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4263 Background: COMPLETE is a registry of peripheral T-cell lymphoma (PTCL) patients from academic and community settings in the US. For the registry, selected biomarkers are recorded to determine which markers may have informed the diagnosis of PTCL. Research staff at participating sites enter data from pathology reports into an electronic data capture (EDC) system; however, pathology reports can be difficult to interpret, particularly for those who have not received specialized training. To ensure that biomarker data in COMPLETE accurately reflects pathology reports, a biomarker QA initiative was undertaken. A similar initiative was pursued by the International T-Cell Project and is the subject of a separate, parallel abstract. Methods: The COMPLETE biomarker QA initiative includes review of on-site data entered for 26 biomarkers commonly assessed in the diagnostic work-up of PTCL. External review by a board-certified pathologist is triggered once a site has entered the biomarker data into the EDC system for enrolled patients and uploaded the de-identified pathology reports. All patients enrolled at a site within 30 days of the first patient were reviewed. A single mismatch between the site-entered data and the reviewer's findings counts as an error. Findings of the review are entered into the EDC system to facilitate analysis and queries are sent to the site in cases where corrections are needed. Results: Data for 119 patients from 43 centers comprising a sum of 3570 biomarker entries were reviewed by an external hematopathologist. The mean number of phenotypic markers assessed per patient was 12 (range: 4–20), and the mean number of gene rearrangement tests performed was 0.2 (range: 0–3). Of the 119 patients, 13 (11%) had no EDC entry errors, 33 (28%) had 1–2 errors, 34 (29%) had 3–5 errors, 31 (26%) had 6–10 errors and the remaining 8 cases (7%) had 11–15 errors. The reviewer agreed with 74% of entries where the site noted a finding was positive; of the remaining entries where the site indicated the findings were positive, the reviewer noted they were negative (12%), not assessed (10%) or indeterminate (5%). For entries where the site indicated the finding was negative, the reviewer was in agreement 79% of the time; for the remaining 21% of negative-reported entries by the site, the reviewer indicated the marker was not assessed (12%), indeterminate (6%) or positive (3%). The reviewer was in agreement with the site in 92% of the entries where the site indicated the marker was not assessed; otherwise, the reviewer found the marker was negative (4%), positive (3%) or indeterminate (1%). The markers accounting for the most errors (each marker accounting for 5% or more of errors) were CD8, CD20, CD30, Epstein-Bar Virus (EBV) and T-cell receptor gamma gene rearrangement (TCR-g). The types of errors for these markers are noted in the table below. | Marker, N (%) Errors Overall | Type of Error Noted by the Reviewer for Markers with the Most Errors, n (%) | |:-------------------------------------------:| --------------------------------------------------------------------------- | ---------------------------------------------- | | Finding is recorded in non-neoplastic cells | Pathology report unclear | Test performed, result not in pathology report | Marker is positive or negative; site notes opposite | Marker is not assessed or indeterminate; site notes positive or negative result | Marker is positive or negative; site notes marker was not assessed | Other | | CD8, 40 (8%) | 1 (3%) | 12 (30%) | 2 (5%) | 11 (28%) | 7 (18%) | 6 (15%) | 1 (3%) | | CD20, 47 (9%) | 24 (51%) | 6 (13%) | 0 (0%) | 5 (11%) | 6 (13%) | 6 (13%) | 0 (0%) | | CD30, 25 (5%) | 5 (20%) | 3 (12%) | 1 (4%) | 3 (12%) | 6 (24%) | 7 (28%) | 0 (0%) | | EBV, 27 (5%) | 1 (4%) | 2 (7%) | 0 (0%) | 0 (0%) | 0 (0%) | 23 (85%) | 1 (4%) | | TCR-g, 24 (5%) | 0 (0%) | 4 (17%) | 0 (0%) | 0 (0%) | 5 (21%) | 12 (50%) | 3 (13%) | Sum may exceed 100% due to rounding. Recording the type of EBV test performed [in situ hybridization (ISH) and immunohistochemistry (IHC)] presented further challenges, accounting for 8% of errors. Common errors included: 1) the site indicated the test was not performed when it was performed per report and 2) the site indicated a particular test (ISH, IHC) was performed but the method used was unclear in the report. Conclusions: Results of the COMPLETE biomarker QA analysis indicate that errors in interpretation or recording of biomarker data from pathology reports are common. For registries and other observational studies, periodic reviews by an external pathologist may guide site training and improve the accuracy of data collection. For interventional clinical trials in PTCL, central review of all biomarker data by expert hematopathologists should be considered. Disclosures: Hsi: Allos: Research Funding; Eli Lilly: Research Funding; Abbott: Research Funding; Cellerant Therapeutics: Research Funding; BD Biosciences: Research Funding; Millenium: Research Funding. Gruver: MedNet Solutions: Honoraria. Foss: Allos: Consultancy; Celgene: Consultancy; Seattle Genetics: Consultancy; Eisai: Consultancy; Celgene: Study Grant, Study Grant Other; Merck: Study Grant, Study Grant Other. Carson: Allos: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau. Pinter-Brown: Allos: Consultancy, Membership on an entity's Board of Directors or advisory committees. Horwitz: Allos: Consultancy, Research Funding. Rosen: Allos: Consultancy, Honoraria. Pro: Celgene: Honoraria, Research Funding; Spectrum : Honoraria; Allos: Honoraria; Seattle Genetics : Research Funding. Gisselbrecht: Allos: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract 2529 The clinical heterogeneity of acute myeloid leukemia (AML) is well established and identification of specific balanced translocations is a critical component of the classification of AML. Patients with translocations are assigned to prognostic groups generally defined as good [e.g., t(8;21)(q22;q22), inv(16) (p13q22), t(15;17)(q24;q21)], intermediate [e.g., t(3;5)(q25;q34), t(9;11)(p22;q23)] and poor [e.g., other MLL translocations, inv(3)(q21q26.2), t(6;9)(p23;q34)]. Further refinement based on the identification of new genetic markers may help tailor prognostic and treatment decisions. In an effort to identify new molecular markers associated with recurrent translocations, we have analyzed 30 cases of AML with apparently balanced translocations using a microarray-based technology coupled with linear DNA amplification [translocation-CGH (tCGH)], providing high resolution mapping of translocation breakpoints and identification of copy number abnormalities at those breakpoints (BkPt-CNAs). Analysis can be multiplexed to interrogate 14 balanced translocations in a single test. Additionally, all cases were assayed by high-density aCGH to identify additional clinically relevant copy number abnormalities (CNAs) throughout the genome. For each sample these test results were compared. Patients were all adults (>40 years of age), 10 males and 20 females, and included t(15;17)(q24;q21) (8 cases), t(8;21) (q22;q22) (6 cases), inv(16)(p13q22) (5 cases), t(9;11)(p22;q23) (5 cases), t(3;5)(q25;q34) (4 cases), t(6;11)(q27;q23) (1 case) and t(6;9)(p23;q34) (1 case). Of the 30 cases, 6 showed BkPt-CNAs in one or both translocation partners. All 6 (100%) of these cases exhibited additional clinically relevant CNAs, whereas only 11/24 (46%) cases with no BkPt-CNAs showed additional CNAs. Thus, BkPt-CNAs are significantly correlated with the presence of secondary clinically relevant alterations (p=0.0208). Importantly, no MLL translocations cases revealed BkPt-CNAs. In contrast, 3 of 4 t(3;5)(q25;q34) cases showed BkPt-CNAs in both genes (NPM1 and MLF1) and additional CNAs, with two cases showing large deletions (>0.5 Mb) at both breakpoints. Alterations involved RSRC1 (mRNA splicing), FBXW11 (E3 ubiquitin protein ligase), STK10 (serine/threonine kinase), FGFR3 (growth factor receptor) and MGMT (O-6-methylguanine-DNA methyltransferase), findings potentially relevant to disease progression and treatment response. Cases of t(15;17), t(8;21) and inv(16) each showed a single case with BkPt-CNAs and these correlated with greater complexity for additional CNAs. For t(15;17), that case showed partial duplication of both the PML and RARA genes (47 kb and 39 kb) yielding a cryptic fusion (missed by karyotype and FISH) that produced the “long” form (bcr1) transcript based on the PML breakpoint and confirmed by rtPCR. This case also had a 24.56 Mb deletion at 13q14.2->q21.33 involving RB1, MIR15A and MIR16-1. In the 7 remaining PML-RARA cases [4 short (bcr3) and 3 long (bcr1)] with no BkPt-CNAs, two cases showed non-complex CNAs [(e.g., -Y (bcr1) or +8 (bcr3)]. Five of six t(8;21) cases exhibited CNAs, but the case with a BkPt-CNA (39 kb within RUNX1) exhibited greater complexity for CNAs (multiple gains and losses over >70 MB of distal 9q) than other cases (e.g., +8 or +15). Similarly, analysis of inv(16) cases demonstrated CNAs in 3 of 5 cases, with the greatest complexity in the case with BkPt-CNAs [deletion in CBFB and MYH11 involving ABCC1 (anion transporter implicated in multiple drug resistance), deletions 9q34.13 involving ABL1 and NUP214], while other cases had single changes of a homozygous deletion of GSTT1 or a heterozygous deletion of the 16p11.2 microdeletion syndrome region, either potentially inherited. The sole case of t(6;9) had only the relevant translocation. In conclusion, analysis of AML cases with balanced translocation by t-CGH and aCGH revealed significant correlation between BkPt-CNAs and the presence of secondary CNAs. Moreover, when BkPt-CNAs were present, the molecular complexity of additional CNAs was increased. This observation held true across various translocations evaluated, with the notable exception of MLL translocations. Thus BkPt-CNAs provide a broadly applicable signature for a subtle molecular instability in AML that, acquired or inherited, may predate translocations and may prove relevant to treatment response and outcome. Disclosures: Schultz: Signature Genomic Laboratories: Employment. Campbell:Abbott Molecular: Honoraria. Furrow:Signature Genomic Laboratories: Employment. Shaffer:Signature Genomic Laboratories: Employment. Ballif:Signature Genomic Laboratories: Employment.