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Background: Cyclin pathway gene alterations are frequent in urothelial tumors and may co-exist with other important aberrations, leading to therapeutic opportunities. We characterized the landscape of cyclin gene alterations in urothelial and non-urothelial urinary tract (UT) malignancies. Patients and Methods: Overall, 6842 urothelial and 897 non-urothelial UT cancers were analyzed (hybrid-capture-based comprehensive genomic profile (Foundation Medicine)). Alteration frequency in cyclin-sensitizing and -resistance genes, and co-occurrence with fibroblast growth factor receptor (FGFR) gene abnormalities were evaluated. Results: Cyclin-activating gene alterations were detected in 47.3% of urothelial and 37.9% of non-urothelial UT cancers. Frequency varied by histology and tumor site. CDKN2A and CDKN2B loss were the most frequent alterations in urothelial tumors (present in 38.5% and 30.4% of patients, respectively). Both genes were less frequently altered in adenocarcinomas (15.2% and 8.9%), but commonly altered in squamous cell carcinomas (74.4% and 39%). Tumors of neuroendocrine origin were relatively silent in activating cyclin alterations, but frequently displayed Rb1 alterations (86% and 83.7% of neuroendocrines and small cell carcinomas). Urachal tumors (n = 79) presented a distinct landscape of cyclin alterations relative to other UT cancers, with less frequent alterations overall. FGF/FGFR genes were altered in 34.9% of urothelial (22.1% in FGFR3), and 19.4% of non-urothelial urinary tract tumors (6.8% FGFR3). Cyclin-activating alterations frequently co-occurred with FGF/FGFR alterations but were in general mutually exclusively with cyclin resistance alterations (RB1/CCNE1). Conclusions: Cyclin pathway activating alterations are common in urinary tract tumors, but frequency varies with histology and tumors sites. Co-occurrence of cyclin and FGFR pathway alterations may inform therapeutic opportunities. Cyclin gene alterations are common in urinary tract tumors and often co-occur with FGF/FGFR aberrations. This article analyzes molecular alterations affecting the cyclin signals and potential co-drivers in key pathways. Rare subtypes of urinary tract cancers are included to identify unique alteration patterns and potential therapeutic opportunities. [ABSTRACT FROM AUTHOR]

The cyclin pathway may confer resistance to standard treatments but also offer novel therapeutic opportunities in prostate cancer. Herein, we analyzed prostate cancer samples (majority metastatic) using comprehensive genomic profiling performed by next‐generation sequencing (315 genes, >500× coverage) for alterations in activating and sensitizing cyclin genes (CDK4 amplification, CDK6 amplification, CCND1, CCND2, CCND3, CDKN2B [loss], CDKN2A [loss], SMARCB1), androgen receptor (AR) gene, and coalterations in genes leading to cyclin inhibitor therapeutic resistance (RB1 and CCNE1). Overall, cyclin sensitizing pathway genomic abnormalities were found in 9.7% of the 5,356 tumors. Frequent alterations included CCND1 amplification (4.2%) and CDKN2A and B loss (2.4% each). Alterations in possible resistance genes, RB1 and CCNE1, were detected in 9.7% (up to 54.6% in neuroendocrine) and 1.2% of cases, respectively, whereas AR alterations were seen in 20.9% of tumors (~27.3% in anaplastic). Cyclin sensitizing alterations were also more frequently associated with concomitant AR alterations. Clinical trials with cyclin inhibitors for prostate cancer are ongoing; thus, characterization of the landscape of cyclin pathway genomic alterations is needed. To that end, this article analyzes prostate cancer samples using comprehensive genomic profiling performed by next‐generation sequencing. [ABSTRACT FROM AUTHOR]

Background: We describe the landscape of cyclin and interactive gene pathway alterations in 190,247 solid tumors. Methods: Using comprehensive genomic profiling (315 genes, >500× coverage), samples were analyzed for alterations in activating/sensitizing cyclin genes (CDK4 amplification, CDK6 amplification, CCND1, CCND2, CCND3, CDKN2B [loss], CDKN2A [loss], SMARCB1), hormone genes (estrogen receptor 1 [ESR1], androgen receptor [AR]), and co‐alterations in genes leading to cyclin inhibitor therapeutic resistance (RB1 and CCNE1). Results: Alterations in at least one cyclin activating/sensitizing gene occurred in 24% of malignancies. Tumors that frequently harbored at least one cyclin alteration were brain gliomas (47.1%), esophageal (40.3%) and bladder cancer (37.9%), and mesotheliomas (37.9%). The most frequent alterations included CDKN2A (13.9%) and CDKN2B loss (12.5%). Examples of unique patterns of alterations included CCND1 amplification in breast cancer (17.3%); CDK4 alterations in sarcomas (12%); CCND2 in testicular cancer (23.4%), and SMARCB1 mutations in kidney cancer (3% overall, 90% in malignant rhabdoid tumors). Alterations in resistance genes RB1 and CCNE1 affected 7.2% and 3.6% of samples. Co‐occurrence analysis demonstrated a lower likelihood of concomitant versus isolated alterations in cyclin activating/sensitizing and resistance genes (odds ratio [OR], 0.35; p <.001), except in colorectal, cervical, and small intestine cancers. AR and cyclin activating/sensitizing alterations in prostate cancer co‐occurred more frequently (vs. AR alterations and wild‐type cyclin activating/sensitizing alterations) (OR, 1.79; p <.001) as did ESR1 and cyclin activating/sensitizing alterations in breast (OR, 1.62; p <.001) and cervical cancer (OR, 4.08; p =.04) (vs. ESR1 and cyclin wild‐type activating/sensitizing alterations). Conclusion: Cyclin pathway alterations vary according to tumor type/histology, informing opportunities for targeted therapy, including for rare cancers. Implications for Practice: Cyclin pathway genomic abnormalities are frequent in human solid tumors, with substantial variation according to tumor site and histology. Opportunities for targeted therapy emerge with comprehensive profiling of this pathway. This article identifies molecular alterations in genes involved in the cyclin activation/sensitizing pathways and reports coexisting resistance and hormone pathway alterations in 190,247 diverse solid tumors that underwent next‐generation sequencing. [ABSTRACT FROM AUTHOR]

Background: In order to ascertain the impact of a biomarker-based (personalized) strategy, we compared outcomes between US Food and Drug Administration (FDA)-approved cancer treatments that were studied with and without such a selection rationale.Methods: Anticancer agents newly approved (September 1998 to June 2013) were identified at the Drugs@FDA website. Efficacy, treatment-related mortality, and hazard ratios (HRs) for time-to-event endpoints were analyzed and compared in registration trials for these agents. All statistical tests were two-sided.Results: Fifty-eight drugs were included (leading to 57 randomized [32% personalized] and 55 nonrandomized trials [47% personalized], n = 38 104 patients). Trials adopting a personalized strategy more often included targeted (100% vs 65%, P < .001), oral (68% vs 35%, P = .001), and single agents (89% vs 71%, P = .04) and more frequently permitted crossover to experimental treatment (67% vs 28%, P = .009). In randomized registration trials (using a random-effects meta-analysis), personalized therapy arms were associated with higher relative response rate ratios (RRRs, compared with their corresponding control arms) (RRRs = 3.82, 95% confidence interval [CI] = 2.51 to 5.82, vs RRRs = 2.08, 95% CI = 1.76 to 2.47, adjusted P = .03), longer PFS (hazard ratio [HR] = 0.41, 95% CI = 0.33 to 0.51, vs HR = 0.59, 95% CI = 0.53 to 0.65, adjusted P < .001) and a non-statistically significantly longer OS (HR = 0.71, 95% CI = 0.61 to 0.83, vs HR = 0.81, 95% CI = 0.77 to 0.85, adjusted P = .07) compared with nonpersonalized trials. Analysis of experimental arms in all 112 registration trials (randomized and nonrandomized) demonstrated that personalized therapy was associated with higher response rate (48%, 95% CI = 42% to 55%, vs 23%, 95% CI = 20% to 27%, P < .001) and longer PFS (median = 8.3, interquartile range [IQR] = 5 vs 5.5 months, IQR = 5, adjusted P = .002) and OS (median = 19.3, IQR = 17 vs 13.5 months, IQR = 8, Adjusted P = .04). A personalized strategy was an independent predictor of better RR, PFS, and OS, as demonstrated by multilinear regression analysis. Treatment-related mortality rate was similar for personalized and nonpersonalized trials.Conclusions: A biomarker-based approach was safe and associated with improved efficacy outcomes in FDA-approved anticancer agents. [ABSTRACT FROM AUTHOR]

A response from the author of the article "Impact of a Biomarker-Based Strategy on Oncology Drug Development: A Meta-analysis of Clinical Trials Leading to FDA Approval" in the previous issue is presented.