Andre Ahr

From latent disseminated cells to overt metastasis: Genetic analysis of systemic breast cancer progression

According to the present view, metastasis marks the end in a sequence of genomic changes underlying the progression of an epithelial cell to a lethal cancer. Here, we aimed to find out at what stage of tumor development transformed cells leave the primary tumor and whether a defined genotype corresponds to metastatic disease. To this end, we isolated single disseminated cancer cells from bone marrow of breast cancer patients and performed single-cell comparative genomic hybridization. We analyzed disseminated tumor cells from patients after curative resection of the primary tumor (stage M0), as presumptive progenitors of manifest metastasis, and from patients with manifest metastasis (stage M1). Their genomic data were compared with those from microdissected areas of matched primary tumors. Disseminated cells from M0-stage patients displayed significantly fewer chromosomal aberrations than primary tumors or cells from M1-stage patients (P < 0.008 and P < 0.0001, respectively), and their aberrations appeared to be randomly generated. In contrast, primary tumors and M1 cells harbored different and characteristic chromosomal imbalances. Moreover, applying machine-learning methods for the classification of the genotypes, we could correctly identify the presence or absence of metastatic disease in a patient on the basis of a single-cell genome. We suggest that in breast cancer, tumor cells may disseminate in a far less progressed genomic state than previously thought, and that they acquire genomic aberrations typical of metastatic cells thereafter. Thus, our data challenge the widely held view that the precursors of metastasis are derived from the most advanced clone within the primary tumor.

T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers

IntroductionLymphocyte infiltration (LI) is often seen in breast cancer but its importance remains controversial. A positive correlation of human epidermal growth factor receptor 2 (HER2) amplification and LI has been described, which was associated with a more favorable outcome. However, specific lymphocytes might also promote tumor progression by shifting the cytokine milieu in the tumor.MethodsAffymetrix HG-U133A microarray data of 1,781 primary breast cancer samples from 12 datasets were included. The correlation of immune system-related metagenes with different immune cells, clinical parameters, and survival was analyzed.ResultsA large cluster of nearly 600 genes with functions in immune cells was consistently obtained in all datasets. Seven robust metagenes from this cluster can act as surrogate markers for the amount of different immune cell types in the breast cancer sample. An IgG metagene as a marker for B cells had no significant prognostic value. In contrast, a strong positive prognostic value for the T-cell surrogate marker (lymphocyte-specific kinase (LCK) metagene) was observed among all estrogen receptor (ER)-negative tumors and those ER-positive tumors with a HER2 overexpression. Moreover ER-negative tumors with high expression of both IgG and LCK metagenes seem to respond better to neoadjuvant chemotherapy.ConclusionsPrecise definitions of the specific subtypes of immune cells in the tumor can be accomplished from microarray data. These surrogate markers define subgroups of tumors with different prognosis. Importantly, all known prognostic gene signatures uniformly assign poor prognosis to all ER-negative tumors. In contrast, the LCK metagene actually separates the ER-negative group into better or worse prognosis.

A clinically relevant gene signature in triple negative and basal-like breast cancer.

IntroductionCurrent prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease.MethodsWe assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables.ResultsSeventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables.ConclusionsWe describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.

Identification of high risk breast-cancer patients by gene expression profiling

We previously used DNA array analyses in the molecular profiling of breast cancers. By cluster analysis of 55 patients, we identified a subpopulation of breast cancers-designated class A-that contained a high number of nodal-positive tumours and that had frequently developed distant metastases at the time of diagnosis. We have now analysed follow-up data from these patients. We found that, despite a median of only 23.5 months of follow-up, 11 of 22 patients in class A progressed to metastatic disease, and three of five patients classified as having a nodal status of N0 in this subpopulation developed distant metastases. Our analysis identifies breast-cancer patients with a high risk of disease recurrence, and could act as a first step towards improved patient-adapted therapy.

Expression of p16 and lack of pRB in primary small cell lung cancer.

The retinoblastoma protein (pRB), p16, and cyclin D1 are major components of the RB pathway, which controls the G1 checkpoint of the cell cycle. Proper regulation of this pathway is crucial for normal cell proliferation. Abnormal forms of these proteins have been found in various types of malignant tumours. In the present report, immunohistochemical techniques were applied to study the expression of pRB, p16, and cyclin D1 in 161 samples of primary small cell lung cancer (SCLC) and 20 samples of non‐small cell lung cancer (NSCLC). While pRB and cyclin D1 staining was negative in 161 specimens of SCLC, expression of p16 was observed in 153 samples. In contrast to SCLC, 16 out of 20 NSCLC cases exhibited pRB expression and 15 showed cyclin D1 expression, but only very weak p16 staining was found in five samples. These observations could provide additional criteria for the distinction between SCLC and NSCLC. Furthermore, these findings, based on primary tissues, implicate different mechanisms in the tumourigenesis of SCLC and NSCLC. Copyright

Molecular classification of breast cancer patients by gene expression profiling.

For many tumuors, pathological subclasses exist which have to be further defined by genetic markers to improve therapy and follow‐up strategies. In this study, cDNA array analyses of breast cancers have been performed to classify tumuors into categories based on expression patterns. Comparing purified normal ductal epithelial cells and corresponding tumour tissues, the expression of only a small fraction of genes was found to be significantly changed. A subset of genes repeatedly found to be differentially expressed in breast cancers was subsequently employed to perform a classification of 82 normal and malignant breast specimens by cluster analysis. This analysis identifies a subgroup of transcriptionally related tumours, designated class A, which can be further subdivided into A1 and A2. Correlation with classical clinicopathological parameters revealed that subgroup A1 was characterized by a high number of node‐positive tumours (14 of 16). In this subgroup there was a disproportionate number of patients who had already developed distant metastases at the time of diagnosis (25% in this subgroup, compared with 5% among the rest of the samples). Taken together, the use of these differentially expressed marker genes in conjunction with sample clustering algorithms provides a novel molecular classification of breast cancer specimens, which facilitates the identification of patients with a higher risk of recurrence. Copyright

Poor Outcome in Estrogen Receptor–Positive Breast Cancers Predicted by Loss of Plexin B1

Purpose: A common characteristic of mammary carcinomas is an inverse relationship between the estrogen receptor (ER) status and the proliferative activity of the tumor. Yet, a subset of ER-positive breast cancers is characterized by a high proliferation, suggesting malfunctions in ER responsiveness that influence the biological and therapeutic behavior of tumor cells. The expression of several ER-dependent genes seems to be dysregulated among those “uncoupled” tumors. One of those genes is plexin B1, a cell-surface receptor for the semaphorin Sema4D (CD 100). However, the biological role of plexin B1 in breast cancer is largely unknown. Experimental Design: Expression data of plexin B1 were obtained from Affymetrix microarray analysis of n = 119 breast cancer specimens. Validation was done by quantitative real-time PCR and protein expression was evaluated by immunohistochemistry. Expression data were compared with clinical characteristics as well as follow-up data of the disease. Results: Low plexin B1 expression levels characterize a more aggressive tumor phenotype. The expression of plexin B1 is strongly correlated with the ER status. However, even among ER-positive tumors, loss of plexin B1 is associated with an impaired prognosis of breast cancer patients in both univariate (all patients, P = 0.0062; ER positive, P = 0.0107) and multivariate analyses (all patients, P = 0.032; ER positive, P = 0.022). Immunohistochemistry reveals that the tumor cells themselves and not the endothelial cells are the major source of plexin B1 expression in the tumor. Conclusion: Plexin B1 acts not only as a new important prognostic but should also represent a predictive marker indicating an endocrine resistance. These data give a new insight in markers that could be involved in endocrine dysregulation of breast cancer.

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. Methodology/Principal Findings We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases). Conclusions/Significance Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Pooling of microarray datasets seems to be a reasonable approach to increase sample size when a heterogeneous disease like breast cancer is concerned. Different methods for the adaption of datasets have been used in the literature. We have analyzed influences of these strategies using a pool of 3,030 Affymetrix U133A microarrays from breast cancer samples. We present data on the resulting concordance with biochemical assays of well known parameters and highlight critical pitfalls. We further propose a method for the inference of cutoff values directly from the data without prior knowledge of the true result. The cutoffs derived by this method displayed high specificity and sensitivity. Markers with a bimodal distribution like ER, PgR, and HER2 discriminate different biological subtypes of disease with distinct clinical courses. In contrast, markers displaying a continuous distribution like proliferation markers as Ki67 rather describe the composition of the mixture of cells in the tumor.

Loss of Plexin B1 is highly prognostic in low proliferating ER positive breast cancers - Results of a large scale microarray analysis

Plexins, cell-surface receptors for semaphorins, are involved in cell adhesion and migration. In the previous work, we demonstrated that the loss of Plexin B1 expression is associated with poor outcome in breast cancer patients. The goal of the present study was a validation of Plexin B1 expression in a large scale microarray dataset from n=1086 breast cancer patients. Plexin B1 correlates with ER status (p<0.001) and is of prognostic significance only in ER positive (p=0.024) but not in ER negative samples (p=0.85). Among ER positive tumours, the loss of Plexin B1 expression is associated with a positive ErbB2 status (p=0.05) and a high Ki67 expression (p=0.016) in univariate analysis. Multivariate Cox regression including all standard parameters among ER positive tumours revealed that Plexin B1 (HR 1.59, 95% confidence interval (CI) 1.03-2.47, p=0.036) remains a significant prognostic marker besides tumour size (HR 2.27, 95% CI 1.33-3.89, p=0.0028) and Ki67 (HR 1.78, 95% CI 1.12-2.84, p=0.0149). Interestingly, the prognostic value of Plexin B1 was pronounced in low proliferating ER positive tumours otherwise characterised by a low risk of recurrence. In conclusion, this study confirms our previous observations suggesting Plexin B1 as a new prognostic marker in ER positive breast cancers.