ATM Signaling to TSC2: Mechanisms and Implications for Cancer Therapy

Ataxia telangiectasia mutated (ATM) is a critical component of the cellular response to DNA damage, where it acts as a damage sensor, and signals to a large network of proteins which execute the important tasks involved in responding to the damage, namely inducing cell cycle checkpoints, inducing DNA repair, modulating transcriptional responses, and regulating cell death pathways if the damage cannot be repaired faithfully. We have now discovered that an additional novel component of this ATM-dependent damage response involves induction of autophagy in response to oxidative stress. In contrast to DNA damage-induced ATM activation however, oxidative stress induced ATM, occurs in the cytoplasm, and does not require nuclear-to-cytoplasmic shuttling of ATM. Using several cell culture systems including MCF7 breast carcinoma cells, SKOV3 ovarian cancer cells, and various lineages of mouse embryonic fibroblasts, we showed that once activated by reactive oxygen species (ROS), ATM signals to mTORC1 to induce autophagy via the LKB1-AMPK-TSC2 pathway. Targeting dysregulation of mTORC1 in Atm-deficient mice, which succumb to lymphomagenesis within 3-4 months of age with daily administration of rapamycin, could significantly extend survival and cause regression of tumors, suggesting that pharmacologically targeting this pathway has therapeutic implications in cancer. We also identified a second contrasting pathway for DNA damage-induced mTORC1 repression which does not require AMPK activation, but does require ATM and TSC2. Several potential mechanisms including mTOR localization and p53-mediated pathways were ruled out however we identified that TSC2 may be an additional cytoplasmic direct ATM substrate that is engaged in response to DNA damage specifically. Lastly, a study was performed to examine whether autophagy induced by ovarian cancer therapeutics (focusing on cisplatin, since paclitaxel does not induce autophagy in the SKOV3 cell line model we used) plays a role in resistance to therapy since autophagy can play both pro-survival mechanisms or be a mechanism of cell death. Using a genetic approach to knock-down Atg5 expression with shRNA in SKOV3 ovarian carcinoma cells, we compared the cytotoxicity of cisplatin in vector or Atg5 knock-down cells, and demonstrated that autophagy does not play any significant role in the response to cisplatin in this cell line.

Gene therapy of cancer: Mechanisms of ``bystander effect'' killing in cells expressing the herpes simplex virus-thymidine kinase gene and its potential clinical applications

At the fore-front of cancer research, gene therapy offers the potential to either promote cell death or alter the behavior of tumor-cells. One example makes use of a toxic phenotype generated by the prodrug metabolizing gene, thymidine kinase (HSVtk) from the Herpes Simplex Virus. This gene confers selective toxicity to a relatively nontoxic prodrug, ganciclovir (GCV). Tumor cells transduced with the HSVtk gene are sensitive to 1-50 $\mu$M GCV; normal tissue is insensitive up to 150-250 $\mu$M GCV. Utilizing these different sensitivities, it is possible to selectively ablate tumor cells expressing this gene. Interestingly, if a HSVtk$\sp+$ expressing population is mixed with a HSVtk$\sp-$ population at high density, all the cells are killed after GCV administration. This phenomenon for killing all neighboring cells is termed the "bystander effect", which is well documented in HSVtk$\sp-$ GCV systems, though its exact mechanism of action is unclear.^ Using the mouse colon carcinoma cell line CT26, data are presented supporting possible mechanisms of "bystander effect" killing of neighboring CT26-tk$\sp-$cells. A major requirement for bystander killing is the prodrug GCV: as dead or dying CT26tk$\sp+$ cells have no toxic effect on neighboring cells in its absence. In vitro, it appears the bystander effect is due to transfer of toxic GCV-metabolites, through verapamil sensitive intracellular-junctions. Additionally, possible transfer of the HSVtk enzyme to bystander cells after GCV addition, may play a role in bystander killing. A nude mouse model suggests that in a 50/50 (tk$\sp+$/tk$\sp-$) mixture of CT26 cells the bystander eradication of tumors does not involve an immune component. Additionally in a possible clinical application, the "bystander effect" can be directly exploited to eradicate preexisting CT26 colon carcinomas in mice by intratumoral implantation of viable or lethally irradiated CT26tk$\sp+$ cells and subsequent GCV administration. Lastly, an application of this toxic phenotype gene to a clinical marking protocol utilizing a recombinant adenoviral vector carrying the bifunctional protein GAL-TEK to eradicate spontaneously-arisen or vaccine-induced fibrosarcomas in cats is demonstrated. ^

Monte Carlo and analytical dose calculations for ocular proton therapy

Uveal melanoma is a rare but life-threatening form of ocular cancer. Contemporary treatment techniques include proton therapy, which enables conservation of the eye and its useful vision. Dose to the proximal structures is widely believed to play a role in treatment side effects, therefore, reliable dose estimates are required for properly evaluating the therapeutic value and complication risk of treatment plans. Unfortunately, current simplistic dose calculation algorithms can result in errors of up to 30% in the proximal region. In addition, they lack predictive methods for absolute dose per monitor unit (D/MU) values. ^ To facilitate more accurate dose predictions, a Monte Carlo model of an ocular proton nozzle was created and benchmarked against measured dose profiles to within ±3% or ±0.5 mm and D/MU values to within ±3%. The benchmarked Monte Carlo model was used to develop and validate a new broad beam dose algorithm that included the influence of edgescattered protons on the cross-field intensity profile, the effect of energy straggling in the distal portion of poly-energetic beams, and the proton fluence loss as a function of residual range. Generally, the analytical algorithm predicted relative dose distributions that were within ±3% or ±0.5 mm and absolute D/MU values that were within ±3% of Monte Carlo calculations. Slightly larger dose differences were observed at depths less than 7 mm, an effect attributed to the dose contributions of edge-scattered protons. Additional comparisons of Monte Carlo and broad beam dose predictions were made in a detailed eye model developed in this work, with generally similar findings. ^ Monte Carlo was shown to be an excellent predictor of the measured dose profiles and D/MU values and a valuable tool for developing and validating a broad beam dose algorithm for ocular proton therapy. The more detailed physics modeling by the Monte Carlo and broad beam dose algorithms represent an improvement in the accuracy of relative dose predictions over current techniques, and they provide absolute dose predictions. It is anticipated these improvements can be used to develop treatment strategies that reduce the incidence or severity of treatment complications by sparing normal tissue. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.