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BackgroundThe goal of this study was to assess the impact of trastuzumab on locoregional failure.METHODSThe analysis included 2763 patients with HER2‐positive (HER2+) breast cancer who were randomly assigned to adjuvant doxorubicin (A), cyclophosphamide (C), paclitaxel (T) and trastuzumab (H) (arm A, AC→T [n = 922]; arm B, AC→T→H [n = 988]; arm C, AC→T+H→H [n = 853]). Radiotherapy was given after AC→T concurrently with H. Radiotherapy was given after lumpectomy (L) or after mastectomy (M) with ≥4 positive lymph nodes but was optional for 1 to 3 positive lymph nodes. Locoregional failures at 10 years (LFR10) as first events were compared using competing risk analysis.ResultsThe median follow‐up was 13.0 years. The first site of failure was local‐only in 96 cases, locoregional in 16 cases, regional in 32 cases, and not specified in 2 cases; LFR10 was 4.8% (95% CI 4.1%‐5.7%). LFR10 was 5.5% (95% CI 4.3%‐7.2%), 4.9% (95% CI 3.7%‐6.4%), and 2.8% (95% CI 1.9%‐4.1%) in arms A, B, and C (B vs A: hazard ratio [HR] 0.91, P = .62; C vs A: HR 0.72, P = .12). For estrogen receptor–positive patients, LFR10 was 3.7% (95% CI 2.8%‐4.8%) and for estrogen receptor–negative patients, it was 6.1% (95% CI 5.0%‐7.4%; HR 0.61, P = .004). Local treatment included L+RT (n = 1044 [38%]), M+RT (n = 1025 [37%]), and M (n = 694 [25%]). LFR10 was 6.% (95% CI 5.0%‐7.8%), 3.0% (95% CI 2.1%‐4.3%), and 5.5% (95% CI 4.0%‐7.4%) for L+RT, M+RT, and M, respectively (M+RT vs L+RT: HR 0.43, P < .001; M vs L+RT: HR 0.88, P = .57). For 1 to 3 positive lymph nodes, LFR10 was 6.5% (95% CI 4.8%‐8.9%), 4.1% (95% CI 2.4%‐7.0%), and 4.3% (95% CI 2.9%‐6.5%) in L+RT, M+RT, and M, respectively (M vs L+RT: HR 0.68, P = .14; M vs M+RT: HR 1.2, P = .6).ConclusionLow 10‐year LFRs were seen regardless of trastuzumab use. Differences in local therapy in patients with 1 to 3 positive lymph nodes did not appear to improve local control. Local therapy studies for HER2+ and other tumor characteristics are important as the role of local therapies continues to evolve.

e19013 Background: Mutation profiling to assess for potentially druggable mutations in NSCLC is being offered at an increasing number of cancer centers throughout North America and internationally. Although data continue to accumulate for the potential value of mutation testing in designing chemotherapeutic regimens, the treatment impact of obtaining information beyond assessment of EGFR and ALK status remains unclear. How best to obtain and clinically utilize these data, including information from exome and whole genome sequencing, also remains unclear. Methods: Patients were reviewed electronically in a multidisciplinary conference regarding indications for testing and results of mutation profiling from various methods, including the mass-spec based LungCarta test, targeted NexGen sequencing, exome, and whole genome sequencing. Outcomes of the multidisciplinary review were communicated back to treating physicians. Results: Mutation testing was performed on 200 patients using a variety of approaches. The majority (>150) were surgically resected stage I and II tumors. Mutations in at least 1 major cancer driver gene, including EGFR, KRAS, MET, BRAF and PIK3CA, were found in 47% of all patients tested. EGFR mutations were present in 14.8% of patients tested, KRAS 21.3%, BRAF 2.6%, PIK3CA 3.2%, and MET 4.5%. A total of 8 patients underwent either exome or whole genome sequencing. A limited number of patients (<10) had mutation results that impacted treatment decisions from this cohort. Conclusions: Mutation profiling can influence treatment decisions in NSCLC, but at a low frequency. The role of exome and or whole genome sequencing for patients with NSCLC is evolving and remains undefined.

Introduction: Globin gene sequencing has long been impeded by repetitive regions, homology and structural variation. Currently, long-read sequencing technology has been limited by high cost and error rate. These challenges can be mitigated by sample multiplexing of targeted capture sequencing and the use of consensus reads, respectively. This ongoing study analyzes the utility of long range sequencing technology in identifying historically problematic beta globin cluster mutations. Materials and methods: Long range sequencing was performed on complex beta globin gene cluster cases utilizing the Sequel platform (Pacific Biosciences, Menlo Park, California, United States). Sample input was 1 microgram of DNA extracted from whole blood. Circular Consensus Sequence (CCS) FASTQ sequences files were mapped to reference genome (GRCh37/hg19) using NGMLR, followed by annotation of variants using a custom bioinformatics workflow. Comparison to multiplex ligation-dependent probe amplification (MLPA), Sanger sequencing, array CGH, short-range NGS (300 bp), hemoglobin protein, and clinical data was performed. Results: CCS read length varied from 1 to 10 kb, average 3.5. Analysis of the beta globin cluster region showed impressive superiority and comprehensiveness to Sanger, MLPA, array CGH and short range sequencing technology: 1) single nucleotide variants (SNVs) were identified to a sensitivity and specificity of 99.5%; 2) large structural variants (SVs) such as large deletions, duplications, insertions, crossovers and fusions spanning many kilobases were characterized in the heterozygous, homozygous and compound heterozygous state to a precise genomic coordinate (table 1); 3) phasing SNVs identified Hb S haplotypes (Central African region (CAR), Benin, Senegal, Arab-Indian and Cameroon). Conclusion: 1) Long range sequencing holds significant promise in genotyping hemoglobin disorders. This method improves efficiency with potential for a single test offering with increased resolution, particularly in large deletion analysis. Complex cases greatly benefited from improved range and resolution. 2) The decreased requirement of blood volume for extensive testing is of particular interest for the pediatric population. 3) Genotyping of Hb S patients detected by positive newborn screening will help predict need for prophylaxis or therapy by assessing variant zygosity, phasing and haplotypes. In conclusion, we believe that long range sequencing has immense potential to contribute extensively to thalassemia and hemoglobinopathy diagnostics and is far superior to the currently available technologies for beta globin cluster analysis. Disclosures Jen: Celgene: Employment.

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.