Search

The role of clinical researchers is vital to cancer progress. The teaching, research, and leadership roles that academic oncologists hold need to be accounted for and appropriately compensated. National metrics are currently inexistent, but are necessary to move the oncology research field forward. Clinical research and routine clinical care must be harmoniously integrated without competing. This article reviews the national landscape of clinical cancer research and proposes a call for action.

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R packageTo understand cancer, researchers need to know which molecules tumor cells use. These so-called ‘biomarkers’ tag cancer cells as being different from healthy cells, and can be used to predict how aggressive a tumor may be, or how well it might respond to treatment. A popular technique for assessing biomarkers across multiple tumors is to use tissue microarrays. This involves taking samples from different tumors and embedding them in a block of wax, which is then cut into micro-thin slices and stained with reagents that can detect specific biomarkers, such as proteins. Each block contains hundreds of samples, which all experience the same conditions. So, any patterns detected in the staining are likely to represent real variations in the biomarkers present. Many cancer studies, however, often compare samples from multiple tissue microarrays, which may increase the risk of technical artifacts: for example, staining may look stronger in one batch of tissue samples than another, even though the amount of biomarker present in these different arrays is roughly the same. These ‘batch effects’ could potentially bias the results of the experiment and lead to the identification of misleading patterns. To evaluate how batch effects impact tissue microarray studies, Stopsack et al. examined 14 wax blocks which contained tumor samples from 1,448 men with prostate cancer. This revealed that for some biomarkers, but not others, there were noticeable differences between tissue microarrays that were clearly the result of batch effects. Stopsack et al. then tested six different ways of fixing these discrepancies using statistical methods. All six approaches were successful, even if the arrays included tumors with different characteristics, such as tumors that had been diagnosed more or less recently. This work highlights the importance of considering batch effects when using tissue microarrays to study cancer. Stopsack et al. have used their statistical approaches to develop freely available software which can reduce the biases that sometimes arise from these technical artifacts. This could help researchers avoid misleading patterns in their data and make it easier to detect real variations in the biomarkers present between tumor samples.

DNA repair genes are commonly altered in metastatic prostate cancer, but BRCA1 mutations are rare. Preliminary studies suggest that higher tumor expression of the BRCA1 protein may be associated with worse prognosis. We undertook a prospective study among men with prostate cancer in the Health Professionals Follow-up Study and evaluated BRCA1 via immunohistochemical staining on tissue microarrays. BRCA1 was expressed in 60 of 589 tumors. Prevalence of BRCA1 positivity was 43% in the 14 men with metastases at diagnosis compared with 9% in non-metastatic tumors [difference, 33 percentage points; 95% confidence interval (CI), 7–59]. BRCA1-positive tumors had 2.16-fold higher Ki-67 proliferative indices (95% CI, 1.18–3.95), higher tumor aneuploidy as predicted from whole-transcriptome profiling, and higher Gleason scores. Among the 575 patients with non-metastatic disease at diagnosis, we evaluated the association between BRCA1 expression and development of lethal disease (metastasis or cancer-specific death, 69 events) during long-term follow-up (median, 18.3 years). A potential weak association of BRCA1 positivity with lethal disease (hazard ratio, 1.61; 95% CI, 0.82–3.15) was attenuated when adjusting for age, Gleason score and clinical stage (hazard ratio, 1.11; 95% CI, 0.54–2.29). In summary, BRCA1 protein expression is a feature of more proliferative and more aneuploid prostate tumors and is more common in metastatic disease. While not well suited as a prognostic biomarker in primary prostate cancer, BRCA1 protein expression may be most relevant in advanced disease.

Systemic metabolic alterations associated with increased consumption of saturated fat and obesity are linked with increased risk of prostate cancer progression and mortality, but the molecular underpinnings of this association are poorly understood. Here, we demonstrate in a murine prostate cancer model, that high-fat diet (HFD) enhances the MYC transcriptional program through metabolic alterations that favour histone H4K20 hypomethylation at the promoter regions of MYC regulated genes, leading to increased cellular proliferation and tumour burden. Saturated fat intake (SFI) is also associated with an enhanced MYC transcriptional signature in prostate cancer patients. The SFI-induced MYC signature independently predicts prostate cancer progression and death. Finally, switching from a high-fat to a low-fat diet, attenuates the MYC transcriptional program in mice. Our findings suggest that in primary prostate cancer, dietary SFI contributes to tumour progression by mimicking MYC over expression, setting the stage for therapeutic approaches involving changes to the diet.