Cancer Personalized Profiling By Deep Sequencing Research Articles

Distinct molecular determinants of treatment‐failure in elderly Hodgkin lymphoma identified by cell‐free DNA profiling: A LYSA Study

Introduction: While nearly all younger patients (pts) with Hodgkin's lymphoma (HL) are cured with conventional chemotherapy, outcomes are significantly worse in those over age 60. Intriguingly, among older HL pts, those with EBV+ disease have even poorer outcomes (Keegan et al., JCO 2005), yet the biological basis for this phenomenon is not understood. The paucity of malignant cells in HL tumors has hampered genomic studies. Circulating tumor DNA (ctDNA) profiling can overcome these limitations, allowing noninvasive genotyping and risk stratification in HL (Spina et al., Blood 2018; Alig et al., ASH 2022). Here, we use a fully non-invasive approach for genomic profiling of elderly HL pts, focusing on the impact of ctDNA and EBV status on clinical outcomes. Methods: We profiled 57 pts (64%) from NCT02414568, a prospective, multicenter phase II LYSA-PVAB study of newly diagnosed classical HL >60 years (Ghesquières et al., ASH2019). All pts received 6 cycles of PVAB (prednisone, vinblastine, doxorubicin, and bendamustine). Plasma cfDNA was genotyped using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq); MRD was monitored using Phased variant Enrichment and Detection Sequencing (PhasED-Seq). Pts were classified into high and low pretreatment ctDNA levels using a threshold of 2.5 log hGE/mL (Alig et al., ASH2022). We defined EBV status either by LMP1 staining of FFPE tumor tissue or by VirCAPP-Seq targeting 180 viral species, where a predefined threshold of 32 genomes/ml was previously validated as 94% accurate (Garofalo et al., ASH 2022). Results: Pretreatment ctDNA was detected in 94% of pts with a median concentration of 2.2% (MAF), which was significantly correlated with disease burden measured by TMTV (p = 0.034). Pts with high pre-treatment ctDNA levels (39%) had significantly worse PFS (HR = 2.3, p = 0.039). EBV+ pts also had inferior PFS (HR = 2.2, p = 0.04) and OS (HR = 3.1, p = 0.028) (Figures A-B). In multivariate analysis, both indices remained independently associated with worse outcomes (HR = 2.5, p = 0.01 for pre-treatment ctDNA and HR = 2.7, p = 0.01 for EBV status). Interestingly, pre-treatment ctDNA levels did not differ between EBV+ and EBV- tumors (p = 0.92), while specific genetic aberrations were significantly associated with respective EBV status (Figure C). Among evaluable pts for MRD monitoring, detection of ctDNA after four cycles of chemotherapy was highly associated with risk of death (Figure D). The research was funded by: NIH/NCI R01 R01CA257655 Keywords: Diagnostic and Prognostic Biomarkers, Genomics, Epigenomics, and Other -Omics, Hodgkin lymphoma Conflicts of interests pertinent to the abstract. C. Rossi Consultant or advisory role: Genmab, Janssen, Incyte Educational grants: Kyte, Abbvie S. K. Alig Consultant or advisory role: Takeda Pharmaceuticals D. M. Kurtz Consultant or advisory role: Adaptive Biotechnologies, Roche, Foresight O. Casasnovas Consultant or advisory role: Roche, Takeda Educational grants: Roche, Takeda, BMS, Janssen A. A. Alizadeh Consultant or advisory role: Adaptive Biotechnologies, Foresight Diagnostics, Roche, BMS, Gilead, Genentech, Karyopharm

Circulating tumour DNA kinetics in recurrent/metastatic head and neck squamous cell cancer patients

PurposeImmune checkpoint blockade (ICB) has become a standard of care in the treatment of recurrent/metastatic head and neck squamous cell cancer (R/M HNSCC). However, only a subset of patients benefit from treatment. Quantification of plasma circulating tumour DNA (ctDNA) levels and on-treatment kinetics may permit real-time assessment of disease burden under selective pressures of treatment. Patients and methodsR/M HNSCC patients treated with systemic therapy, platinum-based chemotherapy (CT) or ICB, underwent serial liquid biopsy sampling. Biomarkers tested included ctDNA measured by CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) and markers of host inflammation measured by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). ResultsAmong 53 eligible patients, 16 (30%) received CT, 30 (57%) ICB [anti-PD1/L1] monotherapy and 7 (13%) combination immunotherapy (IO). Median progression-free survival (PFS) and overall survival (OS) were 2.8 months (95% CI, 1.3–4.3) and 8.2 months (95% CI, 5.6–10.8), respectively. Seven (13%) patients experienced a partial response and 21 (40%) derived clinical benefit. At baseline, median ctDNA variant allele frequency (VAF) was 4.3%. Baseline ctDNA abundance was not associated with OS (p = 0.56) nor PFS (p = 0.54). However, a change in ctDNA VAF after one cycle of treatment (ΔVAF (T1–2)) was predictive of both PFS (p< 0.01) and OS (p< 0.01). Additionally, decrease in ΔVAF identified patients with longer OS despite early radiological progression, 8.2 vs 4.6 months, hazard ratio 0.44 (95% CI, 0.19–0.87) p = 0.03. After incorporating NLR and PLR into multivariable Cox models, ctDNA ∆VAF retained an association with OS. ConclusionsEarly dynamic changes in ctDNA abundance, after one cycle of treatment, compared to baseline predicted both OS and PFS in R/M HNSCC patients on systemic therapy.

Abstract 5707: Tumor and immune determinants of response to anti-BCMA CAR T-cell therapy in multiple myeloma using cell-free DNA

Abstract Background: Multiple myeloma (MM) is an incurable disease with a heterogenous clinical course and genomic landscape. Autologous anti-BCMA chimeric antigen receptor (CAR) T-cells are a promising new therapy, but determinants of response and resistance are not well known. Cell-free DNA (cfDNA) is a useful tool to study MM as it allows for repeated, non-invasive tumor assessment. We apply a novel method for simultaneously tracking tumor mutations and CAR T-cells from cfDNA using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq). Methods: We designed a 480kb CAPP-Seq hybrid capture panel to identify mutations, track tumor burden and minimal residual disease, and detect cfDNA derived from the CAR transgene (CAR-cfDNA) in patients receiving idecabtagene vicleucel (ide-cel). Flow cytometry (FC) for enumeration of CAR T-cells was performed from peripheral blood mononuclear cells (PBMCs) when available. Results: We profiled 153 biologic samples, including plasma, PBMCs, and bone marrow mononuclear cells, from 15 patients receiving ide-cel and 18 healthy controls. We observed a median of 84 SNVs (range 30-277) prior to therapy. Patients with prolonged responses (&amp;gt;90 days) had significantly lower circulating tumor DNA (ctDNA) at day 28 post-infusion than patients with early progression (&amp;lt;90 days) (0.6 vs. 4.6 log haploid genome equivalents (hGE)/mL; p=0.002). Additionally, higher ctDNA at D28 was prognostic for time to progression (TTP) (HR=1.67, p=0.019). We validated CAR-cfDNA detection by comparison with FC from PBMCs at matched timepoints (n=38), finding a significant correlation (rho=0.79, p=3E-09). CAR-cfDNA typically reached its peak level around D14 (median 332 hGE/mL), with ctDNA declining at the same time-point. Thus, CAR-cfDNA levels and ctDNA burden were inversely correlated (rho= -0.3, p=0.019). Surprisingly, peak CAR expansion was not associated with TTP (HR=1, p=0.463). However, lower CAR-cfDNA at D28 was prognostic for inferior TTP (HR=2.68, p=0.011). This suggests CAR persistence may play a more important role in clinical outcomes. Furthermore, among progressors, time until loss of detectable CAR-cfDNA correlated with TTP (rho=0.81, p=0.02). Patients who progressed before day 90 had a median CAR persistence of 28 days. In contrast, patients who progressed after day 90 had a median of 137 days and often had emergent copy number alterations in ctDNA at relapse. This included one case with emergent loss of chr16, where TNFRSF17 (BCMA), resides. This event was detected 36 days prior to clinical relapse; BCMA loss was validated via whole-genome sequencing and immunohistochemistry staining of the tumor. Conclusions: Cell-free DNA is a promising biomarker for mutational genotyping, disease monitoring, and tracking CAR T-cells in MM. The persistence of CAR-cfDNA has particular prognostic importance and novel strategies to increase CAR persistence should be explored. Citation Format: Mia Carleton, Hitomi Hosoya, Kailee L. Tanaka, Brian Sworder, Vanna Hovanky, Bita Sahaf, Matthew J. Frank, George E. Duran, Tian Y. Zhang, Sally Arai, David Iberri, Michaela Liedtke, David B. Miklos, Michael S. Khodadoust, Surbhi Sidana, David M. Kurtz. Tumor and immune determinants of response to anti-BCMA CAR T-cell therapy in multiple myeloma using cell-free DNA. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5707.

486 Liquid biopsy-based analysis using cell-free DNA from melanoma patients

Since the demonstrated clinical success of BRAF/MEK inhibitors in patients with metastatic melanoma harboring BRAF mutations, attention has been focused on liquid biopsy to detect BRAF mutations in cell-free DNA (cfDNA) using droplet digital PCR (ddPCR) or next-generation sequencing methods. In this study, we investigated gene mutations in tumor DNA and cfDNA collected from 43 melanoma patients and evaluate their potential as biomarkers. ddPCR and CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) techniques were performed to detect gene mutations in plasma cfDNA obtained from patients with metastatic melanoma.

Disease Characterization and Response Prediction in Myeloma Patients Undergoing Conventional and Cellular Therapies from Circulating Tumor DNA

Background: Multiple myeloma (MM) is characterized by dramatic biological and clinical heterogeneity. Prognostic biomarkers including genomic alterations and bone marrow (BM) minimal residual disease can resolve some of this heterogeneity; however, these rely on invasive tumor sampling which limits their utility. Here we develop and apply plasma cell-free DNA liquid biopsies via Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) to characterize tumors and quantify treatment response in patients with MM and explore its utility during anti-BCMA CAR T-cell therapy. Methods: We developed a 480 kb MM-specific CAPP-Seq panel, which covers 86 recurrently mutated coding genes as well as other recurrently mutated regions, for disease detection and quantification from circulating tumor DNA (ctDNA). Using this hybrid-capture panel, we profiled 214 plasma, germline and tumor DNA samples from 35 patients including 15 patients undergoing idecabtagene vicleucel (ide-cel) therapy. Samples were sequenced to ultra-high unique molecular depth as previously described (Newman et al, Nat Biotechnol 2016) to quantify ctDNA. Copy number variants (CNVs) were identified using our recently developed method, CANARy (Chabon et al, Nature 2020). Tumor variants and ctDNA levels were identified prior to and following therapy to assess ctDNA dynamics, and were correlated with clinical indices and patient outcomes. Results: We applied CAPP-Seq to samples from 35 patients with newly diagnosed (ND, n=10) or relapsed/refractory (RR, n=25) plasma cell disorders. A median of 87 single-nucleotide variants (SNVs, range 2-283) were detected per case. Among these, a median of 5 mutations/patient were in coding genes such as KRAS (25% of patients), TP53 (25%) and NRAS (19%). Most other SNVs occurred in genomic regions targeted by activation induced deaminase (AID), such as IGH, IGK and IGL. To validate tumor genotyping from plasma, we compared SNVs observed in plasma to paired BM samples in 18 cases. Here, we observed 84% (594/703) of all mutations identified in BM were also present in plasma, including 90% (26/29) of all coding alterations. Interestingly, we detected an additional 259 SNVs in the plasma that were not present in the BM, including KRAS, NRAS, and FAM46C mutations, suggesting ctDNA may better capture spatial tumor heterogeneity. We next utilized CAPP-Seq to quantify disease burden prior to treatment. We detected ctDNA in 97% (31/32) of pretreatment plasma with a median allele frequency of 0.91% (range 0.006 to 36%) and 3.82 log haploid genome equivalents per mL of plasma (hGE/mL). Levels of ctDNA were higher in RRMM compared to NDMM patients (4.26 vs 3.08 log hGE/mL, p=0.02) and correlated with clinical risk factors including R-ISS (stage 1: 2.83 vs stage 2/3: 4.18 log hGE/mL, p=0.01) as well as disease burden measurements including M-spike (rho=0.53, p=0.02) and free light chain ratio (rho=0.40, p=0.03). The fold change in ctDNA level at a median of 36 days from treatment initiation correlated with clinical response (median 0.060 for non-progressors vs 7.0 for progressors, p=0.04). Further, we applied CAPP-Seq to serial samples from 15 patients with RRMM receiving ide-cel. Notably, patients with longer response (>90 days) had significantly lower ctDNA levels at day 28 compared to those with early (<90 days) progression (0.6 vs 4.1 log hGE/mL; p=0.009, Fig 1). Lower ctDNA levels at day 28 predicted longer time to progression (proportional hazard model, p=0.02, HR=1.6). Finally, we assessed clonal evolution upon relapse after ide-cel. In multiple patients we observed emergence of new CNVs in ctDNA, including a case with an emergent loss of chr16, where TNFRSF17 (BCMA) resides (Fig 2). This copy number loss was observed 36 days prior to clinical relapse, was validated through WGS of the relapsed tumor, and resulted in loss of BCMA by immunohistochemistry. Conclusions: Assessment of ctDNA has significant potential utility in MM, as it allows for mutational genotyping, quantification of disease burden, and identification of tumor clonal evolution. Lower levels of ctDNA during treatment and its clearance are associated with improved outcomes, while detection of emergent alterations after targeted therapies may inform subsequent treatment strategies. Use of ctDNA may improve response and relapse prediction and reveal mechanisms of therapy resistance. *SS and DMK are co-senior authors Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Profiling of Circulating Tumor DNA for Noninvasive Disease Detection, Risk Stratification, and MRD Monitoring in Patients with CNS Lymphoma

Abstract Introduction: Clinical outcomes for patients with central nervous system lymphoma (CNSL) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure remains challenging with existing methods. In addition, diagnosis of CNSL requires invasive neurosurgical biopsies that carry procedural risks and often cannot be performed in frail or elderly patients. Circulating tumor DNA (ctDNA) has shown great potential as a noninvasive biomarker in systemic lymphomas. Yet, previous studies revealed low ctDNA detection rates in blood plasma of CNSL patients. In this study, we utilized ultrasensitive targeted high-throughput sequencing technologies to explore the role of ctDNA for disease classification, MRD detection, and early prediction of clinical outcomes in patients with CNSL. Methods: We applied Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) and Phased Variant Enrichment and Detection Sequencing (PhasED-Seq, Kurtz et al, Nat Biotech 2021) to 85 tumor biopsies, 131 plasma samples, and 62 CSF specimens from 92 CNSL patients and 44 patients with other brain cancers or inflammatory cerebral diseases, targeting 794 distinct genetic regions. Concentrations of ctDNA were correlated with radiological measures of tumor burden and tested for associations with clinical outcomes at distinct clinical time points. We further developed a novel classifier to noninvasively distinguish CNS lymphomas from other CNS tumors based on their mutational landscapes in plasma and CSF, using supervised training of a machine learning approach from tumor whole genome sequencing data and own genotyping analyses, followed by its independent validation. Results: We identified genetic aberrations in 100% of CNSL tumor biopsies (n=63), with a median of 262 mutations per patient. Pretreatment plasma ctDNA was detectable in 78% of plasma samples and in 100% of CSF specimens (Fig. 1a), with ctDNA concentrations ranging from 0.0004 - 5.94% allele frequency (AF, median: 0.01%) in plasma and 0.0049 - 50.47% AF (median: 0.62%) in CSF (Fig. 1b). Compared to ctDNA concentrations in patients with systemic diffuse large B-cell lymphoma (DLBCL, data from Kurtz et al., J Clin Oncol, 2018), plasma ctDNA levels in CNSL were in median more than 200-fold lower (Fig. 1b). We observed a significant correlation of ctDNA concentrations with total radiographic tumor volumes (TRTV) measured by MRI (Fig. 1c,d), but no association with clinical risk scores (i.e., MSKCC score) or concurrent steroid treatment. Assessment of ctDNA at pretreatment time points predicted progression-free survival (PFS) and overall survival (OS), both as continuous and binary variable (Fig. 1e,f). Notably, patients could be stratified into risk groups with particularly favorable or poor prognoses by combining ctDNA and TRTV as pretreatment biomarkers (Fig. 1g). Furthermore, ctDNA positivity during curative-intent induction therapy was significantly associated with clinical outcomes, both PFS and OS (Fig. 1h). Finally, we applied our novel machine learning classifier to 207 specimens from an independent validation cohort of CNSL and Non-CNSL patients. We observed high specificity (100%) and positive predictive value (100%) for noninvasive diagnosis of CNSL, with a sensitivity of 57% for CSF and 21% for plasma, suggesting that a significant subset of CNSL patients might be able to forego invasive surgical biopsies. Conclusions: We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings suggest that ctDNA accurately mirrors tumor burden and serves as a valuable clinical biomarker for risk stratification, outcome prediction, and surgery-free lymphoma classification in CNSL. We foresee an important potential future role of ctDNA as a decision-making tool to guide treatment in patients with CNSL. Figure 1 Figure 1. Disclosures Esfahani: Foresight Diagnostics: Current holder of stock options in a privately-held company. Kurtz: Genentech: Consultancy; Roche: Consultancy; Foresight Diagnostics: Consultancy, Current holder of stock options in a privately-held company. Schorb: Riemser Pharma GmbH: Honoraria, Research Funding; Roche: Research Funding; AbbVie: Research Funding. Diehn: BioNTech: Consultancy; RefleXion: Consultancy; Roche: Consultancy; AstraZeneca: Consultancy; Foresight Diagnostics: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CiberMed: Current holder of stock options in a privately-held company, Patents &amp; Royalties; Illumina: Research Funding; Varian Medical Systems: Research Funding. Alizadeh: Foresight Diagnostics: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Gilead: Consultancy; Roche: Consultancy, Honoraria; Celgene: Consultancy, Research Funding; Janssen Oncology: Honoraria; CAPP Medical: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Forty Seven: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Bristol Myers Squibb: Research Funding.

Genomic landscape of circulating tumour DNA in metastatic extramammary Paget's disease.

Although cancer personalized profiling by deep sequencing (CAPP-Seq) of cell-free DNA (cfDNA) has gained attention, the clinical utility of circulating tumour DNA (ctDNA) in extramammary Paget's disease (EMPD) has not been investigated. In this study, genomic alterations in the cfDNA and tumour tissue DNA were investigated in seven patients with metastatic EMPD. CAPP-Seq revealed mutations in 18genes, 11 of which have not yet been reported in EMPD. The variant allele frequency of some of the mutated genes reflected the disease course in patients with EMPD. In one patient, the mutation was detected even though imaging findings revealed no metastasis. In another patient with triple EMPD (genital area and both axilla), cfDNA sequencing detected the mutation in a rib metastatic lesion, which was also detected in both axilla lesions but not the genital region. Investigations of the ctDNA may be useful towards the elucidation of clonal evolution in EMPD.

A Comprehensive Circulating Tumor DNA Assay for Detection of Translocation and Copy-Number Changes in Pediatric Sarcomas.

Most circulating tumor DNA (ctDNA) assays are designed to detect recurrent mutations. Pediatric sarcomas share few recurrent mutations but rather are characterized by translocations and copy-number changes. We applied Cancer Personalized Profiling by deep Sequencing (CAPP-Seq) for detection of translocations found in the most common pediatric sarcomas. We also applied ichorCNA to the combined off-target reads from our hybrid capture to simultaneously detect copy-number alterations (CNA). We analyzed 64 prospectively collected plasma samples from 17 patients with pediatric sarcoma. Translocations were detected in the pretreatment plasma of 13 patients and were confirmed by tumor sequencing in 12 patients. Two of these patients had evidence of complex chromosomal rearrangements in their ctDNA. We also detected copy-number changes in the pretreatment plasma of 7 patients. We found that ctDNA levels correlated with metastatic status and clinical response. Furthermore, we detected rising ctDNA levels before relapse was clinically apparent, demonstrating the high sensitivity of our assay. This assay can be utilized for simultaneous detection of translocations and CNAs in the plasma of patients with pediatric sarcoma. While we describe our experience in pediatric sarcomas, this approach can be applied to other tumors that are driven by structural variants.

Abstract 543: ctDNA MRD detection from plasma and urine and personalized oncogenomic analysis in oligometastatic colorectal cancer

Abstract BACKGROUND: Circulating tumor DNA (ctDNA) can detect molecular residual disease (MRD) in plasma after curative-intent treatment of non-metastatic solid tumor malignancy when prior mutational knowledge from either tumor or pre-treatment plasma is available. We hypothesized that ctDNA MRD analysis without prior mutational knowledge could be performed to assess oligometastatic colorectal cancer (CRC) treated with curative intent by applying an error-corrected ultra-deep targeted sequencing approach. We also investigated urine as an alternative analyte for ctDNA MRD detection in this non-genitourinary setting. METHODS: We applied AVENIO, a ctDNA technology derived from CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) with integrated digital error suppression (iDES), to plasma, tumor, and urine samples acquired on the day of surgery from 24 prospectively enrolled oligometastatic CRC patients. We sequenced patients' white blood cell DNA to remove age-related clonal hematopoiesis variants. Plasma and urine ctDNA MRD detection were then correlated with residual disease present in tumor tissue, and adjuvant treatment strategies were proposed based on plasma ctDNA-inferred tumor mutational burden and targetable genomic alterations. RESULTS: Tumor-naïve plasma ctDNA analysis detected MRD at a median level of 0.62% with 95% sensitivity and 100% specificity. Plasma ctDNA MRD levels correlated with tumor size at the time of surgery (ρ= 0.68, P = 0.0003) and were significantly lower in patients without evidence of disease in their surgical specimen compared to those with residual tumor cells (P = 0.02). In urine, ctDNA MRD detection specificity remained high at 100%, but sensitivity was lower at 64%, with median levels being 11-fold lower than in plasma (P &amp;lt; 0.0001). Cell-free DNA fragments containing mutations were shorter in urine with an average size of 150.1 bp, compared to 180.2 bp in plasma (P &amp;lt; 0.0001). Personalized ctDNA MRD oncogenomic analysis revealed that 81% of patients had a high inferred tumor mutational burden (&amp;gt;10 mutations/megabase), and 10% also had a targetable PIK3CA mutation. CONCLUSION: Tumor-naïve plasma ctDNA analysis can sensitively and specifically detect MRD in patients with oligometastatic CRC after neoadjuvant chemotherapy. Urine-based ctDNA MRD detection is also feasible in these patients; however, it is less sensitive than plasma. Analysis of oligometastatic CRC patients' ctDNA MRD also revealed potentially actionable results that might have implications for personalized adjuvant treatment regimens. Citation Format: Bruna Pellini, Nadja Pejovic, Wenjia Feng, Noah Earland, Peter K. Harris, Abul Usmani, Jeffrey J. Szymanski, Farid Qaium, Jacqueline Mudd, Hyun Kim, Benjamin Tan, Ryan C. Fields, Aadel A. Chaudhuri. ctDNA MRD detection from plasma and urine and personalized oncogenomic analysis in oligometastatic colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 543.

Abstract 2536: Mutational landscape of the bronchial epithelium of individuals at high risk for lung cancer

Abstract Insights into mutational burden of the airway epithelium, the field of cancerization, may play important role in stratifying individuals at different levels of risk for lung cancer. We hypothesized that cytologically normal bronchial epithelium of patients with lung cancer and those at high risk for lung cancer harbor driver mutations. We performed next generation sequence-based targeted CAPP-Seq (CAncer Personalized Profiling by deep Sequencing) and whole exome sequencing (WES) of DNA from the bronchial epithelial cells from the bronchial brushings of individuals at-risk for lung cancer and patients with lung cancer as well as matched lung tumor tissues. We used the Tammemagi risk model to calculate the risk for lung cancer. Both CAPPSeq and WES data analyses showed higher mutation burden in lung tumor tissues compared to bronchial brushings. Using targeted CAPPSeq, a total of 129 nonsynonymous mutations were identified in bronchial brushings. Among individuals without lung cancer, 9 oncogenic driver mutations were identified in 5 out of 13 high risk and 2 out of 7 low risk individuals. Among individuals with cancer, 13 out of 22 brushings harbored 11 driver mutations, of which mutations in TP53 and PTEN were identified in both tumor and brushing matched samples. Mutations detected in the brushings of individuals with and without cancer were TP53, PIK3CA, NOTCH1, KRAS, MET, CUL3, and NF1. Using WES on the DNA from the same brushings (low risk, n=9; high risk, n=8 and lung cancer, n=17), we identified 19478 mutations total, and 90 of that were also identified by CAPPSeq. WES identified 80% of the mutations that were identified by CAPPSeq with &amp;gt;5% variant allelic frequencies (VAF). Mutations identified by CAPPSeq had VAF as low as 0.2% and vast majority of the mutations identified by WES had VAF &amp;gt;5%. Out of 9 mutations in TP53 identified in the brushings by CAPPSeq, 4 were identified by WES. Mutation in KMT2D, a histone methylation enzyme and believed to be a tumor suppressor, was identified by WES in the brushings and the tumor tissues of patients with lung cancer as well as in one high risk brushings. Mutational signature profiling identified tobacco mutational signature in the bronchial epithelium and tobacco, APOBEC and DNA-DSB repair signatures in the tumor tissues. Copy number variation (CNV) data analysis revealed significantly higher CNV levels in tumor samples than brushing samples. Genome-wide CNA profiling identified regions significantly influenced by CNV, such as deletions in chromosome region 19p13.3, which was previously reported to be related to lung cancer. We identified mutations in cancer driver genes in the bronchial brushings of individuals with and without lung cancer. This field of cancerization effect may provide a tool for risk assessment for lung cancer. This research was funded by U01CA196405 to PPM. Citation Format: SM Jamshedur Rahman, Shilin Zhao, Shih-Kai Chu, Yong Zou, Angela B. Hui, Thomas P. Stricker, Chen Heidi, Maximilian Diehn, Pierre P. Massion. Mutational landscape of the bronchial epithelium of individuals at high risk for lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2536.

Reproducibility of m7-FLIPI Risk Scores in Follicular Lymphoma Using Tumor Biopsies and Blood Specimens

Background: Follicular lymphoma (FL) is clinically and genetically heterogeneous. While most patients experience an indolent disease course, with median survival of over 15 years, a portion of patients experience early relapse and poor outcomes. The m7-FLIPI risk score has been described as a clinicogenetic method to accurately determine which patients are at highest risk for early relapse and poor outcomes (Pastore et al., Lancet Oncology 2015; Vindi et al., Blood 2015). However, molecular characterization of the m7 genes currently relies on tumor biopsies, which require an invasive procedure and may be unavailable. Furthermore, it is unclear whether the m7-FLIPI score is consistent over the course of disease. Circulating tumor DNA (ctDNA) is an emerging noninvasive biomarker for tumor detection in diverse lymphomas including FL. In this study, we evaluated the stability of m7-FLIPI status over multiple FL relapses as well as the utility of ctDNA for classification of patients with FL into high- and low-risk m7-FLIPI categories.Methods: We applied Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq), a capture-based method for ultra-deep sequencing (Newman et al., Nat Med 2014; Scherer et al., Sci Transl Med 2016 ) to identify risk via m7-FLIPI in pretreatment tumor and plasma samples of 29 FL patients. In addition, we retrospectively analyzed whole exome sequencing (WES) data of 37 patients from two FL cohorts (Green et al., PNAS 2015; Bouska et al., Leukemia 2017). We first validated in silico our capture-based method for identification of m7-FLIPI-relevant somatic alterations in 258 patients from the original publication (Pastore et al., Lancet Oncology 2015). We then identified non-synonymous mutations in CREBBP, EZH2, ARID1A, MEF2B, FOXO1, EP300, and CARD11 presentin each tumor and plasma sample. Using both CAPP-Seq and WES data (n = 66), we compared m7-FLIPI risk group concordance between paired diagnostic tumor biopsies, paired diagnostic and relapse tumor biopsies, paired FL and transformed FL (tFL) tumor biopsies, and paired diagnostic tumor and plasma samples. FLIPI was calculated at first FL time-point available.Results: We observed a 95% agreement in m7-FLIPI risk category between our capture-based method and methods utilizing full exon coverage. A 100% concordance of m7 gene profile and 100% concordance of m7-FLIPI risk category were seen when comparing paired FL tumor biopsy samples from two separate lymph nodes obtained at diagnosis (n = 5). In addition, a 75% agreement in m7 gene profile and 100% agreement of m7-FLIPI risk category were observed when comparing tumor biopsy samples obtained at FL diagnosis and first relapse (n = 36; median time to relapse of 547 days, range 55 - 4200 days). There was a low concordance in m7 genetic profile between tumor biopsy samples from time of initial diagnosis and transformation (48.4%, n = 31). Importantly, concordance of m7 gene profile and m7-FLIPI risk category was 64.3% and 78.6%, respectively, when assessed by paired tumor and plasma samples obtained at FL diagnosis (n = 14; Figure 1). Furthermore, non-concordant mutations could be present in either the tumor or the plasma sample. Across all FL samples, there was no significant difference in concordance based on individual m7 genes. When analyzing pairs in which mutations were observed, concordance between paired samples was lowest for EZH2 (p = 0.028).Conclusions: m7-FLIPI scores appear consistent between multiple FL tumor samples obtained at the time of diagnosis and across FL relapses. However, concordance in m7 gene profile between paired plasma and tumor samples is lower than between paired tumor biopsies, even at ultra-deep sequencing depths. Optimization of blood samples for assessing m7 gene profile should be performed to improve the reliability of ctDNA for determining m7-FLIPI risk status.Figure 1. Concordance of m7-FLIPI risk category. (A) Re-classification of m7-FLIPI risk category when using paired tumor biopsy and plasma samples from 14 patients. (B) Percentage of discordant samples when comparing diagnostic tumor pairs (n = 5), diagnostic versus relapse tumor pairs (n = 37), and diagnostic tumor versus plasma pairs (n = 14). Concordance was lowest for diagnostic tumor and diagnostic plasma pairs (p = 0.013). [Display omitted] DisclosuresDiehn:Varian Medical Systems: Research Funding; Quanticel Pharmaceuticals: Consultancy; Roche: Consultancy; Novartis: Consultancy. Alizadeh:Gilead: Consultancy; Roche: Consultancy; Genentech: Consultancy; Celgene: Consultancy; CiberMed: Consultancy.

Comparison of Circulating Tumor DNA Recovery from Plasma and Serum

Background: Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for noninvasive disease detection and tumor genotyping in patients with a wide range of malignancies. Circulating tumor DNA often comprises less than 1% of the total cell-free DNA (cfDNA) pool; maximizing recovery of tumor DNA is therefore critical for disease detection and characterization. Levels of ctDNA are dependent upon features of each tumor type, including disease burden (Bettegowda et al., Sci Transl Med 2014; Newman et al., Nat Med 2014). However, ctDNA levels are also potentially sensitive to other analytical considerations, such as the biological source material. In this study, we sought to directly compare plasma and serum samples for ctDNA detection.Methods: We applied Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) to 33 pretreatment serum and 75 pretreatment plasma samples from patients with diffuse large B cell lymphoma (DLBCL), with matched tumor in 40 cases. In addition, we sequenced matched serum and plasma samples from a single phlebotomy in 3 cases. Germline DNA was also sequenced. Plasma and serum samples were assessed for differences in cfDNA concentration, number of mutations identified, and mean tumor allele fraction (AF). We further compared the size distribution between mutated and wild-type DNA fragments and between plasma and serum samples. Finally, we compared the recovery rate of tumor-biopsy confirmed mutations in plasma and serum samples.Results: Serum samples had a higher median concentration of cfDNA per mL compared to matched plasma (serum: 481 ng/mL, range 270-499; plasma: 17.7 ng/mL, range 8.4-30). This discrepancy was also seen across the full cohort (serum: 718 ng/mL, IQR 458-1248; plasma: 33.3 ng/mL, IQR 33.3-73.1; p < 0.0001). Despite higher cfDNA concentration, fewer mutations were identified in serum than in plasma in both paired blood samples (serum: 22 mutations per sample, range 0-299; plasma: 186 mutations per sample, range 60-459) and in the full cohort (serum: 50 mutations per sample, IQR 17.8-162; plasma: 184 mutations per sample, IQR 72-331; p < 0.0001). Serum samples also had lower tumor AF than plasma samples in the paired (serum: 0.85 %AF, range 0.71-1.49; plasma 2.8 %AF, range 1.9-8.22) and full cohorts (serum: 0.40 %AF, IQR 0.04-0.98; plasma: 2.91% AF, IQR 0.88-6.05; p = 0.003; Figure 1A). However, when quantitated as a concentration of tumor molecules per mL of sample, serum and plasma had similar levels of ctDNA (serum: 476 haploid genome equivalents (hGE) per mL, IQR 95-1085; plasma: 292 hGE per mL, IQR 82-990; p = 0.91; Figure 1B). This suggests the higher concentration of total cfDNA found in serum consists mostly of non-tumor derived molecules.In serum, non-mutated molecules were significantly longer compared to molecules harboring tumor mutations (non-mutated: 319 bp, IQR 291-359; mutated: 269 bp, IQR 243-304; p < 0.0001; Figure 1C). This pattern was not seen in plasma (Figure 1D). Interestingly, serum samples also had a longer mean cfDNA fragment length compared to plasma samples (serum: 319 bp, IQR 291-358; plasma: 235 bp, IQR 219-251; p < 0.0001), consistent with the finding that serum contains a higher proportion of non-tumor-derived cfDNA. Finally, when considering mutations identified in tumor biopsies, the percentage of tumor mutations recovered in matched plasma was significantly higher than that in matched serum (serum: 0.4% tumor mutations, IQR 0-40%; plasma: 62% tumor mutations, IQR 28-85%; p = 0.0005; Figure 1E).Conclusions: Plasma and serum contain equivalent absolute quantities of ctDNA. However, plasma contains a higher fraction of ctDNA than serum, facilitating mutation detection and noninvasive genotyping. When possible, blood samples used for cancer detection should be obtained and stored as plasma to optimize ctDNA recovery.Figure 1. Comparison of serum and plasma samples.(A) Mean tumor AF is higher in plasma than in serum (p = 0.003) (B) Total quantity of tumor-derived DNA is equivalent between plasma and serum samples (C) In serum, non-tumor-derived cfDNA has a longer average fragment length than ctDNA (p < 0.0001) (D) In plasma, ctDNA and non-tumor-derived cfDNA show overlapping fragment distributions (E) A greater number of mutations seen in tumor biopsies are recovered in paired plasma samples as compared to paired serum samples (p = 0.0005) [Display omitted] DisclosuresNeelapu:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Poseida Therapeutics, Inc: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karus: Research Funding; Kite Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis Inc.: Research Funding. Nastoupil:TG Therapeutics: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Karus Therapeutics: Research Funding; Celgene: Honoraria, Research Funding. Diehn:Novartis: Consultancy; Roche: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding. Alizadeh:Roche: Consultancy; Genentech: Consultancy; Celgene: Consultancy; CiberMed: Consultancy; Gilead: Consultancy.

Clinical Impact of Somatic Copy Number Alterations in Circulating Tumor DNA from Diverse Lymphoma Subtypes

Introduction:Somatic copy number alterations (SCNAs) constitute a major class of genomic aberrations known to have functional significance across cancers, including prognostic value in lymphoma. For example, diffuse large B-cell lymphoma (DLBCL) patients with tumors harboring SCNAs in MYC, BCL2, or TP53 tend to have inferior outcomes (Quesada, ASH 2016). However, detection of copy number alterations without invasive tissue biopsies remains difficult. We applied Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq), a targeted high-throughput sequencing platform, to noninvasively detect and monitor genome-wide SCNAs in patients with DLBCL, primary mediastinal B-cell lymphoma (PMBCL), and classical Hodgkin lymphoma (cHL).Methods: We profiled pre-treatment blood samples from 209 patients with diverse lymphomas including 168 DLBCL, 20 cHL, and 21 PMBCL, using a custom 314kb gene panel targeting recurrently altered regions in these cancers. Additionally, we sequenced 139 tumor biopsies to use as matched tissue gold standards for SCNA confirmation. In each sample, we interrogated the sequencing read distribution to summarize focal and broad SCNAs directly from plasma. All thresholds were tuned for 95% specificity, which were empirically verified in independent withheld healthy controls. Results were orthogonally validated through tissue karyotyping, fluorescence in situ hybridization (FISH), and whole genome sequencing (WGS) (Fig 1A and Fig 1B).Results: We determined our limit of detection (LOD) for detecting SCNAs in plasma at ~1% circulating tumor fraction with 77% sensitivity; we observed higher sensitivity above 5% circulating tumor fraction (sensitivity = 95%). In our cohort, 70% of patients had over 1% circulating tumor burden with representation from all clinical stages. In DLBCL patients, we detected gains in MYC (12%), BCL2 (24%), BCL6 (19%), and PD-L1 (23%), as well as losses in TP53 (27%) and CDKN2A (19%). SCNAs detected in plasma were 85% concordant with those detected in matched tumor biopsies. Additionally, PD-L1 copy number gains were more frequently detected in both PMBCL (63%, p= 0.026) and cHL (71%, p= 0.012) than in DLBCL (23%), consistent with prior reports (Chapuy B, Blood 2016; Roemer MG, J. Clin. Oncol. 2016).We also demonstrate that noninvasive detection of copy number gains in MYC and BCL2, as well as loss of TP53, are negatively prognostic for survival (p < 0.0001, p= 0.0013, p < 0.0001, respectively) (Fig 1C). Furthermore, we identified associations between DLBCL cell-of-origin classification and specific SCNAs, with non-GCB DLBCL enriched for BCL2, BCL6, and PD-L1 copy number gains (39% vs 17%; 31% vs 15%, and 39% vs 25%, respectively). Circulating tumor burden was not statistically different between GCB and non-GCB patients. TP53 losses were more frequently observed in relapsed/refractory patients compared to treatment naïve patients (22% vs 45%, p= 0.023).We performed a preliminary analysis monitoring disease over time in three patients with clear SCNAs detected in plasma at diagnosis. Across longitudinal blood samples (n= 42), SCNA-based disease detection demonstrated 95% concordance with mutation-based disease detection, with a LOD down to ~0.1% circulating tumor burden. Our results suggest noninvasive SCNA profiling could provide an independent method for detecting residual disease, particularly in tumors characterized by structural alterations. Further validation and expansion of this monitoring method will be explored and presented.Conclusions: Noninvasive identification of genome-wide SCNAs is feasible via targeted next generation sequencing. Our approach allows for both genotyping and monitoring of copy number gains and losses from plasma, without requiring matched germline or tissue specimens. This strategy not only allows for the noninvasive genotyping of tumor SCNAs, but also provides an orthogonal method for monitoring residual disease from liquid biopsies using next generation sequencing.Figure 1: A) Matched tissue FISH staining confirms arm-level PD-L1 amplification (red) detected in plasma. D9Z1 (green) was used as an assay control. B) SCNAs detected in plasma were concordant with karyotype analysis of a matched tumor biopsy. C) Copy number alterations in MYC, BCL2, and TP53 are adversely prognostic for survival in R-CHOP treated DLBCL patients. [Display omitted] DisclosuresHüttmann:Bristol-Myers Squibb, Takeda, Celgene, Roche: Honoraria; Gilead, Amgen: Other: Travel cost. Gaidano:Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. 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; Celgene: Membership on an entity's Board of Directors or advisory committees. Advani:Bayer Healthcare Pharmaceuticals: Research Funding; Gilead: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Celgene: Research Funding; Janssen: Research Funding; Genentech: Research Funding; Seattle Genetics: Research Funding; Nanostring: Consultancy; Spectrum: Consultancy; Millennium: Research Funding; FortySeven: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Agensys: Research Funding; Infinity: Research Funding; Juno Therapeutics: Consultancy; Kura: Research Funding; Merck: Research Funding; Pharmacyclics: Consultancy; Cell Medica: Research Funding; Sutro: Consultancy. Diehn:Varian Medical Systems: Research Funding; Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy. Alizadeh:Genentech: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; CiberMed: Consultancy.

Circulating Tumor DNA Is a Reliable Measure of Tumor Burden at Diagnosis of Diffuse Large B Cell Lymphoma: An International Reproducibility Study

▪Background: Pretreatment tumor burden is a known prognostic factor in diffuse large B cell lymphoma (DLBCL). Components of the International Prognostic Index (IPI), including lactate dehydrogenase (LDH) and clinical stage, attempt to capture tumor burden but lack specificity. Tumor volume is an alternative metric for disease burden, but requires specialized radiographic interpretation. We recently described circulating tumor DNA (ctDNA) levels as a novel quantitative biomarker for disease burden in DLBCL (Scherer F and Kurtz DM, Sci Transl Med 2016). To validate the performance of ctDNA across a diverse cohort of patients and medical centers, we assessed pretreatment ctDNA levels in patients with DLBCL from six centers around the world.Methods: We performed Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) on pretreatment cell-free and germline DNA from patients undergoing curative-intent anthracycline-based therapy. Somatic mutations were identified and variant allele fractions were determined. Levels of ctDNA were determined using the mean tumor allele fraction and the total concentration of cell-free DNA; absolute concentration of ctDNA was expressed as haploid genome equivalents per mL (hGE/mL). Finally, we correlated levels of ctDNA with clinical factors including stage, IPI, pretreatment metabolic tumor volume (MTV), and patient outcomes.Results: A total of 183 patients from six centers were enrolled (Stanford University, n=52; University of Eastern Piedmont, n=36; MD Anderson Cancer Center, n=21; Hospital Le Bocage, Dijon, n=25; the National Cancer Institute, n=33; and University Hospital Essen, n=16). Pretreatment cell-free DNA was sequenced deeply; ctDNA was detectable prior to therapy in 97% of patients. The performance of ctDNA detection was consistent across cohorts, with >90% of patients from each center having detectable ctDNA. Concentration of ctDNA was not significantly different across cohorts, with a median of 295 hGE of tumor DNA / mL (one-way ANOVA, P=0.62; Fig 1A). Using pretreatment plasma, all but 5 patients had mutations identifiable via non-invasive genotyping without knowledge of tumor genotype. In 88 patients with tumor samples available, a median of 81% (interquartile range: 34-92%) of variants identified via non-invasive genotyping were confirmed by tumor sequencing on a per-patient basis.We next sought to correlate levels of ctDNA with independent measures of tumor burden. We found a significant difference in ctDNA levels between patients in different IPI risk groups (one-way ANOVA, P<0.0001; Fig 1B). We also found that patients with higher-stage disease had higher ctDNA levels (P<0.0001); ctDNA levels also correlated with LDH (P<0.0001). We demonstrated a robust correlation between MTV and ctDNA level (P<0.0001; Fig 1C). This correlation was consistent across all three cohorts with MTV available (Essen: P=0.0005, r=0.80; Dijon: P<0.0001, r=0.73; Stanford: P=0.006, r=0.50). Finally, pretreatment ctDNA concentration was continuously associated with EFS and OS (P<0.0001 and P=0.0014). Using the median to divide patients into groups with high or low ctDNA, we found patients with high ctDNA levels had significantly inferior EFS compared to those with low ctDNA (P=0.005, HR 2.7, 95% CI 1.4-4.6; Fig 1D).Conclusions: Circulating tumor DNA is a robust biomarker of disease burden in DLBCL in patients from multiple medical centers across North America and Europe. Patients from different centers have similar levels of ctDNA despite pre-analytical variation. Concentrations of ctDNA correlate with stage, IPI, and tumor volume; furthermore, ctDNA level prior to therapy is prognostic of clinical outcome. This study validates previous findings and provides evidence that ctDNA is a reliable biomarker in aggressive lymphomas that could be used to refine clinical estimates of disease burden including stage.Figure 1: Reproducibility and clinical significance of pretreatment ctDNA. A) A stacked scatter plot compares ctDNA levels from patients across 6 cohorts. B) A stacked scatter plot shows ctDNA levels from patients with different IPI risk categories. C) A scatter plot compares metabolic tumor volume to ctDNA level. D) A Kaplan Meier estimate demonstrates event-free survival from the start of therapy for patients with pretreatment ctDNA level above and below the median value. (ND: Not detected). [Display omitted] DisclosuresHüttmann:Bristol-Myers Squibb, Takeda, Celgene, Roche: Honoraria; Gilead, Amgen: Other: Travel cost. Casasnovas:Roche: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Westin: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; Celgene: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees. Gaidano:Roche: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria. Diehn:Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding. Alizadeh:Gilead: Consultancy; CiberMed: Consultancy; Roche: Consultancy; Genentech: Consultancy; Celgene: Consultancy.

Noninvasive identification of emergent mutations following cytotoxic therapy for lung cancer.

8533 Background: Lung cancer is the leading cause of cancer death world-wide, and chemotherapy and radiation remain backbones of therapy for patients with locoregionally advanced and metastatic disease. However, the genetic mechanisms that mediate resistance to chemotherapy and radiation are largely unclear due to a lack of available tissue at the time of relapse. We hypothesized that circulating tumor DNA (ctDNA) analysis could identify emergent mutations after chemotherapy and radiation that may lead to treatment resistance. Methods: To identify emergent mutations at the time of progression following an initial response to chemotherapy and/or radiation therapy for lung cancer, we utilized CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) to analyze plasma samples and matched leukocytes collected pre-treatment and at the time of relapse. We analyzed a targeted panel enriched for lung cancer drivers and recurrently mutated genes for 27 patients treated with chemoradiation therapy for locoregionally advanced lung cancer. In addition, we performed ultra-deep whole exome sequencing ( &gt; 2000X deduped depth) of pre-treatment and relapse cell-free DNA for 5 patients treated with combination chemotherapy for metastatic lung cancer. Functional enrichment analysis was performed on emergent mutation gene lists to identify significantly enriched pathways. Results: We identified emergent variants in 6 out of 27 patients using targeted sequencing after chemoradiation therapy. Emergent mutations after chemoradiation were enriched for plasma membrane adhesion molecules such as PCDH17, PCDH10, and FAT3 (adjusted P = 0.03). Using ultra-deep whole exome sequencing, we observed emergent mutations in 3 out of 5 patients treated with combination chemotherapy. After combination chemotherapy, there was a trend towards enrichment in mutations in ATP-binding cassette transporters, including ABCA13 and ABCB4 (adjusted P = 0.057). Notably, there were no recurrent emergent mutations within our cohort. Conclusions: Our results suggest that ultra-deep whole exome sequencing can non-invasively identify emergent mutations at the time of progression. Resistance to cytotoxic therapy is likely multi-factorial and analysis in expanded cohorts will be helpful to identify recurrently mutated pathways that may contribute to disease progression after an initial response to therapy.

Liquid biopsy-based analysis by ddPCR and CAPP-Seq in melanoma patients

BackgroundThe development of BRAF/MEK inhibitors in patients with metastatic melanoma harboring BRAF mutations has garnered attention for liquid biopsy to detect BRAF mutations in cell-free DNA (cfDNA) using droplet digital PCR (ddPCR) or next-generation sequencing methods. ObjectiveTo investigate gene mutations in tumor DNA and cfDNA collected from 43 melanoma patients and evaluate their potential as biomarkers. MethodsddPCR and CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) techniques were performed to detect gene mutations in plasma cfDNA obtained from patients with metastatic melanoma. ResultsGene variants, including BRAF, NRAS, TP53, GNAS, and MET, were detectable in the plasma cfDNA, and the results were partially consistent with the results of those identified in the tissues. Among the variants examined, copy numbers of MET mutations were consistent with the disease status in two melanoma patients. ConclusionLiquid biopsy using CAPP-Seq and ddPCR has the potential to detect tumor presence and mutations, especially when tissue biopsies are unavailable. MET mutations in cfDNA may be a potential biomarker in patients with metastatic melanoma.

Abstract PO-069: Circulating tumor DNA kinetics to identify genomic predictors of rapid response to chemoradiation in non-small cell lung cancer

Abstract Introduction: Despite evidence that a subset of patients with locoregionally advanced non-small cell lung cancer (NSCLC) can be cured with radiation doses less than 60 Gy, there are currently no validated approaches to identify patients that could benefit from radiation dose de-escalation. Normal tissue changes including inflammation and fibrosis can be difficult to distinguish from residual disease on standard imaging during and following chemoradiation therapy (CRT), making assessment of treatment response and identification of favorable responders challenging. We hypothesized that circulating tumor DNA (ctDNA) kinetics during CRT could be used as a surrogate of response to identify genomic predictors of rapid response to treatment. Methods: We applied cancer personalized profiling by deep sequencing (CAPP-Seq) ctDNA analysis to 61 patients treated with CRT for Stage II-III NSCLC. We quantified ctDNA concentrations pre-CRT and a median of 21 days into CRT (mid-CRT) to determine the log-fold change in ctDNA concentration and identify “rapid responders.” The association between ctDNA log-fold change as a continuous variable with progression-free survival (PFS) was analyzed using univariable and multivariable regression, including gender, age, and stage as co-variables. The prevalence of driver gene single nucleotide variants in rapid responders versus slow responders was compared for each gene using Fisher’s exact tests with P-values adjusted using the Benjamini-Hochberg procedure. Results: Mid-CRT ctDNA log-fold change was significantly associated with progression-free survival as a continuous variable on both univariable (P=0.02) and multivariable analysis (P=0.03). Among patients whose ctDNA log-fold change was more negative than -2.15, 10/11 (91%) did not recur within the radiation field. We defined ctDNA rapid responders as the 10 patients with the largest decrease in ctDNA concentration mid-CRT without local progression. Compared with slow responders, ctDNA rapid responders had a trend towards more TP53 mutations (P=0.12), but no driver mutations were significantly enriched in rapid responders. Notably, mutations in common driver genes KEAP1, NFE2L2, KRAS, and EGFR were observed in 36% of slow responders and 0% of rapid responders (P=0.03). Conclusions: Our results suggest that ctDNA kinetics during CRT can identify patients responding favorably to treatment. Additional molecular characterization of ctDNA rapid responders may enable identification of patients who could benefit from treatment de-escalation. Citation Format: Everett J. Moding, Yufei Liu, Angela B. Hui, Jianzhong He, Yawei Qiao, Ting Xu, Luyang Yao, Saumil Gandhi, Zhongxing Liao, Millie Das, Kavitha J. Ramchandran, Sukhmani K. Padda, Joel W. Neal, Heather A. Wakelee, Billy W. Loo, Steven H. Lin, Ash A. Alizadeh, Maximilian Diehn. Circulating tumor DNA kinetics to identify genomic predictors of rapid response to chemoradiation in non-small cell lung cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PO-069.

Interim Cell-Free Circulating Lymphoma DNA Analysis of the Phase 2 Accept Trial

Introduction Plasma cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) genotyping in Diffuse Large B-cell Lymphoma (DLBCL) holds great promise for early detection and response prediction. DLBCL is a highly heterogenous disease with diverse tumor behaviour and outcomes, yet the delineation of poor- risk groups remains challenging. Genomically driven molecular risk stratification, using ultradeep next generation sequencing (NGS) provides a comprehensive view of the mutational landscape of lymphoma and enables ultrasensitive disease detection. Here, we present an interim analysis of ctDNA kinetics from ACCEPT, a phase Ib/II study of acalabrutinib with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP) for patients with previously untreated DLBCL. Methods Plasma samples were collected at four time points, at baseline and before the second, third and final cycles of chemotherapy. Extracted cell-free DNA (cfDNA) was quantified by fluorometry, prior to undergoing library preparation and next-generation sequencing using the CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) methodology. The targeted sequencing panel covered 799 regions over 50 genes (&amp;gt;200Kb) known to be involved in lymphomagenesis. Analysis was carried out with a custom bioinformatics pipeline using error-corrected deduplication and a database of known artefacts to reduce background noise and improve sensitivity for low-level somatic variant detection. Patients were closely monitored through the study period by clinical examination and PET-CT scanning to provide objective endpoints against which the ctDNA response could be correlated. Results Fifteen patients had samples analysed for all four time points at the time of the interim analysis and were included in the study. Ten (66%) had late stage disease, nine (60%) poor risk disease (IPI &amp;gt; 2). Early vs late stage of disease at presentation showed a strong correlation with baseline levels of both total cfDNA (3,946 HGE/ml vs 21,613 HGE/ml) and ctDNA (842HGE/ml vs 6,380 HGE/ml). Twelve patients (80%) had trackable variants detected at baseline. The majority of these cleared after the first cycle of chemotherapy (Figure A; representative patient). Two cases (Figures B and C) had variants persistently detectable at high variant allele frequency (VAF) (0.059 and 0.068). These patients both had primary refractory disease. One patient from the first acalabrutinib dose escalation cohort and the second patient was found to be ineligible and withdrawn from the primary analysis because of continuous co-administration of omeprazole during therapy which inhibits acalabrutinib absorption. All others achieved a metabolic complete metabolic remission (mCR) after the final cycle of therapy. A third pattern of response noted was (Figure D) showing disappearance of detectable variants at baseline but progressive emergence of an acquired TP53 variant throughout follow-up that was not detected in the patient's germline sample. This high-risk patient with IPI of 5 and triple-hit DLBCL achieved a mCR and remains in remission in follow-up. Conclusion The clearance of ctDNA from the plasma of the responding patients in our study is consistent with the prior literature, as is the poor prognostic significance of persisting variants after the first cycle of chemotherapy. The rapid kinetics of the changes in ctDNA is noted and is potentially of advantage over other modalities of monitoring, such as PET-CT scan. Data on the entire cohort (n=30) and longer follow-up will be available at the time of the meeting. Figure 1 Disclosures Davies: Roche: Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Roche, Celgene, Kite Pharma, Acerta, Karyopharma, Regeneron, Incyte: Consultancy; Roche, Acerta Pharma, AstraZeneca, Celgene, Gilead, ADC Therapeutics, Gilead: Research Funding; Celegene, Roche, Kite Pharma, Celegene: Honoraria. Johnson:Oncimmune: Consultancy; Bristol-Myers: Honoraria; Celgene: Honoraria; Genmab: Honoraria; Incyte: Honoraria; Kite Pharma: Honoraria; Kymera: Honoraria; MorphoSys: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Boehringer Ingelheim: Consultancy; Epizyme: Consultancy, Research Funding; Janssen: Consultancy; Oncimmune: Consultancy; Epizyme: Consultancy, Research Funding; Janssen: Consultancy. Schuh:Illumina: Other: Consulting fees; Roche: Other: Consulting fees; Gilead: Other: Consulting fees; Abbvie: Other: Consulting fees.

Profiling T-Cell Receptor Diversity and Dynamics during Lymphoma Immunotherapy Using Cell-Free DNA (cfDNA)

Profiling T-Cell Receptor Diversity and Dynamics during Lymphoma Immunotherapy Using Cell-Free DNA (cfDNA)

Abstract IA41: Approaches for personalized medicine in lymphoma through liquid biopsies

Abstract Over the last decade, circulating tumor DNA (ctDNA) has emerged as an important biomarker across a range of cancers, including lymphomas. The potential clinical applications of ctDNA technologies are broad, including tumor mutational genotyping, detection of molecular responses and minimal residual disease, as well as early detection of relapsing disease with specific attention to mechanisms of resistance. Numerous opportunities therefore exist to apply ctDNA methodologies to tailor therapy for individual patients. By applying targeted next-generation sequencing of ctDNA via Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) in patients with lymphoma, these opportunities for precision oncology largely fall into two groups—identification of specific targetable molecular features and quantitative assessment of disease burden and risk. Here we explore these approaches in patients with diffuse large B-cell lymphoma (DLBCL) undergoing first-line or salvage therapy. Utilizing CAPP-Seq from pretreatment plasma, mutational genotyping of diverse somatic alterations is possible, including single-nucleotide variants (SNVs), translocations, and copy number alterations. By combining these mutations through an integrated Bayesian framework, identification of specific disease subtypes is possible, including activated and germinal center B-cell molecular cell-of-origin subtypes. In addition, detection of emergent mutations during and after treatment can provide insights into mechanisms of therapeutic resistance, many of which are potentially targetable. In addition to these insights into disease biology, ctDNA can serve as a quantitative handle on disease burden. By measuring ctDNA disease burden in DLBCL patients from six centers throughout North America and Europe, we demonstrate the prognostic value of ctDNA levels prior to treatment. Additionally, we found that a molecular response to treatment, as detected by the change in ctDNA after one or two cycles of therapy, is highly prognostic for patient outcomes in both front-line and later lines of therapy. Moreover, these molecular response features can be combined with additional outcome predictors, such as the IPI and interim PET/CT scans, to build a dynamic risk model termed the Continuous Individualized Risk Index (CIRI). CIRI provides a personalized probability of likely patient outcomes that updates over time as more information becomes available. Outcome prediction with CIRI significantly improved on predictions using ctDNA molecular response, interim PET/CT scans, or the IPI alone. In summary, liquid biopsies through ctDNA afford numerous opportunities for personalized medicine, ranging from mutational profiling and molecular subtyping to disease quantification and response prediction. Further studies of novel clinical approaches are likely to change treatment paradigms in the near future. Citation Format: David M. Kurtz. Approaches for personalized medicine in lymphoma through liquid biopsies [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr IA41.