Simen Myhre

Integrative Analysis of Cyclin Protein Levels Identifies Cyclin B1 as a Classifier and Predictor of Outcomes in Breast Cancer

Purpose: We studied the expression levels of cyclins B1, D1, and E1 and the implications of cyclin overexpression for patient outcomes in distinct breast cancer subtypes defined by clinical variables and transcriptional profiling. Experimental Design: The expression levels of cyclins B1, D1, and E1 were quantified in 779 breast tumors and 53 cell lines using reverse phase protein arrays and/or transcriptional profiling. Results: Whereas cyclin E1 overexpression was a specific marker of triple-negative and basal-like tumors, cyclin B1 overexpression occurred in poor prognosis hormone receptor–positive, luminal B and basal-like breast cancers. Cyclin D1 overexpression occurred in luminal and normal-like cancers. Breast cancer subgroups defined by integrated expression of cyclins B1, D1, and E1 correlated significantly (P < 0.000001) with tumor subtypes defined by transcriptional profiling and clinical criteria. Across three hormone receptor–positive data sets, cyclin B1 was the dominant cyclin associated with poor prognosis in univariate and multivariate analyses. Although CCNE1 was present in significantly higher copy numbers in basal-like versus other subtypes (ANOVA P < 0.001), CCNB1 gene copy number did not show gain in breast cancer. Instead, cyclin B1 expression was increased in tumors with co-occurrence of TP53 mutations and MYC amplification, a combination that seems to characterize basal-like and luminal B tumors. CCNB1 gene expression was significantly correlated with PLK, CENPE, and AURKB gene expression. Conclusion: Cyclins B1, D1, and E1 have distinct expressions in different breast cancer subtypes. Novel PLK, CENPE, and AURKB inhibitors should be assessed for therapeutic utility in poor prognosis cyclin B1–overexpressing breast cancers.

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

PurposeTo determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST).MethodsReverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.ResultsSix breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).ConclusionWe developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.

Development and Validation of a Gene Profile Predicting Benefit of Postmastectomy Radiotherapy in Patients with High-Risk Breast Cancer: A Study of Gene Expression in the DBCG82bc Cohort

Purpose: To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). Experimental Design: The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Results: Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into “high LRR risk” and “low LRR risk” groups. PMRT significantly reduced risk of LRR in “high LRR risk” patients, whereas “low LRR risk” patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. Conclusions: A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. Clin Cancer Res; 20(20); 5272–80. ©2014 AACR.

Influence of DNA copy number and mRNA levels on the expression of breast cancer related proteins

For a panel of cancer related proteins, the aim was to shed light on which molecular level the expression of each protein was mainly regulated in breast tumors, and to investigate whether differences in regulation were reflected in different molecular subtypes. DNA, mRNA and protein lysates from 251 breast tumor specimens were analyzed using appropriate microarray technologies. Data from all three levels were available for 52 proteins selected for their known involvement in cancer, primarily through the PI3K/Akt pathway. For every protein, in cis Spearman rank correlations between the three molecular levels were calculated across all samples and within each intrinsic gene expression subtype, enabling 63 comparisons altogether due to multiple gene probes matching to single proteins. Subtype‐specific relationships between the three molecular levels were studied by calculating the variance of subtype‐specific correlation and differences between overall and average subtype‐specific correlation. The findings were validated in an external dataset comprising 703 breast tumor specimens. The proteins were sorted into four groups based on the calculated rank correlation values between the three molecular levels. Group A consisted of eight proteins with significant correlation between DNA copy number levels and mRNA expression, and between mRNA expression and protein expression (Bonferroni adjusted p < 0.05). Group B consisted of 14 proteins with significant correlation between mRNA expression and protein expression. Group C consisted of 15 proteins with significant correlation between copy number levels and mRNA expression. For the remaining 25 proteins (group D), no significant correlations was observed. Stratification of tumors according to intrinsic subtype enabled identification of positive correlations between copy number levels, mRNA and protein expression that were undetectable when considering the entire sample set. Protein pairings that either demonstrated high variance in correlation values between subtypes, or between subtypes and the total dataset were studied in particular. The protein expression of cleaved caspase 7 was most highly expressed, and correlated highest to CASP7 gene expression within the basal‐like subtype, accompanied by the lowest amounts of hsa‐miR‐29c. Luminal A‐like subtype demonstrated highest amounts of hsa‐miR‐29c (a miRNA with a putative target sequence in CASP7 mRNA), low expression of cleaved caspase 7 and low correlation to CASP7 gene expression. Such pattern might be an indication of hsa‐miR‐29c miRNA functioning as a repressor of translation of CASP7 within the luminal‐A subtype. Across the entire cohort no correlation was found between CCNB1 copy number and gene expression. However, within most gene intrinsic subtypes, mRNA and protein expression of cyclin B1 was found positively correlated to copy number data, suggesting that copy number can affect the overall expression of this protein. Aberrations of cyclin B1 copy number also identified patients with reduced overall survival within each subtype. Based on correlation between the three molecular levels, genes and their products could be sorted into four groups for which the expression was likely to be regulated at different molecular levels. Further stratification suggested subtype‐specific regulation that was not evident across the entire sample set.

In Silico Ascription of Gene Expression Differences to Tumor and Stromal Cells in a Model to Study Impact on Breast Cancer Outcome

Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures.

Relationship between the prognostic and predictive value of the intrinsic subtypes and a validated gene profile predictive of loco-regional control and benefit from post-mastectomy radiotherapy in patients with high-risk breast cancer

Abstract Background. Breast cancer is characterized by great molecular heterogeneity demonstrated, e.g. by the intrinsic subtypes. Administration of post-mastectomy radiotherapy (PMRT) does, however, not reflect this heterogeneity. A gene profile (DBCG-RT profile) has recently been developed and validated, and has shown prognostic impact in terms of loco-regional failure and predictive impact for PMRT. Reports have also shown predictive value in terms of benefit of PMRT from intrinsic subtypes and derived approximations. The aim of this study was to examine: 1) the agreement between various methods for determining the intrinsic subtypes; and 2) the relationship between the prognostic and predictive impact of the DBCG-RT profile and the intrinsic subtypes. Material and methods. Intrinsic subtypes and the DBCG-RT profile was determined from microarray analysis based on fresh frozen tissue from 191 patients included in the Danish Breast Cancer Cooperative Group (DBCG) 82bc trial. Corresponding formalin-fixed, paraffin-embedded tissue was available from 146 of these patients and from another 890 DBCG82bc patients. Estrogen receptor, progesterone receptor, HER2, CK5/6, Ki-67 and EGFR were combined into immunohistochemical approximations of the intrinsic subtypes. Endpoint considered was loco-regional recurrence (LRR). Results. The DBCG-RT profile identified a group of patients with low risk of LRR and no additional benefit from PMRT among all subtypes. Combining six immunohistochemical markers identified a subgroup of triple negative patients with high risk of LRR and significant benefit from PMRT. Agreement in the different assignments of tumors to the subtypes was suboptimal, and the clinical outcome and predicted benefit from PMRT varied according to the method used for assignment. Conclusion. The prognostic and predictive information obtained from the DBCG-RT profile cannot be substituted by any approximation of the tumors intrinsic subtype. The predictive value of the intrinsic subtypes in terms of PMRT was influenced by the method used for assignment to the intrinsic subtypes.

Development and validation of a gene profile predicting benefit of post-mastectomy radiotherapy in high risk breast cancer patients: A study of gene expression in the DBCG82bc cohort

Purpose: To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). Experimental Design: The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Results: Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into “high LRR risk” and “low LRR risk” groups. PMRT significantly reduced risk of LRR in “high LRR risk” patients, whereas “low LRR risk” patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. Conclusions: A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. Clin Cancer Res; 20(20); 5272–80. ©2014 AACR.

Intrinsic subtypes and benefit from postmastectomy radiotherapy in node-positive premenopausal breast cancer patients who received adjuvant chemotherapy – results from two independent randomized trials

Abstract Background: The study of the intrinsic molecular subtypes of breast cancer has revealed differences among them in terms of prognosis and response to chemotherapy and endocrine therapy. However, the ability of intrinsic subtypes to predict benefit from adjuvant radiotherapy has only been examined in few studies. Methods: Gene expression-based intrinsic subtyping was performed in 228 breast tumors collected from two independent post-mastectomy clinical trials (British Columbia and the Danish Breast Cancer Cooperative Group 82b trials), where pre-menopausal patients with node-positive disease were randomized to adjuvant radiotherapy or not. All patients received adjuvant chemotherapy and a subgroup of patients underwent ovarian ablation. Tumors were classified into intrinsic subtypes: Luminal A, Luminal B, HER2-enriched, Basal-like and Normal-like using the research-based PAM50 classifier. Results: In the British Columbia study, patients treated with radiation had an overall significant lower incidence of locoregional recurrence compared to the controls. For Luminal A tumors the risk of loco-regional recurrence was low and was further lowered by adjuvant radiation. These findings were validated in the DBCG 82b study. The individual data from the two cohorts were merged, the hazard ratio (HR) for loco-regional recurrence associated with giving radiation was 0.34 (0.19 to 0.61) overall and 0.12 (0.03 to 0.52) for Luminal A tumors. Conclusions: In both postmastectomy trials, patients with Luminal A tumors turned out to have a significant lower incidence of loco-regional recurrence when randomized to adjuvant radiotherapy, leaving no indication to omit postmastectomy adjuvant radiation in pre-menopausal high-risk patients with Luminal A tumors. It was not possible to evaluate the effect of radiotherapy among the other subtypes because of limited sample sizes.

Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer

Purpose: To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). Experimental Design: The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Results: Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into “high LRR risk” and “low LRR risk” groups. PMRT significantly reduced risk of LRR in “high LRR risk” patients, whereas “low LRR risk” patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. Conclusions: A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. Clin Cancer Res; 20(20); 5272–80. ©2014 AACR.