Citations

Abstract To identify the genetic factors that influence overall survival in never smokers who have non–small cell lung carcinoma (NSCLC), we conducted a consistency meta-analysis study using genome-wide association approaches for overall survival in 327 never smoker patients with NSCLC from The University of Texas MD Anderson Cancer Center (Houston, TX) and 293 cases from the Mayo Clinic (Rochester, MN). We then conducted a two-pronged validation of the top 25 variants that included additional validation in 1,256 patients with NSCLC from Taiwan and assessment of expression quantitative trait loci (eQTL) and differential expression of genes surrounding the top loci in 70 tumors and matched normal tissues. A total of 94 loci were significant for overall survival in both MD Anderson and Mayo studies in the consistency meta-analysis phase, with the top 25 variants reaching a P value of 10−6. Two variants of these 25 were also significant in the Taiwanese population: rs6901416 [HR, 1.44; 95% confidence interval (CI), 1.01–2.06] and rs10766739 (HR, 1.23; 95%CI, 1.00–1.51). These loci resulted in a reduction of median survival time of at least eight and five months in three populations, respectively. An additional six variants (rs4237904, rs7976914, rs4970833, rs954785, rs485411, and rs10906104) were validated through eQTL analysis that identified significant correlations with expression levels of six genes (LEMD3, TMBIM, ATXN7L2, SHE, ITIH2, and NUDT5, respectively) in normal lung tissue. These genes were also significantly differentially expressed between the tumor and normal lung tissue. These findings identify several novel, candidate prognostic markers for NSCLC in never smokers, with eQTL analysis suggesting a potential biologic mechanism for a subset of these observed associations. Cancer Res; 73(13); 4028–38. ©2013 AACR.

Abstract Introduction: Multivariable models that predict biochemical recurrence (BCR) can indicate patients at higher risk for developing clinical progression after prostatectomy. However, most patients that experience BCR do not metastasize or die from prostate cancer, making BCR an unreliable surrogate endpoint. In this study we compared the clinical and genomic characteristics of prostatectomy patients in order to better delineate the relationship between BCR and aggressive prostate cancer. Methods: Postoperative patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but didn't have progressive disease as evidenced by bone or CT scans); and CP (clinical progression with BCR plus positive CT or bone scans). Patient specimens were from the Mayo Clinic Radical Prostatectomy Registry, selected using a nested case-control design with 14 years median follow-up. RNA expression levels from FFPE tumor specimens were measured with 1.4 million feature whole-transcriptome oligonucleotide microarrays on 106 matching patients from each outcome group (total 318 patients). We analyzed the clinical covariates of the outcome groups in a pairwise manner (NED vs. BCR-only; NED vs. CP; CP vs. BCR-only), using a conditional logistic model in univariable and multivariable analyses. We performed feature reduction for the genomic data set using Kruskal-Wallis tests and regularized logistic regression. Principal Component Analysis (PCA) was used to quantify the variance of the genomic data with respect to the patient outcome groups. Results: Our initial hypothesis was that all three outcome groups would have different clinical and genomic characteristics. Univariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only groups. Multivariable analysis of the clinical variables also failed to show statistically significant differences between NED and BCR-only groups. Among the clinical variables only pathological Gleason score was statistically significant when comparing the BCR-only vs. CP and NED vs. CP groups. Genomic analysis found 363 coding and non-coding features that passed thresholds for differential expression between the three outcome groups. PCA established that the primary source of variance in the genomic data stemmed from the separation of CP from NED and BCR-only patients; whereas only a small amount of variance separates NED and BCR-only groups. The 363 features were further compared in the three outcome groups using Wilcoxon tests, adjusting for false-discovery using Benjamini-Hochberg. At a cutoff of p < 0.001 there were no features differentially expressed between NED and BCR-only, while 52 and 34 statistically significant features were found in the NED vs. CP and CP vs. BCR-only comparisons, respectively. Conclusion: Considering the clinical and genomic results together, we find that: 1) Patients that developed clinical progression were significantly different from those that did not, irrespective of whether they experienced BCR or not; 2) the NED and BCR-only groups were statistically very similar to each other. We therefore conclude that BCR may not be the most appropriate endpoint for predictive models that attempt to stratify patients at risk for progression. This abstract is also presented as Poster B44. Citation Format: Anamaria Crisan, Elai Davicion, R. Jeffery Karnes, Robert B. Jenkins, Mercedeh H. Ghadessi, Christine Buerki, Eric J. Bergstrahl, Nicholas Erho, Ismael A. Vergara, Karla V. Ballman, Peter C. Black, Timothy J. Triche. Biochemical recurrence is not a definitive surrogate endpoint for development of clinically useful predictive models for post-prostatectomy patients [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr PR4.

Abstract Motivations: Gleason score is the most prognostic variable in prostate cancer pathology. Patients with high Gleason (8+) are the most likely to experience biochemical recurrence (BCR), clinical progression (CP) and prostate-cancer specific mortality (PCSM), whereas patients with Gleason ≤6 are expected to have excellent long-term outcomes. However, the majority of prostate cancer patients are diagnosed with Gleason score 7, a heterogeneous group in terms of patient outcomes. In the absence of adverse pathological factors (APF, such as preoperative PSA, SM+, ECE and SVI) most Gleason 7 patients will have excellent outcomes. In contrast, Gleason 7 patients with APF are considered at high-risk for recurrence and therefore are candidates for adjuvant therapy. In this study, we have investigated whether a classifier based on differentially expressed genomic features could be used to improve prognostication of Gleason 7 patients, independent of APF. Methods: Two whole-transcriptome (1.4 million feature) oligonucleotide microarray expression datasets from radical prostatectomy specimens were analyzed. The first one (GEO accession: GSE21032) consisted of 125 fresh frozen (FF) samples from the MSKCC Oncogenome Project and the second dataset had 545 formalin-fixed-paraffin- embedded (FFPE) samples from the Mayo Clinic tumor registry. For genomic feature selection, differential expression in the FF dataset between Gleason ≤6 (n=40) and ≥8 (n=15) was assessed with false discovery rate adjusted t-tests (p<0.05). Next, a k-nearest neighbor (KNN) classifier was trained and tuned using the selected features to predict clinical progression (i.e., positive bone or CT scans after BCR) in the FFPE dataset, again using samples with Gleason score ≤6 (n=63) and ≥8 (n=211). The KNN classifier was evaluated on Gleason score 7 (n=209) FFPE samples (not used for training) using multivariable logistic regression. Results: The KNN classifier segregated the Gleason 7 samples into high and low risk CP groups. The KNN odds ratios were 2.9 (p<0.005), 2.1 (p<0.05) and 2.8 (p<0.05) for BCR, CP and PCSM end-points, respectively when comparing high and low risk groups. Multivariable analysis revealed that the KNN classifier was independently prognostic. Conclusions: A KNN classifier developed using differentially expressed genomic features significantly stratified Gleason score 7 patients for several clinically relevant endpoints. Future validation studies will address the utility of this classifier in predicting pathological Gleason score from biopsy specimens, which may be particularly useful for evaluation of biopsy Gleason 6 cases. In these patients, deferring prostatectomy in favour of active surveillance is an appealing treatment option unless the ‘true’ Gleason of the index lesion is in fact a Gleason 7 or higher, where immediate surgery is likely a life-saving procedure. Citation Format: Nicholas Erho, Thomas Sierocinski, Elai Davicioni, Robert B. Jenkins, Mercedeh Gadhessi, Christine Buerki, Ismael A. Vergara, Anamaria Crisan, Zaid Haddad, Benedikt Zimmermann, Anirban Mitra, Timothy J. Triche. Identification of biomarkers for stratification of Gleason score 7 patients using whole-transcriptome expression profiling [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr B46.

Research in quantitative whole breast ultrasound imaging has been developing rapidly. Recently, we published results from 2D transmission inverse scattering algorithms, based on optimization, incorporating diffraction, refraction, and limited multiple scattering effects, using data collected from an early prototype, which showed the feasibility of high resolution quantitative imaging of the breast tissue speed and attenuation, and concomitant refraction corrected reflection imaging. However, artifact problems in speed and attenuation result from the 2D algorithms and the data characteristics. The reflection algorithm uses the speed map to model refractive effects of rays, so these artifacts are unacceptable. The 3D inverse scattering algorithm presented here, using data from a new prototype, overcomes most of these artifacts. We then use a 3D refraction corrected 360° compounded reflection algorithm for high resolution speckle free reflection images. We discuss the transmission and reflection algorithms and the advanced scanner used to collect the data, as well as initial clinical results from the Mayo Clinic, Breast Cancer Imaging Center, Orange County, and the University California, San Diego. We show examples of cysts, fibroadenomas, calcifications, cancers, and DCIS, in dense, fatty, and average breast tissue, and compare these with hand-held ultrasound, MRI, and mammography, where available.