Predicting future metastasis in GIST by gene expression including CINSARC, Genomic Index, and AURKA
Several recent papers have noted that different gene expression patterns can potentially identify GISTs with a high probability of recurrence or metastasis after complete resection of the primary tumor. The patients with these tumors are candidates for adjuvant Gleevec. (In contrast, at least half of primary GIST patients are potentially cured by surgery and do not need adjuvant Gleevec.) Though current classification schemes can identify low-risk patients, the classification of "intermediate" and "high" risk is not as accurate as desired, and we need better ways to identify the patients who are actually very likely to have recurrence or metastasis.
Gene expression profiling uses special genomic techniques to detect which genes are “turned on” (expressed) to produce proteins within the cell. These techniques detect the mRNA transcribed from active genes. Gene expression profiling can discover which genes are relevant to a certain disease. In contrast, simpler immunohistochemical techniques use protein-specific antibodies and stains to detect whether the final proteins produced by expressed genes are present in the cell. However, immunohistochemistry can only be used after the protein of interest has been previously identified as relevant.
Gene expression and Risk of Recurrence
Japanese investigators Yamaguchi et al (2008) performed a gene expression analysis on 32 frozen GIST samples and then tested the results by applying them to a separate set of 152 different paraffin-embedded samples. In contrast to usual practice, they categorized mitotic rate as <5 or >=5 based on only 10 microscope fields rather than 50 fields. Therefore, their "low" mitotic rate group contained samples that would have been classified as "intermediate" or "high" based on 50 fields. Regardless, Yamaguchi et al did identify genes associated with greater risk of recurrence in gastric tumors and in bowel tumors. Classification of GISTs based on gene expression separated groups with no recurrence in longterm followup from groups with high rates of recurrence. Although their discussion focused on the DPP4 gene encoding CD 26 protein, this was prognostic only for GIST of the stomach. However, their gene expression data hold additional value because they made the data available to other researchers via the Gene Expression Omnibus database.
Genomic Complexity: CINSARC, Genomic Index, and AURKA in GIST
European researchers Chibon et al (2010) described gene expression profiles in a variety of sarcomas and used comparative genomic hybridization to derive a set of 67 genes related to mitotic control, chromosome integrity, and genome complexity. A prognostic scoring scheme using these genes, called CINSARC, correlates with prognosis across many sarcoma types. In a followup study, the same research group (Lagarde et al, 2011) focused on GIST prognosis. CINSARC scores were correlated with metastasis-free survival in GIST. No metastases occurred in the low-CINSARC group, but 62% of the high-CINSARC group developed metastases.
Next the researchers used Quantitative genomic and reverse transcription Polymerase Chain Reaction to analyze which genes were the best predictors in GIST prognosis and defined a 45-gene GIST-specific Genomic Index. The aurora kinase A gene (AURKA) emerged as the the strongest predictor common to CINSARC and GIST-specific gene patterns. Increased AURKA protein resulted from deletion of either the p16 or the RB1 gene in different patients, not from amplification of the AURKA gene. Aurora kinases are involved in cell-cycle regulation. AURKA functions in centrosome and mitotic spindle formation — steps in the cell proliferation cycle that enables tumors to grow. Lagarde et al verified their results using the separate data from Yamaguchi et al.
Classifying risk based on either CINSARC, or on GIST-specific Genomic Index, or on AURKA expression alone yielded better prediction of metastasis than the AFIP risk categories regardless of tumor location. (For more about AFIP categories see this link.) Lagarde et al preferred the Genomic Index as a prognostic tool. Lagarde et al stated "Here, we show that mitotic checkpoint expression and chromosome complexity are strong predicators of metastatic outcome in GISTs. Of particular interest, these signatures can distinguish good from poor prognosis patients classified as intermediate-risk by the current histologic method for risk assessment (which represent around 25% of diagnoses). Comparative genomic hybridization technique is already used in pathology laboratories with formalin-fixed paraffin-embedded samples. Genomic profiling could therefore be a powerful tool to manage imatinib therapy for intermediate- risk patients." However, comparative genomic hybridization is not as easy to perform as the most common pathology test method, immunohistochemistry, which is based on laboratory stains of tissue on slides.
Confirmation using Simpler Immunohistochemical Methods
Finally, a third study conducted by Taiwanese researchers and Jonathan Fletcher (Yen et al, 2012) also confirmed AURKA as a useful prognosticator in GIST using immunohistochemistry. They independently reanalyzed the Yamaguchi et al data, first reclassifying AFIP risk category using <5 versus >=5 mitoses per 50 fields (not the non-standard 10 fields originally used by Yamaguchi et al). Using this reclassification there were 13 high-risk, 3 intermediate-risk, and 19 low-risk patients. The 10 genes most strongly associated with high risk all involved cell cycle progression. Yen et al compared the top 50 genes in the high-risk group of the Yamaguchi et al data to the CINSARC genes; 19 genes were common to both, including AURKA. Irrespective of AFIP category, patients with above-average AURKA expression had much worse recurrence-free survival (only about 10%) than those with less AURKA expression (about 90%).
Next Yen et al examined AURKA in a new group of 142 Taiwanese patients operated on for primary GIST, of whom 46.5% were AFIP low-risk, 19% intermediate-risk, and 34.5% high-risk. They used immunohistochemistry to detect AURKA in paraffin-embedded tumor samples. 38% had high AURKA expression, and AURKA was higher in non-gastric cases. 44 of 142 patients had recurrence. In a multivariate analysis, three factors showed independent prognostic value: tumor size, mitotic rate, and AURKA expression categorized as low versus high. When AURKA expression was included in the analysis, tumor location was no longer a significant predictor.
Successful AURKA classification by immunohistochemistry means that this test could easily be performed as part of pathology analyses for GIST if its value becomes accepted.
Could AURKA be a treatment target in GIST?
AURKA inhibitors are in clinical trials for other cancer types. Perhaps in the future AURKA inhibition might be useful in combination with current drug treatments for GIST. The agreement among the three investigations summarized above definitely implies that AURKA may be useful in discriminating risk of recurrence and thereby indicating which patients most need adjuvant Gleevec. Yen et al stated in conclusion: "AURKA, a cell-cycle regulator, was shown to be an
important prognostic factor of recurrence for primary GIST…. We are currently planning another study to clarify the impact of AURKA expression on the survival of advanced GIST patients receiving imatinib therapy."
Click title to link to PubMed abstract.
Chibon F, Lagarde P, Salas S, Pérot G, Brouste V, Tirode F, Lucchesi C, de Reynies A, Kauffmann A, Bui B, Terrier P, Bonvalot S, Le Cesne A, Vince-Ranchère D, Blay JY, Collin F, Guillou L, Leroux A, Coindre JM, Aurias A.
Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity.
Nature Medicine. 2010 Jul;16(7):781-7.
PubMed PMID: 20581836.
Lagarde P, Pérot G, Kauffmann A, Brulard C, Dapremont V, Hostein I, Neuville A, Wozniak A, Sciot R, Schöffski P, Aurias A, Coindre JM, Debiec-Rychter M, Chibon F.
Mitotic checkpoints and chromosome instability are strong predictors of clinical outcome in gastrointestinal stromal tumors.
Clin Cancer Res. 2012 Feb 1;18(3):826-38.
PubMed PMID: 22167411.
Yamaguchi U, Nakayama R, Honda K, Ichikawa H, Hasegawa T, Shitashige M, Ono M, Shoji A, Sakuma T, Kuwabara H, Shimada Y, Sasako M, Shimoda T, Kawai A, Hirohashi S, Yamada T.
Distinct gene expression-defined classes of gastrointestinal stromal tumor.
J Clin Oncol. 2008 Sep 1;26(25):4100-8.
PubMed PMID: 18757323.
Yen CC, Yeh CN, Cheng CT, Jung SM, Huang SC, Chang TW, Jan YY, Tzeng CH, Chao TC, Chen YY, Yang CY, Ho CL, Fletcher JA.
Integrating Bioinformatics and Clinicopathological Research of Gastrointestinal Stromal Tumors: Identification of Aurora Kinase A as a Poor Risk Marker.
Ann Surg Oncol. 2012 May 17. [Epub ahead of print]
PubMed PMID: 22588468.