It is well known that uncontrolled growth of cancer cells results in imbalance between oxygen demand and angiogenesis. This causes tumor hypoxia, which provokes activation of cellular adaptation mechanisms. Hypoxia-inducible factor HIF-1α plays a key role in hypoxic adaptation of tumor cells. HIF-1a is overexpressed in hypoxia and initializes a cascade including transcription of a network of genes that control several aspects of tumor biology [39-41]. So far, HIF-1α induces transcription of vascular endothelial growth factor (VEGF) to promote endothelial cell proliferation and blood vessel formation [40-43]. Furthermore, HIF-1a stimulates glucose metabolism of cancer cells via up-regulation of glucose transporter (GLUT) 1 and 3 [40-43]. Moreover, also other relevant factors like erythropoietin, inducible nitric oxide synthase, and different matrix metalloproteinases are activated by HIF-1α [39-42]. Overall, these mechanisms result in aggressiveness, increases metastatic potential, and promotes tumor progression [39-42]. In fact, previously, numerous studies identified a significant role of HIF-1α in several malignancies. It has been shown that overexpression of HIF-1α was associated with increased mortality and worse prognosis of HNSCC [10]. In lung cancer, patients with positive HIF-1α expression in tumor tissues had lower overall survival rate than patients with negative HIF-1α expression [12]. Similar results were reported for uterine cervical cancer [11]. Also HIF-1α predicts resistance to anticancer therapy [9-11]. Therefore, visualization of tumor hypoxia may be of great importance. Some novel tracers were proposed to reflect tumor hypoxia HIF-1a. They showed promising preclinical and clinical results [44]. However, they are not widely available and, therefore, cannot be used in daily clinical routine. In contrast, the FDG is available in clinics worldwide. Therefore, the possibility to predict expression of HIF-1α noninvasively with a widely used technique like FDG-PET would be very important. In fact, if metabolic parameters of FDG-PET were associated with expression of HIF-1α, FDG-PET could be used as surrogate biomarker for tumoral hypoxia. Hence, also tumor behavior could be predicted. Theoretically, metabolic activity measuring by FDG-PET may reflect expression of HIF-1α. As mentioned above, HIF-1α induces up-regulation of glucose transporter (GLUT) 1 and 3, which are main cellular mediators for FDG [40-43]. Presumably, parameters of FDG-PET like SUVmax may well correlate with expression of HIF-1α. Some experimental investigations supported this hypothesis [45, 46]. Previously, only few clinical studies investigated this question. Some authors reported promising data about relationships between FDG-PET and HIF-1α. However, because of small number of involved patients, the reported results cannot apply as evident. The present meta-analysis is the first report based on a large sample including different tumors. Our results did not confirm the hypothesis about possible associations between SUVmax derived from FDG-PET and expression of HIF-1α. A wide spectrum of correlation coefficients was identified and the pooled coefficient was 0.27. This finding indicates that FDG-PET is not associated with tumor hypoxia. Furthermore, also in the tumor subgroups, namely NSCLC, NHSCC, uterine cervical cancer, and thymic tumors SUVmax correlated weakly with HIF-1α.
The present meta-analysis identified some major problems. To date, there are no reports analyzing associations between SUVmax and expression of HIF-1α in many frequent and less frequent solid malignancies like breast cancer, colonic cancer, esophageal carcinoma, different sarcomas, pancreatic cancer, hepatocellular carcinoma, and cholangiocellular carcinoma. This is a goal for further investigations.
Furthermore, we found significant heterogeneities among the studies investigating the same tumors. So far, in HNSCC, Grönroos et al. observed an inverse correlation between SUVmax and expression of HIF-1α (-0.194) [14], but in the study of Zhao et al. the correlation coefficient was 0.577 [33]. Moreover, the same work group of Kaira et al. identified different correlations between SUVmax and expression of HIF-1α in NSCLC, namely 0.71 and 0.11 [17, 23]. This finding is difficult to ascertain. The identified variations of the published correlation coefficients might be related to different ratio of tumor subtypes, or different analysis methods of HIF-1α expression. Finally, for uterine cervical cancer and thymic tumors the number of patients was small, namely 78 and 82, respectively. This fact relativizes the estimated correlation coefficients.
Also the present analysis identified some methodological problems of the acquired studies. Most of the acquired reports were retrospective or study design was not given. Furthermore, according the QUADAS criteria, most involved studies showed clinical review bias, diagnostic review bias, partial verification bias, differential verification bias, and incorporation bias.
We dont exactly know how the HIF-1a level was estimated in the studies used in our meta-analyses. High variability in the specificity of HIF-1a antibody used for visualisation of HIF-1a can independently influence the results of correlation between FDG uptake and HIF-1a expression.
In addition, all the studies included in our meta-analyses presumed that there is a direct linear relationship between GLUT-1, Hexokinase-2 and HIF-1a expression. However, this relationship is more complex and more likely to be non-linear as majority of tumor are heteroegenous. Therefore it is expected from outset that there cannot be high correlation between HIF-1a and FDG Uptake as measured by SUVmax (which in itself is dependent on several factors). It remains to be seen how absolute quantification of glucose metabolism using dynamic FDG PET correlates with HIF-1a, GLUT-1 and hexokinase.
Clearly, further prospective studies with well-defined inclusion and exclusion criteria are needed.