Tissue Microarrays for Hypothesis Generation

EDITORIALS Tissue Microarrays for Hypothesis Generation Ethan Dmitrovsky Decades of hypothesis-driven research have led to the discovery of oncogenes and tumor suppressors that affect carcinogenesis. With the advent of powerful genomic and proteomic microarray techniques, a different scientific paradigm is emerging— one in which hypotheses are generated based on the evaluation of global gene expression in cells or tissues. Tissue microarrays are also useful to interrogate expression, at the mRNA or protein level, of a single or small set of gene products in normal, preneoplastic, or malignant tissues. In this issue of the Journal, Gurrieri et al. (1) used tissue microarrays to analyze comprehensively the mRNA and protein expression profiles of the tumor suppressor PML in diverse cancers of hematopoietic and solid tumor origin. They found frequent loss of PML protein but not of PML mRNA in cancers from multiple histopathologic sites. In a few cases, PML mutations or polymorphisms were identified. However, neither these changes nor loss of heterozygosity (LOH) could account for the loss of PML protein. These findings and those obtained after treatment with a proteasome inhibitor, point to a post-transcriptional mechanism for this PML regulation. PML is the most common translocation partner of the retinoic acid receptor alpha (RAR␣) found in acute promyelocytic leukemia (APL) rearrangements (2– 4). Originally called Myl (5), it was renamed PML when its cDNA sequence was reported (6,7). PML is an integral component of nuclear structures known as PML nuclear bodies that are disrupted in APL patients expressing the PML–RAR␣ translocation product (8,9). Early work on PML highlighted its role as a growth-suppressive species in APL and potentially other tumor cell contexts (10,11). PML expression is repressed in small-cell lung cancers (12) as well as in other cancers (13). These findings were built on by in vitro studies and by engineering PML null mice, which turned out to be prone to tumor formation; PML null cells were resistant to apoptotic stimuli mediated through p53 and other tumor suppressors that could affect genomic stability (14 –22). Loss of PML function impaired cellular senescence despite oncogenic signals (3). This work set the stage for comprehensive examination of PML expression and possible PML structural alterations in carcinogenesis, as in the study conducted by Gurrieri et al. (1). Such studies could implicate a functional role for PML in cancers beyond APL. Gurrieri et al. should be commended for undertaking these studies using tissue microarrays to explore patterns of PML mRNA and protein expression in non-APL hematopoietic and solid tumors. The findings confirmed and extended prior work (6 –22) by excluding LOH as the cause of repression of PML protein in the tumor cells examined. Because PML mRNA expression was intact, the authors sought post-transcriptional mechanisms for instability of PML protein. This work is an example of using tissue microarrays for hypothesis generation. On the basis of the results from the tissue microarrays, the 248 EDITORIALS

authors proposed an intriguing mechanism for PML regulation that involves the ubiquitin–proteasome degradation pathway. Treatment with a proteasome inhibitor restored PML protein expression in cell lines that expressed PML mRNA but not PML protein. This result is not surprising because post-translational modification by Sumo-1, the ubiquitin-like modifier, affects PML nuclear bodies (3). It is interesting to note that previous gene profiling studies revealed that the E1-like ubiquitinactivating enzyme UBE1L can be induced by all-trans-retinoic acid treatment of APL cells (23). UBE1L is a retinoic acid target gene that promotes apoptosis and PML–RAR␣ degradation (24). Activation of the ubiquitin–proteasome degradation pathway also occurs during retinoid-induced tumor cell differentiation and chemoprevention (4). Cell cycle regulators were identified as molecular targets for these pharmacologic effects. PML protein may be directly affected by UBE1L or regulators of the proteasome degradation pathway, and future work should explore this possibility. Additional studies are needed to establish whether a proteasome-dependent mechanism was predominantly responsible for repression of the PML protein in the cancers studied by Gurrieri et al. (1). Nevertheless, there are therapeutic implications to finding that regulation of PML was proteasomedependent. Proteasome inhibition has clinical activity (25). Clinical use of a proteasome inhibitor might restore PML expression and thereby confer its activity as a tumor suppressor. Although the currently available proteasome inhibitors do not target PML specifically, clinical treatment with these agents would address whether PML expression can be restored in malignant tumors. The data in Gurrieri et al. (1) are consistent with an important role for PML repression in regulating the growth of cancers in patients. PML repression occurred early during prostate carcinogenesis, even at the transition from prostatic intraepithelial neoplasia to invasive prostate carcinoma. Statistically significant associations were also observed for PML repression in breast and central nervous system cancers. These data would have been strengthened by inclusion of paired normal and malignant tumor specimens from the same patients. Yet, these findings do suggest a regulatory role for PML in the growth of certain tumors. Future work should establish whether restoration of PML expression has therapeutic benefits. It is also important to consider other post-transcriptional mechanisms for PML silencing.

Affiliation of author: Departments of Medicine and of Pharmacology and Toxicology, the Norris Cotton Cancer Center, Dartmouth Medical School, Hanover, NH, and Dartmouth-Hitchcock Medical Center, Lebanon, NH. Correspondence to: Ethan Dmitrovsky, MD, Department of Pharmacology and Toxicology, Remsen 7650, Dartmouth Medical School, Hanover, NH 03755 (e-mail: [email protected]). DOI: 10.1093/jnci/djh068 Journal of the National Cancer Institute, Vol. 96, No. 4, © Oxford University Press 2004, all rights reserved.

Journal of the National Cancer Institute, Vol. 96, No. 4, February 18, 2004

It is not unexpected to find that PML might have a role in other cancers in addition to APL. Prior work revealed that other translocation partners have regulatory roles beyond the hematopoietic malignancy in which they were first found, as in rearrangements of myc in Burkitt’s lymphoma (26). A similar case could be made for the retinoblastoma gene product, Rb. This tumor suppressor was first identified in a childhood cancer, but it has a critical role in regulating growth of other malignancies (27). The article by Gurrieri et al. has prioritized those tumors in which PML might function in carcinogenesis by determining clinical settings in which PML expression was frequently lost or repressed. The study by Gurrieri et al. (1) underscores how useful tissue microarrays can be in uncovering key steps in carcinogenesis. This is an example of merging hypothesis-driven and hypothesis-generating strategies to uncover a potential molecular target for cancer therapy or chemoprevention. Because PML repression is frequent in many different malignancies, it is now important to determine precisely how repression occurs and what its biologic impact is. Whether PML repression can be pharmacologically overcome should be studied. A translational research approach is needed to address these points. Preliminary data have implicated the ubiquitin–proteasome pathway in this PML regulation. However, it is important to explore which post-transcriptional or post-translational mechanisms are engaged to alter PML expression. Different mechanisms could cooperate to affect PML expression. In summary, the article by Gurrieri et al. (1) highlights an emerging approach in molecular oncology. Hypothesis-driven and hypothesis-generating strategies are not mutually exclusive. Merging these approaches will hasten the process of translating discoveries from the bench to the bedside and back again. We should apply genomic, proteomic, and tissue-based microarray techniques to this purpose.

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