Heterogeneity In Patient Response Research Articles

Multifaceted Mechanisms of Cisplatin Resistance in Long-Term Treated Urothelial Carcinoma Cell Lines.

Therapeutic efficacy of cisplatin-based treatment of late stage urothelial carcinoma (UC) is limited by chemoresistance. To elucidate underlying mechanisms and to develop new approaches for overcoming resistance, we generated long-term cisplatin treated (LTT) UC cell lines, characterised their cisplatin response, and determined the expression of molecules involved in cisplatin transport and detoxification, DNA repair, and apoptosis. Inhibitors of metallothioneins and Survivin were applied to investigate their ability to sensitise towards cisplatin. Cell growth, proliferation, and clonogenicity were examined after cisplatin treatment by MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, EdU (5-ethynyl-2’-deoxyuridine) incorporation assay, and Giemsa staining, respectively. Cell cycle distribution and apoptosis were quantified by flow cytometry. mRNA and protein expressions were measured by real-time quantitative (qRT)-PCR, western blot, or immunofluorescence staining. LTTs recovered rapidly from cisplatin stress compared to parental cells. In LTTs, to various extents, cisplatin exporters and metallothioneins were induced, cisplatin adduct levels and DNA damage were decreased, whereas expression of DNA repair factors and specific anti-apoptotic factors was elevated. Pharmacological inhibition of Survivin, but not of metallothioneins, sensitised LTTs to cisplatin, in an additive manner. LTTs minimise cisplatin-induced DNA damage and evade apoptosis by increased expression of anti-apoptotic factors. The observed diversity among the four LTTs highlights the complexity of cisplatin resistance mechanisms even within one tumour entity, explaining heterogeneity in patient responses to chemotherapy.

Hydroxocobalamin for the treatment of cardiac surgery-associated vasoplegia: a case series.

Vasoplegia is a clinical syndrome marked by severe arteriolar vasodilatation, hypotension, and low systemic vascular resistance refractory to multiple vasopressor treatment. We report our experience with hydroxocobalamin (B12) infusion as a potential rescue adjunct for refractory vasoplegia during cardiopulmonary bypass (CPB). We performed a retrospective chart review of 33 patients undergoing cardiac surgery between 1 January 2013 and 31 December 2015, who were given intravenous B12 for refractory hypotension during, or immediately following, CPB. We assessed mean arterial pressure (MAP) responses using semi-parametric group-based models (trajectory analysis). Vasopressor use was evaluated by norepinephrine-equivalent rates calculated five minutes prior, and up to 60 min following, B12 administration. Patients were mostly male (82%), had a mean (SD) age of 53 (13) yr, and median (IQR) EuroSCORE mortality index of 9 [4-40]. Four patterns of MAP responses to B12 were identified. In Group 1 ("poor responders") nine of 33 patients (27%) had the highest median [IQR] mortality risk (EuroSCORE 40 [4-52]), lowest mean pre-B12 MAP (50 mmHg), and minimal hemodynamic response in spite of continued vasopressor support. In contrast, Group 2 "responders" (8/33, 24%) showed a brisk MAP response (> 15 mmHg) to B12, sustained for > 60 min post-infusion, with 50% vasopressor reduction. Groups 3 and 4 had the lowest median mortality risk (EuroSCORE 8) and highest pre-B12 MAP (72 mmHg). Although Group 3 patients ("sustainers"; 9/33, 27%) showed a sustained MAP improvement, those in Group 4 ("rebounders"; 7/33, 21%) were characterized by hypertensive overshoot followed by a decrease in MAP. These data indicate considerable heterogeneity in patient response to B12, potentially dependent on both patient preoperative condition and non-standardized time of administration. B12 may provide a useful alternative therapy for refractory hypotension and vasoplegia, but controlled clinical trials to assess efficacy are needed.

Mode of progesterone administration in euploid, single embryo transfer cycles: is there a heterogeneity of patient response?

Mode of progesterone administration in euploid, single embryo transfer cycles: is there a heterogeneity of patient response?

Maximizing Intervention Effectiveness

Frequently, policymakers seek to roll out an intervention previously proven effective in a research study, perhaps subject to resource constraints. However, since different subpopulations may respond differently to the same treatment, there is no a priori guarantee that the intervention will be as effective in the targeted population as it was in the study. How then should policymakers target individuals to maximize intervention effectiveness? We propose a novel robust optimization approach that leverages evidence typically available in a published study. Our approach is tractable -- real-world instances are easily optimized in minutes with off-the-shelf software -- and flexible enough to accommodate a variety of resource and fairness constraints. We compare our approach with current practice by proving tight, performance guarantees for both approaches which emphasize their structural differences. We also prove an intuitive interpretation of our model in terms of regularization, penalizing differences in the demographic distribution between targeted individuals and the study population. Although the precise penalty depends on the choice of uncertainty set, we show for special cases that we can recover classical penalties from the covariate matching literature on causal inference. Finally, using real data from a large teaching hospital, we compare our approach to current practice in the particular context of reducing emergency department utilization by Medicaid patients through case management. We find that our approach can offer significant benefits over current practice, particularly when the heterogeneity in patient response to the treatment is large.

Maximizing Intervention Effectiveness

Frequently, policy makers seek to roll out an intervention previously proven effective in a research study, perhaps subject to resource constraints. However, because different subpopulations may respond differently to the same treatment, there is no a priori guarantee that the intervention will be as effective in the targeted population as it was in the study. How then should policy makers target individuals to maximize intervention effectiveness? We propose a novel robust optimization approach that leverages evidence typically available in a published study. Our model can be easily optimized in minutes for realistic instances with off-the-shelf software and is flexible enough to accommodate a variety of resource and fairness constraints. We compare our approach with current practice by proving performance guarantees for both approaches, which emphasize their structural differences. We also prove an intuitive interpretation of our model in terms of regularization, penalizing differences in the demographic distribution between targeted individuals and the study population. Although the precise penalty depends on the choice of uncertainty set, we show that for special cases we can recover classical penalties from the covariate matching literature on causal inference. Finally, using real data from a large teaching hospital, we compare our approach to common practice in the particular context of reducing emergency department utilization by Medicaid patients through case management. We find that our approach can offer significant benefits over common practice, particularly when the heterogeneity in patient response to the treatment is large. This paper was accepted by Chung-Piaw Teo, optimization.

Capturing complexity in pulmonary system modelling

Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.

Amplifying Each Patient's Voice: A Systematic Review of Multi-criteria Decision Analyses Involving Patients.

Qualitative methods tend to be used to incorporate patient preferences into healthcare decision making. However, for patient preferences to be given adequate consideration by decision makers they need to be quantified. Multi-criteria decision analysis (MCDA) is one way to quantify and capture the patient voice. The objective of this review was to report on existing MCDAs involving patients to support the future use of MCDA to capture the patient voice. MEDLINE and EMBASE were searched in June 2014 for English-language papers with no date restriction. The following search terms were used: 'multi-criteria decision*', 'multiple criteria decision*', 'MCDA', 'benefit risk assessment*', 'risk benefit assessment*', 'multicriteri* decision*', 'MCDM', 'multi-criteri* decision*'. Abstracts were included if they reported the application of MCDA to assess healthcare interventions where patients were the source of weights. Abstracts were excluded if they did not apply MCDA, such as discussions of how MCDA could be used; or did not evaluate healthcare interventions, such as MCDAs to assess the level of health need in a locality. Data were extracted on weighting method, variation in patient and expert preferences, and discussion on different weighting techniques. The review identified ten English-language studies that reported an MCDA to assess healthcare interventions and involved patients as a source of weights. These studies reported 12 applications of MCDA. Different methods of preference elicitation were employed: direct weighting in workshops; discrete choice experiment surveys; and the analytical hierarchy process using both workshops and surveys. There was significant heterogeneity in patient responses and differences between patients, who put greater weight on disease characteristics and treatment convenience, and experts, who put more weight on efficacy. The studies highlighted cognitive challenges associated with some weighting methods, though patients' views on their ability to undertake weighting tasks was positive. This review identified several recent examples of MCDA used to elicit patient preferences, which support the feasibility of using MCDA to capture the patient voice. Challenges identified included, how best to reflect the heterogeneity of patient preferences in decision making and how to manage the cognitive burden associated with some MCDA tasks.

Hospital Advertising

Does hospital advertising influence patient choice and health outcomes? We examine more than 220,000 individual patient-level visits over 24 months in Massachusetts to answer this question. We find that patients are positively influenced by hospital advertising; seeing a television advertisement for a given hospital makes a patient more likely to select that hospital. We also observe significant heterogeneity in patient response depending on insurance status, medical conditions, and demographic factors like age, gender, and race. For example, patients with more restrictive forms of insurance are less sensitive to advertisements. Our demand model allows us to study the impact of a ban on hospital advertising, which has been recently considered by policy makers. We find that banning hospital advertising can hurt patient health outcomes through increased hospital readmissions. This is because hospital advertisements drive patients to higher quality hospitals, which tend to advertise more and have lower readmission rates. However, we do not find a significant change in the overall mortality rate.

Abstract A070: Genetic knockdown screens across tumor types unravel a diverse tumor “immune-modulatome” landscape

Abstract Clinical trials with immune-checkpoint blockade antibodies have bolstered the importance of immune therapy as the standard of care for cancer patients, but it has also simultaneously highlighted the heterogeneity in patient responses to such treatment. These heterogeneities can be explained to a certain extent by the lack of tumor-specific expression of the targeted immune checkpoint molecules. But in many cases it can also be conceived that tumors either develop resistance to a targeted immune-checkpoint node by circumventing it or more than one player is involved in a concerted action to subvert the T cell response. In either scenario, we lack a holistic understanding of the putative genes in the tumor genome that could functionally suppress the immune response. To bridge this gap, we employed a high-throughput RNAi-mediated knockdown of upto 2800 genes (~50% associated with surface molecules) in MCF7 (breast), M579-A2 (melanoma) and PANC-1 (pancreatic) tumor cell lines and co-cultured them with either antigen-specific T cell clones or respective patient-derived and tumor-specific infiltrating lymphocytes (TILs) to assess the impact on anti-tumor immunity using a luciferase-based readout. Primary hit-list was further subjected to a secondary screen based on multi-cytokine profiling of the T cells. Our investigation revealed a few salient caveats of tumor-mediated immune suppression. Firstly, we discovered a family of orphan receptors, which were never attributed to the immune system before, to actively suppress the T cells in a manner comparable to the currently defined immune-checkpoint molecules, such as PD-L1. Secondly, the trans-versatility of these novel molecules across the tumor types was highly limited, with only 3-14 common molecules being involved in two or more tumor types. This leads us to our third observation that there exists a complex organ-specific orchestration of peripheral immune tolerance, which needs to be taken into account when devising immune-checkpoint blockade therapies. Amongst the key immunosuppressive candidate genes, CCR9 was validated to directly subvert T cell responses in melanoma, breast and pancreatic cancer in the in vitro and in vivo tumor models. Additionally we have verified TiMi1, an orphan G-protein coupled receptor, to mediate strong immunosuppression in melanoma and pancreatic cancer against the respective TILs. Knockdown of both CCR9 and TiMi1, either via siRNAs or shRNAs, in tumor cells significantly increased Th1 cytokine secretion by TILs along with elevated tumor lysis in vitro and in vivo xenotransplanted mouse models. Moreover, they both induce a highly immunosuppressive genetic signature in the encountering TILs. While TiMi1 appeared to modulate calcium-dependent signaling, CCR9 regulated STAT signaling in T cells leading to an immunosuppressed phenotype. Overall, these candidates represent attractive targets for cancer immunotherapy either through function blocking antibodies or small molecules. In conclusion, we here report an effective genetic screen strategy in multiple tumor types that has the potential to uncover novel modifiers of anti-tumor immunity. Extensively validated candidates from these screens are attractive targets for cancer immunotherapy that will allow us to further expand our limited arsenal of immune-checkpoint inhibitors, with the overall goal of increasing patient responses to such treatments. Citation Format: Nisit Khandelwal, Tillmann Michels, Marco Breinig, Antonio Sorrentino, Isabel Poschke, Rienk Offringa, Michal Lotem, Michael Boutros, Philipp Beckhove. Genetic knockdown screens across tumor types unravel a diverse tumor “immune-modulatome” landscape. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr A070.

WE-D-BRB-02: Predicting Outcomes of Primary and Metastatic Pancreatic Cancer with Principles of Mass Transport

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

WE-D-BRB-00: Modeling Cancer Complexity

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

WE-D-BRB-01: Fragile Proteins and the Origins of Cancer

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

WE-D-BRB-03: Geometrization of Tumor Evolution

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

SAT0070 Levels of 14-3-3Eta Predict Good Eular Response to Anti-TNF Treatment in Patients with Rheumatoid Arthritis

Background14-3-3eta (η) is a mechanistic biomarker that, as a soluble ligand, dose-dependently induces key joint damage and inflammatory factors, including TNF-alpha (α). There is a high clinical unmet need in...

Local control for identifying subgroups of interest in observational research: persistence of treatment for major depressive disorder

Caregivers are regularly faced with decisions between competing treatments. Large observational health care databases provide a golden opportunity for research on heterogeneity in patient response to guide caregiver decisions, due to their sample size, diverse populations, and real-world setting. Local control is a promising tool for using observational data to detect patient subgroups with differential response on one treatment relative to another. While standard data mining approaches find subgroups with optimal responses for a particular population, detecting subgroups that reveal treatment differences while also adjusting for confounding in observational data is challenging. Local control utilizes unsupervised clustering to form non-parametric patient-level counterfactual treatment differences and displays them as an observed distribution of effect-size estimates. Classification and regression trees (CART) then find the factors that drive the greatest outcome differentiation between treatments. In this manuscript, we demonstrate the use of this two-step strategy using local control plus CART to identify depression patients most (least) likely to benefit from treatment with duloxetine relative to extended-release venlafaxine. Prior medication costs and age were found to be factors most associated with differential outcome, with prior medication costs remaining as an important factor after sensitivity analyses using a second dataset.

TRAJECTORIES OF CHANGE IN ANXIETY SEVERITY AND IMPAIRMENT DURING AND AFTER TREATMENT WITH EVIDENCE-BASED TREATMENT FOR MULTIPLE ANXIETY DISORDERS IN PRIMARY CARE

Coordinated Anxiety Learning and Management (CALM) is a model for delivering evidence-based treatment for anxiety disorders in primary care. Compared to usual care, CALM produced greater improvement in anxiety symptoms. However, mean estimates can obscure heterogeneity in treatment response. This study aimed to identify (1) clusters of participants with similar patterns of change in anxiety severity and impairment (trajectory groups); and (2) characteristics that predict trajectory group membership. The CALM randomized controlled effectiveness trial was conducted in 17 primary care clinics in four US cities in 2006-2009. 1,004 English- or Spanish-speaking patients age 18-75 with panic, generalized anxiety, social anxiety, and/or posttraumatic stress disorder participated. The Overall Anxiety Severity and Impairment Scale was administered repeatedly to 482 participants randomized to CALM treatment. Group-based trajectory modeling was applied to identify trajectory groups and multinomial logit to predict trajectory group membership. Two predicted trajectories, representing about two-thirds of participants, were below the cut-off for clinically significant anxiety a couple of months after treatment initiation. The predicted trajectory for the majority of remaining participants was below the cut-off by 9 months. A small group of participants did not show consistent improvement. Being sicker at baseline, not working, and reporting less social support were associated with less favorable trajectories. There is heterogeneity in patient response to anxiety treatment. Adverse circumstances appear to hamper treatment response. To what extent anxiety symptoms improve insufficiently because adverse patient circumstances contribute to suboptimal treatment delivery, suboptimal treatment adherence, or suboptimal treatment response requires further investigation.

Psychological trajectories in the year after a newly diagnosed seizure

Underdiagnosed depression and anxiety are well-recognized issues in chronic epilepsy, but the evolution of these symptoms after diagnosis is not well understood. We aimed to identify mood trajectories after a first seizure, and to examine factors impacting these trajectories. Seventy-four patients were evaluated at 1, 3, and 12 months with (1) the Hospital Anxiety and Depression Scale, and (2) a semistructured interview assessing patients' initial psychological reaction to the seizure at 1 month (limited vs. pervasive loss of control). The SAS Institute's TRAJ data modelling procedure was employed to delineate trajectories. Two depression and three anxiety trajectories were identified, with significant overlap. The majority of patients (≈ 74%) followed a trajectory with low depression throughout the study, and either low or moderate anxiety. A minority followed trajectories with high depression and anxiety from diagnosis (≈ 16%). Patients with high levels of distress were adversely affected by seizure recurrence and antiepileptic drugs (AEDs), whereas those with low levels were not. Trajectories were predicted by the patient's sense of loss of control early after diagnosis and were weakly related to demographic and medical variables (age, gender, education, relationship status, psychiatric history, and prior epileptic events). Methods that account for heterogeneity in patient responses are critical for developing a clinically relevant understanding of adjustment after a newly diagnosed seizure. Most patients appear to be resilient in the face of early seizures, whereas those at risk of longer-term psychological difficulties may be evident from diagnosis. Early screening for depression and anxiety is warranted.

Sci-Fri PM: Delivery - 11: Accuracy considerations in modern radiation oncology: An update.

The most recent reviews of accuracy requirements in radiation oncology were published in the 1990s, primarily in an era that was transitioning from 2-D to 3-D conformal radiation therapy (CRT). Since then, the technology associated with radiation oncology has changed dramatically. The combination of various forms of imaging for radiation therapy planning, treatment planning software, dose delivery technology including 4-D considerations as well as in-room daily image guidance has resulted in new perspectives on accuracy considerations. The underlying hypothesis for the use of these advanced technologies is that loco-regional control of cancer remains a significant barrier to cancer cure for many common cancers and that better dose distributions will translate into better outcomes. However, further clinical gain using these new technologies may be limited by single or compounded uncertainties associated with the entire treatment process. Thus, it is important to understand what factors should be considered in determining accuracy requirements as well as the realistic expectations of uncertainties that exist within the total treatment process. The need for accuracy is based on clinical requirements such as the steepness of dose-response curves, inherent heterogeneity in patient response to treatment, and the level of accuracy that is practically achievable. Statements on accuracy are dependent on the technology used and the reality of what is practically achievable and necessary. This review highlights some of the major differences between accuracy requirements as determined in the 2-D RT and 3-D CRT era versus the modern era of intensity modulated, image-guided, 4-D radiation therapy.

Abstract 405: Innate immunity in taxol-mediated tumor regression

Abstract Taxol is a common chemotherapeutic used against many tumor types, including breast, ovarian and lung tumors. However, the mechanism by which it promotes tumor regression is unclear. While in cell culture taxol kills dividing cells, its ability to promote regression of slow growing tumors in some patients suggests it may have additional actions in vivo. To better understand taxol drug response in vivo we profiled xenograft tumors 1, 4, and 7 days post-drug treatment for changes gene expression and cytokine levels. Enrichment of genes associated with immune response pathways, and an increase in chemotactic cytokines Rantes and Mip-1α, suggest a pro-inflammatory immune response. In accordance, immunohistochemistry confirms that tumor associated macrophages (TAMs) are recruited following treatment. While TAMs are generally considered pro-tumorigenic, our system suggests that macrophages may play a cytotoxic role under certain conditions. Understanding the mechanisms by which macrophages can be pro or anti-tumor growth following drug treatment will shed light on the heterogeneity in patient response and may suggest more efficacious, targeted treatment strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 405. doi:1538-7445.AM2012-405

Genetic basis for personalized medicine in asthma

There is heterogeneity in patient responses to current asthma medications. Significant progress has been made identifying genetic polymorphisms that influence the efficacy and potential for adverse effects to asthma drugs, including; β2-adrenergic receptor agonists, corticosteroids and leukotriene modifiers. Pharmacogenetics holds great promise to maximise clinical outcomes and minimize adverse effects. Asthma is heterogeneous with respect to clinical presentation and inflammatory mechanisms underlying the disease, which is likely to contribute to variable results in clinical trials targeting specific inflammatory mediators. Genome-wide association studies have begun to identify genes underlying asthma (e.g., IL1RL1), which represent future therapeutic targets. In this article, we review and update the pharmacogenetics of current asthma therapies and discuss the genetics underlying selected Phase II and future targets.