Anti-Human IgG1 monoclonal antibody, clone NL 16

The extent and composition of the immune response in a breast cancer is one important prognostic factor for the disease. The aim of the current work was to refine the analysis of the humoral component of an immune response in breast tumors by quantifying mRNA expression of different immunoglobulin classes and study their association with prognosis. We used RNA-Seq data from two local population-based breast cancer cohorts to determine the expression of IGJ and immunoglobulin heavy (IGH) chain-encoding RNAs. The association with prognosis was investigated and public data sets were used to corroborate the findings. Except for IGHE and IGHD, mRNAs encoding heavy chains were generally detected at substantial levels and correlated with other immune-related genes. High IGHGT mRNA was associated with factors related to poor prognosis such as estrogen receptor negativity, HER2 amplification, and high grade, whereas high IGHA2 mRNA levels were primarily associated with lower age at diagnosis. High IGHA2 and IGJ mRNA levels were associated with a more favorable prognosis both in univariable and multivariable Cox models. When adjusting for other prognostic factors, high IGHG1 mRNA levels were positively associated with improved prognosis. To our knowledge, these results are the first to demonstrate that expression of individual Ig class types has prognostic implications in breast cancer.

Background B-cell chronic lymphocytic leukemia (CLL) is a heterogeneous disease and the most common adult leukemia in western countries. IgVH mutational status distinguishes two major types of CLL, each associated with a different prognosis and survival. Sequencing identified NOTCH1 and SF3B1 as the two main recurrent mutations. We described a novel method to clarify how these mutations affect gene expression by finding small-scale signatures that predict the IgVH, NOTCH1 and SF3B1 mutations. We subsequently defined the biological pathways and correlation networks involved in disease development, with the potential goal of identifying new drugable targets. Methods We modeled a microarray dataset consisting of 48807 probes derived from 163 samples. The use of Fisher's ratio and fold change combined with feature elimination allowed us to identify the minimum number of genes with the highest predictive mutation power and, subsequently, we applied network and pathway analyses of these genes to identify their biological roles. Results The mutational status of the patients was accurately predicted (94-99%) using small-scale gene signatures: 13 genes for IgVH, 60 for NOTCH1 and 22 for SF3B1. LPL plays an important role in the case of the IgVH mutation, whereas MSI2, LTK, TFEC and CNTAP2 are involved in the NOTCH1 mutation, and RPL32 and PLAGL1 are involved in the SF3B1 mutation. Four high discriminatory genes (IGHG1, MYBL1, NRIP1 and RGS1) are common to these three mutations. The IL-4-mediated signaling events pathway appears to be involved as a common mechanism and suggests an important role of the immune response mechanisms and antigen presentation. Conclusions This retrospective analysis served to provide a deeper understanding of the effects of the different mutations in CLL disease progression, with the expectation that these findings will be clinically applied in the near future to the development of new drugs.