Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach

Colorectal cancer is a heterogeneous disease that manifests through diverse clinical scenarios. During many years, our knowledge about the variability of colorectal tumors was limited to the histopathological analysis from which generic classifications associated with different clinical expectations are derived. However, currently we are beginning to understand that under the intense pathological and clinical variability of these tumors there underlies strong genetic and biological heterogeneity. Thus, with the increasing available information of inter-tumor and intra-tumor heterogeneity, the classical pathological approach is being displaced in favor of novel molecular classifications. In the present article, we summarize the most relevant proposals of molecular classifications obtained from the analysis of colorectal tumors using powerful high throughput techniques and devices. We also discuss the role that cancer systems biology may play in the integration and interpretation of the high amount of data generated and the challenges to be addressed in the future development of precision oncology. In addition, we review the current state of implementation of these novel tools in the pathological laboratory and in clinical practice.     

Targeting Signaling Pathways in Epithelial Ovarian Cancer

Ovarian carcinoma (OC) is the most lethal gynecological malignancy. Response to platinum-based chemotherapy is poor in some patients and, thus, current research is focusing on new therapy options. The various histological types of OC are characterized by distinctive molecular genetic alterations that are relevant for ovarian tumorigenesis. The understanding of these molecular pathways is essential for the development of novel therapeutic strategies.

Purpose

We want to give an overview on the molecular genetic changes of the histopathological types of OC and their role as putative therapeutic targets.

In Depth Review of Existing Data

In 2012, the vascular endothelial growth factor (VEGF) inhibitor, bevacizumab, was approved for OC treatment. Bevacizumab has shown promising results as single agent and in combination with conventional chemotherapy, but its target is not distinctive when analyzed before treatment. At present, mammalian target of rapamycin (mTOR) inhibitors, poly-ADP-ribose polymerase (PARP) inhibitors and components of the EGFR pathway are in the focus of clinical research. Interestingly, some phytochemical substances show good synergistic effects when used in combination with chemotherapy.

Conclusion

Ongoing studies of targeted agents in conjunction with chemotherapy will show whether there are alternative options to bevacizumab available for OC patients. Novel targets which can be assessed before therapy to predict efficacy are needed. The assessment of therapeutic targets is continuously improved by molecular pathological analyses on tumor tissue. A careful selection of patients for personalized treatment will help to reduce putative side effects and toxicity.     

Optimal Decision Rules for Biomarker-Based Subgroup Selection for a Targeted Therapy in Oncology

Throughout recent years, there has been a rapidly increasing interest regarding the evaluation of so-called targeted therapies. These therapies are assumed to show a greater benefit in a pre-specified subgroup of patients—commonly identified by a predictive biomarker—as compared to the total patient population of interest. This situation has led to the necessity to develop biostatistical methods allowing an efficient evaluation of such treatments. Among others, adaptive enrichment designs have been proposed as a solution. These designs allow the selection of the most promising patient population based on an efficacy analysis at interim and restricting recruitment to these patients afterwards. As has recently been shown, the performance of the applied interim decision rule in such a design plays a crucial role in ensuring a successful trial. In this work, we investigate the situation when the primary outcome of the trial is a binary variable. Optimal decision rules are derived which incorporate the uncertainty about the treatment effects. These optimal decision rules are evaluated with respect to their performance in an adaptive enrichment design in terms of correct selection probability and power, and are compared to proposed ad hoc decision rules. Our methods are illustrated by means of a clinical trial example.     

Molecular Classification and Pharmacogenetics of Primary Plasma Cell Leukemia: An Initial Approach toward Precision Medicine

Primary plasma cell leukemia (pPCL) is a rare and aggressive variant of multiple myeloma (MM) which may represent a valid model for high-risk MM. This disease is associated with a very poor prognosis, and unfortunately, it has not significantly improved during the last three decades. New high-throughput technologies have allowed a better understanding of the molecular basis of this disease and moved toward risk stratification, providing insights for targeted therapy studies. This knowledge, added to the pharmacogenetic profile of new and old agents in the analysis of efficacy and safety, could contribute to help clinical decisions move toward a precision medicine and a better clinical outcome for these patients. In this review, we describe the available literature concerning the genomic characterization and pharmacogenetics of plasma cell leukemia (PCL).