Prediction of Kidney Allograft Discard Before Procurement: The Kidney Discard Risk Index.

OBJECTIVES: There is an 18.9% discard rate among kidney allografts. Here, we aimed to determine predictors of kidney discard and construct an index to identify high-probability discard kidney allografts prior to procurement. MATERIALS AND METHODS: A total of 102 246 potential kidney allograft donors from the Organ Procurement and Transplantation Network database were used in this analysis. The cohort was randomized into 2 groups. The training set included 67% of the cohort and was used to derive a predictive index for discard that comprised 21 factors identified by univariate and multivariate logistic regression analysis. The validation set included 33% and was used to internally validate the kidney discard risk index. RESULTS: In 77.3% of donors, at least 1 kidney was used for transplant, whereas in 22.7% of donors, both kidneys were discarded. The kidney discard risk index was highly predictive of discard with a C statistic of 0.89 (0.88-0.89). The bottom 10th percentile had a discard rate of 0.73%, whereas the top 10th percentile had a discard rate of 83.65%. The 3 most predictive factors for discard were age, creatinine level, and hepatitis C antibody status. CONCLUSIONS: We identified 21 factors predictive of discard prior to donor procurement and used these to develop a kidney discard risk index with a C statistic of 0.89.

A signal-based method for finding driver modules of breast cancer metastasis to the lung.

Tumor metastasis is mainly caused by somatic genomic alterations (SGAs) that perturb pathways regulating metastasis-relevant activities and thus help the primary tumor to adapt to the new microenvironment. Identifying drivers of metastasis, i.e. SGAs, sheds light on the metastasis mechanism and provides guidance for targeted therapy. In this paper, we introduce a novel method to search for SGAs driving breast cancer metastasis to the lung. First, we search for transcriptomic modules with genes that are differentially expressed in breast cell lines with strong metastatic activities to the lung and co-expressed in a large number of breast tumors. Then, for each transcriptomic module, we search for a set of SGA genes (driver modules) such that genes in each driver module carry a common signal regulating the transcriptomic module. Evaluations indicate that many genes in driver modules are indeed related to metastasis, and our methods have identified many new driver candidates. We further choose two novel metastatic driver genes, BCL2L11 and CDH9, for in vitro verification. The wound healing assay reveals that inhibiting either BCL2L11 or CDH9 will enhance the migration of cell lines, which provides evidence that these two genes are suppressors of tumor metastasis.

An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem.

BACKGROUND: The mutual exclusivity of somatic genome alterations (SGAs), such as somatic mutations and copy number alterations, is an important observation of tumors and is widely used to search for cancer signaling pathways or SGAs related to tumor development. However, one problem with current methods that use mutual exclusivity is that they are not signal-based; another problem is that they use heuristic algorithms to handle the NP-hard problems, which cannot guarantee to find the optimal solutions of their models. METHOD: In this study, we propose a novel signal-based method that utilizes the intrinsic relationship between SGAs on signaling pathways and expression changes of downstream genes regulated by pathways to identify cancer signaling pathways using the mutually exclusive property. We also present a relatively efficient exact algorithm that can guarantee to obtain the optimal solution of the new computational model. RESULTS: We have applied our new model and exact algorithm to the breast cancer data. The results reveal that our new approach increases the capability of finding better solutions in the application of cancer research. Our new exact algorithm has a time complexity of [Formula: see text](Note: Following the recent convention, we use a star * to represent that the polynomial part of the time complexity is neglected), which has solved the NP-hard problem of our model efficiently. CONCLUSION: Our new method and algorithm can discover the true causes behind the phenotypes, such as what SGA events lead to abnormality of the cell cycle or make the cell metastasis lose control in tumors; thus, it identifies the target candidates for precision (or target) therapeutics.