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Most relapsed/refractory large B cell lymphoma (r/rLBCL) patients receiving anti-CD19 chimeric antigen receptor (CAR19) T cells relapse. To characterize determinants of resistance, we profiled over 700 longitudinal specimens from two independent cohorts (n = 65 and n = 73) of r/rLBCL patients treated with axicabtagene ciloleucel. A method for simultaneous profiling of circulating tumor DNA (ctDNA), cell-free CAR19 (cfCAR19) retroviral fragments, and cell-free T cell receptor rearrangements (cfTCR) enabled integration of tumor and both engineered and non-engineered T cell effector-mediated factors for assessing treatment failure and predicting outcomes. Alterations in multiple classes of genes are associated with resistance, including B cell identity (PAX5 and IRF8), immune checkpoints (CD274), and those affecting the microenvironment (TMEM30A). Somatic tumor alterations affect CAR19 therapy at multiple levels, including CAR19 T cell expansion, persistence, and tumor microenvironment. Further, CAR19 T cells play a reciprocal role in shaping tumor genotype and phenotype. We envision these findings will facilitate improved chimeric antigen receptor (CAR) T cells and personalized therapeutic approaches.

Minimal residual disease (MRD) is the most powerful prognostic factor in pediatric acute lymphoblastic leukemia (ALL). Real-time quantitative polymerase chain reaction (RQ-PCR) represents the gold standard for molecular MRD assessment and risk-based stratification of front-line treatment. In the protocols of the Italian Association of Pediatric Hematology and Oncology (AIEOP) and the Berlin-Frankfurth-Munschen (BFM) group AIEOP-BFM ALL2009 and ALL2017, B-lineage ALL patients with high RQ-PCR-MRD at day+33 and positive at day+78 are defined slow early responders (SERs). Based on results of the AIEOP-BFM ALL2000 study, these patients are treated as high-risk also when positive MRD signal at day +78 is below the lower limit of quantification of RQ-PCR ("positive not-quantifiable," POS-NQ). To assess whether droplet digital polymerase chain reaction (ddPCR) could improve patients' risk definition, we analyzed MRD in 209 pediatric B-lineage ALL cases classified by RQ-PCR as POS-NQ and/or negative (NEG) at days +33 and/or +78 in the AIEOP-BFM ALL2000 trial. ddPCR MRD analysis was performed on 45 samples collected at day +78 from SER patients, who had RQ-PCR MRD ≥ 5.0 * 10-4 at day+33 and POS-NQ at day+78 and were treated as medium risk (MR). The analysis identified 13 of 45 positive quantifiable cases. Most relapses occurred in this patients' subgroup, while ddPCR NEG or ddPCR-POS-NQ patients had a significantly better outcome (P < 0.001). Overall, in 112 MR cases and 52 standard-risk patients, MRD negativity and POS-NQ were confirmed by the ddPCR analysis except for a minority of cases, for whom no differences in outcome were registered. These data indicate that ddPCR is more accurate than RQ-PCR in the measurement of MRD, particularly in late follow-up time points, and may thus allow improving patients' stratification in ALL protocols.

PURPOSE Patients with Diffuse Large B-cell Lymphoma (DLBCL) in need of immediate therapy are largely under-represented in clinical trials. The diagnosis-to-treatment interval (DTI) has recently been described as a metric to quantify such patient selection bias, with short DTI being associated with adverse risk factors and inferior outcomes. Here, we characterized the relationships between DTI, circulating tumor DNA (ctDNA), conventional risk factors, and clinical outcomes, with the goal of defining objective disease metrics contributing to selection bias. PATIENTS AND METHODS We evaluated pretreatment ctDNA levels in 267 patients with DLBCL treated across multiple centers in Europe and the United States using Cancer Personalized Profiling by Deep Sequencing. Pretreatment ctDNA levels were correlated with DTI, total metabolic tumor volumes (TMTVs), the International Prognostic Index (IPI), and outcome. RESULTS Short DTI was associated with advanced-stage disease ( P < .001) and higher IPI ( P < .001). We also found an inverse correlation between DTI and TMTV ( RS = −0.37; P < .001). Similarly, pretreatment ctDNA levels were significantly associated with stage, IPI, and TMTV (all P < .001), demonstrating that both DTI and ctDNA reflect disease burden. Notably, patients with shorter DTI had higher pretreatment ctDNA levels ( P < .001). Pretreatment ctDNA levels predicted short DTI independent of the IPI ( P < .001). Although each risk factor was significantly associated with event-free survival in univariable analysis, ctDNA level was prognostic of event-free survival independent of DTI and IPI in multivariable Cox regression (ctDNA: hazard ratio, 1.5; 95% CI [1.2 to 2.0]; IPI: 1.1 [0.9 to 1.3]; −DTI: 1.1 [1.0 to 1.2]). CONCLUSION Short DTI largely reflects baseline tumor burden, which can be objectively measured using pretreatment ctDNA levels. Pretreatment ctDNA levels therefore have utility for quantifying and guarding against selection biases in prospective DLBCL clinical trials.

p50, the mature product of NFKB1, is constitutively produced from its precursor, p105. Here, we identify BARD1 as a p50-interacting factor. p50 directly associates with the BARD1 BRCT domains via a C-terminal phospho-serine motif. This interaction is induced by ATR and results in mono-ubiquitination of p50 by the BARD1/BRCA1 complex. During the cell cycle, p50 is mono-ubiquitinated in S phase and loss of this post-translational modification increases S phase progression and chromosomal breakage. Genome-wide studies reveal a substantial decrease in p50 chromatin enrichment in S phase and Cycln E is identified as a factor regulated by p50 during the G1 to S transition. Functionally, interaction with BARD1 promotes p50 protein stability and consistent with this, in human cancer specimens, low nuclear BARD1 protein strongly correlates with low nuclear p50. These data indicate that p50 mono-ubiquitination by BARD1/BRCA1 during the cell cycle regulates S phase progression to maintain genome integrity., p50 is a constitutively produced NF-κB subunit that modulates the response to DNA damage. Here, the authors show that activation of ATR during S phase induces p50 interaction with BARD1 resulting in p50 mono-ubiquitination, facilitating cell cycle progression and promoting chromosome integrity.

Background: Characterization of T-cell receptor (TCR) diversity and dynamics is increasingly critical to understanding therapeutic immune responses targeting tumors. Current TCR profiling methods generally require invasive tissue biopsies that capture a single snapshot of immune activity or are limited by the sheer diversity of the circulating TCR repertoire. In theory, T-cells with the greatest turnover could best reflect pivotal immune dynamics from both circulating and tissue-derived compartments, including non-circulating tissue-resident memory T-cells (Trm). To noninvasively capture such responses in the blood, we developed and benchmarked a high-throughput TCR profiling approach using plasma, optimized for the fragmented nature of cfDNA and the non-templated nature of rearranged TCRs. We then applied this method for residual disease monitoring in mature T-cell lymphomas (TCL) without circulating disease and for characterizing immune dynamics after anti-CD19 chimeric antigen receptor (CAR19) T-cell therapy of B-cell lymphomas with axicabtagene ciloleucel. Methods: We developed SABER (Sequence Affinity capture & analysis By Enumeration of cell-free Receptors) as a technique for TCR enrichment and analysis of fragmented rearrangements shed in cfDNA and applied this method using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq). We used SABER to profile a total of 381 samples (300 cfDNA and 81 PBMC samples) from 75 lymphoma patients and 18 healthy controls. After mapping sequencing reads (hg38) to identify candidate rearrangements within TCR loci, unique cfDNA fragments were resolved by a novel strategy to define consensus of unique molecular identifiers clustered by Levenshtein distances, followed by CDR3-anchoring for enumeration of final receptor clonotypes. SABER thus leverages information from fragmented TCRs, a critical requirement for cfDNA, to make V gene, CDR3, and J gene assignments after deduplication-mediated error-correction. We benchmarked SABER against established amplicon-based TCR-β targeted sequencing (LymphoTrack, Invivoscribe) and repertoire analysis methods (MiXCR; Bolotin et al, 2015 Nature Methods) when considering both cfDNA and PBMC samples from healthy adults and TCL patients. We assessed SABER performance for tracking clonal molecular disease in patients with mature TCLs from both cellular and cell-free circulating compartments (n=9). Malignant TCL clonotypes were identified in tumor specimens using clonoSEQ (Adaptive Biotechnologies). Finally, we evaluated TCR repertoire dynamics over time in 66 DLBCL patients after CAR19 T-cell therapy. Results: SABER demonstrated superior recovery of TCR clonotypes from cfDNA compared to both amplicon sequencing (LymphoTrack, Invivoscribe) and hybrid-capture methods when enumerating receptors using MiXCR (Fig. 1A). When applied to blood samples from TCL patients, SABER identified the malignant clonal TCR-β rearrangement in 8/9 (88.9%) cases, with significantly improved detection in cfDNA (p=0.015, Fig. 1B). Specifically, tumoral TCR clonotype was detectable only in cfDNA in 6 cases (75%), cfDNA-enriched in 1 case (12.5%), and detectable only in PBMCs in 1 case (12.5%). We applied SABER to monitor TCR repertoire dynamics in cfDNA after CAR T-cell therapy of patients with relapsed/refractory DLBCL and observed increased T-cell turnover and repertoire expansion (greater total TCR-β clonotypes) (Fig. 1C). As early as 1-week after CAR19 infusion, TCR repertoire size was significantly correlated both with cellular CAR19 T-cell levels by flow cytometry (p=0.008) as well as with retroviral CAR19 levels in cfDNA (p=2.20e-07) suggesting faithful monitoring of CAR T-cell activity (Fig. 1D). TCR repertoire size one month after infusion was significantly associated with longer progression-free survival (HR 0.246, 95% CI 0.080-0.754, p=0.014). Conclusions: SABER has a favorable profile for cfDNA TCR repertoire capture when compared to existing methods and could thus have potential broad applicability to diverse disease contexts. Given the higher abundance of lymphoma-derived TCRs in cfDNA than intact circulating leukocytes, SABER holds promise for monitoring minimal residual disease in T-cell lymphomas. This approach also holds promise for monitoring T-cell repertoire changes including after CAR T-cell therapy and for predicting therapeutic responses. Disclosures Kurtz: Genentech: Consultancy; Foresight Diagnostics: Other: Ownership; Roche: Consultancy. Kim:Corvus: Research Funding; Eisai: Membership on an entity's Board of Directors or advisory committees, Research Funding; Elorac: Research Funding; Forty Seven Inc: Research Funding; Galderma: Membership on an entity's Board of Directors or advisory committees, Research Funding; Horizon Pharma: Consultancy, Research Funding; Innate Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kyowa-Kirin Pharma: Research Funding; Medivir: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; miRagen: Research Funding; Neumedicine: Consultancy, Research Funding; Portola: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Solingenix: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Trillium: Research Funding. Mackall:Lyell Immunopharma: Consultancy, Current equity holder in private company; BMS: Consultancy; Allogene: Current equity holder in publicly-traded company; Apricity Health: Consultancy, Current equity holder in private company; Nektar Therapeutics: Consultancy; NeoImmune Tech: Consultancy. Miklos:Kite-Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding; Adaptive Biotech: Consultancy, Other: Travel support, Research Funding; Juno-Celgene-Bristol-Myers Squibb: Consultancy, Other: Travel support, Research Funding; Novartis: Consultancy, Other: Travel support, Research Funding; Allogene Therapeutics Inc.: Research Funding; Pharmacyclics: Consultancy, Other: Travel support, Patents & Royalties, Research Funding; Janssen: Consultancy, Other: Travel support; Miltenyi Biotec: Research Funding. Diehn:Varian Medical Systems: Research Funding; Illumina: Research Funding; Roche: Consultancy; AstraZeneca: Consultancy; RefleXion: Consultancy; BioNTech: Consultancy. Khodadoust:Seattle Genetics: Consultancy; Kyowa Kirin: Consultancy. Alizadeh:Janssen: Consultancy; Genentech: Consultancy; Pharmacyclics: Consultancy; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

8518 Background: Detection of circulating tumor DNA (ctDNA) has prognostic value in lung cancer and could facilitate minimal residual disease (MRD) driven approaches. However, the sensitivity of ctDNA detection is suboptimal due to the background error rates of existing assays. We developed a novel method leveraging multiple mutations on a single cell-free DNA molecule (“phased variants” or PVs) resulting in an ultra-low error profile. Here we develop and apply this approach to improve MRD in localized NSCLC. Methods: To identify the prevalence of PVs, we reanalyzed whole genome sequencing (WGS) from 2,538 tumors and 24 cancer types from the pan-cancer analysis of whole genomes (PCAWG). We applied Phased Variant Enrichment and Detection Sequencing (PhasED-Seq) to track personalized PVs in localized NSCLC. We compared PhasED-Seq to a single nucleotide variant (SNV)-based ctDNA method. Results: In the PCAWG dataset, we found that PVs were common in both lung squamous cell carcinomas (LUSC, median 1,268/tumor; rank 2nd) and adenocarcinomas (LUAD, median 655.5/tumor; rank 3rd). However, PVs did not occur in stereotyped genomic regions. Thus, to leverage PhasED-Seq, we performed tumor/normal WGS to identify PVs, followed by design of personalized panels targeting PVs to allow deep cfDNA sequencing. We performed personalized PhasED-Seq for 5 patients with localized NSCLC. PVs were identified from WGS of tumor FFPE and validated by targeted resequencing in all cases (median 248/case). The background rate of PVs was lower than that of SNVs, even when considering duplex molecules (background: SNVs, 3.8e-5; duplex SNVs, 1.0e-5; PVs, 1.2e-6; P < 0.0001). We next assessed PhasED-Seq for MRD detection in 14 patient plasma samples. Both SNVs and PhasED-Seq had high specificity in healthy control cfDNA (95% and 97% respectively). Using SNVs, ctDNA was detected in 5/14 samples; PhasED-Seq detected all of these with nearly identical tumor fractions (Spearman rho = 0.97). However, PhasED-Seq also detected MRD in an additional 5 samples containing tumor fractions as low as 0.000094% (median 0.0004%). We analyzed serial samples from a patient with stage III LUAD treated with chemoradiotherapy (CRT) and durvalumab. SNV-based ctDNA and PhasED-Seq detected similar MRD levels (0.8%) prior to therapy. However, 3 samples collected during CRT, as well as before and during immunotherapy, were undetectable by SNVs. SNV-based ctDNA then re-emerged at disease recurrence. PhasED-Seq detected MRD in all 3 samples not detected by SNVs with tumor fractions as low as 0.00016%, including prior to immunotherapy (8 months prior to progression). Similar improvements were seen in samples not detected by SNVs from 2 additional patients. Conclusions: Personalized ctDNA monitoring via PVs is feasible and improves MRD detection in localized NSCLC. PhasED-Seq allows clinical studies testing personalized treatment based on MRD.

Purpose Diffuse large B cell (DLBCL)-like post-transplant lymphoproliferative disorder (PTLD) affects up to 10% of thoracic transplant recipients. Although 50% of PTLDs can be related to Epstein-Barr virus (EBV) infection, and high or increasing EBV viral load may precede EBV+ PTLD, preemptive monitoring of EBV titers does not reliably predict PTLD . Consequently, we assessed the utility of circulating tumor DNA (ctDNA), a promising biomarker for many cancers including DLBCL , for early detection of PTLD. Methods We used Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) to analyze diagnostic tumor and plasma samples and matched germline DNA in a cohort of 10 heart and lung transplant recipients who developed PTLD to identify somatic alterations. We compared mutational patterns in PTLD to 173 de novo DLBCL cases. We additionally sequenced serial post-transplant plasma samples preceding diagnosis in 5 patients (2 EBV-, 3 EBV+), and during or after treatment in 8 patients, to determine the dynamics of ctDNA and compared these patterns to clinical outcomes and EBV titers. Results There was no significant difference in mutational burden between patients with PTLD and de novo DLBCL (median PTLD variants: 216, IQR = 79 - 321; median de novo DLBCL variants: 143.5, IQR = 45 - 243.8; p = 0.35), although there was a trend toward greater variant count in EBV- compared to EBV+ PTLD (p = 0.064). The prevalence of alterations in known driver genes was similar between PTLD and de novo DLBCL. Prediagnostic plasma samples were available for 5 cases, and we detected ctDNA in all patients prior to clinical diagnosis (mean lead time = 114 days, maximum lead time 1.5 years). Levels of ctDNA during and after treatment were concordant with EBV titers in EBV+ disease and with clinical response in all patients. Conclusion PTLD patients have detectable ctDNA prior to clinical diagnosis. Development of screening tools utilizing both ctDNA and EBV titers would facilitate early detection of PTLD in the transplant population.

Background: Anti-CD19 chimeric antigen receptor (CAR19) T-cells have significant activity in patients with relapsed/refractory DLBCL (rrDLBCL). While the majority of rrDLBCL patients receiving axicabtagene ciloleucel (Axi-cel)achieve complete responses, a significant subset of patients experience disease progression (Locke FL, et al. Lancet Oncol. 2019). Circulating tumor DNA (ctDNA) analysis has demonstrated utility for predicting therapeutic benefit in DLBCL, as well as for detecting emergent resistance mechanisms to targeted therapies. Here we apply cell-free DNA (cfDNA) analysis to patients receiving Axi-cel, to characterize molecular responses, resistance mechanisms, and to track CAR19 cells. Methods: We performed Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) on DNA from germline and plasma samples collected prior to CAR T-cell infusion, multiple time-points post infusion, and, where available, at the time of relapse from 30 patients receiving Axi-cel for rrDLBCL at Stanford University. We designed a novel hybrid-capture panel and analysis pipeline designed to detect both tumor variants, as well as Axi-cel specific recombinant retroviral sequences to quantify CAR19 levels in cfDNA. Tumor variants were identified prior to and following Axi-cel therapy to assess for emergent variants, and Axi-cel specific sequences were quantified. Results: The median follow-up for the 30 patients after Axi-cel infusion was 10 months, with 47% (14/30) of patients experiencing disease progression after Axi-cel therapy. We identified an average of 164.3 SNVs per case (range:1-685) before Axi-cel therapy; the most common coding variants identified at baseline were in MLL2 (29.2%), BCL2 (22.5%), and TP53 (19.3%). When treated as a continuous variable, pretreatment ctDNA levels were prognostic of PFS (HR 2.16, 95% CI 1.11-4.21, P=0.02). Using a previously established ctDNA threshold to stratify disease burden (2.5 log10(hGE/mL); Kurtz et al. JCO 2018), we observed significantly superior PFS in patients with low pretreatment ctDNA levels treated with Axi-cel (Fig. 1A). In the majority of Axi-cel treated patients (62.9%), ctDNA was detectable at day 28, and PFS was significantly longer in patients with undetectable ctDNA at this time-point (Fig. 1B). Multiple putative resistance mechanisms were identified at relapse after Axi-cel, including emergent variants in CD19, HVEM, and TP53, as well as copy number gains in PD-L1 (Fig. 1C). For example, in one patient, a CD19 stop-gain mutation, which was not detected prior to treatment or at the time of the first interim PET scan, emerged at the time of relapse (Fig. 1D). Finally, we found cfDNA evidence for Axi-cel DNA in 74% of patients 28 days after therapy, including in patients without evidence of circulating CAR T-cells in PBMCs. Axi-cel levels in cfDNA as measured by CAPP-Seq were significantly correlated with CAR19 flow cytometry (Pearson r=0.55, P=.015; Fig. 1E). Conclusions: Baseline and interim ctDNA measurements have prognostic significance in DLBCL patients being treated with CAR19 T-cells, and potential emergent resistance mutations, including in CD19, can be identified in patients via cfDNA analysis. Quantification of CAR19 T-cells using cfDNA is significantly correlated with flow cytometric quantification, indicating that these cells can be quantified via cfDNA. Taken together, these data indicate that cfDNA analysis is a powerful tool for predicting response to CAR19 therapy, identifying genomic determinants of resistance and quantifying CAR19 cells, which may in turn inform the next therapeutic steps. Figure 1: A) Kaplan Meier analysis of PFS, with patients stratified based on pre-Axi-cel therapy ctDNA level, above and below a previously established threshold (2.5 log10[haploid Genome Equivalents/mL]). B) A Kaplan Meier plot depicting PFS stratification for patients with detectable versus undetectable ctDNA at day 28 after Axi-cel infusion. C) Oncoprint depicting selected emergent and baseline tumor variants in progressors and non-progressors after Axi-cel therapy. D) Change in mean ctDNA variant allele frequency (VAF) and emergence of a CD19 stop-gain mutation (CD19 pTrpX) at the time of relapse in a patient who initially achieved a CR at day 28 after CAR19 infusion. E) Relationship between CAR19 T-cell quantification by cfDNA and flow cytometry. (ND: Not detected) Disclosures Kurtz: Roche: Consultancy. Chabon:Lexent Bio Inc: Consultancy. Khodadoust:Corvus Pharmaceuticals: Research Funding. Majzner:Xyphos Inc.: Consultancy; Lyell Immunopharma: Consultancy. Mackall:Obsidian: Research Funding; Lyell: Consultancy, Equity Ownership, Other: Founder, Research Funding; Nektar: Other: Scientific Advisory Board; PACT: Other: Scientific Advisory Board; Bryologyx: Other: Scientific Advisory Board; Vor: Other: Scientific Advisory Board; Roche: Other: Scientific Advisory Board; Adaptimmune LLC: Other: Scientific Advisory Board; Glaxo-Smith-Kline: Other: Scientific Advisory Board; Allogene: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Apricity Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Diehn:Roche: Consultancy; Quanticell: Consultancy; Novartis: Consultancy; AstraZeneca: Consultancy; BioNTech: Consultancy. Miklos:Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Juno: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees. Alizadeh:Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

African-American men have the highest incidence of and mortality from prostate cancer. Whether a biological basis exists for this disparity remains unclear. Exome sequencing (n = 102) and targeted validation (n = 90) of localized primary hormone-naïve prostate cancer in African-American men identified several gene mutations not previously observed in this context, including recurrent loss-of-function mutations in ERF, an ETS transcriptional repressor, in 5% of cases. Analysis of existing prostate cancer cohorts revealed ERF deletions in 3% of primary prostate cancers and mutations or deletions in ERF in 3% to 5% of lethal castration-resistant prostate cancers. Knockdown of ERF confers increased anchorage-independent growth and generates a gene expression signature associated with oncogenic ETS activation and androgen signaling. Together, these results suggest that ERF is a prostate cancer tumor-suppressor gene. More generally, our findings support the application of systematic cancer genomic characterization in settings of broader ancestral diversity to enhance discovery and, eventually, therapeutic applications. Significance: Systematic genomic sequencing of prostate cancer in African-American men revealed new insights into prostate cancer, including the identification of ERF as a prostate cancer gene; somatic copy-number alteration differences; and uncommon PIK3CA and PTEN alterations. This study highlights the importance of inclusion of underrepresented minorities in cancer sequencing studies. Cancer Discov; 7(9); 973–83. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 920

Introduction. Minimal residual disease (MRD) is the most powerful prognostic factor in acute lymphoblastic leukemia (ALL). Currently, real-time quantitative PCR (RQ-PCR) is the most widely used molecular method for MRD assessment, rigorously standardized within the EuroMRD consortium. According to the EuroMRD guidelines (Van der Velden et al. Leukemia 2007), a non-negligible fraction of patients with very low MRD levels are classified as positive not-quantifiable (PNQ), a definition that may result problematic in the clinical practice. Digital-droplet-PCR (ddPCR) allows an absolute quantification without the need of a standard curve and has the potential to overcome some limitations of RQ-PCR. High degrees of efficiency, sensitivity and accuracy have been reported for ddPCR compared to RQ-PCR, but no established guidelines for ddPCR MRD analysis and interpretation have so far been defined and its ability to correctly evaluate very low MRD levels is still under investigation. In the present study, we assessed MRD by ddPCR in pediatric ALL cases classified as PNQ and/or negative by RQ-PCR at days +33 and/or +78 of the AIEOP-BFM ALL 2000 trial, to evaluate the potential of ddPCR for low MRD quantification and patients' risk stratification. Patients and Methods. A total of 211 pediatric ALL patients enrolled in the AIEOP-BFM ALL 2000 trial were included in the study. We analyzed 124 B-lineage ALL patients defined as intermediate risk (IR) who had high positive MRD at day +33 and at day +78 were either PNQ (n=45, Slow Early Responders (SER)) or negative (n=79). A case-control design was applied to 36 B- and T-lineage relapsed ALL patients (cases) who at day +33 had PNQ MRD (n=12, IR) or were negative (n=24, standard risk (SR)) and to matched controls (21 and 30 patients who did not present a relapse). ddPCR analysis was performed as previously published (Della Starza et al, BJH 174, 541-9, 2016), by using 1.5 μg and 3.0 μg DNA of the follow-up samples. In the absence of an international consensus, data have been analyzed using two alternative guidelines; results are reported according to Della Starza et al (BJH 2016). Results. Among 45 SER patients, ddPCR performed on 1.5 μg DNA of PNQ samples at day +78 revealed that 13 were quantifiable (Q), 16 PNQ and 16 negative (NEG) . When 3.0 μg of DNA were used (41/45 samples due to material availability), 12 were Q, 19 PNQ and 10 NEG. Event-free survival (EFS) curves are shown in Fig. 1a. Among the 79 patients with high positive MRD at day +33 but who were negative at day +78, ddPCR on 1.5 μg DNA of day +78 identified 5 as Q, 17 PNQ and 57 NEG. When 3.0 μg DNA was used (77/79 samples), 9 patients were Q, 27 PNQ and 41 NEG. EFS curves are reported in Fig. 1b. When ddPCR was applied to 33 PNQ samples at day +33, 2 were Q, 9 PNQ and 22 NEG; when using 3.0 μg of DNA, 1 was Q, 15 were PNQ and 17 NEG. EFS curves are shown in Fig. 1c. Lastly, ddPCR on 1.5 μg of day +33 DNA of 54 SR patients showed 5 PNQ and 49 NEG, whilst by using 3.0 μg on 53 sample, 7 were PNQ and 46 NEG. Conclusions. Our data demonstrate that ddPCR is a very promising tool for the evaluation of MRD in ALL cases with very low or negative RQ-PCR MRD results. In particular, among SER patients most relapses occurred in cases with quantifiable ddPCR MRD at day +78, while patients with negative or PNQ MRD by ddPCR at day +78 had a better outcome. Based on these results, high-risk treatment could be offered only to ddPCR quantifiable cases. Among patients with highly positive MRD at day +33 and negative at day +78, the small number of cases with quantifiable disease by ddPCR at present does not allow to establish the impact of quantification; consistently with SER patients, the outcome was similar for patients with negative or PNQ MRD by ddPCR at day +78. Similarly, among patients with PNQ MRD by RQ-PCR at day +33, a similar outcome was observed for cases negative or PNQ by ddPCR. Lastly, in most SR patients ddPCR confirmed the negative results of RQ-PCR at day +33, associated with an extremely good kinetics of disease reduction, independently of the MRD PCR method. Overall, our data indicate that ddPCR is as sensitive as RQ-PCR and can provide a potentially more accurate prognostic stratification for cases defined as PNQ MRD by RQ-PCR, in view of its ability to quantify without a standard curve. The application of ddPCR in a prospective clinical protocol with international guidelines is needed to define whether it can result in an overall improvement of pediatric ALL patients' stratification and outcome. Disclosures Foà: AMGEN: Other: ADVISORY BOARD; JANSSEN: Other: ADVISORY BOARD, Speakers Bureau; CELTRION: Other: ADVISORY BOARD; INCYTE: Other: ADVISORY BOARD; ABBVIE: Other: ADVISORY BOARD, Speakers Bureau; ROCHE: Other: ADVISORY BOARD, Speakers Bureau; NOVARTIS: Speakers Bureau; CELGENE: Other: ADVISORY BOARD, Speakers Bureau; GILEAD: Speakers Bureau.

7572Background: Diffuse large B cell (DLBCL)-like post-transplant lymphoproliferative disorder (PTLD) affects 2-5% of transplant recipients. Although 50% of PTLDs can be related to Epstein-Barr vir...

e18051Background: Transplant recipients experience a 2 to 10-fold increased risk of head and neck cancers (HNC) compared to the general population, with about 10-15% of transplant recipients develo...

Inherited mutations in DNA-repair genes such as BRCA2 are associated with increased risks of lethal prostate cancer. Although the prevalence of germline mutations in DNA-repair genes among men with localized prostate cancer who are unselected for family predisposition is insufficient to warrant routine testing, the frequency of such mutations in patients with metastatic prostate cancer has not been established.We recruited 692 men with documented metastatic prostate cancer who were unselected for family history of cancer or age at diagnosis. We isolated germline DNA and used multiplex sequencing assays to assess mutations in 20 DNA-repair genes associated with autosomal dominant cancer-predisposition syndromes.A total of 84 germline DNA-repair gene mutations that were presumed to be deleterious were identified in 82 men (11.8%); mutations were found in 16 genes, including BRCA2 (37 men [5.3%]), ATM (11 [1.6%]), CHEK2 (10 [1.9% of 534 men with data]), BRCA1 (6 [0.9%]), RAD51D (3 [0.4%]), and PALB2 (3 [0.4%]). Mutation frequencies did not differ according to whether a family history of prostate cancer was present or according to age at diagnosis. Overall, the frequency of germline mutations in DNA-repair genes among men with metastatic prostate cancer significantly exceeded the prevalence of 4.6% among 499 men with localized prostate cancer (P0.001), including men with high-risk disease, and the prevalence of 2.7% in the Exome Aggregation Consortium, which includes 53,105 persons without a known cancer diagnosis (P0.001).In our multicenter study, the incidence of germline mutations in genes mediating DNA-repair processes among men with metastatic prostate cancer was 11.8%, which was significantly higher than the incidence among men with localized prostate cancer. The frequencies of germline mutations in DNA-repair genes among men with metastatic disease did not differ significantly according to age at diagnosis or family history of prostate cancer. (Funded by Stand Up To Cancer and others.).

11565Background: Implementing cancer precision medicine requires interpretation of each potentially clinically relevant and biologically meaningful genomic alteration to aid clinical decision-makin...

5009Background: Inherited mutations in genes that regulate DNA repair processes such as BRCA1 and BRCA2 associate with increased risks of developing lethal prostate cancer (PC), and with response t...

1500Background: The expanding appeal of clinical tumor profiling has led to many sequencing strategies, ranging from small gene panels to exomes, with or without patient-matched germline data. This diversity of approaches may engender uncertainty about their benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling, and emerging immunotherapies that leverage genome-wide data. Methods: We modeled common tumor profiling modalities - large (n = 300 genes) and small (n= 15 or 48 genes) panels - onto clinical whole exomes (WES) from 157 patients with lung or colon adenocarcinoma. We created a tumor-only analysis algorithm to assess germline false positives (variants erroneously called as somatic), impact of patient ethnicity on results, and neoantigen detection. Results: The germline false positive rate with tumor-only large panel sequencing was 14% (144/1012 variants). For patients whose results underwent molecular pathologist review, 50/54 (93%) false positi...