Exploring phenotype-associated modules in an oral cavity tumor using an integrated framework

Surgical resection and reconstructive techniques using autologous femoral head bone-grafting in treating partial acetabular defects arising from primary pelvic malignant tumors

Background

The aim of this study is to present and evaluate surgical resection and reconstructive techniques using autologous femoral head bone-grafting in treating partial acetabular defects arising from primary pelvic malignant tumors.

Methods

From January 2009 until January 2015, a total of 20 primary pelvic malignancy cases involving the acetabulum were retrospectively investigated. Surgical resections and reconstructions were conducted based on the type of the tumor with custom osteotomy guides and autologous femoral head bone-grafting. In all cases, prosthesis survival period, complication occurrence, and clinical outcomes data were collected and analyzed.

Results

Thirteen male and 7 female patients with an average age of 48 years old (range 23-69 years old) were followed for a median of 69 months (range 48-112 months). Of these cases, 17 included chondrosarcomas and 3 additional patients with a malignant giant cell tumor of bone (MBGCT) as proven by pathology. During follow-up, 3 cases of chondrosarcoma recurred (15%), of which two cases received hemi-pelvic amputation, 1 case of MBGCT relapsed and developed pulmonary metastases. Two cases of acetabular prosthesis with an impending dislocation received closed reduction followed by 6 weeks of hip abduction brace fixation. One case of prosthesis loosening was revised. In another case a deep infection occurred with debridement and prosthesis removal. Musculoskeletal Tumor Society 1993 (MSTS-93) score was utilized to conduct functional evaluation: 13 cases were good, 6 were average and one was poor.

Conclusion

The precision of the osteotomies performed is likely crucial for this type of reconstructive strategy to be successful. The use of custom guides for acetabular osteotomies and femoral head reconstruction can improve functional outcomes with relatively low complications at the intermediate length of follow-up.

Causal inference of gene regulation with subnetwork assembly from genetical genomics data

Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer. Network rewiring clearly characterizes the dynamic regulatory systems of distinct disease states. This study is the first to associate an RXRB-causal network with increased risks of nodal metastasis, tumor relapse, distant metastases and poor survival for oral cancer. Thus, identifying crucial upstream drivers of a signal cascade can facilitate the discovery of potential biomarkers and effective therapeutic targets.

It's the machine that matters: Predicting gene function and phenotype from protein networks

Increasing knowledge about the organization of proteins into complexes, systems, and pathways has led to a flowering of theoretical approaches for exploiting this knowledge in order to better learn the functions of proteins and their roles underlying phenotypic traits and diseases. Much of this body of theory has been developed and tested in model organisms, relying on their relative simplicity and genetic and biochemical tractability to accelerate the research. In this review, we discuss several of the major approaches for computationally integrating proteomics and genomics observations into integrated protein networks, then applying guilt-by-association in these networks in order to identify genes underlying traits. Recent trends in this field include a rising appreciation of the modular network organization of proteins underlying traits or mutational phenotypes, and how to exploit such protein modularity using computational approaches related to the internet search algorithm PageRank. Many protein network-based predictions have recently been experimentally confirmed in yeast, worms, plants, and mice, and several successful approaches in model organisms have been directly translated to analyze human disease, with notable recent applications to glioma and breast cancer prognosis.