While the contribution of specific tumor suppressor networks to cancer development has been the subject of considerable recent study, it remains unclear how alterations in these networks are integrated to influence the response of tumors to anti-cancer treatments. Here, we show that mechanisms commonly used by tumors to bypass early neoplastic checkpoints ultimately determine chemotherapeutic response and generate tumor-specific vulnerabilities that can be exploited with targeted therapies. Specifically, evaluation of the combined status of ATM and p53, two commonly mutated tumor suppressor genes, can help to predict the clinical response to genotoxic chemotherapies. We show that in p53-deficient settings, suppression of ATM dramatically sensitizes tumors to DNA-damaging chemotherapy, whereas, conversely, in the presence of functional p53, suppression of ATM or its downstream target Chk2 actually protects tumors from being killed by genotoxic agents. Furthermore, ATM-deficient cancer cells display strong nononcogene addiction to DNA-PKcs for survival after DNA damage, such that suppression of DNA-PKcs in vivo resensitizes inherently chemoresistant ATM-deficient tumors to genotoxic chemotherapy. Thus, the specific set of alterations induced during tumor development plays a dominant role in determining both the tumor response to conventional chemotherapy and specific susceptibilities to targeted therapies in a given malignancy.[Keywords: Cancer; ATM; p53; DNA-PK; chemotherapy; mouse models] Supplemental material is available at http://www.genesdev.org.

Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)-based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for highresolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.Chemotherapy remains the frontline therapy for systemic malignancies. However, drug development has been severely hampered by an inability to efficiently elucidate mechanisms of drug action. This limits both the development of modified compounds with improved efficacy and the capability to predict mechanisms of drug resistance and select optimal patient populations for a given agent. Although drug-target interactions have traditionally been examined using biochemical approaches 1 , a number of genetic strategies have been developed to identify pathways targeted by uncharacterized small molecules. A wellestablished genetic approach to drug classification is chemogenomic profiling in yeast [2][3][4][5][6] . In this approach, bar-coded yeast deletion strains are exposed to select agents, and genotypedependent drug sensitivity is used to identify genes and pathways affected by a given drug, * hemann@mit.edu. Author contributionsH.J., J.R.P. and M.T.H. designed experiments. H.J. and J.R.P. performed RNAi knockdown and treatment studies. J.R.P. developed the computational approaches and performed all of the computational analyses. R.T.W. developed and characterized the B-ALL cell line. H.J., J.R.P., D.A.L. and M.T.H. analyzed the data and wrote the manuscript. Competing financial interestsThe authors declare no competing financial interests. Additional informationSupplementary information is available online at http://www.nature.com/naturechemicalbiology/. Reprints and permissions information is available online at http://npg.nature.com/reprintsandpermissions/. NIH Public Access Author ManuscriptNat Chem Biol. Author manuscript; available in PMC 2011 August 1. 5,7,8 . This approach has proven quite powerful and has been broadly disseminated; however, its efficacy in interrogating cancer chemotherapeutics is limited by the lack of conservation of certain drug targets from yeast to mammals. This is a particular problem in the context of targeted therapeutics, which are frequently direct...

A requirement for understanding morphogenesis is being able to quantify expansion at the cellular scale. Here, we present new software (RootflowRT) for measuring the expansion profile of a growing root at high spatial and temporal resolution. The software implements an image processing algorithm using a novel combination of optical flow methods for deformable motion. The algorithm operates on a stack of nine images with a given time interval between each (usually 10 s) and quantifies velocity confidently at most pixels of the image. The root does not need to be marked. The software calculates components of motion parallel and perpendicular to the local tangent of the root's midline. A variation of the software has been developed that reports the overall root growth rate versus time. Using this software, we find that the growth zone of the root can be divided into two distinct regions, an apical region where the rate of motion, i.e. velocity, rises gradually with position and a subapical region where velocity rises steeply with position. In both zones, velocity increases almost linearly with position, and the transition between zones is abrupt. We observed this pattern for roots of Arabidopsis, tomato (Lycopersicon lycopersicum), lettuce (Lactuca sativa), alyssum (Aurinia saxatilis), and timothy (Phleum pratense). These velocity profiles imply that relative elongation rate is regulated in a step-wise fashion, being low but roughly uniform within the meristem and then becoming high, but again roughly uniform, within the zone of elongation. The executable code for RootflowRT is available from the corresponding author on request.Growth underlies life. Although organisms may be distinguished from crystals by reproduction, there would be nothing to reproduce without growth. In plants, growth is important not only for development of the organism but also for physiology. An animal runs, rolls over, bites, or plays dead; instead, a plant bends away, repositions its leaves, thickens its stem, or makes thorns. All these examples, among many others, involve growth.The first step to understanding how a plant grows is measurement. Growth overall can be measured by following the displacement of a terminus, such as the tip of a blade of grass. By attaching the tip to a position transducer, the displacement can be measured accurately (e.g. Hsiao et al., 1970; Degli Agosti et al., 1997; Frensch, 1997), and tip displacement has been measured at even greater accuracy by interferometry (Fox and Puffer, 1976;Jiang and Staude, 1989). Although such methods are useful for characterizing the overall growth output of an organ, attaching a transducer may disturb the plant, and conditions for interferometry are exacting. More fundamentally, these methods are limited because they record growth in one dimension and because they cannot be used to measure the distribution of growth within the organ. The distribution of growth reflects the growth behavior of component cells and, therefore, is linked to the underlying mechanisms powering expansi...

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