Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts

Estrogen receptor alpha somatic mutations Y537S and D538G confer breast cancer endocrine resistance by stabilizing the activating function-2 binding conformation

Somatic mutations in the estrogen receptor alpha (ERα) gene (ESR1), especially Y537S and D538G, have been linked to acquired resistance to endocrine therapies. Cell-based studies demonstrated that these mutants confer ERα constitutive activity and antiestrogen resistance and suggest that ligand-binding domain dysfunction leads to endocrine therapy resistance. Here, we integrate biophysical and structural biology data to reveal how these mutations lead to a constitutively active and antiestrogen-resistant ERα. We show that these mutant ERs recruit coactivator in the absence of hormone while their affinities for estrogen agonist (estradiol) and antagonist (4-hydroxytamoxifen) are reduced. Further, they confer antiestrogen resistance by altering the conformational dynamics of the loop connecting Helix 11 and Helix 12 in the ligand-binding domain of ERα, which leads to a stabilized agonist state and an altered antagonist state that resists inhibition.

Around one in every eight women will be diagnosed with breast cancer in their lifetime. Hormone-based therapies – also referred to antiestrogen drugs – target a protein called estrogen receptor alpha and are effective treatments for the majority of these cancers. Unfortunately, about half of patients will develop recurrent breast cancers even though the cancer continues to produce the target of the drugs.

The estrogen receptor alpha drives breast cancer in a number of ways, many of which require the receptor to be activated by binding to the hormone estrogen. When estrogen binds it causes the receptor to change shape to expose a surface where other proteins called coactivators can bind. Once a coactivator is bound, the estrogen receptor is active and signals the cancer cell to grow, divide, invade local tissues, and spread to new sites in the body.

Antiestrogen drugs competitively block the binding of estrogen to the receptor and cause the receptor to take on a different shape that inhibits the binding of the coactivator. However, recent studies identified mutations at specific sites in the gene that encodes estrogen receptor alpha in a large subset of patients with breast cancers that have spread. These mutations make the receptor resistant to antiestrogen drugs, and two mutations (called Y537S and D538G) account for approximately 70% of cases. However, it was not clear how these mutations altered the activity of estrogen receptor alpha at the molecular level.

Fanning, Mayne, Dharmarajan et al. now show these two most common mutations allow estrogen receptor alpha to bind to the coactivator in the absence of hormone. This unfortunately also reduces the effectiveness of one of the mostly widely administered antiestrogen therapies – a drug called tamoxifen. However, Fanning, Mayne, Dharmarajan et al. also show that the newer and more potent antiestrogens that are currently under examination in clinical trials should be highly effective at treating the cancers with the mutated versions of estrogen receptor alpha.

Applying the knowledge gained from these new findings toward the development of new antiestrogens could help reverse the impact of these common mutations. If successful, these new drugs will provide life-saving treatments for many breast cancer patients.

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients' tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.

p53 deficiency linked to B cell translocation gene 2 (BTG2) loss enhances metastatic potential by promoting tumor growth in primary and metastatic sites in patient-derived xenograft (PDX) models of triple-negative breast cancer

Background

Despite advances in early diagnosis and treatment of cancer patients, metastasis remains the major cause of mortality. TP53 is one of the most frequently mutated genes in human cancer, and these alterations can occur during the early stages of oncogenesis or as later events as tumors progress to more aggressive forms. Previous studies have suggested that p53 plays a role in cellular pathways that govern metastasis. To investigate how p53 deficiency contributes to late-stage tumor growth and metastasis, we developed paired isogenic patient-derived xenograft (PDX) models of triple-negative breast cancer (TNBC) differing only in p53 status for longitudinal analysis.

Methods

Patient-derived isogenic human tumor lines differing only in p53 status were implanted into mouse mammary glands. Tumor growth and metastasis were monitored with bioluminescence imaging, and circulating tumor cells (CTCs) were quantified by flow cytometry. RNA-Seq was performed on p53-deficient and p53 wild-type tumors, and functional validation of a lead candidate gene was performed in vivo.

Results

Isogenic p53 wild-type and p53-deficient tumors metastasized out of mammary glands and colonized distant sites with similar frequency. However, p53-deficient tumors metastasized earlier than p53 wild-type tumors and grew faster in both primary and metastatic sites as a result of increased proliferation and decreased apoptosis. In addition, greater numbers of CTCs were detected in the blood of mice engrafted with p53-deficient tumors. However, when normalized to tumor mass, the number of CTCs isolated from mice bearing parental and p53-deficient tumors was not significantly different. Gene expression profiling followed by functional validation identified B cell translocation gene 2 (BTG2), a downstream effector of p53, as a negative regulator of tumor growth both at primary and metastatic sites. BTG2 expression status correlated with survival of TNBC patients.

Conclusions

Using paired isogenic PDX-derived metastatic TNBC cells, loss of p53 promoted tumor growth and consequently increased tumor cell shedding into the blood, thus enhancing metastasis. Loss of BTG2 expression in p53-deficient tumors contributed to this metastatic potential by enhancing tumor growth in primary and metastatic sites. Furthermore, clinical data support conclusions generated from PDX models and indicate that BTG2 expression is a candidate prognostic biomarker for TNBC.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0673-9) contains supplementary material, which is available to authorized users.

Integrated Bottom-Up and Top-Down Proteomics of Patient-Derived Breast Tumor Xenografts

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0-30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0-30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

Prognostic and Predictive Biomarkers of Endocrine Responsiveness for Estrogen Receptor Positive Breast Cancer

The estrogen-dependent nature of breast cancer is the fundamental basis for endocrine therapy. The presence of estrogen receptor (ER), the therapeutic target of endocrine therapy, is a prerequisite for this therapeutic approach. However, estrogen-independent growth often exists de novo at diagnosis or develops during the course of endocrine therapy. Therefore ER alone is insufficient in predicting endocrine therapy efficacy. Several RNA-based multigene assays are now available in clinical practice to assess distant recurrence risk, with majority of these assays evaluated in patients treated with 5 years of adjuvant endocrine therapy. While MammaPrint and Oncotype Dx are most predictive of recurrence risk within the first 5 years of diagnosis, Prosigna, Breast Cancer Index (BCI), and EndoPredict Clin have also demonstrated utility in predicting late recurrence. In addition, PAM50, or Prosigna, provides further biological insights by classifying breast cancers into intrinsic molecular subtypes. Additional strategies are under investigation in prospective clinical trials to differentiate endocrine sensitive and resistant tumors and include on-treatment Ki-67 and Preoperative Endocrine Prognostic Index (PEPI) score in the setting of neoadjuvant endocrine therapy. These biomarkers have become important tools in clinical practice for the identification of low risk patients for whom chemotherapy could be avoided. However, there is much work ahead toward the development of a molecular classification that informs the biology and novel therapeutic targets in high-risk disease as chemotherapy has only modest benefit in this population. The recognition of somatic mutations and their relationship to endocrine therapy responsiveness opens important opportunities toward this goal.

Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.

Proteomic Characterization of Head and Neck Cancer Patient-Derived Xenografts

Despite advances in treatment approaches for head and neck squamous cell carcinoma (HNSCC), survival rates have remained stagnant due to the paucity of preclinical models that accurately reflect the human tumor. Patient-derived xenografts (PDX) are an emerging model system where patient tumors are implanted directly into mice. Increased understanding of the application and limitations of PDXs will facilitate their rational use. Studies to date have not reported protein profiles of PDXs. Therefore, we developed a large cohort of HNSCC PDXs and found that tumor take rate was not influenced by the clinical, pathologic, or processing features. Protein expression profiles, from a subset of the PDXs, were characterized by reverse-phase protein array and the data was compared with The Cancer Genome Atlas HNSCC data. Cluster analysis revealed that HNSCC PDXs were more similar to primary HNSCC than to any other tumor type. Interestingly, while a significant fraction of proteins were expressed similarly in both primary HNSCC and PDXs, a subset of proteins/phosphoproteins were expressed at higher (or lower) levels in PDXs compared with primary HNSCC. These findings indicate that the proteome is generally conserved in PDXs, but mechanisms for both positive and negative model selection and/or differences in the stromal components exist.

IMPLICATIONS

Proteomic characterization of HNSCC PDXs demonstrates potential drivers for model selection and provides a framework for improved utilization of this expanding model system.

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.

Endocrine resistance in breast cancer--An overview and update

Tumors that express detectable levels of the product of the ESR1 gene (estrogen receptor-α; ERα) represent the single largest molecular subtype of breast cancer. More women eventually die from ERα+ breast cancer than from either HER2+ disease (almost half of which also express ERα) and/or from triple negative breast cancer (ERα-negative, progesterone receptor-negative, and HER2-negative). Antiestrogens and aromatase inhibitors are largely indistinguishable from each other in their abilities to improve overall survival and almost 50% of ERα+ breast cancers will eventually fail one or more of these endocrine interventions. The precise reasons why these therapies fail in ERα+ breast cancer remain largely unknown. Pharmacogenetic explanations for Tamoxifen resistance are controversial. The role of ERα mutations in endocrine resistance remains unclear. Targeting the growth factors and oncogenes most strongly correlated with endocrine resistance has proven mostly disappointing in their abilities to improve overall survival substantially, particularly in the metastatic setting. Nonetheless, there are new concepts in endocrine resistance that integrate molecular signaling, cellular metabolism, and stress responses including endoplasmic reticulum stress and the unfolded protein response (UPR) that provide novel insights and suggest innovative therapeutic targets. Encouraging evidence that drug combinations with CDK4/CDK6 inhibitors can extend recurrence free survival may yet translate to improvements in overall survival. Whether the improvements seen with immunotherapy in other cancers can be achieved in breast cancer remains to be determined, particularly for ERα+ breast cancers. This review explores the basic mechanisms of resistance to endocrine therapies, concluding with some new insights from systems biology approaches further implicating autophagy and the UPR in detail, and a brief discussion of exciting new avenues and future prospects.

NFκB affects estrogen receptor expression and activity in breast cancer through multiple mechanisms

Estrogen receptor (ER) and NFκB are two widely expressed, pleiotropic transcription factors that have been shown to interact and affect one another's activity. While the ability of ER to repress NFκB activity has been extensively studied and is thought to underlie the anti-inflammatory activity of estrogens, how NFκB signaling affects ER activity is less clear. This is a particularly important question in breast cancer since activation of NFκB in ER positive tumors is associated with failure of endocrine and chemotherapies. In this review, we provide an update on the multiple mechanisms by which NFκB can influence ER activity, including down-regulation of ER expression, enhanced ER recruitment to DNA, and increased transcriptional activity of both liganded and unliganded ER. Additionally, a novel example of NFκB potentiation of ER-dependent gene repression is reviewed. Together, these mechanisms can alter response to endocrine therapies and may underlie the poor outcome for women with ER positive tumors that have active NFκB signaling.

The molecular, cellular and clinical consequences of targeting the estrogen receptor following estrogen deprivation therapy

During the past 20 years our understanding of the control of breast tumor development, growth and survival has changed dramatically. The once long forgotten application of high dose synthetic estrogen therapy as the first chemical therapy to treat any cancer has been resurrected, refined and reinvented as the new biology of estrogen-induced apoptosis. High dose estrogen therapy was cast aside once tamoxifen, from its origins as a failed "morning after pill", was reinvented as the first targeted therapy to treat any cancer. The current understanding of the mechanism of estrogen-induced apoptosis is described as a consequence of acquired resistance to long term antihormone therapy in estrogen receptor (ER) positive breast cancer. The ER signal transduction pathway remains a target for therapy in breast cancer despite "antiestrogen" resistance, but becomes a regulator of resistance. Multiple mechanisms of resistance come into play: Selective ER modulator (SERM) stimulated growth, growth factor/ER crosstalk, estrogen-induced apoptosis and mutations of ER. But it is with the science of estrogen-induced apoptosis that the next innovation in women's health will be developed. Recent evidence suggests that the glucocorticoid properties of medroxyprogesterone acetate blunt estrogen-induced apoptosis in estrogen deprived breast cancer cell populations. As a result breast cancer develops during long-term hormone replacement therapy (HRT). A new synthetic progestin with estrogen-like properties, such as the 19 nortestosterone derivatives used in oral contraceptives, will continue to protect the uterus from unopposed estrogen stimulation but at the same time, reinforce apoptosis in vulnerable populations of nascent breast cancer cells.

Precision medicine in breast cancer: genes, genomes, and the future of genomically driven treatments

Remarkable progress in sequencing technology over the past 20 years has made it possible to comprehensively profile tumors and identify clinically relevant genomic alterations. In breast cancer, the most common malignancy affecting women, we are now increasingly able to use this technology to help specify the use of therapies that target key molecular and genetic dependencies. Large sequencing studies have confirmed the role of well-known cancer-related genes and have also revealed numerous other genes that are recurrently mutated in breast cancer. This growing understanding of patient-to-patient variability at the genomic level in breast cancer is advancing our ability to direct the appropriate treatment to the appropriate patient at the appropriate time--a hallmark of "precision cancer medicine." This review focuses on the technological advances that have catalyzed these developments, the landscape of mutations in breast cancer, the clinical impact of genomic profiling, and the incorporation of genomic information into clinical care and clinical trials.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach

A massive amount of somatic mutations has been cataloged in large-scale projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium projects. The majority of the somatic mutations found in tumor genomes are neutral 'passenger' rather than damaging "driver" mutations. Now, understanding their biological consequences and prioritizing them for druggable targets are urgently needed. Thanks to the rapid advances in structural genomics technologies (e.g. X-ray), large-scale protein structural data has now been made available, providing critical information for deciphering functional roles of mutations in cancer and prioritizing those alterations that may mediate drug binding at the atom resolution and, as such, be druggable targets. We hypothesized that mutations at protein-ligand binding-site residues are likely to be druggable targets. Thus, to prioritize druggable mutations, we developed SGDriver, a structural genomics-based method incorporating the somatic missense mutations into protein-ligand binding-site residues using a Bayes inference statistical framework. We applied SGDriver to 746,631 missense mutations observed in 4997 tumor-normal pairs across 16 cancer types from The Cancer Genome Atlas. SGDriver detected 14,471 potential druggable mutations in 2091 proteins (including 1,516 recurrently mutated proteins) across 3558 cancer genomes (71.2%), and further identified 298 proteins harboring mutations that were significantly enriched at protein-ligand binding-site residues (adjusted p value < 0.05). The identified proteins are significantly enriched in both oncoproteins and tumor suppressors. The follow-up drug-target network analysis suggested 98 known and 126 repurposed druggable anticancer targets (e.g. SPOP and NR3C1). Furthermore, our integrative analysis indicated that 13% of patients might benefit from current targeted therapy, and this -proportion would increase to 31% when considering drug repositioning. This study provides a testable strategy for prioritizing druggable mutations in precision cancer medicine.

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

Droplet digital polymerase chain reaction assay for screening of ESR1 mutations in 325 breast cancer specimens

Droplet digital polymerase chain reaction (ddPCR), which could perform thousands of PCRs on a nanoliter scale simultaneously, would be an attractive method to massive parallel sequencing for identifying and studying the significance of low-frequency rare mutations. Recent evidence has shown that the key potential mechanisms of the failure of aromatase inhibitors-based therapy involve identifying activating mutations affecting the ligand-binding domain of the ESR1 gene. Therefore, the detection of ESR1 mutations may be useful as a biomarker predicting an effect of the treatment. We aimed to develop a ddPCR-based method for the sensitive detection of ESR1 mutations in 325 breast cancer specimens, in which 270 primary and 55 estrogen receptor-positive (ER+) metastatic breast cancer (MBC) specimens. Our ddPCR assay could detect the ESR1 mutant molecules with low concentration of 0.25 copies/μL. According to the selected cutoff, ESR1 mutations occurred in 7 (2.5%) of 270 primary breast cancer specimens and in 11 (20%) of 55 ER+ MBC specimens. Among the 11 MBC specimens, 5 specimens (45.5%) had the most common ESR1 mutation, Y537S, 4 specimens (36.3%) each had D538G, Y537N, and Y537C. Interestingly, 2 patients had 2 ESR1 mutations, Y537N/D538G and Y537S/Y537C, and 2 patients had 3 ESR1 mutations, Y537S/Y537N/D538G. Biopsy was performed in heterochrony in 8 women twice. In 8 women, 4 women had primary breast cancer and MBC specimens and 4 women had 2 specimens when treatment was failure. Four of these 8 women acquired ESR1 mutation, whereas no ESR1 mutation could be identified at first biopsy. ddPCR technique could be a promising tool for the next-generation sequencing-free precise detection of ESR1 mutations in endocrine therapy resistant cases and may assist in determining the treatment strategy.

Developmental Insights into Breast Cancer Intratumoral Heterogeneity

Breast cancer is no longer considered a single disease, but instead is made up of multiple subtypes with genetically and most likely epigenetically heterogeneous tumors composed of numerous clones. Both the hierarchical cancer stem cell and clonal evolution models have been invoked to help explain this intratumoral heterogeneity. Several recent studies have helped define the functional interactions among the different cellular subpopulations necessary for the evolution of this complex ecosystem. These interactions involve paracrine interactions that include locally acting Wnt family members, reminiscent of the signaling pathways important for normal mammary gland development and stem cell self-renewal. In this review, we discuss the interactions among various cell populations in both normal and tumor tissues. A better understanding of these interactions, especially in the metastatic setting, will be important for the development of improved combinatorial therapies designed to prevent relapse and to ultimately decrease mortality.

Studying platinum sensitivity and resistance in high-grade serous ovarian cancer: Different models for different questions

High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among all gynecological cancers. Patients are generally diagnosed in an advanced stage with the majority of cases displaying platinum resistant relapses. Recent genomic interrogation of large numbers of HGSOC patient samples indicated high complexity in terms of genetic aberrations, intra- and intertumor heterogeneity and underscored their lack of targetable oncogenic mutations. Sub-classifications of HGSOC based on expression profiles, termed 'differentiated', 'immunoreactive', 'mesenchymal' and 'proliferative', were shown to have prognostic value. In addition, in almost half of all HGSOC patients, a deficiency in homologous recombination (HR) was found that potentially can be targeted using PARP inhibitors. Developing precision medicine requires advanced experimental models. In the current review, we discuss experimental HGSOC models in which resistance to platinum therapy and the use of novel therapeutics can be carefully studied. Panels of better-defined primary cell lines need to be established to capture the full spectrum of HGSOC subtypes. Further refinement of cell lines is obtained with a 3-dimensional culture model mimicking the tumor microenvironment. Alternatively, ex vivo ovarian tumor tissue slices are used. For in vivo studies, larger panels of ovarian cancer patient-derived xenografts (PDXs) are being established, encompassing all expression subtypes. Ovarian cancer PDXs grossly retain tumor heterogeneity and clinical response to platinum therapy is preserved. PDXs are currently used in drug screens and as avatars for patient response. The role of the immune system in tumor responses can be assessed using humanized PDXs and immunocompetent genetically engineered mouse models. Dynamic tracking of genetic alterations in PDXs as well as patients during treatment and after relapse is feasible by sequencing circulating cell-free tumor DNA and analyzing circulating tumor cells. We discuss how various models and methods can be combined to delineate the molecular mechanisms underlying platinum resistance and to select HGSOC patients other than BRCA1/2-mutation carriers that could potentially benefit from the synthetic lethality of PARP inhibitors. This integrated approach is a first step to improve therapy outcomes in specific subgroups of HGSOC patients.

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to "rescue" the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.

Estrogen Receptor Folding Modulates cSrc Kinase SH2 Interaction via a Helical Binding Mode

The estrogen receptors (ERs) feature, next to their transcriptional role, important nongenomic signaling actions, with emerging clinical relevance. The Src Homology 2 (SH2) domain mediated interaction between cSrc kinase and ER plays a key role in this; however the molecular determinants of this interaction have not been elucidated. Here, we used phosphorylated ER peptide and semisynthetic protein constructs in a combined biochemical and structural study to, for the first time, provide a quantitative and structural characterization of the cSrc SH2-ER interaction. Fluorescence polarization experiments delineated the SH2 binding motif in the ER sequence. Chemical shift perturbation analysis by nuclear magnetic resonance (NMR) together with molecular dynamics (MD) simulations allowed us to put forward a 3D model of the ER-SH2 interaction. The structural basis of this protein-protein interaction has been compared with that of the high affinity SH2 binding sequence GpYEEI. The ER features a different binding mode from that of the "two-pronged plug two-hole socket" model in the so-called specificity determining region. This alternative binding mode is modulated via the folding of ER helix 12, a structural element directly C-terminal of the key phosphorylated tyrosine. The present findings provide novel molecular entries for understanding nongenomic ER signaling and targeting the corresponding disease states.

Efficacy of SERD/SERM Hybrid-CDK4/6 Inhibitor Combinations in Models of Endocrine Therapy-Resistant Breast Cancer

PURPOSE

Endocrine therapy, using tamoxifen or an aromatase inhibitor, remains first-line therapy for the management of estrogen receptor (ESR1)-positive breast cancer. However, ESR1 mutations or other ligand-independent ESR1 activation mechanisms limit the duration of response. The clinical efficacy of fulvestrant, a selective estrogen receptor downregulator (SERD) that competitively inhibits agonist binding to ESR1 and triggers receptor downregulation, has confirmed that ESR1 frequently remains engaged in endocrine therapy-resistant cancers. We evaluated the activity of a new class of selective estrogen receptor modulators (SERM)/SERD hybrids (SSH) that downregulate ESR1 in relevant models of endocrine-resistant breast cancer. Building on the observation that concurrent inhibition of ESR1 and the cyclin-dependent kinases 4 and 6 (CDK4/6) significantly increased progression-free survival in advanced patients, we explored the activity of different SERD- or SSH-CDK4/6 inhibitor combinations in models of endocrine therapy-resistant ESR1(+) breast cancer.

EXPERIMENTAL DESIGN

SERDs, SSHs, and the CDK4/6 inhibitor palbociclib were evaluated as single agents or in combination in established cellular and animal models of endocrine therapy-resistant ESR1(+) breast cancer.

RESULTS

The combination of palbociclib with a SERD or an SSH was shown to effectively inhibit the growth of MCF7 cell or ESR1-mutant patient-derived tumor xenografts. In tamoxifen-resistant MCF7 xenografts, the palbociclib/SERD or SSH combination resulted in an increased duration of response as compared with either drug alone.

CONCLUSIONS

A SERD- or SSH-palbociclib combination has therapeutic potential in breast tumors resistant to endocrine therapies or those expressing ESR1 mutations. See related commentary by DeMichele and Chodosh, p. 4999.

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.

Treatment strategies for advanced hormone receptor-positive and human epidermal growth factor 2-negative breast cancer: the role of treatment order

Although survival rates among patients with breast cancer have improved in recent years, those diagnosed with advanced disease with distant metastasis face a 5-year survival rate of less than 25%, making the management of these patients an area still in significant need of continued research. Selecting the optimal treatment order from among the variety of currently available therapy options presents a relevant challenge for medical oncologists. With the understanding that the majority of patients with breast cancer and those who succumb to this disease have HR-positive disease, this review will focus on treatment options and treatment order in patients with HR-positive advanced breast cancer. While endocrine therapy is considered the preferred treatment for first-line therapy in HR-positive/HER2-negative breast cancer, selection of the specific agent depends on the menopausal status of the patient. Palbociclib, a cyclin-dependent kinase (CDK) 4/6 inhibitor, is also recommended as first-line treatment in patients with ER-positive/HER2-negative disease. In patients with endocrine therapy-resistant disease, specific strategies include sequencing of other antiestrogen receptor agents, or agents that target other molecular pathways. Future treatment strategies for patients with primary or secondary resistance to endocrine therapy for advanced disease are discussed. These strategies include first-line therapy with high-dose fulvestrant or everolimus (in combination with exemestane or letrozole or with other endocrine therapies), use of the PI3K inhibitors (e.g., buparlisib, alpelisib, pictilisib, taselisib), entinostat, CDK 4/6 inhibitors (e.g., palbociclib, ribociclib, abemaciclib), and novel selective estrogen receptor degradation agents that may enhance the targeting of acquired mutations in the ESR1 gene.

Optimal management of hormone receptor positive metastatic breast cancer in 2016

Hormone receptor positive tumors represent the most common form of breast cancer and account for most of the deaths from the disease. Endocrine therapy represents the main initial therapeutic strategy for these patients and has been associated with significant clinical benefits in a majority of patients. While in early stages endocrine therapy is administered as part of a curative approach once clinical metastases develop, the disease is considered incurable and the main management objectives are tumor control and quality of life. The two major clinical paradigms of always indicating endocrine therapy in the absence of visceral crises and sequencing endocrine treatments have been guiding our therapeutic approach to these patients. However, for many decades, we have delivered endocrine therapy with a 'one size fits all' approach by applying agents that interfere with hormone receptor signaling equally in every clinical patient scenario. We have been unable to incorporate the well-known biologic principle of different degrees of hormone receptor dependency in our therapeutic recommendations. Recent developments in the understanding of molecular interactions of hormone signaling with other important growth factor, metabolic and cell division pathways have opened the possibility of improving results by modulating hormone signaling and interfering with resistance mechanisms yet to be fully understood. Unfortunately, limitations in the design of trials conducted in this area have made it difficult to develop predictive biomarkers and most of the new combinations with targeted agents, even though showing improvements in clinical endpoints, have been directed to an unselected population of patients. In this review we explore some of the current and most relevant literature in the management of hormone receptor positive advance breast cancer.

Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients

PURPOSE

Given the clinical relevance of ESR1 mutations as potential drivers of resistance to endocrine therapy, this study used sensitive detection methods to determine the frequency of ESR1 mutations in primary and metastatic breast cancer, and in cell-free DNA (cfDNA).

EXPERIMENTAL DESIGN

Six ESR1 mutations (K303R, S463P, Y537C, Y537N, Y537S, D538G) were assessed by digital droplet PCR (ddPCR), with lower limits of detection of 0.05% to 0.16%, in primary tumors (n = 43), bone (n = 12) and brain metastases (n = 38), and cfDNA (n = 29). Correlations between ESR1 mutations in metastatic lesions and single (1 patient) or serial blood draws (4 patients) were assessed.

RESULTS

ESR1 mutations were detected for D538G (n = 13), Y537S (n = 3), and Y537C (n = 1), and not for K303R, S463P, or Y537N. Mutation rates were 7.0% (3/43 primary tumors), 9.1% (1/11 bone metastases), 12.5% (3/24 brain metastases), and 24.1% (7/29 cfDNA). Two patients showed polyclonal disease with more than one ESR1 mutation. Mutation allele frequencies were 0.07% to 0.2% in primary tumors, 1.4% in bone metastases, 34.3% to 44.9% in brain metastases, and 0.2% to 13.7% in cfDNA. In cases with both cfDNA and metastatic samples (n = 5), mutations were detected in both (n = 3) or in cfDNA only (n = 2). Treatment was associated with changes in ESR1 mutation detection and allele frequency.

CONCLUSIONS

ESR1 mutations were detected at very low allele frequencies in some primary breast cancers, and at high allele frequency in metastases, suggesting that in some tumors rare ESR1-mutant clones are enriched by endocrine therapy. Further studies should address whether sensitive detection of ESR1 mutations in primary breast cancer and in serial blood draws may be predictive for development of resistant disease. See related commentary by Gu and Fuqua, p. 1034.

Clonotyping for precision oncology

Advances in identifying subpopulations of cancer cells and reconstructing the clonal evolution of tumors greatly enhance our understanding of the molecular events within a patient and their context relative to one another. In the rapidly unfolding era of personalized medicine, the ability to monitor clonal dynamics throughout patient care has significant clinical implications for the appropriate development or application of targeted therapies as well as understanding the potential mechanisms driving resistance. In this review, we discuss advances in biotechnology and bioinformatics that improve precision treatment by dissecting clonal evolution, focusing first on the initial discoveries in lymphomas and leukemias followed by the more recent applications to advance our understanding of prostate cancer (PCa).

Optimization of a Novel Binding Motif to (E)-3-(3,5-Difluoro-4-((1R,3R)-2-(2-fluoro-2-methylpropyl)-3-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indol-1-yl)phenyl)acrylic Acid (AZD9496), a Potent and Orally Bioavailable Selective Estrogen Receptor Downregulator and Antagonist

The discovery of an orally bioavailable selective estrogen receptor downregulator (SERD) with equivalent potency and preclinical pharmacology to the intramuscular SERD fulvestrant is described. A directed screen identified the 1-aryl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole motif as a novel, druglike ER ligand. Aided by crystal structures of novel ligands bound to an ER construct, medicinal chemistry iterations led to (E)-3-(3,5-difluoro-4-((1R,3R)-2-(2-fluoro-2-methylpropyl)-3-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indol-1-yl)phenyl)acrylic acid (30b, AZD9496), a clinical candidate with high oral bioavailability across preclinical species that is currently being evaluated in phase I clinical trials for the treatment of advanced estrogen receptor (ER) positive breast cancer.

Biobanking of patient and patient-derived xenograft ovarian tumour tissue: efficient preservation with low and high fetal calf serum based methods

Using patient-derived xenografts (PDXs) for preclinical cancer research demands proper storage of tumour material to facilitate logistics and to reduce the number of animals needed. We successfully established 45 subcutaneous ovarian cancer PDXs, reflecting all histological subtypes, with an overall take rate of 68%. Corresponding cells from mouse replaced human tumour stromal and endothelial cells in second generation PDXs as demonstrated with mouse-specific vimentin and CD31 immunohistochemical staining. For biobanking purposes two cryopreservation methods, a fetal calf serum (FCS)-based (95%v/v) “FCS/DMSO” protocol and a low serum-based (10%v/v) “vitrification” protocol were tested. After primary cryopreservation, tumour take rates were 38% and 67% using either the vitrification or FCS/DMSO-based cryopreservation protocol, respectively. Cryopreserved tumour tissue of established PDXs achieved take rates of 67% and 94%, respectively compared to 91% using fresh PDX tumour tissue. Genotyping analysis showed that no changes in copy number alterations were introduced by any of the biobanking methods. Our results indicate that both protocols can be used for biobanking of ovarian tumour and PDX tissues. However, FCS/DMSO-based cryopreservation is more successful. Moreover, primary engraftment of fresh patient-derived tumours in mice followed by freezing tissue of successfully established PDXs is the preferred way of efficient ovarian cancer PDX biobanking.

Prognostic and functional importance of the engraftment-associated genes in the patient-derived xenograft models of triple-negative breast cancers

We aimed to identify the factors affecting the successful tumor engraftment in breast cancer patient-derived xenograft (PDX) models. Further, we investigated the prognostic significance and the functional importance of the PDX engraftment-related genes in triple-negative breast cancers (TNBC). The clinico-pathologic features of 81 breast cancer patients whose tissues were used for PDX transplantation were analyzed to identify the factors affecting the PDX engraftment. A gene signature associated with the PDX engraftment was discovered and its clinical importance was tested in a publicly available dataset and in vitro assays. Nineteen out of 81 (23.4 %) transplanted tumors were successfully engrafted into the PDX models. The engraftment rate was highest in TNBC when compared to other subtypes (p = 0.001) and in recurrent or chemotherapy-resistant tumors compared to newly diagnosed primary tumors (p = 0.024). PDX tumors originated from the TNBC cases showed more rapid tumor growth in mice. Gene expression profiling showed that down-regulation of genes involved in the tumor-immune interaction was significantly associated with the successful PDX engraftment. The engraftment gene signature was associated with worse survival outcome when tested in publicly available mRNA datasets of TNBC cases. Among the engraftment-related genes, PHLDA2, TKT, and P4HA2 showed high expression in triple-negative breast cancer cell lines, and siRNA-based gene silencing resulted in reduced cell invasion and proliferation in vitro. Our results show that the PDX engraftment may reflect the aggressive phenotype in breast cancer. Genes associated with the PDX engraftment may provide a novel prognostic biomarker and therapeutic targets in TNBC.

Positron emission tomography of tumour [(18)F]fluoroestradiol uptake in patients with acquired hormone-resistant metastatic breast cancer prior to oestradiol therapy

PURPOSE

Whereas anti-oestrogen therapy is widely applied to treat oestrogen receptor (ER) positive breast cancer, paradoxically, oestrogens can also induce tumour regression. Up-regulation of ER expression is a marker for oestrogen hypersensitivity. We, therefore, performed an exploratory study to evaluate positron emission tomography (PET) with the tracer 16α-[(18)F]fluoro-17β-oestradiol ((18)F-FES) as potential marker to select breast cancer patients for oestradiol therapy.

METHODS

Eligible patients had acquired endocrine-resistant metastatic breast cancer that progressed after ≥2 lines of endocrine therapy. All patients had prior ER-positive histology. Treatment consisted of oestradiol 2 mg, three times daily, orally. Patients underwent (18)F-FES-PET/CT imaging at baseline. Tumour (18)F-FES-uptake was quantified for a maximum of 20 lesions and expressed as maximum standardised uptake value (SUVmax). CT-scan was repeated every 3 months to evaluate treatment response. Clinical benefit was defined as time to radiologic or clinical progression ≥24 weeks.

RESULTS

(18)F-FES uptake, quantified for 255 lesions in 19 patients, varied greatly between lesions (median 2.8; range 0.6-24.3) and between patients (median 2.5; range 1.1-15.5). Seven (37%) patients experienced clinical benefit of oestrogen therapy, eight progressed (PD), and four were non-evaluable due to side effects. The positive and negative predictive value (PPV/NPV) of (18)F-FES-PET for response to treatment were 60% (95% CI: 31-83%) and 80% (95% CI: 38-96%), respectively, using SUVmax >1.5.

CONCLUSION

(18)F-FES-PET may aid identification of patients with acquired antihormone resistant breast cancer that are unlikely to benefit from oestradiol therapy.

Roles for miRNAs in endocrine resistance in breast cancer

Therapies targeting estrogen receptor alpha (ERα), including selective ER modulators such as tamoxifen, selective ER downregulators such as fulvestrant (ICI 182 780), and aromatase inhibitors such as letrozole, are successfully used in treating breast cancer patients whose initial tumor expresses ERα. Unfortunately, the effectiveness of endocrine therapies is limited by acquired resistance. The role of microRNAs (miRNAs) in the progression of endocrine-resistant breast cancer is of keen interest in developing biomarkers and therapies to counter metastatic disease. This review focuses on miRNAs implicated as disruptors of antiestrogen therapies, their bona fide gene targets and associated pathways promoting endocrine resistance.

Clonal evolution and tumor-initiating cells: New dimensions in cancer patient treatment

Human cancer is not a uniform disease but a plethora of disparate tumor types and subtypes. The differences that exist between individual tumors (intertumoral heterogeneity) present a significant roadblock to the eradication of cancer. It has also become increasingly clear that variations across individual tumors (intratumoral heterogeneity) have important implications to cancer progression and treatment efficacy. Therefore, in order to improve patient care and develop novel chemotherapeutics, the evolving tumor landscape needs to be further explored. Next-generation sequencing (NGS) technologies are revolutionizing the cancer research arena by providing state-of-the-art, high-speed methods of genome sequencing at single-nucleotide resolution, thus enabling an unprecedented detection of tumor-specific genetic abnormalities. These anomalies can be quantified to reveal specific frequencies of DNA alterations that correspond to distinct clonal populations within a given tumor. As such, NGS approaches have also been utilized to explore the heterogeneous landscape of patient tumors as well as to match metastatic and/or recurrent growths and patient-derived engrafts. By sequencing in this manner--through time so to speak--cancer researchers can track shifting clonal populations, make important inferences about tumor evolution and potentially identify tumor subclones that could be viably targeted. This exciting new territory has important implications for the competing clonal evolution and cancer stem cell models of tumor heterogeneity, and also offers a new dimension for cancer treatment and profound hope for patients in the coming years.

Estrogen receptor mutations and functional consequences for breast cancer

A significant number of estrogen receptor α (ERα)-positive breast tumors develop resistance to endocrine therapy and recur with metastatic disease. Several mechanisms of endocrine resistance have been proposed, including genetic alterations that lead to ERs with altered protein sequence. By altering the conformation of the protein and increasing the interaction with coactivators, point mutations in ESR1, the gene encoding ERα, promote an active form of the receptor in the absence of hormone that assists tumor cells to evade hormonal treatments. Recent studies have confirmed that ESR1 point mutations frequently occur in metastatic breast tumors that are refractory to endocrine therapy, and suggest the development of novel strategies that may be more effective in controlling ER signaling and benefit patients with recurrent and metastatic disease.

From bench to bedside: What do we know about hormone receptor-positive and human epidermal growth factor receptor 2-positive breast cancer?

Breast cancer is a heterogeneous disease. Thanks to extensive efforts from research scientists and clinicians, treatment for breast cancer has advanced into the era of targeted medicine. With the use of several well-established biomarkers, such as hormone receptors (HRs) (i.e., estrogen receptor [ER] and progesterone receptor [PgR]) and human epidermal growth factor receptor-2 (HER2), breast cancer patients can be categorized into multiple subgroups with specific targeted treatment strategies. Although therapeutic strategies for HR-positive (HR+) HER2-negative (HER2-) breast cancer and HR-negative (HR-) HER2-positive (HER2+) breast cancer are well-defined, HR+ HER2+ breast cancer is still an overlooked subgroup without tailored therapeutic options. In this review, we have summarized the molecular characteristics, etiology, preclinical tools and therapeutic options for HR+ HER2+ breast cancer. We hope to raise the attention of both the research and the medical community on HR+ HER2+ breast cancer, and to advance patient care for this subtype of disease.

Development and Characterization of Bladder Cancer Patient-Derived Xenografts for Molecularly Guided Targeted Therapy

BACKGROUND

The overarching goal of this project is to establish a patient-derived bladder cancer xenograft (PDX) platform, annotated with deep sequencing and patient clinical information, to accelerate the development of new treatment options for bladder cancer patients. Herein, we describe the creation, initial characterization and use of the platform for this purpose.

METHODS AND FINDINGS

Twenty-two PDXs with annotated clinical information were established from uncultured unselected clinical bladder cancer specimens in immunodeficient NSG mice. The morphological fidelity was maintained in PDXs. Whole exome sequencing revealed that PDXs and parental patient cancers shared 92-97% of genetic aberrations, including multiple druggable targets. For drug repurposing, an EGFR/HER2 dual inhibitor lapatinib was effective in PDX BL0440 (progression-free survival or PFS of 25.4 days versus 18.4 days in the control, p = 0.007), but not in PDX BL0269 (12 days versus 13 days in the control, p = 0.16) although both expressed HER2. To screen for the most effective MTT, we evaluated three drugs (lapatinib, ponatinib, and BEZ235) matched with aberrations in PDX BL0269; but only a PIK3CA inhibitor BEZ235 was effective (p<0.0001). To study the mechanisms of secondary resistance, a fibroblast growth factor receptor 3 inhibitor BGJ398 prolonged PFS of PDX BL0293 from 9.5 days of the control to 18.5 days (p<0.0001), and serial biopsies revealed that the MAPK/ERK and PIK3CA-AKT pathways were activated upon resistance. Inhibition of these pathways significantly prolonged PFS from 12 day of the control to 22 days (p = 0.001). To screen for effective chemotherapeutic drugs, four of the first six PDXs were sensitive to the cisplatin/gemcitabine combination, and chemoresistance to one drug could be overcome by the other drug.

CONCLUSION

The PDX models described here show good correlation with the patient at the genomic level and known patient response to treatment. This supports further evaluation of the PDXs for their ability to accurately predict a patient's response to new targeted and combination strategies for bladder cancer.

ESR1 Mutations in Circulating Plasma Tumor DNA from Metastatic Breast Cancer Patients

PURPOSE

Mutations in the estrogen receptor (ER)α gene, ESR1, have been identified in breast cancer metastases after progression on endocrine therapies. Because of limitations of metastatic biopsies, the reported frequency of ESR1 mutations may be underestimated. Here, we show a high frequency of ESR1 mutations using circulating plasma tumor DNA (ptDNA) from patients with metastatic breast cancer.

EXPERIMENTAL DESIGN

We retrospectively obtained plasma samples from eight patients with known ESR1 mutations and three patients with wild-type ESR1 identified by next-generation sequencing (NGS) of biopsied metastatic tissues. Three common ESR1 mutations were queried for using droplet digital PCR (ddPCR). In a prospective cohort, metastatic tissue and plasma were collected contemporaneously from eight ER-positive and four ER-negative patients. Tissue biopsies were sequenced by NGS, and ptDNA ESR1 mutations were analyzed by ddPCR.

RESULTS

In the retrospective cohort, all corresponding mutations were detected in ptDNA, with two patients harboring additional ESR1 mutations not present in their metastatic tissues. In the prospective cohort, three ER-positive patients did not have adequate tissue for NGS, and no ESR1 mutations were identified in tissue biopsies from the other nine patients. In contrast, ddPCR detected seven ptDNA ESR1 mutations in 6 of 12 patients (50%).

CONCLUSIONS

We show that ESR1 mutations can occur at a high frequency and suggest that blood can be used to identify additional mutations not found by sequencing of a single metastatic lesion.

The changing role of ER in endocrine resistance

Estrogen receptor (ER) is expressed in approximately 70% of newly diagnosed breast tumors. Although endocrine therapy targeting ER is highly effective, intrinsic or acquired resistance is common, significantly jeopardizing treatment outcomes and minimizing overall survival. Even in the presence of endocrine resistance, a continued role of ER signaling is suggested by several lines of clinical and preclinical evidence. Indeed, inhibition or down-regulation of ER reduces tumor growth in preclinical models of acquired endocrine resistance, and many patients with recurrent ER+ breast tumors progressing on one type of ER-targeted treatment still benefit from sequential endocrine treatments that target ER by a different mechanism. New insights into the nature and biology of ER have revealed several mechanisms sustaining altered ER signaling in endocrine-resistant tumors, including deregulated growth factor receptor signaling that results in ligand-independent ER activation, unbalanced ER co-regulator activity, and genomic alterations involving the ER gene ESR1. Therefore, biopsies of recurrent lesions are needed to assess the changes in epi/genomics and signaling landscape of ER and associated pathways in order to tailor therapies to effectively overcome endocrine resistance. In addition, more completely abolishing the levels and activity of ER and its co-activators, in combination with selected signal transduction inhibitors or agents blocking the upstream or downstream targets of the ER pathway, may provide a better therapeutic strategy in combating endocrine resistance.

Translational value of mouse models in oncology drug development

Much has been written about the advantages and disadvantages of various oncology model systems, with the overall finding that these models lack the predictive power required to translate preclinical efficacy into clinical activity. Despite assertions that some preclinical model systems are superior to others, no single model can suffice to inform preclinical target validation and molecule selection. This perspective provides a balanced albeit critical view of these claims of superiority and outlines a framework for the proper use of existing preclinical models for drug testing and discovery. We also highlight gaps in oncology mouse models and discuss general and pervasive model-independent shortcomings in preclinical oncology work, and we propose ways to address these issues.

Tumorigenesis: it takes a village

Although it is widely accepted that most cancers exhibit some degree of intratumour heterogeneity, we are far from understanding the dynamics that operate among subpopulations within tumours. There is growing evidence that cancer cells behave as communities, and increasing attention is now being directed towards the cooperative behaviour of subclones that can influence disease progression. As expected, these interactions can add a greater layer of complexity to therapeutic interventions in heterogeneous tumours, often leading to a poor prognosis. In this Review, we highlight studies that demonstrate such interactions in cancer and postulate ways to overcome them with better-designed therapeutic strategies.

New concepts in breast cancer genomics and genetics

Massively parallel DNA and RNA sequencing approaches have generated data on thousands of breast cancer genomes. In this review, we consider progress largely from the perspective of new concepts and hypotheses raised so far. These include challenges to the multistep model of breast carcinogenesis and the discovery of new defects in DNA repair through sequence analysis. Issues for functional genomics include the development of strategies to differentiate between mutations that are likely to drive carcinogenesis and bystander background mutations, as well as the importance of mechanistic studies that examine the role of mutations in genes with roles in splicing, histone methylation, and long non-coding RNA function. The application of genome-annotated patient-derived breast cancer xenografts as a potentially more reliable preclinical model is also discussed. Finally, we address the challenge of extracting medical value from genomic data. A weakness of many datasets is inadequate clinical annotation, which hampers the establishment of links between the mutation spectra and the efficacy of drugs or disease phenotypes. Tools such as dGene and the DGIdb are being developed to identify possible druggable mutations, but these programs are a work in progress since extensive molecular pharmacology is required to develop successful ‘genome-forward’ clinical trials. Examples are emerging, however, including targeting HER2 in HER2 mutant breast cancer and mutant ESR1 in ESR1 endocrine refractory luminal-type breast cancer. Finally, the integration of DNA- and RNA-based sequencing studies with mass spectrometry-based peptide sequencing and an unbiased determination of post-translational modifications promises a more complete view of the biochemistry of breast cancer cells and points toward a new discovery horizon in our understanding of the pathophysiology of this complex disease.

Patient-derived tumour xenografts as models for breast cancer drug development

PURPOSE OF REVIEW

Patient-derived xenografts (PDXs) are becoming increasing popular as a preclinical tool for evaluating novel therapeutic strategies in cancer. These models maintain the biological characteristics of the donor tumours and have a predictive power in the translation of cancer therapeutics into clinical settings. This review focuses on the rapidly growing body of literature on PDX models of breast cancer and their applications and challenges in cancer drug development.

RECENT FINDINGS

Several articles in the last 2 years have reported that breast cancer PDXs can reproduce the phenotype and diversity of patients' tumours. This preservation of breast cancer biology involves a number of different aspects, including gene expression patterns, mutational status, drug response and tumour architecture. These models have been shown to be a valuable tool for the identification of new treatment targets, rational drug combinations, biomarkers and mechanisms of drug resistance.

SUMMARY

The development of relevant, predictive models is key to increase the success rate for new drugs. PDX models of breast cancer hold the promise for the development of new and more efficient therapeutic strategies.

Features of aggressive breast cancer

BACKGROUND

Aggressive breast cancer is a term commonly used in literature to describe breast cancer with a poor prognosis. Identifying and understanding the factors associated with aggressiveness could be helpful to the management of patients with breast cancer. Breast cancer is a heterogeneous disease, both clinically and biologically, which may be responsible for the wide range of survival durations for patients with metastatic disease.

AIM

The goal of this study was to identify the factors most often described in association with aggressive metastatic breast cancer (MBC).

METHODS

A systematic review was performed by querying PubMed from January 1, 2012 to June 1, 2014 for "metastatic breast cancer" ("aggressive" or "poor prognosis" or "high risk"). The level of evidence to support each potential prognostic factor of aggressive MBC was also reviewed.

RESULTS

The identified factors were grouped into 3 principle categories: clinical, biological, and patient related. Because patient-related factors may not be indicative of inherent cancer aggressiveness, this review focused only on clinical and biological factors. The factors with the highest levels of evidence to support associations with survival in metastatic breast cancer were visceral metastases, number of metastatic sites, disease-free interval, presence of CTCs, triple-negative disease, and tumour grade.

CONCLUSION

Identification of these factors and understanding their contribution to the aggressiveness of MBC and disease progression may lead to more personalized treatment in this patient population.

Subclonal diversification of primary breast cancer revealed by multiregion sequencing

The sequencing of cancer genomes may enable tailoring of therapeutics to the underlying biological abnormalities driving a particular patient's tumor. However, sequencing-based strategies rely heavily on representative sampling of tumors. To understand the subclonal structure of primary breast cancer, we applied whole-genome and targeted sequencing to multiple samples from each of 50 patients' tumors (303 samples in total). The extent of subclonal diversification varied among cases and followed spatial patterns. No strict temporal order was evident, with point mutations and rearrangements affecting the most common breast cancer genes, including PIK3CA, TP53, PTEN, BRCA2 and MYC, occurring early in some tumors and late in others. In 13 out of 50 cancers, potentially targetable mutations were subclonal. Landmarks of disease progression, such as resistance to chemotherapy and the acquisition of invasive or metastatic potential, arose within detectable subclones of antecedent lesions. These findings highlight the importance of including analyses of subclonal structure and tumor evolution in clinical trials of primary breast cancer.

Clinical management of breast cancer heterogeneity

Traditionally, intertumour heterogeneity in breast cancer has been documented in terms of different histological subtypes, treatment sensitivity profiles, and clinical outcomes among different patients. Results of high-throughput molecular profiling studies have subsequently revealed the true extent of this heterogeneity. Further complicating this scenario, the heterogeneous expression of the oestrogen receptor (ER), progesterone receptor (PR), and HER2 has been reported in different areas of the same tumour. Furthermore, discordance, in terms of ER, PR and HER2 expression, has also been reported between primary tumours and their matched metastatic lesions. High-throughput molecular profiling studies have confirmed that spatial and temporal intratumour heterogeneity of breast cancers exist at a level beyond common expectations. We describe the different levels of tumour heterogeneity, and discuss the strategies that can be adopted by clinicians to tackle treatment response and resistance issues associated with such heterogeneity, including a rationally selected combination of agents that target driver mutations, the targeting of deleterious passenger mutations, identifying and eradicating the 'lethal' clone, targeting the tumour microenvironment, or using adaptive treatments and immunotherapy. The identification of the most-appropriate strategies and their implementation in the clinic will prove highly challenging and necessitate the adoption of radically new practices for the optimal clinical management of breast malignancies.

Noninvasive detection of activating estrogen receptor 1 (ESR1) mutations in estrogen receptor-positive metastatic breast cancer

BACKGROUND

Activating mutations in the estrogen receptor 1 (ESR1) gene are acquired on treatment and can drive resistance to endocrine therapy. Because of the spatial and temporal limitations of needle core biopsies, our goal was to develop a highly sensitive, less invasive method of detecting activating ESR1 mutations via circulating cell-free DNA (cfDNA) and tumor cells as a "liquid biopsy."

METHODS

We developed a targeted 23-amplicon next-generation sequencing (NGS) panel for detection of hot-spot mutations in ESR1, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), tumor protein p53 (TP53), fibroblast growth factor receptor 1 (FGFR1), and fibroblast growth factor receptor 2 (FGFR2) in 48 patients with estrogen receptor-α-positive metastatic breast cancer who were receiving systemic therapy. Selected mutations were validated using droplet digital PCR (ddPCR).

RESULTS

Nine baseline cfDNA samples had an ESR1 mutation. NGS detected 3 activating mutations in ESR1, and 3 hot-spot mutations in PIK3CA, and 3 in TP53 in baseline cfDNA, and the ESR1 p.D538G mutation in 1 matched circulating tumor cell sample. ddPCR analysis was more sensitive than NGS and identified 6 additional baseline cfDNA samples with the ESR1 p.D538G mutation at a frequency of <1%. In serial blood samples from 11 patients, 4 showed changes in cfDNA, 2 with emergence of a mutation in ESR1. We also detected a low frequency ESR1 mutation (1.3%) in cfDNA of 1 primary patient who was thought to have metastatic disease but was clear by scans.

CONCLUSIONS

Early identification of ESR1 mutations by liquid biopsy might allow for cessation of ineffective endocrine therapies and switching to other treatments, without the need for tissue biopsy and before the emergence of metastatic disease.

ESR1 mutations—a mechanism for acquired endocrine resistance in breast cancer

Approximately 70% of breast cancers are oestrogen receptor α (ER) positive, and are, therefore, treated with endocrine therapies. However, about 25% of patients with primary disease and almost all patients with metastases will present with or eventually develop endocrine resistance. Despite the magnitude of this clinical challenge, the mechanisms underlying the development of resistance remain largely unknown. In the past 2 years, several studies unveiled gain-of-function mutations in ESR1, the gene encoding the ER, in approximately 20% of patients with metastatic ER-positive disease who received endocrine therapies, such as tamoxifen and aromatase inhibitors. These mutations are clustered in a 'hotspot' within the ligand-binding domain (LBD) of the ER and lead to ligand-independent ER activity that promotes tumour growth, partial resistance to endocrine therapy, and potentially enhanced metastatic capacity; thus, ER LBD mutations might account for a mechanism of acquired endocrine resistance in a substantial fraction of patients with metastatic disease. In general, the absence of detectable ESR1 mutations in patients with treatment-naive disease, and the correlation between the frequency of patients with tumours harbouring these mutations and the number of endocrine treatments received suggest that, under selective treatment pressure, clonal expansion of rare mutant clones occurs, leading to resistance. Preclinical and clinical development of rationale-based novel therapeutic strategies that inhibit these ER mutants has the potential to substantially improve treatment outcomes. We discuss the contribution of ESR1 mutations to the development of acquired resistance to endocrine therapy, and evaluate how mutated ER can be detected and targeted to overcome resistance and improve patient outcomes.

Proteomic analysis of colon and rectal carcinoma using standard and customized databases

Understanding proteomic differences underlying the different phenotypic classes of colon and rectal carcinoma is important and may eventually lead to a better assessment of clinical behavior of these cancers. We here present a comprehensive description of the proteomic data obtained from 90 colon and rectal carcinomas previously subjected to genomic analysis by The Cancer Genome Atlas (TCGA). Here, the primary instrument files and derived secondary data files are compiled and presented in forms that will allow further analyses of the biology of colon and rectal carcinoma. We also discuss new challenges in processing these large proteomic datasets for relevant proteins and protein variants.