S100 Calcium Binding Protein Family Members Associate With Poor Patient Outcome and Response to Proteasome Inhibition in Multiple Myeloma

Molecular and immunological mechanisms of clonal evolution in multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). It is known that early genetic mutations in post-germinal center B/plasma cells are the cause of myelomagenesis. The acquisition of additional chromosomal abnormalities and distinct mutations further promote the outgrowth of malignant plasma cell populations that are resistant to conventional treatments, finally resulting in relapsed and therapy-refractory terminal stages of MM. In addition, myeloma cells are supported by autocrine signaling pathways and the tumor microenvironment (TME), which consists of diverse cell types such as stromal cells, immune cells, and components of the extracellular matrix. The TME provides essential signals and stimuli that induce proliferation and/or prevent apoptosis. In particular, the molecular pathways by which MM cells interact with the TME are crucial for the development of MM. To generate successful therapies and prevent MM recurrence, a thorough understanding of the molecular mechanisms that drive MM progression and therapy resistance is essential. In this review, we summarize key mechanisms that promote myelomagenesis and drive the clonal expansion in the course of MM progression such as autocrine signaling cascades, as well as direct and indirect interactions between the TME and malignant plasma cells. In addition, we highlight drug-resistance mechanisms and emerging therapies that are currently tested in clinical trials to overcome therapy-refractory MM stages.

Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM

Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here we introduce an active learning method that identifies minimal but highly informative gene sets that enable the identification of cell types, physiological states and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data by employing an active support vector machine (ActiveSVM) classifier. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across, for example, cell atlas and disease-characterization datasets. The discovery of small but highly informative gene sets should enable reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery and genetic screens.