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Abstract Abstract 2702 Background: CXCR5 [chemokine (C-X-C motif) receptor 5; also known as Burkitt lymphoma receptor 1 (BCR1)] is expressed on mature B-cells, subsets of CD4+ and CD8+ T-cells, and skin-derived migratory dendritic cells. CXCL13 is the ligand for CXCR5, and together they are involved in guiding B-cells into the B-cell zones of secondary lymphoid organs as well as T-cell migration. CXCR5 is also expressed in malignant cells in NHL patients. Single nucleotide polymorphisms (SNPs) in CXCR5 have not been extensively studied, although two studies reported that SNPs from this chromosome 11 gene were associated with risk of primary biliary cirrhosis in a European population and with risk of NHL in an Asian population. This study evaluated the role of common germline genetic variation in the risk and prognosis of the common NHL subtypes of diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), marginal zone lymphoma (MZL) and peripheral T-cell lymphoma (PTCL). Methods: Ten SNPs in CXCR5 were genotyped using a clinic-based study of 2694 NHL cases (including 586 DLBCL, 588 FL, 137 MCL, 230 MZL, and 158 PTCL) and 1521 controls enrolled from 2002–2009 in the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma SPORE. Clinical data were abstracted for all NHL patients and all were prospectively followed for disease progression, retreatment, transformation and death. SNPs were selected from HapMap using a standard tagging approach (r2≥0.80 and minor allele frequency (MAF) ≥0.05) and included SNPs 5kb upstream and downstream from the gene. Genotyping was conducted using a custom Illumina iSelect panel. All SNPs were in Hardy-Weinberg equilibrium in the control group. The most prevalent homozygous genotype was used as the reference group, and each SNP was modeled as having a log-additive effect in the regression model. For risk of NHL, odds ratios (ORs) and 95% confidence intervals were calculated using unconditional logistic regression, adjusted for age and gender. For prognosis, hazard ratios (HR) were calculated for both event-free (EFS) and overall (OS) survival using Cox proportional hazards regression, adjusting for age, treatment type, and IPI (DLBCL, PTCL), FLIPI (FL), or MIPI (MCL). To determine risk of FL transformation to DLBCL, a competing risk Cox model was used. A p<0.05 was declared as statistically significant. Results: Of the 10 CXCR5 tag SNPs in our study, 5 were associated with risk of NHL, with rs1790192 (MAF=0.39 among controls) having the strongest association (OR=1.19, 95%CI 1.08–1.30; p=0.0003). This SNP was strongly associated with FL (OR=1.44, 95%CI 1.25–1.66; p=3.1×10−7), and was also associated with DLBCL (OR=1.16, 95%CI 1.01–1.33; p=0.038) and PTCL (OR=1.29, 95%CI 1.02–1.64; p=0.037) but not MCL or MZL. For FL, none of the SNPs were associated with EFS or OS, although rs1790192 was associated with better outcome among FL patients that were observed (HR=0.64; 95%CI 0.47–0.87; p=0.0039). For DLBCL, none of the SNPs were associated with EFS, while three SNPs were associated with OS: rs523604 (HR=0.79; 95%CI 0.64–0.97; p=0.028), rs12363277 (HR=0.57; 95%CI 0.33–0.99; p=0.046), and rs3922 (HR=0.79; 95%CI 0.65–0.97; p=0.027). The latter SNP was also associated with risk of transformation from FL to DLBCL (HR=0.61; 95%CI 0.40–0.91; p=0.016). For MCL, MZL and PTCL, none of the SNPs were associated with either EFS or OS. Conclusions: Several germline CXCR5 polymorphisms were associated with NHL risk, particularly rs1790192 with risk of FL as well as DLBCL and PTCL. CXCR5 SNPs did not appear to be important in prognosis for most NHL subtypes, although rs1790192 was associated with a better outcome in untreated FL and rs3922 was associated with OS in DLBCL patients and with risk of FL transformation. These results provide additional evidence for a role of host genetic variation in CXCR5 in lymphomagenesis, mainly in risk of disease development, although there may also be a role for CXCR5 in FL and DLBCL prognosis. Replications of these findings in additional studies are needed. Disclosures: Ansell: Seattle Genetics, Inc.: Research Funding.

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 Abstract 2441 Background: CLL patients with mutated IGHVs (M-CLL) have clinical outcomes better than patients with unmutated BCRs (U-CLL). It is believed that this difference stems from the fact that somatic mutations have altered the structure of the BCR such that it no longer binds stimulatory (auto)antigens and therefore cannot deliver trophic signals to the leukemic cells. We sought to determine if this belief could be corroborated by analyzing the types (silent vs. conservative vs. non-conservative) and locations (FRs v CDRs) of mutations that occurred in IGHVs of M-CLL clones and then comparing the time to first therapy (TTFT) in patients with different IGHV features. Methods: Using IGMT software, we analyzed the IGHV sequences of 1560 cases and characterized their mutations in several respects: first, if IGHV mutations altered amino acid structure (silent vs. replacement); second, if mutations occurred in CDRs (antigen binding domains) or FRs (scaffolds of the BCR); third, if mutations were conservative or non-conservative (as determined by charge, hydropathy, size). TTFT for patients was correlated with various combinations of the above parameters. Differences in TTFT were estimated by the method of Kaplan and Meier; differences were assessed using the log rank test. Results: First we compared TTFT in patients who had only silent mutations (n=32) to those with no mutations (n=563) and found a trend toward longer TTFT in patients with silent mutations (3.0 vs. 3.6 yrs, p=0.09). Because the statistical significance of this result was likely influenced by the small numbers of clones with only silent mutations, we expanded the group to include IGHVs with replacement mutations of only the conservative type (n=83) since these would be unlikely to havemajor impact on BCR structure. Comparison of this group with those with no mutations for differences in TTFT was significant (3.0 vs. 3.9 yrs, p=0.0007). Furthermore, comparisons of patients with silent plus conservative mutations in FRs only (n=123) versus patients with no mutations showed a clear difference in TTFT (3.0 vs. 4.1, respectively; (p<0.003). In both of the above instances, non-conservative replacement mutations do not have a significantly greater impact on TTFT than the silent plus conservative combination. Conclusions: It is assumed that M-CLL patients have a clinical course superior to U-CLL patients because IGHV somatic mutations change the structure of the M-CLL BCR such that it no longer binds stimulatory antigens. For this theory to be correct, only replacement mutations and in particular non-conservative replacement mutations that would alter amino structure of the IGHV/D/J rearrangements would have relevance. Silent mutations, which by definition do not change amino acid structure, should be irrelevant in this regard, and conservative mutations, which substitute amino acids with similar chemical properties to the original amino acid, would be unlikely to result in major changes in the BCR. Therefore, the fact that the combination of only silent plus conservative mutations, which probably would not result in changes in BCR structure and therefore antigen binding, is associated with a lengthened TTFT militates against a loss of antigen binding as a reason for improved clinical course. Therefore, we suggest that another explanation for the enhanced survival of M-CLL patients should be sought. One possibility is that the cells that have the capacity to mutate their IGHVs are different in origin and/or biologic properties from those that cannot, and these account for the differences in survival between M- and U-CLL patients. (This work was supported NIH grant PO1-CA81534 of the CLL Research Consortium.) Disclosures: Byrd: Genzyme Corporation: Research Funding. Kipps: GlaxoSmithKline: Research Funding.

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 The existence of stereotyped B cell receptor immunoglobulins (BcR IG) in chronic lymphocytic leukemia (CLL) strongly implicated antigen selection in disease ontogeny. We have previously shown that the stereotyped fraction encompasses ~30% of all CLL and includes multiple subsets with distinct BcR IG configuration and variable size. Eventually, certain major subsets emerged as distinct clinical entities, exemplified by subset #2 (IGHV3-21/IGLV3-21, ~2.5-3% of all CLL, mixed somatic hypermutation (SHM) status) of a particularly aggressive clinical course, thus, sharply contrasting subset #4 (IGHV4-34/IGKV2-30, ~1% of all CLL, mutated IGHV genes, M-CLL), a prototype for indolent disease. Here, taking advantage of a multi-institutional cohort of 21,123 CLL IG rearrangements, almost three times the size of the largest previous study, and the availability of validated, purpose-built immunoinformatics methods, we reappraised BcR IG stereotypy especially focusing on major subsets and the degree of their sequence similarity to related minor subsets. Stereotypy discovery was performed with ARResT/Teiresias, while stereotypy assignment to existing subsets previously deemed as major was performed with ARResT/AssignSubsets (http://bat.infspire.org/arrest/). In the present study, a subset was characterized as major if representing > 0.2% of the cohort (i.e. at least 50 cases). Minor subsets closely related to major ones (termed satellite) were identified applying the following criteria: (i) usage of IGHV genes from the same phylogenetic clan; (ii) VH CDR3 length difference ranging from -2 to +2 compared to the respective major subset; (iii) shared VH CDR3 sequence motif; and, (iv) -2 to +2 difference in the offset of the VH CDR3 motif compared to the respective major subset. In total, 7378/21123 (34.9%) IG sequences were grouped into subsets with stereotyped VH CDR3, with the previously characterized 19 major subsets accounting collectively for 2594 sequences (12.3%) of the cohort: of these, 12 included cases with unmutated IGHV genes (U-CLL), 6 concerned M-CLL and 1 (subset #2) included cases with mixed SHM status. Four additional subsets exceeded 50 cases, and, thus, were also considered as 'major'. These results reinforce the notion that not all CLL will end up being stereotyped but rather that a plateau for stereotypy exists at ~1/3 of the cohort. Subset #2 was the largest subset (n=572, 2.7%), while subset #1 (IGHV clan I (IGHV1,5,7 subgroups)/IGKV1(D)-39) was the most frequent subset within U-CLL (n=515, 2.4%) and subset #4 the most common M-CLL subset (n=192, 0.9%), hence displaying remarkable consistency regarding their frequency in all cohorts published since the pioneering studies. Altogether, Teiresias and AssignSubsets gave concordant results for previously identified major subsets, illustrating the validity of our approach. Satellite subsets were sought for individually for each major subset. In general, few satellite subsets were identified, most of which concerned U-CLL major subsets. That notwithstanding, notable cases of satellite subsets were exemplified by major subset #1 and its satellite subset #99 from which it differed only in VH CDR3 length (13 aminoacids in subset #1 versus 14 in subset #99); interestingly, both subsets displayed equally aggressive clinical course. Another example concerned subset #8 (IGHV4-39/IGKV1(D)-39, U-CLL), an aggressive subset with very high risk for Richter's transformation, that, except for a one-aminoacid difference in VH CDR3 length, was otherwise identical to satellite subset #215, also displaying clinical aggressiveness. Overall, our results confirm that major subsets can be robustly identified and are consistent in relative size, hence representing distinct disease subgroups amenable to compartmentalized research with the potential of overcoming the pronounced heterogeneity of CLL. Most major subsets display unique sequence motifs, however satellite subsets exist, especially within U-CLL. Considering ever-increasing evidence that major stereotyped subsets may represent distinct disease subgroups, the existence of satellite subsets reveals a novel aspect of repertoire restriction and has implications for refined molecular classification of CLL. Disclosures Shanafelt: Genentech: Research Funding; GlaxoSmithkKine: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Cephalon: Research Funding; Hospira: Research Funding. Gaidano:Roche: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau. Niemann:Janssen: Consultancy; Abbvie: Consultancy; Roche: Consultancy; Gilead: Consultancy. Langerak:F. Hofmann-LaRoche, Genentech: Research Funding; InVivoScribe Technologies: Patents & Royalties: Royalties are provided to European Network (EuroClonality). Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Kater:Celgene: Research Funding; Gilead: Research Funding; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding. Stilgenbauer:Amgen: Consultancy, Honoraria, Other: Travel grants, Research Funding; Gilead: Consultancy, Honoraria, Other: Travel grants , Research Funding; Genentech: Consultancy, Honoraria, Other: Travel grants , Research Funding; Celgene: Consultancy, Honoraria, Other: Travel grants , Research Funding; Boehringer Ingelheim: Consultancy, Honoraria, Other: Travel grants , Research Funding; Genzyme: Consultancy, Honoraria, Other: Travel grants , Research Funding; AbbVie: Consultancy, Honoraria, Other: Travel grants, Research Funding; GSK: Consultancy, Honoraria, Other: Travel grants , Research Funding; Janssen: Consultancy, Honoraria, Other: Travel grants , Research Funding; Mundipharma: Consultancy, Honoraria, Other: Travel grants , Research Funding; Novartis: Consultancy, Honoraria, Other: Travel grants , Research Funding; Pharmacyclics: Consultancy, Honoraria, Other: Travel grants , Research Funding; Hoffmann-La Roche: Consultancy, Honoraria, Other: Travel grants , Research Funding; Sanofi: Consultancy, Honoraria, Other: Travel grants , Research Funding. Hallek:Gilead: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Mundipharma: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau. Rosenquist:Gilead Sciences: Speakers Bureau. Ghia:Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Roche: Honoraria, Research Funding; Adaptive: Consultancy; Abbvie: Consultancy, Honoraria. Stamatopoulos:Novartis: Honoraria, Research Funding; Abbvie: Honoraria, Other: Travel expenses; Janssen: Honoraria, Other: Travel expenses, Research Funding; Gilead: Consultancy, Honoraria, Research Funding.

Abstract Background: CLL patients (pts) treated with novel targeted therapies continue to experience disease progression. Approximately 7-15% of relapsed CLL treated with ibrutinib or venetoclax developed Richter's syndrome (RS), an aggressive high grade lymphoma transformation. Standard chemotherapy has limited efficacy in RS. We hypothesized that checkpoint blockade would re-establish anti-tumor immune response in progressive CLL/RS and here we aim to update the clinical data and correlative analysis of MC1485: a phase 2 study of PD-1 blocking antibody pembrolizumab in relapsed/refractory CLL and RS. Methods: Relapsed/refractory CLL including RS enrolled in the CLL arm of MC1485 trial was reported here. The primary end point of this study is overall response rate (ORR). Pts with prior allogeneic stem cell transplant were excluded. Pembrolizumab 200 mg was administered intravenously every 3 weeks. NCI CTCAE v4.0 and IWCLL 2008 criteria were used for non-hematological adverse events (AE) and for CLL grade hematological AE. Tumor expression of PD-1 and PD-L1 were assessed with standard immunohistochemical (IHC) staining. Percentage expressions of individual antigens were analyzed with whole-slide scanning followed by image analysis with Image-Pro software (Media Cybernetics). Fluorescence in situ hybridization (FISH) was performed on available CLL/RS tissue sections to assess copy number of chromosome 9p region that contains PD-L1 and PD-L2. Results: 25 pts including 16 CLL and 9 RS (biopsy-proven large cell lymphoma) were enrolled in CLL arm. The median age was 69 years (46-81). Twelve (48%) pts had del(17p) or monosomy 17 or TP53 mutation. The median number of prior therapies was 4 (1-10). 96%, 72% or 48% of pts had received alkylator and anti-CD20 antibody, purine analog or anthracycline. Fifteen (60%) pts had prior ibrutinib and 12 (48%) progressed clinically while receiving ibrutinib, including 6 who progressed to RS and 6 who developed progressive CLL. The median number of pembrolizumab doses that pts received was 3 (1 to 21), administered over a median treatment duration of 11 weeks (1 to 56). Drug-related AE occurred in 21 pts (88%) with the most common ones being neutropenia in 9 (37%), cough in 7 (29%) and dyspnea in 6 (25%) pts. Drug-related grade 3 or above AE occurred in 9 (38%) pts with most common ones being thrombocytopenia (5, 21%), dyspnea (2, 8%), and fatigue (2, 8%). The most common immune-related AE was liver enzyme elevation (12%, 8% G3) and was reversible with therapy interruption or steroid therapy. Based on investigator assessment using the Revised Response Criteria for Lymphomas and IWCLL 2008 criteria, one RS pt had complete response (CR, 4%), 3 RS had partial responses (PR, 12%). Among 9 RS pts, 1 CR (11%), 3 PR (33%), 3 SD (33%) and 2 PD (22%) were observed. Thus the ORR in RS was 44%. No CLL pts achieved a CR/PR, 3 had SD and 9 had progressive disease (PD). The ORR of all pts was 16%. Of the 4 RS who had CR/PR, responses occurred early after 2 cycles of therapy and median duration of therapy for CR/PR pts was 8.3 months with data cut-off by June 30th 2016 (Figure 1A). The causes for therapy discontinuation in RS were PD (3, 33%), alternate therapy (2, 22%), and thrombocytopenia due to increased marrow CLL (1, 11%). Increased marrow CLL was observed in 2 RS pts with PR to RS phase of disease assessed by PET, thus the protocol was amended to allow addition of idelalisib/ibrutinib to control the underlying marrow CLL. For RS pts who had a CR/PR or SD, variable nodal responses were observed (Figure 1B). In particular, 6 RS who had prior ibrutinib experienced CR or PR or SD with nodal responses. After a median follow-up of 10.2 (1.9 to 16.1) months, the median overall survival (OS) for all pts was 10.7 months (95% confidence interval [CI], 5.4 to not reached). The 6 month OS rate for RS and CLL pts were 73% and 59%, respectively. Biomarker assessment using IHC analysis on 10 pts with available tumor/nodal tissues (6 RS and 4 CLL) showed an increased expression of PD-L1 (p = 0.02) and a trend for an increased expression of PD-1 (p = 0.1) in the group of pts with CR/ PR versus pts in the group with no clinical response. One out of 10 tested showed polysomic for chromosome 9p. Conclusion: Pembrolizumab had an acceptable safety profile in CLL and RS patients. We confirmed that pembrolizumab has substantial therapeutic activity in RS. Single-agent pembrolizumab does not appear to have clear activity in CLL. Disclosures Ding: Merck: Research Funding. Parikh:Pharmacyclics: Honoraria, Research Funding. Shanafelt:GlaxoSmithKline: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; Hospira: 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 #the authors contributed equally to this work While survival outcomes after HLA-matched unrelated donor (URD) blood and marrow transplant (BMT) have significantly improved over the last two decades, about 40% of patients die before 1-year post URD BMT. Previously we performed a genome-wide association study (GWAS) named DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT) to investigate the contributions of common genetic variants on survival outcomes. To address the specific contributions of low frequency (≤ 1%) exonic variants on survival outcomes, we used the DISCOVeRY-BMT cohorts typed with the Illumina HumanExome BeadChip containing 242,901 variants, which are mainly protein-coding variants. We used a gene-based test (the optimal sequence kernel association test (SKAT-O)) to evaluate the cumulative effects of multiple genetic variants within a gene on overall survival (OS), transplant-related mortality (TRM), and disease-related mortality (DRM) 1 year after URD BMT. SKAT-O measures the overall significance of the gene and allows the variants to have different directions and magnitude of effects. We used a 2-stage study design: SKAT-O analysis was carried out in Cohort 1 consisting of 1,972 AML, ALL or MDS patients reported to the Center for International Blood and Marrow Transplant Research from 2000 to 2008 and their 2,006 10/10 HLA-matched URD. Genes with a suggestive association with each outcome (P < 1×10-3) entered the final meta-analysis including Cohort 1 and an independent cohort (Cohort 2), consisting of 503 recipients and 520 10/10 HLA-matched URD from 2009 to 2011. The analysis included recipients and URD of European descent due to the low frequency of other populations. All association models included age at BMT, diagnosis (AML, ALL, MDS), disease status at BMT, cell source (peripheral blood, marrow), year of BMT, and principal components from EIGENSTRAT to control for population stratification. Only coding variants (missense and nonsense) with minor allele frequency ≤ 1% were included in our analysis, which resulted in > 12,000 genes tested in our gene-based analysis in recipients and donors. Exome-wide significance was set at Pmeta< 4.10×10-6 after Bonferroni correction for the total number of genes tested. The likely pathogenicity of these variants was determined in silico using SIFT and PolyPhen. In recipients, we identified OR51D1, a member of the olfactory receptor gene family, as significantly associated with both OS and TRM. The OS association with ORD51D1 is driven by TRM as the same six missense variants contribute to both TRM and OS, but not to DRM. In donors, four (ALPP, EMID1, SLC44A5, LRP1), one (HHAT), and two (LYZL4, NT5E) genes were significantly associated with OS, TRM, and DRM, respectively (Table). ALPP encodes an alkaline phosphatase. SLC44A5 is a member of the solute carrier gene family. LRP1 (low density lipoprotein receptor-related protein 1) encodes an endocytic receptor involved in several cellular processes, including intracellular signaling, lipid homeostasis, and clearance of apoptotic cells. HHAT encodes an enzyme that acts within the hedgehog secretory pathway, which is involved in hematologic malignancy. LYZL4 is a member of a family of lysozyme-like genes, which have a protective role in host defense. The encoded protein of NT5E, CD270, is used as a determinant of lymphocyte differentiation. The likely pathogenicity of several of the functional variants in all significant genes identified was predicted to be both (possibly or probably) damaging and deleterious (Table), thus indicating that the amino acid substitution of the variant may not be well tolerated. One damaging variant in LRP1 has been identified as a somatic mutation in hematopoietic and lymphoid tissue. Our study is the first analysis of the low-frequency coding variant contribution to URD BMT survival outcomes. We found that the missense variants contributing to many of these significant gene associations shows evidence of pathogenicity and thus it is biologically plausible these variants are contributing to survival outcomes. Further confirmation of these findings, and studies of the functional consequences of protein-coding changes in these genes, may provide more individualized risk prediction and prognosis as well as alternative donor selection strategies. Table. Table. Disclosures McCarthy: Gamida Cell: Honoraria, Membership on an entity's Board of Directors or advisory committees; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; The Binding Site: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Hahn:Novartis: Equity Ownership; NIH: Research Funding. Sucheston-Campbell:NIH/NCI: Research Funding.