Asmann, Yan W.

Abstract Objective: Allogeneic stem cell transplant (ASCT) is a potentially curative treatment for many hematologic malignancies. HLA match is the principal factor in donor selection but the total antigenic landscape as seen by the donor immune cells extends beyond the HLA and minor HLA antigens. Thus, despite "perfect" HLA match graft versus host disease (GVHD) caused by alloreactive donor immune cells reacting to disparate antigens in the recipient, remains a significant cause of morbidity and mortality. Whole exome sequencing (WES) which assays all the protein coding nucleotide sequences provides a powerful means to assess the entire antigenic makeup of the individual. We examined the WES of sibling donor-recipient pairs undergoing BMT to assess the influence of the extent of genetic disparity and specific gene polymorphisms on GVHD. Methods: Sixteen matched sibling donor-recipient pairs receiving allogeneic stem cell transplants between June 2008 and April 2013 had samples available for retrospective WES. Transplant data including conditioning regimen, age of donor and recipient at the time of transplant, development and type of GVHD and cause of death were recorded. The sequencing was performed using Illumina HiSeq2000 and the single nucleotide variants (SNVs) and small insertions and deletions (INDELs) were identified using the haplotype caller within the Genome Analysis Toolkit GATK v2.7. Functional variants including non-synonymous, frame-shift, truncating, and splice site SNVs and INDELs identified were compared between donor and recipient and among those with GVHD and those without GVHD (non-GVHD). One-sided Fisher's exact test was used to determine the statistical significance of the SNPs in GVHD and non-GVHD. Candidate genes with a p value of <0.05 were further evaluated for their biochemical pathway and possible role in inflammatory or immune responses. Results: Nine of 16 patients had a diagnosis of AML or high grade myelodysplastic syndrome, 3 had a diagnosis of non- Hodgkin lymphoma, 1 had Hodgkin lymphoma, 1 had multiple myeloma, and 2 had CML. Six patients were conditioned with a myeloablative regimen, others with a reduced intensity conditioning regimen. Eight of 16 patients were female and 7 of 16 donors were female. Nine of 16 patients developed stage II-IV acute and/or chronic GVHD with 7 of them developing GVHD of the skin. A total of 12,623 genes with 43,670 functional variants were identified using WES. 134 genes had a p value < 0.05 and enrichment analysis was performed on these genes. 12 out of 134 genes were associated with gene ontology (GO) term "immune system development" with an unadjusted enrichment p-value 0.0015. Three (IL31RA, OSMR, SIRPA) out of 134 genes were associated with "inflammation_Jak-STAT pathway" with an adjusted enrichment p-value 0.07. Variants of IL31RA, OSMR, and PTX3 were identified as genes with polymorphisms associated with GVHD (p= 0.003, 0.01, and 0.01, respectively) with 5 variants identified in IL31RA, 7 variants in OSMR, and 2 variants in PTX3. Further investigation of these three genes revealed their role in inflammatory or immune responses. Discussion: GVHD is a cytokine mediated immune-inflammatory process that can occur despite "perfect" HLA matching. WES identified the extensive genetic variability between donors and recipients implying large allo-reactivity potential that can lead to GVHD. Quantification of genetic variation may allow for more precise determination of GVHD risk and better selection of donors. In our cohort, WES identified polymorphisms in IL31RA, OSMR, and PTX3 as statistically significantly associated with development of GVHD. The protein products of these three genes have each been shown to be involved in inflammatory processes and are influenced by other cytokines. IL31RA and OSMR are found in many tissues including the skin and lungs. They function both independently and as a heterodimer receptor capable of binding multiple cytokines including IL-31, IL-6, and TNF-α. PTX3 is a reactive protein that increases in response to cytokine release related to systemic inflammation. Levels of this protein have been used as a prognostic marker in many disease processes with high levels correlating with poor outcomes. The small sample size of our cohort precludes any firm conclusions but provides promising hypotheses for further evaluation in larger studies. Disclosures No relevant conflicts of interest to declare.

Abstract Background Next Generation sequencing (NGS) is a powerful tool to identify somatic mutations associated with tumor onset and drug response. While it is well suited for high quality fresh/frozen samples, NGS is not proven for FFPE tissue which is the most common type of clinical specimen. Since the nucleic acids can be readily extracted from FFPE samples for a variety of genomic analyses, a comparative mutational analysis of paired frozen and FFPE tissues is urgently needed. Our long term goal is to establish a lab protocol to detect mutations in FFPE tumors using a targeted capture and sequencing approach for genes of interest. This pilot study focuses on the comparison of FFPE and frozen samples to test the validity of using FFPE tissues in such application. Methods Gene Selection: 128 genes associated with known pathogenic mutations in lymphoma Sample Selection: 9 diffuse large B-cell lymphoma (DLBCL) cases with FFPE, frozen and germline samples, as well as 10 frozen normal lymphatic tissues as references for CNV detections Capture Probe Design: We targeted coding exons and UTR, as well as the evolutionarily conserved intronic regions. The capture probes were designed using the Agilent eArray tool. The titling density of the probes was set to 3 probes overlapping with every base in the target region to improve the capture efficiency in FFPE samples. The least stringent masking of the repeat regions was allowed to include regions with small repeats that are shorter than the length of the sequencing reads (100-bp). In addition, boosting parameters were picked to set various levels of probe replication in different regions in order to minimize the local coverage differences (e.g. between regions of different GC contents) Sequencing and Bioinformatics: The target capture and sequencing were performed by the Mayo Clinic Medical Genome Facility. The reads were mapped to Human Reference Genome Build 37 using Novalign, and SNVs were called using GATK. The CNVs were identified using an in-house developed algorithm, patternCNV. Results The designed probes covered 99.65937% of the target regions. We generated 2.2-6.7 Gbp of reads per sample, 57.4-71.5% of which were on target. This equalled an average coverage of 2100-6700 folds which is 10-30 times higher than the minimal coverage recommended by Agilent. Due to this high coverage, we observed duplicate reads that accounted for 7.7-73.5% of the total reads. When we analysed the data with and without the duplicated reads, the concordance of the called SNVs was between 84-93% out of 207-249 mutated positions per trio-sample. There were 7.8-8.9% and 1.1-2.2% unique SNVs per sample by excluding or including duplicate reads, respectively. The dis-concordances were mostly missed calls, where a SNV was observed in only 1 or 2 of the trio samples. The missed calls from frozen samples ranged from 0-10.4% compared to 1.4-10.4% from the FFPE tissues, with 0.88-2.4% more SNVs missed in FFPE. Further analyses showed that all of the missing calls came from the lack of or low coverage of the corresponding positions. There were also differences of the called SNVs between the trio samples. However, this was extremely rare. Only 2 out of the 9 trio samples at a total of 3 positions had disagreements in called SNVs between FFPE and frozen tissues, all due to the allelic imbalance where the percentage of reads supporting the alternative alleles were below 20%. Therefore, this dis-concordance can be removed by back-filling of the read-level information for each position. Unfortunately only 11.9-47.4% of the CNVs called in frozen tissues were identified in FFPE samples, due to the widely various coverage in FFPE samples. The consequent large noises of the log ratio values between the FFPEs and normal references significantly reduced the sensitivity for CNV calling. Conclusions This pilot study compared the performance of SNV and CNV detection in FFPE and paired frozen tissues using a target capture and sequencing approach. With a capture probe design strategized to benefit FFPE samples, we observed SNV detection rates in FFPE that were only slightly lower (0.88-2.4%) than those of frozen tissues due to poor coverage of some positions in FFPE samples. With a proper back-filling step, there was no dis-concordance of the called SNVs between FFPE and frozen samples. However, CNV detections in FFPE were more problematic due to the un-predictable regional coverage in FFPE samples. Disclosures: No relevant conflicts of interest to declare.

Abstract Tumors arise and evolve by iterative steps of mutation, subclonal selection, and clonal expansion due to growth advantage of the fittest subclones and external mutation induction and selection pressure from radiation and drug therapies. Various studies have shown that greater clonal complexities in primary tumors are correlated with poor clinical outcome of tumor progression and/or drug resistance. Recently, we have reported that very few of the driver mutations, all of which were clonal, detected in primary diffuse large B-cell lymphoma (DLBCL) were associated with DLBCL outcome as measured by 24-month event-free survival (EFS24). In the current project, we identified and studied subclonal mutations in primary DLBCL tumors and assessed their associations with EFS24, as well as the role of activation-induced cytidine deaminase (AID) in genomic mutations occurred in DLBCL tumors. The detection of subclonal mutations is still a significant bioinformatics challenge. In addition, identification of clonal mutations in samples with lower tumor purities faces similar challenges due to the low concentration of reads supporting the mutant alleles. The current methods of somatic mutation calling are not sufficiently sensitive to identify the low concentration mutations in tumor DNA sequencing data. We implemented a bioinformatics workflow for low-concentration and subclonal mutation detection which is based on the positional read pile-up data and a back-fill approach (PUB). The pile-up files were first generated using the re-aligned and re-calibrated BAM files after read alignment using Burrows-Wheeler Aligner (BWA). The variant positions with alternative bases were annotated by attributes defined in the Variant Quality Score Recalibration (VQSR). PUB then used a boosting method and a generalized linear model (GLM) to train a model of 'good quality' variants using common variants from HapMap, and prioritized and called clonal and subclonal variants based on the trained model. The VQSR attributes related to alternative allele depth were less-weighed in order to call subclonal mutations. The somatic mutations were then identified by Fisher's Exact Test using sequencing depths of the reference and alternative alleles from paired tumor and germline sequencing data. The exome sequencing data of paired tumor and peripheral blood from 48 newly diagnosed DLBCL patients were analyzed using PUB. Thirty-six of the 48 patients achieved EFS24 with the other 12 patients experienced primary treatment failure. Most of the tumors studied had tumor purities between 40-70%. PUB identified substantially higher number of somatic mutations, both clonal and subclonal, compared to those detected using existing somatic callers. We observed that the prevalence of mutations in previously reported driver genes were higher using thresholds of mutation concentration ≥ 5% and Fisher's Exact Test p ≤ 0.05, including EZH2 (mutated in 26% of DLBCL tumors analyzed by PUB, compared to 12.7% as previously reported), MLL2 (72% vs. 31%), CD79B (22% vs. 14%), TNFRSF14 (36% vs. 20%), MEF2B (22% vs. 16.4%), CARD11 (46% vs. 21.8%), and MYD88 (18% vs. 11%). In addition, other genes involved tumorigenesis that have not been previously linked to DLBCL also harbored both clonal and subclonal mutations with substantial prevalence, including FGFR3 (12%), KIT (24%), and ATM (28%). Furthermore, association of genes displaying clonal and subclonal mutations with EFS24 identified potential biomarkers for DLBCL outcome. Among these, two genes EPGN and ASTE are involved in epithelial growth factor receptor signaling and had association p values of 0.0001 and 0.0016, respectively. The oncogene MAFB was also associated with EFS24 (p= 0.0016). We searched for AID induced mutations among all identified variant positions and concluded that there was no evidence of AID site enrichments compared to a simulated data set. In summary, we developed and applied a sensitive bioinformatics pipeline for the identification of both clonal and low concentration somatic mutations in primary DLBCL exome sequencing data which further revealed the clonal complexity of the primary DLBCL tumors. Several of the genes were identified as potential biomarkers for DLBCL outcome. Disclosures Maurer: Kite Pharma: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Celldex: Research Funding. Link:Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding. Cerhan:Kite Pharma: Research Funding.

Abstract Abstract 149 Background. There are few established factors that predict risk of follicular lymphoma (FL) transformation, and only two studies have evaluated the role of germline genetic variation and risk of transformation. One study reported an association of a BCL6 SNP (397C>G) with risk of transformation, and a second study reported an association with rs6457327, which is in the HLA region of 6p21. This study evaluated the risk of FL transformation in the modern treatment era with germline SNPs that were previously reported to be associated with FL transformation (BCL6SNPs and rs6457327), along with SNPs (N=45) previously associated with either risk of FL or FL prognosis. Methods. Newly diagnosed FL patients were prospectively enrolled from 2002–2009 into the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma SPORE. Peripheral blood for genotyping was collected at enrollment, and clinical and treatment data were abstracted from medical records. All patients were actively followed for disease progression, retreatment, transformation and death. For this analysis, only FL patients with grade I-IIIa were included. Patients with composite DLBCL, FL grade IIIb, or other evidence of transformation at diagnosis were excluded. Transformation was based on either 1) biopsy confirmation of FL grade IIIb, DLBCL, or higher grade B-cell lymphoma; or 2) clinical indicators of transformation based on the published literature, including sudden rise in LDH, rapid discordant localized nodal growth, new involvement of unusual extranodal sites, new B-symptoms, or hypercalcemia. Genotyping was conducted using a custom Illumina iSelect panel. Cox regression was used to estimate Hazard Ratios (HRs) and 95% confidence intervals (CI) for individual SNPs with time to transformation, with death modeled as a competing risk. Each SNP was modeled with the most prevalent homozygous genotype used as the reference group, and each SNP was modeled as having a log-additive (per minor allele) effect in the regression model. An ordinal test was used to assess the trend, and p<0.05 was considered statistically significant. Results. There were 631 newly diagnosed grade I-IIIa FL patients, of whom 532 had DNA and were successfully genotyped. The median age at diagnosis was 60 years (range 23–93) and 53% were male. FLIPI distribution was 42% 0–1, 34% 2 and 24% 3–5. Initial therapies included observation (32%), rituximab (R) monotherapy (13%), alkylator-based chemotherapy +/− R (20%), and anthracycline-based chemotherapy +/− R (19%). At a median of 59 months of follow-up (range, 11–110), 58 patients died, 254 had an event (death, progression or retreatment) and 54 patients (10%) transformed. Transformation was biopsy proven in 43 of the 54 patients (80%). The SNP rs6457327 on chromosome 6p that was previously reported to be associated with transformation was positively associated with risk of transformation in our data (HR=1.40; 95%CI 0.97–2.04; p=0.07), although this association was weaker but still consistent with the prior report. Further, we evaluated additional typed SNPs from the HLA region of 6p, and found additional associations (p<0.05) with HLA-DRA (3 SNPs), HLA-DQA2 (2 SNPs), HLA-DOA (2 SNPs) and HLA-DPB1/HLA-DPA2 (2 SNPs), of which rs10484569 from HLA-DPB1/HLA-DPA2 was the most strongly associated with risk of transformation (HR=3.01; 95%CI 1.48–6.09; p=0.002). None of the BCL6 or other previously reported risk SNPs were associated with transformation. Of the SNPs previously associated with FL prognosis, only the FCGR2A rs1801274 (HR=1.41; 95%CI 0.94–2.10; p=0.09) and CD46 (CD46 molecule, complement regulatory protein) rs2466571 (HR=1.72; 95%CI 1.16–2.55; p=0.007) were associated with risk of transformation. For CD46, one additional SNP (rs2796269) was also associated with transformation (HR=0.61; 95%CI 0.40–0.95; p=0.03). Further adjustment for FLIPI score and treatment (observation versus all other) did not alter these associations. Conclusions. The previously reported association of rs6457327 with risk of transformation in the modern treatment era was replicated. In addition, several HLA loci and two FL prognosis SNPs were linked to transformation risk. The remaining published FL risk or prognosis SNPs were not associated with transformation. Our results support a role for germline genetic variation in the HLA region, FCRGR2A and CD46 in transformation of FL. Disclosures: No relevant conflicts of interest to declare.

Abstract Introduction Tumor specific somatic mutations can potentially influence patient response to targeted immunotherapies. Since somatic mutations generate neoantigens, novel tumor-specific peptides can be processed, loaded into MHC class I molecules, and presented to T-cells as potentially immunogenic targets. Indeed, both somatic mutation burden and neoantigen burden have been correlated with responsiveness to checkpoint inhibitor blockade in highly mutagenic advanced melanoma and lung cancer. However, correlation of response with mutation and neoantigen burden has not been reported for cancers of lower characteristic mutational burden such as multiple myeloma (MM). Indeed early results of checkpoint inhibitors in MM have been underwhelming. We therefore wished to better understand the mutation and neoantigen landscape of MM patients as it relates to responsiveness to current and future therapies. Materials/Methods Somatic mutation burden, neoantigen load, and response to therapy was obtained using interim data from theCoMMpassstudy (NCT01454297), a study by the Multiple Myeloma Research Foundation that collects longitudinal clinical and molecular data on 1,000 newly diagnosed MM patients from baseline through treatment and disease progression. Next-generation exome sequencing data was used to call somatic mutations andbioinformaticallydetermine patient specific HLA type. Neoantigen binding affinity was predicted for all unique patient specific somatic missense mutation and HLA allele combinations using a well validated neural network and machine learning based algorithm. Binding affinity of the corresponding non-mutated peptide was also predicted to identify mutated neoantigens witha high binding affinity while the wild type counterpart has low predicted binding affinity. Results For 627 patients with available sequencing and clinical data, mutation and neoantigen profiles were created. Within this population, the mean overall canonical somatic mutation load was 405.84 (s=608.55) mutations per patient. These mutations included 38,222 unique missense mutations in 12,994 genes. The mean somatic missense mutation load was 64.51(s=95.29) mutations per patient including immunoglobulin genes (52.70, s=94.85 excluding immunoglobulins). There was a positive linear correlation between mutation burden and neoantigen burden. The mean neoantigen load using thresholds of mutant peptide binding affinity (half maximal inhibitory concentration, IC50) less than 500nM was 69.57 (s=124.76) neoantigens (58.53, s=123.58 excluding immunoglobulins). Limiting binding affinity of wild type peptide sequence to greater than 500nM decreased the mean neoantigen load to 23.84 (s=53.55) neoantigens (20.74, s=53.28 excluding immunoglobulins). Progression free survival (PFS) following induction therapy was used to determine correlates of mutation burden excluding immunoglobulin mutations with response. Kaplan-Meier survival analysis revealed significantly shorter PFS time in patients with greater than average tumor somatic missense mutation load (N=126) relative to those with less than average mutation load (49.1% vs. 73.0% PFS at 2yrs, p=0.0055, Figure 1) Similarly, patients with greater than average neoantigen load (N=164) had significantly shorter PFS relative to those with less than average neoantigen load (48.0% vs. 74.2% PFS at 2yrs, p=0.00259, Figure 2). Moreover, patients with both high mutation and neoantigen load (N=85) had significantly shorter PFS relative to those with less than average mutation and neoantigen load (45.3% vs. 72% PFS at 2yrs, p= 0.0168). Conclusion While mutation and neoantigen load has been correlated with response to targeted immunotherapies in highly mutated solid tumors, we report here a negative correlation between mutation, neoantigen load and MM patient survival. This indicates that a high mutation andneoantigen load negatively impacts survival of myeloma patients under current standard of care. It is hypothesized that this is because current treatments fail to recruit T cell immunity necessary to exploit tumor neoantigens fundamental to immune-mediated killing of cancer cells. This presents an encouraging basis for pursuing targeted and personalized immunotherapies for multiple myeloma, especially for patients with a higher mutational burden. PFS and mutation load. PFS and mutation load. PFS and neoantigen load. PFS and neoantigen load. Disclosures Lonial: BMS: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Novartis: Consultancy; BMS: Consultancy; Millenium: Consultancy; Merck: Consultancy.

Abstract To date, whole genome and exome sequencing studies of follicular lymphoma (FL) have primarily focused on identification of small site mutations that are recurrent in FL tumorigenesis or involved in tumor clonal evolution. A comprehensive genomic and transcriptomic survey of various mutation types including large structural variants (SVs) in FL cases with detailed clinical annotations and long-term follow-up has not been accomplished. To gain insight into genetic biomarkers that may predict clinical features, we performed exome and whole genome mate-pair sequencing of fresh frozen tumor and paired peripheral blood DNAs, and transcriptome sequencing of tumor RNAs, from 8 FL patients. The patients we selected were either below the median age of FL onset (n=7, median 54.5 yrs) or had a family history of lymphoma (n=1). These patients were clinically diverse, and included patients who had Grade 1 or 2 disease (n=4), classified as “indolent”; and patients with Grade 3a disease (n=2) or who subsequently had pathologic transformation (n=2), classified as “aggressive”. The coding regions of the genome (exome) were captured using Agilent SureSelect Target Capture Kit V2.0, and sequenced at 100-bp paired-end. The single nucleotide variants (SNVs) and small insertions and deletions (INDELs) were called using The Genome Analysis Toolkit (GATK), and the exon level copy number variants (CNVs) we identified using patternCNV. The whole-genome mate-pair libraries of both normal and tumor DNAs with 3kb insert sizes were sequenced paired end at 50-bp. The large SVs of CNVs, large INDELs, translocations, and inversions were identified using the SnowShoes-SV algorithm. The RNA sequencing libraries of 8 FL tumors were constructed using Illumina TruSeq protocol, and sequenced at 50-bp paired end. The RNA-Seq data were analyzed using TopHat and the fusion transcripts were identified using SnowShoes-FTD. Our analysis of SNVs and INDELs revealed mutations in previously reported genes including MLL2, CREBBP, TNFRSF14, and histone cluster genes (HIST1H2AM, HIST1H2BD). In addition, we identified novel recurrent mutations in cysteine-rich PAK1 inhibitor (CRIPAK) in 25% of the tumors. In a secondary analysis performed by Sanger sequencing or re-analysis of publically available RNA sequencing data, we identified CRIPAK mutations in 44% of FL (n=32) and 28% of DLBCL tumors (n=102). Bioinformatics analysis shows that the coding region of CRIPAK is highly enriched with the protein functional domain, post-SET, which is usually found in histone lysine methyltransferases (HMTase) genes including MLL2 and EZH2 that are known to be important in lymphomagenesis. Interestingly, CRIPAK is part of the same regulatory network consisted of previously identified lymphoma genes including MLL2, EZH2, CREBBP, and EP300, according to the shortest path algorithm by MetaCore (Philadelphia, Pennsylvania). Recurrent SVs identified in the FL tumors included the well-known IGH-BCL2 translocation, or t(14;18), in 6 out of 8 cases (75%) and the chr1q amplifications in 4 out of 8 (50%) tumors. Other non-recurrent large CNVs involving entire chromosomes or chromosome arms, as well as other inter- and intra-chromosomal structural variants were detected in individual tumors. In addition, we identified and validated 6 fusion transcripts from the transcriptome sequencing data in 3 out of 8 cases (38%). While our sample size is small, we found that SNVs and INDELs in MLL2, CREBBP, TNFRSF14, CRIPAK, and histone cluster genes, as well as t(14;18), did not distinguish indolent or aggressive tumors. However, all aggressive (4/4, 100%) and none of the indolent tumors had gains in chr1q; and the presence of RNA fusion transcripts were observed in aggressive tumors only (3/4, 75%). Additionally, we found that aggressive tumors had higher numbers of genes with point mutations (SNVs and short INDELs) (40 ± 7.6 vs. 26 ± 2.6; aggressive vs. indolent), higher numbers of genes impacted by copy number aberrations (1060 ± 263.7 vs. 233 ± 133.9), and higher numbers of large SVs (24 ± 10 vs. 6 ± 1.6). Taken together, our comprehensive analysis of 8 FL tumors reveals genetic diversity among newly diagnosed FL patients, identifies novel and recurrent mutations in CRIPAK, and finds that high tumor complexity and DNA instability may be indicators of aggressive disease. Disclosures No relevant conflicts of interest to declare.

Abstract Abstract 3637 Background: A recent large genome-wide association study confirmed the important role of the major histocompatibility complex in classical Hodgkin Lymphoma (cHL) susceptibility (rs6903608), but also identified significant associations with genetic variants outside of the HLA region at chromosomal regions 2p16.1 (REL, rs1432295), 8q24.21 (PVT1, rs2608053, rs2019960), and 10p14 (GATA3, rs501764, rs485411). These single nucleotide polymorphisms (SNPs) are localized in or near important genes involved in lymphoma pathogenesis: REL, which encodes for c-rel, is a member of NFkB pathway; PVT1, which is downstream of the MYC locus, encodes for non-coding microRNAs, some of which have oncogenic properties; and GATA3, which is a critical transcriptional factor for priming lymphoid T-cells for Th2 phenotype, is aberrantly expressed in HL. We investigated whether these six SNPs described in cHL etiology also influence the outcome of newly diagnosed cHL patients in two independent cohorts. Patients and Methods: The first cohort consisted of 342 cHL patients from the Groupe d'Etude des Lymphomes de l'Adulte (GELA) who were prospectively enrolled between 1998 and 2002. The second cohort consisted of 259 patients prospectively enrolled in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). DNA for genotyping was extracted from a blood samples collected at diagnosis, and genotyping was conducted using Taqman technology (ABI Prism 7000, Applied Biosystems) for HLA-DRA, PVT1, GATA3 and PCR-based restriction fragment length polymorphism for REL in GELA cohort and Illumina iSelect for the 6 SNPs in MER cohort. Both cohorts were followed for disease progression, re-treatment and death. In the GELA series, the median age was 32 years (range, 15–93), 57% were male, and 72% had an Ann Arbor stage I–II. Histology was nodular sclerosis in 84%, and 29% of available cases (n=40/140) were EBV positive. In the MER series, the median age was 38 years (range, 18–89), 53% were male, and 53% had an Ann Arbor stage I-II. Nodular sclerosis was the most common subtype (62%) and 23 of 115 available cases were EBV positive (20%). The International Prognostic Score (IPS) was 0–1 in 46% and 52% of GELA and MER patients, respectively. ABVD regimen was used for 60% and 88% of the GELA and MER patients, respectively. The 5-year progression free survival (PFS) was 83% and 75% in GELA and MER cohorts, respectively. We estimated the prognostic value of each individual SNP for PFS for allelic and genotypic (dominant and recessive) modes of transmission in each series. In order to increase power, we then performed a meta-analysis of the two studies. Results: Consistent with prior reports, the minor allele frequencies for REL (rs1432295), HLA-DRA (rs6903608), PVT1 (rs2608053), PVT1 (rs2019960), GATA3 (rs501764) and GATA3 (rs485411) were 0.44, 0.50, 0.43, 0.29, 0.22, 0.28 in the GELA and 0.46, 0.41, 0.46, 0.27, 0.25, 0.31 in the MER series, respectively. In GELA, GATA3 (rs501767) was associated with PFS in the ordinal model (HR=1.66, 95%CI 1.11–2.49, P=.01), while there were suggestive associations for PVT1 (rs2608053) (HR=1.76, 95%CI 0.95–3.26, P=.07) and REL (rs1432295) (HR=0.63, 95%CI 0.37–1.06, P=.08) in the dominant model. In the MER, PVT1 (rs2608053) was associated with PFS in the ordinal model (HR=1.42, 95%CI 0.99–2.03, P=.06), while there was a suggestive association with HLA-DRA (rs6903608) in the dominant model (HR=0.64, 95%CI 0.39–1.05, P=.08) for whole cohort but also for EBV negative cHL (HR=0.41, 95%CI 0.17–0.99, P=.05). In a meta-analysis of the two studies, PVT1 (rs2608053) was significantly associated with PFS in the ordinal (HR=1.34, 95%CI 1.04–1.73, P=.02) and the dominant (HR=1.88, 95%CI 1.20–2.95, P=.006) models. Further adjustment for IPS had minimal impact on these associations (ordinal HR=1.30, 95%CI 1.00–1.70; dominant HR=1.78, 95%CI 1.13–2.83). Patients with PVT1(rs2608053) GG, GA, AA genotypes had a 5-year PFS rate of 83%, 74% and 73% in GELA and 86%, 70%, and 73% in the MER, respectively. Conclusions: The non-HLA cHL susceptibility locus PVT1 (rs2608053) was associated with PFS in two independent cohorts of cHL cases, and after adjustment for the IPS. While associations with GATA3 and REL were only observed in one of the studies, their evaluation in other studies is warranted. Functional studies of PVT1 in cHL pathogenesis should be pursued. Disclosures: No relevant conflicts of interest to declare.

Abstract We previously identified five single nucleotide polymorphisms (SNPs) at four susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (GWAS). To further elucidate genetic susceptibility to DLBCL, we sought to validate two loci at 3q13.33 and 3p24.1 that were suggestive in the original GWAS with additional genotyping. In the meta-analysis (5662 cases and 9237 controls) of the four original GWAS discovery scans and three replication studies, the 3q13.33 locus (rs9831894; minor allele frequency [MAF] = 0.40) was associated with DLBCL risk [odds ratio (OR) = 0.83, P = 3.62 × 10−13]. rs9831894 is in linkage disequilibrium (LD) with additional variants that are part of a super-enhancer that physically interacts with promoters of CD86 and ILDR1. In the meta-analysis (5510 cases and 12 817 controls) of the four GWAS discovery scans and four replication studies, the 3p24.1 locus (rs6773363; MAF = 0.45) was also associated with DLBCL risk (OR = 1.20, P = 2.31 × 10−12). This SNP is 29 426-bp upstream of the nearest gene EOMES and in LD with additional SNPs that are part of a highly lineage-specific and tumor-acquired super-enhancer that shows long-range interaction with AZI2 promoter. These loci provide additional evidence for the role of immune function in the etiology of DLBCL, the most common lymphoma subtype.

Abstract Existing risk models underperform among women who have undergone a benign breast disease (BBD) biopsy (&gt; one million performed annually in the US) and with respect to estimating the risk of aggressive BCs. We've shown that molecular pathologic analysis of BBD biopsies can improve individual risk prediction, compared with standard risk models relying on self-reported factors, and provide insights into mechanisms mediating BC risk. BC risk factors such as obesity and ethanol use are proposed to increase production of cytokines and chemokines, resulting in chronic inflammation, and leading to production of DNA damaging free radicals and growth factors (e.g., VEGF, IGFs) that activate pro-carcinogenic pathways (e.g. NF-KB and JAK/STAT). The effects of most BC risk factors on immunity are poorly understood. Further, whereas experimental models implicate immunity throughout carcinogenesis, knowledge of immune markers and mechanisms related to BC development among women is limited. Therefore, we aim to define tissue immune cell content throughout BC development, and particularly at the earliest stages, to improve risk assessment and discover immune-based prevention strategies. Our study combines novel resources and technologies to define the immune landscape in: 1) normal breast tissues in relation to BC risk factors, 2) BBD with respect to BC risk, and 3) invasive BC in relation to neoantigen expression and molecular subtype. We will perform NanoString GeoMxTM Digital Spatial Profiling (DSP) to quantitatively map expression of key immune proteins in healthy breast tissues donated to the Komen Tissue Bank (KTB) in relation to BC risk factors and in BBD biopsies from two cohorts to predict BC risk. We will re-analyze top prognostic markers in BBD biopsies using Vectra multiplex IF staining with machine learning algorithms to refine how immune cell content and organization impacts prognosis, and assess critical immune pathways related to BBD progression with the NanoString PanCancer IO 360 panel, which provides RNA expression of 770 immune genes (13 signatures). Finally, we will evaluate immune responses in BCs categorized for predicted neoantigen load and underlying mutation type with our novel bioinformatics pipeline that enables accurate prediction of MHC class I and class II missense, fusion and frameshift mutation neoantigens generated through faulty DNA repair, aberrant DNA and RNA splicing, insertions, and deletions. Our project demonstrates a novel approach to defining the immune processes in early stages of BC development which may improve BC risk prediction and prevention. We present a plan to comprehensively analyze immunity throughout BC development and define changes that accompany transitions from normal to BBD and BBD to BC. Citation Format: Doug Hinerfeld, Keith Knutson, Derek Radisky, E. Aubrey Thompson, Yan Asmann, Kim McCoy, Amy Degnim, Jodi Carter, Stacey Winham, Michele Cote, Jeroen van der Laak, Mark Sherman. Analysis of the immune microenvironment to advance breast cancer risk prediction and prevention [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 651.