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TUMOR HOMOLOGY WITH SELF AS A BIOMARKER FOR RESPONSE TO CHECKPOINT INHIBITOR THERAPY [Meeting Abstract]

Richard, G; Steinberg, G; Ruggiero, N; Martin, W; De, Groot A
Background As tumor genomes are shaped by their interaction with the immune system, a phenomenon known as immunoediting, it is critical to understand how immunotherapies impact this process. Checkpoint inhibitors directly influence T cells responding to neoantigens, as such, these therapies drastically affect the genomes of surviving tumor clones. Similar to the concept of immune camouflage, where genomes of pathogens evolve in a way to avoid immune detection, we hypothesized that tumor clones surviving checkpoint inhibition therapy harbor mutations more prone to immune avoidance. Methods We analyzed a published cohort of Nivolumab-treated melanoma patients (n=41) for which tumor samples were collected from the same site prior ('Pre' samples) and during ('On' samples) Nivolumab therapy.1 The immunogenic and tolerance potential of mutations from the Pre and On samples were evaluated with the Ancer neoantigen screening platform,2 which includes the EpiMatrix algorithm to identify HLA class I and HLA class II neoepitopes and the JanusMatrix algorithm to evaluate neoepitopes for homology with the self genome. Prior work with JanusMatrix showed that neoantigens highly homologous to self might be inhibitory.3 Results Tumor samples collected during Nivolumab therapy demonstrated increased homology (self-like) scores from their matched pre-therapy samples (paired t test, p=0.0475). While this increase in homology with self was significant across the cohort, the effect was more pronounced in patients exhibiting complete (CR) or partial responses (PR), compared to patients with stable (SD) or progressive disease (PD). An ANOVA analysis confirmed that increase in homology after Nivolumab therapy was significantly greater in CR/PR patients, as opposed to SD or PD patients (p=0.0005). This observation was supported by Receiver Operating Characteristic (ROC) analysis discriminating CR/PR patients from SD/PD patients based on differences in homology with self between Pre and On treatment samples (AUC=0.7484, p=0.0313). A comparative ROC analysis employing baseline patient tumor mutation burden (TMB) yielded non-conclusive results (AUC= 0.5054, p=0.9613). Conclusions Our Ancer analysis highlights that Nivolumab therapy affects the tolerance profile of tumors in a manner that is consistent with the concepts of immunoediting and immune camouflaging. Interestingly, tumors in patients with favorable outcomes demonstrated the greatest increase in selflike neoepitopes. This observation suggests that collecting tumor biopsies shortly after the initiation of checkpoint inhibitor therapy and evaluating their tolerance profile may be employed as a prognostic biomarker. Furthermore, this approach highlights in silico tools may distinguish effector from tolerance inducing neoepitopes, a critical feature for designing novel neoantigen-based precision immunotherapies
EMBASE:639737282
ISSN: 2051-1426
CID: 5379522

Stealthier mutanomes are induced after nivolumab immunotherapy [Meeting Abstract]

Richard, G; Princiotta, M; Steinberg, G; Martin, W; De, Groot A
Background As tumor genomes are shaped by their interaction with the immune system, a phenomenon known as immunoediting, it is critical to understand how immunotherapies impact this process. Checkpoint inhibitors directly influence T cells responding to neoantigens, as such, these therapies drastically affect the genomes of surviving tumor clones. Similar to the concept of immune camouflage, observed in infectious diseases, where genomes of pathogens evolve in a way to avoid immune detection, we hypothesized that tumor clones surviving checkpoint inhibition therapy harbor mutations more prone to immune avoidance. Methods We analyzed a cohort of nivolumab-treated melanoma patients (n=41) for which tumor samples were collected from the same site prior (Pre samples) and during (On samples) nivolumab therapy.1 The immunogenic and tolerance potential of mutations from the Pre and On samples were evaluated with the Ancer neoantigen screening platform,2 which includes the EpiMatrix algorithm to identify HLA-I and HLA-II neoepitopes and the JanusMatrix algorithm to evaluate neoepitopes homology with self. Prior work with JanusMatrix showed epitopes highly homologous to self can be inhibitory.3 Matching Pre and On therapy samples were compared to identify mutations deleted (unique to the Pre samples), maintained (found in both the Pre and On samples), and induced while on therapy (unique to the On samples). Results Mutations from the On therapy samples had a lower immunogenic potential than mutations found in the Pre therapy samples (figure 1A, Mann-Whitney test, p=0.0001). After further distinguishing mutations deleted, maintained, and induced while on therapy, we observed that newly induced mutations had a significantly lower immunogenic potential compared to other mutations (Kruskal-Wallis test, p<0.0001). In addition, newly induced mutations were more homologous to self than other mutations (figure 1B, Kruskal-Wallis test, p<0.0001), indicating a greater likelihood for these new mutations to be tolerated by the immune system. In summary, we showed that mutations generated after nivolumab therapy are less immunogenic and more tolerated than mutations found prior to therapy. Abstract 313 Figure 1 Immunogenicity (A) and tolerance (B) potentials of mutations found in matching melanoma tumor samples collected before (Pre) and during (On) nivolumab therapy. Conclusions Our Ancer analysis suggests that nivolumab therapy affects the immunogenicity and tolerance profiles of newly generated mutations in a manner that is consistent with the concepts of immunoediting and immune camouflaging. Mutations induced after therapy appear less immunogenic and more self-like, illustrating a potential mechanism tumors employ to avoid immune surveillance. Furthermore, our approach highlights in silico tools can distinguish effector from tolerance inducing neoepitopes, a critical feature for designing novel neoantigen-based precision immunotherapies
EMBASE:636986055
ISSN: 2051-1426
CID: 5138552

Integrating CD8 and CD4 effector neo-epitope content with regulatory T cell epitope exclusion is a superior prognostic biomarker for bladder cancer patient compared to their tumor mutation burden [Meeting Abstract]

Richard, G; Sweis, R; Ardito, M; Garcia, T; Moise, L; Princiotta, M; Bridon, D; Martin, W; Berdugo, G; Balar, A; Steinberg, G; De, Groot A
Background We hypothesized that neo-epitope-based prediction using an advanced in silico T cell epitope screening system (AncerTM) may better identify patients with improved prognosis than tumor mutation burden. Analysis of genomic data derived from the muscle-invasive bladder cancer (BLCA) cohort of The Cancer Genome Atlas (TCGA) database for CD4, CD8, and Treg neo-epitopes was performed to determine whether AncerTM would improve prognostic stratification compared to tumor mutational burden (TMB). Methods BLCA patient mutanomes (n=412) were retrieved from the TCGA and evaluated with AncerTM, an innovative and automated neo-epitope screening platform that combines proprietary machine learningbased HLA I and HLA II neo-epitope identification tools with removal of inhibitory regulatory T cell epitopes for neo-epitope ranking and personalized cancer vaccine design. BLCA patients were separated based on median TMB or neo-epitope burdens. We investigated the effect of integrating both CD8 and CD4 neo-epitope burdens as most mutanome pipelines exclusively focus on the identification of Class I neo-epitopes. Overall survival was analyzed using the Kaplan-Meier method and differences analyzed by log-rank testing. Results Compared to low TMB, high TMB was significantly associated with improved survival (p = 0.0001, difference of 38.5 months in median survival, Figure 1). Improved differentiation of median survival times was obtained when separating patients based on their Class I neoepitope content, as estimated by AncerTM (p < 0.0001, difference of 59.8 months in median survival). Adding Class II neo-epitope burden further increased separation of OS times, showcased by a 69.6-month increase in median survival for BLCA patients with both high CD8 and high CD4 neo-epitope contents compared to other patients (p = 0.0001). Since we discovered that Class II neo-epitopes can induce inhibitory responses, we further evaluated whether the screening of these detrimental sequences could improve our analysis. Upon identifying Class II neo-epitopes likely to induce T effector (Teff) responses, we found that the median survival of patients with high CD8 and high CD4 Teff contents was extended by nearly 4 months to 73.4 months compared, to the remainder of the cohort (p < 0.0001, Figure 2). Conclusions Our analysis suggests that optimal host-immune recognition of CD8+, CD4+, and Treg epitopes plays a key role in cancer survival. While defining CD8 neo-epitope burden enhanced associations with OS, the inclusion of CD4 Teff neo-epitope burden substantially helped identify long-term survivors. These results suggest that defining the number of true neo-epitopes using AncerTM may represent a novel prognostic or predictive biomarker
EMBASE:629890492
ISSN: 2051-1426
CID: 4227412

Gut microbial response to anti-TNF therapy in IBD: An IBDREMEDY Study. (IBD research Mentoring education ny) [Meeting Abstract]

Ann, C L; Zoya, G; Martin, W; Lisa, M; Melissa, R; Arun, S; David, H; Dana, L; Garrett, L
BACKGROUND: While the contribution of enteric microbes to the pathogenesis of inflammatory bowel disease (IBD) is widely accepted, understanding of this interaction is limited. Previous studies have demonstrated dysbiosis in IBD, though microbiome changes over the course of disease and remission are not wellcharacterized. Furthermore, the effects of IBD treatments on the gut microbiome are unknown. Among IBD therapies, anti-tumor necrosis factor (TNF) medications are some of the most potent, but also carry the risk of substantial adverse effects, such as severe infections and malignancies. Depending on the IBD subtype and anti-TNF medication, approximately 30-80% of patients will not respond to therapy. Our hypothesis is that anti-TNF treatments affect the microbiome composition of the gut, and that changes in the microbiome may correlate with disease activity. We further hypothesize that there are gut microbiome characteristics that may predict response to anti-TNF medications. To evaluate this, we are exploring the correlation between the gut microbiome and clinical response following initial treatment with anti-TNF therapy. METHODS: Fecal samples were collected immediately prior to each dose of anti-TNF therapy at 0, 2, and 6 weeks for biologic-naive IBD patients being treated with either infliximab or adalimumab. Disease index surveys, quality of life surveys, smoking, diet, medication, and clinical data were collected at each timepoint. Stool DNA was extracted, and Illumina Miseq sequencing was used to evaluate the V4 region of the 16S rRNA gene. Following standard upstream processing, microbiome composition was characterized using QIIME 1.8 and LEfSe bioinformatics software, referencing the Greengenes database (version gg-13-8). RESULTS: We present data on our first 5 patients. Three are female. Three have ulcerative colitis; 2 have Crohn's disease. One patient (NAT01) was started on anti- TNF therapy due to adverse side effects of thiopurines, while the remainder were started on anti-TNFs due to disease severity. We found a relative abundance of Eubacterium spp. prior to anti-TNF exposure (timepoint a), and a relative abundance of Ruminococcus spp. at 6 weeks post-treatment (timepoint c) [Figure 1]. Microbiome changes follow characteristic trends along 2D and 3D Principal Coordinate Analysis [Figure 2]. All 5 patients reported clinical improvement or sustained clinical control following initiation of anti-TNF therapy, though certain patients still had continued disease activity [Table 1]. CONCLUSIONS: The preliminary data of this ongoing study indicate that microbial communities are indeed altered in a characteristic fashion following anti-TNF therapy, though it is currently unclear whether these changes reflect the effects of anti-TNF medications or differences in disease activity. Variations in post-treatment gut microbiome changes may correlate with disease activity, or potentially disease phenotype. In ongoing analyses, we hope to better understand how the gut microbiome may be altered by IBD treatment and disease activity, or potentially predict response to therapy. Should we find certain bacterial compositions that correlate with clinical outcomes, these bacterial characteristics could be used to give (Figure presented) clinicians key pieces of information needed to optimize treatment decisions in the care of IBD patients
EMBASE:71811594
ISSN: 1078-0998
CID: 1514682

The evolution of eukaryotes [Letter]

Martin, William; Dagan, Tal; Koonin, Eugene V; Dipippo, Jonathan L; Gogarten, J Peter; Lake, James A
PMID: 17463271
ISSN: 0036-8075
CID: 282042