Gene profiling for the prediction of Tumor Response to treatment:The case of Immunotherapy”

Cancer is a heterogeneous disease in most respects, including its cellularity, different genetic alterations and diverse clinical behaviors. The combinatorial origin, the heterogeneity of malignant cells, and the variable host background produce multiple tumor subclasses. Many analytical methods have been used to study human tumors and to classify them into homogeneous groups that can predict clinical behavior. Currently, cancer classifications are principally based on clinical and histomorphologic features that only partially reflect this heterogeneity, reducing the probability of the most appropriate diagnostic, prognostic and therapeutic strategy for each patient. Furthermore, virtually all current anticancer agents do not differentiate between cancerous and normal cells, resulting in sometimes disastrous toxicity and an inconstant efficacy. The development of innovative drugs that selectively target cancer cells while sparing normal tissues is very promising and underscores the importance of dissecting the cascade of molecular events that underlie cancer development, progression and sensitivity to antineoplastic agents. Since these phenomena are sustained by the derangement of multiple genes, biotechnological tools allowing the simultaneous study of hundreds or thousands of molecular targets are greatly welcome and provide investigators with a unique opportunity to decipher the many enigmas that surround cell physiology and disease. Over the last decade– prompted also by the sequencing of the human genome–investigators have devised several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and DNA arrays. The availability of such large amounts of information has shifted the attention of scientists towards a non-reductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, DNA arrays have become prominent because they are easier to use, do not require large-scale DNA sequencing, and allow the parallel quantification of thousands of genes from multiple samples. Hopefully, by integrating this powerful analytic tool with other high throughput techniques, such as tissue microarray and proteomics, investigators will be able to comprehensively describe the molecular portrait of the biological phenomena underlying tumor pathogenesis, aggressiveness and response to therapy. DNA array technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor: accordingly, it is of paramount importance for both researchers and clinicians to know the principles underlying this laboratory tool in order to critically appreciate the results originating from this biotechnology. In the present book, we describe the main features of microarray technology– from DNA array construction to data analysis–and discuss its key applications by reviewing some of the most interesting results already achieved in the field of oncology.