Secreted phospholipase A2 group IIa (sPLA2-IIa) has been shown to promote tumor genesis and cell proliferation. The properties of this group of enzymes are utilized in liposomal drug delivery of chemotherapy. sPLA2-IIa is also under investigation as a possible treatment target in itself, and as a prognostic marker. The expression of sPLA2-IIa in breast cancer has not been examined extensively, and never using immunohistochemistry. We sought to investigate the expression of sPLA2-IIa in a cohort of advanced breast cancer patients with correlation to known clinicopathologic risk factors and survival. Material from 525 breast cancer patients (426 primary tumors and 99 metastases or local recurrences) was examined for sPLA2-IIa expression using immunohistochemistry. Out of these, 262 showed expression of sPLA2-IIa. We found that there was no correlation to clinicopathologic characteristics, and no impact of sPLA2-IIa expression on prognosis. However, we found that a large proportion of patients in our study had high levels of sPLA2-IIa expression, and that sPLA2-IIa was equally expressed in primary tumors and metastases. These findings may be significant in the future development of liposomal drug delivery or targeted sPLA2-IIa treatment.

PurposeAnthracyclines remain a cornerstone in the treatment of primary and advanced breast cancer (BC). This study has evaluated the predictive value of a multigene mRNA-based drug response predictor (DRP) in the treatment of advanced BC with epirubicin. The DRP is a mathematical method combining in vitro sensitivity and gene expression with clinical genetic information from > 3000 clinical tumor samples.MethodsFrom a DBCG cohort, 140 consecutive patients were treated with epirubicin between May 1997 and November 2016. After patient informed consent, mRNA was isolated from archival formalin-fixed paraffin-embedded primary breast tumor tissue and analyzed using Affymetrix arrays. Using time to progression (TTP) as primary endpoint, the efficacy of epirubicin was analyzed according to DRP combined with clinicopathological data collected retrospectively from patients’ medical records. Statistical analysis was done using Cox proportional hazards model stratified by treatment line.ResultsMedian TTP was 9.3 months. The DRP was significantly associated to TTP (P = 0.03). The hazard ratio for DRP scores differing by 50 percentage points was 0.55 (95% CI –0.93, one-sided). A 75% DRP was associated with a median TTP of 13 months compared to 7 months following a 25% DRP. Multivariate analysis showed that DRP was independent of age and number of metastases.ConclusionThe current study prospectively validates the predictive capability of DRP regarding epirubicin previously shown retrospectively allowing the patients predicted to be poor responders to choose more effective alternatives. Randomized prospective studies are needed to demonstrate if such an approach will lead to increased overall survival.

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e12119 Background: Neoadjuvant treatment for breast cancer (BC) is an important remedy if the tumors are responding adequately. Still, there is no biomarker guidance resulting in many patients receiving chemotherapy with no efficient antitumor effect. We study a multigene mRNA-based technology for drug response prediction (DRP). DRP has been thoroughly validated in other settings, latest in prediction of epirubicin efficacy in advanced BC [1]. Here are the results of the DRP method in the prediction of response to neoadjuvant doxorubicin in early BC. Methods: The DRP correlates sensitivity of the applicable drug in cell lines with background mRNA. This is combined with gene expression patterns from >2.000 tumors of different origin to ensure clinically usefulness. The higher DRP score, the higher likelihood of response. Blinded predictions of doxorubicin DRP scores were compared to response data which originated from a phase II trial [2], where the study population received neoadjuvant doxorubicin and cyclophosphamide (N=279). The patients had histologically confirmed primary invasive breast adenocarcinoma (T2-3, N0-3, M0, tumor size ≥ 2.0 cm, ER+/- and HER2+/-). Statistical analysis was done using logistic regression. Results: Table shows the distribution of response and DRP scores, showing higher DRPs are correlated to better clinical outcome. This is demonstrated by logistic regression ( p=0.002), odds ratio for response 2.2 (95% CI:1.3-3.4). Multivariate analysis showed that the DRP was independent of other covariates. Conclusions: The DRP can predict which patients will be high likelihood responders to neoadjuvant doxorubicin. Modern multigene technologies may help assist clinicians in choosing between upfront surgery or neoadjuvant chemotherapy. Complete response (CR), Partial response (PR), Stable disease (SD), Progression of Disease (PD). 1. Buhl ASK, et al. (2018): Predicting efficacy of epirubicin by a multigene assay in advanced breast cancer within a Danish Breast Cancer Cooperative Group (DBCG) cohort: a retrospective-prospective blinded study. Breast cancer research and treatment. doi: 10.1007/s10549-018-4918-4. 2. Horak CE et al. (2013): Biomarker analysis of neoadjuvant doxorubicin/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer. Clinical cancer research. doi: 10.1158/1078-0432.ccr-12-1359.[Table: see text]

Background Even with positive oestrogen receptor (ER+) status some advanced breast cancer (ABC) patients fail to benefit from endocrine therapy (ET). A method that previously predicted other drugs in various cancers was evaluated. Here multigene markers based on aromatase inhibitor (AI) effect in vitro were used for prediction of AI benefit in ER+ ABC patients. Simultaneously effects of long-term ET on predictive efficacy was evaluated. Methods The Drug Response Predictors (DRPs) are based on correlations between baseline gene expression and growth inhibition patterns of exemestane, anastrozole and letrozole, respectively, in the National Cancer Institute 60 cell lines. The genes were controlled for expression in 3,500 tumours. In a Danish Breast Cancer Cooperative Group cohort of 695 ABC patients with complete gene expression and time-to-progression (TTP) data, 414 received an AI as monotherapy. Hereof, 57 received anastrozole, 166 received exemestane, and 327 received letrozole. mRNA was isolated from archival formalin-fixed paraffin embedded tumour tissue and run on microarray and 60% of the tumours were from time of primary diagnosis. Medical records of the patients were assessed for TTP for all treatments given for ABC. Results The DRPs were tested in subsets 1) with no adjuvant ET and 2) with adjuvant ET. In 1) the anastrozole DRP predicted benefit of anastrozole (hazard ratio (HR) was 0.21 upper 95%-confidence interval limit (CI) 0.76, p=0.023) but not in 2). Dichotomised by a DRP of 50, the anastrozole DRP did predict benefit (HR=0.16, upper 95%-CI 0.75, p=0.026). Only in 1) the exemestane DRP predicted benefit of exemestane (HR=0.57, upper 95%-CI 1.00, p=0.0497). The letrozole DRP had no predictive value. Additionally, we tested each DRPs ability to predict other AIs. Only the anastrozole DRP predicted benefit of overall AI treatment, in 1)with an HR of 0.76 (upper 95%-CI 0.99, p=0.044) and in 2) with an HR of 0.71 (upper 95%-CI 0.92, p=0.015). The anastrozole DRP did though not predict benefit of letrozole. All tests are one-sided, alpha=5%. Conclusions Among the DRPs for AIs, the anastrozole DRP was strongest with clinically relevant prediction of TTP in AI treated ER+ ABC patients. Trial registration: ClinicalTrials.gov NCT01861496. BackgroundApproximately 70% of women with advanced breast cancer (ABC) have oestrogen-receptor positive

e12532 Background: Exemestaneis a steroidal aromatase inhibitor used in the treatment of postmenopausal patients with estrogen receptor(ER)-positive adjuvant and advanced breast cancer. We aimed to determine the predictive value of a multigene mRNA-based mathematical algorithm (Drug Response Predictor (DRP)) for benefit of exemestane. The DRP is founded on measuring the full cancer transcriptome in sensitive and drug resistant cell lines compared with expression patterns in tumors and broadly validated in several studies (Wang et al. JNCI (2013) 105 (17): 1284-1291.) (Knudsen, S. et al. PLoS One (2014) 9(2): e87415.) (Christensen, TD et al. J Clin Oncol (2016) 34: suppl; abstr e12056.) (Kappel, IB et al. J Clin Oncol (2016) 34: suppl; abstr e20007.). Methods: Among 838 consecutive patients from a DBCG cohort with advanced breast cancer treated at 10 participating sites we identified 163 patients who between November 2008 and November 2015 initiated exemestane. All but one patient were ER-positive. Patients were evaluated every 3 to 4 months using CT scans and clinical examination. After patient informed consent mRNA was extracted and assayed on Affymetrix Gene Chip U133p2 arrays from formalin fixed paraffin embedded diagnostic biopsies. The primary endpoint was progression free survival (PFS). Analysis of the DRP’s ability to predict PFS was performed using a Cox regression model adjusted for treatment line. Results: Median PFS was 8.5 months. Of the 163 patients, 101 received prior adjuvant antihormone therapy and 60 did not. Data regarding adjuvant therapy was inaccessible for two patients. Hazard ratios for patients with predicted good vs. poor effect of exemestane are shown in the table. Conclusions: In a clinical setting, a mathematical algorithm using mRNA from a diagnostic biopsy can in patients unexposed to previous adjuvant endocrine therapy with statistical significance predict who will benefit from exemestane. [Table: see text]

Currently, the clinical situation in advanced breast cancer is such that a variety of drugs are available with very little guidance on their selection. Estrogen receptor status, HER2 status and subtypes based on PAM50 can be used to stratify patients to treatment with antiestrogen therapy, anti-HER2 therapy and adjuvant chemotherapy [1]. However, no drug-specific biomarkers are currently available for personalizing treatment to patients. Personalization -choice of assay & strategyThe original approach of biomarker searches was based on biological knowledge of mechanism of action for each specific drug. In breast cancer, estrogen receptor is used as a simple, single biomarker identified by staining with immunohistochemistry (IHC) and is used to select patients to treatment with selective estrogen-receptor modulator tamoxifen or aromatase inhibitors like letrozole giving an approximately 30% response rate in estrogen receptor positive breast cancer patients [2]. Other examples are topoisomerase 2 to predict efficacy of topoisomerase 2 inhibiting drugs such as epirubicine [3] and BRCA mutations being used to guide treatment with PARP inhibitors with response rates varying from 20 to 60% [4]. Lastly, using HER2/ErbB2 levels on IHC can be used to select patients to treatment with trastuzumab, pertuzumab or lapatinib. However, knowing the target is only part of the solution. Trastuzumab given to HER2 positive patients produces a response rate of 25%, not 100% [5].Many projects are prospectively searching for targetable mutations with next-generation sequencing (NGS) and trying to target these. The dream of finding an extremely efficient drug for a specific mutation has been alive since imatinib became available for chronic myeloid leukemia patients with Philadelphia chromosome mutations [6]. This dream of targeting single mutations is driving projects such as FoundationOne that was launched as an assay searching for targetable mutations [7,8]. Another example is the Copenhagen Prospective cohort with a Phase I study of a variety of mixed tumors selected for targeted anticancer treatments based on mutational status, for example, BRAF mutated treated with BRAF inhibitor combination (EGFRi/MEKi) therapy like vemurafenib + panitumumab [9]. The overall response rate from the latter study was only 15% albeit in a heavily pretreated cohort. The NGS gives a thorough deep sequencing of DNA in a very efficient manner. But currently the use of NGS is limited to single-hit mutations or tumor mutational burden and not capturing collective interactions in data.Whether it is protein with IHC or DNA with NGS, the approach of searching for single biomarkers to select drugs has brought substantial treatment benefits into advanced breast cancer but sadly we have yet to see actual cure. Further, these approaches do not aid the clinician in choosing between drugs when there are several drugs to choose from.A completely different take on the matter of personalizing treatment in oncology is taken by mimicking a standard strategy from microbi...