Supplementary Material 1 from CHIR-124, a Novel Potent Inhibitor of Chk1, Potentiates the Cytotoxicity of Topoisomerase I Poisons In vitro and In vivo

Abstract Expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), as measured by immunohistochemistry, is routinely used to guide appropriate therapy in breast cancer. HER2 targeted therapy is used to treat HER2 overexpressing patients and endocrine therapy is used to treat ER+ or PR+ patients. However, therapeutic options are limited for patients who are triple negative, relapsed HER2 overexpressed patients, and ER+ or PR+ patients who are refractory to endocrine therapy. For these breast cancer patients, immunotherapy has the potential to improve survival by targeting cancer cells. Although, the role of immune response in breast cancer is not fully understood, studies have observed high number of natural killer (NK) cells, B cells, and cytotoxic T cells suppress tumor growth while high number of macrophages, and regulatory T cells (Treg) promote tumor growth. The purpose of this study is to measure levels of immune cells infiltration (cytotoxic, helper, and regulatory T cells, NK cells, & macrophages) between different ER, PR, and HER2 IHC subtypes. Tissue microarrays were constructed from 106 breast cancer patients consisting of 36 triple negatives, 11 HER2 overexpressed (ER-/PR-), 7 ER+/PR-/HER2-, 24 ER+/PR+/HER2-, 8 ER+/PR-/HER2+, and 20 ER+/PR+/HER2+ assessed by a pathologist based on immunohistochemical stains. Utilizing the constructed TMAs, MultiOmyx hyperplexed immunofluorescence assay was performed on IHC4 (HER2, ER, PR, Ki67), and immune (CD3, CD4, CD8, CD45RO, CD56, CD68, FOXP3) markers. MultiOmyx technology enables visualization and characterization of multiple biomarkers from a single 4 micron formalin-fixed paraffin-embedded tissue section. PanCK marker was used to differentiate between tumor and stromal regions. A representative tumor was assessed for immune infiltration in each of the following subtypes: ER+/PR+/HER2-, ER+/PR+/HER2+, ER+/PR-/HER2-, ER+/PR-/HER2+, HER2 overexpressed ER-/PR-, and triple negative. Level of immune cells infiltration is defined as a percentage of positive immune cells relative to the total number of stromal cells. Percentage of immune cells infiltration in each IHC subtypes is 5.6% (ER+/PR+/HER2-), 14.7% (ER+/PR+/HER2+), 33% (ER+/PR-/HER2-), 9% (ER+/PR-/HER2+), 58% (HER2 overexpressed ER-/PR-), and 16% in triple negative. Comprehensive analysis of all 106 breast cancer patients will be reported. Differences in the levels of immune cells infiltration across different ER, PR, and HER2 IHC subtypes suggests that some subtypes may benefit from immunotherapy targeting immune checkpoints (anti-PD-L1, anti-CTLA-4) while other subtypes may benefit from immune activation (adoptive T cell therapy). Citation Format: Pinky Tripathi, Nam Tran, Raghavkrishna Padmanabhan, Richard Hartsfield, Edward J. Moler, Nicholas Hoe, Kenneth Bloom. Measurement of immune infiltration in ER, PR, and HER2 IHC subtypes reveals populations that may benefit from immunotherapy. [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 A131.

Abstract A comprehensive signaling pathway analysis is critical to characterize cancer pathogenesis, in which malignant cells evolve from nonmalignant cells, in heterogeneous tissues comprised of both healthy and pathological cells. Although significant advances in technologies have led to improved understanding of cancer biology, comprehensive profiling utilizing multiple biomarkers remains a technical challenge due to limited sample availability. GE Healthcare MultiOmyx multiplexed immunofluorescence (IF) assay overcomes this sample limitation and staining up to 60 protein biomarkers has been demonstrated in a single formalin-fixed, paraffin-embedded (FFPE) slide. In this study, proximity ligation assay (PLA) technology is adapted to expand MultiOmyx assay capabilities by enabling detection of protein-protein interactions (PPIs) and post-translational modification (PTMs) from a single FFPE slide. The MultiOmyx assay utilizes a pair of directly conjugated Cyanine dye-labeled (Cy3, Cy5) antibodies per round of staining. Each round of staining is imaged and followed by novel dye inactivation chemistry, enabling repeated rounds of staining. The PLA technology utilizes a pair of directly conjugated proximity probes to detect proteins of interest. Proximal binding of these probes lead to ligation and DNA amplification using rolling circle amplification (RCA). Amplified DNA is detected by hybridizing Cyanine dye-labeled oligonucleotides. Herein we report a comprehensive analysis of key receptor tyrosine kinases (RTKs) (HER1, HER2, HER3, cMET, others) along with their downstream signaling proteins (PI3K, phospho AKT, and phospho ERK1/2) in 10 colorectal cancer (CRC) samples using the standard MultiOmyx assay. A PLA-adapted MultiOmyx assay is utilized to detect dimerization partners (HER1:HER2, HER2:HER3, HER1:HER3) and RTK phosphorylation using separate antibodies against the RTK and the phosphorylation site. Protein IF staining revealed heterogeneous expression and activation across different samples. High EGFR and HER3 expression correlated with positive staining for AKT, through EGFR:HER3 dimer. High expression of EGFR correlated with positive staining for phospho Erk1/2, through EGFR:HER2 dimer. Additionally, intra-tumor heterogeneity was observed, with varied expression and activation of EGFR, HER2, HER3, and cMET. Current IHC and multiplexed IF assays measures the expression levels of individual proteins but overlook the measurement of PPIs and PTMs, which are crucial to understanding the biology of pathway signaling. The PLA-adapted MultiOmyx assay enables true comprehensive pathway signaling analysis at a single cell level by providing spatial context and quantitative analysis of protein expression, protein-protein interactions, and protein activations (phosphorylation). Citation Format: Qingyan Au, Flora Sahafi, Kathy Nguyen, Raghav Padmanabhan, Edward J. Moler, Nicholas Hoe. Quantification of protein complexes and post-translational modifications in colorectal cancer utilizing combinted MultiOmyxTM and PLA assay from a single FFPE slide. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4578.

PD1 ligands (PDL1) are often upregulated on the cell surface of many different tumors. The primary role of PDL1 in cancer is to inhibit T-cell mediated immune response. Two general mechanisms for PDL1 expression on tumor cells have been proposed. Innate immune resistance, in which PDL1 expression is induced by the constitutive oncogenic signaling, and adaptive immune resistance, in which PDL1 expression is induced by T-cells releasing interferon-γ (IFNγ) and activating the STAT signaling pathway. In order to differentiate between these two mechanisms, IFNγ mRNA expression is measured as an effective alternative to detecting IFNγ protein. Detection of cytokines by IHC is challenging as secreted proteins are widely diffused and the associated staining pattern appears to lack cellular specificity. RNAscope RNA in situ hybridization (ISH) assay is utilized to measure Interferon-γ (IFNγ) mRNA expression, and MultiOmyxTM multiplexed assay (demonstrated to stain up to 60 protein biomarkers) is utilized to measure CD3, CD4, CD8, CD56, CD68, PD1, and PDL1 protein expression. In this study, combined MO and RNAscope ISH assays, enabled identification of individual cells with characteristic mRNA and protein expression profile. The MultiOmyx assay utilizes a pair of directly conjugated Cyanine dye-labeled (Cy3, Cy5) antibodies per round of staining. Each Cy-dye conjugated antibody recognizes different target proteins. Each round of staining is imaged and followed by novel dye inactivation chemistry, enabling repeated rounds of staining. RNAscope is a novel RNA ISH assay capable of single-molecule detection in individual cells, utilizing hybridization mediated signal amplification. The assay utilizes a pair of RNA target specific oligonucleotide probes, which sequentially hybridize to preamplifier, amplifier, and fluorophore label probes. Utilizing MultiOmyx and RNAscope assays, this study proposed to profile both RNA and protein expression in lung, breast, melanoma, colorectal, esophageal, and prostate cancer samples. Differentiating PDL1 expression induced in response to inflammatory signals produced by an activated T-cell, from PDL1 expression induced by constitutive oncogenic signaling, has potential implications in effectiveness of PD1 blockade therapy. According to the proposed mechanisms, PD1 blockade as a mono therapy may only benefit individuals with strong endogenous immune response. In individuals with weak endogenous immune response, combinational therapies consisting of both immune activation and PD1-pathway blockade may be more effective than either mono therapy alone. Citation Format: Qingyan Au, Kathy Nguyen, Michael S. Lazare, Edward J. Moler, Nicholas Hoe. Detection of IFNγ induced PDL1 expression by combined in situ RNA analysis and protein profiling from a single FFPE slide. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5135.

Background Primary care providers with limited time and resources bear a heavy responsibility for chronic disease prevention or progression. Reliable clinical tools are needed to risk stratify patients for more targeted care. This exploratory study examined the care of patients who had been risk stratified regarding their likelihood of clinically progressing to type 2 diabetes. Methods This was a retrospective chart review pilot study conducted to assess a primary care provider’s use of a risk screening test. In this quality improvement project, the result of the risk screening was examined in relation to its influence on medical management and clinical impact on patients at risk for diabetes. All providers were board certified in family medicine and had more than 10 years clinical experience in managing diabetes and prediabetes. No specific clinical practice guidelines were mandated for patient care in this pilot study. Physicians in the practice group received an orientation to the diabetes risk measure and its availability for use in a pilot study to be conducted over a 6-month period. We identified the 696 nondiabetic adults in family practices who received a risk screening test (PreDx®, a multi-marker blood test that estimates the 5-year likelihood of conversion to type 2 diabetes) between June and November 2011 for a 6-month sample. A comparison group of 2,002 patients from a total database of 3.2 million patients who did not receive the risk test was randomly selected from the same clinical database after matching for age, sex, selected diagnoses, and metabolic risk factors. Patient groups were compared for intensity of care provided and clinical impact. Results Compared to patients with a similar demographic and diagnostic profile, patients who had the risk test received more intensive primary care and had better clinical outcome than comparison patients. Risk-tested patients were more likely to return for follow-up visits, be monitored for relevant cardio-metabolic risk factors, and receive prescription medications with P<0.001. Further, intensity of care was associated with the level of risk test result: patients with moderate or high scores were more likely to return for follow-up visits and receive prescription medications than patients with low scores. All P-values for comparison patients between the low and moderate groups, low and high groups, and moderate and high groups resulted in P<0.001. Risk-tested patients were more likely than their comparison group counterparts to achieve weight reduction, lowered blood pressure, and improved blood glucose and cholesterol as demonstrated by P-values of <0.001. Conclusion Use of a risk stratification test in primary care may help providers to more effectively identify high risk patients, manage diabetes risk, increase patient involvement in diabetes risk management, and improve clinical outcomes. A randomized controlled study is the next step to investigate the impact of diabetes risk stratification in primary care.

We sought to evaluate the impact of diabetes prevention costs and effectiveness on the projected return on investment (ROI) from the perspectives of a US health care payer and a large, self-insured employer using an improved risk stratification tool. A model comprised of a closed cohort with four Markov health-states was developed to project diabetes-specific costs and offsets due to incident diabetes and utilization of prevention resources. Subjects identified as “at-risk” for diabetes in an annual health risk appraisal would be tested and stratified into high or moderate-to-low risk groups. Parameters of the screen for at-risk subjects were based upon published impaired fasting plasma glucose prevalence of an insured US population. High risk subjects optionally enter a diabetes prevention program. Parameters for the risk stratification test were based upon published data for a multiple biomarker risk assessment test (PreDx DRS). Cost inputs included direct and indirect medical costs of diabetes and pre-diabetes, and the cost of stratification testing ($250). A range of intervention costs and effectiveness were examined. Model outputs included projected costs, savings, and number of life years and diabetes-free years saved. At a published annual prevention program cost of $850 and intervention effectiveness of 58%, employers would see a positive ROI by year 2 that increases through year 5. Savings at year 5 represent a return of $1.71 for every $1 spent on diabetes prevention, with 167 diabetes cases prevented, 547 diabetes-free years and 6.3 life years saved per 10,000 employees. Payers could achieve cost savings at lower program costs and/or increased effectiveness. The ROI depends strongly on reported intervention effectiveness in the range of 31%-72% and is moderately sensitive to cost variations. Cost savings for employers and payers are possible using risk stratification in conjunction with an effective prevention program to reduce diabetes incidence.

Background: Given the increasing worldwide incidence of diabetes, methods to assess diabetes risk which would identify those at highest risk are needed. We compared two risk-stratification approaches for incident type 2 diabetes mellitus (T2DM); factors of metabolic syndrome (MetS) and a previously developed diabetes risk score, PreDx (R) Diabetes Risk Score (DRS). DRS assesses 5 yr risk of incident T2DM based on the measurement of 7 biomarkers in fasting blood.Methodology/Principal Findings: DRS was evaluated in baseline serum samples from 4,128 non-diabetic subjects in the Inter99 cohort (Danes aged 30-60) for whom diabetes outcomes at 5 years were known. Subjects were classified as having MetS based on the presence of at least 3 MetS risk factors in baseline clinical data. The sensitivity and false positive rate for predicting diabetes using MetS was compared to DRS. When the sensitivity was fixed to match MetS, DRS had a significantly lower false positive rate. Similarly, when the false positive rate was fixed to match MetS, DRS had a significantly higher specificity. In further analyses, subjects were classified by presence of 0-2, 3 or 4-5 risk factors with matching proportions of subjects distributed among three DRS groups. Comparison between the two risk stratification schemes, MetS risk factors and DRS, were evaluated using Net Reclassification Improvement (NRI). Comparing risk stratification by DRS to MetS factors in the total population, the NRI was 0.146 (p = 0.008) demonstrating DRS provides significantly improved stratification. Additionally, the relative risk of T2DM differed by 15 fold between the low and high DRS risk groups, but only 8-fold between the low and high risk MetS groups.Conclusions/Significance: DRS provides a more accurate assessment of risk for diabetes than MetS. This improved performance may allow clinicians to focus preventive strategies on those most in need of urgent intervention.

BACKGROUND:Personalized medicine requires diagnostic tests that stratify patients into distinct groups that may differentially benefit from targeted treatment approaches. This study compared the costs and benefits of two approaches for identifying those at high risk of developing type 2 diabetes for entry into a diabetes prevention program. The first approach identified high risk patients using impaired fasting glucose (IFG). The second approach used the PreDx Diabetes Risk Score (DRS) to further stratify IFG patients into high-risk and moderate-risk groups.METHODS:A Markov model was developed to simulate the incidence and disease progression of diabetes and consequent costs and quality-adjusted life expectancy (QALY), comparing alternative approaches for identifying high-risk patients. We modeled direct medical costs, including the costs of the stratification testing, over a 10-year time horizon from a US payer perspective.RESULTS:Stratification of IFG patients by the DRS method leads to improved identification and prevention among those at highest risk. At 5 years, the number needed to treat (NNT) in the IFG-only approach was 39 patients to prevent one case of diabetes compared to an NNT of 15 in the IFG + DRS approach. When compared to IFG alone, the IFG + DRS approach results in an incremental cost-effectiveness ratio (ICER) of $17,100/QALY gained at 5 years and would become cost saving in 10 years. In contrast and as compared to no stratification, the IFG-only approach would produce an ICER of $235,500/QALY gained at 5 years and $94,600/QALY gained at 10 years. The study findings are limited by the generalizability of the DRS validation study and uncertainty regarding the long-term effectiveness of diabetes prevention.CONCLUSIONS:The analysis indicates that the cost-effectiveness of diabetes prevention can be improved by better identification of patients at highest risk for diabetes using the DRS.

We have determined the adsorption site and interlayer spacings of c(2×2) N2/Ni(100),(√3 × √3)R30° and (1.5 × 1.5)R18° CO adsorbed on Cu(111), using ARPEFS and a full Multiple-Scattering, Spherical Wave (MSSW) calculation program. The nitrogen molecule stands upright at an atop site, with a N-Ni bond length of 2.25(1) Å, a N-N bond length of 1.10(7) Å, and a first layer Ni-Ni spacing of 1.76(4) Å. The C-Cu bond length is 1.91 (1) Å in the (√3 × √3)R30° phase and 1.91(2) Å in the (1.5 × 1.5)R18° phase. The first layer Cu-Cu spacing is 2.07(3) Å in the (√ × √3)R30° phase. The first layer Cu-Cu spacing in the (1.5 × 1.5)R18° phase is 2.01(4) Å, a contraction of 3 % from the clean metal value of 2.07 Å.

BACKGROUND Higher medical care costs have been associated with the number of metabolic syndrome components present, but the association with future medical costs has not been described. Furthermore, the independent cost contribution of each component alone and in combination with other components is unknown. METHODS We identified 57,420 nondiabetic adults aged ≥30 with all metabolic syndrome components measured in 2003-2004 and with 5 years of follow-up data available. We calculated and compared total annualized direct medical costs across the number of metabolic syndrome components present and for all possible combinations of metabolic syndrome components. The independent contribution to costs of each component was isolated by adjusting for age, sex, the other metabolic syndrome components, incident diabetes, number of years with diabetes, cardiovascular (CVD) hospitalization, and years after hospitalization. RESULTS Annualized age- and sex-adjusted medical costs incurred over follow-up increased with each additional metabolic syndrome component present. After full adjustment, hypertension ($550), obesity ($366), low high-density lipoprotein (HDL) ($363), and high triglycerides ($317) were significantly associated with higher annual costs (P < 0.001 for all), but impaired fasting glucose was not. Further analysis indicated that costs were significantly elevated for each of these components only among those who did not develop diabetes or were not hospitalized for CVD. CONCLUSIONS Incident diabetes or CVD hospitalizations accounted for the association between each metabolic syndrome component and future costs when these events occurred, but the elevated costs associated with metabolic syndrome components were observed even when these events did not occur. Further research is needed to understand the underlying morbidity that is driving the increased costs.

Aims Because metabolic syndrome (MetS) is defined as any three of five criteria, not all persons with MetS have the same risk factors Whether the combinations of criteria confer equal diabetes risk is unknownMethods We identified 58,056 non-diabetic adults age >=30 with all MetS components measured in 2003-2004 We estimated age- and sex-adjusted diabetes incidence over 5 years for all possible combinations of MetS componentsResults The overall incidence rate of diabetes was 12 5/1000 person-years (95% Cl 12 1-12 9) Although incidence increased with the MetS factor count, incidence varied by >9-fold in patients with 3 risk factors, >5-fold in patients with 4 factors, and >54-fold in patients with <3 factors All two-factor combinations that included hyperglycemia had higher incidence rates than three- or four-factor combinations that did not For example, incidence in patients with only hyperglycemia and obesity was 21.7/1000 person-years (95% Cl 17.4-27 1), compared to 11 4 (9 8-13 4) among those with four components without hyperglycemiaConclusions Diabetes risk increases exponentially with MetS factor count, but vanes substantially depending upon which factors are present Hyperglycemia, regardless of the presence of MetS, is a much stronger predictor of incident diabetes than MetS without hyperglycemia (C) 2010 Elsevier Ireland Ltd All rights reserved

Diabetes increases the risk of cardiovascular disease (CVD) and heart failure, as well as other serious complications, such as renal disease and depression. However, these conditions are often present prior to diabetes diagnosis. We sought to determine whether they increase the risk of developing diabetes independent of other risk factors.

Because metabolic syndrome (MetS) is a known risk factor for cardiovascular disease and diabetes, associated medical care costs should be elevated. However, the extent to which costs associated wit...

Kolberg et al. (1) report from the Inter99 cohort that a risk score incorporating six biomarkers (adiponectin, C-reactive protein, ferritin, interleukin-2 receptor A, glucose, and insulin) is useful in the prediction of type 2 diabetes and could be recommended as a tool for identification of high-risk individuals. So far, however, there is little evidence that a procedure based partly on nonroutinely measured biomarkers is superior to risk scores based on personally known variables or on routinely measured clinical data. An area under the receiver operating characteristic curve (AROC) of 0.76 is reported by the investigators, indicating a rather moderate diagnostic accuracy of the …

Background:Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx™ Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Methods:Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. Results:The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. Conclusions:The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors.