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Circulating tumor DNA (ctDNA) is an emerging biomarker for many cancers, but the limited sensitivity of current detection methods reduces its utility for diagnosing minimal residual disease. Here we describe phased variant enrichment and detection sequencing (PhasED-Seq), a method that uses multiple somatic mutations in individual DNA fragments to improve the sensitivity of ctDNA detection. Leveraging whole-genome sequences from 2,538 tumors, we identify phased variants and their associations with mutational signatures. We show that even without molecular barcodes, the limits of detection of PhasED-Seq outperform prior methods, including duplex barcoding, allowing ctDNA detection in the parts-per-million range in patient samples. We profiled 678 specimens from 213 patients with B-cell lymphomas, including serial cell-free DNA samples before and during therapy for diffuse large B-cell lymphoma. In patients with undetectable ctDNA by CAPP-Seq after two cycles of therapy, an additional 25% have ctDNA detectable by PhasED-Seq and have worse outcomes. Finally, we demonstrate the application of PhasED-Seq to solid tumors., Editorial summary The sensitivity of circulating tumor DNA detection is improved by identifying sequences with two or more mutations.

BACKGROUND: New ultrasensitive methods for detecting residual disease after surgery are needed in human papillomavirus–associated oropharyngeal squamous cell carcinoma (HPV+OPSCC). METHODS: To determine whether the clearance kinetics of circulating tumor human papillomavirus DNA (ctHPVDNA) is associated with postoperative disease status, a prospective observational study was conducted in 33 patients with HPV+OPSCC undergoing surgery. Blood was collected before surgery, postoperative days 1 (POD 1), 7, and 30 and with follow-up. A subcohort of 12 patients underwent frequent blood collections in the first 24 hours after surgery to define early clearance kinetics. Plasma was run on custom droplet digital polymerase chain reaction (ddPCR) assays for HPV genotypes 16, 18, 33, 35, and 45. RESULTS: In patients without pathologic risk factors for recurrence who were observed after surgery, ctHPVDNA rapidly decreased to 350 copies/mL) and remained elevated until adjuvant treatment (n = 3/3). Patients with intermediate POD 1 ctHPVDNA levels (1.2–58.4 copies/mL) all possessed pathologic risk factors for microscopic residual disease (n = 9/9). POD 1 ctHPVDNA levels were higher in patients with known adverse pathologic risk factors such as extranodal extension >1 mm (P = .0481) and with increasing lymph nodes involved (P = .0453) and were further associated with adjuvant treatment received (P = .0076). One of 33 patients had a recurrence that was detected by ctHPVDNA 2 months earlier than clinical detection. CONCLUSIONS: POD 1 ctHPVDNA levels are associated with the risk of residual disease in patients with HPV+OPSCC undergoing curative intent surgery and thus could be used as a personalized biomarker for selecting adjuvant treatment in the future.

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.

7565 Background: Detection of circulating tumor DNA (ctDNA) has prognostic value in diverse tumors, including DLBCL. Despite uses for assessing molecular response to therapy, current methods using immunoglobulin or hybrid-capture sequencing have suboptimal sensitivity, particularly when disease-burden is low. This contributes to a high false negative rate at key milestones such as at the end of therapy (EOT; Kumar A, ASH 2020). We explored the utility of detecting multiple mutations (phased variants, PVs) on individual cell-free DNA (cfDNA) strands to improve MRD in DLBCL. Methods: We applied Phased Variant Enrichment and Detection Sequencing to track PVs from 485 specimens from 117 DLBCL patients undergoing first-line therapy. We sequenced cfDNA prior to, during, and after therapy to assess the prognostic value of MRD. We compared the performance of PhasED-Seq to current techniques, including SNV-based CAPP-Seq and duplex sequencing. Results: To establish its detection limit for ctDNA, we compared the background error-profile of of PVs and SNVs in cfDNA sequencing from healthy subjects. PV-detection by PhasED-Seq demonstrated a lower background profile than SNVs, even when considering duplex molecules (n = 12; 8.0e-7 vs 3.3e-5 and 1.2e-5; P < 0.0001). We also assessed analytical sensitivity within a ctDNA limiting dilution series from 3 patients, simulating tumor fractions from 0.1% to 0.00005% (1:2,000,000). PhasED-Seq outperformed SNV-based methods and duplex sequencing for recovery of expected tumor content below 0.01% (P < 0.0001 and P = 0.005 respectively by paired t-test). We then explored disease detection in clinical samples. We identified SNVs and PVs from pretreatment tumor or plasma and followed these variants in serial cfDNA. Using SNV-based methods, 40% and 59% of patients had undetectable ctDNA after 1 or 2 cycles (n = 82 and 88). However, 24% and 25% of these cases had detectable ctDNA by PhasED-Seq. Importantly, MRD detection by PhasED-Seq was prognostic for event-free survival even in patients with undetectable ctDNA by SNVs. We next explored the utility of PhasED-Seq at the EOT in 19 subjects, 5 of whom experienced eventual disease progression. While only 2/5 cases with progression had detectable disease at EOT using SNVs, PhasED-Seq detected all 5/5 cases. PhasED-Seq also correctly identified all patients (14/14) without clinical relapse as having no residual disease, including one patient who discontinued therapy after 1 cycle due to toxicity, but remains in remission > 5 years after this single treatment. This resulted in superior classification of patients for EFS using PVs compared with SNVs (C-statistic: 0.98 vs 0.60, P = 0.02). Conclusions: Tracking PVs results in significantly lower background rates than SNV-based approaches, enabling detection to parts per million range. PhasED-Seq improves on disease detection in DLBCL at the EOT, allowing possible MRD-driven consolidative approaches.

Korrespondenz zu 10.1200/JCO.2018.78.5246

BACKGROUND Selection biases can impair the generalizability of clinical trials. Studies investigating aggressive diseases such as Diffuse Large B-cell Lymphoma (DLBCL) can be particularly affected by such biases since clinical urgency and need for therapy may not allow the requisite extensive screening and consent processes for trials. Diagnosis-to-Treatment Interval (DTI) has recently been proposed as a novel metric to capture this phenomenon (Maurer et al, JCO, 2018), and short DTI is associated with both adverse clinical factors and adverse clinical outcomes. Intriguingly, DTI was independent of clinical risk factors like the International Prognostic Index (IPI) suggesting that widely applied prognostic scores do not adequately reflect risk factors considered for clinical decision making. In this study, we aim to assess whether pretreatment levels of circulating tumor DNA (ctDNA) are associated with shorter DTI and may constitute an objective measure of clinical urgency. METHODS We quantified pretreatment ctDNA levels in plasma samples from 178 patients treated in 5 US and European centers for large cell lymphoma (DLBCL, Follicular lymphoma grade 3b, or High-grade-B-cell-lymphoma) using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) as previously described (Kurtz, JCO 2018; Scherer, STM 2016). Pretreatment ctDNA levels were correlated with DTI, clinical factors and treatment outcome. RESULTS Pretreatment ctDNA was detectable in 175/178 cases. Median number of single nucleotide variants (SNV) detected per patient was 129 (range 0-628). Pretreatment ctDNA levels ranged from 0 - 1.4 x105 haploid genome equivalents per milliliter of plasma (hGE/ml, median 239). Median DTI was 19 days (range 0-141, Figure 1A) and was similar in distribution to 2 previously described cohorts from the US and Europe (Maurer et al, JCO 2018). Shorter DTI was associated with higher ctDNA levels (RS=-0.39, P= 1.4 x10-7, Figure 1B). Patients with longer DTI had improved Event-Free Survival (EFS, Hazard Ratio (HR) for DTI: 0.9/week, P= 0.03). However, this association was lost when adjusting for pretreatment ctDNA levels (HR for DTI: 0.95/week, P= 0.39; HR for log10(ctDNA): 1.7, P= 5.8 x10-5). In a multivariate analysis including DTI, ctDNA and IPI, only ctDNA levels were significantly associated with EFS (HR for log10(ctDNA): 1.6, P= 0.002, n=178, Figure 1C). Pretreatment ctDNA levels remained the only prognostic factor for EFS in a second multivariate analysis also considering pretreatment metabolic tumor volume (MTV, HR for log10(ctDNA): 1.8, P= 0.01, n=93, Figure 1D). DISCUSSION Shorter DTI is associated with higher pretreatment ctDNA levels in patients with aggressive B-cell lymphomas. When comparing to established factors (DTI, IPI, MTV), pretreatment ctDNA levels appear to best predict clinical outcomes. This suggests that quantification of ctDNA better reflects disease burden and treatment urgency than existing clinical biomarkers. Pretreatment ctDNA level may therefore be a valuable metric for disease aggressiveness of patients included in clinical trials, and may help identify studies suffering from selection bias. This may be particularly useful for noncontrolled Phase I/II single arm trials, but also for stratification in randomized trials. Disclosures Kurtz: Roche: Consultancy. Dührsen:Alexion: Honoraria; Novartis: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Honoraria; Takeda: Consultancy, Honoraria; Celgene: Research Funding; CPT: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Teva: Honoraria; Roche: Honoraria, Research Funding. Hüttmann:Takeda: Honoraria; Gilead: Honoraria; University Hospital Essen: Employment. Westin:Juno: Other: Advisory Board; Novartis: Other: Advisory Board, Research Funding; Janssen: Other: Advisory Board, Research Funding; Kite: Other: Advisory Board, Research Funding; Curis: Other: Advisory Board, Research Funding; Celgene: Other: Advisory Board, Research Funding; 47 Inc: Research Funding; Unum: Research Funding; MorphoSys: Other: Advisory Board; Genentech: Other: Advisory Board, Research Funding. Gaidano:AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astra-Zeneca: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sunesys: Consultancy, Honoraria. Rossi:Abbvie: Honoraria, Other: Scientific advisory board; Janseen: Honoraria, Other: Scientific advisory board; Roche: Honoraria, Other: Scientific advisory board; Astra Zeneca: Honoraria, Other: Scientific advisory board; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Diehn:Novartis: Consultancy; BioNTech: Consultancy; AstraZeneca: Consultancy; Quanticell: Consultancy; Roche: Consultancy. Alizadeh:Pfizer: Research Funding; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Genentech: Consultancy; Roche: Consultancy.

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.

Summary Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to “win probability” models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient’s course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI’s broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.

Introduction: Cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) have an emerging diagnostic role in multiple malignancies including in lymphomas (Kurtz et al ASH 2017). In classical Hodgkin Lymphoma (cHL), malignant Reed Sternberg (RS) cells are rare, requiring laser capture microdissection from archival tissues or flow sorting from viable tumor cell suspensions for genotyping. We profiled ctDNA in cHL to assess the utility of ctDNA in the noninvasive evaluation of somatic single nucleotide variants (SNVs), somatic copy number alterations (SCNAs), and tumor EBV status. Methods: A total of 53 subjects with HL (29 with early stage and 24 with advanced disease) were studied encompassing a total of 95 blood and tissue samples (72 from Stanford, 23 from UZ Leuven). Plasma samples were sequenced with CAPP-Seq (Newman et al Nat Biotech 2016), using a panel informed by the genotyping of primary tumor biopsies. The genotypes of cHL patients were compared to that of 189 patients with other B-cell malignancies. Given the thoracic distribution of most cHL, we also compared ctDNA levels to that of 55 lung carcinomas. ctDNA levels were calculated as the product of the cfDNA concentration and the mean allelic fraction of somatic mutations. Results: The median pretreatment ctDNA level in cHL was 125 hGE/mL (15 - 5277 hGE/mL), corresponding to a median variant allelic fraction (VAF) of 3.2% (0.3 - 13.9%) (Fig 1A). Pretreatment ctDNA burden was greater in cHL cases than in follicular lymphoma (FL) cases (p = 0.002), but was not significantly different from that of diffuse large B-cell lymphoma (DLBCL) (p = 0.26). Plasma genotyping in cHL and DLBCL also identified similar numbers of SNVs, recovering a median of 108 mutations in cHL and 117 mutations in DLBCL (p = 0.53). In samples with available diagnostic PET/CT, pre-treatment ctDNA levels in cHL were significantly correlated with total metabolic tumor volume (MTV) (Spearman ρ = 0.615, p = 0.006) (Fig 1B), but not with diagnostic PET/CT SUVmax, stage, bulky status (>10 cm), B-symptoms, or presence of extranodal disease. Surprisingly, despite the lower tumor purity of RS cells in cHL tumor masses than that of malignant B-cells in DLBCL, the relationship between ctDNA and PET/CT estimates of disease burden in cHL was highly similar to that of DLBCL. Specifically, cHL and DLBCL were statistically indistinguishable for the ratio between ctDNA levels and MTV (mean ctDNA/MTV of 2.1 vs 1.5 hGE/mL per cm3 tumor, p = 0.38), and both were significantly higher than that of non small cell lung carcinoma (NSCLC) (p < 0.0001) (Fig 1C). In patients with available mid-treatment cfDNA (n = 10), we monitored ctDNA concentrations and observed that circulating tumor burden falls rapidly, with a third of our patients reaching undetectable levels within the first month after start of therapy. PD-L1 copy number gains, previously shown to be prognostic for survival in cHL treated with checkpoint inhibitors, were observed in 42% of cHL patients with ctDNA VAFs above our SCNA limit of detection (1%) and were genotyped significantly more frequently than in other non-PMBCL B-cell malignancies (42% vs 18%, p = 0.005) (Fig 1D). Coding SNVs in the most commonly mutated genes involved STAT6 (24%), SOCS1 (20%), GNA13 (20%), TNFAIP3 (18%), and B2M (16%) while noncoding SNVs in IGK and IGH were more abundant in cHL and DLBCL respectively (Fig 1E). EBV tumor cell presence has previously been shown to be prognostic in cHL (Keegan et al JCO 2005). Prior to therapy, EBV cfDNA constituted a significantly larger fraction of total cfDNA in patients confirmed by EBER ISH to have EBV+ cHL than in either EBER-negative cHL patients or healthy controls (p < 0.0001) (Fig 1F). Conclusions: Levels of ctDNA in cHL are higher than might be expected based on tumor purity, with pre-treatment levels similar to DLBCL and higher than FL. ctDNA allows for reliable noninvasive genotyping of cHL at diagnosis, encompassing coding and non-coding SNVs and additional clinically significant factors such as tumor EBV status and SCNAs. Additional cases are currently being profiled and expanded analyses of genotyping and monitoring will also be presented at the meeting. Disclosures Dührsen: Celgene: Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Roche: Honoraria, Research Funding; Janssen: Honoraria; Amgen: Research Funding; Gilead: Consultancy, Honoraria. Hüttmann:Roche: Other: Travel expenses; Celgene: Other: Travel expenses. Gaidano:Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Morphosys: Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Westin:Apotex: Membership on an entity's Board of Directors or advisory committees; Celgen: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals Corporation: Membership on an entity's Board of Directors or advisory committees. Advani:Regeneron: Research Funding; Kyowa: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Infinity: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Millenium: Research Funding; Cell Medica: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Agensys: Research Funding; Forty Seven Inc.: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Gilead/Kite: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Autolus: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Celgene: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Bayer: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Merck: Research Funding.

Purpose Outcomes for patients with diffuse large B-cell lymphoma remain heterogeneous, with existing methods failing to consistently predict treatment failure. We examined the additional prognostic value of circulating tumor DNA (ctDNA) before and during therapy for predicting patient outcomes. Patients and Methods We studied the dynamics of ctDNA from 217 patients treated at six centers, using a training and validation framework. We densely characterized early ctDNA dynamics during therapy using cancer personalized profiling by deep sequencing to define response-associated thresholds within a discovery set. These thresholds were assessed in two independent validation sets. Finally, we assessed the prognostic value of ctDNA in the context of established risk factors, including the International Prognostic Index and interim positron emission tomography/computed tomography scans. Results Before therapy, ctDNA was detectable in 98% of patients; pretreatment levels were prognostic in both front-line and salvage settings. In the discovery set, ctDNA levels changed rapidly, with a 2-log decrease after one cycle (early molecular response [EMR]) and a 2.5-log decrease after two cycles (major molecular response [MMR]) stratifying outcomes. In the first validation set, patients receiving front-line therapy achieving EMR or MMR had superior outcomes at 24 months (EMR: EFS, 83% v 50%; P = .0015; MMR: EFS, 82% v 46%; P < .001). EMR also predicted superior 24-month outcomes in patients receiving salvage therapy in the first validation set (EFS, 100% v 13%; P = .011). The prognostic value of EMR and MMR was further confirmed in the second validation set. In multivariable analyses including International Prognostic Index and interim positron emission tomography/computed tomography scans across both cohorts, molecular response was independently prognostic of outcomes, including event-free and overall survival. Conclusion Pretreatment ctDNA levels and molecular responses are independently prognostic of outcomes in aggressive lymphomas. These risk factors could potentially guide future personalized risk-directed approaches.

Background Diffuse large B-cell lymphoma (DLBCL) is a genetically and clinically heterogeneous disease. The cell-of-origin (COO) classification subdivides DLBCL into the transcriptionally defined activated B-cell (ABC) and germinal center B-cell (GCB) subtypes. Recently, 2 novel classifiers based on genetic features were independently proposed further unraveling the diversity of DLBCL [Schmitz et al, NEJM2018; Chapuy et al, Nat Med2018]. The concordance between the 2 novel classification systems has not yet been systematically studied. However, both classifiers are largely complementary to COO subtypes, and describe overlapping genotypes. We previously demonstrated the feasibility of COO classification by noninvasive plasma genotyping in a limited gene panel using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) [Scherer et al, STM2016], and this approach has now been replicated by others. In this study, we take first steps toward a comprehensive non-invasive classification of novel DLBCL genetic subtypes using a limited gene panel. Methods We analyzed genetic profiling of 476 DLBCL patients reported by Schmitz et al (NEJM 2018) as a training set to build 2 classifiers in a limited gene panel applicable to plasma genotyping from CAPP-Seq: (1) A COO classifier (i.e. ABC, GCB and Unclassified); (2) A comprehensive genetic classifier (i.e. EZB, BN2, MCD, N1 and Other as defined in Schmitz et al, NEJM 2018). Features were limited to genetic alterations captured by our plasma genotyping panel, and those with population frequency of at least 10% in at least one genetic subtype. Our final model comprised 100 features: 64 recurrently mutated genes, 26 amplifications, 7 deletions and 3 translocations (BCL2, BCL6 and MYC). After cross-validation in the training set, we applied the 2 classifiers to the dataset from Chapuy et al (Nat Med 2018) as well as pretreatment plasma genotyping data from patients previously reported by our group [Kurtz et al, JCO 2018]. Results We first evaluated our 2 classifiers in a 10-fold cross-validation (CV) framework in the NEJM 2018 dataset of Schmitz et al. Despite modest performance of our GCB/ABC classification, COO labels had the expected significant prognostic associations (Fig. 1A). Overall accuracy of our second classifier to determine novel genetic subtypes was 82% (Fig. 1B). Consistent with the original study, inferred MCD and N1 subtypes had adverse prognosis compared to EZB and BN2 (Fig. 1C). We next applied our classifiers to the Chapuy et al (Nat Med 2018) dataset. Again, consistent with findings by Schmitz et al (NEJM 2018), the EZB subset of GCB cases had inferior outcome compared to non-EZB cases (Fig. 1D). We next examined the cross-correlation between the two classifiers and observed the expected enrichment patterns of ABCs in the MCD subset and enrichment of GCBs in the EZB subset (Fig. 1E). Finally, we applied our classifiers to plasma genotyping data previously reported by our group [Kurtz et al., JCO 2018]. We restricted the analysis to cases with a mean variant allele fraction ≥0.5% (n=68). Similar to the original study, 59% of cases (40/68) were labeled unclassifiable (i.e. Other). We compared the distribution of COO subtypes within the Schmitz genetic clusters. Representation of ABC and GCB within the clusters inferred from Plasma genotyping (Fig. 1F) was similar to the distribution from Tumor genotyping (Fig. 1E). Conclusions We describe 2 new classifiers applicable to noninvasive plasma genotyping data that recapitulate transcriptionally and genetically defined DLBCL subtypes. Using independent datasets, we show the feasibility of classification with a limited feature set with good prediction accuracy and prognostic stratification of defined subtypes. Genotyping of pretreatment plasma samples suggest that comprehensive non-invasive classification of genetic subtypes of DLBCL is achievable. Disclosures Kurtz: Roche: Consultancy. Diehn:BioNTech: Consultancy; Quanticell: Consultancy; Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy. Alizadeh:Roche: Consultancy; Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Pfizer: Research Funding.

Background: Circulating tumor DNA (ctDNA) is an emerging biomarker in non-Hodgkin lymphomas (NHLs). Current methods for ctDNA minimal residual disease (MRD) are limited by two factors - low input DNA amounts and high background error rates. VDJ sequencing (i.e., IgHTS) has low background but is limited by low cell-free DNA (cfDNA). Tracking multiple mutations via CAPP-Seq improves sensitivity, but detection is limited by background errors. Clustered mutations have been described in multiple cancers including NHLs and potentially have lower error rates. We explored clustered mutations from whole-genome sequencing (WGS) to identify 'phased variants' (PVs), defined as multiple mutations on a single DNA molecule (Fig 1A). We designed a method to capture PVs for improved ctDNA detection and explored its utility for MRD in DLBCL. Methods: We reanalyzed WGS from 1455 tumors across 11 cancer types. We identified genomic regions recurrently containing PVs and designed an assay for deep cfDNA sequencing. We applied this assay to 171 patients with large B-cell lymphomas. We compared the performance of PVs for disease detection to current ctDNA techniques, including CAPP-Seq and duplex sequencing. Results: To utilize PVs, mutations must occur within a typical cfDNA strand (~170bp). We measured the frequency of putative PVs in WGS, focusing on pairs of mutations occurring within We designed an approach for enriching PVs from ~115kb (Phased variant Enrichment Sequencing, PhasE-Seq) and other regions recurrently mutated in B-NHLs. We compared PhasE-Seq and CAPP-Seq using tumor and plasma samples from 16 patients. Compared to CAPP-Seq, PhasE-Seq yielded more SNVs and PVs per case (median SNVs: 331 vs 114, P We then compared PhasE-Seq to alternative methods for MRD detection. We used limiting dilutions of patient ctDNA down to 1:1,000,000 to establish the detection limit (LOD, Fig 1D). PhasE-Seq outperformed CAPP-Seq and duplex sequencing for recovery of expected tumor content, with a high degree of linearity down to ~1:1,000,000. We applied standard CAPP-Seq and PhasE-Seq to patient cfDNA samples after two cycles of front-line therapy (n=92). We previously reported a 2.5-log reduction in ctDNA as prognostic at this time-point (Kurtz, JCO 2018). Using CAPP-Seq, 58% (53/92) of samples were undetectable. Using PhasE-Seq, 30% (16/53) of samples not detected by CAPP-Seq had evidence of MRD, with levels as low as 2:1,000,000. In patients with ctDNA undetected by CAPP-Seq, detection by PhasE-Seq significantly stratified outcomes (Fig 1E). Conclusions: PVs are frequent in NHLs, likely due to AID, and correlate with disease biology. PhasE-Seq allows for superior detection of ctDNA, including MRD detection in the majority of patients after 2 cycles. Targeted sequencing of ctDNA should consider PVs to maximize detection and guide precision approaches. Figure 1: A) Structure of phased variants B) Distribution of putative PVs from WGS data C) Genomic enrichment in PVs in lymphoma subtypes D) Dilution series comparing PhasE-Seq, CAPP-Seq, and duplex sequencing E) Waterfall plot showing ctDNA level vs outcome; undetectable ctDNA by CAPP-Seq is highlighted F) EFS of patients with undetectable ctDNA by CAPP-Seq after 2 cycles, stratified by PhasE-Seq Disclosures Kurtz: Roche: Consultancy. Diehn:Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy; BioNTech: Consultancy; Quanticell: Consultancy. Alizadeh:Pharmacyclics: Consultancy; Janssen: Consultancy; Genentech: Consultancy; Roche: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Pfizer: Research Funding.

Background : Infectious disease plays a central role in malignancy, with up to one in six cancers having a microbial association (Parkin Int. J. Cancer 2006). Lymphomas in particular are associated with multiple viral pathogens, including Epstein Barr virus (EBV), Kaposi Sarcoma herpesvirus (KSHV), and HIV. Sequencing of cell-free DNA (cfDNA) is an emerging technique in the diagnosis and surveillance of cancer. While studies to date have focused primarily on tumor-associated somatic variants, cfDNA may also provide insight into the infectious and immune state of cancer patients. We examined cfDNA from lymphoma patients of multiple histologic subtypes to characterize viral detection and dynamics. Methods: Plasma from 360 pre-treatment patients with various lymphoma histologies was analyzed along with that of 69 healthy adults. Multiple samples per patient were included when available. All samples underwent deep sequencing with error correction by CAPP-Seq (Newman Nat Biotech 2016). Reads were filtered for homology to the human genome and endogenous retroviruses, mapped to NCBI consensus genomes for human-hosted viral species, and filtered by breadth of genomic coverage. Viral read count was normalized by total sequencing depth to determine viral read fraction (VRF). EBV fragment size was assessed via single-read BLAST alignment length considering reads with expect value < 1E-5. Integration sites were assessed with the VirusClip package (Ho Oncotarget 2015). Results: Patients with most lymphoma histologic subtypes had viral loads not significantly different from those of healthy adults. However, post-transplant lymphoproliferative disorder (PTLD) patients receiving immunosuppression for solid organ transplants had significantly increased total viremia (Fig 1A) and EBV levels (Fig 1B) when compared to healthy adults and non-transplant DLBCL patients. EBER+ classical Hodgkin lymphoma (cHL) displayed no difference in total viremia but had significantly elevated EBV. In an EBV-positive PTLD patient, cfDNA viral levels tracked both clinical viral qPCR and circulating tumor DNA (ctDNA) levels in serial samples leading to diagnosis (Fig 1C). Elevated EBV levels were also present in a subset of non-transplant DLBCL. In a cohort of DLBCL patients treated with frontline R-CHOP-like chemotherapy (n=152), individuals with pre-treatment EBV frequency greater than VRF 1E-7 had significantly higher risk of disease progression at three years (HR 1.8, CI 1.0-3.4, p=0.013) (Fig 1D). Immunosuppression in transplant patients is associated with the expansion of the endogenous anellovirus family (De Vlaminck Cell 2013). Accordingly, anellovirus was detected significantly more often in PTLD patients (91% of samples) compared to DLBCL NOS (2.8%) and controls (1.4%) (Fig 1E, p < 0.0001). As the standard-of-care R-CHOP regimen for DLBCL has activity against both B- and T- lymphocytes, we hypothesized that an immunosuppressive effect might be observed. In non-transplant DLBCL patients receiving R-CHOP (n=31), we detected anellovirus in 6% of samples at the time of first chemotherapy infusion, 16% immediately before cycle 2, but in no samples from post-treatment patients in complete response (Fig 1F). Viral integration into the host genome is associated with malignant transformation. We profiled a cohort of EBER+ cHL (n=8) and found circulating EBV/human chimeric reads suggesting integration in all cases. Viral fragment size distribution also distinguishes integrated DNA from shorter free episomes and may increase cancer screening performance (Lam PNAS 2018). We profiled EBV fragment sizes in cHL and PTLD patients grouped by EBER positivity. Plasma from EBER+ cHL and PTLD patients was significantly enriched in longer fragments (Fig 1G), suggesting nucleosomal protection of EBV integrated within tumor genomes but not their benign episomal counterparts. Conclusions: Viral infection in lymphoma has diagnostic and prognostic significance: elevated circulating EBV levels are associated with active PTLD (Kanakry Blood 2016) and poor outcomes in advanced HL (Kanakry Blood 2013) and DLBCL (Tisi Leuk & Lymph 2015). Our work demonstrates the utility of cfDNA sequencing for simultaneous characterization of malignancy, infection, and immunosuppression. The integration of viral dynamics into cfDNA analysis may assist in risk stratification and treatment monitoring in lymphoma patients. Disclosures Dührsen: Amgen: Research Funding; Celgene: Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Roche: Honoraria, Research Funding; Gilead: Consultancy, Honoraria; Janssen: Honoraria. Hüttmann:Celgene: Other: Travel expenses; Roche: Other: Travel expenses. Meignan:F. Hoffman-La Roche Ltd: Honoraria. Casasnovas:Janssen: Consultancy; Takeda: Honoraria; Janssen: Honoraria; MSD: Honoraria; Merck: Honoraria; Gilead Sciences: Honoraria; Celgene: Honoraria; Roche: Consultancy; Roche: Research Funding; takeda: Consultancy; Gilead Sciences: Consultancy; Roche: Honoraria; Gilead Sciences: Research Funding; merck: Consultancy; MSD: Consultancy. Westin:Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Apotex: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals Corporation: Membership on an entity's Board of Directors or advisory committees; Celgen: Membership on an entity's Board of Directors or advisory committees. Gaidano:Amgen: Consultancy, Honoraria; Morphosys: Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Advani:Bayer: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Agensys: Research Funding; Infinity: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Merck: Research Funding; Janssen: Research Funding; Cell Medica: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Kyowa: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Millenium: Research Funding; Celgene: Research Funding; Kura: Research Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board, Research Funding; Regeneron: Research Funding; Autolus: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Gilead/Kite: Membership on an entity's Board of Directors or advisory committees, Other: Participated in an advisory board; Forty Seven Inc.: Research Funding; Celgene: Research Funding.

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...

7538 Background: Patients with diffuse large B cell lymphoma (DLBCL) exhibit significant differences in clinical outcome based on cell-of-origin (COO). Patients are categorized as having germinal-center-like (GCB) or activated-B-cell-like (ABC) disease based on RNA microarray and histopathological analyses of tumor biopsies. We recently described an accurate sequencing-based method for determination of COO in DLBCL utilizing stereotyped differences in mutations (Scherer et al., 2016). Here, we further explore the mutational patterns in patients with differing molecular subtypes of DLBCL based on sequencing of circulating tumor DNA. Methods: We applied cancer personalized profiling by deep sequencing (CAPP-Seq) to pretreatment plasma samples and matched germline from a cohort of 115 patients with DLBCL. We then identified somatic alterations, which were used to determine COO molecular subtypes as previously described. Finally, we compared mutational patterns in patients with GCB and non-GCB DLBCL. Results: We detected a significantly greater number of total mutations (GCB: 1766 ± 160 mutations per Mb of targeted sequencing; non-GCB: 1364 ± 150 mutations per Mb of targeted sequencing; p < 0.05) and coding mutations (GCB: 145 ± 21 mutations per Mb of targeted sequencing; non-GCB: 28 ± 8.5 mutations per Mb of targeted sequencing; p < 0.001), particularly in immunoglobulin (Ig) regions (p < 0.05). In addition, GCB and non-GCB samples exhibited distinct mutational patterns within Ig regions. GCB samples were enriched for mutations in regions of switch mu (Sμ) (p < 0.01) and IGHV2-70 (p < 0.01), while non-GCB samples were enriched for mutations in regions of IGHG3 (p < 0.03), IGHV4-34 (p < 0.03), and IGLL5 (p < 0.05). GCB samples were also significantly enriched for coding mutations in SOCS1 (p < 0.01), a gene not included in our original COO classifier. Conclusions: Patients with GCB and non-GCB DLBCL exhibit distinct mutational patterns across both Ig and non-Ig loci of the genome. These differences in mutational patterns can be used to classify molecular subtypes noninvasively, potentially providing further utility to noninvasive genotyping and liquid biopsies.