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Background: Age-related macular degeneration (AMD) is a leading cause of blindness that affects the central region of the retinal pigmented epithelium (RPE), choroid, and neural retina. Initially characterized by an accumulation of sub-RPE deposits, AMD leads to progressive retinal degeneration, and in advanced cases, irreversible vision loss. Although genetic analysis, animal models, and cell culture systems have yielded important insights into AMD, the molecular pathways underlying AMD's onset and progression remain poorly delineated. We sought to better understand the molecular underpinnings of this devastating disease by performing the first comparative transcriptome analysis of AMD and normal human donor eyes.

Previous work by Del Re et al. describing the emergence of KRAS mutations following treatment of non-small cell lung cancer patients with EGFR tyrosine kinase inhibitors was inadvertently omitted from the reference list of this Article and should have been cited as follows. The statement in the Results section 'While it is well established that KRAS activation is a mechanism of acquired resistance in colorectal cancer patients treated with EGFR-targeting monoclonal antibodies (mAbs) 25,26,31,32 , this is to our knowledge the first report of EGFR mutant NSCLC patients acquiring activating mutations in KRAS following treatment with an EGFR TKI 10,11,23' , and the identical statement in the Discussion section, should both have read 'While it is well established that KRAS activation is a mechanism of acquired resistance in colorectal cancer patients treated with EGFR-targeting monoclonal antibodies (mAbs) 25,26,31,32 , here we show that EGFR mutant NSCLC patients can also acquire activating mutations in KRAS following treatment with a third generation EGFR TKI. The acquisition of KRAS mutations in EGFR mutant NSCLC patients following treatment with first line EGFR TKIs has recently been reported (Del Re et al.), although these mutations have not been detected in other similar first line cohorts 10,11,23,24' .Del Re et al. contribution of KRAS mutations and c.2369C4T (p.T790M) EGFR to acquired resistance to EGFR-TKIs in EGFR mutant NSCLC: a study on circulating tumor DNA. Oncotarget

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3591 Background: Adjuvant chemotherapy is offered to most pts with Stage III CRC, and to a subset with Stage II disease deemed at high-risk for recurrence. Nevertheless, risk stratification strategies remain suboptimal. Detection of minimal residual disease (MRD) through ctDNA analysis has been shown to identify pts at high recurrence risk in Stage II CRC, but not Stage III disease. Methods: The next-generation sequencing based AVENIO ctDNA Surveillance Kit (Research Use Only) was used to identify single nucleotide variants (SNVs) in tumor tissue within a cohort of 145 Stage II and III CRC pts following R0 surgical resection (n = 86 and 59 respectively; median follow-up = 32.1 mo). The same assay was used to monitor ctDNA with a single post-operative blood sample (mean surgery-to-phlebotomy time: 10 days). Regions from 197 genes recurrently mutated in CRC were interrogated, and pts were classified as ctDNA positive (+) or negative (-) in plasma based on the detection of SNVs previously identified in tumor tissue. Results: Variants were identified in 99% of tumors (n = 144) with a median of 4 SNVs/sample (range 1-24) and all post-operative plasma samples were successfully profiled. Pts with detectable ctDNA (n = 12) displayed a significantly shorter 2-year relapse-free survival (RFS; 17% vs 88%; HR 10.3; 95% CI 2.3-46.9; p < 0.00001), time to recurrence (TTR; HR 20.6; 95% CI 3.1-139.0; p < 0.00001) and overall survival (OS; HR 3.4; 95% CI 0.5-25.8; p = 0.041) than ctDNA- pts (n = 132). 11 (92%) of ctDNA+ pts developed recurrence compared to 9 (7%) of ctDNA- pts. Monitoring multiple variants doubled sensitivity of MRD detection compared to tracking a single driver mutation. TTR was shorter in ctDNA+ vs ctDNA- Stage II (HR 23.1, 95% CI 0.28-1900.4; p < 0.00001) and stage III pts (HR 17.9; 95% CI 2.7-117.3, p < 0.00001). TTR of Stage II and III ctDNA- pts was similar (p = 0.7). Conclusions: Our results indicate that ctDNA analysis can detect MRD within days after complete resection of CRC and accurately identifies pts at high risk of recurrence in both Stage II and III CRC. MRD detection via ctDNA sequencing may allow personalization of adjuvant treatment strategies.

Background: Molecular characterization of tumors currently relies in large part on invasive tissue biopsies, which can often be unavailable or limiting. For example, while biological heterogeneity between diffuse large B-cell lymphoma (DLBCL) patients can be stratified using classification of cell-of-origin (COO), current methods relying on tumor gene expression profiles (GEP) or multi-parametric immunohistochemistry (IHC) require adequate tissue, and can be hampered by modest accuracy and reproducibility. Circulating tumor DNA (ctDNA) is an emerging noninvasive biomarker for tumor detection with potential for DLBCL disease monitoring. Here, we evaluated the utility ofctDNA for COO classification of patients with DLBCL. Methods: We previously built and trained a novel DLBCL COO Naive Bayesian classifier, using somatic alterations in 32 genes and associated with COO subtypes that were previously classified by Affymetrix mRNA GEP of frozen tumors (n=142 Training; Scherer et al., ASH Annual Meeting 2015 / ASCO Annual Meeting 2016). Here, we applied CAPP-Seq, a capture-based targeted high-throughput sequencing (HTS) method (Newman et al., Nat Med 2014), to genetically profile and classify 146 DLBCL patients in independent cohorts. We employed a test/validation framework (n=76 Test, n=70 Validation) to classify patients diagnosed and treated at Stanford University, MD Anderson Cancer Center, or at the University of Eastern Piedmont, Italy. We compared COO classification from pre-treatment plasma samples (n=119), tumor biopsy tissues (n=82, of which 71% were from FFPE), or both (n=41) against the current clinical standard immunohistochemistry method (Hans algorithm, n=117). Kaplan Meier analyses and Cox proportional-hazard models were used to assess clinical utility of biopsy-free and tumor genotyping for prediction of clinical outcomes from time of DLBCL diagnosis. Results: Genotyping of both test and validation cohorts using either tumor or plasma samples allowed COO classification in 100% of patients (GCB=77 / non-GCB=69) with 77% overall concordance to blinded and centralized Hans IHC classification. As expected, histologically transformed (tFL/DLBCL) and double-hit DLBCLs (MYC and BCL2/BCL6 translocated) were strongly biased toward GCB DNA subtype (26/29, 90%), while primary central nervous system lymphomas were almost invariably classified as ABC/non-GCB by ctDNA (6/7, 87%). The concordance between DNA-based COO classification from tumor vs. plasma was 88% (36/41). In the test cohort, COO classification by genotyping from either tissue or plasma was significantly associated with PFS (P=0.003 and 0.02, respectively, Cox proportional-hazard), while classification by Hans criteria failed to show a significant survival difference (P=0.25). The superior outcome of patients with GCB DNA COO subtype was validated when extended to the full cohort (P=0.02, Cox proportional-hazard; P=0.03, log-rank (Figure 1)). We observed a similar, albeit not significant, trend for overall survival. Conclusions: DLBCL COO subtypes can be accurately classified using somatic alterations detectable as circulating tumor DNA. We envision that this approach might help overcome some of the limitations of mRNA GEP and protein/IHC-based methods, including the requirement for invasive biopsies, tissue availability, and suboptimal assay performance. Furthermore, this strategy could support future clinical trials in helping to identify poor-risk groups at diagnosis, to guide future therapy selection, and to improve treatment decisions for patients with DLBCL. Figure 1. Figure 1. Disclosures Newman: Roche: Consultancy. Lovejoy:Roche: Employment. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Gaidano:Janssen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; Morphosys: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Speakers Bureau. Diehn:Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding. Alizadeh:Gilead: Consultancy; Celgene: Consultancy; Genentech: Consultancy; Roche: Consultancy.

Background and Methods DLBCL patients exhibit striking heterogeneity in therapeutic responses, yet current methods to assess residual disease have shown suboptimal performance. Circulating tumor DNA (ctDNA) is a promising noninvasive biomarker for disease burden. While High-Throughput Sequencing of immunoglobulin genes (Ig-HTS) has shown utility for ctDNA detection in DLBCL, only a small number of tumor markers are profiled, limiting its sensitivity and broad applicability (Kurtz DM, Blood 2015 and Roschewski M, Lancet Oncology 2015). Here, we studied DLBCL patients using CAPP-Seq, a recently developed approach for ultrasensitive assessment of ctDNA that profiles multiple genomic regions across hundreds of kilobases in a single HTS assay (Newman AM, Nat. Med. 2014). Results We designed a 250kb DLBCL CAPP-Seq panel targeting 1053 regions from 268 genes, incorporating recurrent single nucleotide variants (SNVs), insertions/deletions (indels) and rearrangements, and evaluated its genotyping performance on diverse tumors from 63 patients (Table 1). We detected somatic mutations in 98% of patients (median: 143 variants/pt), including AID-related events in well-known hotspots. Moreover, we detected 100% of BCL2/BCL6 rearrangements and most MYC translocations previously identified by FISH (n = 30). Biopsy-confirmed tumor mutations were detectable with 99.3% specificity in 97% of pretreatment plasma samples (n = 37) with a range of 0.007% to 32% mean fractional abundance. Median recovery rates for both SNVs and fusions in plasma exceeded 85%, indicating robust tumor genotyping performance. Furthermore, in most patients, paired allelic fractions between tumor biopsies and pretreatment plasma samples were significantly correlated (P < 0.05), demonstrating that ctDNA can accurately mirror the mutational profiles found in tumor tissues. We next evaluated the utility of direct biopsy-free genotyping of pretreatment plasma using a novel ctDNA genotyping approach. We noninvasively identified variants in 97% of cases (n=37), and confirmed a median of 95% of SNVs per cfDNA sample in paired tumor biopsies, indicating that plasma DNA is an effective surrogate for direct tumor genotyping. Having established the technical performance in pretreatment cfDNA, we next assessed utility of CAPP-Seq for minimal residual disease (MRD) detection (Figure 1). Plasma samples at time points of complete response (n = 22) were collected from 10 patients, all of whom ultimately relapsed. Each plasma sample was then analyzed for evidence of biopsy-confirmed mutations from pretreated tumors. Strikingly, ctDNA was detected in 9 of 10 patients (90%), including 3 of 3 isolated CNS relapses, with fractional abundances as low as 0.004%. Among patients with CAPP-Seq detectable MRD, the median time between first detection and relapse was 162 days, and all patients whose MRD levels were uniformly below the detection limit had blood collections >8 months prior to relapse. Conclusions To summarize, we validated robust technical performance of a novel approach for noninvasive tumor genotyping in DLBCL using pretreatment plasma, and for MRD surveillance. Given the demonstrated advantages of CAPP-Seq in the setting of radiographic remission, we envision its utility for improved relapse prediction. Moreover, we anticipate that this biopsy-free genotyping approach will have applications for monitoring subclonal dynamics and emergent mutations in response to therapy. FS, DMK and AMN contributed equally. Disclosures Newman: Roche: Consultancy. Klass:Roche: Employment. Diehn:Roche: Consultancy. Alizadeh:Genentech: Consultancy; Celgene: Consultancy; Roche: Consultancy.

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