Anti-tumor immunoglobulin M increases lung metastasis in an experimental model of malignant melanoma

The Evolving Landscape of B Cells in Cancer Metastasis

Metastasis is the leading cause of cancer mortality. Functional and clinical studies have documented diverse B-cell and antibody responses in cancer metastasis. The presence of B cells in tumor microenvironments and metastatic sites has been associated with diverse effects that can promote or inhibit metastasis. Specifically, B cells can contribute to the spread of cancer cells by enhancing tumor cell motility, invasion, angiogenesis, lymphangiogenesis, and extracellular matrix remodeling. Moreover, they can promote metastatic colonization by triggering pathogenic immunoglobulin responses and recruiting immune suppressive cells. Contrastingly, B cells can also exhibit antimetastatic effects. For example, they aid in enhanced antigen presentation, which helps activate immune responses against cancer cells. In addition, B cells play a crucial role in preventing the dissemination of metastatic cells from the primary tumor and secrete antibodies that can aid in tumor recognition. Here, we review the complex roles of B cells in metastasis, delineating the heterogeneity of B-cell activity and subtypes by metastatic site, antibody class, antigen (if known), and molecular phenotype. These important attributes of B cells emphasize the need for a deeper understanding and characterization of B-cell phenotypes to define their effects in metastasis.

Improved antitumor activity and reduced myocardial toxicity of doxorubicin encapsulated in MPEG-PCL nanoparticles

Doxorubicin (Dox) is a broad-spectrum antitumor drug used for the treatment of many types of malignant tumors. Although it possesses powerful antitumor activity, its clinical application is seriously encumbered by its unselective distribution and systemic toxicities, particularly myocardial toxicity. Thus, it is imperative to modify Dox to decrease its systemic toxicities and improve its therapeutic index. In the present study, we adopted a novel type of monomethoxy poly(ethylene glycol)-poly(ε-caprolactone) (MPEG-PCL) micelles to encapsulate Dox to prepare Dox-loaded MPEG-PCL (Dox/MPEG-PCL) nanoparticles by a controllable self-assembly process. The cellular uptake efficiency and cell proliferation inhibition of the Dox/MPEG-PCL nanoparticles were examined. The antitumor activity of the Dox/MPEG-PCL nanoparticles was tested on a multiple pulmonary metastasis model of melanoma on C57BL/6 mice. Systemic toxicities and survival time were compared between the mice treated with the Dox/MPEG-PCL nanoparticles and free Dox. The potential myocardial toxicity of the Dox/MPEG-PCL nanoparticles was investigated using a prolonged observation period. Encapsulation of Dox in MPEG-PCL nanoparticles significantly improved the cellular uptake and cell proliferation inhibition of Dox in vivo. Intravenous injection of Dox/MPEG-PCL nanoparticles obtained significant inhibition of the growth and metastasis of melanoma in the lung and prolonged survival time compared with free Dox (P<0.05). The Dox/MPEG-PCL nanoparticles did not show obvious additional systemic toxicities compared with free Dox during the treatment time. During the prolonged observation period, obvious decreased cardiac toxicity was observed in the Dox/MPEG-PCL nanoparticle-treated mice compared with that observed in the free Dox-treated mice. These results indicated that encapsulating Dox with MPEG-PCL micelles could significantly promote its antitumor activity and reduce its toxicity to the myocardium.

Mimicking the germinal center reaction in hybridoma cells to isolate temperature-selective anti-PEG antibodies

Modification of antibody class and binding properties typically requires cloning of antibody genes, antibody library construction, phage or yeast display and recombinant antibody expression. Here, we describe an alternative “cloning-free” approach to generate antibodies with altered antigen-binding and heavy chain isotype by mimicking the germinal center reaction in antibody-secreting hybridoma cells. This was accomplished by lentiviral transduction and controllable expression of activation-induced cytidine deaminase (AID) to generate somatic hypermutation and class switch recombination in antibody genes coupled with high-throughput fluorescence-activated cell sorting (FACS) of hybridoma cells to detect altered antibody binding properties. Starting from a single established hybridoma clone, we isolated mutated antibodies that bind to a low-temperature structure of polyethylene glycol (PEG), a polymer widely used in nanotechnology, biotechnology and pharmaceuticals. FACS of AID-infected hybridoma cells also facilitated rapid identification of class switched variants of monoclonal IgM to monoclonal IgG. Mimicking the germinal center reaction in hybridoma cells may offer a general method to identify and isolate antibodies with altered binding properties and class-switched heavy chains without the need to carry out DNA library construction, antibody engineering and recombinant protein expression.

Coclustering of human cancer microarrays using Minimum Sum-Squared Residue coclustering

It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of previously undetectable biological cellular processes of genes as well as macroscopic phenotypes of related samples. In order to simultaneously cluster genes and conditions, we have previously developed a fast co-clustering algorithm, Minimum Sum-Squared Residue Co-clustering (MSSRCC), which employs an alternating minimization scheme and generates what we call co-clusters in a checkerboard structure. In this paper, we propose specific strategies that enable MSSRCC to escape poor local minima and resolve the degeneracy problem in partitional clustering algorithms. The strategies include binormalization, deterministic spectral initialization, and incremental local search. We assess the effects of various strategies on both synthetic gene expression datasets and real human cancer microarrays and provide empirical evidence that MSSRCC with the proposed strategies performs better than existing co-clustering and clustering algorithms. In particular, the combination of all the three strategies leads to the best performance. Furthermore, we illustrate coherence of the resulting co-clusters in a checkerboard structure, where genes in a co-cluster manifest the phenotype structure of corresponding specific samples, and evaluate the enrichment of functional annotations in Gene Ontology (GO).