A novel c-kit positive biphenotypic acute leukemia cell line, TMBL-1, carrying a p53 point mutation.

We established and characterized a c-kit positive cell line from the bone marrow of a patient with biphenotypic acute leukemia (BAL). The cell line, designated TMBL-1, carried a His-175 mutant p53. The immunophenotype of the primary leukemia cells at diagnosis was cytoplasmic CD3+, CD7+, CD13+, CD33-, interleukin-7 (IL-7) receptor+ and c-kit -. However, leukemia cells in relapse and TMBL-1 cells were CD33+ and c-kit +. Immunophenotypically, TMBL-1 is a BAL cell line that coexpresses T-lymphoid and myeloid markers which fulfill the criteria of the European Group for the Immunological Characterization of Leukemia. Stem cell factor (SCF), a key regulator of hematopoiesis signaling through c-kit, enhanced the proliferation of TMBL-1 cells. Direct sequencing revealed the conversion at codon 175 of the p53 gene in the TMBL-1 cells. Primary leukemia cells in relapse also carried the same point mutation but not at diagnosis. Moreover, TMBL-1 cells are sensitive to paclitaxel, which could induce p53-independent apoptosis. The biphenotypic features and p53 mutation may be associated with progression to a more malignant type. This cell line may provide new information on the role of SCF in the overlapping area between early T-lymphoid/myeloid cells, and help in the design of new therapies targeted towards p53 mutations.

Subspace-based support vector machines for pattern classification.

In this paper, we discuss subspace-based support vector machines (SS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, for each class we define the weighted similarity measure using the vectors called dictionaries that represent the class, and optimize the weights so that the margin between classes is maximized. Because the similarity measure is defined for each class, for a data sample the similarity measure to which the data sample belongs needs to be the largest among all the similarity measures. Introducing slack variables, we define these constraints either by equality constraints or inequality constraints. As a result we obtain subspace-based least squares SVMs (SSLS-SVMs) and subspace-based linear programming SVMs (SSLP-SVMs). To speedup training of SSLS-SVMs, which are similar to LS-SVMs by all-at-once formulation, we also propose SSLS-SVMs by one-against-all formulation, which optimize each similarity measure separately. Using two-class problems, we clarify the difference of SSLS-SVMs and SSLP-SVMs and evaluate the effectiveness of the proposed methods over the conventional methods with equal weights and with weights equal to eigenvalues.