This review article talks about the events in B cell development and differentiation that might go absurd leading to lymphomas. Some key Immunoregulatory genes play an important role in auto-immune diseases with higher risk for lymphomas.

Nat Rev Immunol. 2002 Dec;2(12):920-32.
Lymphoid malignancies: the dark side of B-cell differentiation.
Shaffer AL1, Rosenwald A, Staudt LM.
Author information
Abstract
When the regulation of B-cell differentiation and activation is disrupted, lymphomas and leukaemias can occur. The processes that normally create immunoglobulin diversity might be misdirected, resulting in oncogenic chromosomal translocations that block differentiation, prevent apoptosis and/or promote proliferation. Prolonged or unregulated antigenic stimulation might contribute further to the development and progression of some malignancies. Lymphoid malignancies often resemble normal stages of B-cell differentiation, as shown by molecular techniques such as gene-expression profiling. The similarities and differences between malignant and normal B cells indicate strategies for the treatment of these cancers.

PMID: 12461565 [PubMed – indexed for MEDLINE]

Kidd et al use computational methods to predict the effect of >1,300 drugs on specific immune cells. Findings were then tested by examining electronic medical records.

Nat Biotechnol. 2016 Jan;34(1):47-54. doi: 10.1038/nbt.3367. Epub 2015 Nov 30.
Mapping the effects of drugs on the immune system.
Kidd BA1,2, Wroblewska A1, Boland MR3, Agudo J1, Merad M4,5,6, Tatonetti NP3, Brown BD1,5,6, Dudley JT1,2.
Author information
Abstract
Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 interactions. The resulting immune-cell pharmacology map is used to predict how five drugs influence four immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.

PMID: 26619012 [PubMed – in process] PMCID: PMC4706827 [Available on 2016-07-01]

1. Battich et al. use sm-FISH to study variance of transcript abundance across cell populations. They found that nuclear retention is an important mechanism to maintain uniformity of protein expression in the face of stochastic transcription. Foundational work!

   Cell. 2015 Dec 17;163(7):1596-610. doi: 10.1016/j.cell.2015.11.018.
   Control of Transcript Variability in Single Mammalian Cells.
   Battich N1, Stoeger T1, Pelkmans L2.
   Author information
   Abstract
   A central question in biology is whether variability between genetically identical cells exposed to the same culture conditions is largely stochastic or deterministic. Using image-based transcriptomics in millions of single human cells, we find that while variability of cytoplasmic transcript abundance is large, it is for most genes minimally stochastic and can be predicted with multivariate models of the phenotypic state and population context of single cells. Computational multiplexing of these predictive signatures across hundreds of genes revealed a complex regulatory system that controls the observed variability of transcript abundance between individual cells. Mathematical modeling and experimental validation show that nuclear retention and transport of transcripts between the nucleus and the cytoplasm is central to buffering stochastic transcriptional fluctuations in mammalian gene expression. Our work indicates that cellular compartmentalization confines transcriptional noise to the nucleus, thereby preventing it from interfering with the control of single-cell transcript abundance in the cytoplasm.
   Copyright © 2015 Elsevier Inc. All rights reserved.
   PMID: 26687353 [PubMed – in process]