Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of <7 days from specimen receipt to report. The results demonstrate that the scalable assay accurately and reproducibly detects small variants, copy number alterations, microsatellite instability (MSI) and tumor mutational burden (TMB) from 40ng DNA, and multiple gene fusions, including known and unknown partners and splice variants from 20ng RNA. 717 tumor samples and reference materials with previously known alterations in 96 cancer-related genes were sequenced to evaluate assay performance. All variant classes were reliably detected at consistent and reportable variant allele percentages with >99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

BackgroundCyclospora cayetanensis is a food-borne intestinal human parasite that causes outbreaks of diarrhea. There is a need for efficient laboratory methods for strain-level characterization to assist in outbreak investigations. By using next generation sequencing, genomic sequences can be obtained and compared to identify potential genotyping markers. However, there is no method available to propagate this parasite in the laboratory. Therefore, genomic DNA must be extracted from oocysts purified from human stool. The objective of this study was to apply optimized methods to purify C. cayetanensis oocysts and extract DNA in order to obtain high-quality whole genome sequences with minimum contamination of DNA from other organisms.ResultsOocysts from 21 human stool specimens were separated from other stool components using discontinuous density gradient centrifugation and purified further by flow cytometry. Genomic DNA was used to construct Ovation Ultralow libraries for Illumina sequencing. MiSeq sequencing reads were taxonomically profiled for contamination, de novo assembled, and mapped to a draft genome available in GenBank to assess the quality of the resulting genomic sequences. Following all purification steps, the majority (81–99%) of sequencing reads were from C. cayetanensis. They could be assembled into draft genomes of around 45 MB in length with GC-content of 52%.ConclusionsDensity gradients performed in the presence of a detergent followed by flow cytometry sorting of oocysts yielded sufficient genomic DNA largely free from contamination and suitable for whole genome sequencing of C. cayetanensis. The methods described here will facilitate the accumulation of genomic sequences from various samples, which is a prerequisite for the development of typing tools to aid in outbreak investigations.

Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

e21113 Background: The clinical utility of immune checkpoint inhibitor (ICI) has been well established for CTLA-4 and PD-1/PD-L1 axis. A new ICI targeting LAG3 expressing T-cells was recently approved in combination with PD-1 inhibitor for advanced melanoma, with studies underway in other cancers. In this study, we assess LAG3 expression in a pan-cancer cohort, co-expression with other known ICI targets, and the survival and response implications of LAG3 expression in patients with non-small cell lung cancer (NSCLC) treated with pembrolizumab. Methods: A discovery cohort (DC) of 15,630 FFPE tumors of 35 histologies was evaluated by comprehensive immune profiling (CIP). LAG3 expression, measured by RNA-seq, was labeled as high (rank ≥75), moderate (rank 25-74), and low (rank < 25), and compared with tumor inflammation and cellular proliferation (expression of cellular proliferation markers such as ki-67) phenotypes. Co-expression analysis with other ICI targets was performed using Spearman correlation (rs) with p-values reported. In a retrospective cohort (RC) of 72 metastatic NSCLC patients treated with pembrolizumab alone, Kaplan-Meier analysis was performed to test for differences in overall survival (OS) and progression free survival (PFS). Differences in objective response rate (ORR) were determined using chi squared test. Results: A majority of patients in the DC had moderate LAG3 expression (46.63%). LAG3 high was most predominant in gynecological cancers such as uterine (42%), ovarian (39%) and cervical cancers (37%). High LAG3 was associated with high cellular proliferation (p < 0.0001), regardless of tumor inflammation. LAG3 was significantly co-expressed with other ICI targets such as PD-1 (rs> 0.6, p < 0.05), PD-L1 (rs> 0.4, p < 0.05), PD-L2 (rs> 0.6, p < 0.05) and TIM3 (rs> 0.5, p < 0.05). This finding was supported by significant association of PD-L1 by immunohistochemistry and LAG3 expression (p = 0.002) in the RC. 15% of DC was both PD-1 high and LAG3 high (2344/15,630). Similar results were observed for LAG3 high and PD-L1 high (1870/15,630; 12%). LAG3 high patients in RC showed significantly higher OS (median OS = not reached; p = 0.016) and PFS (median PFS = not reached; p < 0.0001) compared the LAG3 low patients (median OS = 12 months; median PFS = 6.5 months). LAG3 high cases had significantly (ORR = 65%, p = 0.005) improved ICI response compared to LAG3 low cases (ORR = 35%). Conclusions: Our study showed a wide dynamic range of LAG3 expression across a pan-cancer cohort of solid tumors, with highest prevalence observed in gynecological cancers. Additionally, LAG3 is strongly associated with proliferation and significantly improved outcomes for patients with NSCLC, treated with pembrolizumab alone. Furthermore, significant LAG3 co-expression with other ICI targets suggests its plausible use in clinical trial selection and patient stratification for combination immunotherapy strategies.

Genetic redundancy can obscure phenotypic effects of single-gene mutations. Two individual mutations may be viable separately but are lethal when combined, thus synthetically linking the two gene products in an essential process. Synthetic genetic arrays (SGA), in which defined mutations are combined, provide a powerful approach to identify novel genetic interactions and redundant pathways. A genome-scale SGA can offer an initial assignment of function to hypothetical genes by uncovering interactions with known genes or pathways. Here, we take advantage of the chromosomal conjugation system of to combine individual donor and recipient mutations on a genome-wide scale. We demonstrated the feasibility of a high-throughput mycobacterial SGA (mSGA) screen by using mutants of and as query genes, which were combined with an arrayed library of transposon mutants by conjugation. The mSGA identified interacting genes that we had predicted and, most importantly, identified novel interacting genes - encoding both proteins and a ncRNA. In combination with other molecular genetic approaches, the mSGA has great potential to both reduce the high number of conserved hypothetical protein annotations in mycobacterial genomes and further define mycobacterial pathways and gene interactions. is the model organism of choice for the study of mycobacterial pathogens because it is a fast growing, non-pathogenic species encoding many genes that are conserved throughout mycobacteria. In this work, we describe a synthetic genetic array (mSGA) approach for , which combines mutations on a genome-wide scale with high efficiency. Analysis of the double-mutant strains allows identification of interacting genes and pathways that are normally hidden by redundant biological pathways. The mSGA is a powerful genetic tool that allows functions to be assigned to the many conserved hypothetical genes found in all mycobacterial species.

No abstract

BackgroundCancer testis antigens (CTAs) are tumor antigens that have a highly tissue-restricted expression but are often expressed in diverse malignancies. With their highly immunogenic expression limited to tumor cells, CTAs have become a prime target for cancer vaccinations and T-cell-based therapy with chimeric T-cell receptors. In this study, we investigated the landscape of 17 CTA (NY-ESO-1, LAGE-1A, and 15 other CTAs) in the context of the tumor immune microenvironment of real-world clinical tumors spanning multiple histologies.MethodsRNA-seq was performed on 5450 FFPE tumors, and the expression of each of the 17 CTAs were classified as Positive (nRPM≥20) or Negative (nRPM<20). Pearson correlation analysis was conducted on the nRPM values for each CTA to determine co-expression relationships between any of the 17 CTAs. In order to visualize patterns in the CTA expression landscape, heatmap analysis was performed, using hierarchical clustering with Pearson’s correlation as a distance measure to reveal patterns in CTA status across all samples and CTAs.Results5450 tumor samples analyzed in this study spanned 39 histologic types of tumor and were predominantly composed of lung cancer (40.4%) followed by colorectal cancer (10.6%) and breast cancer (8.6%). Positive CTA prevalence ranged from 2.4% (GAGE13) to 31.5% (XAGE1B). A high degree of significant correlation between the expression of all CTAs was observed, with only GAGE10, XAGE1B, MLANA, MAGEA4, GAGE13, and SSX2 having a no significant correlation with at least one other CTA. Three key groups of co-expressed CTAs were observed: 1) NY-ESO-1, LAGE-1A, MAGEA12, MAGEA3, MAGEA1, MAGEA10, and MAGEA4 (0.36≤R≤0.82); 2) GAGE12J, GAGE2, GAGE1, GAGE13 (0.58≤R≤0.72); 3) SSX2, BAGE, MAGEC2 (0.4≤R≤0.58). The three remaining CTAs (GAGE10, XAGE1B, and MLANA) had little or no (R<0.22) correlation with any other CTA or each other. Clustering CTAs across all samples revealed three CTA expression clusters: 1) samples that express a collection of multiple CTAs; 2) samples that express mostly XAGE1B, over-represented by lung cancer (p=1.51e-296); 3) samples that express mostly GAGE10, over-represented by neuroendocrine tumors (p=1.64e-05).ConclusionsAcross multiple cancer subtypes, the expression of a CTA occurs in the context of other CTAs, and specific groups of CTAs are likely to co-express, forming expression patterns characteristic to tumor subgroups. These findings provide a scientific base for selecting appropriate CTAs and designing multiplex vaccination in immunotherapies of a variety of tumors. Further studies are needed to understand the relationship of these CTAs with traditional and emerging immune-oncology biomarkers.

67 Background: Progress in unraveling the complex tumor immune microenvironment (TIME) has aided our understanding of the host immune response to ICI therapies. To gain further insight, we associate a 160-gene tumor inflammation signature (TIS) with cell proliferation status and response to ICI. Methods: 242 FFPE tumor samples from lung, RCC and melanoma were evaluated by RNA-seq to measure expression levels of 394 immune related genes. TIS (weak, moderate, strong) and cell proliferation status (poor, moderate, high) were determined by algorithmic analysis of selected gene pathways. All cases were evaluated for association with ORR to ICIs using RECIST criterion. Furthermore, 13 pre-post ICI treated biopsy pairs were studied to understand the dynamics of these signatures following treatment. Results: In both weakly and moderately inflamed tumors, ORR was highest in moderately proliferative tumors 32.6% (15/46) and 37.8% (14/37), respectively (Table). Surprisingly, in strongly inflamed tumors, both highly and moderately proliferative tumors had a high response rate of 55% (11/20) and 43.2% (16/37), whereas poorly proliferative tumors have a significantly lower response rate of 12.5% (3/24; p= 0.03). 6 of 13 pre-post ICI treated cases demonstrated increased inflammation post treatment, with 83% (5/6) demonstrating concurrent decrease in cell proliferation. Conclusions: Together, TIS and cell proliferation predict response to ICI in NSCLC, RCC and melanoma. The data suggests that in strongly inflamed and highly proliferative tumors, the cell proliferation signal could be attributed to antigen stimulated T-cell proliferation, whereas in other categories of inflammation, moderately proliferative tumors contain signal from both immune cells and tumor cells. Further studies are required to determine the relationship between these signatures in hot and cold tumors. [Table: see text]