Britta Weigelt

Breast cancer metastasis: markers and models

Breast cancer starts as a local disease, but it can metastasize to the lymph nodes and distant organs. At primary diagnosis, prognostic markers are used to assess whether the transition to systemic disease is likely to have occurred. The prevailing model of metastasis reflects this view — it suggests that metastatic capacity is a late, acquired event in tumorigenesis. Others have proposed the idea that breast cancer is intrinsically a systemic disease. New molecular technologies, such as DNA microarrays, support the idea that metastatic capacity might be an inherent feature of breast tumours. These data have important implications for prognosis predicition and our understanding of metastasis.

A large-scale RNAi screen in human cells identifies new components of the p53 pathway

RNA interference (RNAi) is a powerful new tool with which to perform loss-of-function genetic screens in lower organisms and can greatly facilitate the identification of components of cellular signalling pathways. In mammalian cells, such screens have been hampered by a lack of suitable tools that can be used on a large scale. We and others have recently developed expression vectors to direct the synthesis of short hairpin RNAs (shRNAs) that act as short interfering RNA (siRNA)-like molecules to stably suppress gene expression. Here we report the construction of a set of retroviral vectors encoding 23,742 distinct shRNAs, which target 7,914 different human genes for suppression. We use this RNAi library in human cells to identify one known and five new modulators of p53-dependent proliferation arrest. Suppression of these genes confers resistance to both p53-dependent and p19ARF-dependent proliferation arrest, and abolishes a DNA-damage-induced G1 cell-cycle arrest. Furthermore, we describe siRNA bar-code screens to rapidly identify individual siRNA vectors associated with a specific phenotype. These new tools will greatly facilitate large-scale loss-of-function genetic screens in mammalian cells.

Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle

BACKGROUND Plasma-derived cell-free tumor DNA (ctDNA) constitutes a potential surrogate for tumor DNA obtained from tissue biopsies. We posit that massively parallel sequencing (MPS) analysis of ctDNA may help define the repertoire of mutations in breast cancer and monitor tumor somatic alterations during the course of targeted therapy. PATIENT AND METHODS A 66-year-old patient presented with synchronous estrogen receptor-positive/HER2-negative, highly proliferative, grade 2, mixed invasive ductal-lobular carcinoma with bone and liver metastases at diagnosis. DNA extracted from archival tumor material, plasma and peripheral blood leukocytes was subjected to targeted MPS using a platform comprising 300 cancer genes known to harbor actionable mutations. Multiple plasma samples were collected during the fourth line of treatment with an AKT inhibitor. RESULTS Average read depths of 287x were obtained from the archival primary tumor, 139x from the liver metastasis and between 200x and 900x from ctDNA samples. Sixteen somatic non-synonymous mutations were detected in the liver metastasis, of which 9 (CDKN2A, AKT1, TP53, JAK3, TSC1, NF1, CDH1, MML3 and CTNNB1) were also detected in >5% of the alleles found in the primary tumor sample. Not all mutations identified in the metastasis were reliably identified in the primary tumor (e.g. FLT4). Analysis of ctDNA, nevertheless, captured all mutations present in the primary tumor and/or liver metastasis. In the longitudinal monitoring of the patient, the mutant allele fractions identified in ctDNA samples varied over time and mirrored the pharmacodynamic response to the targeted therapy as assessed by positron emission tomography-computed tomography. CONCLUSIONS This proof-of-principle study is one of the first to demonstrate that high-depth targeted MPS of plasma-derived ctDNA constitutes a potential tool for de novo mutation identification and monitoring of somatic genetic alterations during the course of targeted therapy, and may be employed to overcome the challenges posed by intra-tumor genetic heterogeneity. REGISTERED CLINICAL TRIAL: www.clinicaltrials.gov, NCT01090960.BACKGROUND Plasma-derived cell-free tumor DNA (ctDNA) constitutes a potential surrogate for tumor DNA obtained from tissue biopsies. We posit that massively parallel sequencing (MPS) analysis of ctDNA may help define the repertoire of mutations in breast cancer and monitor tumor somatic alterations during the course of targeted therapy. PATIENT AND METHODS A 66-year-old patient presented with synchronous estrogen receptor-positive/HER2-negative, highly proliferative, grade 2, mixed invasive ductal-lobular carcinoma with bone and liver metastases at diagnosis. DNA extracted from archival tumor material, plasma and peripheral blood leukocytes was subjected to targeted MPS using a platform comprising 300 cancer genes known to harbor actionable mutations. Multiple plasma samples were collected during the fourth line of treatment with an AKT inhibitor. RESULTS Average read depths of 287x were obtained from the archival primary tumor, 139x from the liver metastasis and between 200x and 900x from ctDNA samples. Sixteen somatic non-synonymous mutations were detected in the liver metastasis, of which 9 (CDKN2A, AKT1, TP53, JAK3, TSC1, NF1, CDH1, MML3 and CTNNB1) were also detected in >5% of the alleles found in the primary tumor sample. Not all mutations identified in the metastasis were reliably identified in the primary tumor (e.g. FLT4). Analysis of ctDNA, nevertheless, captured all mutations present in the primary tumor and/or liver metastasis. In the longitudinal monitoring of the patient, the mutant allele fractions identified in ctDNA samples varied over time and mirrored the pharmacodynamic response to the targeted therapy as assessed by positron emission tomography-computed tomography. CONCLUSIONS This proof-of-principle study is one of the first to demonstrate that high-depth targeted MPS of plasma-derived ctDNA constitutes a potential tool for de novo mutation identification and monitoring of somatic genetic alterations during the course of targeted therapy, and may be employed to overcome the challenges posed by intra-tumor genetic heterogeneity. REGISTERED CLINICAL TRIAL www.clinicaltrials.gov, NCT01090960.

Gene expression profiles of primary breast tumors maintained in distant metastases

It has been debated for decades how cancer cells acquire metastatic capability. It is unclear whether metastases are derived from distinct subpopulations of tumor cells within the primary site with higher metastatic potential, or whether they originate from a random fraction of tumor cells. Here we show, by gene expression profiling, that human primary breast tumors are strikingly similar to the distant metastases of the same patient. Unsupervised hierarchical clustering, multidimensional scaling, and permutation testing, as well as the comparison of significantly expressed genes within a pair, reveal their genetic similarity. Our findings suggest that metastatic capability in breast cancer is an inherent feature and is not based on clonal selection.

Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists

Breast cancer is a heterogeneous disease encompassing a variety of entities with distinct morphological features and clinical behaviors. Although morphology is often associated with the pattern of molecular aberrations in breast cancers, it is also clear that tumors of the same histological type show remarkably different clinical behavior. This is particularly true for ‘basal-like cancer’, which is an entity defined using gene expression analysis. The purpose of this article was to review the current state of knowledge of basal-like breast cancers, to discuss the relationship between basal-like and triple-negative breast cancers, and to clarify practical implications of these diagnoses for pathologists and oncologists.

The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade.

In the last decade, the development of microarrays and the ability to perform massively parallel gene expression analysis of human tumours were received with great excitement by the scientific community. The promise of microarrays was of apocalyptic dimensions, with some experts envisaging that it would be a matter of a few years for this technology to replace traditional clinicopathological markers in clinical practice and treatment decision‐making. The replacement of histopathology by high‐tech and more objective approaches to cancer diagnosis, prognostication and prediction was, at that time, a foregone conclusion. Ten years after the initial publications of translational research studies using microarrays, one cannot deny that this technology has changed the way breast cancer is perceived. It has brought the concept of breast cancer heterogeneity to the forefront of cancer research, and the fact that distinct subtypes of breast cancer are completely different diseases that affect the same anatomical site. Furthermore, it has led to the development of prognostic and predictive ‘gene signatures’, which are yet to be fully incorporated into clinical practice. Importantly, though, the prognostic and predictive power of microarrays has been shown to be complementary to, rather than a replacement for, traditional clinicopathological parameters. Here we endeavour to provide a fair and balanced assessment of what microarray‐based gene expression analysis has taught us in the last decade and its contribution to breast cancer classification, prognostication and prediction. Copyright

Breast cancer molecular profiling with single sample predictors: a retrospective analysis

BACKGROUND Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes. METHODS Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort. FINDINGS Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves. INTERPRETATION Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed. FUNDING Breakthrough Breast Cancer, Cancer Research UK.

Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer.

Noninvasive mutation tracking in plasma can detect circulating tumor DNA arising from residual micrometastatic disease and thus identify patients at high risk of recurrence. Risk of recurrence Predicting whether a cancer patient will relapse remains a formidable challenge in modern medicine. Fortunately, circulating tumor DNA (ctDNA) present in the blood may give clues on residual disease—cancer cells left behind to seed new tumors even after treatment. Garcia-Murillas et al. developed a personalized ctDNA assay based on digital polymerase chain reaction to track mutations over time in patients with early-stage breast cancer who had received apparently curative treatments, surgery, and chemotherapy. Mutation tracking in serial samples accurately predicted metastatic relapse—in several instances, months before clinical relapse (median of ~8 months). Such unprecedented early prediction could allow for intervention before the reappearance of cancer in high-risk patients. In addition, the authors were able to shed light on the genetic events driving such metastases, by massively parallel sequencing of the ctDNA, which could inform new drug-based therapies on the basis of the patients’ individual mutations. The identification of early-stage breast cancer patients at high risk of relapse would allow tailoring of adjuvant therapy approaches. We assessed whether analysis of circulating tumor DNA (ctDNA) in plasma can be used to monitor for minimal residual disease (MRD) in breast cancer. In a prospective cohort of 55 early breast cancer patients receiving neoadjuvant chemotherapy, detection of ctDNA in plasma after completion of apparently curative treatment—either at a single postsurgical time point or with serial follow-up plasma samples—predicted metastatic relapse with high accuracy [hazard ratio, 25.1 (confidence interval, 4.08 to 130.5; log-rank P < 0.0001) or 12.0 (confidence interval, 3.36 to 43.07; log-rank P < 0.0001), respectively]. Mutation tracking in serial samples increased sensitivity for the prediction of relapse, with a median lead time of 7.9 months over clinical relapse. We further demonstrated that targeted capture sequencing analysis of ctDNA could define the genetic events of MRD, and that MRD sequencing predicted the genetic events of the subsequent metastatic relapse more accurately than sequencing of the primary cancer. Mutation tracking can therefore identify early breast cancer patients at high risk of relapse. Subsequent adjuvant therapeutic interventions could be tailored to the genetic events present in the MRD, a therapeutic approach that could in part combat the challenge posed by intratumor genetic heterogeneity.

Histological and molecular types of breast cancer: is there a unifying taxonomy?

Breast cancer is a complex and heterogeneous disease, comprising multiple tumor entities associated with distinctive histological patterns and different biological features and clinical behaviors. Microarray-based high-throughput technologies have been employed to unravel the molecular characteristics of breast cancer, including its proclivity to disseminate to distant sites, and the molecular basis for histological grade. In addition, a breast cancer molecular taxonomy based solely on transcriptomic analysis has been proposed. Most microarray studies have focused on invasive ductal carcinomas of no special type, neglecting the important information about the biology and clinical behavior of breast cancers conveyed by histological type. Histological special types of breast cancer account for up to 25% of all invasive breast cancers. The histopathological characteristics of these cancers might be driven by specific genetic alterations, providing direct evidence for genotypic–phenotypic correlations between morphological patterns and molecular changes in breast cancer. We review the historical aspects of breast cancer taxonomy, discuss the possible origins of the diversity of breast cancer and propose an approach for the identification of novel therapeutic targets on the basis of histological special types of breast cancer.

Histological types of breast cancer: How special are they?

Breast cancer is a heterogeneous disease, comprising multiple entities associated with distinctive histological and biological features, clinical presentations and behaviours and responses to therapy. Microarray‐based technologies have unravelled the molecular underpinning of several characteristics of breast cancer, including metastatic propensity and histological grade, and have led to the identification of prognostic and predictive gene expression signatures. Furthermore, a molecular taxonomy of breast cancer based on transcriptomic analysis has been proposed. However, microarray studies have primarily focused on invasive ductal carcinomas of no special type. Owing to the relative rarity of special types of breast cancer, information about the biology and clinical behaviour of breast cancers conveyed by histological type has not been taken into account. Histological special types of breast cancer account for up to 25% of all invasive breast cancers. Recent studies have provided direct evidence of the existence of genotypic–phenotypic correlations. For instance, secretory carcinomas of the breast consistently harbour the t(12;15) translocation that leads to the formation of the ETV6–NTRK3 fusion gene, adenoid cystic carcinomas consistently display the t(6;9) MYB–NFIB translocation and lobular carcinomas consistently show inactivation of the CDH1 gene through multiple molecular mechanisms. Furthermore, histopathological and molecular analysis of tumours from conditional mouse models has provided direct evidence for the causative role of specific genes in the genesis of specific histological special types of breast cancer. Here we review the associations between the molecular taxonomy of breast cancer and histological special types, discuss the possible origins of the heterogeneity of breast cancer and propose an approach for the identification of novel therapeutic targets based on the study of histological special types of breast cancer.