Abstract 4649: Duplex Sequencing detects rare subclonal variants that mark early carcinogenesis and preneoplastic clonal evolution

Cancer is a disease of somatic evolution, characterized by the natural selection of genomic mutations that facilitate enhanced cell survival and proliferation. Although many thousands of tumor genomes have now been sequenced, our ability to identify early genetic patterns of clonal selection in both humans and model organisms have been hampered by inadequately sensitive methodologies for identifying mutations during the long period between their occurrence and the final outgrowth of a clinically apparent tumor.

Sensitive molecular tests are capable of measuring minute levels of cancer cells in tissue samples, however no existing method is satisfactory at scaling to high-throughput detection at the rate of one cancer-associated variant per more than a million normal cells. NGS is used to study the genetic variation in cell populations, although the accuracy of standard NGS methods limit our ability to detect sub-clones below ~1%. Duplex Sequencing (DS) is a sensitive NGS error-correction method which increases the accuracy of base calls by more than 100,000-fold. DS enables precise reconstruction of the original double-stranded source molecule while overcoming both chemical and technical artifacts that arise during library preparation and sequencing.

Here we present the use of Duplex Sequencing, in both human and mouse tissues, for measuring sub-clones at allelic fractions less than 1×10-4 for an early glimpse into pre-neoplastic evolution. We illustrate how, merely weeks after mutagen exposure, we observe emerging clones of cells bearing cancer-driving mutations in histologically normal mouse tissue. Furthermore, we illustrate how similar patterns of clonal selection can be seen in multiple otherwise healthy tissues of humans as part of normal aging. We discuss how variations in the pattern and rate of accumulation of rare cancer-associated mutations offers a new preclinical tool for evaluating carcinogenicity of chemicals, as well as a potential clinical tool for assessing life-integrated carcinogenic processes and cancer risk in humans.

Citation Format: Charles C. Valentine, Mark Fielden, Robert Young, Jake Higgins, Lindsey Williams, Tan Li, Rohan Kulkarni, Sheroy Minocherhomji, Rosana Risques, Patrick Danaher, Jesse Salk. Duplex Sequencing detects rare subclonal variants that mark early carcinogenesis and preneoplastic clonal evolution [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 4649.

The Immune Microenvironment in Hormone Receptor-Positive Breast Cancer Before and After Preoperative Chemotherapy

PURPOSE

Hormone receptor-positive/HER2-negative (HR⁺/HER2^-) breast cancer is associated with low levels of stromal tumor-infiltrating lymphocytes (sTIL) and PD-L1, and demonstrates poor responses to checkpoint inhibitor therapy. Evaluating the effect of standard chemotherapy on the immune microenvironment may suggest new opportunities for immunotherapy-based approaches to treating HR⁺/HER2^- breast tumors.

EXPERIMENTAL DESIGN

HR⁺/HER2^- breast tumors were analyzed before and after neoadjuvant chemotherapy. sTIL were assessed histologically; CD8⁺ cells, CD68⁺ cells, and PD-L1 staining were assessed immunohistochemically; whole transcriptome sequencing and panel RNA expression analysis (NanoString) were performed.

RESULTS

Ninety-six patients were analyzed from two cohorts (n = 55, Dana-Farber cohort; n = 41, MD Anderson cohort). sTIL, CD8, and PD-L1 on tumor cells were higher in tumors with basal PAM50 intrinsic subtype. Higher levels of tissue-based lymphocyte (sTIL, CD8, PD-L1) and macrophage (CD68) markers, as well as gene expression markers of lymphocyte or macrophage phenotypes (NanoString or CIBERSORT), correlated with favorable response to neoadjuvant chemotherapy, but not with improved distant metastasis-free survival in these cohorts or a large gene expression dataset (N = 302). In paired pre-/postchemotherapy samples, sTIL and CD8⁺ cells were significantly decreased after treatment, whereas expression analyses (NanoString) demonstrated significant increase of multiple myeloid signatures. Single gene expression implicated increased expression of immunosuppressive (M2-like) macrophage-specific genes after chemotherapy.

CONCLUSIONS

The immune microenvironment of HR⁺/HER2^- tumors differs according to tumor biology. This cohort of paired pre-/postchemotherapy samples suggests a critical role for immunosuppressive macrophage expansion in residual disease. The role of macrophages in chemoresistance should be explored, and further evaluation of macrophage-targeting therapy is warranted.

Immune profiling of pre- and post-treatment breast cancer tissues from the SWOG S0800 neoadjuvant trial

BACKGROUND

How the immune microenvironment changes during neoadjuvant chemotherapy of primary breast cancer is not well understood.

METHODS

We analyzed pre- and post-treatment samples from 60 patients using the NanoString PanCancer IO360™ assay to measure the expression of 750 immune-related genes corresponding to 14 immune cell types and various immune functions, and assessed TIL counts and PD-L1 protein expression by immunohistochemistry. Treatment associated changes in gene expression levels were compared using t-test with Bonferroni correction. TIL count, PD-L1 protein and immune metagenes were compared using Wilcoxon test. Baseline immune markers were correlated with pathologic complete response (pCR) using estrogen receptor and treatment arm adjusted logistic regression.

RESULTS

At baseline, high TIL counts and high expression of chemoattractant cytokines (CCL21, CCL19) and cytotoxic T cell markers were associated with higher pCR rate. High expression of stromal genes (VEGFB, TGFB3, PDGFB, FGFR1, IGFR1), mast and myeloid inflammatory cell metagenes, stem cell related genes (CD90, WNT11, CTNNB1) and CX3CR1, and IL11RA were associated with residual disease (RD). After treatment, in cases with pCR, TIL counts and most immune genes decreased significantly. Among RD cases, TIL counts and PD-L1 expression did not change but cellular stress and hypoxia associated genes (DUSP1, EGR1), and IL6, CD36, CXCL2, CD69 and the IL8/VEGF metagene increased.

CONCLUSIONS

Activated T cells in the tumor microenvironment are associated with pCR whereas stromal functions are associated with residual disease. Most immune functions decrease during neoadjuvant chemotherapy but several immunotherapy targets (PD-L1, IL6, IL8) remain expressed in RD suggesting potential therapeutic strategies.

Abstract B096: Dissecting the flames from the fire: Distribution of immune checkpoints in hot and cold tumors

Introduction: Numerous immune checkpoint inhibitors are being developed for the clinic, but identifying the population of patients most likely to respond remains a significant challenge. PD-(L)1 blocking antibodies have been approved for multiple indications, but even in those indications the majority of patients fail to respond to PD-(L)1 monotherapy. Consequently, diagnostic assays have been developed to identify patients with a higher likelihood of response. PD-L1 immunohistochemistry is the platform for multiple assays currently being used in the clinical as companion and complementary diagnostics for the PD-(L)1 checkpoint inhibitors, but those assays have limited sensitivity and selectivity and have inherent risk of subjective interpretation bias. Tumor mutation burden is in development as a proxy readout for a tumor’s potential to prime immune responses, but it does not measure the actual presence of an immune response, and it is not able to inform treatment decisions if there is the option of more than one immunomodulatory intervention. Gene expression assays have the advantage of being a sensitive, selective, and quantitative assay which can directly measure immune biology, and may overcome many of the limitations of the other assay platforms. The Tumor Inflammation Signature (TIS) has been developed on the NanoString® platform as an 18-gene signature of a suppressed immune response within the tumor and has been developed as a clinically validated assay which enriches for response to anti-PD-1 (Ayers, JCI 2017). We have recently evaluated the distribution of TIS in The Cancer Genome Atlas (TCGA) database to understand the prevalence and distribution of immune “hot” vs “cold” tumors by indication (Danaher, JITC 2018). We now extend that work to evaluate the expression of individual immune checkpoint molecules after segregating tumors by TIS to understand the distribution of immune checkpoints across indications and within the context of a preexisting immune response. Methods: We leverage biostatistical analysis of the RNA-seq data in the TCGA database to evaluate the expression of the TIS signature and individual immune checkpoints. Results: We observe that the expression of many immune checkpoint molecules is directly proportional to the degree of immune infiltrate within the tumor as measured by TIS. As such, there is a distribution of IO targets across indications, with inflamed tumors expressing greater median levels of immune checkpoints vs noninflamed tumors. Within individual indication, we also see a distribution of hot and cold tumors, and a corresponding distribution of checkpoint molecules, indicating that there may be some subpopulations of patients with the potential to respond to immune checkpoint blockade even in an indication that is nonresponsive in an unselected population. Furthermore, we also observe increased expression of particular immune checkpoints in subpopulations of certain tumors. For example, certain bladder cancers express PD-L1 at higher levels than would be predicted by TIS alone, despite the fact that CD274, the gene that encodes for PD-L1, is one of the genes which is in the TIS. Likewise, we see elevated LAG3 expression in a fraction of sarcomas above the expected level based on TIS. Conclusions: Close evaluation of the expression levels of immune checkpoints may guide clinical development of combination immunotherapies. Furthermore, these findings could lead to the development of novel diagnostic assays based on gene signatures that could be used in combination with TIS to segregate patients who would benefit from monotherapy alone versus those who need combination strategies.

Citation Format: Sarah Warren, Tressa Hood, Patrick Danaher, Alessandra Cesano. Dissecting the flames from the fire: Distribution of immune checkpoints in hot and cold tumors [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B096.

A gene expression assay for simultaneous measurement of microsatellite instability and anti-tumor immune activity

Background

Clinical benefit from checkpoint inhibitors has been associated in a tumor-agnostic manner with two main tumor traits. The first is tumor antigenicity, which is typically measured by tumor mutation burden, microsatellite instability (MSI), or Mismatch Repair Deficiency using gene sequence platforms and/or immunohistochemistry. The second is the presence of a pre-existing adaptive immune response, typically measured by immunohistochemistry (e.g. single analyte PD-L1 expression) and/or gene expression signatures (e.g. tumor “inflamed” phenotype). These two traits have been shown to provide independent predictive information. Here we investigated the potential of using gene expression to predict tumor MSI, thus enabling the measurement of both tumor antigenicity and the level of tumor inflammation in a single assay, possibly reducing sample requirement, turn-around time, and overall cost.

Methods

Using The Cancer Genome Atlas RNA-seq datasets with the greatest MSI-H incidence, i.e. those from colon (n = 208), stomach (n = 269), and endometrial (n = 241) cancers, we trained an algorithm to predict tumor MSI from under-expression of the mismatch repair genes MLH1, PMS2, MSH2, and MSH6 and from 10 additional genes with strong pan-cancer associations with tumor hypermutation. The algorithms were validated on the NanoString nCounter™ platform in independent cohorts of colorectal (n = 52), endometrial (n = 11), and neuroendocrine (n = 4) tumors pre-characterized using the MMR immunohistochemistry assay.

Results

In the validation cohorts, the algorithm showed high prediction accuracy of tumor MSI status, with sensitivity of at least 88% attained at thresholds chosen to achieve 100% specificity. Furthermore, MSI status was compared to the Tumor Inflammation Signature (TIS), an analytically validated diagnostic assay which measures a suppressed adaptive immune response in the tumor and enriches for response to immune checkpoint blockade. TIS score was largely independent of MSI status, suggesting that measuring both parameters may identify more patients that would respond to immune checkpoint blockade than either assay alone.

Conclusions

Development of a gene expression signature of MSI status raises the possibility of a combined diagnostic assay on a single platform which measures both tumor antigenicity and presence of a suppressed adaptive immune response. Such an assay would have significant advantages over multi-platform assays for both ease of use and turnaround time and could lead to a diagnostic test with improved clinical performance.

Electronic supplementary material

The online version of this article (10.1186/s40425-018-0472-1) contains supplementary material, which is available to authorized users.

Abstract 558: Prognostic gene signature use in checkpoint inhibitor monotherapy for melanoma

Introduction: The successful deployment of checkpoint inhibitors in cancer immunotherapy relies on the responsiveness of an individual's immune system for relief of that particular blockade in the cancer immunity cycle. As most patients fail to respond to immunotherapy, there is a need for biomarkers that can predict clinical benefit of the therapeutic by identifying the patient population most likely to respond. Gene expression signatures characterizing basal immune state within tumor have shown utility in retrospective analysis of clinical trials. This study utilizes tumor and PBMCs from patients with metastatic melanoma receiving either anti-CTLA-4 or anti-PD-1/PD-L1 to characterize local and peripheral patterns of gene expression associated with clinical benefit of therapy. The goal of this study is to evaluate existing gene signatures and develop novel signatures that predict response to checkpoint inhibitors in melanoma.

Methods: Pretreatment biopsies from metastatic lesions of melanoma stage IV patients treated with ipilimumab (anti-CTLA4) or pembrolizumab (anti-PD-L1) were retrieved from the Department of Pathology archives. Cohorts were assembled of 30 patients receiving each drug, equally stratified between responders and nonresponders. RNA expression in tumor biopsies will be profiled with a pilot version of the NanoString® IO360 gene expression panel. In parallel, pre- and post-treatment PBMC from independent cohorts receiving either ipilimumab (anti-CTLA4) or pembrolizumab (anti-PD-1) were collected and profiled with the NanoString PanCancer Immune Panel (gene expression) and Immune Profiling Panel (protein expression).

Results: Patterns of gene expression in the tumor biopsies and gene and protein expression in the PBMCs will be assessed for correlation to clinical outcome (PFS, OS, ORR). Specifically, the Tumor Inflammation Signature described by Ayers et al. (1), an investigational 18-gene signature of suppressed adaptive immune response that enriches for clinical response to pembrolizumab, will be assessed in these cohorts. Other patterns of gene and protein expression that correlate with response to immunotherapy or lack thereof will also be evaluated.

Conclusion: Correlating patterns of gene expression from tumor with clinical response can lead to the development of biomarkers to better select patients who will respond to immunotherapy. It can also lead to identifying immune evasion. Gene and protein profiling from a readily accessed sample such as PBMC may give insights into early on-treatment signatures of efficacy. Utilization of a clinical grade platform such as the NanoString nCounter® may speed the development of diagnostic assays that can be used to predict and monitor patient response to immunotherapy.

Reference: 1. Ayers et al., J Clin Invest 2017;127:2930.

Citation Format: Mariaelena Capone, Gabriele Madonna, Paolo Ascierto, Patrick Danaher, SuFey Ong, Sarah Warren, Joseph M. Beechem, Alessandra Cesano. Prognostic gene signature use in checkpoint inhibitor monotherapy for melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 558.

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

The Tumor Inflammation Signature (TIS) is an investigational use only (IUO) 18-gene signature that measures a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. To explore this immune phenotype within and across tumor types, we applied the TIS algorithm to over 9000 tumor gene expression profiles downloaded from The Cancer Genome Atlas (TCGA). As expected based on prior evidence, tumors with known clinical sensitivity to anti-programmed cell death protein 1 (PD-1) blockade had higher average TIS scores. Furthermore, TIS scores were more variable within than between tumor types, and within each tumor type a subset of patients with elevated scores was identifiable although with different prevalence associated with each tumor type, the latter consistent with the observed clinical responsiveness to anti PD-1 blockade. Notably, TIS scores only minimally correlated with mutation load in most tumors and ranking tumors by median TIS score showed differing association to clinical sensitivity to PD-1/PD-1 ligand 1 (PD-L1) blockade than ranking of the same tumors by mutation load. The expression patterns of the TIS algorithm genes were conserved across tumor types yet appeared to be minimally prognostic in most cancers, consistent with the TIS score serving as a pan-cancer measurement of the inflamed tumor phenotype. Characterization of the prevalence and variability of TIS will lead to increased understanding of the immune status of untreated tumors and may lead to improved indication selection for testing immunotherapy agents.

Comparison of Clinical Features in a Population of Basic Military Trainees Versus the General Department of Defense Beneficiary Population Presenting With Influenza

INTRODUCTION

Upper respiratory tract infection (URI) is a well-documented cause of morbidity, extra expense, and lost training time among basic military trainees (BMTs). The goal of this study was to characterize the clinical presentation of influenza in the BMT population and to better understand how this presentation differs from that of the general Department of Defense (DoD) beneficiary population (non-BMTs).

MATERIALS AND METHODS

Clinical and demographic data were collected in a prospective study that enrolled DoD beneficiaries presenting to medical treatment facilities in San Antonio, Texas, with URI symptoms between January 2005 and March 2011. Vital signs and symptom duration were collected at the time of enrollment along with basic demographic information.

RESULTS

Among 4,448 participants enrolled, 466 (10.5%) tested positive for influenza: 198 of 3,103 BMTs (6.4%) vs. 268 of 1,345 non-BMTs (20%) (p < 0.01); 412 of 466 had complete data for nine symptom-related variables. BMTs were more likely to be Caucasian males and younger than non-BMTs. BMTs had a higher temperature at the time of presentation (101.5°F vs. 100.5°F, p < 0.01). BMTs presented less frequently than non-BMTs with chills (79.7% vs. 94.4%, p < 0.01), malaise (62.1% vs. 87.0%, p < 0.01), nausea (30.2% vs. 43.0%, p < 0.01), and vomiting (12.1% vs. 21.7%, p = 0.01). Multiple logistic regression analysis showed that BMTs were less likely to have the four symptoms compared to non-BMTs even after controlling for gender and age (chills: odds ratio [OR] = 0.3, 95% confidence interval [CI] = 0.1-0.6, p < 0.01; malaise: OR = 0.5, 95% CI = 0.3-0.8, p < 0.01; nausea: OR = 0.5, 95% CI = 0.3-0.8, p < 0.01; vomiting: OR = 0.4, 95% CI = 0.2-0.8, p < 0.01). Although there was no difference in the frequency of subjective fever between the two groups, reported duration of fever was significantly shorter in BMTs than non-BMTs: median of 1 day (range 0-10) vs. 2 days (range 0-8) (p < 0.01). BMTs presented with a composite symptom index mean of 6.2 (standard deviation = 1.4) symptoms, whereas non-BMTs presented with a mean of 6.9 (standard deviation = 1.3) symptoms (p < 0.01).

CONCLUSIONS

The pretest probability of a BMT presenting with URI symptoms having influenza is significantly lower than that for the general DoD beneficiary population. BMTs with influenza presented sooner, with higher fever, and with fewer overall symptoms than the general DoD beneficiary population. These differences are likely attributable to early reporting and response bias and less likely attributed to age. Military efforts to identify BMTs with suspected influenza infection early and to refer them for treatment promptly are efficacious.

Capturing the complexity of the immune microenvironment of acute myeloid leukemia with 3D biology technology

50

Background: Tumor phenotypes are dictated not only by the neoplastic cell component, but also by the tumor microenvironment (TME), which includes immune and inflammatory cells. We characterized the immune ecosystem of the bone marrow (BM) TME in patients with acute myeloid leukemia (AML) to correlate transcriptomic and proteomic profiles with patient outcome. Methods: We used the nCounter system (NanoString Technologies) to characterize BM specimens from 42 children and 28 adults with AML. Ninety BM samples (63 from de novo AML, 18 from AML in complete remission [CR] and 9 from relapsed AML) were analyzed using the RNA Pan-Cancer Immune Profiling Panel. In situ leukemia-immune system interactions were visualized using Digital Spatial Profiling (DSP). Results: Hierarchical clustering identified subgroups with heightened expression of lymphocyte-associated genes, including NK cell and cytotoxicity genes (“immune-enriched” samples). They also expressed CD8A, IFNG, FOXP3, the cell chemoattractants CXCL9, CXCL10, and inhibitory molecules including IDO1 and the immune checkpoints LAG3, CTLA4 and PD-L1. Conversely, ‘immune-depleted’ samples over-expressed genes associated with mast cell functions and CD8 T-cell exhaustion and showed low expression of T-cell and B-cell genes. Importantly, relapse-free survival (RFS) and overall survival (OS) were significantly shorter in patients with “immune-enriched” compared with “immune-depleted” AMLs (2.2 vs. 18.3 months, p = 0.0064, and 6.3 vs. 22.4 months, p = 0.017, respectively). DSP of the BM TME showed the co-localization of CD8 T cells with FoxP3 Treg cells and PD-L1- and VISTA-expressing cell types, an immune landscape which is consistent with the establishment of adaptive immune resistance mechanisms of immune escape. Conclusions: Our study has identified heterogeneous immune profiles in children and adults with AML. From a clinical standpoint, ‘immune enriched’ AMLs might be amenable to immunotherapy approaches tailored to the BM microenvironment, including blockade of co-inhibitory molecules and/or small-molecule IDO1 inhibitors. Grant support: Roger Counter Foundation, UK; Qatar National Research Fund (#NPRP8-2297-3-494).

Validating critical analytical variables of a multiplexed gene expression assay measuring tumor inflammation designed to predict response to anti-PD1 therapy

203

Background: The development and analytical performance of the Tumor Inflammation Signature (TIS) assay has been described previously. The TIS is an investigational use RNA expression assay on the nCounter Dx Analysis System, which is being evaluated as a patient enrichment biomarker for treatment with pembrolizumab single agent across multiple solid tumor types. Here we describe the analytical validation of the RNA input range and analytical precision starting from RNA isolated from formalin fixed paraffin embedded (FFPE) tissue blocks. Methods: Analytical validation of TIS assay performance across an RNA input range was performed using samples from 11 tumor types. The analytical precision between sites, instruments, reagent lots, and users was measured, using RNA samples isolated from FFPE tissue blocks. Results: The assay was validated across the specified RNA input range with ≥ 94% concordance at the minimum specified RNA input (50ng). The total standard deviation of the TIS score was < 0.04 units across three sites with ≥98% concordance between sites. The 6 users across the three sites did not significantly contribute to the assay variability. There was 100% concordance in biomarker high/low categorization between multiple reagent lots and multiple instruments. Conclusions: The analytical performance of the NanoString TIS assay has been validated to give consistent results across the RNA input range and between site, instrument, assay user, and reagent kit lot. The assay is well suited for decentralized clinical testing and is currently under investigation as a biomarker to enrich for response to anti-PD1 therapy across multiple tumor types.

Development of gene expression signatures characterizing the tumor-immune interaction

205

Background: The efficacy of anti-tumor immunity depends on diverse factors, including not just abundance of immune cell populations but also activities of those populations and of tumor cells. Many of these processes are onerous to assay, but all are reflected in a tumor’s gene expression profile. Using a novel method, we develop gene expression signatures measuring a variety of biological processes underlying the tumor-immune interaction. These signatures fall into categories including antigen availability, structural barriers to immune infiltration, inhibitory signaling by both immune and tumor cells, inhibitory metabolism, pro-immune signaling, killing of tumor cells, tumor receptiveness to immune signaling, and tumor proliferation and death. Methods: We develop a method to train signatures of biological processed by synthesizing biological knowledge and large gene expression datasets. For a given process, we use literature searches and expert knowledge to derive lists of candidate genes. We then evaluate the co-expression of these candidate genes in data from The Cancer Genome Atlas (TCGA), discarding genes whose co-expression patterns are incompatible with their measuring their putative biological process. This approach safeguards the interpretability of our signatures: we only report signatures whose genes show evidence for measuring the desired biology. Finally, we further exploit co-expression patterns to obtain optimal weights for each signature gene. Results: We attempted to train signatures of over 30 biological processes involved in immune oncology. Of these, 17 candidate gene sets displayed sufficient evidence for measuring their putative biology. We show these signatures provide granular but intelligible descriptions of both immunotherapy datasets and single samples. We find they improve power in differential expression analyses and in training of predictors of drug response. Conclusions: The signatures we derive convert gene expression data into measurements of biological processes central to immune oncology, and they improve statistical power and interpretation of results in immunotherapy studies. Our training procedure ensures these signatures measure their intended biology.

Clinical Characteristics of Parainfluenza Virus Infection among Healthy Subjects with Influenza-like Illness

Background

Parainfluenza virus (PIV) is a chief cause of croup. Less is known about the role of PIV in causing influenza-like illness (ILI) among healthy adults and children. We evaluated clinical characteristics of PIV compared with influenza (flu) infection in healthy subjects diagnosed with ILI.

Methods

The Acute Respiratory Infection Consortium (ARIC) conducted an observational, longitudinal study of ILI at five US military treatment facilities from 2009 to 2016. Participants were otherwise healthy military members, retirees, and their dependents. Symptom data were captured prospectively on days 0, 3, and 7 by interview and patient diary. Nasopharyngeal specimens were collected for etiologic determination by multiplex assay (Diatherix Laboratories, LLC.) Severity scores were calculated for upper respiratory, lower respiratory, GI, composite and systemic symptoms

Results

PIV did not account for a large proportion of ILI in our population with 43/961(4.7%) PIV+ vs. 153/961(15.9%) that were flu+. Age &lt; 5 years was associated with increased detection of PIV (10% in &lt;5 years vs. 3.2% in 5–65 years, P &lt; 0.01). Additionally, on multivariable analysis, the presence of a child aged &lt;5 years in the household was associated with an increased risk of PIV detection (OR = 2.58; 95% CI:1.39, 4.80). Sex, geographic location, year of detection, race/ethnicity, smoking status and obesity were all unrelated to PIV detection. Codetections occurred in 8/43 (18.6%) subjects, but codetected viruses did not show any specific pattern, with 5 different viruses found. When comparing demographic characteristics of ILI caused by flu vs. PIV, the only difference was that flu+ subjects were more often ≥5 years (P &lt; 0.01). Comparing symptom profile and severity of adults with PIV + ILI vs. flu+, we found no differences in the presence or severity of 20 symptoms, nor in severity scores for each of the 5 categories. Rates of hospitalization, antibiotic use, or duration of illness were also indistinguishable.

Conclusion

This is one of a few studies to detail the clinical characteristics of PIV presenting as ILI in healthy subjects. PIV is more often detected in young children with ILI. Although PIV was detected 25% as often as flu, it had an indistinguishable clinical course from influenza associated ILI in adults.

Disclosures

L. Malone, diatherix: Employee, Salary. E. Grigorenko, Diatherix Laboratories: Employee, Salary. D. Stalons, Diatherix Laboratories: Employee, Salary.

The Incidence of Ischemic Colitis after Repair of Ruptured Abdominal Aortic Aneurysms Is Decreasing in the Endovascular Era

BACKGROUND

Ischemic colitis (IC) is a well-described complication of ruptured abdominal aortic aneurysms (rAAAs). The purpose of this study was to compare the incidence of IC in patients with rAAA undergoing open repair (OR) versus endovascular aneurysm repair (EVAR) at a single institution. In addition, we analyzed the incidence of IC before and after the implementation of a formal rupture AAA protocol (rEVAR protocol).

METHODS

A retrospective analysis of prospectively collected data on all patients presenting with rAAA to our institution between January 2002 and October 2013 was performed. Variables were analyzed for association with IC. Comparisons were made using Pearson's chi-squared test or Fisher's exact test for categorical variables, Student's t-test for continuous variables, and logistic regression for multivariate analysis. Significance was set at P < 0.05.

RESULTS

Three hundred three patients with rAAA presented over the 10-year study period. One hundred ninety-one patients underwent OR and 89 patients underwent endovascular repair. Twenty-three patients died either in the emergency department, en route to the operating room, or after choosing comfort care. Predictive factors of IC included estimated blood loss, corresponding need for resuscitation, and duration of procedure. Of patients who underwent OR, the rate of IC was 21% (40/191). This was significantly higher than patients who underwent EVAR, 7% (6/89), P < 0.05. The type of intervention did not influence 30-day mortality in patients with IC. However, only 17% (1/6) of patients who had IC following EVAR required colectomy versus 48% (19/40) of patients with IC following OR (P = 0.21). Implementation of our formal rEVAR protocol decreased the incidence of IC significantly from 37.1% (36/97) to 6.4% (10/157), P < 0.001.

CONCLUSIONS

The incidence of IC has decreased significantly in the endovascular era but continues to portend a poor prognosis. Implementation of a formal, multidisciplinary rEVAR protocol in our institution decreased the incidence of IC.

Verification of the analytical performance of a molecular diagnostic for response to anti-PD1 therapy on the nCounter Dx Analysis System

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Background: Pembrolizumab is a humanized anti-PD1 antibody that is approved for use in advanced melanoma, recurrent or metastatic head and neck squamous cell carcinoma, and metastatic non-small-cell lung cancer. It has also shown clinical activity in a number of other tumor types in clinical trials, but there is need for a precise and accurate test that can identify patients most likely to benefit from therapy. We have previously described the development and analytical performance of a NanoString RNA expression clinical trial assay, referred to here as the Tumor Inflammation Signature (TIS) assay, which is being evaluated as a patient enrichment biomarker in multiple solid tumor types for treatment with pembrolizumab. Here we describe additional performance data and analytical verification of reproducibility from tissue and RNA input range in multiple tumor types. Methods: Linearity and specificity were assessed with single targets or pools of in vitro transcribed RNAs with sequences matching the 18 biomarker and 10 normalization gene probe targets. The verification of the previously reported analytical precision from RNA and reproducibility from tissue was performed using independent samples from 11 tumor types. The biological variability of the signature within a patient sample was evaluated by testing multiple core punches from formalin fixed paraffin embedded (FFPE) tissue blocks. Results: The TIS assay’s measurement of the 28 genes was linear across a wide dynamic range ( ≥ 4 logs) and was highly specific with < 1% cross reactivity between probes. The assay was verified across the specified RNA input range with > 90% concordance at the low end (50ng) of the RNA input range. The total standard deviation of the anti-PD1 Predictor Score from tissue was verified as < 5% of the signature score range and > 90% concordance in biomarker high/low categorization within the biological replicates. Conclusions: The analytical performance of the NanoString TIS assay was verified to be robust. The assay is well suited for decentralized clinical testing and is currently under investigation to identify responders to anti-PD1 therapy in multiple tumor types in several clinical studies.

Abstract P6-07-01: Development of a Prosigna® (PAM50)-based classifier for the selection of advanced triple negative breast cancer (TNBC) patients for treatment with enzalutamide

Background: Enzalutamide is an orally administered androgen receptor (AR) inhibitor approved by the FDA for use in men with metastatic castrate-resistant prostate cancer. A recent phase II study of enzalutamide in patients with advanced, AR positive, TNBC (NCT01889238) demonstrated significant improvements in both PFS and OS for patients whose tumors exhibited a gene expression (Gx) profile enriched in AR signaling and luminal biology. A PAM50-based signature was developed from the phase 2 study which used next generation RNA sequencing (NGS) to identify patients likely to respond to enzalutamide. We transitioned the test to the NanoString (NS) nCounter® Analysis System using Prosigna reagents to support clinical validation in a phase 3 trial. Here we describe the development and analytical performance of the NanoString Androgen Gene Expression Profiling Assay-1 (NS-AR-01). Methods: The NS-AR-01 algorithm coefficients were calibrated from the Predict AR algorithm by testing FFPE tumor tissue from patients who were pre-screened but not enrolled in the phase II study with both platforms (NGS and NS). Three unique algorithms were developed and subsequently challenged with an independent sample set with NGS data to provide an unbiased evaluation of the concordance of the platforms. A pre-specified clinical accuracy verification study was performed through prediction of NS-AR-01 scores from the NGS Gx data from the patients included in the phase 2 study efficacy analysis. The final NS-AR-01 algorithm was selected based on performance in the clinical accuracy verification. The final NS-AR-01 algorithm was evaluated in the 118 patients included in the ITT analysis, as well as those treated with 0-1 lines of prior therapy. The analytical performance of the assay was characterized by testing precision from RNA, reproducibility from FFPE tissue, sensitivity to RNA input amounts, and the impact of common interferents. Results: All three algorithm translations met the pre-specified clinical accuracy verification acceptance criteria. The final NS-AR-01 algorithm generated a hazard ratio most similar to that observed from the NGS algorithm. The total standard deviation when testing multiple FFPE sections from the same block was &lt; 1.5% of the score range with an empirical concordance rate of 100% for biomarker status. The range of RNA input specified for Prosigna was successfully verified for NS-AR-01 (125ng–500ng total RNA). The assay was demonstrated to be robust to common interferents including non-tumor tissue. Conclusions: Based on these results, NS-AR-01 is an accurate, precise, and robust assay for the identification of advanced TNBC patients who may respond to treatment with enzalutamide. The assay is well suited to clinical applications, and its ability to identify responders to enzalutamide will be evaluated in future investigational studies.Background: Enzalutamide is an orally administered androgen receptor (AR) inhibitor approved by the FDA for use in men with metastatic castrate-resistant prostate cancer. A recent phase II study of enzalutamide in patients with advanced, AR positive, TNBC (NCT01889238) demonstrated significant improvements in both PFS and OS for patients whose tumors exhibited a gene expression (Gx) profile enriched in AR signaling and luminal biology. A PAM50-based signature was developed from the phase 2 study which used next generation RNA sequencing (NGS) to identify patients likely to respond to enzalutamide. We transitioned the test to the NanoString (NS) nCounter® Analysis System using Prosigna reagents to support clinical validation in a phase 3 trial. Here we describe the development and analytical performance of the NanoString Androgen Gene Expression Profiling Assay-1 (NS-AR-01). Methods: The NS-AR-01 algorithm coefficients were calibrated from the Predict AR algorithm by testing FFPE tumor tissue from patients who were pre-screened but not enrolled in the phase II study with both platforms (NGS and NS). Three unique algorithms were developed and subsequently challenged with an independent sample set with NGS data to provide an unbiased evaluation of the concordance of the platforms. A pre-specified clinical accuracy verification study was performed through prediction of NS-AR-01 scores from the NGS Gx data from the patients included in the phase 2 study efficacy analysis. The final NS-AR-01 algorithm was selected based on performance in the clinical accuracy verification. The final NS-AR-01 algorithm was evaluated in the 118 patients included in the ITT analysis, as well as those treated with 0-1 lines of prior therapy. The analytical performance of the assay was characterized by testing precision from RNA, reproducibility from FFPE tissue, sensitivity to RNA input amounts, and the impact of common interferents. Results: All three algorithm translations met the pre-specified clinical accuracy verification acceptance criteria. The final NS-AR-01 algorithm generated a hazard ratio most similar to that observed from the NGS algorithm. The total standard deviation when testing multiple FFPE sections from the same block was &lt; 1.5% of the score range with an empirical concordance rate of 100% for biomarker status. The range of RNA input specified for Prosigna was successfully verified for NS-AR-01 (125ng–500ng total RNA). The assay was demonstrated to be robust to common interferents including non-tumor tissue. Conclusions: Based on these results, NS-AR-01 is an accurate, precise, and robust assay for the identification of advanced TNBC patients who may respond to treatment with enzalutamide. The assay is well suited to clinical applications, and its ability to identify responders to enzalutamide will be evaluated in future investigational studies.

Citation Format: Danaher P, Skewis L, Mashadi-Hossein A, Ram N, Gowen-MacDonald J, Harris E, Ferree S, Buckingham W. Development of a Prosigna® (PAM50)-based classifier for the selection of advanced triple negative breast cancer (TNBC) patients for treatment with enzalutamide [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-07-01.

Abstract PD5-06: Prognostic value of molecular tumor infiltrating lymphocyte (mTIL) signatures in HER2-positive breast cancer patients in N9831 and FinHer/FinXX trials

Background: While previous study showed that the enrichment of immune-related gene expression was associated with outcome in HER2+ patients receiving sequential or concurrent trastuzumab (H), stromal tumor infiltrating lymphocytes (sTIL) have not been consistently shown to associate with outcome in this group of patients. Given that TIL scoring may be subjective, we analyzed molecular signatures of different subsets of tumor infiltrating immune cell populations, using NanoStringTM gene expression data to assess molecular TIL (mTIL) signature enrichment and intrinsic subtype as a function of relapse-free survival (RFS).

Methods: NanoStringTM technology was used to quantify mRNA in samples from 1,280 patients in N9831, 168 patients in FinHer, and 170 patients in FinXX. In N9831, patients in arm A were treated with chemotherapy alone (AC-T), arm B received chemotherapy followed by sequential H (AC-T-H), and arm C received H concurrently with chemotherapy (AC-TH). In the FinHer trial, H was given concurrently for 9 weeks and either 1 year or 9 weeks in FinXX trial. Cox proportional hazard ratio (HR) was used to determine the association of each gene signature with RFS. Different immune subset signatures, including CD45, B-cells, CD8 T-cells, cytotoxic-cells, and T-cells were analyzed using algorithms developed by NanoString.

Results: In N9831, CD45, cytotoxic-cell, and T-cell signatures were significantly associated with improved RFS in patients receiving chemotherapy alone and AC-T-H. However, none of the mTIL signatures were significantly associated with outcome in patients receiving AC-TH. Patients lacking CD45 enrichment had better outcome when H was given concurrently with chemotherapy. The 10-year Kaplan-Meier estimates for RFS in arm B patients with CD45 enrichment or no enrichment were 81.3% and 72.6%, respectively (HR 0.63 [95% CI, 0.42-0.93]; p = 0.02), and in arm C were 83.6% and 79.8%, respectively (HR 0.79, 95%CI 0.49-1.28; p = 0.34). Among patients with HER2-enriched subtype, all of the mTIL signatures were associated with improved RFS in arm A (AC-T) and B (AC-T-H) but remained non-significant in arm C (AC-TH). In patients with luminal subtypes, mTIL signatures were not significantly associated with outcome in patients treated with chemotherapy alone. Similar findings were observed in the FinHer and FinXX trials, in which, none of mTIL signatures were significantly associated with outcome among patients who received H.

Conclusion: This analysis sheds light on previous discrepancy between immune-related gene signature and sTIL findings. Our data also suggests that the poor prognosis associated with lack of infiltrating immune cells can be partly overcome by the concomitant administration of H with chemotherapy. mTIL signatures, specifically CD45, cytoxic, and T cells, were prognostically associated with improved outcome in patients receiving chemotherapy without concurrent trastuzumab. Understanding the role of the immune system in response to H will require a higher degree of granularity than can be achieved by histological quantification of TILs. Further studies are needed to validate the significance of mTIL signatures as predictive or prognostic biomarker in HER+ patients.

Citation Format: Chumsri S, Serie DJ, Mashadi-Hossein A, Tenner KS, Lauttia SL, Moreno-Aspitia A, McLaughlin SA, Nassar A, Warren S, Danaher P, Colon-Otero G, Lindman H, Joensuu H, Perez EA, Thompson EA. Prognostic value of molecular tumor infiltrating lymphocyte (mTIL) signatures in HER2-positive breast cancer patients in N9831 and FinHer/FinXX trials [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD5-06.