Search

Matthew B Schabath,1,2 Tapashi B Dalvi,3 Hongyue A Dai,4 Alan L Crim,4 Anita Midha,5 Norah Shire,3 Nicholas T Gimbrone,1 Jill Walker,6 Danielle M Greenawalt,7 David Lawrence,8 James R Rigas,9 Robert Brody,9 Danielle Potter,9 Naveen S Kumar,4 Shane A Huntsman,4 Jhanelle E Gray2 1Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; 2Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; 3Oncology R&D, AstraZeneca, Gaithersburg, MD, USA; 4M2Gen, Tampa, FL, USA; 5Department of Personalised Healthcare and Biomarkers, AstraZeneca, Cambridge, UK; 6Department of Precision Medicine Oncology, AstraZeneca, Cambridge, UK; 7Department of iMED Oncology Informatics, AstraZeneca, Waltham, MA, USA; 8Department of Global Medicines Development, AstraZeneca, Cambridge, UK; 9Department of Global Medical Affairs Oncology, AstraZeneca, Gaithersburg, MD, USACorrespondence: Matthew B SchabathH. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USATel +1 813 745 4150Fax +1 813 745 6525Email matthew.schabath@moffitt.orgPurpose: To characterize programmed cell death ligand-1 (PD-L1) expression in relation to survival and gene mutation status in patients with advanced NSCLC. The study also explored the influence of tumor mutational burden (TMB) on PD-L1 expression and patient characteristics.Patients and methods: Adult patients with histologically or cytologically documented Stage IIIB/Stage IV/recurrent/progressive NSCLC, Eastern Cooperative Oncology Group performance status 0 to 3, and >2 lines of prior systemic treatment regimens were included in this retrospective analysis. Patients were treated from 1997 to 2015 at H. Lee Moffitt Cancer Center and Research Institute, Tampa, or at 7 community centers across the United States. PD-L1 expression level was determined using the VENTANA PD-L1 (SP263) Assay. EGFR and KRAS mutation status and ALK rearrangements were determined by targeted DNA sequencing; these were obtained from clinical records where targeted DNA sequencing was not performed. TMB was calculated as the total number of somatic mutations per sample.Results: From a total of 136 patients included in the study, 23.5% had tumors with high PD-L1 expression (≥25%). There were no significant differences in patient characteristics, overall survival (OS), and progression-free survival (PFS) between patients with high PD-L1 expression (median OS: 39.5 months; median PFS: 15.8 months) vs low PD-L1 expression (

e22167 Background: Ovarian cancer is a histologically and molecularly heterogeneous disease that has a clear need for prognostic and predictive tests to improve treatment and outcomes. The TCC prot...

e15017 Background: Molecular-based pt assessment is commonly used to identify proper candidates for targeted therapies. The objective of our investigation was to examine the utility of the Total Ca...

470 Background: Sunitinib is a standard of care for advanced RCC. Despite efforts to identify predictive molecular markers for patient selection, none are available, likely due to multiple resistance mechanisms. Using the Total Cancer Care (TCC) database, which integrates patient clinical, molecular, and biospecimen data, we devised a tumor genomics and transcriptomics experiment to identify differences between RCC patients who derive prolonged clinical benefit from sunitinib versus those who are resistant. Methods: A discovery set of 34 RCC patients treated with sunitinib at the approved regimen were identified in the TCC database (n=16 treated for ≤6 months, having primarily discontinued for reasons other than tolerability; n=18 treated for ≥18 months). Tumor samples were analyzed by whole exome sequencing (WES) and by parallel 400-gene expression profiling. Following gene mutation identification and supervised gene expression analysis, molecular differences between the two groups were identified and tested for potential association with treatment duration. Results: Of the 34 cases identified, 24 remained for analysis following sample QC failure and clinical review (n=10 and 14 treated for ≤6 and ≥18 months, respectively). Gene expression analysis revealed a 37-gene signature associated with treatment duration: MAPK8 (JNK1) was a leading candidate biomarker (Pearson correlation with log [treatment duration]=–0.70; p=0.06 after Bonferroni multiplicity correction). Pathway-based WES analyses identified 25 potential variants of interest, none remaining statistically significant after correction. However, following genome-wide analysis, a single variant in an intronic region of ING3 was statistically associated with treatment duration (p=0.02). Conclusions: Activation of the PI3K/AKT pathway was a marker of resistance to sunitinib. In contrast, activation of the angiogenic, NOTCH, or JAK-STAT pathways was, to some degree, associated with sensitivity to therapy. However, neither VHL alteration nor lack of expression, nor alteration in chromatin-rearrangement genes, was associated with sunitinib treatment duration. These findings require further validation in a larger and independent cohort.

BACKGROUND Gene expression profiling using gene-discovery, high-density microarray technologies is a powerful tool. One potential application is the development of tumor classifiers that predict the site of origin. For this technology to be relevant, however, it must be applicable to tumor biopsy samples, which most often are fine-needle aspiration biopsy (FNAB) samples. METHODS Surgically resected tumors were sampled by FNAB using different gauge needles. A portion of the excised tumor was also collected. RNA samples were extracted using standard techniques and the quality and quantity of the RNA samples were measured for each sample. Thirteen representative FNAB samples and two representative tissue samples were submitted for microarray analysis and then subjected to a tumor classifier. RESULTS Fourteen of 18 samples analyzed for quantity and quality of RNA yielded an adequate amount of RNA (> 1 μg total RNA). Tumor type contributed to the RNA yield because one of the four inadequate samples was retrieved from a patient with lobular carcinoma of the breast and the other three samples were retrieved from patients with retroperitoneal sarcomas. Of the 13 samples submitted for microarray analysis, 9 were classified correctly as to tumor type using a tissue-based tumor classifier. CONCLUSIONS The authors demonstrated that FNABs reproducibly obtained an adequate amount of RNA for microarray analysis when a standardized collection procedure was used. Furthermore, the samples generated interpretable gene expression profiles that could be matched accurately with a tumor classifier established on tissue specimens. The current study showed that FNAB produced adequate material for microarray analysis when utilizing a standardized collection procedure. Cancer (Cancer Cytopathol) 2005. © 2005 American Cancer Society.