Alternating Electric Fields (Tumor-Treating Fields Therapy) Can Improve Chemotherapy Treatment Efficacy in Non-Small Cell Lung Cancer Both In Vitro and In Vivo

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related deaths worldwide. Common treatment modalities for NSCLC include surgery, radiotherapy, chemotherapy, and, in recent years, the clinical management paradigm has evolved with the advent of targeted therapies. Despite such advances, the impact of systemic therapies for advanced disease remains modest, and as such, the prognosis for patients with NSCLC remains poor. Standard modalities are not without their respective toxicities and there is a clear need to improve both efficacy and safety for current management approaches. Tumor-treating fields (TTFields) are low-intensity, intermediate-frequency alternating electric fields that disrupt proper spindle microtubule arrangement, thereby leading to mitotic arrest and ultimately to cell death. We evaluated the effects of combining TTFields with standard chemotherapeutic agents on several NSCLC cell lines, both in vitro and in vivo. Frequency titration curves demonstrated that the inhibitory effects of TTFields were maximal at 150 kHz for all NSCLC cell lines tested, and that the addition of TTFields to chemotherapy resulted in enhanced treatment efficacy across all cell lines. We investigated the response of Lewis lung carcinoma and KLN205 squamous cell carcinoma in mice treated with TTFields in combination with pemetrexed, cisplatin, or paclitaxel and compared these to the efficacy observed in mice exposed only to the single agents. Combining TTFields with these therapeutic agents enhanced treatment efficacy in comparison with the respective single agents and control groups in all animal models. Together, these findings suggest that combining TTFields therapy with chemotherapy may provide an additive efficacy benefit in the management of NSCLC

Understanding the errors of SHAPE-directed RNA structure modeling

Single-nucleotide-resolution chemical mapping for structured RNA is being rapidly advanced by new chemistries, faster readouts, and coupling to computational algorithms. Recent tests have shown that selective 2'-hydroxyl acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in modeling the helices of RNA secondary structure. Here, we benchmark the method using six molecules for which crystallographic data are available: tRNA(phe) and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs gave an overall false negative rate (FNR) of 17% and a false discovery rate (FDR) of 21%, with at least one helix prediction error in five of the six cases. Extensive variations of data processing, normalization, and modeling parameters did not significantly mitigate modeling errors. Only one varation, filtering out data collected with deoxyinosine triphosphate during primer extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual structure modeling errors are explained by the insufficient information content of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping analysis. Beyond these benchmark cases, bootstrapping suggests a low level of confidence (<50%) in the majority of helices in a previously proposed SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA modeling is not always unambiguous, and helix-by-helix confidence estimates, as described herein, may be critical for interpreting results from this powerful methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011

Alternating electric fields (TTFields) inhibit metastatic spread of solid tumors to the lungs

Tumor treating fields (TTFields) are low intensity, intermediate frequency, alternating electric fields used to treat cancerous tumors. This novel treatment modality effectively inhibits the growth of solid tumors in vivo and has shown promise in pilot clinical trials in patients with advanced stage solid tumors. TTFields were tested for their potential to inhibit metastatic spread of solid tumors to the lungs in two animal models: (1) Mice injected with malignant melanoma cells (B16F10) into the tail vein, (2) New Zealand White rabbits implanted with VX-2 tumors within the kidney capsule. Mice and rabbits were treated using two-directional TTFields at 100–200 kHz. Animals were either monitored for survival, or sacrificed for pathological and histological analysis of the lungs. The total number of lung surface metastases and the absolute weight of the lungs were both significantly lower in TTFields treated mice then in sham control mice. TTFields treated rabbits survived longer than sham control animals. This extension in survival was found to be due to an inhibition of metastatic spread, seeding or growth in the lungs of TTFields treated rabbits compared to controls. Histologically, extensive peri- and intra-tumoral immune cell infiltration was seen in TTFields treated rabbits only. These results raise the possibility that in addition to their proven inhibitory effect on the growth of solid tumors, TTFields may also have clinical benefit in the prevention of metastatic spread from primary tumors

Massively Parallel RNA Chemical Mapping with a Reduced Bias MAP-seq Protocol

Chemical mapping methods probe RNA structure by revealing and leveraging correlations of a nucleotide's structural accessibility or flexibility with its reactivity to various chemical probes. Pioneering work by Lucks and colleagues has expanded this method to probe hundreds of molecules at once on an Illumina sequencing platform, obviating the use of slab gels or capillary electrophoresis on one molecule at a time. Here, we describe optimizations to this method from our lab, resulting in the MAP-seq protocol (Multiplexed Accessibility Probing read out through sequencing), version 1.0. The protocol permits the quantitative probing of thousands of RNAs at once, by several chemical modification reagents, on the time scale of a day using a table-top Illumina machine. This method and a software package MAPseeker (http://simtk.org/home/map_seeker) address several potential sources of bias, by eliminating PCR steps, improving ligation efficiencies of ssDNA adapters, and avoiding problematic heuristics in prior algorithms. We hope that the step-by-step description of MAP-seq 1.0 will help other RNA mapping laboratories to transition from electrophoretic to next-generation sequencing methods and to further reduce the turnaround time and any remaining biases of the protocol.Comment: 22 pages, 5 figure

HIV Reservoirs and Immune Surveillance Evasion Cause the Failure of Structured Treatment Interruptions: A Computational Study

Continuous antiretroviral therapy is currently the most effective way to treat HIV infection. Unstructured interruptions are quite common due to side effects and toxicity, among others, and cannot be prevented. Several attempts to structure these interruptions failed due to an increased morbidity compared to continuous treatment. The cause of this failure is poorly understood and often attributed to drug resistance. Here we show that structured treatment interruptions would fail regardless of the emergence of drug resistance. Our computational model of the HIV infection dynamics in lymphoid tissue inside lymph nodes, demonstrates that HIV reservoirs and evasion from immune surveillance themselves are sufficient to cause the failure of structured interruptions. We validate our model with data from a clinical trial and show that it is possible to optimize the schedule of interruptions to perform as well as the continuous treatment in the absence of drug resistance. Our methodology enables studying the problem of treatment optimization without having impact on human beings. We anticipate that it is feasible to steer new clinical trials using computational models

Computational Models of HIV-1 Resistance to Gene Therapy Elucidate Therapy Design Principles

Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents

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We consider an iterative decoding and detection system for magnetic recording channels. The system uses a zero-forcing equalizer to shape the channel response to a target polynomial, called a partial-response (PR) polynomial. We assume that the noise at the input to the equalizer is AWGN. Hence, noise enhancement and coloration occur at the output of the equalizer. The output of the equalizer, ri, can be expressed as r = a +