error bars represent mean ± s.e.m.</p

Expression profiles of antibacterial response genes in healthy and breast cancer tissue (n = 9).

<p>Healthy specimens were obtained from patients undergoing reduction mammoplasty, with no evidence of breast cancer. <b>A</b>) Heatmap of gene expression values generated using non-supervised hierarchical clustering <b>B</b>) Expression profiles of antimicrobial response genes. p-values from Student’s paired t-test are shown, with p<0.05 considered significant. Error bars represent mean ± s.e.m.</p

Risk factors for pregnancy failure in patients with anti-phospholipid syndrome treated with conventional therapies: a multicentre, case-control study

OBJECTIVE: To identify the risk factors associated with pregnancy failure in patients with APS treated with conventional therapy. METHODS: A multicentre, case-control study was conducted to compare APS patients with successful and unsuccessful pregnancy outcomes. We retrospectively considered 410 pregnancies of women diagnosed with primary APS. The study focused on 57 unsuccessful pregnancies (considered the study population) and 57 successful pregnancies (considered the control population) matched for age and therapy. All the patients had been treated with conventional protocol treatments including low-dose aspirin and/or heparin. The clinical and laboratory features of the two groups of women diagnosed with APS were compared. RESULTS: The independent risk factors for pregnancy failure were: (i) the presence of SLE or other autoimmune diseases [odds ratio (OR) 6.0; 95% CI 1.7, 20.8; P = 0.01]; (ii) history of both thrombosis and pregnancy morbidity (OR 12.1; 95% CI 1.3, 115.3; P = 0.03); and (iii) triple [Immunoglobulin (Ig) G/IgM aCLs plus IgG/IgM anti-\u3b2(2) glycoprotein I antibodies plus LA] aPL positivity (OR 4.1; 95% CI 1.0, 16.7; P = 0.05). APS patients diagnosed on the basis of a single positive test and/or history of pregnancy morbidity alone were generally found to have successful pregnancies. CONCLUSION: It would seem from these findings that the risk of pregnancy failure in APS women planning to conceive can be stratified on the basis of some specific clinical and laboratory features