ABL fusion oncogene transformation and inhibitor sensitivity are mediated by the cellular regulator RIN1.

ABL gene translocations create constitutively active tyrosine kinases that are causative in chronic myeloid leukemia, acute lymphocytic leukemia and other hematopoietic malignancies. Consistent retention of ABL SH3/SH2 autoinhibitory domains, however, suggests that these leukemogenic tyrosine kinase fusion proteins remain subject to regulation. We resolve this paradox, demonstrating that BCR-ABL1 kinase activity is regulated by RIN1, an ABL SH3/SH2 binding protein. BCR-ABL1 activity was increased by RIN1 overexpression and decreased by RIN1 silencing. Moreover, Rin1(-/-) bone marrow cells were not transformed by BCR-ABL1, ETV6-ABL1 or BCR-ABL1(T315I), a patient-derived drug-resistant mutant, as judged by growth factor independence. Rescue by ectopic RIN1 verified a cell autonomous mechanism of collaboration with BCR-ABL1 during transformation. Sensitivity to the ABL kinase inhibitor imatinib was increased by RIN1 silencing, consistent with RIN1 stabilization of an activated BCR-ABL1 conformation having reduced drug affinity. The dependence on activation by RIN1 to unleash full catalytic and cell transformation potential reveals a previously unknown vulnerability that could be exploited for treatment of leukemic cases driven by ABL translocations. The findings suggest that RIN1 targeting could be efficacious for imatinib-resistant disease and might complement ABL kinase inhibitors in first-line therapy.

Diffusion network architectures for implementation of Gibbs samplers with applications to assignment problems.

In this paper, analog circuit designs for implementations of Gibbs samplers are presented, which offer fully parallel computation. The Gibbs sampler for a discrete solution space (or Boltzmann machine) can be used to solve both deterministic and probabilistic assignment (association) problems. The primary drawback to the use of a Boltzmann machine for optimization is its computational complexity, since updating of the neurons is typically performed sequentially. We first consider the diffusion equation emulation of a Boltzmann machine introduced by Roysam and Miller (1991), which employs a parallel network of nonlinear amplifiers. It is shown that an analog circuit implementation of the diffusion equation requires a complex neural structure incorporating matched nonlinear feedback amplifiers and current multipliers. We introduce a simpler implementation of the Boltzmann machine, using a "constant gradient" diffusion equation, which eliminates the need for a matched feedback amplifier. The performance of the Roysam and Miller network and the new constant gradient (CG) network is compared using simulations for the multiple-neuron case, and integration of the Chapman-Kolmogorov equation for a single neuron. Based on the simulation results, heuristic criteria for establishing the diffusion equation boundaries, and neuron sigmoidal gain are obtained. The final CG analog circuit is suitable for VLSI implementation, and hence may offer rapid convergence.

Computing association probabilities using parallel Boltzmann machines.

A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta(i)(j) that the i th measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.