Current and future diagnostic and treatment strategies for patients with invasive lobular breast cancer.

BACKGROUND: Invasive lobular breast cancer (ILC) is the second most common type of breast cancer after invasive breast cancer of no special type (NST), representing up to 15% of all breast cancers. DESIGN: Latest data on ILC are presented, focusing on diagnosis, molecular make-up according to the European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets (ESCAT) guidelines, treatment in the early and metastatic setting and ILC-focused clinical trials. RESULTS: At the imaging level, magnetic resonance imaging-based and novel positron emission tomography/computed tomography-based techniques can overcome the limitations of currently used imaging techniques for diagnosing ILC. At the pathology level, E-cadherin immunohistochemistry could help improving inter-pathologist agreement. The majority of patients with ILC do not seem to benefit as much from (neo-)adjuvant chemotherapy as patients with NST, although chemotherapy might be required in a subset of high-risk patients. No differences in treatment efficacy are seen for anti-human epidermal growth factor receptor 2 (HER2) therapies in the adjuvant setting and cyclin-dependent kinases 4 and 6 inhibitors in the metastatic setting. The clinical utility of the commercially available prognostic gene expression-based tests is unclear for patients with ILC. Several ESCAT alterations differ in frequency between ILC and NST. Germline BRCA1 and PALB2 alterations are less frequent in patients with ILC, while germline CDH1 (gene coding for E-cadherin) alterations are more frequent in patients with ILC. Somatic HER2 mutations are more frequent in ILC, especially in metastases (15% ILC versus 5% NST). A high tumour mutational burden, relevant for immune checkpoint inhibition, is more frequent in ILC metastases (16%) than in NST metastases (5%). Tumours with somatic inactivating CDH1 mutations may be vulnerable for treatment with ROS1 inhibitors, a concept currently investigated in early and metastatic ILC. CONCLUSION: ILC is a unique malignancy based on its pathological and biological features leading to differences in diagnosis as well as in treatment response, resistance and targets as compared to NST.

Spatial interplay of lymphocytes and fibroblasts in estrogen receptor-positive HER2-negative breast cancer.

In estrogen-receptor-positive, HER2-negative (ER+HER2-) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER+HER2- breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER+HER2- breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER+HER2- breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER+HER2- breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER+HER2- patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+HER2- breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions.

A gene signature to predict high tumor-infiltrating lymphocytes after neoadjuvant chemotherapy and outcome in patients with triple-negative breast cancer.

Background: In patients with triple-negative breast cancer (TNBC), the extent of tumor-infiltrating lymphocytes (TILs) in the residual disease after neoadjuvant chemotherapy (NACT) is associated with better prognosis. Our objective was to develop a gene signature from pretreatment samples to predict the extent of TILs after NACT and then to test its prognostic value on survival. Patients and methods: Using 99 pretreatment samples, we generated a four-gene signature associated with high post-NACT TILs. Prognostic value of the signature on distant relapse-free survival (DRFS) was first assessed on the training set (n = 99) and then on an independent validation set (n = 115). Results: A four-gene signature combining the expression levels of HLF, CXCL13, SULT1E1, and GBP1 was developed in baseline samples to predict the extent of lymphocytic infiltration after NACT. In a multivariate analysis performed on the training set, this signature was associated with DRFS [hazard ratio (HR): 0.28, for a one-unit increase in the value of the four-gene signature, 95% confidence interval (CI): 0.13-0.63)]. In a multivariate analysis performed on an independent validation set, the four-gene signature was significantly associated with DRFS (HR: 0.17, 95% CI: 0.06-0.43). The four-gene signature added significant prognostic information when compared with the clinicopathologic pretreatment model (likelihood ratio test in the training set P = 0.004 and in the validation set P = 0.002). Conclusions: A four-gene signature predicts high levels of TILs after anthracycline-containing NACT and outcome in patients with TNBC and adds prognostic information to a clinicopathological model at diagnosis.

Characterization of human breast cancer tissues by infrared imaging.

Fourier Transform InfraRed (FTIR) spectroscopy coupled to microscopy (IR imaging) has shown unique advantages in detecting morphological and molecular pathologic alterations in biological tissues. The aim of this study was to evaluate the potential of IR imaging as a diagnostic tool to identify characteristics of breast epithelial cells and the stroma. In this study a total of 19 breast tissue samples were obtained from 13 patients. For 6 of the patients, we also obtained Non-Adjacent Non-Tumor tissue samples. Infrared images were recorded on the main cell/tissue types identified in all breast tissue samples. Unsupervised Principal Component Analyses and supervised Partial Least Square Discriminant Analyses (PLS-DA) were used to discriminate spectra. Leave-one-out cross-validation was used to evaluate the performance of PLS-DA models. Our results show that IR imaging coupled with PLS-DA can efficiently identify the main cell types present in FFPE breast tissue sections, i.e. epithelial cells, lymphocytes, connective tissue, vascular tissue and erythrocytes. A second PLS-DA model could distinguish normal and tumor breast epithelial cells in the breast tissue sections. A patient-specific model reached particularly high sensitivity, specificity and MCC rates. Finally, we showed that the stroma located close or at distance from the tumor exhibits distinct spectral characteristics. In conclusion FTIR imaging combined with computational algorithms could be an accurate, rapid and objective tool to identify/quantify breast epithelial cells and differentiate tumor from normal breast tissue as well as normal from tumor-associated stroma, paving the way to the establishment of a potential complementary tool to ensure safe tumor margins.

Frequent incidence of BARD1-truncating mutations in germline DNA from triple-negative breast cancer patients.

Triple-negative breast cancer (TNBC) accounts for 10-20% of all breast cancers (BCs), and conventional chemotherapy is the only effective systemic treatment. Germline BRCA1/2 mutations are found in approximately 15% of TNBC patients. In the past, we have documented pathogenic mutations in BARD1, a BRCA1 interacting protein, in families at high risk for BC. In this study, we have analyzed germline DNA from 61 estrogen receptor negative patients (of which 42 were TNBC) for the presence of mutations in the BRCA1, BRCA2 and BARD1 gene. BRCA1/2 mutations were found in 8 out of 42 (19%) TNBC patients, but not in the ER-/HER2+ cohort. We also found four good candidate pathogenic BARD1 mutations in the TNBC cohort, including two protein-truncating mutations (p.Gln564Ter and p.Arg641Ter). Our data suggest that TNBC patients are enriched for pathogenic BARD1 germline mutations as compared to control samples and high BC risk families. Ten of the 42 investigated TNBC patients carry a BRCA pathway mutation (in BRCA1, BRCA2 or BARD1) rendering them susceptible to homologous recombination deficiency. These patients should become eligible for exploring the efficacy of poly (ADP-ribose) polymerase (PARP) inhibitors.

Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial.

BACKGROUND: We have previously shown the prognostic importance of tumor-infiltrating lymphocytes (TILs) in newly diagnosed triple-negative breast cancer (TNBC) using tumor samples from a large clinical trial cohort. In this study, we aimed to validate these findings and also investigate associations with trastuzumab benefit in HER2-overexpressing disease (HER2+). PATIENTS AND METHODS: A prospective-retrospective study was conducted using samples from the FinHER adjuvant, phase III trial that enrolled 1010 early-stage BC patients, 778 of whom were HER2-nonamplified. Those with HER2+ disease (n = 232) were randomized to 9 weeks of trastuzumab or no trastuzumab in addition to chemotherapy. Two pathologists independently quantified stromal TILs in 935 (92.6%) available slides. The primary end point of distant disease-free survival (DDFS) and interactions with trastuzumab were studied in Cox regression models. RESULTS: Confirming our previous findings, in TNBC (n = 134) each 10% increase in TILs was significantly associated with decreased distant recurrence in TNBC; for DDFS the hazard ratio adjusted for clinicopathological factors: 0.77; 95% confidence interval (CI) 0.61-0.98, P = 0.02. In HER2+ BC (n = 209), each 10% increase in lymphocytic infiltration was significantly associated with decreased distant recurrence in patients randomized to the trastuzumab arm (DDFS P interaction = 0.025). CONCLUSIONS: Higher levels of TILs present at diagnosis were significantly associated with decreased distant recurrence rates in primary TNBC. These results confirm our previous data and further support that TILs should be considered as a robust prognostic factor in this BC subtype. We also report for the first time an association between higher levels of TILs and increased trastuzumab benefit in HER2+ disease. Further research into why some TN and HER2+ BCs can or cannot generate a host antitumor immune response and how trastuzumab can favorably alter the immune microenvironment is warranted.

Breast cancer and melanoma cell line identification by FTIR imaging after formalin-fixation and paraffin-embedding.

Over the past few decades, Fourier transform infrared (FTIR) spectroscopy coupled to microscopy has been recognized as an emerging and potentially powerful tool in cancer research and diagnosis. For this purpose, histological analyses performed by pathologists are mostly carried out on biopsied tissue that undergoes the formalin-fixation and paraffin-embedding (FFPE) procedure. This processing method ensures an optimal and permanent preservation of the samples, making FFPE-archived tissue an extremely valuable source for retrospective studies. Nevertheless, as highlighted by previous studies, this fixation procedure significantly changes the principal constituents of cells, resulting in important effects on their infrared (IR) spectrum. Despite the chemical and spectral influence of FFPE processing, some studies demonstrate that FTIR imaging allows precise identification of the different cell types present in biopsied tissue, indicating that the FFPE process preserves spectral differences between distinct cell types. In this study, we investigated whether this is also the case for closely related cell lines. We analyzed spectra from 8 cancerous epithelial cell lines: 4 breast cancer cell lines and 4 melanoma cell lines. For each cell line, we harvested cells at subconfluence and divided them into two sets. We first tested the "original" capability of FTIR imaging to identify these closely related cell lines on cells just dried on BaF2 slides. We then repeated the test after submitting the cells to the FFPE procedure. Our results show that the IR spectra of FFPE processed cancerous cell lines undergo small but significant changes due to the treatment. The spectral modifications were interpreted as a potential decrease in the phospholipid content and protein denaturation, in line with the scientific literature on the topic. Nevertheless, unsupervised analyses showed that spectral proximities and distances between closely related cell lines were mostly, but not entirely, conserved after FFPE processing. Finally, PLS-DA statistical analyses highlighted that closely related cell lines are still successfully identified and efficiently distinguished by FTIR spectroscopy after FFPE treatment. This last result paves the way towards identification and characterization of cellular subtypes on FFPE tissue sections by FTIR imaging, indicating that this analysis technique could become a potential useful tool in cancer research.

Change in the microenvironment of breast cancer studied by FTIR imaging.

Fourier transform infrared (FTIR) imaging was applied on histopathological specimens of breast cancer of different tumor histological grades. Focus was given to the extracellular matrix. FTIR spectral changes were observed when examining the extracellular matrix close to and far from carcinoma. Major changes were observed, in particular in the relative intensities of the collagen bands at 1640 and 1630 cm(-1). PCA analysis and global fitting indicate a continuous progression in collagen spectral features when moving away from the tumor. These preliminary results suggest FTIR spectral features present in the 1700-1600 cm(-1) spectral range could be used as spectral markers to identify cancer-induced modifications in collagen. This chemical imaging approach to analyze the breast cancer microenvironment could be used in the future for improving diagnostics of breast cancer.

Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients.

Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-alpha (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Here we describe in details the contents and quality controls for the gene expression and clinical data associated with the study published by Desmedt and colleagues in the Journal of Clinical Oncology in 2011 (Desmedt et al., 2011). We also provide R code to easily access the data and perform the quality controls and basic analyses relevant to this dataset.

Genomic grade adds prognostic value in invasive lobular carcinoma.

BACKGROUND: The prognostic value of histologic grade (HG) in invasive lobular carcinoma (ILC) remains uncertain, and most ILC tumors are graded as HG2. Genomic grade (GG) is a 97-gene signature that improves the prognostic value of HG. This study evaluates whether GG may overcome the limitations of HG in ILC. METHODS: Gene expression data were generated from frozen tumor samples, and GG calculated according to the expression of 97 genes. The prognostic value of GG was assessed in a stratified Cox regression model for invasive disease-free survival (IDFS) and overall survival (OS). RESULTS: A total of 166 patients were classified by GG. HG classified 33 (20%) tumors as HG1, 120 (73%) as HG2 and 12 (7%) as HG3. GG classified 106 (64%) tumors as GG low (GG1), 29 (17%) as GG high (GG3) and 31 (19%) as equivocal (cases not classified as GG1 or GG3). The median follow-up time was 6.5 years. In multivariate analyses, GG was associated with IDFS [HR(GG3 vs GG1) 5.6 (2.1-15.3); P < 0.001] and OS [HR(GG3 vs GG1) 7.2, 95% CI (1.6-32.2); P = 0.01]. CONCLUSIONS: GG outperformed HG in ILC and added prognostic value to classic clinicopathologic variables, including nodal status.

Modeling invasive breast cancer: growth factors propel progression of HER2-positive premalignant lesions.

The HER2/neu oncogene encodes a receptor-like tyrosine kinase whose overexpression in breast cancer predicts poor prognosis and resistance to conventional therapies. However, the mechanisms underlying aggressiveness of HER2 (human epidermal growth factor receptor 2)-overexpressing tumors remain incompletely understood. Because it assists epidermal growth factor (EGF) and neuregulin receptors, we overexpressed HER2 in MCF10A mammary cells and applied growth factors. HER2-overexpressing cells grown in extracellular matrix formed filled spheroids, which protruded outgrowths upon growth factor stimulation. Our transcriptome analyses imply a two-hit model for invasive growth: HER2-induced proliferation and evasion from anoikis generate filled structures, which are morphologically and transcriptionally analogous to preinvasive patients' lesions. In the second hit, EGF escalates signaling and transcriptional responses leading to invasive growth. Consistent with clinical relevance, a gene expression signature based on the HER2/EGF-activated transcriptional program can predict poorer prognosis of a subgroup of HER2-overexpressing patients. In conclusion, the integration of a three-dimensional cellular model and clinical data attributes progression of HER2-overexpressing lesions to EGF-like growth factors acting in the context of the tumor's microenvironment.

Largest ever DNA methylation dataset for breast cancer completed.

DNA methylation dataset for human breast tumor tissue could aid in the diagnosis of the disease.

An advanced discrete state-discrete event multiscale simulation model of the response of a solid tumor to chemotherapy: Mimicking a clinical study.

In this paper an advanced, clinically oriented multiscale cancer model of breast tumor response to chemotherapy is presented. The paradigm of early breast cancer treated by epirubicin according to a branch of an actual clinical trial (the Trial of Principle, TOP trial) has been addressed. The model, stemming from previous work of the In Silico Oncology Group, National Technical University of Athens, is characterized by several crucial new features, such as the explicit distinction of proliferating cells into stem cells of infinite mitotic potential and cells of limited proliferative capacity, an advanced generic cytokinetic model and an improved tumor constitution initialization technique. A sensitivity analysis regarding critical parameters of the model has revealed their effect on the behavior of the biological system. The favorable outcome of an initial step towards the clinical adaptation and validation of the simulation model, based on the use of anonymized data from the TOP clinical trial, is presented and discussed. Two real clinical cases from the TOP trial with variable molecular profile have been simulated. A realistic time course of the tumor diameter and a reduction in tumor size in agreement with the clinical data has been achieved for both cases by selection of reasonable model parameter values, thus demonstrating a possible adaptation process of the model to real clinical trial data. Available imaging, histological, molecular and treatment data are exploited by the model in order to strengthen patient individualization modeling. The expected use of the model following thorough clinical adaptation, optimization and validation is to simulate either several candidate treatment schemes for a particular patient and support the selection of the optimal one or to simulate the expected extent of tumor shrinkage for a given time instant and decide on the adequacy or not of the simulated scheme.

'In silico' oncology for clinical decision making in the context of nephroblastoma.

The present paper outlines the initial version of the ACGT (Advancing Clinico-Genomic Trials) -- an Integrated Project, partly funded by the EC (FP6-2005-IST-026996)I-Oncosimulator as an integrated software system simulating in vivo tumour response to therapeutic modalities within the clinical trials environment aiming to support clinical decision making in individual patients. Cancer treatment optimization is the main goal of the system. The document refers to the technology of the system and the clinical requirements and the types of medical data needed for exploitation in the case of nephroblastoma. The outcome of an initial step towards the clinical adaptation and validation of the system is presented and discussed. Use of anonymized real data before and after chemotherapeutic treatment for the case of the SIOP 2001/GPOH nephroblastoma clinical trial constitutes the basis of the clinical adaptation and validation process. By using real medical data concerning nephroblastoma for a single patient in conjunction with plausible values for the model parameters (based on available literature) a reasonable prediction of the actual tumour volume shrinkage has been made possible. Obviously as more and more sets of medical data are exploited the reliability of the model "tuning" is expected to increase. The successful performance of the initial combined ACGT Oncosimulator platform, although usable up to now only as a test of principle, has been a particularly encouraging step towards the clinical translation of the system, being the first of its kind worldwide.

Gene expression predictors in breast cancer: current status, limitations and perspectives.

Breast cancer is characterised by a wide heterogeneity regarding outcome and drug sensitivity. A better prediction of these two parameters at the individual level should improve patient management and therefore also improve both the quality of life and the overall survival of the patient. Several molecular predictors for prognosis (MammaPrint or Oncotype DX) and drug prediction (DLD30, SET index) have been generated using DNA-based arrays or RT-PCR, some of these being tested in phase III trials. Although they exhibit good metric performance and should improve the quality of care in the next decade, these predictors are considered suboptimal regarding the potential of the technology. New study design and arrays should generate more powerful second generation gene signatures.

A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?

MOTIVATION: Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data mining tools emerged as a promising technology to bring additional insights into BC biology and to improve the quality of prognostication. The aim of this work is to assess quantitatively the accuracy of prediction obtained with state-of-the-art data analysis techniques for BC microarray data through an independent and thorough framework. RESULTS: Due to the large number of variables, the reduced amount of samples and the high degree of noise, complex prediction methods are highly exposed to performance degradation despite the use of cross-validation techniques. Our analysis shows that the most complex methods are not significantly better than the simplest one, a univariate model relying on a single proliferation gene. This result suggests that proliferation might be the most relevant biological process for BC prognostication and that the loss of interpretability deriving from the use of overcomplex methods may be not sufficiently counterbalanced by an improvement of the quality of prediction. AVAILABILITY: The comparison study is implemented in an R package called survcomp and is available from http://www.ulb.ac.be/di/map/bhaibeka/software/survcomp/.

A significant proportion of elderly patients develop hormone-dependant "luminal-B" tumours associated with aggressive characteristics.

INTRODUCTION: To investigate the influence of ageing on the incidence of breast cancer (BC) molecular subtypes, patient age at diagnosis was correlated with bio-pathological data collected retrospectively from 2723 consecutive patients diagnosed/treated at our Institute between 2000 and 2003. METHODS: According to their bio-characteristics, 61% of the samples could be assigned to a molecular subtype: the "HER-2+", the "ER & HER2 negative" or one of the two "luminal-like" subtypes divided according to their histological grade ("A" [HER-2-/ER+/grade 1-2] and "B" [HER-2-/ER+/grade 3]). RESULTS AND CONCLUSION: Age is highly influencing the incidence of BC molecular subtypes. Patients younger than 40 develop a statistically higher rate of high grade proliferating "HER-2" (27%) and "ER & HER2 negative" (31%) BC whereas patients older than 50 develop mostly less aggressive hormone-dependant "luminal-A" BC (>67%). Nevertheless, a significant proportion of patients older than 70 develop "luminal-B" (19%) tumours associated with high proliferation, high grade, large size and nodal invasion.