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Background: Phase Ib, single-arm trials are common in oncology development. Since these trials are not powered for statistical significance, Go/No-Go decisions are largely driven by observed trends in ORR relative to historic control. Here we develop a bootstrapping method to systematically compare secondary endponts, such as the waterfall plot, to historic control data. In addition, we identify an actionable Go threshold and estimate the expected false positive and false negative rates associated with that threshold. Method: A proprietary mathematical model was used to simulate thousands of single-arm trials (n = 30) with a combination of pembrolizumab and a novel agent with miserable (Rx0), modest (Rx1), moderate (Rx2) or magnificent (Rx3) efficacy. A bootstrapping method was used to compare waterfall plots from these trials to a simulated Phase III pembrolizumab monotherapy control arm (n = 511). Results: The bootstrapping method provides a visualization of the observed waterfall plot for the single-arm combination trial relative to the range of possible outcomes from historic control data (5th, median, 95th percentile). The method also provides a score reflecting the probability that the observed data is different from the historic control. An ROC analysis showed a strong ability to separate drugs with modest (AUROC = 83%), moderate (AUROC = 96%) and magnificent combination efficacy (AUROC = 99%) from drugs with miserable efficacy. The method was shown to effectively move drugs with a range of efficacy through an in-silico pipeline while rejecting drugs without combination efficacy. Conclusion: This bootstrapping method allows comparisons of secondary endpoints from early clinical trials to historic control data. The visualization is intuitive, and the quantification provides an actionable Go-threshold. The method also provides an estimate of the risk associated with that recommendation. We suggest incorporation of this as a component of Go/No-Go decision making in early oncology trials. Citation Format: Aparna Mohan, Raunak Dutta, Jacqueline Buros-Novik, Omobolaji Akala, Brian Gregory Topp. A bootstrapping method to optimize go/no-go decisions from single-arm, signal-finding studies in oncology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4375.

Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimer’s disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is an antagonist at the group II metabotropic glutamate receptor (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of cerebrovascular accumulation of oligomeric AβE22Q (APPE693Q= “Dutch APP”) reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of the Dutch APP mice. Here we show that (i) administration of BCI-838, physical exercise, or a combination of BCI-838 and physical exercise enhanced AHN in a four-month old mouse model of AD amyloid pathology (APPKM670/671NL/ PSEN1Δexon9 = APP/PS1), (ii) administration of BCI-838 alone or associated with physical exercise led to stimulation of AHN and improvement in both spatial and recognition memory, (iii) significantly, the hippocampal dentate gyrus transcriptome of APP/PS1 mice following BCI-838 treatment up-regulated brain-derived neurotrophic factor (BDNF), PIK3C2A of the PI3K-MTOR pathway, and metabotropic glutamate receptors, and down-regulated EIF5A of ketamine-modulating mTOR activity, and finally (iv) qPCR findings validate a significantly strong association between increased BDNF levels and BCI-838 treatment. Our study points to BCI-838 as a safe and orally active compound capable of mimicking the beneficial effect of exercise on AHN, learning behavior, and anxiety in a mouse model of AD neuropathology.

Non-alcoholic steatohepatitis (NASH) is a rising health challenge, with no approved drugs. We used a computational drug repositioning strategy to uncover a novel therapy for NASH, identifying a GABA-B receptor agonist, AZD3355 (Lesogaberan) previously evaluated as a therapy for esophageal reflux. AZD3355’s potential efficacy in NASH was tested in human stellate cells, human precision cut liver slices (hPCLS), and in vivo in a well-validated murine model of NASH. In human stellate cells AZD3355 significantly downregulated profibrotic gene and protein expression. Transcriptomic analysis of these responses identified key regulatory nodes impacted by AZD3355, including Myc, as well as MAP and ERK kinases. In PCLS, AZD3355 down-regulated collagen1α1, αSMA and TNF-α mRNAs as well as secreted collagen1α1. In vivo, the drug significantly improved histology, profibrogenic gene expression, and tumor development, which was comparable to activity of obeticholic acid in a robust mouse model of NASH, but awaits further testing to determine its relative efficacy in patients. These data identify a well-tolerated clinical stage asset as a novel candidate therapy for human NASH through its hepatoprotective, anti-inflammatory and antifibrotic mechanisms of action. The approach validates computational methods to identify novel therapies in NASH in uncovering new pathways of disease development that can be rapidly translated into clinical trials.

Background RECIST1 is commonly used to characterize intermediate outcomes for clinical trials in the context of solid tumors, and it is largely based on a standardized measure of tumor size known as the sum of longest diameters (SLD). In recent years, the FDA has granted accelerated approvals for several new compounds based on improvements in RECIST-based surrogate outcomes like overall response rate and progression-free survival.2 However, there are concerns regarding the robustness of these surrogate endpoints relative to overall survival (OS),3 4 and it is not known whether their prognostic value is similar across TKI, chemotherapy, and immunotherapy regimens. Methods We have developed a Bayesian meta-analytic joint model for longitudinal SLD and OS in order to predict Phase III outcomes from early Phase II data. We validated this model in extensive simulation studies. The model utilizes a generalized Stein-Fojo equation5 to characterize SLD over time in terms of 3 parameters: f (proportion of tumor that is treatment-susceptible), ks (the decay rate among susceptible cells), and kg (the growth rate among resistant cells). Two quantities [tumor shrinkage (f * ks) and tumor regrowth ((1-f) * kg)] are then associated with survival in the context of a proportional-hazards survival model. We estimated this model using Stan6 on a dataset of >6,000 subjects in 15 randomized clinical trials in advanced non-small cell lung cancer. Results Both tumor shrinkage and tumor regrowth were found to be associated with OS (HR for tumor shrinkage: median 0.51, 90% CrI 0.42 - 0.61; HR for tumor regrowth: median 1.24, 90% CrI 1.18 - 1.32). There is a stronger association between tumor shrinkage and OS among patients randomized to a PD-1/PD-L1 inhibitor, either as a monotherapy or in combination with a CTLA-4 inhibitor, than among patients in other trial arms (figure 1). By contrast, there were negligible differences across treatment classes in the association between tumor regrowth and OS. Conclusions Our results suggest that not all reductions in tumor size are equal. A patient with a certain degree of tumor shrinkage on the PD-1/PD-L1 inhibitor will have lower mortality risk than a patient with a similar degree of shrinkage on the other regimens evaluated. More research is needed to determine whether the result is unique to this particular PD-1/PD-L1 inhibitor, to determine what mechanisms of action mediate these treatment-specific effects, and to develop improved surrogate measures of treatment efficacy. References Eisenhauer EA, Therasse P, Bogaerts J, et al: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228–247. Of Health USD, Services H, Others: Clinical trial endpoints for the approval of non-small cell lung cancer drugs and biologics: guidance for Industry, 2015 Mandrekar SJ, An M-W, Meyers J, et al: Evaluation of Alternate Categorical Tumor Metrics and Cut Points for Response Categorization Using the RECIST 1.1 Data Warehouse. J Clin Orthod32:841–850, 2014 Blumenthal GM, Karuri SW, Zhang H, et al: Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non-small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses. J Clin Oncol33:1008–1014, 2015 Stein WD, Figg WD, Dahut W, et al: Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist13:1046–1054, 2008 Carpenter B, Gelman A, Hoffman MD, et al: Stan: A probabilistic programming language [Internet]. jitc-2020-SITC2020.0277.pdf

BACKGROUND N-of-1 trials promise to help individuals make more informed decisions about treatment selection through structured experiments that compare treatment effectiveness by alternating treatments and measuring their impacts in a single individual. We created a digital platform that automates the design, administration, and analysis of N-of-1 trials. Our first N-of-1 trial, the app-based Brain Boost Study, invited individuals to compare the impacts of two commonly consumed substances (caffeine and L-theanine) on their cognitive performance. OBJECTIVE The purpose of this study is to evaluate critical factors that may impact the completion of N-of-1 trials to inform the design of future app-based N-of-1 trials. We will measure study completion rates for participants that begin the Brain Boost Study and assess their associations with study duration (5, 15, or 27 days) and notification level (light or moderate). METHODS Participants will be randomized into three study durations and two notification levels. To sufficiently power the study, a minimum of 640 individuals must begin the study, and 97 individuals must complete the study. We will use a multiple logistic regression model to discern whether the study length and notification level are associated with the rate of study completion. For each group, we will also compare participant adherence and the proportion of trials that yield statistically meaningful results. RESULTS We completed the beta testing of the N1 app on a convenience sample of users. The Brain Boost Study on the N1 app opened enrollment to the public in October 2019. More than 30 participants enrolled in the first month. CONCLUSIONS To our knowledge, this will be the first study to rigorously evaluate critical factors associated with study completion in the context of app-based N-of-1 trials. CLINICALTRIAL ClinicalTrials.gov NCT04056650; https://clinicaltrials.gov/ct2/show/NCT04056650 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/16362

Robust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of immune cell content around a tumor by immunohistochemistry, flow cytometry, or mass cytometry allows measurement of only up to a few dozen markers at a time, limiting the number of immune cell types identified. Immune cell type abundances may instead be estimatedin silicoby deconvolving gene expression mixtures from bulk RNA sequencing of tumor tissue. By measuring tens of thousands of transcripts at once, bulk RNA-seq provides a rich input to algorithms that quantify cell type abundances in the tumor microenvironment, affording the potential to quantify the states of a greater number of immune cell types (given adequate training data). Here, we first review existing methods for deconvolution and evaluate their performance on synthetic mixtures. Then we develop a Bayesian inference approach, namedinfino, that learns to distinguish immune cell expression phenotypes and deconvolve mixtures. In contrast to earlier approaches,infinoaccepts RNA sequencing data, models transcript expression variability, and exploits the relationships between cell types to improve deconvolution accuracy and allow interrogation from the level of broad categories to the level of finest granularity. The resulting probability distributions of immune infiltration could be applied to numerous questions concerning the diverse ecology of immune cell types, including assessment of the association of immune infiltration with response to immunotherapy, and study of the expression profile and presence of elusive T cell subcompartments, such as T cell exhaustion.

Objectives: Dynamics of tumor size have long been used for the clinical diagnosis, staging, prognosis and treatment of non-small cell lung cancer (NSCLC). The aim of this work was to develop a joint statistical framework to interrogate the longitudinal tumor burden, the appearance of new lesions, and changes in therapy following progression in a population of NSCLC patients treated with an EGFR inhibitor gefinitib to predict, given early results, time to progression (PFS) and overall survival at both population and individual levels. Methods: The model builds on a previously developed joint model for tumor size and survival. The tumor size model was extended to estimate the probability of a new lesion. For the survival submodel, two additional biomarkers were evaluated: the derivative of the sum of longest diameters (SLD) and the probability of new lesions. The model was fitted in a Bayesian framework using STAN. Model was developed from 434 EGFR+, NSCLC patients treated with gefitinib or carboplatin+paclitaxel (‘IPASS’, NCT00322452). Predictive performance was evaluated on a cohort of 102 EGFR+ NSCLC patients (‘IFUM’, NCT01203917). Results: Overall, the model fit well to the observed data and the predictive performance was strong in both within- and out-of-sample evaluations. Surprisingly, SLD derivative and new-lesion association parameters appear to negatively impact model performance, indicating further improvement of these submodels might be necessary. The model recapitulates response evaluation criteria in solid tumors (RECIST) outcomes with nearly 90% accuracy. When comparing progression-specific hazards, it appears the drug effect is mediated by tumor size dynamics rather than new lesion incidence. Conclusion: This Bayesian joint model of tumor size and survival accurately recapitulates the RECIST-based outcomes and generates well calibrated predictions of survival. It offers insights regarding the relative predictive value of the components of RESICT in NSCLC. Citation Format: James Dunyak, Jacqueline Buros-Novik, Eric Novik, Hong Yan, Nidal Al-Huniti. Prediction of survival based on tumor size dynamics and new lesions in EGFR mutation-positive non-small cell lung cancer patients treated with gefitinib or carboplatin and paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1674.

Background:Inhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.Methods and Findings:The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.Patients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.Conclusions:These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

Background Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. Methods and findings The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. Conclusions These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients., Alexandra Snyder and colleagues reveal the complex nature of the immune response to checkpoint blockade in metastatic urothelial cancer patients. Time-varying effects of somatic mutation load on survival are reported., Author summary Why was this study done? A new type of cancer treatment called checkpoint blockade therapy activates the immune system to fight cancer. When these therapies work, patients with advanced disease can experience long-lasting disease control or even cures. However, most patients will not experience these benefits, and it is crucial to identify these patients in advance so that we can develop better treatments for them. What did the researchers do and find? In this study, we studied 29 patients with advanced bladder cancers treated with a checkpoint blockade drug called atezolizumab. We examined features of the tumor and the immune system, as well as clinical features. We found that these features were related to each other, and to the success of therapy, in various ways. Patients who had a diverse repertoire of T cells in their blood tended to survive longer. Patients who had poor clinical prognostic factors, like having cancer that had traveled to their liver, tended to have worse survival. What did the research findings mean? This study demonstrates that we need to take the tumor, immune system, and clinical picture into account if we are to improve the efficacy of immune-mobilizing therapies in cancer. Some patients may be too sick to benefit from checkpoint blockade therapy, despite, in some cases, having biomarkers in their tumors that would predict benefit.

4518Background: Mutation load and neoantigen burden predict benefit from anti-PD1 therapy across several cancers but are generally low in RCC. Pan-cancer comparisons highlight RCC for its enriched ...

Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole-exome sequencing to examine non-small-cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.

Objective: Recent genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) identified 9 novel risk loci. Discovery of functional variants within genes at these loci is required to confirm their role in Alzheimer disease (AD). Single nucleotide polymorphisms that influence gene expression (eSNPs) constitute an important class of functional variants. We therefore investigated the influence of the novel LOAD risk loci on human brain gene expression. Methods: We measured gene expression levels in the cerebellum and temporal cortex of autopsied AD subjects and those with other brain pathologies (∼400 total subjects). To determine whether any of the novel LOAD risk variants are eSNPs, we tested their cis -association with expression of 6 nearby LOAD candidate genes detectable in human brain ( ABCA7, BIN1, CLU, MS4A4A, MS4A6A, PICALM ) and an additional 13 genes ±100 kb of these SNPs. To identify additional eSNPs that influence brain gene expression levels of the novel candidate LOAD genes, we identified SNPs ±100 kb of their location and tested for cis -associations. Results: CLU rs11136000 ( p = 7.81 × 10 −4 ) and MS4A4A rs2304933/rs2304935 ( p = 1.48 × 10 −4 –1.86 × 10 −4 ) significantly influence temporal cortex expression levels of these genes. The LOAD-protective CLU and risky MS4A4A locus alleles associate with higher brain levels of these genes. There are other cis -variants that significantly influence brain expression of CLU and ABCA7 ( p = 4.01 × 10 −5 –9.09 × 10 −9 ), some of which also associate with AD risk ( p = 2.64 × 10 −2 –6.25 × 10 −5 ). Conclusions: CLU and MS4A4A eSNPs may at least partly explain the LOAD risk association at these loci. CLU and ABCA7 may harbor additional strong eSNPs. These results have implications in the search for functional variants at the novel LOAD risk loci.

Background: Patients with peripheral arterial disease (PAD) represent a high-risk patient subset in the setting of non-ST-segment elevation acute coronary syndromes (NSTE ACS). The efficacy and safety of early invasive management for such patients remains unclear. Hypothesis: Early invasive management would be well tolerated and effective among patients with NSTE ACS and PAD. Methods: Patients from the Treat angina with Aggrastat and determine Cost of Therapy with an Invasive or Conservative Strategy—Thrombolysis In Myocardial Infarction (TAC-TICS-TIMI) 18 trial were stratified by the presence or absence of PAD and assessed with respect to baseline clinical factors. The outcomes of patients with PAD and NSTE ACS were examined with respect to treatment assignment to either early invasive therapy or conservative treatment of NSTE ACS. Finally, the bleeding and stroke rates of patients with PAD managed invasively were compared with patients with PAD managed conservatively. Results: Of 2,219 patients with NSTE ACS overall, 166 (7.5%) had concomitant PAD. Compared with those patients without PAD, those with PAD were older (75 vs. 62 years, p < 0.001) and were more likely to have high-risk clinical features, including prior histories of bypass surgery (39 vs. 20%, p < 0.001) or diabetes mellitus (38 vs. 27%, p = 0.002), and more ST-segment depression on their 12-lead electrocardiogram (43 vs. 29%, p < 0.001). Among such patients, early invasive management was associated with significant reductions in the risk of myocardial infarction (MI) at 30 days (11.4 vs. 2.3%, p = 0.03). At 180 days, compared with early conservative management, early invasive treatment for patients with PAD and NSTE ACS was associated with similar reductions in MI (12.7 vs. 3.5%, p = 0.04), and was also accompanied by significant reductions in risk of death(10.1 vs. 2.3%, p = 0.05). No excess in bleeding or stroke rates was noted among patients with PAD managed invasively. Conclusions: Among patients with NSTE ACS and a history of PAD, early invasive management is well tolerated and accompanied by significant reductions in morbidity and mortality when compared with a more conservative, ischemia-driven approach.