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Molecular Profiling in the Practice of Radiation Oncology

Molecular Profiling in the Practice of Radiation Oncology

Oncology (Williston Park). 31(7):571–572.

In their article in the current issue of ONCOLOGY, Dr. Williams and colleagues define the relationships between genetic variation and patients’ response to radiation therapy.[1] The article provides an abbreviated but excellent overview of models of clinical response utilizing genomic determinants, presented within the context of prognostic and predictive biomarkers. Genomic complexity is a characteristic common to many tumors, and can serve as an independent predictor of radioresistance and disease progression in some disease sites.[2] Many other examples of genomic biomarkers are expertly discussed within the article. It is also worth noting that the first laboratory-based attempts to develop a predictive or prognostic biomarker of clinical radiosensitivity used the surviving fraction at 2 Gy (SF2) of cells harvested from tumor tissue or from normal tissue samples obtained by clinical biopsies.[3-5] Although effective, measuring the cellular clonogenicity of patient-derived material proved too laborious and impractical for routine clinical adoption. There is also a large body of literature describing the repair of radiation-induced DNA breaks as indicators of radiosensitivity.[6] These types of cell-based radiosensitivity assays never gained widespread adoption. Those assays have given way to the more robust molecular approaches aimed at uncovering genomic signatures of toxicity and radiation response,[7] as Dr. Williams et al discuss. In their article, they suggest that after 2 decades of rapid innovation in DNA-based technologies and sequence databases, genomic biomarkers of radiosensitivity have reached the point of clinical implementation, albeit not for routine use in most cases. The first genomic classifier panels of cellular radiosensitivity used small numbers of gene markers, and in vivo verification was not possible. Moreover, the function of the classifier genes and their role in assessing radiosensitivity was not always clear. These early limitations have now been addressed by the development of newer radiosensitivity signatures.

A strength of the article is the authors’ discussion of the evolution and clinical implementation of radiation sensitivity gene classifier models, with descriptions of both the initial discoveries and, when applicable, their clinical evaluation. They describe the validation of genomic classifiers, beginning with the early work of Amundson and colleagues at the National Cancer Institute,[8] and evolving to the radiosensitivity indices of the more general radiosensitivity index (RSI) from Ahmed et al and Torres-Roca et al[9-11] at the H. Lee Moffitt Cancer Center and the breast-specific radiosensitivity signature from Speers et al at the University of Michigan.[12] At the core of these cell line–based gene expression discovery panels is SF2. The approach taken by Zhao et al at the University of Michigan in developing the prostate cancer Post-Operative Radiation Therapy Outcomes Score,[13] a 24-gene signature prognostic for rates of metastasis after postoperative radiation therapy to the prostate, was to incorporate many of the genes identified in such cell line–based SF2 studies, as well as other genes related to DNA damage and radiation response.

Notably, the RSI model offers considerable promise for routine clinical use; however, it is possible that the RSI will be augmented or supplanted by disease site–specific predictive gene expression signatures. The RSI, which assigns a numerical value that is directly proportional to tumor radioresistance, has been successfully applied to radiosensitivity evaluation in a range of disease sites. The RSI is proving to be a robust genomic classifier with clinical predictive potential.[9,10,14,15] Furthermore, clinical applicability of the RSI appears to extend to metastatic disease, since in one study the RSI was used to define radioresistant and radiosensitive brain metastases across all histologies.[9]

Although Dr. Williams and colleagues review a range of genomic classifiers, other biological and genomic indicators should also be considered as predictive or prognostic biomarkers of radiation response. Biologic tumor variables that define or regulate cellular radiosensitivity (eg, tumor hypoxia, vascularity) may enhance the specificity of the gene classifier panels. Biologic classifiers known to correlate with radiation sensitivity, such as the mutation status of ATM, p53, or RAS, could also increase the functionality of genomic classifier panels, and these were only discussed briefly. The inclusion of other molecular-based parameters, such as small noncoding microRNAs that regulate gene expression at the post-transcriptional level, could also enhance the usefulness of a genomic classifier panel, since microRNAs contribute to radiosensitivity.[16] Development of techniques to quantify circulating tumor DNA in serially collected plasma samples offers a real potential to assess treatment efficacy during and after therapy.[17-19] Finally, there is also growing interest in the use of proteomic biomarkers for predicting response to radiation therapy and defining radioresistance,[20] which could provide selective information in addition to that generated by the genomic classifiers.

In their discussion of the international genome-wide association studies conducted by the Radiogenomics Consortium, the authors also address research into the response of normal tissue to radiation therapy. New information about the potential role of single nucleotide polymorphisms within DNA repair genes and the inter-individual radiation response of normal tissue and tumor tissue[21] could complement the use of specific genomic classifiers of tumor response.

As with all complex molecular profiling technologies, the largest hurdles to the widespread adoption of genomic classifiers are the cost of implementation, the turnaround time of the complex clinical genomic analysis by laboratories that are able to offer this service, and the very rapid development of genomic technologies that frequently outpace their clinical validation. As a result, some genomic signatures are being released by companies prematurely, without classic validation methods or US Food and Drug Administration approval, in the hope that use of the applications will become formalized as a result of their expanded availability. Nonetheless, as specific candidate genes become more well established and gene expression assays gain sophistication, the value in clinical outcomes prediction and treatment selection is expected to transform the practice of radiation oncology.

Financial Disclosure: Dr. Pollack collaborates on research with GenomeDx Biosciences. Dr. Marples has no significant financial interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this article.

References

1. Williams NL, Dan T, Zaorsky NG, et al. The role of genomic techniques in predicting response to radiation therapy. Oncology (Williston Park). 2017;31:562-70.

2. Ouillette P, Fossum S, Parkin B, et al. Aggressive chronic lymphocytic leukemia with elevated genomic complexity is associated with multiple gene defects in the response to DNA double-strand breaks. Clin Cancer Res. 2010;16:835-47.

3. Fertil B, Malaise EP. Intrinsic radiosensitivity of human cell lines is correlated with radioresponsiveness of human tumors: analysis of 101 published survival curves. Int J Radiat Oncol Biol Phys. 1985;11:1699-707.

4. West C, Hendry J. Prediction of radiotherapy response using SF2: is it methodology or mythology? Radiother Oncol. 1994;31:86-8.

5. Burnet NG, Wurm R, Nyman J, Peacock JH. Normal tissue radiosensitivity: how important is it? Clin Oncol (R Coll Radiol). 1996;8:25-34.

6. Jeggo P, Lavin MF. Cellular radiosensitivity: how much better do we understand it? Int J Radiat Biol. 2009;85;1061-81.

7. Lockhart DJ, Winzeler EA. Genomics, gene expression and DNA arrays. Nature. 2000;405:827-36.

8. Amundson SA, Do KT, Vinikoor LC, et al. Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen. Cancer Res. 2008;68:415-24.

9. Ahmed KA, Berglund AE, Welsh EA, et al. The radiosensitivity of brain metastases based upon primary histology utilizing a multigene index of tumor radiosensitivity. Neuro Oncol. 2017 Mar 31. [Epub ahead of print]

10. Ahmed KA, Chinnaiyan P, Fulp WJ, et al. The radiosensitivity index predicts for overall survival in glioblastoma. Oncotarget. 2015;6:34414-22.

11. Torres-Roca JF, Fulp WJ, Caudell JJ, et al. Integration of a radiosensitivity molecular signature into the assessment of local recurrence risk in breast cancer. Int J Radiat Oncol Biol Phys. 2015;93:631-8.

12. Speers C, Zhao S, Liu M, et al. Development and validation of a novel radiosensitivity signature in human breast cancer. Clin Cancer Res. 2015;21:3667-77.

13. Zhao SG, Chang SL, Spratt DE, et al. Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol. 2016;17:1612-20.

14. Strom T, Hoffe SE, Fulp W, et al. Radiosensitivity index predicts for survival with adjuvant radiation in resectable pancreatic cancer. Radiother Oncol. 2015;117:159-64.

15. Eschrich SA, Fulp WJ, Pawitan Y, et al. Validation of a radiosensitivity molecular signature in breast cancer. Clin Cancer Res. 2012;18:5134-43.

16. Hummel R, Hussey DJ, Haier J. MicroRNAs: predictors and modifiers of chemo- and radiotherapy in different tumour types. Eur J Cancer. 2010;46:298-311.

17. Chaudhuri AA, Binkley MS, Osmundson EC, et al. Predicting radiotherapy responses and treatment outcomes through analysis of circulating tumor DNA. Semin Radiat Oncol. 2015;25:305-12.

18. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14:985-90.

19. Newman AM, Bratman SV, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20:548-54.

20. Feng XP, Yi H, Li MY, et al. Identification of biomarkers for predicting nasopharyngeal carcinoma response to radiotherapy by proteomics. Cancer Res. 2010;70:3450-62.

21. West C, Rosenstein BS, Alsner J, et al. Establishment of a radiogenomics consortium. Int J Radiat Oncol Biol Phys. 2010;76:1295-6.

 
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