Adjuvant therapy in renal cell carcinoma

We tested the hypothesis that the prediction of renal cancer-specific survival can be improved if traditional predictor variables are used within a prognostic nomogram. Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme. Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates. The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.

Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

Tumor progression depends on sequential events, including a switch to the angiogenic phenotype (i.e., initial recruitment of blood vessels). Failure of a microscopic tumor to complete one or more early steps in this process may lead to delayed clinical manifestation of the cancer. Microscopic human cancers can remain in an asymptomatic, non-detectable, and occult state for the life of a person. Clinical and experimental evidence suggest that human tumors can persist for long periods of time as microscopic lesions that are in a state of dormancy (i.e., not expanding in tumor mass). Because it is well established that tumor growth beyond the size of 1-2 mm is angiogenesis-dependent, we hypothesized that presentation of large tumors is attributed to a switch to the angiogenic phenotype in otherwise microscopic, dormant tumors. Although clinically important, the biology of human tumor dormancy is poorly understood. The development of animal models which recapitulate the clinically observed timing and proportion of dormant tumors which switch to the angiogenic phenotype are reviewed here. The contributing molecular mechanisms involved in the angiogenic switch and different strategies for isolation of both angiogenic and non-angiogenic tumor cell populations from otherwise heterogeneous human tumor cell lines or surgical specimens are also summarized. Several imaging techniques have been utilized for the qualitative and quantitative detection of microscopic tumors in mice and their strengths and limitations are discussed. The animal models employed here permitted further studies of the angiogenic switch. These models also allowed development of an angiogenesis-based panel of blood and urine biomarkers that can be quantified and used to detect microscopic tumors before or during the angiogenic switch. If the information obtained from these animal models is translatable to the clinic, it may be possible in the future to liberate the management of cancer from a dependency on anatomical site years before it becomes symptomatic and detectable.