Development and validation of a gene expression score that predicts response to fulvestrant in breast cancer patients

DNA methylation and gene expression profiling reveal potential association of retinol metabolism related genes with hepatocellular carcinoma development

Background

Aberrant DNA methylation patterns play a critical role in the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms associated with these aberrantly methylated genes remain unclear. This study aimed to comprehensively investigate the methylation-driven gene expression alterations in HCC using a multi-omics dataset.

Methods

Whole genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) techniques were used to assess the methylation and gene expression profiles of HCC tissues (HCCs) and normal adjacent tissues (NATs). The candidate genes' potential function was further investigated using single-cell RNA sequencing (scRNA seq) data.

Results

We observed widespread hypomethylation in HCCs compared to NATs. Methylation levels in distinct genomic regions exhibited significant differences between HCCs and NATs. We identified 247,632 differentially methylated regions (DMRs) and 4,926 differentially expressed genes (DEGs) between HCCs and NATs. Integrated analysis of DNA methylation and RNA-seq data identified 987 methylation-driven candidate genes, with 970 showing upregulation and 17 showing downregulation. Four genes involved in the retinol metabolic pathway, namely ADH1A, CYP2A6, CYP2C8, and CYP2C19, were identified as hyper-downregulated genes. Their expression levels could stratify HCCs into three subgroups with distinct survival outcomes, immune cell infiltration, and tumor microenvironments. Validation of these findings in an independent dataset yielded similar outcomes, confirming the high concordance and potential prognostic value of these genes. ScRNA seq data revealed the low expression of these genes in immune cells, emphasizing their role in promoting malignant cell proliferation and migration. In conclusion, this study provides insights into the molecular characteristics of HCC, revealing the involvement of retinol metabolism-related genes in the development and progression of HCC. These findings have implications for HCC diagnosis, prognosis prediction, and the development of therapeutic targets.

Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer

Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.

A novel drug specific mRNA biomarker predictor for selection of patients responding to dovitinib treatment of advanced renal cell carcinoma and other solid tumors

PURPOSE

Dovitinib is a receptor tyrosine kinase inhibitor of VEGFR1-3, PDGFR, FGFR1/3, c-KIT, FLT3 and topoisomerase 1 and 2. The drug response predictor (DRP) biomarker algorithm or DRP-Dovitinib is being developed as a companion diagnostic to dovitinib and was applied retrospectively.

PATIENTS AND METHODS

Archival tumor samples were obtained from consenting patients in a phase 3 trial comparing dovitinib to sorafenib in renal cell carcinoma patients and the DRP-Dovitinib was applied. The biomarker algorithm combines the expression of 58 messenger RNAs relevant to the in vitro sensitivity or resistance to dovitinib, including genes associated with FGFR, PDGF, VEGF, PI3K/Akt/mTOR and topoisomerase pathways as well as ABC drug transport, and provides a likelihood score between 0-100%.

RESULTS

The DRP-Dovitinib divided the dovitinib treated RCC patients into two groups, sensitive (n = 49, DRP score >50%) or resistant (n = 86, DRP score ≤ 50%) to dovitinib. The DRP sensitive population was compared to the unselected sorafenib arm (n = 286). Median progression-free survival (PFS) was 3.8 months in the DRP sensitive dovitinib arm and 3.6 months in the sorafenib arm (hazard ratio 0.71, 95% CI 0.51-1.01). Median overall survival (OS) was 15.0 months in the DRP sensitive dovitinib arm and 11.2 months in the sorafenib arm (hazard ratio 0.69, 95% CI 0.48-0.99). The observed clinical benefit increased with increasing DRP score. At a cutoff of 67% the median OS was 20.6 months and the median PFS was 5.7 months in the dovitinib arm. The results were confirmed in five smaller phase II trials of dovitinib which showed a similar trend.

CONCLUSION

The DRP-Dovitinib shows promise as a potential biomarker for identifying advanced RCC patients most likely to experience clinical benefit from dovitinib treatment, subject to confirmation in an independent prospective trial of dovitinib in RCC patients.

Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: Transforming the healthcare applications

During the past two decades, the era of digitalization in pharmaceutical device manufacturing has gained significant momentum for maintaining human health. From various available technologies, internet of things (IoT) sensors are being increasingly used as wearable devices (e.g., smart watches, wrist bands, mobile phones, tablets, implantable pumps, etc.) that enable real-time monitoring of data. Such devices are integrated with smart materials that typically monitor the real-time data (blood pressure, blood sugar, heart and pulse rate, cytokine levels, etc.) to advise patients and physicians. Hence, there has been a great demand for wearable devices as potential tools for remote clinical trial monitoring in cancers and other diseases and they are proving to be very cost-effective.

IHC-based Ki67 as response biomarker to tamoxifen in breast cancer window trials enrolling premenopausal women

Window studies are gaining traction to assess (molecular) changes in short timeframes. Decreased tumor cell positivity for the proliferation marker Ki67 is often used as a proxy for treatment response. Immunohistochemistry (IHC)-based Ki67 on tissue from neo-adjuvant trials was previously reported to be predictive for long-term response to endocrine therapy for breast cancer in postmenopausal women, but none of these trials enrolled premenopausal women. Nonetheless, the marker is being used on this subpopulation. We compared pathologist assessed IHC-based Ki67 in samples from pre- and postmenopausal women in a neo-adjuvant, endocrine therapy focused trial (NCT00738777), randomized between tamoxifen, anastrozole, or fulvestrant. These results were compared with (1) IHC-based Ki67 scoring by AI, (2) mitotic figures, (3) mRNA-based Ki67, (4) five independent gene expression signatures capturing proliferation, and (5) blood levels for tamoxifen and its metabolites as well as estradiol. Upon tamoxifen, IHC-based Ki67 levels were decreased in both pre- and postmenopausal breast cancer patients, which was confirmed using mRNA-based cell proliferation markers. The magnitude of decrease of Ki67 IHC was smaller in pre- versus postmenopausal women. We found a direct relationship between post-treatment estradiol levels and the magnitude of the Ki67 decrease in tumors. These data suggest IHC-based Ki67 may be an appropriate biomarker for tamoxifen response in premenopausal breast cancer patients, but anti-proliferative effect size depends on estradiol levels.

Icb-1 expression inhibits growth and fulvestrant response of breast cancer cells and affects survival of breast cancer patients

PURPOSE

Human gene icb-1 recently has been reported to be part of a gene expression score predicting response to antiestrogen fulvestrant in breast cancer patients. In the present study, we examined to what extent icb-1 expression would affect the response of breast cancer cells to this antiestrogen in vitro and investigated underlying molecular mechanisms. Using open access mRNA data, we elucidated the significance of icb-1 expression for survival of breast cancer patients.

METHODS

Icb-1 gene expression was knocked down by RNAi. Breast cancer cell growth after treatment with fulvestrant was assessed using the Cell Titer Blue assay. Gene expression was analyzed by Western blot analysis or RT-qPCR. Survival analyses were performed using bioinformatical online tools and data.

RESULTS

Knockdown of icb-1 in T-47D breast cancer cells significantly increased growth of this cell line and also elevated the growth-stimulatory effect of E2 (p < 0.001). After treatment with different concentrations of fulvestrant, icb-1 knockdown cells exhibited a significantly enhanced response to this drug (p < 0.01). On the molecular level, icb-1 knockdown led to elevated expression of ESR1 and its target gene TFF1 (pS2) and enhanced E2-triggered up-regulation of proliferation genes. Finally, bioinformatical meta-analysis of gene expression data of 3951 breast cancer patients revealed that high icb-1 expression increases their relapse-free survival (HR = 0.87, p < 0.05).

CONCLUSION

The presented data further support a tumor-suppressive role of icb-1 in breast cancer and suggest an inhibitory effect of this gene on fulvestrant action, which both are suggested to be mediated by suppression of cellular E2 response.

Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature

Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.

Estrogen Receptor Pathway Activity Score to Predict Clinical Response or Resistance to Neoadjuvant Endocrine Therapy in Primary Breast Cancer

Endocrine therapy is important for management of patients with estrogen receptor (ER)-positive breast cancer; however, positive ER staining does not reliably predict therapy response. We assessed the potential to improve prediction of response to endocrine treatment of a novel test that quantifies functional ER pathway activity from mRNA levels of ER pathway-specific target genes. ER pathway activity was assessed on datasets from three neoadjuvant-treated ER-positive breast cancer patient cohorts: Edinburgh: 3-month letrozole, 55 pre-/2-week/posttreatment matched samples; TEAM IIa: 3- to 6-month exemestane, 49 pre-/28 posttreatment paired samples; and NEWEST: 16-week fulvestrant, 39 pretreatment samples. ER target gene mRNA levels were measured in fresh-frozen tissue (Edinburgh, NEWEST) with Affymetrix microarrays, and in formalin-fixed paraffin-embedded samples (TEAM IIa) with qRT-PCR. Approximately one third of ER-positive patients had a functionally inactive ER pathway activity score (ERPAS), which was associated with a nonresponding status. Quantitative ERPAS decreased significantly upon therapy (P < 0.001 Edinburgh and TEAM IIa). Responders had a higher pretreatment ERPAS and a larger 2-week decrease in activity (P = 0.02 Edinburgh). Progressive disease was associated with low baseline ERPAS (P = 0.03 TEAM IIa; P = 0.02 NEWEST), which did not decrease further during treatment (P = 0.003 TEAM IIa). In contrast, the staining-based ER Allred score was not significantly associated with therapy response (P = 0.2). The ERPAS identified a subgroup of ER-positive patients with a functionally inactive ER pathway associated with primary endocrine resistance. Results confirm the potential of measuring functional ER pathway activity to improve prediction of response and resistance to endocrine therapy.

Prediction of fulvestrant efficacy in patients with advanced breast cancer: retrospective-prospective evaluation of the predictive potential of a multigene expression assay

BACKGROUND

Fulvestrant is a selective oestrogen receptor (ER) degrader used as monotherapy and combination therapy for ER positive HER2 negative advanced breast cancer (ABC) in postmenopausal women. The drug response predictor (DRP), is a mathematical algorithm based on the expression of multiple genes in the tumour. The fulvestrant DRP algorithm has previously shown effect in BC. In this study, we investigated the DRP's potential in predicting fulvestrant benefit.

METHOD

Among 695 patients with ABC prospectively included in a Danish Breast Cancer Cooperative Group (DBCG) cohort we retrospectively included 226 patients who received fulvestrant as monotherapy. The DRP result was based on mRNA extracted from tumour biopsies and analysed using Affymetrix array. Primary endpoint was time to progression (TTP).

RESULTS

For patients who received fulvestrant in line one to four and were previously unexposed to adjuvant endocrine therapy, we identified a hazard ratio (HR) of 0.44 (90% confidence interval (90% CI) upper limit of 1.08, one sided p = 0.066) for a predicted positive vs negative outcome. A weaker association was seen when including patients exposed to adjuvant endocrine treatment or received fulvestrant in fifth or later lines. Exploratory analyses showed that the DRP was efficient when using recent biopsies for DRP estimate and among recently treated patients.

CONCLUSION

The DRP showed a potential in predicting fulvestrant treatment but was not significant in the overall analysis. Use of older biopsies, long-term endocrine treatment and multiple therapies between biopsy used for analysis and fulvestrant treatment, probably affect the predictive accuracy.

Guidelines for using sigQC for systematic evaluation of gene signatures

With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.

Molecular profiling of hormone receptor-positive, HER2-negative breast cancers from patients treated with neoadjuvant endocrine therapy in the CARMINA 02 trial (UCBG-0609)

BACKGROUND

Postmenopausal women with large, hormone receptor (HR)-positive/HER2-negative and low-proliferative breast cancer derived a benefit from neoadjuvant endocrine therapy (NET) in the CARMINA02 trial. This study was designed to correlate gene expression and mutation profiles with both response to NET and prognosis.

METHODS

Gene expression profiling using RNA sequencing was performed in 86 pre-NET and post-NET tumor samples. Targeted next-generation sequencing of 91 candidate breast cancer-associated genes was performed on DNA samples from 89 patients. Molecular data were correlated with radiological response and relapse-free survival.

RESULTS

The transcriptional profile of tumors to NET in responders involved immune-associated genes enriched in activated Th1 pathway, which remained unchanged in non-responders. Immune response was confirmed by analysis of tumor-infiltrating lymphocytes (TILs). The percentage of TILs was significantly increased post-NET compared to pre-NET samples in responders (p = 0.0071), but not in non-responders (p = 0.0938). Gene expression revealed that lipid metabolism was the main molecular function related to prognosis, while PPARγ is the most important upstream regulator gene. The most frequently mutated genes were PIK3CA (48.3%), CDH1 (20.2%), PTEN (15.7%), TP53 (10.1%), LAMA2 (10.1%), BRCA2 (9.0%), MAP3K1 (7.9%), ALK (6.7%), INPP4B (6.7%), NCOR1 (6.7%), and NF1 (5.6%). Cell cycle and apoptosis pathway and PIK3CA/AKT/mTOR pathway were altered significantly more frequently in non-responders than in responders (p = 0.0017 and p = 0.0094, respectively). The average number of mutations per sample was significantly higher in endocrine-resistant tumors (2.88 vs. 1.64, p = 0.03), but no difference was observed in terms of prognosis. ESR1 hotspot mutations were detected in 3.4% of treatment-naive tumors.

CONCLUSIONS

The Th1-related immune system and lipid metabolism appear to play key roles in the response to endocrine therapy and prognosis in HR-positive/HER2-negative breast cancer. Deleterious somatic mutations in the cell cycle and apoptosis pathway and PIK3CA/AKT/mTOR pathway may be relevant for clinical management.

TRIAL REGISTRATION

This trial is registered with ClinicalTrials.gov ( NCT00629616 ) on March 6, 2008, retrospectively registered.

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

Purpose

Anthracyclines 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.

Methods

From 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.

Results

Median 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.

Conclusion

The 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.

Molecular prediction of adjuvant cisplatin efficacy in Non-Small Cell Lung Cancer (NSCLC)-validation in two independent cohorts

INTRODUCTION

Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented.

METHODS

The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1) snap-frozen tumors from 133 patients with completely resected stage 1B-2 NSCLC randomized to adjuvant cisplatin and vinorelbine (ACV, n = 71) or no adjuvant treatment (OBS, n = 62) and 2) formalin-fixed paraffin-embedded (FFPE) tumors from 95 patients with completely resected stage 1A-3B NSCLC receiving adjuvant cisplatin and vinorelbine.

RESULTS

The combined cisplatin and vinorelbine profiles showed: 1) univariate Hazard Ratio (HR) for sensitive versus resistant of 0.265 (95% CI:0.079-0.889, p = 0.032) in the ACV cohort and a HR of 0.28 in a multivariate model (95% CI:0.08-1.04, p = 0.0573); 2) significant prediction at 3 year survival from surgery in univariate (HR = 0.138 (95% CI:0.035-0.537), p = 0.004) and multivariate analysis (HR = 0.14 (95% CI:0.030-0.6), p = 0.0081). No discrimination was found in the OBS cohort (HR = 1.328, p = 0.60). The cisplatin predictor alone had similar figures with 1) univariate HR of 0.37 (95% CI:0.12-1.15, p = 0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03-0.59, p = 0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis.

CONCLUSIONS

Profiles derived from snap-frozen and FFPE NSCLC tissue were prognostic and predictive in the patients that received cisplatin and vinorelbine but not in the cohort that did not receive adjuvant treatment.

Phylogenomic detection and functional prediction of genes potentially important for plant meiosis

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries.

Drug response prediction in high-risk multiple myeloma

A Drug Response Prediction (DRP) score was developed based on gene expression profiling (GEP) from cell lines and tumor samples. Twenty percent of high-risk patients by GEP70 treated in Total Therapy 2 and 3A have a progression-free survival (PFS) of more than 10years. We used available GEP data from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged PFS, HR=2.4 (1.2-4.9, P=0.014) and those with predicted sensitivity to bortezomib had a HR 5.7 (1.2-27, P=0.027). In case of predicted sensitivity to bortezomib, a better response to treatment was found (P=0.022). This method may provide us with a tool for identifying candidates for effective personalized medicine and spare potential non-responders from suffering toxicity.

Anthropometric, clinical and molecular determinants of treatment outcomes in postmenopausal, hormone receptor positive metastatic breast cancer patients treated with fulvestrant: Results from a real word setting

To characterize determinants of treatment outcome in a real world population of 161 post-menopausal hormone receptor-positive metastatic breast cancer patients treated with fulvestrant. Descriptive statistics for demographics, anthropometrics, clinical and molecular characteristic were compared across subgroups of sensitivity/resistance to prior endocrine therapy and tested in uni/multivariate models. Clinical benefit was more common in sensitive patients with higher estrogen receptor expression and when fulvestrant was given in first line (p=0.02 and 0.046). In resistant patients, PFS was longer with lower BMI (p=0.01). Among endocrine sensitive women, longer PFS was associated with fulvestrant in first-line, single metastasis and no visceral involvement (p=0.01, 0.003 and 0.01). OS was shorter in resistant patients with HER2-positive disease and if fulvestrant was given in second and subsequent line (p=0.03). In sensitive patients, we observed worse OS with multiple metastases (p=0.008). Multivariate analyses confirmed longer PFS in resistant patients with lower BMI and older age (p=0.002 and 0.007). OS in resistant patients was negatively influenced by HER2 positivity and fulvestrant in second and subsequent line (p=0.04). In sensitive women, multiple metastases were associated with poorer survival (p=0.002). This evidence encourages considering patient and disease characteristics in decision making and outcome interpretation for patients candidate to fulvestrant.

Randomized phase II trial of fulvestrant alone or in combination with bortezomib in hormone receptor-positive metastatic breast cancer resistant to aromatase inhibitors: a New York Cancer Consortium trial

The proteasome inhibitor bortezomib enhances the effect of the selective estrogen receptor (ER) downregulator (SERD) fulvestrant by causing accumulation of cytoplasmic ER aggregates in preclinical models. The purpose of this trial was to determine whether bortezomib enhanced the effectiveness of fulvestrant. One hundred eighteen postmenopausal women with ER-positive metastatic breast cancer resistant to aromatase inhibitors (AIs) were randomized to fulvestrant alone (Arm A-500 mg intramuscular (i.m.) day -14, 1, 15 in cycle 1, and day 1 of additional cycles) or in combination with bortezomib (Arm B-1.6 mg/m² intravenous (i.v.) on days 1, 8, 15 of each cycle). The study was powered to show an improvement in median progression-free survival (PFS) from 5.4 to 9.0 months and compare PFS rates at 6 and 12 months (α=0.10, β=0.10). Patients with progression on fulvestrant could cross over to the combination (arm C). Although there was no difference in median PFS (2.7 months in both arms), the hazard ratio for PFS in Arm B versus Arm A (referent) was 0.73 (95% confidence interval (CI)=0.49, 1.09, P=0.06, 1-sided log-rank test, significant at the prespecified 1-sided 0.10 α level). At 12 months, the PFS proportion in Arm A and Arm B was 13.6% and 28.1% (P=0.03, 1-sided χ²-test; 95% CI for difference (14.5%)=-0.06, 29.1%). Of 27 patients on arm A who crossed over to the combination (arm C), 5 (18%) were progression-free for at least 24 weeks. Bortezomib likely enhances the effectiveness of fulvestrant in AI-resistant, ER-positive metastatic breast cancer by reducing acquired resistance, supporting additional evaluation of proteasome inhibitors in combination with SERDs.

In-depth characterization of breast cancer tumor-promoting cell transcriptome by RNA sequencing and microarrays

Numerous studies have reported the existence of tumor-promoting cells (TPC) with self-renewal potential and a relevant role in drug resistance. However, pathways and modifications involved in the maintenance of such tumor subpopulations are still only partially understood. Sequencing-based approaches offer the opportunity for a detailed study of TPC including their transcriptome modulation. Using microarrays and RNA sequencing approaches, we compared the transcriptional profiles of parental MCF7 breast cancer cells with MCF7-derived TPC (i.e. MCFS). Data were explored using different bioinformatic approaches, and major findings were experimentally validated. The different analytical pipelines (Lifescope and Cufflinks based) yielded similar although not identical results. RNA sequencing data partially overlapped microarray results and displayed a higher dynamic range, although overall the two approaches concordantly predicted pathway modifications. Several biological functions were altered in TPC, ranging from production of inflammatory cytokines (i.e., IL-8 and MCP-1) to proliferation and response to steroid hormones. More than 300 non-coding RNAs were defined as differentially expressed, and 2,471 potential splicing events were identified. A consensus signature of genes up-regulated in TPC was derived and was found to be significantly associated with insensitivity to fulvestrant in a public breast cancer patient dataset. Overall, we obtained a detailed portrait of the transcriptome of a breast cancer TPC line, highlighted the role of non-coding RNAs and differential splicing, and identified a gene signature with a potential as a context-specific biomarker in patients receiving endocrine treatment.

Cell Line Derived 5-FU and Irinotecan Drug-Sensitivity Profiles Evaluated in Adjuvant Colon Cancer Trial Data

PURPOSE

This study evaluates whether gene signatures for chemosensitivity for irinotecan and 5-fluorouracil (5-FU) derived from in vitro grown cancer cell lines can predict clinical sensitivity to these drugs.

METHODS

To test if an irinotecan signature and a SN-38 signature could identify patients who benefitted from the addition of irinotecan to 5-FU, we used gene expression profiles based on cell lines and clinical tumor material. These profiles were applied to expression data obtained from pretreatment formalin fixed paraffin embedded (FFPE) tumor tissue from 636 stage III colon cancer patients enrolled in the PETACC-3 prospective randomized clinical trial. A 5-FU profile developed similarly was assessed by comparing the PETACC-3 cohort with a cohort of 359 stage II colon cancer patients who underwent surgery but received no adjuvant therapy.

RESULTS

There was no statistically significant association between the irinotecan or SN-38 profiles and benefit from irinotecan. The 5-FU sensitivity profile showed a statistically significant association with relapse free survival (RFS) (hazard ratio (HR) = 0.54 (0.41-0.71), p<1e-05) and overall survival (HR = 0.47 (0.34-0.63), p<1e-06) in the PETACC-3 subpopulation. The effect of the 5-FU profile remained significant in a multivariable Cox Proportional Hazards model, adjusting for several relevant clinicopathological parameters. No statistically significant effect of the 5-FU profile was observed in the untreated cohort of 359 patients (relapse free survival, p = 0.671).

CONCLUSION

The irinotecan predictor had no predictive value. The 5-FU predictor was prognostic in stage III patients in PETACC-3 but not in stage II patients with no adjuvant therapy. This suggests a potential predictive ability of the 5-FU sensitivity profile to identify colon cancer patients who may benefit from 5-FU, however, any biomarker predicting benefit for adjuvant 5-FU must be rigorously evaluated in independent cohorts. Given differences between the two study cohorts, the present results should be further validated.

Treating gynecologic malignancies with selective estrogen receptor downregulators (SERDs): promise and challenges

Endometrial and ovarian cancers are estrogen-dependent gynecologic malignancies. Although many are estrogen receptor (ER) positive, treatment with the selective estrogen receptor modulator (SERM) tamoxifen, a tissue selective partial-agonist, has demonstrated only modest clinical benefit. Selective estrogen receptor downregulators (SERDs) are pure ER antagonists showing a benefit for advanced ER positive breast cancer, which has bolstered their potential use for ER positive gynecologic malignancies. We summarize these preclinical and clinical data, suggesting that a subpopulation of patients with endometrial or ovarian cancer exists in which treatment with SERDs results in improved outcome. However, the full potential of SERDs for a gynecologic malignancies will be realized only when the appropriate predictive biomarkers are identified. Additionally, a further understanding ER signaling in the context of ovarian and endometrial tissues that appear to involve c-Src and other kinase pathways is needed to successfully address the emergence of resistance with rationally designed combination therapies.

Overview of Genetically Engineered Mouse Models of Breast Cancer Used in Translational Biology and Drug Development

Breast cancer is a heterogeneous condition with no single standard of treatment and no definitive method for determining whether a tumor will respond to therapy. The development of murine models that faithfully mimic specific human breast cancer subtypes is critical for the development of patient-specific treatments. While the artificial nature of traditional in vivo xenograft models used to characterize novel anticancer treatments has limited clinical predictive value, the development of genetically engineered mouse models (GEMMs) makes it possible to study the therapeutic responses in an intact microenvironment. GEMMs have proven to be an experimentally tractable platform for evaluating the efficacy of novel therapeutic combinations and for defining the mechanisms of acquired resistance. Described in this overview are several of the more popular breast cancer GEMMs, including details on their value in elucidating the molecular mechanisms of this disorder.

Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients

Epithelial-mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co-opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer-specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer-specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease-free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial- and mesenchymal-like ovarian cancers to therapeutic regimes administered with or without paclitaxelin vivo and demonstrated that mesenchymal-like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival.

Development and blind clinical validation of a microRNA based predictor of response to treatment with R-CHO(E)P in DLBCL

MicroRNAs (miRNA) are a group of short noncoding RNAs that regulate gene expression at the posttranscriptional level. It has been shown that microRNAs are independent predictors of outcome in patients with diffuse large B-cell lymphoma (DLBCL) treated with the drug combination R-CHOP. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of doxorubicine, cyclophosphamide, vincristine and etoposide as well as the baseline microRNA expression of the 60 cell lines, a microRNA based response predictor to CHOP was developed. The response predictor consisting of 20 microRNAs was blindly validated in a cohort of 116 de novo DLBCL patients treated with R-CHOP or R-CHOEP as first line treatment. The predicted sensitivity based on diagnostic FFPE samples matched the clinical response, with decreasing sensitivity in poor responders (P = 0.03). When the International Prognostic Index (IPI) was included in the prediction analysis, the separation between responders and non-responders improved (P = 0.001). Thirteen patients developed relapse, and five patients predicted sensitive to their second and third line treatment survived a median 1194 days, while eight patients predicted not sensitive to their second and third line treatment survived a median 187 days (90% CI: 485 days versus 227 days). Among the latter group it was predicted that four would have been sensitive to another second line treatment than the one they received. The predictions were almost the same when diagnostic biopsies were used as when relapse biopsies were used. These preliminary findings warrant testing in a larger cohort of relapse patients to confirm whether the miRNA based predictor can select the optimal second line treatment and increase survival.