Single cell spatial transcriptomic profiling of childhood-onset lupus nephritis reveals complex interactions between kidney stroma and infiltrating immune cells

Children with systemic lupus erythematosus (SLE) are at increased risk of developing kidney disease, termed childhood-onset lupus nephritis (cLN). Single cell transcriptomics of dissociated kidney tissue has advanced our understanding of LN pathogenesis, but loss of spatial resolution prevents interrogation of in situ cellular interactions. Using a technical advance in spatial transcriptomics, we generated a spatially resolved, single cell resolution atlas of kidney tissue (>400,000 cells) from eight cLN patients and two controls. Annotated cells were assigned to 35 reference cell types, including major kidney subsets and infiltrating immune cells. Analysis of spatial distribution demonstrated that individual immune lineages localize to specific regions in cLN kidneys, including myeloid cells trafficking to inflamed glomeruli and B cells clustering within tubulointerstitial immune hotspots. Notably, gene expression varied as a function of tissue location, demonstrating how incorporation of spatial data can provide new insights into the immunopathogenesis of SLE. Alterations in immune phenotypes were accompanied by parallel changes in gene expression by resident kidney stromal cells. However, there was little correlation between histologic scoring of cLN disease activity and glomerular cell transcriptional signatures at the level of individual glomeruli. Finally, we identified modules of spatially-correlated gene expression with predicted roles in induction of inflammation and the development of tubulointerstitial fibrosis. In summary, single cell spatial transcriptomics allows unprecedented insights into the molecular heterogeneity of cLN, paving the way towards more targeted and personalized treatment approaches.

An information theoretic approach to detecting spatially varying genes

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.

Abstract 5637: Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue

Cancer research across drug development, molecular biomarkers, and patient response depends on understanding biology that is dependent on complex interactions between malignant, immune, and stromal cells. To survive clearance mechanisms, a tumor can rely on a myriad of escape strategies, and the microenvironment is architected around the current path of escape. To enable a more comprehensive picture of tumor biology, we have developed the CosMx™ Spatial Molecular Imager (SMI) technology to capture a snapshot of thousands of RNA species resolved subcellularly from a single, standard histopathology slide. Building upon the previously released panels, this study tests a new 6,000-plex panel, the highest RNA plex measured in situ within human tissue, allowing the imputation of a spatial whole transcriptome in the tissue. We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on an FFPE human liver cancer tissue (~1 cm2 area) using the CosMx SMI. This RNA panel covers broad biological areas with special emphasis on oncology, immunology, and signal transduction, such that all cancer researchers can benefit from the direct detection of targets of interest (sans imputation) in intact tissue. Analysis algorithms were developed to allow robust assessments of cell types, cell states, cell-cell interactions, and pathway activation. Imputation based on reference profiles from HCA, TCGA, and other public repositories allows estimation of non-measured transcripts at a ratio of approximately 1:3, compared to the approximate 1:20-1:70 imputations performed previously for spatial data.Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE liver cancer tissue section at single-cell subcellular resolution. We were able to accurately map known reference profiles from scRNA-seq into this sample while identifying cancer-specific malignant, immune, and stromal cells in this tissue sample using this ultra-high plex RNA panel. In addition, we constructed sample-specific spatial neighborhoods, defined by cell types, cell states, and nearly unlimited sets of biological pathways through the imputed whole transcriptome. Finally, we measured >1,000 ligand-receptor interactions between key cell types of adjacent cells in the tissue, identifying mechanisms for tumor-mediated escape as well as reactive re-architecting of the native stroma which defines the trajectory of cancer’s evolution. Single-cell spatial measurements of gene expression at 6,000 plex from a single FFPE slide has the potential to transform our understanding of tumor biology and facilitate the next advances in cancer research by extracting the highest data density possible from rare specimens collected during patient treatment.

Citation Format: Shanshan He, Michael Patrick, Jason W. Reeves, Patrick Danaher, Julian Preciado, Joseph Phan, Erin Piazza, Zachary Reitz, Lidan Wu, Rustem Khafizov, Haiyan Zhai, Michael Rhodes, David Ruff, Joseph Beechem. Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue. [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 5637.

Highly Multiplexed Spatially Resolved Proteomic and Transcriptional Profiling of the Glioblastoma Microenvironment Using Archived Formalin-Fixed Paraffin-Embedded Specimens

Glioblastoma is a heterogeneous tumor for which effective treatment options are limited and often insufficient. Few studies have examined the intratumoral transcriptional and proteomic heterogeneity of the glioblastoma microenvironment to characterize the spatial distribution of potential molecular and cellular therapeutic immunooncology targets. We applied an integrated multimodal approach comprised of NanoString GeoMx Digital Spatial Profiling, single-cell RNA-seq (scRNA-seq), and expert neuropathologic assessment to characterize archival formalin-fixed paraffin-embedded glioblastoma specimens. Clustering analysis and spatial cluster maps highlighted the intratumoral heterogeneity of each specimen. Mixed cell deconvolution analysis revealed that neoplastic and vascular cells were the prominent cell types throughout each specimen, with macrophages, oligodendrocyte precursors, neurons, astrocytes, and oligodendrocytes present in lower abundance and illustrated the regional distribution of the respective cellular enrichment scores. The spatial resolution of the actionable immunotherapeutic landscape showed that robust B7H3 gene and protein expression was broadly distributed throughout each specimen and identified STING and VISTA as potential targets. Lastly, we uncovered remarkable variability in VEGFA expression and discovered unanticipated associations between VEGFA, endothelial cell markers, hypoxia, and the expression of immunoregulatory genes, indicative of regionally distinct immunosuppressive microdomains. This work provides an early demonstration of the ability of an integrated panel-based spatial biology approach to characterize and quantify the intrinsic molecular heterogeneity of the glioblastoma microenvironment.

Longitudinal transcriptional analysis of peripheral blood leukocytes in COVID-19 convalescent donors

BACKGROUND

SARS-CoV2 can induce a strong host immune response. Many studies have evaluated antibody response following SARS-CoV2 infections. This study investigated the immune response and T cell receptor diversity in people who had recovered from SARS-CoV2 infection (COVID-19).

METHODS

Using the nCounter platform, we compared transcriptomic profiles of 162 COVID-19 convalescent donors (CCD) and 40 healthy donors (HD). 69 of the 162 CCDs had two or more time points sampled.

RESULTS

After eliminating the effects of demographic factors, we found extensive differential gene expression up to 241 days into the convalescent period. The differentially expressed genes were involved in several pathways, including virus-host interaction, interleukin and JAK-STAT signaling, T-cell co-stimulation, and immune exhaustion. A subset of 21 CCD samples was found to be highly "perturbed," characterized by overexpression of PLAU, IL1B, NFKB1, PLEK, LCP2, IRF3, MTOR, IL18BP, RACK1, TGFB1, and others. In addition, one of the clusters, P1 (n = 8) CCD samples, showed enhanced TCR diversity in 7 VJ pairs (TRAV9.1_TCRVA_014.1, TRBV6.8_TCRVB_016.1, TRAV7_TCRVA_008.1, TRGV9_ENST00000444775.1, TRAV18_TCRVA_026.1, TRGV4_ENST00000390345.1, TRAV11_TCRVA_017.1). Multiplexed cytokine analysis revealed anomalies in SCF, SCGF-b, and MCP-1 expression in this subset.

CONCLUSIONS

Persistent alterations in inflammatory pathways and T-cell activation/exhaustion markers for months after active infection may help shed light on the pathophysiology of a prolonged post-viral syndrome observed following recovery from COVID-19 infection. Future studies may inform the ability to identify druggable targets involving these pathways to mitigate the long-term effects of COVID-19 infection.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT04360278 Registered April 24, 2020.

Abstract C038: Repeat RNA dysregulation of cellular states in the pancreatic cancer microenvironment

Aberrant transcription of the repeat RNAs is a common feature in epithelial cancers including PDAC, but the function of these non-coding RNAs in cancer development is relatively unexplored. We have found that these repeat RNAs are sensed and replicate like retroviruses, and now have identified the ability of these viral-like elements to be transmitted from cancer cells through extracellular vesicles (EVs). PDAC-derived EVs applied to cancer-associated fibroblasts (CAFs) activates interferon-stimulated genes (ISGs) and is able to drive CAFs towards an inflammatory CAF (iCAF) phenotype with concordant loss of myofibroblast CAF (myCAF) marker genes. Using in-vitro transcription, we demonstrate that individual repeat RNAs (HSATII, HERVK (env), LINE-1 5’UTR and LINE-1 3’UTR) are sufficient to induce ISG response in CAFs with HSATII and HERVK (env) having the most potent ISG response. In contrast, PDAC cells were found to induce epithelial-mesenchymal transition (EMT) with loss of epithelial gene expression. To determine the potential sensor of HSATII repeat RNAs, we utilized CRISPR/Cas9 knockout of the viral RNA sensors RIG-I, MDA5, and MAVS in PDAC and CAF cells. Notably, these sensors were important for PDAC repeat RNA sensing and response, but not in CAF cells. Evaluating the innate immune pathway further downstream, we used genetic knockout of IRF3 with CRISPR/Cas9 knockout and find significant downregulation of key EMT genes that are shared with myCAF markers (ACTA2, FN1, SERPINE1). Interestingly, HSATII RNA activated IRF3 dependent EMT genes in PDAC cells, but induced IRF3 degradation in CAF cells that results in loss of myCAF gene expression. Furthermore, we found that conditioned media from HSATII transfected CAF activates EMT-related gene expression (ACTA2, FN1, SERPINE1) in PDAC cell lines, which indicates an cell extrinsic mechanism to augment EMT induction in PDAC cells. We utilized next generation spatial transcriptomic platforms NanoString GeoMx and CosMx to understand the spatial distribution of repeat RNAs in human PDAC tumors. We find that repeat RNAs can be found as a gradient from PDAC cells to the surround tumor microenvironment consistent with delivery of these RNA species. Analysis of over 300,000 individual cells in 3 PDAC tumor specimens, we find that high repeat PDAC cells have lost epithelial gene expression and high repeat CAFs have lost myCAF gene expression. Altogether, these findings support the “infection” of repeat RNAs disrupts cellular identity in both tumor cells and the CAF microenvironment as a mechanism for tumor progression.

Citation Format: Eunae You, Luli Zou, Patrick Danaher, Ildiko E. Phillips, Michael J. Raabe, Bidish Patel, Amaya Pankaj, Khrystyna North, Sean Kim, Youngmi Kim, Martin Aryee, David T. Ting. Repeat RNA dysregulation of cellular states in the pancreatic cancer microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C038.

Abstract 3878: Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager

The spatial interactions between the immune system and tumor cells greatly influence antitumoral immunity. Characterization of immune cell composition and infiltration within the tumor niche informs prognosis, drug delivery efficiency, and therapeutic efficacy. However, few methods exist to query large numbers of immune biomarkers at subcellular spatial resolution. The CosMx™ Spatial Molecular Imager is the first platform to demonstrate simultaneous single-cell and subcellular detection of over 100 proteins on standard, biobanked, FFPE tissue samples. This high-plex protein panel detects key drivers of cancer progression and immune cell activation states. Here, we apply the CosMx 100-plex immuno-oncology assay on a set of breast cancer biopsies and demonstrate its quantitative and spatial capabilities. Key to CosMx protein technology is an antibody-oligonucleotide-conjugate 64-bit encoding method, not a cyclic exchange method. The encoding scheme is enabled by a 20nm hybridization-based optical barcode. The CosMx system uses a fully automated, cyclic microfluidics imaging system, high-resolution optics and 3D capability. The raw cyclic encoded 4-color tissue images are decoded using a robust automated decoding algorithm that detects protein sub-cellular localization and quantifies expression level. CosMx SMI produces protein localization maps for each target, which characterizes tissue microenvironment heterogeneity while providing spatial information. Additionally, accurate segmentation of individual cells enables spatial single-cell protein expression analysis, facilitating further mining and analyses of cellular subpopulations. The CosMx protein assay reagents were validated on multi-organ FFPE tissue microarrays and 35 human FFPE cell lines, including overexpression lines for key targets and cellular activation states, such as GITR, CD278, PD-L1, and PD-1. Benchmarking to multiple orthogonal datasets (e.g., the Human Protein Atlas, Cancer Cell Line Encyclopedia, and low-plex IHC) demonstrates that the assay is highly sensitive and specific. CosMx SMI protein assay can be coupled with SMI’s 1000-plex RNA-detection assay; together, such a multi-omics platform can generate an unprecedented information-rich view of spatial biology that could usher in novel discoveries about health and disease. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Citation Format: Zachary R. Lewis, Tien Phan-Everson, Gary Geiss, Mithra Korukonda, Ruchir Bhatt, Carl Brown, Dwayne Dunaway, Joseph Phan, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Yan Liang, Emily Brown, Brian Birditt, Giang Ong, Hye Son Yi, Erin Piazza, Vikram Devgan, Nicole Ortogero, Patrick Danaher, Sarah Warren, Michael Rhodes, Joseph Beechem. Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3878.

Adaptive immune signature in HER2-positive breast cancer in NCCTG (Alliance) N9831 and NeoALTTO trials

Trastuzumab acts in part through the adaptive immune system. Previous studies showed that enrichment of immune-related gene expression was associated with improved outcomes in HER2-positive (HER2+) breast cancer. However, the role of the immune system in response to lapatinib is not fully understood. Gene expression analysis was performed in 1,268 samples from the North Central Cancer Treatment Group (NCCTG) N9831 and 244 samples from the NeoALTTO trial. In N9831, enrichment of CD45 and immune-subset signatures were significantly associated with improved outcomes. We identified a novel 17-gene adaptive immune signature (AIS), which was found to be significantly associated with improved RFS among patients who received adjuvant trastuzumab (HR 0.66, 95% CI 0.49-0.90, Cox regression model p = 0.01) but not in patients who received chemotherapy alone (HR 0.96, 95% CI 0.67-1.40, Cox regression model p = 0.97). This result was validated in NeoALTTO. Overall, AIS-low patients had a significantly lower pathologic complete response (pCR) rate compared with AIS-high patients (χ2 p < 0.0001). Among patients who received trastuzumab alone, pCR was observed in 41.7% of AIS-high patients compared with 9.8% in AIS-low patients (OR of 6.61, 95% CI 2.09-25.59, logistic regression model p = 0.003). More importantly, AIS-low patients had a higher pCR rate with an addition of lapatinib (51.1% vs. 9.8%, OR 9.65, 95% CI 3.24-36.09, logistic regression model p < 0.001). AIS-low patients had poor outcomes, despite receiving adjuvant trastuzumab. However, these patients appear to benefit from an addition of lapatinib. Further studies are needed to validate the significance of this signature to identify patients who are more likely to benefit from dual anti-HER2 therapy. ClinicalTrials.gov Identifiers: NCT00005970 (NCCTG N9831) and NCT00553358 (NeoALTTO).

Metabolic adaptation of ovarian tumors in patients treated with an IDO1 inhibitor constrains antitumor immune responses

To uncover underlying mechanisms associated with failure of indoleamine 2,3-dioxygenase 1 (IDO1) blockade in clinical trials, we conducted a pilot, window-of-opportunity clinical study in 17 patients with newly diagnosed advanced high-grade serous ovarian cancer before their standard tumor debulking surgery. Patients were treated with the IDO1 inhibitor epacadostat, and immunologic, transcriptomic, and metabolomic characterization of the tumor microenvironment was undertaken in baseline and posttreatment tumor biopsies. IDO1 inhibition resulted in efficient blockade of the kynurenine pathway of tryptophan degradation and was accompanied by a metabolic adaptation that shunted tryptophan catabolism toward the serotonin pathway. This resulted in elevated nicotinamide adenine dinucleotide (NAD⁺), which reduced T cell proliferation and function. Because NAD⁺ metabolites could be ligands for purinergic receptors, we investigated the impact of blocking purinergic receptors in the presence or absence of NAD⁺ on T cell proliferation and function in our mouse model. We demonstrated that A2a and A2b purinergic receptor antagonists, SCH58261 or PSB1115, respectively, rescued NAD⁺-mediated suppression of T cell proliferation and function. Combining IDO1 inhibition and A2a/A2b receptor blockade improved survival and boosted the antitumor immune signature in mice with IDO1 overexpressing ovarian cancer. These findings elucidate the downstream adaptive metabolic consequences of IDO1 blockade in ovarian cancers that may undermine antitumor T cell responses in the tumor microenvironment.

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

953 Transcriptional analysis of leukocytes from COVID convalescent donors reveals persistent activation of the innate and adaptive immune system

Background

Coronavirus disease 2019 (COVID-19) results in robust but dysregulated acute immune response characterized by pro-inflammatory cytokine production and T-cell exhaustion, but little is known concerning immune response following recovery. We assessed immune function in convalescent plasma donors (CCD) who had recovered from COVID-19.

Methods

The cellular immune response and T-cell receptor (TCR) diversity in CCD was investigated using the nCounter host response and TCR diversity panels. 270 CCD and 40 healthy donor (HD) blood samples collected 11 to 193 days after diagnosis were analyzed. The CCD samples were from 162 donors, 69 donated more than once. All HD donated only once.

Results

Many genes were differentially expressed for months following infection. Analysis of samples collected 0 to 90 days post-diagnosis found that 19 of 773 genes were differentially expressed among CCD and HD (FDR < 0.05) (figure 1a). At 90 to 120 days, 120 to 150 and >150 post-diagnosis, 13, 58 and 4 genes were differentially expressed respectively (FDR < 0.05) (figures 1b-d). At 120 to 150 days the differentially expressed genes included those in Treg differentiation, type III interferon signaling and chemokine signaling pathways. 76 genes were differently expressed at least once during the time windows described above. (Figure 1e). Among CCD, the expression of CTLA-4, ICOS, ICOSLG, OSM and CXCR4 were initially elevated but fell to HD levels at the end of the study period. The expression of LILRA6, CCR2 and CX3CR1 increased or remained elevated throughout (figure 1f).

A subset of samples departed notably from the average trend. The transcriptome of each CCD sample was scored by its similarity to the mean transcriptome of HD samples. This analysis revealed 21 CCD samples from 19 unique donors were highly perturbed from HD samples (figure 2a). Among these highly perturbed samples 80% were collected > 90 days post-diagnosis. The perturbed samples clustered into two groups, labelled P1 and P2 (figure 2b) and displayed dysregulation of distinct gene sets (figures 2c, 2d). The P1 were characterized by increased expression of genes in myeloid inflammation, type 1 interferon and innate immune signaling pathways, lower COVID antibody levels and increased T-cell receptor diversity. P2 were characterized by highly up-regulated CD44, BCL2, TGFB1, IL18BP, IL27RA, and IL11RA.

Abstract 953 Figure 1

Longitudinal trends in CCD gene expression. a-d: Differential expression results in HD vs. 4 time windows of CCD. Genes with FDR <0.1 are labeled; e: average CCD log2 fold-changes from HD over time. Color is only given for times where the Loess regression is different from the mean HD with p < 0.05; f: longitudinal results for selected genes. Orange lines connect CCD samples over time. Blue lines show inner 95% quantiles of HD samples

Abstract 953 Figure 2

CCD with more severe departure from HD gene expression. a: CCD samples (in orange) were scored for perturbation from the mean HD (in blue), and 21 highly perturbed sample subsets emerged; b: clustering of the 21 highly perturbed patients. The dendrogram was cut to define two groups. c: volcano plots comparing expression in P1 (left) and P2 (right) vs. CCD; d: longitudinal trends of selected genes perturbed in P1 and P2

Conclusions

Immune dysregulation in CCD continues at least 6 months post-infection. Some CCDs experienced marked transcriptional changes which may be the result of COVID-19 reactivation and could be responsible for long-haul syndrome.

Acknowledgements

N/A

Trial Registration

NCT04360278

References

N/A

Ethics Approval

N/A

Consent

N/A

50 In-situ visualization and measurement of tumor-infiltrating lymphocytes (TILs) on intact FFPE renal cell carcinoma (RCC) tissue using the spatial molecular imager (SMI)

Background

Although cancer immunotherapies can effectively restore T cell-mediated immunity leading to sustained clinical responses, these responses are unpredictable partly due to highly heterogeneous phenotypes of tumor-infiltrating lymphocytes (TILs) between patients. Thus, understanding such TILs and their roles in the context of tumor microenvironments (TME) may lead to developing better immunotherapy solutions. The spatial molecular imager (SMI) is a novel spatial transcriptomics platform that allows spatially resolved high-dimensional cellular phenotyping for comprehensive TIL profiling. SMI uses fluorescent molecular barcodes to enable in-situ measurement of biological targets on an intact tissue sample. Here, we characterize comprehensive TIL phenotypes and visualize landscape of TILs directly on intact formalin-fixed paraffin-embedded (FFPE) tissues using a 1000+-plex RNA panel.

Methods

To build multi-omics TIL profiling data sets for renal cell carcinoma (RCC) tissues, we employed scRNA-seq, mass cytometry (CyTOF) and SMI. Peripheral blood mononuclear cells and dissociated cells from matched RCC tumor and adjacent normal tissues were analyzed by CyTOF and single-cell sequencing. Then, SMI profiling of matching FFPE tissues was used to visualize TILs in the context of the TME and to understand relationships between high-dimensional cellular heterogeneity and the spatial organization of cells within a tumor tissue.

Results

CyTOF and scRNA-seq analysis of dissociated cells was used to determine the gene expression profiles of numerous cellular subsets. TCR sequencing was also used to assess the extent of clonal expansion and clonotypic relationships between blood and tumor. Consistent with our previous reports, T cell populations could be segregated based on markers associated with chronic T cell receptor signaling and many T cells with an exhausted phenotype were clonally expanded in the tumor but not the blood. In contrast, T cell clonotypes with bystander phenotypes in the tumor were readily detected as expanded clones in the blood, supporting notion that not all tumor-infiltrating T cells are specific for tumor antigens. SMI analysis of matched tumor tissue was used to accurately quantify the densities and to determine the spatial organization of all T cell subsets. In addition, computational methods were used to describe distinct cellular niches within tumors with accurately defined cellular compositions.

Conclusions

High dimensional cellular profiling highlights the abundance of bystander T cell infiltration of RCC tumors. Comprehensive spatial profiling by SMI provides spatial context to the highly diverse immune cell composition of tumor infiltrates.

Ethics Approval

Fully anonymous human material was obtained from Northwest Biotrust and given IRB designation of non-human subjects research.

1512. Influenza vaccine effectiveness wanes over the influenza season: results from five military treatment facilities

Background

Influenza vaccination can reduce influenza burden, but questions have arisen about the duration of vaccine protection. While the timing of vaccination varies, annual receipt of influenza vaccine is mandated for active duty military personnel. The goal of this analysis is to determine if influenza vaccine effectiveness decreases over time. A secondary goal of this analysis is to determine if repeated influenza vaccination is associated with risk for influenza.

Methods

Otherwise healthy individuals presenting for treatment of acute respiratory infections at 5 military treatment facilities from 2009 to 2018 were enrolled in the Acute Respiratory Infection Consortium (ARIC) study. Individuals with complete demographics, influenza vaccination in the two years prior to illness, and influenza laboratory results were included in this analysis (n=1,273). Multivariate logistic regression was used to calculate the odds of an influenza diagnosis according to time since influenza vaccination, categorized in 90-day periods. The model also included age, race, month of diagnosis, influenza season, and whether the participant received 4+ influenza vaccinations in the past 5 years.

Results

One hundred and ninety-two individuals (15%) had laboratory confirmed influenza (Table 1). Participants were mostly active duty, male, and white. Over half of the participants received 4+ influenza vaccinations in the past 5 years. Participants who were vaccinated 90-179 and 180+ days ago had greater odds of being diagnosed with influenza than did individuals who were vaccinated &lt; 90 days prior to illness onset (Table 2). Participants who were 18-24 years old had lower odds of influenza than individuals in other age groups. Vaccine experience (vaccinated against influenza for at least four of the past five years), race, and ethnicity were not statistically significantly associated with influenza diagnosis.

Table 1. Characteristics of individuals included in the analysis of waning influenza vaccine effectiveness in the ARIC study

Conclusion

Influenza vaccination was most effective 14-89 days post-vaccination and effectiveness decreased thereafter. Repeat influenza vaccination, however, was not significantly associated with greater odds of influenza. The waning effectiveness of influenza vaccination indicates additional consideration be given to the timing of vaccination.

Disclaimer

Disclosures

All Authors: No reported disclosures

1229. Volatile Biomarkers of Influenza Infection in the Breath

Background

Annual influenza epidemics cause significant morbidity and mortality. New, emerging strains threaten to cause catastrophic pandemics. Assay of exhaled breath for volatile organic compounds (VOCs) via gas chromatography-mass spectroscopy (GC-MS) is an emerging diagnostic modality ideally suited to fill the gap in influenza diagnostics.

Methods

Patients with influenza like illness (ILI) presenting to the Troop Medical Clinic on JBSA Fort Sam Houston, TX, from 3/2017 to 3/2019 submitted a 2-minute breath sample in addition to a nasopharyngeal swab collected for polymerase chain reaction (PCR) assay for influenza virus. ILI was defined as temperature > 100.4⁰F AND respiratory symptoms like cough, sputum production, chest pain and/or sore throat. Breath VOCs were assayed with GC-MS and data were analyzed in order to identify the significant breath VOC biomarkers that discriminated between ILI patients with and without a PCR assay positive for influenza with greater than random accuracy.

Results

Demographic, clinical, PCR and breath data were available for 237 episodes of ILI. PCR was positive for influenza for 32 episodes (30 influenza A and 2 B). The median age of participants was 21 (IQR 19, 23) and 69% were male. There were no differences in age, gender, education, race, or smoking, between the influenza positive and negative groups. Likewise, there was no difference in days of limited activity or missed work, or symptoms at presentation between the groups. The algorithm achieved near maximal predictive accuracy of 78% with four biomarkers (74% sensitivity and 70% specificity). Based on their mass spectra, these biomarker VOCs were tentatively identified as 2-amino-1-propanol, 2-butanamine, n-nitro, 3-methyl-hexanal, and heptane, which are consistent with products of oxidative stress.

Figure. Accuracy, Sensitivity, and Specificity of Influenza Breath Test. Receiver operating characteristic (ROC) of the breath test (sensitivity versus 1-specificity). The accuracy of the breath test was 78%. With a cutoff point at the “shoulder” of the ROC curve, the test had 74% sensitivity and 70% specificity.

Conclusion

Our findings bolster available benchtop and clinical data suggesting that breath testing may be a useful diagnostic modality for influenza infection. The next step will be to study the predictive algorithm developed in this protocol in a blinded validation cohort. If the predictive algorithm performs well in a validation study, adaptation for its use in a portable, tabletop GC would be warranted to allow for a rapid, accurate, universal point-of-care influenza diagnostic test.

Disclosures

Michael Phillips, MD, Menssana Research, Inc (Grant/Research Support)

Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX^® (Genomic Health.), Prosigna^™ (NanoString Technologies, Inc.), MammaPrint^® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational “modelling” of test results using published algorithms and secondly a “training” approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a “modelling” approach without reference to real world test results. Simultaneously we show that a “training” approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

Abstract 1688: High-throughput immune cell phenotyping using GeoMx DSP reveals Non-Small Cell Lung Cancers (NSCLC) are divided into distinct immunological subtypes

GeoMx DSP reveals NSCLCs are divided into distinct immunological subtypes, and local immune signatures of tumor sub-regions differentially affect neighboring tumor biology. Although cancer immunotherapy is considered an effective cancer-treatment option, means to measure its effect on sub-tumor regions are on demands. Despite its popularity, scRNA-seq only reveals cell populations found within a tissue but is mute on roles in tumor microenvironment (TME) like the impact of one cell type on another's behavior. To gain insights inaccessible to single-cell methods, we demonstrate a harmonized analysis of scRNA-seq and NanoString GeoMx™ data in lung tumors. This approach reveals the spatial distribution of cell populations defined via scRNA-seq, enabling rich descriptions of cells' responses to each other and to their locations within the tumor. GeoMx Digital Spatial Profiler (GeoMx DSP) is based on barcoding technology that enables spatially resolved, digital characterization of proteins or mRNA in a highly multiplexed (over 1,500-plex) assay. The oligonucleotide tags cleaved from discrete regions are quantitated by NGS, and counts are mapped back to tissue location, yielding a spatially-resolved digital profile of analyte abundance. To measure immune infiltrates in tumors, we employed machine learning techniques to classify gene sets of immune cell phenotypes and built an immune cell phenotyping panel for GeoMx DSP. Gene expression data from subregions of a tumor tissue were analyzed through this immune cell phenotyping pipeline to quantify immune infiltrates. We applied scRNA-seq to predefine the cell populations present in NSCLC tumors, and we applied the GeoMx platform to measure these populations across dozens of FFPE tumor sections. We found that there were three immunological subtypes; cold, myeloid enriched and lymphoid enriched groups. This finding was validated using traditional methods, such as FACS or immunohistochemistry analysis. In addition, we also profiled immune infiltrates of sub-tumor regions within a tumor tissue and found that spatial profiling of subregions and their neighboring environment (TME) enabled us to measure how TME differentially affects tumor biology. These measurements enable us to describe the distribution of immune populations across spatial variables like tumor interior vs. margin, to catalog the degree to which immune populations traffic together within the tumor, and to correlate gene expression in tumor cells with neighboring immune populations. These analyses demonstrate the ability of spatial RNA profiling to reach conclusions inaccessible to single-cell data alone. “FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.”

Citation Format: Youngmi Kim, Patrick Danaher, Brenn Nelson, Maddy Griswold, Margaret Hoang, McGarry A. Houghton, Joseph M. Beechem. High-throughput immune cell phenotyping using GeoMx DSP reveals Non-Small Cell Lung Cancers (NSCLC) are divided into distinct immunological subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1688.

Abstract 1364: Spatially-resolved in situ expression profiling using the GeoMx™ Cancer Transcriptome Atlas panel in FFPE tissue

The emerging field of spatial genomics represents a significant advance for biology. To drive new discoveries in spatial genomics and immuno-oncology, we introduce the GeoMx Cancer Transcriptome Atlas (CTA) Panel for comprehensive spatial analysis of cancer pathways using the Nanostring GeoMx Digital Spatial Profiler (DSP). We demonstrate profiling of 1600+ immuno-oncology targets in the tumor, microenvironment, and immune compartments of archival FFPE tissue sections, coupled to downstream Next Generation Sequencing (NGS) readout to enable high-throughput workflows. High-plex spatial RNA molecular profiling with GeoMx CTA was performed as follows:

1. Photocleavable DNA oligonucleotides tags were coupled to 8000+ in situ hybridization probes targeting 1600+ genes. These reagents were allowed to bind targets directly on slide-mounted FFPE tissue sections.

2. ROIs were identified and selected using GeoMx DSP, and ROI-specific oligonucleotide tags were released using ultraviolet exposure.

3. Released oligonucleotide tags from each ROI were collected and deposited into designated wells on a microtiter plate, allowing well indexing of each ROI during NGS library preparation.

4. After indexing, the entire plate was pooled into a single tube for purification and then sequenced on an Illumina instrument.

5. NGS reads were processed into digital counts and mapped back to each ROI, generating a map of transcript activity within the tissue architecture.

We compared data from experiments in which bulk RNA-seq and GeoMx DSP using the CTA Panel were performed on the same samples. Overall, we found good correlation between pseudo-bulk GeoMx CTA (sum of ROIs) and RNA-seq from the same tissue specimen. Individually, however, each ROI showed a distinct expression pattern from bulk, and ROI expression patterns clustered based on similar tissue morphology. Importantly, GeoMx CTA was able to detect a higher number of genes with low expression within the microenvironment and immune spatial compartment compared to bulk RNA-seq, providing a detailed look at the anti-tumor immune response. Lastly, we profiled similar tissues using a novel 18000+ gene whole transcriptome panel and found further enrichment of low-expressers relative to RNA-seq, revealing novel spatial biology previously masked by bulk assays. Together, these data demonstrate that GeoMx offers high sensitivity for genome-scale expression profiling while preserving critical information about tissue architecture. GeoMx DSP technology is for Research Use Only and not for use in diagnostic procedures.

Citation Format: Margaret L. Hoang, Michelle Kriner, Zoey Zhou, Zach Norgaard, Kristina Sorg, Chris Merritt, Erin Piazza, Marty Ross, Robin Fropf, Nileshi Saraf, Patrick Danaher, Michael Rhodes, Joseph Beechem. Spatially-resolved in situ expression profiling using the GeoMx™ Cancer Transcriptome Atlas panel in FFPE tissue [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1364.

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection

The relationship of SARS-CoV-2 lung infection and severity of pulmonary disease is not fully understood. We analyzed autopsy specimens from 24 patients who succumbed to SARS-CoV-2 infection using a combination of different RNA and protein analytical platforms to characterize inter- and intra- patient heterogeneity of pulmonary virus infection. There was a spectrum of high and low virus cases that was associated with duration of disease and activation of interferon pathway genes. Using a digital spatial profiling platform, the virus corresponded to distinct spatial expression of interferon response genes and immune checkpoint genes demonstrating the intra-pulmonary heterogeneity of SARS-CoV-2 infection.

Multiplex digital spatial profiling of proteins and RNA in fixed tissue

Digital Spatial Profiling (DSP) is a method for highly multiplex spatial profiling of proteins or RNAs suitable for use on formalin-fixed, paraffin-embedded (FFPE) samples. The approach relies on (1) multiplexed readout of proteins or RNAs using oligonucleotide tags; (2) oligonucleotide tags attached to affinity reagents (antibodies or RNA probes) through a photocleavable (PC) linker; and (3) photocleaving light projected onto the tissue sample to release PC oligonucleotides in any spatial pattern across a region of interest (ROI) covering 1 to ~5,000 cells. DSP is capable of single-cell sensitivity within an ROI using the antibody readout, with RNA detection feasible down to ~600 individual mRNA transcripts. We show spatial profiling of up to 44 proteins and 96 genes (928 RNA probes) in lymphoid, colorectal tumor and autoimmune tissues by using the nCounter system and 1,412 genes (4,998 RNA probes) by using next-generation sequencing (NGS). DSP may be used to profile not only proteins and RNAs in biobanked samples but also immune markers in patient samples, with potential prognostic and predictive potential for clinical decision-making.

Abstract B63: Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment

Background: Acute myeloid leukemia (AML) is characterized by clonal expansion of poorly differentiated myeloid precursors, resulting in impaired hematopoiesis and often bone marrow (BM) failure. The general therapeutic approach in patients with AML has not changed substantially in more than 30 years. Investigation of new strategies, including immunotherapy, remains a priority. Tumor phenotypes are dictated not only by major oncogene drivers, but also by the tumor immunologic microenvironment (TIME) which is inherently immunosuppressive, is equipped to hamper effector T-cell function and includes immune and inflammatory cells, soluble mediators such as interferon (IFN)-gamma and extracellular matrix components. Herein, we profiled the TIME of AML to identify gene signatures that are reflective of general immune status and predictive of antileukemia immune potential.

Methods: We used the hybridization-based nCounter® system (NanoString Technologies®, Seattle, WA) and the RNA Pan-Cancer Immune Profiling PanelTM to analyze BM aspirates from 290 adults and 40 children with newly diagnosed, nonpromyelocytic AML. Data were normalized to a set of reference genes and log2-transformed for bioinformatics analysis. The clinical relevance of the association between immune gene signatures and patient outcome was further validated in silico using publicly available cancer transcriptomic datasets (The Cancer Genome Atlas; TCGA).

Results: We identified distinct sets of co-expressed genes corresponding to innate immunity, adaptive immunity and IFN-gamma signaling. BM samples with IFN-gamma-dominant gene expression profiles showed up-regulation of actionable immune checkpoints, such as B7-H3, PD-L1 and CTLA-4. A set of 4 differentially expressed immune genes between children and adults with AML (FDR&lt;0.005) was associated with adverse cytogenetic features, leukemia relapse and shorter patient survival, and also stratified survival in the TCGA cohort of adult patients with AML. Finally, all four genes were amplified, deleted or mutated in 7% of 51,175 TCGA solid tumors, with the highest frequency of mutations being detected in primary CNS lymphoma (40%) and diffuse large B-cell lymphoma (25%).

Conclusions: Our study identified distinct immune gene programs in the TIME of patients with newly diagnosed AML. From a translational standpoint, immune-enriched and IFN-gamma-dominant AMLs may benefit from immune checkpoint blockade and from new immunotherapy approaches, including dual-affinity T-cell redirecting antibodies targeting CD123.

Grant support: John and Lucille van Geest Foundation, UK; Roger Counter Foundation, UK; Qatar National Research Fund (#NPRP8-2297-3-494).

Citation Format: Sergio Rutella, Jayakumar Vadakekolathu, Tressa Hood, Stephen Reeder, Sarah E. Warren, Patrick Danaher, Jan Davidson-Moncada, Alessandra Cesano, Joseph M. Beechem, Sarah K. Tasian, Mark D. Minden. Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B63.

Development of Gene Expression-Based Biomarkers on the nCounter® Platform for Immuno-Oncology Applications

Biomarkers based on transcriptional profiling can be useful in the measurement of complex and/or dynamic physiological states where other profiling strategies such as genomic or proteomic characterization are not able to adequately measure the biology. One particular advantage of transcriptional biomarkers is the ease with which they can be measured in the clinical setting using robust platforms such as the NanoString nCounter system. The nCounter platform enables digital quantitation of multiplexed RNA from small amounts of blood, formalin-fixed, paraffin-embedded tumors, or other such biological samples that are readily available from patients, and the chapter uses it as the primary example for diagnostic assay development. However, development of diagnostic assays based on RNA biomarkers on any platform requires careful consideration of all aspects of the final clinical assay a priori, as well as design and execution of the development program in a way that will maximize likelihood of future success. This chapter introduces transcriptional biomarkers and provides an overview of the design and development process that will lead to a locked diagnostic assay that is ready for validation of clinical utility.

2791. Burden of Respiratory Infections in Trainees Higher Than Healthcare Records Indicate: Results from an Anonymous Survey

Background

Methods

Results

Conclusion

Disclosures

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

8

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.