Point mutations of protein kinases and individualised cancer therapy

The Effect of CacyBP/SIP on the Phosphorylation of ERK1/2 and p38 Kinases in Clear Cell Renal Cell Carcinoma

The prognosis for patients with RCC is very poor because this cancer is diagnosed mainly in the metastatic stage and is resistant to radio- and chemotherapy. According to recent research, CacyBP/SIP exhibits phosphatase activity against MAPK and may be involved in many cellular processes. This function has not been studied in RCC so far, so we decided to test whether CacyBP/SIP has phosphatase function against ERK1/2 and p38 in high-grade clear cell RCC. The research material consisted of fragments of clear cell RCC, whereas the comparative material consisted of the adjacent normal tissues. Immunohistochemistry and qRT-PCR were used to identify the expression of CacyBP/SIP, ERK1/2, and p38. The studies showed an increase in immunoreactivity and gene expression of the parameters examined in clear cell RCC compared with normal tissues. Only in the case of ERK1/2 was it shown that the expression of the MAPK3 gene was downregulated and the MAPK1 gene was higher in clear cell RCC. These studies demonstrated that CacyBP/SIP lacked phosphatase function against ERK1/2 and p38 in high-grade clear cell RCC. Further research is needed because a better understanding of the role of CacyBP/SIP and MAPK offers hope for the treatment of urological cancer.

Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.

Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics

Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein-gene relationships as well as induces sparsity in both protein-gene and protein-survival relationships, to select ge-nomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAV-IOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers.

Thyroid cancer has a small impact on patient–partner relationships and their frequency of sexual activity

Objective:

This cross-sectional survey examined changes in perceived relationships and sexual activity in a sample of thyroid cancer patients and their partners, taking into account sociodemographic and disease-related variables, as well as such outcome measures as anxiety, depression, fatigue, and quality of life (QoL).

Method:

A total of 38 patients with thyroid cancer who were being treated at the department of nuclear medicine in Zürich or Lucerne over the preceding seven years, as well as their partners, completed questionnaires about the quality of their relationships (RQ), about perceptions of changes in their relationships, and about their frequency of sexual activity. They also filled out prevalidated questionnaires related to anxiety, depression, fatigue, and QoL.

Results:

Some 17 patients (44.7%) and 16 partners (42.1 %) reported that the cancer diagnosis had changed their relationships. Of these, 10 (26.3%) patients and 9 (23.7%) partners reported positive changes only, while 7 patients (18.4%) and 7 partners (18.4%) reported mixed or negative changes. A perceived mixed/negative relationship change was associated with increased depression and lower RQ in patients and partners, as well as with increased anxiety in patients. While the frequency of sexual activity only changed in roughly half of patients and partners (16 patients [42.1%] and 20 partners [52.6%]), increased sexual activity was associated with lower physical QoL scores and a higher depression score than in counterparts who reported no change.

Significance of Results:

Compared to other cancer sites, in our sample thyroid cancer had a relatively small impact on patient–partner relationships and levels of intimacy. We found that screening patients and their partners with a simple question—“Did the diagnosis of cancer change your relationship?”—can lead to early detection of couples who are potentially at risk for perceived negative relationship changes and can facilitate timely psychosocial referral for couple's therapy.

Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics

It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct simultaneous variable selection at the pathway (group level) and the gene (within-group) level. To adapt to the overlapping group structure present in the pathway–gene hierarchy of the data, we developed an overlap-HSVS method that introduces latent partial effect variables that partition the marginal effect of the covariates and corresponding weights for a proportional shrinkage of the partial effects. Combining gene expression data with prior pathway information from the KEGG databases, we identified several gene–pathway combinations that are significantly associated with clinical outcomes of multiple myeloma. Biological discoveries support this relationship for the pathways and the corresponding genes we identified.

BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA

Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that allow for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underlying different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.

Targeted therapy for brain metastases

The prevention and treatment of brain metastases is an increasingly important challenge in oncology. Improved understanding of the molecular pathogenesis of a number of cancers has led to the development of highly active targeted therapies for patients with specific oncogenic events. Such therapies include EGFR inhibitors for lung cancer, HER2/neu inhibitors for breast cancer, and BRAF inhibitors for melanoma. This review will discuss the development of these targeted therapy approaches, existing data about their role in the management of brain metastasis, and opportunities and challenges for future research in this critical area.

Genetic interactions in cancer progression and treatment

As cancer cell genomes are unveiled at a breathtaking pace, the genetic principles at play in cancer are emerging in all their complexity, prompting the assessment of classical genetic interaction models. Here, we discuss the implications of these findings for cancer progression and heterogeneity and for the development of new therapeutic approaches.

Targeted therapy for melanoma: a primer

Melanoma is the most aggressive form of skin cancer. Unfortunately, despite recent improvements for some solid tumors, the prevalence and mortality of melanoma continue to increase. The identification of activating mutations in melanoma, combined with a growing appreciation of the different pattern of genetic changes in the anatomically defined melanoma subtypes, has become the focus of a concerted effort to translate these discoveries into personalized therapeutic approaches for this disease. This article reviews the known mutations, amplifications, and deletions in kinase signaling pathways that have been implicated in melanoma; the prevalence of these genetic events in clinicopathologically defined melanoma subtypes; and the results of clinical trials that use targeted therapy approaches to block aberrantly activated pathways resulting from these mutations. The challenges that must be overcome to achieve improved outcomes with targeted therapies in melanoma in the future are also discussed.

Analysis of the genome to personalize therapy for melanoma

The treatment of cancer is being revolutionized by an improved understanding of the genetic events that occur in tumors. Advances in the understanding of the prevalence and patterns of mutations in melanoma have recently led to impressive results in trials of personalized, targeted therapies for this disease. In this review, we will discuss the molecular targets that have been validated clinically, additional genetic events that are candidates for future trials, and the challenges that remain to improve outcomes further in this aggressive disease.

Activity of dasatinib against L576P KIT mutant melanoma: molecular, cellular, and clinical correlates

Point mutations in the KIT receptor tyrosine kinase gene have recently been identified in mucosal, acral lentiginous, and chronically sun-damaged melanomas. We have identified the first human melanoma cell line with an endogenous L576P mutation, the most common KIT mutation in melanoma ( approximately 30-40%). In vitro testing showed that the cell viability of the L576P mutant cell line was not reduced by imatinib, nilotinib, or sorafenib small molecule KIT inhibitors effective in nonmelanoma cells with other KIT mutations. However, the viability of the mutant cells was reduced by dasatinib at concentrations as low as 10 nM (P = 0.004). Molecular modeling studies found that the L576P mutation induces structural changes in KIT that reduce the affinity for imatinib (DeltaDeltaGbind = -2.52 kcal/mol) but not for dasatinib (DeltaDeltaGbind = +0.32 kcal/mol). Two metastatic melanoma patients with the L576P KIT mutation were treated with dasatinib, including one patient previously treated with imatinib. Both patients had marked reduction (>50%) and elimination of tumor F18-fluorodeoxyglucose (FDG)-avidity by positron emission tomography (PET) imaging after dasatinib treatment. These data support the selective inhibitory effect of dasatinib against cells harboring the most common KIT mutation in melanoma, and thus has therapeutic implications for acrallentiginous, chronic sun-damaged, and mucosal melanomas.

siRNA vs. shRNA: similarities and differences

RNA interference (RNAi) is a natural process through which expression of a targeted gene can be knocked down with high specificity and selectivity. Using available technology and bioinformatics investigators will soon be able to identify relevant bio molecular tumor network hubs as potential key targets for knockdown approaches. Methods of mediating the RNAi effect involve small interfering RNA (siRNA), short hairpin RNA (shRNA) and bi-functional shRNA. The simplicity of siRNA manufacturing and transient nature of the effect per dose are optimally suited for certain medical disorders (i.e. viral injections). However, using the endogenous processing machinery, optimized shRNA constructs allow for high potency and sustainable effects using low copy numbers resulting in less off-target effects, particularly if embedded in a miRNA scaffold. Bi-functional design may further enhance potency and safety of RNAi-based therapeutics. Remaining challenges include tumor selective delivery vehicles and more complete evaluation of the scope and scale of off-target effects. This review will compare siRNA, shRNA and bi-functional shRNA.

Fibroblast growth factor receptor 1-transformed mammary epithelial cells are dependent on RSK activity for growth and survival

Fibroblast growth factor receptor 1 (FGFR1) is frequently amplified and highly expressed in lobular carcinomas of the breast. In this report, we evaluated the biological activity of FGFR1 in a wide range of in vitro assays. Conditional activation of FGFR1 in the nontransformed MCF10A human mammary cell line, MCF10A, resulted in cellular transformation marked by epidermal growth factor-independent cell growth, anchorage-independent cell proliferation and survival, loss of cell polarity, and epithelial-to-mesenchymal transition. Interestingly, small-molecule or small interfering RNA inhibition of ribosomal S6 kinase (RSK) activity induced death of the FGFR1-transformed cells, but not of the parental MCF10A cell line. The dependence of FGFR1-transformed cells on RSK activity was further confirmed in cell lines derived from mouse and human lobular carcinomas that possess high FGFR1 activity. Taken together, these results show the transforming activity of FGFR1 in mammary epithelial cells and identify RSK as a critical component of FGFR1 signaling in lobular carcinomas, thus implicating RSK as a candidate therapeutic target in FGFR1-expressing tumors.

The PIK3CA gene as a mutated target for cancer therapy

The development of targeted therapies with true specificity for cancer relies upon exploiting differences between cancerous and normal cells. Genetic and genomic alterations including somatic mutations, translocations, and amplifications have served as recent examples of how such differences can be exploited as effective drug targets. Small molecule inhibitors and monoclonal antibodies directed against the protein products of these genetic anomalies have led to cancer therapies with high specificity and relatively low toxicity. Recently, our group and others have demonstrated that somatic mutations in the PIK3CA gene occur at high frequency in breast and other cancers. Moreover, the majority of mutations occur at three hotspots, making these ideal targets for therapeutic development. Here we review the literature on PIK3CA mutations in cancer, as well as existing data on PIK3CA inhibitors and inhibitors of downstream effectors for potential use as targeted cancer therapeutics.

Challenges in the search for drugs to treat central nervous system disorders

The history of drug discovery spans approximately 200,000 years. For much of this time, the identification of therapeutic agents was empirical, with the shift to a more hypothesis-driven approach occurring in the late 19th century. Since then, the objective has changed from identifying an active drug and its mechanism of action to determining therapeutic potential only after identifying drug-like compounds that interact with a target site. Although the emphasis on target identification, or "targephilia," has yielded novel drugs, overall it appears to have slowed the drug discovery process, especially for compounds used in treating central nervous system (CNS) disorders. This is because the "targephilic" approach requires a good understanding of target physiology and its integration with the target organ, with a hierarchical integration from in vitro cellular and functional tissue studies to animal models that reasonably predict human responses. Because the majority of CNS drugs were discovered empirically, drug discovery in this area appears less amenable to target-based approaches than it seems for other types of therapeutics. Improving the success rate in CNS drug discovery requires a more pharmacometric-based approach, with a renewed emphasis on defining basic CNS function in intact animals and a more systematic in vivo screening of novel structures. Efforts must also be directed toward defining the sites of action of existing CNS drugs to aid in the design of second-generation agents with improved efficacy and safety.

Identification of a novel recepteur d'origine nantais/c-met small-molecule kinase inhibitor with antitumor activity in vivo

Recepteur d'origine nantais (RON) is a receptor tyrosine kinase closely related to c-Met. Both receptors are involved in cell proliferation, migration, and invasion, and there is evidence that both are deregulated in cancer. Receptor overexpression has been most frequently described, but other mechanisms can lead to the oncogenic activation of RON and c-Met. They include activating mutations or gene amplification for c-Met and constitutively active splicing variants for RON. We identified a novel inhibitor of RON and c-Met, compound I, and characterized its in vitro and in vivo activities. Compound I selectively and potently inhibited the kinase activity of RON and c-Met with IC(50)s of 9 and 4 nmol/L, respectively. Compound I inhibited hepatocyte growth factor-mediated and macrophage-stimulating protein-mediated signaling and cell migration in a dose-dependent manner. Compound I was tested in vivo in xenograft models that either were dependent on c-Met or expressed a constitutively active form of RON (RONDelta160 in HT-29). Compound I caused complete tumor growth inhibition in NIH3T3 TPR-Met and U-87 MG xenografts but showed only partial inhibition in HT-29 xenografts. The effect of compound I in HT-29 xenografts is consistent with the expression of the activating b-Raf V600E mutation, which activates the mitogen-activated protein kinase pathway downstream of RON. Importantly, tumor growth inhibition correlated with the inhibition of c-Met-dependent and RON-dependent signaling in tumors. Taken together, our results suggest that a small-molecule dual inhibitor of RON/c-Met has the potential to inhibit tumor growth and could therefore be useful for the treatment of patients with cancers where RON and/or c-Met are activated.

Pancreatic cancer

The past two decades have witnessed an explosion in our understanding of pancreatic cancer, and it is now clear that pancreatic cancer is a disease of inherited (germ-line) and somatic gene mutations. The genes mutated in pancreatic cancer include KRAS2, p16/CDKN2A, TP53, and SMAD4/DPC4, and these are accompanied by a substantial compendium of genomic and transcriptomic alterations that facilitate cell cycle deregulation, cell survival, invasion, and metastases. Pancreatic cancers do not arise de novo, and three distinct precursor lesions have been identified. Experimental models of pancreatic cancer have been developed in genetically engineered mice, which recapitulate the multistep progression of the cognate human disease. Although the putative cell of origin for pancreatic cancer remains elusive, minor populations of cells with stem-like properties have been identified that appear responsible for tumor initiation, metastases, and resistance of pancreatic cancer to conventional therapies.

PIK3CA-activating mutations and chemotherapy sensitivity in stage II-III breast cancer

Introduction

In vitro evidence suggests that PIK3CA (phosphatidylinositol 3-kinase, catalytic, alpha polypeptide) activation may be associated with altered chemotherapy sensitivity in cancer.

Methods

Tumor DNA from 140 patients with stage II–III breast cancer undergoing neoadjuvant chemotherapy was sequenced for PIK3CA mutations on exons 1, 9, and 20. Mutation status was correlated with clinical/pathological parameters and chemotherapy response as (a) pathological complete response (pCR) versus residual cancer or (b) quantitative residual cancer burden (RCB) scores, including stratification for estrogen receptor (ER) expression status, type of chemotherapy, and by exons.

Results

Twenty-three patients (16.4%) harbored a PIK3CA mutation, with 12, 11, and 0 mutations located in exons 9, 20, and 1, respectively. PIK3CA exon 9 mutations were more frequent among node-negative (52% versus 25%; P = 0.012) than node-positive tumors, particularly among ER-positive tumors. pCR rates and RCB scores were similar among patients with the wild-type and mutant PIK3CA genes, even after stratification by ER status, chemotherapy regimen (anthracycline versus anthracycline plus paclitaxel), or exon.

Conclusion

PIK3CA mutations are not associated with altered sensitivity to preoperative anthracycline-based or taxane-based chemotherapies in ER-positive and ER-negative breast tumors. In this study, PIK3CA mutation was associated with a decreased rate of node-positive disease, particularly among ER-positive tumors.

Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer

Mitogen-activated protein kinase (MAPK) cascades are key signaling pathways involved in the regulation of normal cell proliferation, survival and differentiation. Aberrant regulation of MAPK cascades contribute to cancer and other human diseases. In particular, the extracellular signal-regulated kinase (ERK) MAPK pathway has been the subject of intense research scrutiny leading to the development of pharmacologic inhibitors for the treatment of cancer. ERK is a downstream component of an evolutionarily conserved signaling module that is activated by the Raf serine/threonine kinases. Raf activates the MAPK/ERK kinase (MEK)1/2 dual-specificity protein kinases, which then activate ERK1/2. The mutational activation of Raf in human cancers supports the important role of this pathway in human oncogenesis. Additionally, the Raf-MEK-ERK pathway is a key downstream effector of the Ras small GTPase, the most frequently mutated oncogene in human cancers. Finally, Ras is a key downstream effector of the epidermal growth factor receptor (EGFR), which is mutationally activated and/or overexpressed in a wide variety of human cancers. ERK activation also promotes upregulated expression of EGFR ligands, promoting an autocrine growth loop critical for tumor growth. Thus, the EGFR-Ras-Raf-MEK-ERK signaling network has been the subject of intense research and pharmaceutical scrutiny to identify novel target-based approaches for cancer treatment. In this review, we summarize the current status of the different approaches and targets that are under evaluation and development for the therapeutic intervention of this key signaling pathway in human disease.