Introduction

Article Outline

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It is almost a truism to say that not all patients will react the same way to radiotherapy. For patients with ostensibly identical tumor characteristics (location, pathology, size, and stage) and given the same treatment schedule, some patients will be cured and some will not. In addition, some patients will suffer adverse normal tissue reactions and some will not. Knowing the probable outcome to a given treatment for a given patient with a given tumor would certainly help. It would allow other treatments, or other doses, to be considered, avoiding ineffective treatments with their associated toxicities. This issue focuses on predictive assays and their biological and clinical impact. Special attention is given to new assays that may be applied in the near future for cancer patients treated with radiotherapy and combined modalities.

The ability to predict outcome to a given treatment is, of course, not the only thing the treating physician would like to know. If the prediction result says a particular tumor is resistant, it would also be very useful to know what other treatments would be effective against this tumor. This is the next step after prediction and will require further detailed biological knowledge of the tumor and patient. However, the development of robust and accurate prediction techniques for current radiotherapy, for radiotherapy combined with chemotherapy, and for radiotherapy combined with new molecular-targeted drugs remains an important and worthwhile goal. Furthermore, such predictive assays, in addition to being useful in practice for the clinician, can also give insights into causes of failure, leading to design of more effective interventions in the future. They can, therefore, lead to that important second step after prediction.

Methods available for understanding and predicting biological behavior have undergone a revolution in the last decade or two. In the past, studies to predict response to therapy involved measuring one, or at most a few, parameters. These included attempts to assess intrinsic radiosensitivity, repopulating ability and hypoxia, 3 factors known to influence outcome after radiotherapy and other treatment modalities. Such assays included survival after 2 Gy of cells extracted from tumors; proliferation rate with exogenous or endogenous markers; hypoxia using electrodes or endogenous markers; and indirect assays such as p53, apoptosis, DNA breaks, and a host of others but all measured as single parameters in different studies. These have provided some useful proof-of-principle data, but none have evolved into useful and routine clinical predictors.

One reason for the relative lack of success is the complexity of tumor biology, with many factors contributing to the success or failure of the treatment, the relative contributions of each factor varying considerably between tumors. Measurement of a single parameter is, therefore, unlikely to succeed as a robust predictor. Unfortunately, it was not possible or practical in the past to measure multiple parameters. This has now changed. Genome-wide methods are now available for studies at the DNA and RNA levels, and methods to look at thousands if not tens of thousands of proteins are making rapid progress. We are now in the era of genomics, transcriptomics, proteomics, metabolomics, and others. The problem is now a different one—how to cope with the mass of information. But this is a “luxurious” problem; the data can be obtained, we just need to know how best to distinguish signal from noise.

In this issue, it was not possible to cover all the exciting research currently ongoing in the area of outcome prediction. However, we have selected experts to describe the progress in what we believe are some of the main areas. These include the prediction of both tumor and normal tissue response. Topics include single nucleotide polymorphisms, comparative genomic hybridization, tissue microarrays, expression microarrays, proteomics, and functional imaging. There is a particular focus on new possibilities that may be applied in the future for cancer patients treated with radiotherapy and combined modalities. Each article gives an overview of the current preclinical and clinical status of the methods.

The first article by Bentzen is a critical reflection on what we have learned from our failures with classical cellular predictive assays, leading to proposals as to how we can prevent the same mistakes being made with the new high-throughput assays in radiation oncology. While he describes the opportunities of these new assays, he also provides some key concepts in the analysis of high-throughput predictive and prognostic markers.

Costa and coworkers then describe the comparative genomic hybridization technique, which depicts copy number changes in genomic DNA in normal or tumor samples. It has already provided useful information on biological changes in a variety of tumor types and in the near future is likely to lead to better cancer classification, prognosis, and outcome prediction.

How to obtain more and faster prognostic and predictive information from tumor tissue blocks is described by Voduc and coworkers. They discuss the status, challenges, and potential of tissue microarrays, a semi-high-throughput technology for the analysis of molecular markers, allowing up to thousands of small tissue samples to be studied simultaneously by immunohistochemistry and other methods. This is likely to become an increasingly used method for prediction in the future.

The ability of microarray analyses to study the expression of many thousands of genes in a single experiment is highlighted by Nuyten and van de Vijver. They use breast cancer as an example of how this powerful technique can predict survival and, as a consequence, lead to treatment adaptation based on individual tumor characteristics. This has already seen routine use as a laboratory tool and will undoubtedly see more use in the clinic for prognosis and prediction in the near future.

In addition to the information being gained at the DNA and RNA levels, techniques to look at proteins on a larger and ever more sophisticated scale are making rapid progress. Wouters describes the technological developments of mass spectrometry-based proteomics and how these techniques are and can be used in the clinic. He also stresses the importance of blood and tumor banking with attention paid to protocols that will facilitate proteomics experiments in the future.

With all the efforts on what can be learned from analyzing tumor biopsies, we still should not forget that the individual radiation sensitivity of normal tissues varies significantly from patient to patient. Alsner and coworkers focus their attention on the genetic component of radiation-induced morbidity and the potential of single nucleotide polymorphisms to detect which patients are likely to develop severe adverse reactions and who will not. They also discuss how this powerful genome-wide method can best be used in future studies, what to do and what not to do.

Finally, Nimmagadda and coworkers describe the exciting progress in targeted molecular imaging including allowing visualization of in vivo metabolism, proliferation, hypoxia, apoptosis, angiogenesis, receptors, and even gene expression. They describe several novel and sophisticated strategies for imaging reporters. They also discuss how imaging can not only help in understanding tumor biology but also help in treatment planning and treatment monitoring.

All these new exciting methods will undoubtedly lead to a better understanding of the tumor behavior and to the discovery of new targets. Such developments will benefit from large-scale international cooperation to expedite rapid introduction into the clinic. As an important spin-off, progress in developing predictive and prognostic markers is also likely to result in a major reduction in the number of patients required for clinical trials aimed at testing new agents for a specific target. It will allow selection of appropriate patients with specific markers related to a certain target, therefore leading to a much faster validation of new treatment approaches.

We believe that this issue of Seminars in Radiation Oncology provides an excellent overview of important current areas in the predictive assay field, which we hope will stimulate further preclinical and clinical development to the eventual benefit of the cancer patient.