Trastuzumab: unusual responses and toxicities

Managing the oncologic patient with suspected pneumonia in the intensive care unit

INTRODUCTION

Solid cancer patients are frequently admitted in intensive care units for critical events. Improving survival rates in this setting is considered an achievable goal today. Respiratory failure is the main reason for admission, representing a primary target for research.

AREAS COVERED

This review presents a diagnostic and therapeutic algorithm for pneumonia and other severe respiratory events in the solid cancer population. It aims to increase awareness of the risk factors and the different etiologies in this changing scenario in which neutropenia no longer seems to be a decisive factor in poor outcome. Bacterial pneumonia is the leading cause, but opportunistic diseases and non-infectious etiologies, especially unexpected adverse effects of radiation, biological drugs and monoclonal antibodies, are becoming increasingly frequent. Options for respiratory support and diagnostics are discussed and indications for antibiotics in the management of pneumonia are detailed. Expert commentary: Prompt initiation of critical care to facilitate optimal decision-making in the management of respiratory failure, early etiological assessment and appropriate antibiotic therapy are cornerstones in management of severe pneumonia in oncologic patients.

An overview of drug development for metastatic breast cancer

The prevalence of breast cancer is increasing as more women are living with the disease. Outcomes have improved as a result of progress in all major aspects of multidisciplinary care. These include surgery, radiotherapy, hormonal therapy, chemotherapy and newer targeted drugs. Two aspects merit particular attention here. First, there is an understanding now that cancer is a heterogenous disease and a 'one-size-fits-all' approach is becoming redundant, albeit slowly. Second, basic science and an appreciation of cellular molecular targets in those different types of breast cancer is being translated into the clinic and has led to the development of exciting new drugs for both triple negative and HER2-positive relapsed disease. An improved understanding of endocrine resistance remains an unmet need in drug development and here, it appears worthwhile to adopt less conventional approaches. Better trial design with a focus on biomarkers should lower barriers to regulatory approval as well as increase cost effectiveness.

Analysis of kinase gene expression patterns across 5681 human tissue samples reveals functional genomic taxonomy of the kinome

Kinases play key roles in cell signaling and represent major targets for drug development, but the regulation of their activation and their associations with health and disease have not been systematically analyzed. Here, we carried out a bioinformatic analysis of the expression levels of 459 human kinase genes in 5681 samples consisting of 44 healthy and 55 malignant human tissues. Defining the tissues where the kinase genes were transcriptionally active led to a functional genomic taxonomy of the kinome and a classification of human tissues and disease types based on the similarity of their kinome gene expression. The co-expression network around each of the kinase genes was defined in order to determine the functional context, i.e. the biological processes that were active in the cells and tissues where the kinase gene was expressed. Strong associations for individual kinases were found for mitosis (69 genes, including AURKA and BUB1), cell cycle control (73 genes, including PLK1 and AURKB), DNA repair (49 genes, including CHEK1 and ATR), immune response (72 genes, including MATK), neuronal (131 genes, including PRKCE) and muscular (72 genes, including MYLK2) functions. We then analyzed which kinase genes gain or lose transcriptional activity in the development of prostate and lung cancers and elucidated the functional associations of individual cancer associated kinase genes. In summary, we report here a systematic classification of kinases based on the bioinformatic analysis of their expression in human tissues and diseases, as well as grouping of tissues and tumor types according to the similarity of their kinome transcription.