Massion, Pierre P.

Abstract This study assessed the inter-user reproducibility of Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel analytical tool that risk stratifies lung adenocarcinomas (ADCs) according to defined computed tomography (CT) structural characteristics. CANARY detects nine distinct voxel signatures in ADCs based on standard CT imaging, thereby defining nodule characteristics correlating with patient outcomes, and corresponding to invasion or lepidic growth histologically. A software user segments the borders of each ADC prior to voxel analysis, introducing potential variability into the assessment. While CANARY is a promising method, it requires validation of the analytical variability between users and prediction of accuracy in an independent cohort. To evaluate the reproducibility of CANARY analysis, three independent users who are not part of the CANARY development team segmented and analyzed 50 biopsy-confirmed primary lung ADCs from Vanderbilt University Medical Center. The CT scans of ADCs were selected retrospectively based on the following criteria: ADC-histology proven on biopsy, diagnosed between 2009-2015, less than 3cm in greatest diameter, and stages IA-IV. Users followed a standard operating procedure established at the Mayo Clinic, and were blinded to clinical characteristics and patient outcomes. Results: To measure inter-user variability of ADC voxel composition, the intraclass correlation coefficient (ICC) was calculated based upon the percentage of each voxel subtype within the whole ADC. An ICC of 1 reflects high reproducibility between users. Amongst all 50 ADCs, the average ICC for all nine voxel types was 0.828 (95% CI 0.76, 0.895). The ICC of the four voxel types associated with invasive features on CT was 0.865 (95% CI 0.805, 0.924). ICCs were also calculated using a logarithmic transformation for data normalization, generating an ICC of 0.745 (95% CI 0.663, 0.826) for all nine voxel types, and an ICC of 0.995 (95% CI 0.993, 0.997) for the four voxel types associated with invasion. Conclusions: (1) CANARY analysis of lung ADC voxel signatures appears to be reproducible amongst users, making it a reliable tool for the evaluation of ADC voxel density. (2) Correlation of invasive signatures associated with more aggressive ADCs was nearly perfect amongst users. Additional validation metrics for CANARY with larger datasets are being evaluated, including the accuracy of tumor prognostic predictions between users and analysis of ADC datasets from other institutions. Citation Format: Erica C. Nakajima, Michael P. Frankland, Tucker Johnson, Sanja L. Antic, Ronald A. Karwoski, Bennett Landman, Heidi Chen, Ronald C. Walker, Brian J. Bartholmai, Tobias Peikert, Srinivasan Rajagopalan, Pierre P. Massion, Fabien Maldonado. Assessing the reproducibility of computer-aided nodule assessment and risk yield (CANARY) method to characterize lung adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3723. doi:10.1158/1538-7445.AM2017-3723

Abstract Background: Current management of lung nodules is complicated by nontherapeutic resections and missed chances for cure. We hypothesized that a serum proteomic signature may add diagnostic information beyond that provided by combined clinical and radiographic data. Methods: Cohort A included 265 and cohort B 114 patients. Using multivariable logistic regression analysis we calculated the area under the receiver operating characteristic curve (AUC) and quantified the added value of a previously described serum proteomic signature beyond clinical and radiographic risk factors for predicting lung cancer using the integration discrimination improvement (IDI) index. Results: The average computed tomography (CT) measured nodule size in cohorts A and B was 37.83 versus 23.15 mm among patients with lung cancer and 15.82 versus 17.18 mm among those without, respectively. In cohort A, the AUC increased from 0.68 to 0.86 after adding chest CT imaging variables to the clinical results, but the proteomic signature did not provide meaningful added value. In contrast, in cohort B, the AUC improved from 0.46 with clinical data alone to 0.61 when combined with chest CT imaging data and to 0.69 after adding the proteomic signature (IDI of 20% P = 0.0003). In addition, in a subgroup of 100 nodules between 5 and 20 mm in diameter, the proteomic signature added value with an IDI of 15% (P ≤ 0.0001). Conclusions: The results show that this serum proteomic biomarker signature may add value to the clinical and chest CT evaluation of indeterminate lung nodules. Impact: This study suggests a possible role of a blood biomarker in the evaluation of indeterminate lung nodules. Cancer Epidemiol Biomarkers Prev; 21(5); 786–92. ©2012 AACR.

Abstract The phosphatidylinositol 3-kinases (PI3K) are a family of enzymes that relay important cellular growth control signals. Recently, a large-scale mutational analysis of eight PI3K and eight PI3K-like genes revealed somatic mutations in PIK3CA, which encodes the p110α catalytic subunit of class IA PI3K, in several types of cancer, including glioblastoma multiforme. In that report, 4 of 15 (27%) glioblastomas contained potentially oncogenic PIK3CA mutations. Subsequent studies, however, showed a significantly lower mutation rate ranging from 0% to 7%. Given this disparity and to address the relation of patient age to mutation frequency, we examined 10 exons of PIK3CA in 73 glioblastoma samples by PCR amplification followed by direct DNA sequencing. Overall, PIK3CA mutations were found in 11 (15%) samples, including several novel mutations. PIK3CA mutations were distributed in all sample types, with 18%, 9%, and 13% of primary tumors, xenografts, and cell lines containing mutations, respectively. Of the primary tumors, PIK3CA mutations were identified in 21% and 17% of pediatric and adult samples, respectively. No evidence of PIK3CA gene amplification was detected by quantitative real-time PCR in any of the samples. This study confirms that PIK3CA mutations occur in a significant number of human glioblastomas, further indicating that therapeutic targeting of this pathway in glioblastomas is of value. Moreover, this is the first study showing PIK3CA mutations in pediatric glioblastomas, thus providing a molecular target in this important pediatric malignancy. (Mol Cancer Res 2006;4(10):709–14)