Characterization of IGH locus breakpoints in multiple myeloma indicates a subset of translocations appear to occur in pregerminal center B cells.

Translocations in myeloma are thought to occur solely in mature B cells in the germinal center through class switch recombination (CSR). We used a targeted captured technique followed by massively parallel sequencing to determine the exact breakpoints in both the immunoglobulin heavy chain (IGH) locus and the partner chromosome in 61 presentation multiple myeloma samples. The majority of samples (62%) have a breakpoint within the switch regions upstream of the IGH constant genes and are generated through CSR in a mature B cell. However, the proportion of CSR translocations is not consistent between cytogenetic subgroups. We find that 100% of t(4;14) are CSR-mediated; however, 21% of t(11;14) and 25% of t(14;20) are generated through DH-JH recombination activation gene-mediated mechanisms, indicating they occur earlier in B-cell development at the pro-B-cell stage in the bone marrow. These 2 groups also generate translocations through receptor revision, as determined by the breakpoints and mutation status of the segments used in 10% and 50% of t(11;14) and t(14;20) samples, respectively. The study indicates that in a significant number of cases the translocation-based etiological events underlying myeloma may arise at the pro-B-cell hematological progenitor cell level, much earlier in B-cell development than was previously thought.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.