RAS-mediated tumor stress adaptation and the targeting opportunities it presents

Cellular stress is known to function in synergistic cooperation with oncogenic mutations during tumorigenesis to drive cancer progression. Oncogenic RAS is a strong inducer of a variety of pro-tumorigenic cellular stresses, and also enhances the ability of cells to tolerate these stresses through multiple mechanisms. Many of these oncogenic, RAS-driven, stress-adaptive mechanisms have also been implicated in tolerance and resistance to chemotherapy and to therapies that target the RAS pathway. Understanding how oncogenic RAS shapes cellular stress adaptation and how this functions in drug resistance is of vital importance for identifying new therapeutic targets and therapeutic combinations to treat RAS-driven cancers

Leukemia-associated RhoGEF (LARG) is a novel RhoGEF in cytokinesis and required for the proper completion of abscission.

Proper completion of mitosis requires the concerted effort of multiple RhoGEFs. Here we show that leukemia-associated RhoGEF (LARG), a RhoA-specific RGS-RhoGEF, is required for abscission, the final stage of cytokinesis, in which the intercellular membrane is cleaved between daughter cells. LARG colocalizes with α-tubulin at the spindle poles before localizing to the central spindle. During cytokinesis, LARG is condensed in the midbody, where it colocalizes with RhoA. HeLa cells depleted of LARG display apoptosis during cytokinesis with unresolved intercellular bridges, and rescue experiments show that expression of small interfering RNA-resistant LARG prevents this apoptosis. Moreover, live cell imaging of LARG-depleted cells reveals greatly delayed fission kinetics in abscission in which a population of cells with persistent bridges undergoes apoptosis; however, the delayed fission kinetics is rescued by Aurora-B inhibition. The formation of a Flemming body and thinning of microtubules in the intercellular bridge of cells depleted of LARG is consistent with a defect in late cytokinesis, just before the abscission event. In contrast to studies of other RhoGEFs, particularly Ect2 and GEF-H1, LARG depletion does not result in cytokinetic furrow regression nor does it affect internal mitotic timing. These results show that LARG is a novel and temporally distinct RhoGEF required for completion of abscission

RGS-RhoGEFs: interactions with G proteins and identification of novel subcellular locations and functions

Abstract not availabl

Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia

<div><p>Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.</p></div

Loci of single-bundle hypoxia gradients.

<p>Circles (red) defined by the <i>r</i>_<i>m</i> found by the Intensity-Sample-Ray-Bundles algorithm for each of the three centers we specified, corresponding to vessel locations in the registered H&E image. Here we show <i>m</i> = 1 sector (2<i>π</i> radians per sector) for each center. Sectors are labeled with red numbers, counterclockwise, just outside of the red sector contour.</p

Hypoxia gradient analysis.

<p>Intensity level analysis produced by the Intensity-Sample-Ray-Bundles algorithm for centers 1 (left 3 panels), 2 (middle 3 panels), and 3 (right 3 panels). Intensity-Sample-Ray-Bundles creates three plots of the data, where the horizontal axis denotes distance from the center (pixels), and the vertical axis denotes intensity level. The first panel shows every ray measurement (light gray), upon which (blue) and (red) are overlaid; its title gives <i>r</i>_<i>m</i> (pixels). The second panel shows (blue) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (<i>l</i>, pixels), slope (<i>s</i>), and least squares error (<i>e</i>, pixels) for each fitted segment. The third panel shows (red) ± (gray), overlaid with segmented least squares fits to (black); its title gives the length (<i>l</i>, pixels), slope (<i>s</i>), and least squares error (<i>e</i>, pixels) for each fitted segment. The segmented least square fits are given by a dynamic programming algorithm using a cost parameter <i>C</i> = 200.</p