PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer

A randomized Phase I pre-operative window trial of transdermal endoxifen in women planning mastectomy: Evaluation of dermal safety, intra-mammary drug distribution, and biologic effects

Breast cancer prevention only requires local exposure of the breast to active drug. However, oral preventive agents entail systemic exposure, causing adverse effects that limit acceptance by high-risk women. Drug-delivery through the breast skin is an attractive option, but requires demonstration of dermal safety and drug distribution throughout the breast. We formulated the tamoxifen metabolite (E/Z)-endoxifen for transdermal delivery and tested it in a placebo-controlled, double-blinded Phase I trial with dose escalation from 10 to 20 mg daily. The primary endpoint was dermal toxicity. Thirty-two women planning mastectomy were randomized (2:1) to endoxifen-gel or placebo-gel applied to both breasts for 3-5 weeks. Both doses of endoxifen-gel incurred no dermal or systemic toxicity compared to placebo. All endoxifen-treated breasts contained the drug at each of five sampling locations; the median per-person tissue concentration in the treated participants was 0.6 ng/g (IQR 0.4-1.6), significantly higher (p < 0.001) than the median plasma concentration (0.2 ng/mL, IQR 0.2-0.2). The median ratio of the more potent (Z)-isomer to (E)-isomer at each breast location was 1.50 (IQR 0.96-2.54, p < 0.05). No discernible effects of breast size or adiposity on tissue concentrations were observed. At the endoxifen doses and duration used, and the tissue concentration achieved, we observed a non-significant overall reduction of tumor proliferation (Ki67 LI) and significant downregulation of gene signatures known to promote cancer invasion (FN1, SERPINH1, PLOD2, PDGFA, ITGAV) (p = 0.03). Transdermal endoxifen is an important potential breast cancer prevention agent but formulations with better dermal penetration are needed.

Diabetes-associated breast cancer is molecularly distinct and shows a DNA damage repair deficiency

Diabetes commonly affects patients with cancer. We investigated the influence of diabetes on breast cancer biology using a 3-pronged approach that included analysis of orthotopic human tumor xenografts, patient tumors, and breast cancer cells exposed to diabetes/hyperglycemia-like conditions. We aimed to identify shared phenotypes and molecular signatures by investigating the metabolome, transcriptome, and tumor mutational burden. Diabetes and hyperglycemia did not enhance cell proliferation but induced mesenchymal and stem cell-like phenotypes linked to increased mobility and odds of metastasis. They also promoted oxyradical formation and both a transcriptome and mutational signatures of DNA repair deficiency. Moreover, food- and microbiome-derived metabolites tended to accumulate in breast tumors in the presence of diabetes, potentially affecting tumor biology. Breast cancer cells cultured under hyperglycemia-like conditions acquired increased DNA damage and sensitivity to DNA repair inhibitors. Based on these observations, we conclude that diabetes-associated breast tumors may show an increased drug response to DNA damage repair inhibitors.

Ki67 assessment protocol as an integral biomarker for avoiding radiotherapy in the LUMINA breast cancer trial

AIMS

The LUMINA trial demonstrated a very low local recurrence rate in women ≥55 years with low-risk luminal A breast cancer (defined as grade I-II, T1N0, hormone receptor positive, HER2 negative and Ki67 index ≤13.25%) treated with breast-conserving surgery and endocrine therapy (but no other systemic therapy), supporting the safe omission of radiation in these women. Here we describe the protocol for Ki67 assessment, the companion diagnostic used to guide omission of adjuvant radiotherapy.

METHODS

Ki67 immunohistochemistry was performed on full-face sections at one of three regional labs. Pathologists trained in the International Ki67 in Breast Cancer Working Group (IKWG) method demarcated tumour areas on scanned slides and scored 100 nuclei from each of at least five randomly selected 1-mm fields. For cases with high Ki67 heterogeneity, further virtual cores were selected and scored in order to confidently assign a case as luminal A (≤13.25%) or B (>13.25%). Interlaboratory variability was assessed through an annual quality assurance programme during the study period.

RESULTS

From the quality assurance programme, the mean Ki67 index across all cases/labs was 13%. The observed intraclass correlation coefficient (ICC) and kappa statistics were ≥0.9 and ≥0.7, respectively, indicating a substantial level of agreement. Median scoring time was 4 min per case. The IKWG-recommended scoring method, performed directly from slides, requiring up to four scored fields, is concordant with the LUMINA scoring method (ICC ≥ 0.9).

CONCLUSION

Ki67 is a practical, reproducible, and inexpensive biomarker that can identify low-risk luminal A breast cancers as potential candidates for radiation de-escalation.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01791829.

Longitudinal multi-omics study of palbociclib resistance in HR-positive/HER2-negative metastatic breast cancer

BACKGROUND

Cyclin-dependent kinase 4/6 inhibitor (CDK4/6) therapy plus endocrine therapy (ET) is an effective treatment for patients with hormone receptor-positive/human epidermal receptor 2-negative metastatic breast cancer (HR+/HER2- MBC); however, resistance is common and poorly understood. A comprehensive genomic and transcriptomic analysis of pretreatment and post-treatment tumors from patients receiving palbociclib plus ET was performed to delineate molecular mechanisms of drug resistance.

METHODS

Tissue was collected from 89 patients with HR+/HER2- MBC, including those with recurrent and/or metastatic disease, receiving palbociclib plus an aromatase inhibitor or fulvestrant at Samsung Medical Center and Seoul National University Hospital from 2017 to 2020. Tumor biopsy and blood samples obtained at pretreatment, on-treatment (6 weeks and/or 12 weeks), and post-progression underwent RNA sequencing and whole-exome sequencing. Cox regression analysis was performed to identify the clinical and genomic variables associated with progression-free survival.

RESULTS

Novel markers associated with poor prognosis, including genomic scar features caused by homologous repair deficiency (HRD), estrogen response signatures, and four prognostic clusters with distinct molecular features were identified. Tumors with TP53 mutations co-occurring with a unique HRD-high cluster responded poorly to palbociclib plus ET. Comparisons of paired pre- and post-treatment samples revealed that tumors became enriched in APOBEC mutation signatures, and many switched to aggressive molecular subtypes with estrogen-independent characteristics. We identified frequent genomic alterations upon disease progression in RB1, ESR1, PTEN, and KMT2C.

CONCLUSIONS

We identified novel molecular features associated with poor prognosis and molecular mechanisms that could be targeted to overcome resistance to CKD4/6 plus ET.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03401359. The trial was posted on 18 January 2018 and registered prospectively.

Learning vector quantized representation for cancer subtypes identification

BACKGROUND AND OBJECTIVE

Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

METHODS

This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input.

RESULTS

Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.

CONCLUSION

Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Prognostic value of intrinsic subtypes in hormone-receptor-positive metastatic breast cancer: systematic review and meta-analysis

BACKGROUND

In hormone receptor-positive (HoR+) breast cancer (BC), gene expression analysis identifies luminal A (LumA), luminal B (LumB), human epidermal growth factor receptor 2 (HER2)-enriched (HER2-E), basal-like (BL) intrinsic subtypes and a normal-like group. This classification has an established prognostic value in early-stage HoR+ BC. Here, we carried out a trial-level meta-analysis to determine the prognostic ability of subtypes in metastatic BC (MBC).

MATERIALS AND METHODS

We systematically reviewed all the available prospective phase II/III trials in HoR+ MBC where subtype was assessed. The primary endpoint was progression-free survival (PFS)/time to progression (TTP) of the LumA subtype compared to non-LumA. Secondary endpoints were PFS/TTP of each individual subtype, according to treatment, menopausal and HER2 status and overall survival (OS). The random-effect model was applied, and heterogeneity assessed through Cochran's Q and I². Threshold for significance was set at P < 0.05. The study was registered in PROSPERO (ID: CRD42021255769).

RESULTS

Seven studies were included (2536 patients). Non-LumA represented 55.2% and was associated with worse PFS/TTP than LumA [hazard ratio (HR) 1.77, P < 0.001, I² = 61%], independently of clinical HER2 status [P_{subgroup difference} (P_sub) = 0.16], systemic treatment (P_sub = 0.96) and menopausal status (P_sub = 0.12). Non-LumA tumors also showed worse OS (HR 2.00, P < 0.001, I² = 65%), with significantly different outcomes for LumB (PFS/TTP HR 1.46; OS HR 1.41), HER2-E (PFS/TTP HR 2.39; OS HR 2.08) and BL (PFS/TTP HR 2.67; OS HR 3.26), separately (PFS/TTP P_sub = 0.01; OS P_sub = 0.005). Sensitivity analyses supported the main result. No publication bias was observed.

CONCLUSIONS

In HoR+ MBC, non-LumA disease is associated with poorer PFS/TTP and OS than LumA, independently of HER2, treatment and menopausal status. Future trials in HoR+ MBC should consider this clinically relevant biological classification.

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs

Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Molecular Subtyping of Invasive Breast Cancer Using a PAM50-Based Multigene Expression Test-Comparison with Molecular-Like Subtyping by Tumor Grade/Immunohistochemistry and Influence on Oncologist's Decision on Systemic Therapy in a Real-World Setting

In intermediate risk hormone receptor (HR) positive, HER2 negative breast cancer (BC), the decision regarding adjuvant chemotherapy might be facilitated by multigene expression tests. In all, 142 intermediate risk BCs were investigated using the PAM50-based multigene expression test Prosigna® in a prospective multicentric study. In 119/142 cases, Prosigna® molecular subtyping was compared with local and two central (C1 and C6) molecular-like subtypes relying on both immunohistochemistry (IHC; HRs, HER2, Ki-67) and IHC + tumor grade (IHC+G) subtyping. According to local IHC, 35.4% were Luminal A-like and 64.6% Luminal B-like subtypes (local IHC+G subtype: 31.9% Luminal A-like; 68.1% Luminal B-like). In contrast to local and C1 subtyping, C6 classified >2/3 of cases as Luminal A-like. Pairwise agreement between Prosigna® subtyping and molecular-like subtypes was fair to moderate depending on molecular-like subtyping method and center. The best agreement was observed between Prosigna® (53.8% Luminal A; 44.5% Luminal B) and C1 surrogate subtyping (Cohen’s kappa = 0.455). Adjuvant chemotherapy was suggested to 44.2% and 88.6% of Prosigna® Luminal A and Luminal B cases, respectively. Out of all Luminal A-like cases (locally IHC/IHC+G subtyping), adjuvant chemotherapy was recommended if Prosigna® testing classified as Prosigna® Luminal A at high / intermediate risk or upgraded to Prosigna® Luminal B.

Impact of Duration of Neoadjuvant Aromatase Inhibitors on Molecular Expression Profiles in Estrogen Receptor-positive Breast Cancers

PURPOSE

Aromatase inhibitor (AI) treatment is the standard of care for postmenopausal women with primary estrogen receptor-positive breast cancer. The impact of duration of neoadjuvant endocrine therapy (NET) on molecular characteristics is still unknown. We evaluated and compared changes of gene expression profiles under short-term (2-week) versus longer-term neoadjuvant AIs.

EXPERIMENTAL DESIGN

Global gene expression profiles from the PeriOperative Endocrine Therapy for Individualised Care (POETIC) trial (137 received 2 weeks of AIs and 47 received no treatment) and targeted gene expression from 80 patients with breast cancer treated with NET for more than 1 month (NeoAI) were assessed. Intrinsic subtyping, module scores covering different cancer pathways and immune-related genes were calculated for pretreated and posttreated tumors.

RESULTS

The differences in intrinsic subtypes after NET were comparable between the two cohorts, with most Luminal B (90.0% in the POETIC trial and 76.3% in NeoAI) and 50.0% of HER2 enriched at baseline reclassified as Luminal A or normal-like after NET. Downregulation of proliferative-related pathways was observed after 2 weeks of AIs. However, more changes in genes from cancer-signaling pathways such as MAPK and PI3K/AKT/mTOR and immune response/immune-checkpoint components that were associated with AI-resistant tumors and differential outcome were observed in the NeoAI study.

CONCLUSIONS

Tumor transcriptional profiles undergo bigger changes in response to longer NET. Changes in HER2-enriched and Luminal B subtypes are similar between the two cohorts, thus AI-sensitive intrinsic subtype tumors associated with good survival might be identified after 2 weeks of AI. The changes of immune-checkpoint component expression in early AI resistance and its impact on survival outcome warrants careful investigation in clinical trials.

The tumor immune microenvironment of primary and metastatic HER2- positive breast cancers utilizing gene expression and spatial proteomic profiling

BACKGROUND

The characterization of the immune component of the tumor microenvironment (TME) of human epidermal growth factor receptor 2 positive (HER2+) breast cancer has been limited. Molecular and spatial characterization of HER2+ TME of primary, recurrent, and metastatic breast tumors has the potential to identify immune mediated mechanisms and biomarker targets that could be used to guide selection of therapies.

METHODS

We examined 15 specimens from eight patients with HER2+ breast cancer: 10 primary breast tumors (PBT), two soft tissue, one lung, and two brain metastases (BM). Using molecular profiling by bulk gene expression TME signatures, including the Tumor Inflammation Signature (TIS) and PAM50 subtyping, as well as spatial characterization of immune hot, warm, and cold regions in the stroma and tumor epithelium using 64 protein targets on the GeoMx Digital Spatial Profiler.

RESULTS

PBT had higher infiltration of immune cells relative to metastatic sites and higher protein and gene expression of immune activation markers when compared to metastatic sites. TIS scores were lower in metastases, particularly in BM. BM also had less immune infiltration overall, but in the stromal compartment with the highest density of immune infiltration had similar levels of T cells that were less activated than PBT stromal regions suggesting immune exclusion in the tumor epithelium.

CONCLUSIONS

Our findings show stromal and tumor localized immune cells in the TME are more active in primary versus metastatic disease. This suggests patients with early HER2+ breast cancer could have more benefit from immune-targeting therapies than patients with advanced disease.

Identification of nuclear export inhibitor-based combination therapies in preclinical models of triple-negative breast cancer

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High-throughput drug screening reveals promising therapeutic candidates for TNBC.

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KPT-330, an XPO1 inhibitor, and GSK2126458 exhibit synergism in preclinical models of TNBC.

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XPO1 is overexpressed in basal-like breast tumors.

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XPO1 expression is associated with PIK3CA, MTOR, and MKI67 expression at the single-cell level.

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XPO1 overexpression in basal-like patients is associated with greater rates of metastases.

An estimated 284,000 Americans will be diagnosed with breast cancer in 2021. Of these individuals, 15–20% have basal-like triple-negative breast cancer (TNBC), which is known to be highly metastatic. Chemotherapy is standard of care for TNBC patients, but chemoresistance is a common clinical problem. There is currently a lack of alternative, targeted treatment strategies for TNBC; this study sought to identify novel therapeutic combinations to treat basal-like TNBCs. For these studies, four human basal-like TNBC cell lines were utilized to determine the cytotoxicity profile of 1363 clinically-used drugs. Ten promising therapeutic candidates were identified, and synergism studies were performed in vitro. Two drug combinations that included KPT-330, an XPO1 inhibitor, were synergistic in all four cell lines. In vivo testing of four basal-like patient-derived xenografts (PDX) identified one combination, KPT-330 and GSK2126458 (a PI3K/mTOR inhibitor), that decreased tumor burden in mice significantly more than monotherapy with either single agent. Bulk and single-cell RNA-sequencing, immunohistochemistry, and analysis of published genomic datasets found that XPO1 was abundantly expressed in human basal-like TNBC cell lines, PDXs, and patient tumor samples. Within basal-like PDXs, XPO1 overexpression was associated with increased proliferation at the cellular level. Within patient datasets, XPO1 overexpression was correlated with greater rates of metastasis in patients with basal-like tumors. These studies identify a promising potential new combination therapy for patients with basal-like breast cancer.

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

Background

The prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.

Methods

We performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture, TIC) in tumors. Then, we studied associations between these immune profiles and relapse-free and overall survival among the different intrinsic molecular subtypes of breast cancer defined by PAM50 classification.

Results

TIC estimates the abundance of 15 immune cell subsets. Both myeloid and lymphoid subpopulations show different spread among intrinsic molecular breast cancer subtypes. A high abundance of myeloid cells was associated with poor outcome, while lymphoid cells were associated with favorable prognosis. Unsupervised clustering describing the 15 immune cell subsets revealed four subgroups of breast tumors associated with distinct patient survival, but independent from PAM50. Adding this information to clinical stage and PAM50 strongly improves the prediction of relapse or death.

Conclusions

Our findings make it possible to refine the survival stratification of early patients with breast cancer by incorporating TIC in addition to PAM50 and clinical tumor burden in a prognostic model validated in training and validation cohorts.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

Practical consensus recommendations regarding the management of hormone receptor positive early breast cancer in elderly women

Breast cancer is a leading cause of death among women, and its incidence increases with age. Currently the treatment of breast cancer in older patients is almost identical to their younger counterparts. This expert group used data from published literature, practical experience and opinion of a large group of academic oncologists to arrive at these practical consensus recommendations for the benefit of community oncologists regarding the management of early breast cancer specifically in elderly women.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Subtyping of male breast cancer by PAM50 and immunohistochemistry: a pilot study of a consecutive Danish cohort

Male breast cancer (MBC) is a rare disease that is still to be fully understood. In female breast cancer, molecular subtyping by gene expression has proven its significance. In this study, we characterize a consecutive cohort of MBC patients surgically treated from 1997 to 2017, identified at our institution (N = 37), and report the association between molecular subtypes found by a surrogate panel of immunohistochemical (IHC) markers, and the PAM50 signature, as well as risk of recurrence score and overall survival for the different subtypes. PAM50 subtypes were determined using the nCounter FLEX system instrument and software. The distribution of molecular subtypes according to the PAM50 signature was as follows: 56% luminal B, 39% luminal A, and 5% basal-like. None of the tumors were HER2-enriched. Using IHC surrogate markers, we found 80% luminal B-like, 15% luminal A-like, and 5% basal-like. None were HER2-positive (non-luminal). We found a strong statistical association between subtypes found by PAM50 signature and the IHC surrogate markers (p < 0.001). Furthermore, we found luminal A tumors to be smaller in size compared to luminal B tumors (p = 0.04). Patients with luminal A subtype tumors had the lowest ROR scores with a mean of 39, whereas patients with luminal B subtype tumors had a mean ROR score of 69. Significant worse overall survival for luminal B tumors compared to luminal A tumors was seen (p = 0.02). Male breast cancer seems to be a mainly luminal disease, with luminal B being the most frequent subtype. Further studies are needed to ensure correct therapeutic strategies for this select group of patients.

Gene Expression Assay in the Management of Early Breast Cancer

The addition of adjuvant chemotherapy to hormonal therapy is often considered questionable in patients with estrogen receptor-positive early breast cancer. Low risk of disease relapse after endocrine treatment alone and/or a low sensitivity to chemotherapy are reasons behind not all patients benefit from chemotherapy. Most of the patients could be exposed to unnecessary treatment- related adverse events and health care costs when treatment decision-making is based only on classical clinical histological features. Gene expression profile has been developed to refine physician's decision-making process and to tailor personalized treatment to patients. In particular, these tests are designed to spare patients the side effects of unnecessary treatment, and ensure that adjuvant chemotherapy is correctly recommended to patients with early breast cancer. In this review, we will discuss the main diagnostic tests and their potential clinical applications (Oncotype DX, MammaPrint, PAM50/Prosigna, EndoPredict, MapQuant Dx, IHC4, and Theros-Breast Cancer Gene Expression Ratio Assay).

Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug Treatment Concentrations

Lack of efficacy and a low overall success rate of phase I-II clinical trials are the most common failures when it comes to advancing cancer treatment. Current drug sensitivity screenings present several challenges including differences in cell growth rates, the inconsistent use of drug metrics, and the lack of translatability. Here, we present a patient-derived 3D culture model to overcome these limitations in breast cancer (BCa). The human plasma-derived 3D culture model (HuP3D) utilizes patient plasma as the matrix, where BCa cell lines and primary BCa biopsies were grown and screened for drug treatments. Several drug metrics were evaluated from relative cell count and growth rate curves. Correlations between HuP3D metrics, established preclinical models, and clinical effective concentrations in patients were determined. HuP3D efficiently supported the growth and expansion of BCa cell lines and primary breast cancer tumors as both organoids and single cells. Significant and strong correlations between clinical effective concentrations in patients were found for eight out of ten metrics for HuP3D, while a very poor positive correlation and a moderate correlation was found for 2D models and other 3D models, respectively. HuP3D is a feasible and efficacious platform for supporting the growth and expansion of BCa, allowing high-throughput drug screening and predicting clinically effective therapies better than current preclinical models.

Highly robust model of transcription regulator activity predicts breast cancer overall survival

BACKGROUND

While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes.

METHODS

Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome.

RESULT

We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients.

CONCLUSION

Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Oral metronomic vinorelbine combined with endocrine therapy in hormone receptor-positive HER2-negative breast cancer: SOLTI-1501 VENTANA window of opportunity trial

Background

The biological effect of oral metronomic vinorelbine (mVNB) alone or in combination with endocrine therapy in patients with hormone receptor-positive (HR+)/HER2-negative breast cancer has been scarcely addressed.

Methods

Postmenopausal women with untreated stage I–III HR+/HER2-negative breast cancer were randomized (1:1:1) to receive 3 weeks of letrozole (LTZ) 2.5 mg/day, oral mVNB 50 mg 3 days/week, or the combination. The primary objective was to evaluate, within PAM50 Luminal A/B disease, if the anti-proliferative effect of LTZ+mVNB was superior to monotherapy. An anti-proliferative effect was defined as the mean relative decrease of the PAM50 11-gene proliferation score in combination arm vs. both monotherapy arms. Secondary objectives included the evaluation of a comprehensive panel of breast cancer-related genes and safety. An unplanned analysis of stromal tumor-infiltrating lymphocytes (sTILs) was also performed. PAM50 analyses were performed using the nCounter®-based Breast Cancer 360™ gene panel, which includes 752 genes and 32 signatures.

Results

Sixty-one patients were randomized, and 54 paired samples (89%) were analyzed. The main patient characteristics were mean age of 67, mean tumor size of 1.7 cm, mean Ki67 of 14.3%, stage I (55.7%), and grades 1–2 (90%). Most baseline samples were PAM50 Luminal A (74.1%) or B (22.2%). The anti-proliferative effect of 3 weeks of LTZ+mVNB (− 73.2%) was superior to both monotherapy arms combined (− 49.9%; p = 0.001) and mVNB (− 19.1%; p < 0.001). The anti-proliferative effect of LTZ+mVNB (− 73.2%) was numerically higher compared to LTZ (− 65.7%) but did not reach statistical significance (p = 0.328). LTZ+mVNB induced high expression of immune-related genes and gene signatures, including CD8 T cell signature and PDL1 gene and low expression of ER-regulated genes (e.g., progesterone receptor) and cell cycle-related and DNA repair genes. In tumors with ≤ 10% sTILs at baseline, a statistically significant increase in sTILs was observed following LTZ (paired analysis p = 0.049) and LTZ+mVNB (p = 0.012). Grade 3 adverse events occurred in 3.4% of the cases.

Conclusions

Short-term mVNB is well-tolerated and presents anti-proliferative activity alone and in combination with LTZ. The high expression of immune-related biological processes and sTILs observed with the combination opens the possibility of studying this combination with immunotherapy. Further investigation comparing these biological results with other metronomic schedules or drug combinations is warranted.

Trial registration

NCT02802748, registered 16 June 2016.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s13058-019-1195-z.

Liver- and Microbiome-derived Bile Acids Accumulate in Human Breast Tumors and Inhibit Growth and Improve Patient Survival

PURPOSE

Metabolomics is a discovery tool for novel associations of metabolites with disease. Here, we interrogated the metabolome of human breast tumors to describe metabolites whose accumulation affects tumor biology.

EXPERIMENTAL DESIGN

We applied large-scale metabolomics followed by absolute quantification and machine learning-based feature selection using LASSO to identify metabolites that show a robust association with tumor biology and disease outcome. Key observations were validated with the analysis of an independent dataset and cell culture experiments.

RESULTS

LASSO-based feature selection revealed an association of tumor glycochenodeoxycholate levels with improved breast cancer survival, which was confirmed using a Cox proportional hazards model. Absolute quantification of four bile acids, including glycochenodeoxycholate and microbiome-derived deoxycholate, corroborated the accumulation of bile acids in breast tumors. Levels of glycochenodeoxycholate and other bile acids showed an inverse association with the proliferation score in tumors and the expression of cell-cycle and G₂-M checkpoint genes, which was corroborated with cell culture experiments. Moreover, tumor levels of these bile acids markedly correlated with metabolites in the steroid metabolism pathway and increased expression of key genes in this pathway, suggesting that bile acids may interfere with hormonal pathways in the breast. Finally, a proteome analysis identified the complement and coagulation cascade as being upregulated in glycochenodeoxycholate-high tumors.

CONCLUSIONS

We describe the unexpected accumulation of liver- and microbiome-derived bile acids in breast tumors. Tumors with increased bile acids show decreased proliferation, thus fall into a good prognosis category, and exhibit significant changes in steroid metabolism.

A Pathology-Based Combined Model to Identify PAM50 Non-luminal Intrinsic Disease in Hormone Receptor-Positive HER2-Negative Breast Cancer

Background: In hormone receptor-positive (HR+)/HER2-negative breast cancer, the HER2-enriched and Basal-like intrinsic subtypes are associated with poor outcome, low response to anti-estrogen therapy and high response to chemotherapy. To date, no validated biomarker exists to identify both molecular entities other than gene expression.

Methods: PAM50 subtyping and immunohistochemical data were obtained from 8 independent studies of 1,416 HR+/HER2-negative early breast tumors. A non-luminal disease score (NOLUS) from 0 to 100, based on percentage of estrogen receptor (ER), progesterone receptor (PR) and Ki67 tumor cells, was derived in a combined cohort of 5 studies (training dataset) and tested in a combined cohort of 3 studies. The performance of NOLUS was estimated using Area Under the ROC Curve (AUC).

Results: In the training dataset (n = 903) and compared to luminal disease, non-luminal disease had lower percentage of ER-positive cells (median 65.2 vs. 86.2%, p < 0.01) and PR-positive cells (33.2 vs. 56.4%, p < 0.01) and higher percentage of Ki67-positive cells (18.2 vs. 13.1%, p = 0.01). A NOLUS formula was derived: −0.45^*ER −0.28^*PR +0.27^*Ki67 + 73.02. The proportion of non-luminal tumors in NOLUS-positive (≥51.38) and NOLUS-negative (<51.38) groups was 52.6 and 8.7%, respectively. In the testing dataset (n = 514), NOLUS was found significantly associated with non-luminal disease (p < 0.01) with an AUC 0.902. The proportion of non-luminal tumors in NOLUS-positive and NOLUS-negative groups was 76.9% (56.4–91.0%) and 2.6% (1.4–4.5%), respectively. The sensitivity and specificity of the pre-specified cutoff was 59.3 and 98.7%, respectively.

Conclusions: In the absence of gene expression data, NOLUS can help identify non-luminal disease within HR+/HER2-negative breast cancer.

Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer

Background

We recently showed PAM50 gene expression data can be represented by five quantitative, orthogonal, multi-gene breast tumor traits. These novel tumor ‘dimensions’ were superior to categorical intrinsic subtypes for clustering in high-risk breast cancer pedigrees, indicating potential to represent underlying genetic susceptibilities and biological pathways. Here we explore the prognostic and predictive utility of these dimensions in a sub-study of GEICAM/9906, a Phase III randomized prospective clinical trial of paclitaxel in breast cancer.

Methods

Tumor dimensions, PC1–PC5, were calculated using pre-defined coefficients. Univariable and multivariable Cox proportional hazards (PH) models for disease-free survival (DFS) were used to identify associations between quantitative dimensions and prognosis or response to the addition of paclitaxel. Results were illustrated using Kaplan–Meier curves.

Results

Dimensions PC1 and PC5 were associated with DFS (Cox PH p = 6.7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10⁻⁷ and p = 0.036), remaining significant after correction for standard clinical–pathological prognostic characteristics. Both dimensions were selected in the optimal multivariable model, together with nodal status and tumor size (Cox PH p = 1.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10⁻¹²). Interactions with treatment were identified for PC3 and PC4. Response to paclitaxel was restricted to tumors with low PC3 and PC4 (log-rank p = 0.0021). Women with tumors high for PC3 or PC4 showed no survival advantage.

Conclusions

Our proof-of-concept application of quantitative dimensions illustrated novel findings and clinical utility beyond standard clinical–pathological characteristics and categorical intrinsic subtypes for prognosis and predicting chemotherapy response. Consideration of expression data as quantitative tumor dimensions offers new potential to identify clinically important patient subsets in clinical trials and advance precision medicine.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-05097-5) contains supplementary material, which is available to authorized users.

Molecular characterisation of aromatase inhibitor-resistant advanced breast cancer: the phenotypic effect of ESR1 mutations

Background

Several thousand breast cancer patients develop resistance to aromatase inhibitors (AIs) each year in the UK. Rational treatment requires an improved molecular characterisation of resistant disease.

Materials and methods

The mutational landscape of 198 regions in 16 key breast cancer genes and RNA expression of 209 genes covering key pathways was evaluated in paired biopsies before AI treatment and at progression on AI from 48 patients. Validity of findings was assessed in another five ESR1-mutated tumours progressing on AI.

Results

Eighty-nine mutations were identified in 41 matched pairs (PIK3CA in 27%; CDH1 in 20%). ESR1 (n = 5), ERBB2 (n = 1) and MAP2K4 (n = 1) had mutations in the secondary sample only. There was very high heterogeneity in gene expression between AI-resistant tumours with few patterns apparent. However, in the ESR1-mutated AI-resistant tumours, expression of four classical oestrogen-regulated genes (ERGs) was sevenfold higher than in ESR1 wild-type tumours, a finding confirmed in the second set of ESR1-mutated tumours. In ESR1 wild-type AI-resistant tumours ERG expression remained suppressed and was uncoupled from the recovery seen in proliferation.

Conclusions

Major genotypic and phenotypic heterogeneity exists between AI-resistant disease. ESR1 mutations appear to drive oestrogen-regulated processes in resistant tumours.

Basal biomarkers nestin and INPP4B predict gemcitabine benefit in metastatic breast cancer: Samples from the phase III SBG0102 clinical trial

In a formal prospective-retrospective analysis of the phase III SBG0102 clinical trial randomizing metastatic breast cancer patients to gemcitabine-docetaxel or to single agent docetaxel, patients with basal-like tumors by PAM50 gene expression had significantly better overall survival in the gemcitabine arm. By immunohistochemistry (IHC), triple negative status was not predictive, but more specific biomarkers have since become available defining basal-like by nestin positivity or loss of inositol-polyphosphate-4-phosphate (INPP4B). Here, we evaluate their capacity to identify which patients benefit from gemcitabine in the metastatic setting. Nestin and INPP4B staining and interpretation followed published methods. A prespecified statistical plan evaluated the primary hypothesis that patients with basal-like breast cancer, defined as "nestin+ or INPP4B-", would have superior overall survival on gemcitabine-docetaxel when compared to docetaxel. Interaction tests, Kaplan-Meier curves and forest plots were used to assess prognostic and predictive capacities of biomarkers relative to treatment. Among 239 cases evaluable for our study, 36 (15%) had been classified as basal-like by PAM50. "Nestin+ or INPP4B-" was observed in 41 (17%) of the total cases and was significantly associated with PAM50 basal-like subtype. Within an estimated median follow-up of 13 years, patients assigned as IHC basal "nestin+ or INPP4B-" had significantly better overall survival on gemcitabine-docetaxel versus docetaxel monotherapy (HR = 0.31, 95%CI: 0.16-0.60), whereas no differences were observed for other patients (HR = 0.99), p-interaction < 0.01. In the metastatic setting, women with IHC basal breast cancers defined as "nestin+ or INPP4B-" have superior overall survival when randomized to gemcitabine-containing chemotherapy compared to docetaxel alone. These findings need to be validated using larger prospective-retrospective phase III clinical trials series.

Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival

Background

Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis.

Methods

We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from “The Cancer Genome Atlas” and the Clinical Proteomic Tumor Analysis Consortium.

Results

We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3′ untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes.

Conclusions

Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0602-x) contains supplementary material, which is available to authorized users.

Prognostic role for the derived neutrophil-to-lymphocyte ratio in early breast cancer: a GEICAM/9906 substudy

PURPOSE

Elevated markers of host inflammation, a hallmark of cancer, have been associated with worse outcomes in several solid tumors. Here, we explore the prognostic role of the derived neutrophil-to-lymphocyte ratio (dNLR), across different tumor subtypes, in patients with early breast cancer.

PATIENTS AND METHODS

This was a retrospective analysis of 1246 patients with lymph node-positive, operable early breast cancer enrolled in the GEICAM/9906 trial, a multicenter randomized phase 3 study evaluating adjuvant chemotherapy. dNLR was calculated as the ratio of neutrophils and the difference between total leukocytes and neutrophils in peripheral blood before chemotherapy. Disease-free survival (DFS) and overall survival were explored using a Cox proportional hazard analysis.

RESULTS

The analysis comprised 1243 (99.8%) patients with dNLR data, with a median follow-up of 10 years. Data on intrinsic subtypes were available from 818 (66%) patients (luminal A 34%, luminal B 32%, HER2-enriched 21% and basal-like 9%). Median dNLR was 1.35 [interquartile range (IQR) 1.08-1.71]. In the whole population, dNLR was not prognostic after adjustment for clinico-pathological factors. However, dNLR ≥ 1.35 was independently associated with worse DFS in the hormone receptor-negative/HER2+ population (HR 2.86; p = 0.038) and in patients with one to three lymph node metastases (HR 1.32, p = 0.032). There was a non-significant association with worse DFS in non-luminal and in HER2-enriched tumors (HR 1.40, p = 0.085 and HR 1.53, p = 0.067). No significant interaction was observed between the treatment arm and dNLR.

CONCLUSION

Elevated dNLR appears to be an adverse prognostic factor in hormone receptor-negative early breast cancer.

TRIAL REGISTRATION

EudraCT: 2005-003108-12 (retrospectively registered 28/06/2005). ClinicalTrials.gov Identifier: NCT00129922 (retrospectively registered 10/08/2005). Results of this study were presented in part at the 2016 ESMO conference October 7-11, 2016, Copenhagen, Denmark (oral presentation).

Outcomes of single versus double hormone receptor-positive breast cancer. A GEICAM/9906 sub-study

BACKGROUND

Retrospective data suggest better outcomes for patients with double hormonal receptor (oestrogen [ER] and progesterone receptor [PgR])-positive (dHR+) early breast cancer, compared with single hormonal receptor-positive, sHR+, (ER+/PgR- or ER-/PgR+) disease. Here, we evaluate the classification according to intrinsic subtypes and clinical outcomes of sHR+ versus dHR+ in HER2-negative breast cancer patients enrolled in GEICAM/9906 study (NCT00129922).

METHODS

Archival tumours were retrieved retrospectively for the analysis of ER, PgR and HER2 status and classified into intrinsic subtypes using the PAM50 gene expression assay. Disease-free survival (DFS) and overall survival (OS) were explored using a Cox proportional hazard analysis.

RESULTS

Data on intrinsic subtypes were available in 571 (50%) patients with ER+ and/or PR+, and HER2-negative primary tumours. The incidence of luminal A and luminal B subtypes were 52%/36% in dHR+ tumours (ER+/PgR+), and 15%/58% in ER+/PgR-tumours. ER-/PgR+ tumours were mainly luminal A (52%). Compared with ER+/PgR+ patients, DFS was similar in ER-/PgR+ (hazard ratio [HR] 1.15, 95% confidence interval [CI] 0.57-2.34, p = 0.70) but worse in ER+/PgR- patients (HR 1.60, 95% CI 1.12-2.28, p < 0.01). Similar results were observed for OS (HR 1.50, p = 0.30 and HR 1.86, p < 0.01, respectively).

CONCLUSIONS

The ER+/PgR- group is characterised by higher proliferation and worse outcomes. In spite of the ER-/PgR+ subgroup resembles ER+/PgR+ disease in terms of molecular subtypes and outcomes, the small number of patients in this subgroup prevents from drawing any conclusions.

TRIAL REGISTRATION

EudraCT: 2005-003108-12 (retrospectively registered 28/06/2005). CLINICALTRIALS.

GOV IDENTIFIER

NCT00129922 (retrospectively registered 10/08/2005).

Circular RNAs and their associations with breast cancer subtypes

Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse biological functions. They are implicated in modulation of gene expression thus affecting various cellular and disease processes. Based on existing bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq to identify and report expressed circRNAs. Circ-Seq also provides informative genomic annotation along circRNA fused junctions thus allowing prioritization of circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast cancer cell lines and validated one of the large circRNAs identified. Circ-Seq was then applied to a larger cohort of breast cancer samples (n = 885) provided by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue samples. Notably, circRNA results reveal that normal-adjacent tissues in estrogen receptor positive (ER+) subtype have relatively higher numbers of circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers were observed in normal breast-mammary tissues from the Genotype-Tissue Expression (GTEx) project. Finally, we observed that number of circRNAs in normal-adjacent samples of ER+ subtype is inversely correlated to the risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting that circRNA frequency may be a marker for cell proliferation in breast cancer. The Circ-Seq workflow will function for both single and multi-threaded compute environments. We believe that Circ-Seq will be a valuable tool to identify circRNAs useful in the diagnosis and treatment of other cancers and complex diseases.

Systematic review of the clinical and economic value of gene expression profiles for invasive early breast cancer available in Europe

Gene expression profiles with prognostic capacities have shown good performance in multiple clinical trials. However, with multiple assays available and numerous types of validation studies performed, the added value for daily clinical practice is still unclear. In Europe, the MammaPrint, OncotypeDX, PAM50/Prosigna and Endopredict assays are commercially available. In this systematic review, we aim to assess these assays on four important criteria: Assay development and methodology, clinical validation, clinical utility and economic value. We performed a literature search covering PubMed, Embase, Web of Science and Cochrane, for studies related to one or more of the four selected assays. We identified 147 papers for inclusion in this review. MammaPrint and OncotypeDX both have evidence available, including level IA clinical trial results for both assays. Both assays provide prognostic information. Predictive value has only been shown for OncotypeDX. In the clinical utility studies, a higher reduction in chemotherapy was achieved by OncotypeDX, although the number of available studies differ considerably between tests. On average, economic evaluations estimate that genomic testing results in a moderate increase in total costs, but that these costs are acceptable in relation to the expected improved patient outcome. PAM50/prosigna and EndoPredict showed comparable prognostic capacities, but with less economical and clinical utility studies. Furthermore, for these assays no level IA trial data are available yet. In summary, all assays have shown excellent prognostic capacities. The differences in the quantity and quality of evidence are discussed. Future studies shall focus on the selection of appropriate subgroups for testing and long-term outcome of validation trials, in order to determine the place of these assays in daily clinical practice.

Assessing the Clinical Benefit of Systemic Adjuvant Therapies for Early Breast Cancer

Oncologic therapy is currently undergoing significant changes. A number of innovative targeted medications currently in clinical development have raised high expectations. With that in mind, discussions about terms such as "clinical benefit" and "clinical relevance" are highly topical. This also applies to further developments in the field of adjuvant systemic therapies for early-stage breast cancer. As the treatment aim is curative, assessment of the clinical benefit of adjuvant therapies must be largely based on efficacy outcomes. The focus must be on improving disease-free survival rates and lowering the risk of recurrence. Because of the current low mortality rates, statements about overall survival rates are only possible after very long observation periods. Consequently, new drugs in adjuvant therapies should be considered as offering a clinical benefit, if they reduce the risk of recurrence below current low levels of risk. The evidence for established adjuvant therapy standards in early-stage breast cancer can be used as objective criteria for comparison. This review article considers the requirements for clinical benefit of new adjuvant therapies for early breast cancer, based on examples from adjuvant endocrine therapy, adjuvant polychemotherapy and adjuvant anti-HER2 therapy.

Prognostic immune-related gene models for breast cancer: a pooled analysis

Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER−) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER− breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER−, LN+, and LN− breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER− and LN+ tumors.

A PAM50-Based Chemoendocrine Score for Hormone Receptor-Positive Breast Cancer with an Intermediate Risk of Relapse

Purpose: Hormone receptor-positive (HR+) breast cancer is clinically and biologically heterogeneous, and subgroups with different prognostic and treatment sensitivities need to be identified.Experimental Design: Research-based PAM50 subtyping and expression of additional genes was performed on 63 patients with HR+/HER2- disease randomly assigned to neoadjuvant multiagent chemotherapy versus endocrine therapy in a phase II trial. The biology associated with treatment response was used to derive a PAM50-based chemoendocrine score (CES). CES's predictive ability was evaluated in 4 independent neoadjuvant data sets (n = 675) and 4 adjuvant data sets (n = 1,505). The association of CES, intrinsic biology, and PAM50 risk of relapse (ROR) was explored across 6,007 tumors.Results: Most genes associated with endocrine sensitivity were also found associated with chemotherapy resistance. In the chemotherapy test/validation data sets, CES was independently associated with pathologic complete response (pCR), even after adjusting for intrinsic subtype. pCR rates of the CES endocrine-sensitive (CES-E), uncertain (CES-U), and chemotherapy-sensitive (CES-C) groups in both data sets combined were 25%, 11%, and 2%, respectively. In the endocrine test/validation data sets, CES was independently associated with response. Compared with ROR, >90% of ROR-low and ROR-high tumors were identified as CES-E and CES-C, respectively; however, each CES group represented >25% of ROR-intermediate disease. In terms of survival outcome, CES-C was associated with poor relapse-free survival in patients with ROR-intermediate disease treated with either adjuvant endocrine therapy only or no adjuvant systemic therapy, but not in patients treated with (neo)adjuvant chemotherapy.Conclusions: CES is a genomic signature capable of estimating chemoendocrine sensitivity in HR+ breast cancer beyond intrinsic subtype and risk of relapse. Clin Cancer Res; 23(12); 3035-44. ©2016 AACR.

Network Analysis Reveals A Signaling Regulatory Loop in the PIK3CA-mutated Breast Cancer Predicting Survival Outcome

Mutated genes are rarely common even in the same pathological type between cancer patients and as such, it has been very challenging to interpret genome sequencing data and difficult to predict clinical outcomes. PIK3CA is one of a few genes whose mutations are relatively popular in tumors. For example, more than 46.6% of luminal-A breast cancer samples have PIK3CA mutated, whereas only 35.5% of all breast cancer samples contain PIK3CA mutations. To understand the function of PIK3CA mutations in luminal A breast cancer, we applied our recently-proposed Cancer Hallmark Network Framework to investigate the network motifs in the PIK3CA-mutated luminal A tumors. We found that more than 70% of the PIK3CA-mutated luminal A tumors contain a positive regulatory loop where a master regulator (PDGF-D), a second regulator (FLT1) and an output node (SHC1) work together. Importantly, we found the luminal A breast cancer patients harboring the PIK3CA mutation and this positive regulatory loop in their tumors have significantly longer survival than those harboring PIK3CA mutation only in their tumors. These findings suggest that the underlying molecular mechanism of PIK3CA mutations in luminal A patients can participate in a positive regulatory loop, and furthermore the positive regulatory loop (PDGF-D/FLT1/SHC1) has a predictive power for the survival of the PIK3CA-mutated luminal A patients.

Hormonal Modulation of Breast Cancer Gene Expression: Implications for Intrinsic Subtyping in Premenopausal Women

Clinics are increasingly adopting gene-expression profiling to diagnose breast cancer subtype, providing an intrinsic, molecular portrait of the tumor. For example, the PAM50-based Prosigna test quantifies expression of 50 key genes to classify breast cancer subtype, and this method of classification has been demonstrated to be superior over traditional immunohistochemical methods that detect proteins, to predict risk of disease recurrence. However, these tests were largely developed and validated using breast cancer samples from postmenopausal women. Thus, the accuracy of such tests has not been explored in the context of the hormonal fluctuations in estrogen and progesterone that occur during the menstrual cycle in premenopausal women. Concordance between traditional methods of subtyping and the new tests in premenopausal women is likely to depend on the stage of the menstrual cycle at which the tissue sample is taken and the relative effect of hormones on expression of genes versus proteins. The lack of knowledge around the effect of fluctuating estrogen and progesterone on gene expression in breast cancer patients raises serious concerns for intrinsic subtyping in premenopausal women, which comprise about 25% of breast cancer diagnoses. Further research on the impact of the menstrual cycle on intrinsic breast cancer profiling is required if premenopausal women are to benefit from the new technology of intrinsic subtyping.

OPTIMA prelim: a randomised feasibility study of personalised care in the treatment of women with early breast cancer

BACKGROUND

There is uncertainty about the chemotherapy sensitivity of some oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers. Multiparameter assays that measure the expression of several tumour genes simultaneously have been developed to guide the use of adjuvant chemotherapy for this breast cancer subtype. The assays provide prognostic information and have been claimed to predict chemotherapy sensitivity. There is a dearth of prospective validation studies. The Optimal Personalised Treatment of early breast cancer usIng Multiparameter Analysis preliminary study (OPTIMA prelim) is the feasibility phase of a randomised controlled trial (RCT) designed to validate the use of multiparameter assay directed chemotherapy decisions in the NHS.

OBJECTIVES

OPTIMA prelim was designed to establish the acceptability to patients and clinicians of randomisation to test-driven treatment assignment compared with usual care and to select an assay for study in the main RCT.

DESIGN

Partially blinded RCT with adaptive design.

SETTING

Thirty-five UK hospitals.

PARTICIPANTS

Patients aged ≥ 40 years with surgically treated ER-positive HER2-negative primary breast cancer and with 1-9 involved axillary nodes, or, if node negative, a tumour at least 30 mm in diameter.

INTERVENTIONS

Randomisation between two treatment options. Option 1 was standard care consisting of chemotherapy followed by endocrine therapy. In option 2, an Oncotype DX(®) test (Genomic Health Inc., Redwood City, CA, USA) performed on the resected tumour was used to assign patients either to standard care [if 'recurrence score' (RS) was > 25] or to endocrine therapy alone (if RS was ≤ 25). Patients allocated chemotherapy were blind to their randomisation.

MAIN OUTCOME MEASURES

The pre-specified success criteria were recruitment of 300 patients in no longer than 2 years and, for the final 150 patients, (1) an acceptance rate of at least 40%; (2) recruitment taking no longer than 6 months; and (3) chemotherapy starting within 6 weeks of consent in at least 85% of patients.

RESULTS

Between September 2012 and 3 June 2014, 350 patients consented to join OPTIMA prelim and 313 were randomised; the final 150 patients were recruited in 6 months, of whom 92% assigned chemotherapy started treatment within 6 weeks. The acceptance rate for the 750 patients invited to participate was 47%. Twelve out of the 325 patients with data (3.7%, 95% confidence interval 1.7% to 5.8%) were deemed ineligible on central review of receptor status. Interviews with researchers and recordings of potential participant consultations made as part of the integral qualitative recruitment study provided insights into recruitment barriers and led to interventions designed to improve recruitment. Patient information was changed as the result of feedback from three patient focus groups. Additional multiparameter analysis was performed on 302 tumour samples. Although Oncotype DX, MammaPrint(®)/BluePrint(®) (Agendia Inc., Irvine, CA, USA), Prosigna(®) (NanoString Technologies Inc., Seattle, WA, USA), IHC4, IHC4 automated quantitative immunofluorescence (AQUA(®)) [NexCourse BreastTM (Genoptix Inc. Carlsbad, CA, USA)] and MammaTyper(®) (BioNTech Diagnostics GmbH, Mainz, Germany) categorised comparable numbers of tumours into low- or high-risk groups and/or equivalent molecular subtypes, there was only moderate agreement between tests at an individual tumour level (kappa ranges 0.33-0.60 and 0.39-0.55 for tests providing risks and subtypes, respectively). Health economics modelling showed the value of information to the NHS from further research into multiparameter testing is high irrespective of the test evaluated. Prosigna is currently the highest priority for further study.

CONCLUSIONS

OPTIMA prelim has achieved its aims of demonstrating that a large UK clinical trial of multiparameter assay-based selection of chemotherapy in hormone-sensitive early breast cancer is feasible. The economic analysis shows that a trial would be economically worthwhile for the NHS. Based on the outcome of the OPTIMA prelim, a large-scale RCT to evaluate the clinical effectiveness and cost-effectiveness of multiparameter assay-directed chemotherapy decisions in hormone-sensitive HER2-negative early breast would be appropriate to take place in the NHS.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN42400492.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 10. See the NIHR Journals Library website for further project information. The Government of Ontario funded research at the Ontario Institute for Cancer Research. Robert C Stein received additional support from the NIHR University College London Hospitals Biomedical Research Centre.

CrossLink: a novel method for cross-condition classification of cancer subtypes

BACKGROUND

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another.

METHODS

To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature.

RESULTS

We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles.

CONCLUSIONS

A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression-Based Meta-Analysis

PURPOSE

To provide further insight into the role of proliferation and other cellular processes in chemosensitivity and resistance, we evaluated the association of a diverse set of gene expression signatures with response to neoadjuvant chemotherapy (NAC) in breast cancer.

EXPERIMENTAL DESIGN

Expression data from primary breast cancer biopsies for 1,419 patients in 17 studies prior to NAC were identified and aggregated using common normalization procedures. Clinicopathologic characteristics, including response to NAC, were collected. Scores for 125 previously published breast cancer-related gene expression signatures were calculated for each tumor.

RESULTS

Within each receptor-based subgroup or PAM50 subtype, breast tumors with high proliferation signature scores were significantly more likely to achieve pathologic complete response to NAC. To distinguish "proliferation-associated" from "proliferation-independent" signatures, we used correlation and linear modeling approaches. Most signatures associated with response to NAC were proliferation associated: 90.5% (38/42) in ER⁺/HER2^- and 63.3% (38/60) in triple-negative breast cancer (TNBC). Proliferation-independent signatures predictive of response to NAC in ER⁺/HER2^- breast cancer were related to immune activity, while those in TNBC comprised a diverse set of signatures, including immune, DNA damage, signaling pathways (PI3K, AKT, Ras, and EGFR), and "stemness" phenotypes.

CONCLUSIONS

Proliferation differences account for the vast majority of predictive capacity of gene expression signatures in neoadjuvant chemosensitivity for ER⁺/HER2^- breast cancers and, to a lesser extent, TNBCs. Immune activation signatures are proliferation-independent predictors of pathologic complete response in ER⁺/HER2^- breast cancers. In TNBCs, significant proliferation-independent signatures include gene sets that represent a diverse set of cellular processes. Clin Cancer Res; 22(24); 6039-50. ©2016 AACR.

Biological subtyping of early breast cancer: a study comparing RT-qPCR with immunohistochemistry

The biological subtype of breast cancer influences the selection of systemic therapy. Distinction between luminal A and B cancers depends on consistent assessment of Ki-67, but substantial intra-observer and inter-observer variability exists when immunohistochemistry (IHC) is used. We compared RT-qPCR with IHC in the assessment of Ki-67 and other standard factors used in breast cancer subtyping. RNA was extracted from archival breast tumour tissue of 769 women randomly assigned to the FinHer trial. Cancer ESR1, PGR, ERBB2 and MKI67 mRNA content was quantitated with an RT-qPCR assay. Local pathologists assessed ER, PgR and Ki-67 expression using IHC. HER2 amplification was identified with chromogenic in situ hybridization (CISH) centrally. The results were correlated with distant disease-free survival (DDFS) and overall survival (OS). qPCR-based and IHC-based assessments of ER and PgR showed good concordance. Both low tumour MKI67 mRNA (RT-qPCR) and Ki-67 protein (IHC) levels were prognostic for favourable DDFS [hazard ratio (HR) 0.42, 95 % CI 0.25–0.71, P = 0.001; and HR 0.56, 0.37–0.84, P = 0.005, respectively] and OS. In multivariable analyses, cancer MKI67 mRNA content had independent influence on DDFS (adjusted HR 0.51, 95 % CI 0.29–0.89, P = 0.019) while Ki-67 protein expression had not any influence (P = 0.266) whereas both assessments influenced independently OS. Luminal B patients treated with docetaxel-FEC had more favourable DDFS and OS than those treated with vinorelbine-FEC when the subtype was defined by RT-qPCR (for DDFS, HR 0.52, 95 % CI 0.29–0.94, P = 0.031), but not when defined using IHC. Breast cancer subtypes approximated with RT-qPCR and IHC show good concordance, but cancer MKI67 mRNA content correlated slightly better with DDFS than Ki-67 expression. The findings based on MKI67 mRNA content suggest that patients with luminal B cancer benefit more from docetaxel-FEC than from vinorelbine-FEC.

Signatures of breast cancer metastasis at a glance

Gene expression profiling has yielded expression signatures from which prognostic tests can be derived to facilitate clinical decision making in breast cancer patients. Some of these signatures are based on profiling of whole tumor tissue (tissue signatures), which includes all tumor and stromal cells. Prognostic markers have also been derived from the profiling of metastasizing tumor cells, including circulating tumor cells (CTCs) and migratory-disseminating tumor cells within the primary tumor. The metastasis signatures based on CTCs and migratory-disseminating tumor cells have greater potential for unraveling cell biology insights and mechanistic underpinnings of tumor cell dissemination and metastasis. Of clinical interest is the promise that stratification of patients into high or low metastatic risk, as well as assessing the need for cytotoxic therapy, might be improved if prognostics derived from these two types of signatures are used in a combined way. The aim of this Cell Science at a Glance article and accompanying poster is to navigate through both types of signatures and their derived prognostics, as well as to highlight biological insights and clinical applications that could be derived from them, especially when they are used in combination.

Prognostic ability of EndoPredict compared to research-based versions of the PAM50 risk of recurrence (ROR) scores in node-positive, estrogen receptor-positive, and HER2-negative breast cancer. A GEICAM/9906 sub-study

There are several prognostic multigene-based tests for managing breast cancer (BC), but limited data comparing them in the same cohort. We compared the prognostic performance of the EndoPredict (EP) test (standardized for pathology laboratory) with the research-based PAM50 non-standardized qRT-PCR assay in node-positive estrogen receptor-positive (ER+) and HER2-negative (HER2−) BC patients receiving adjuvant chemotherapy followed by endocrine therapy (ET) in the GEICAM/9906 trial. EP and PAM50 risk of recurrence (ROR) scores [based on subtype (ROR-S) and on subtype and proliferation (ROR-P)] were compared in 536 ER+/HER2− patients. Scores combined with clinical information were evaluated: ROR-T (ROR-S, tumor size), ROR-PT (ROR-P, tumor size), and EPclin (EP, tumor size, nodal status). Patients were assigned to risk-categories according to prespecified cutoffs. Distant metastasis-free survival (MFS) was analyzed by Kaplan–Meier. ROR-S, ROR-P, and EP scores identified a low-risk group with a relative better outcome (10-year MFS: ROR-S 87 %; ROR-P 89 %; EP 93 %). There was no significant difference between tests. Predictors including clinical information showed superior prognostic performance compared to molecular scores alone (10-year MFS, low-risk group: ROR-T 88 %; ROR-PT 92 %; EPclin 100 %). The EPclin-based risk stratification achieved a significantly improved prediction of MFS compared to ROR-T, but not ROR-PT. All signatures added prognostic information to common clinical parameters. EPclin provided independent prognostic information beyond ROR-T and ROR-PT. ROR and EP can reliably predict risk of distant metastasis in node-positive ER+/HER2− BC patients treated with chemotherapy and ET. Addition of clinical parameters into risk scores improves their prognostic ability.

Comparison of prognostic and predictive impact of genomic or central grade and immunohistochemical subtypes or IHC4 in HR+/HER2- early breast cancer: WSG-AGO EC-Doc Trial

INTRODUCTION

Potential prognostic and predictive markers in early, intermediate-risk breast cancer (BC) include histological grade, Ki-67, genomic signatures, e.g. genomic grade index (GGI), and intrinsic subtypes. Their prognostic/predictive impact in hormone receptor (HR: ER and/or PR) positive/HER2- BC is controversial. WSG-AGO EC-Doc demonstrated superior event-free survival (EFS) in patients with 1-3 positive lymph node receiving epirubicin/cyclophosphamide-docetaxel (EC-Doc) versus 5-fluoruracil/epirubicin/cyclophosphamide (FEC).

METHODS

In a representative trial subset, we quantify concordance among factors used for clinical chemotherapy indication. We investigate the impact of central histology (n = 772), immunohistochemistry for intrinsic subtyping and IHC4, and dichotomous (GG) or continuous (GGI) genomic grade (n = 472) on patient outcome and benefit from taxane chemotherapy, focusing on HR+/HER2- patients (n = 459).

RESULTS

Concordance of local grade (LG) with central (CG) or genomic grade was modest. In HR+/HER2- patients, low (GG-1: 16%), equivocal (GG-EQ: 17%), and high (GG-3: 67%) GG were associated with respective 5-year EFS of 100%, 93%, and 85%. GGI was prognostic for EFS within all LG subgroups and within CG3, whereas IHC4 was prognostic only in CG3 tumors.In unselected and HR+/HER2- patients, CG3 and luminal-A-like subtype entered the multivariate EFS model, but not IHC4 or GG. In the whole population, continuous GGI entered the model [hazard ratio (H.R.) of 75th versus 25th = 2.79; P = 0.01], displacing luminal-A-like subtype; within HR+/HER2- (H.R. = 5.36; P < 0.001), GGI was the only remaining prognostic factor.In multivariate interaction analysis (including central and genomic grade), luminal-B-like subtype [HR+ and (Ki-67 ≥20% or HER2+)] was predictive for benefit of EC-Doc versus FEC in unselected but not in HR+/HER2- patients.

CONCLUSION

In the WSG-AGO EC-Doc trial for intermediate-risk BC, CG, intrinsic subtype (by IHC), and GG provide prognostic information. Continuous GGI (but not IHC4) adds prognostic information even when IHC subtype and CG are available. Finally, the high interobserver variability for histological grade and the still missing validation of Ki-67 preclude indicating or omitting adjuvant chemotherapy based on these single factors alone.

TRIAL REGISTRATION

The WSG-AGO/EC-Doc is registered at ClinicalTrials.gov, NCT02115204.

PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance)

PAM50 intrinsic breast cancer subtypes are prognostic independent of standard clinicopathologic factors. CALGB 9741 demonstrated improved recurrence-free (RFS) and overall survival (OS) with 2-weekly dose-dense (DD) versus 3-weekly therapy. A significant interaction between intrinsic subtypes and DD-therapy benefit was hypothesized. Suitable tumor samples were available from 1,471 (73%) of 2,005 subjects. Multiplexed gene-expression profiling generated the PAM50 subtype call, proliferation score, and risk of recurrence score (ROR-PT) for the evaluable subset of 1,311 treated patients. The interaction between DD-therapy benefit and intrinsic subtype was tested in a Cox proportional hazards model using two-sided alpha = 0.05. Additional multivariable Cox models evaluated the proliferation and ROR-PT scores as continuous measures with selected clinical covariates. Improved outcomes for DD therapy in the evaluable subset mirrored results from the complete data set (RFS; hazard ratio = 1.20; 95% confidence interval = 0.99-1.44) with 12.3-year median follow-up. Intrinsic subtypes were prognostic of RFS (P < 0.0001) irrespective of treatment assignment. No subtype-specific treatment effect on RFS was identified (interaction P = 0.44). Proliferation and ROR-PT scores were prognostic for RFS (both P < 0.0001), but no association with treatment benefit was seen (P = 0.14 and 0.59, respectively). Results were similar for OS. The prognostic value of PAM50 intrinsic subtype was greater than estrogen receptor/HER2 immunohistochemistry classification. PAM50 gene signatures were highly prognostic but did not predict for improved outcomes with DD anthracycline- and taxane-based therapy. Clinical validation studies will assess the ability of PAM50 and other gene signatures to stratify patients and individualize treatment based on expected risks of distant recurrence.