Karin Jirström

The Human Protein Atlas—a tool for pathology

Tissue‐based diagnostics and research is incessantly evolving with the development of new molecular tools. It has long been realized that immunohistochemistry can add an important new level of information on top of morphology and that protein expression patterns in a cancer may yield crucial diagnostic and prognostic information. We have generated an immunohistochemistry‐based map of protein expression profiles in normal tissues, cancer and cell lines. For each antibody, altogether 708 spots of tissues and cells are analysed and the resulting images and data are presented as freely available in the Human Protein Atlas (www.proteinatlas.org). The new version 4 of the atlas, including more than 5 million images of immunohistochemically stained tissues and cells, is based on 6122 antibodies, representing 5011 human proteins encoded by approximately 25% of the human genome. The gene‐centric database includes a putative classification of proteins in various protein classes, both functional classes, such as kinases or transcription factors and project‐related classes, such as candidate genes for cancer or cardiovascular diseases. For each of the internally generated antibodies, the exact antigen sequence is presented, together with a visualization of application‐specific validation data, including a protein array assay, western blot analysis, immunohistochemistry and, in most cases, immunofluorescent‐based confocal microscopy. The updated version also includes new search algorithms to allow complex queries regarding expression profiles, protein classes and chromosome location. Thus, the presented Human Protein Atlas provides a resource for pathology‐based biomedical research, including protein science and biomarker discovery. Copyright

Increased claudin-4 expression is associated with poor prognosis and high tumour grade in breast cancer

The role of intercellular tight junctions in breast epithelial cells is traditionally thought to be in maintaining polarity and barrier function. However, claudin‐4, a tight junction protein, is overexpressed in breast tumour cells compared to normal epithelial cells, which generally corresponds to a loss in polarity. The aim of this study was to investigate the distribution and potential clinical value of claudin‐4 in breast cancer, and to evaluate its usefulness as a prognostic and predictive biomarker. Expression of claudin‐4 was initially examined by Western blot analysis in a cohort of 88 breast tumours, and was found to correlate positively with tumour grade and negatively with ER. Claudin‐4 expression was then evaluated by immunohistochemistry in a larger cohort of 299 tumours represented on a tissue microarray. Claudin‐4 expression correlated positively with tumour grade and Her2, and negatively with ER. High claudin‐4 expression was also associated with worse breast cancer‐specific survival (p = 0.003), recurrence‐free survival (p = 0.025) and overall survival (p = 0.034). Multivariate analysis revealed that claudin‐4 independently predicted survival in the entire cohort (HR 1.95; 95%CI 1.01–3.79; p = 0.047) and in the ER positive subgroup treated with adjuvant tamoxifen (HR 4.34; 95%CI 1.14–16.53; p = 0.032). This relationship between increased claudin‐4 expression and adverse outcome was validated at the mRNA level in a DNA microarray dataset of 295 breast tumours. We conclude that high levels of claudin‐4 protein are associated with adverse outcome in breast cancer patients, including the subgroup of patients treated with adjuvant tamoxifen.

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer

IntroductionManual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer.MethodsTwo cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis.ResultsThe accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearmans ρ = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681).ConclusionsIn summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

PKCα expression is a marker for breast cancer aggressiveness

BackgroundProtein kinase C (PKC) isoforms are potential targets for breast cancer therapy. This study was designed to evaluate which PKC isoforms might be optimal targets for different breast cancer subtypes.ResultsIn two cohorts of primary breast cancers, PKCα levels correlated to estrogen and progesterone receptor negativity, tumor grade, and proliferative activity, whereas PKCδ and PKCε did not correlate to clinicopathological parameters. Patients with PKCα-positive tumors showed poorer survival than patients with PKCα-negative tumors independently of other factors. Cell line studies demonstrated that PKCα levels are high in MDA-MB-231 and absent in T47D cells which proliferated slower than other cell lines. Furthermore, PKCα silencing reduced proliferation of MDA-MB-231 cells. PKCα inhibition or downregulation also reduced cell migration in vitro.ConclusionsPKCα is a marker for poor prognosis of breast cancer and correlates to and is important for cell functions associated with breast cancer progression.

CENP-F expression is associated with poor prognosis and chromosomal instability in patients with primary breast cancer

DNA microarrays have the potential to classify tumors according to their transcriptome. Tissue microarrays (TMAs) facilitate the validation of biomarkers by offering a high‐throughput approach to sample analysis. We reanalyzed a high profile breast cancer DNA microarray dataset containing 96 tumor samples using a powerful statistical approach, between group analyses. Among the genes we identified was centromere protein‐F (CENP‐F), a gene associated with poor prognosis. In a published follow‐up breast cancer DNA microarray study, comprising 295 tumour samples, we found that CENP‐F upregulation was significantly associated with worse overall survival (p < 0.001) and reduced metastasis‐free survival (p < 0.001). To validate and expand upon these findings, we used 2 independent breast cancer patient cohorts represented on TMAs. CENP‐F protein expression was evaluated by immunohistochemistry in 91 primary breast cancer samples from cohort I and 289 samples from cohort II. CENP‐F correlated with markers of aggressive tumor behavior including ER negativity and high tumor grade. In cohort I, CENP‐F was significantly associated with markers of CIN including cyclin E, increased telomerase activity, c‐Myc amplification and aneuploidy. In cohort II, CENP‐F correlated with VEGFR2, phosphorylated Ets‐2 and Ki67, and in multivariate analysis, was an independent predictor of worse breast cancer‐specific survival (p = 0.036) and overall survival (p = 0.040). In conclusion, we identified CENP‐F as a biomarker associated with poor outcome in breast cancer and showed several novel associations of biological significance.

Growth differentiation factor 15: a prognostic marker for recurrence in colorectal cancer

Background:Growth differentiation factor 15 (GDF15) belongs to the transforming growth factor beta superfamily and has been associated with activation of the p53 pathway in human cancer. The aim of this study was to assess the prognostic value of GDF15 in patients with colorectal cancer (CRC).Methods:Immunohistochemistry and tissue microarrays were used to analyse GDF15 protein expression in 320 patients with CRC. In a subgroup of 60 patients, the level of GDF15 protein in plasma was also measured using a solid-phase proximity ligation assay.Results:Patients with CRC with moderate to high intensity of GDF15 immunostaining had a higher recurrence rate compared with patients with no or low intensity in all stages (stages I–III) (HR, 3.9; 95% CI, 1.16–13.15) and in stage III (HR, 10.32; 95% CI, 1.15–92.51). Patients with high plasma levels of GDF15 had statistically shorter time to recurrence (P=0.041) and reduced overall survival (P=0.002).Conclusion:Growth differentiation factor 15 serves as a negative prognostic marker in CRC. High expression of GDF15 in tumour tissue and high plasma levels correlate with an increased risk of recurrence and reduced overall survival.

Hypoxia inducible factor-1alpha is a prognostic marker in premenopausal patients with intermediate to highly differentiated breast cancer but not a predictive marker for tamoxifen response.

Hypoxia is common in many solid tumours, including breast cancer. Hypoxia triggers the expression of hypoxia inducible factor‐1α (HIF‐1α), and HIF‐1α has been associated with an impaired prognosis in breast cancer and down‐regulation of the oestrogen receptor (ER), potentially affecting the treatment efficiency of antioestrogens. The role of HIF‐1α regarding prognostic and treatment predictive information in breast cancer has not been established and we therefore analyzed HIF‐1α using immunohistochemistry in a cohort of 377 premenopausal stage II breast cancers arranged in a tissue microarray. The patients were included in a randomized trial with either 2 years of tamoxifen or no adjuvant treatment. The tamoxifen treatment effect could be studied in subgroups of breast cancer and pure prognostic information could be scrutinized for untreated control patients. HIF‐1α was scored as positive in 24% of the tumours and correlated positively to tumour size, Nottingham histological grade (NHG), Ki‐67, Her2 and cyclin E expression and negatively to lymph node status, cyclin D1, ER and PR (progesterone receptor) expression. Surprisingly, there was no difference in tamoxifen response for patients with high or low HIF‐1α expressing tumours. In lymph node‐positive patients as well as NHG 1/2 tumours, high HIF‐1α protein expression was significantly associated with an impaired recurrence‐free survival (p = 0.014, 0.018). When analyzing the subgroup of NHG 1/2 tumours, a high HIF‐1α expression was the only independent significant prognostic marker in multivariate analysis, including standard prognostic markers, suggesting that HIF‐1α might be a useful prognostic marker in this subgroup of breast cancer, with a rather good but diverse prognosis.

The transcription factor Sox11 is a prognostic factor for improved recurrence-free survival in epithelial ovarian cancer

BACKGROUND Current prognostic molecular markers for epithelial ovarian cancer (EOC) are insufficient. The aim of the current study was to investigate the role of Sox11 in EOC. METHODS Using an in silico transcriptomic screen, Sox11 was identified as a potential EOC biomarker. Sox11 protein expression was evaluated using immunohistochemistry (IHC) in 76 EOC cases, which were analysed using automated algorithms to develop a quantitative scoring model. RESULTS Sox11 mRNA expression was upregulated in EOC compared to normal tissues. Automated analysis of Sox11 in the EOC cohort revealed high expression of Sox11, in 40% of tumours, who had an improved recurrence-free survival (RFS) (p=0.002). Multivariate analysis confirmed that Sox11 was an independent predictor of improved RFS after controlling for stage and grade. CONCLUSIONS These data suggest that Sox11 is a new prognostic marker in EOC. Loss of Sox11 is associated with a decreased RFS and a more aggressive phenotype.

HMG-CoA reductase expression in breast cancer is associated with a less aggressive phenotype and influenced by anthropometric factors

Although several studies have reported on the anti‐tumoural properties exerted by 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase (HMG‐CoAR) inhibitors (statins), the in vivo expression of HMG‐CoAR in human cancer has been considerably less investigated. In our study, we examined the immunohistochemical expression of HMG‐CoAR in 511 incident breast cancers within the Malmö Diet and Cancer Study in order to explore its relationship to established clinicopathological and tumour biological parameters. Furthermore, the potential influence of estrogen exposure on HMG‐CoAR expression was assessed by performing Coxs proportional hazards analyses of the relationship between the use of hormone replacement therapy (HRT), obesity (waist circumference) and tumour‐cell specific HMG‐CoAR expression. We found that HMG‐CoAR was present in various fractions and intensities in the cytoplasm, sometimes with a membranous pattern, but not in the tumour cell nuclei. The expression of HMG‐CoAR was associated with a smaller tumour size (p = 0.02), low histological grade (p = 0.001), low Ki67 index (p = 0.004), ERα+ (p = 0.02), ERβ+ (p = 0.005), and high p27 expression (p = <0.001). The incidence of tumours with a high HMG‐CoAR‐expression was increased among HRT‐users, although this was not statistically significant in a heterogeneity analysis. Obesity was significantly associated with a high HMG‐CoAR expression assessed both as a high (>50%) fraction of positive cells (relative risk: 2.06; 95% confidence interval: 1.20–3.51), and a strong staining intensity (2.33: 1.08–5.02). In summary, we demonstrate that HMG‐CoAR is differentially expressed in breast cancer and that a high expression is associated with prognostically favourable tumour parameters. Moreover, estrogen related life‐style and anthropometric factors might indeed regulate HMG‐CoAR expression.

Breast tumours following combined hormone replacement therapy express favourable prognostic factors

The aim of the present study was to evaluate the association between different types of hormone replacement therapy (HRT) and risk of specific breast cancer subgroups. A population‐based prospective cohort study including 12,583 peri‐ or postmenopausal women were followed using record‐linkage with national cancer registries. During an average follow‐up of 4.5 years, 332 cases of invasive breast cancer were diagnosed. Tumour samples were available from 283 cases. These tumours were re‐evaluated according to histological type, grade, and mitotic index. Evaluation of tumours included estrogen and progesterone receptor status (ERα, ERβ and PgR), as well as expression of Ki67, HER2, cyclin D1 and p27. The incidence of breast cancer in current users of combined HRT (CHRT) was significantly higher than in non‐users. The difference corresponded to an adjusted relative risk (95% confidence interval) of 3.01 (2.35–3.84) as obtained using a Coxs proportional hazards analysis. CHRT was associated with lobular tumours (3.48:1.99–6.10), grade 1 tumours (4.46:2.79–7.13) and tumours with a low mitotic index (4.35:2.99–6.34). CHRT was not related to any specific subgroup in terms of ERα‐, ERβ‐ or PgR‐expression. CHRT was associated with low proliferating tumours, defined by the Ki67 index (3.58:2.60–4.93), HER2 amplified tumours (4.40:1.93–10.06), low expression of the oncogene cyclin D1 (3.14:2.32–4.23) and high expression of the tumour suppressor gene p27 (3.47:2.40–5.01). Use of estrogen‐alone HRT (ERT) was not associated with any statistically significant risk of breast cancer. We conclude that the use of CHRT is associated with an increased incidence of breast tumours with comparatively favourable prognostic factors.