Essential gene pathways for glioblastoma stem cells: clinical implications for prevention of tumor recurrence.

Glioblastoma (World Health Organization/WHO grade IV) is the most common and most aggressive adult glial tumor. Patients with glioblastoma, despite being treated with gross total resection and post-operative radiation/chemotherapy, will almost always develop tumor recurrence. Glioblastoma stem cells (GSC), a minor subpopulation within the tumor mass, have been recently characterized as tumor-initiating cells and hypothesized to be responsible for post-treatment recurrence because of their enhanced radio-/chemo-resistant phenotype and ability to reconstitute tumors in mouse brains. Genome-wide expression profile analysis uncovered molecular properties of GSC distinct from their differentiated, proliferative progeny that comprise the majority of the tumor mass. In contrast to the hyperproliferative and hyperangiogenic phenotype of glioblastoma tumors, GSC possess neuroectodermal properties and express genes associated with neural stem cells, radial glial cells, and neural crest cells, as well as portray a migratory, quiescent, and undifferentiated phenotype. Thus, cell cycle-targeted radio-chemotherapy, which aims to kill fast-growing tumor cells, may not completely eliminate glioblastoma tumors. To prevent tumor recurrence, a strategy targeting essential gene pathways of GSC must be identified and incorporated into the standard treatment regimen. Identifying intrinsic and extrinsic cues by which GSC maintain stemness properties and sustain both tumorigenesis and anti-apoptotic features may provide new insights into potentially curative strategies for treating brain cancers

Triple positive breast cancer. A distinct subtype?

Breast cancer is a heterogeneous disease, and within the HER-2 positive subtype this is highly exemplified by the presence of substantial phenotypical and clinical heterogeneity, mostly related to hormonal receptor (HR) expression. It is well known how HER-2 positivity is commonly associated with a more aggressive tumor phenotype and decreased overall survival and, moreover, with a reduced benefit from endocrine treatment. Preclinical studies corroborate the role played by functional crosstalks between HER-2 and estrogen receptor (ER) signaling in endocrine resistance and, more recently, the activation of ER signaling is emerging as a possible mechanism of resistance to HER-2 blocking agents. Indeed, HER-2 positive breast cancer heterogeneity has been suggested to underlie the variability of response not only to endocrine treatments, but also to HER-2 blocking agents. Among HER-2 positive tumors, HR status probably defines two distinct subtypes, with dissimilar clinical behavior and different sensitivity to anticancer agents. The triple positive subtype, namely, ER/PgR/Her-2 positive tumors, could be considered the subset which most closely resembles the HER-2 negative/HR positive tumors, with substantial differences in biology and clinical outcome. We argue on whether in this subgroup the "standard" treatment may be considered, in selected cases, i.e., small tumors, low tumor burden, high expression of both hormonal receptors, an overtreatment. This article review the existing literature on biologic and clinical data concerning the HER-2/ER/PgR positive tumors, in an attempt to better define the HER-2 subtypes and to optimize the use of HER-2 targeted agents, chemotherapy and endocrine treatments in the various subsets

Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer‐related mortality. Despite significant advances made in the treatment of other cancers, current chemotherapies offer little survival benefit in this disease. Pancreaticoduodenectomy offers patients the possibility of a cure, but most will die of recurrent or metastatic disease. Hence, preventing metastatic disease in these patients would be of significant benefit. Using principal component analysis (PCA), we identified a LOX/hypoxia signature associated with poor patient survival in resectable patients. We found that LOX expression is upregulated in metastatic tumors from Pdx1‐Cre KrasG12D/+ Trp53R172H/+ (KPC) mice and that inhibition of LOX in these mice suppressed metastasis. Mechanistically, LOX inhibition suppressed both migration and invasion of KPC cells. LOX inhibition also synergized with gemcitabine to kill tumors and significantly prolonged tumor‐free survival in KPC mice with early‐stage tumors. This was associated with stromal alterations, including increased vasculature and decreased fibrillar collagen, and increased infiltration of macrophages and neutrophils into tumors. Therefore, LOX inhibition is able to reverse many of the features that make PDAC inherently refractory to conventional therapies and targeting LOX could improve outcome in surgically resectable disease

Numerically Stable Recurrence Relations for the Communication Hiding Pipelined Conjugate Gradient Method

Pipelined Krylov subspace methods (also referred to as communication-hiding methods) have been proposed in the literature as a scalable alternative to classic Krylov subspace algorithms for iteratively computing the solution to a large linear system in parallel. For symmetric and positive definite system matrices the pipelined Conjugate Gradient method outperforms its classic Conjugate Gradient counterpart on large scale distributed memory hardware by overlapping global communication with essential computations like the matrix-vector product, thus hiding global communication. A well-known drawback of the pipelining technique is the (possibly significant) loss of numerical stability. In this work a numerically stable variant of the pipelined Conjugate Gradient algorithm is presented that avoids the propagation of local rounding errors in the finite precision recurrence relations that construct the Krylov subspace basis. The multi-term recurrence relation for the basis vector is replaced by two-term recurrences, improving stability without increasing the overall computational cost of the algorithm. The proposed modification ensures that the pipelined Conjugate Gradient method is able to attain a highly accurate solution independently of the pipeline length. Numerical experiments demonstrate a combination of excellent parallel performance and improved maximal attainable accuracy for the new pipelined Conjugate Gradient algorithm. This work thus resolves one of the major practical restrictions for the useability of pipelined Krylov subspace methods.Comment: 15 pages, 5 figures, 1 table, 2 algorithm

Focal adhesion kinase: Insight into molecular roles and functions in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Due to the high incidence of post-operative recurrence after current treatments, the identification of new and more effective drugs is required. In previous years, new targetable genes/pathways involved in HCC pathogenesis have been discovered through the help of high-throughput sequencing technologies. Mutations in TP53 and β-catenin genes are the most frequent aberrations in HCC. However, approaches able to reverse the effect of these mutations might be unpredictable. In fact, if the reactivation of proteins, such as p53 in tumours, holds great promise as anticancer therapy, there are studies arguing that chronic activation of these types of molecules may be deleterious. Thus, recently the efforts on potential targets have focused on actionable mutations, such as those occurring in the gene encoding for focal adhesion kinase (FAK). This tyrosine kinase, localized to cellular focal contacts, is over-expressed in a variety of human tumours, including HCC. Moreover, several lines of evidence demonstrated that FAK depletion or inhibition impair in vitro and in vivo HCC growth and metastasis. Here, we provide an overview of FAK expression and activity in the context of tumour biology, discussing the current evidence of its connection with HCC development and progression

Wavemoth -- Fast spherical harmonic transforms by butterfly matrix compression

We present Wavemoth, an experimental open source code for computing scalar spherical harmonic transforms (SHTs). Such transforms are ubiquitous in astronomical data analysis. Our code performs substantially better than existing publicly available codes due to improvements on two fronts. First, the computational core is made more efficient by using small amounts of precomputed data, as well as paying attention to CPU instruction pipelining and cache usage. Second, Wavemoth makes use of a fast and numerically stable algorithm based on compressing a set of linear operators in a precomputation step. The resulting SHT scales as O(L^2 (log L)^2) for the resolution range of practical interest, where L denotes the spherical harmonic truncation degree. For low and medium-range resolutions, Wavemoth tends to be twice as fast as libpsht, which is the current state of the art implementation for the HEALPix grid. At the resolution of the Planck experiment, L ~ 4000, Wavemoth is between three and six times faster than libpsht, depending on the computer architecture and the required precision. Due to the experimental nature of the project, only spherical harmonic synthesis is currently supported, although adding support or spherical harmonic analysis should be trivial.Comment: 13 pages, 6 figures, accepted by ApJ