Advances in the Molecular Characterization of IBC
As stated earlier, IBC is diagnosed primarily on the basis of a set of clinical characteristics, with pathological confirmation of invasive carcinoma also required. Characteristic pathological findings that can aid in the diagnosis of IBC include the presence of dermal lymphatic invasion by invasive tumor emboli, leading to obstruction of the lymphatic drainage and resulting in a clinical appearance of erythema and edema mimicking an inflammatory process.[13,14] However, the absence of a dermal lymphatic invasion does not preclude a diagnosis of IBC. Molecular alterations that have been reported with high incidence in IBC include negative hormone receptor status, overexpression and/or gene amplification of human epidermal growth factcor receptor 2 (HER2), overexpression of epidermal growth factor receptor (EGFR), high S-phase fraction, high-grade p53 mutation, overexpression of E-cadherin, dysfunction of mucin 1, overexpression of RhoC GTPase (a gene involved in cytoskeletal reorganization), loss of the LIBC (lost in inflammatory breast cancer) gene, and a high level of angiogenesis. Studies have also demonstrated high levels of expression of multiple chemokine receptors in IBC. In a recent study, Cabliogu et al looked at the prognostic implications of the chemokine receptors CXCR4 and CCR7, as well as HER2 and EGFR, in IBC. Working with a sample of 44 cases of IBC, the authors reported high levels of expression of CXCR4, EFGR, and HER2 amplification that were associated with a higher risk of recurrence and death. However, the molecular alterations described here are not specific to IBC. Several studies looking at these molecular markers have shown that in multivariate analysis, IBC status itself still remains an independent adverse prognostic feature; this indicates that other molecular differences are probably present that distinguish IBC from non-IBC.
Gene expression profiling
To better define the differences between IBC and non-IBC that are present at the molecular level, high-throughput molecular technologies have been utilized that allow for the analysis of several thousand genes in a tumor sample simultaneously. Over the past decade, several studies have specifically looked at expression profiling. To the best of our knowledge, six groups have profiled clinical samples of IBC and reported their results.[21-30] One of the earliest publications, from Bertucci et al in 2004, used cDNA microarrays containing approximately 8000 genes to profile 37 IBC and 44 non-IBC prechemotherapy tumor samples. The authors made a number of important observations, including their finding of the presence of extensive transcriptional heterogeneity in IBC, and their demonstration that IBC exhibited overexpression of basal, immune, and vascular gene clusters and exhibited underexpression of luminal gene clusters compared with non-IBC tumors. In addition, they identified a 109-gene signature that discriminated between the two sample types. Using the same data set, Bertucci et al further demonstrated that similar to non-IBC, five molecular subtypes (defined according to the Stanford intrinsic gene set)—including luminal A, luminal B, basal, HER2-positive, and normal-like—could be demonstrated in IBC. Higher frequencies of basal and HER2-positive IBC samples were observed compared with the frequencies of these two subtypes in non-IBC samples.
Van Laere et al in 2005 used cDNA microarrays containing approximately 6000 genes to profile 16 IBC and 18 non-IBC prechemotherapy samples. Using supervised analysis, the authors identified 953 genes that exhibited a 1.5-fold difference in expression between the two sample types. Of these genes, 756 (retained after the exclusion of genes related to the expression of estrogen receptor 1 and HER2) resulted in a perfect segregation of IBC and non-IBC samples. Using the same data set, the investigators further identified five different cell-of-origin subtypes in the two sample sets, with the basal (37%) and HER2-positive (13%) subtypes representing 50% of the IBC samples. Using Affymetrix technology on a set of 19 IBC and 40 non-IBC samples, the investigators confirmed their initial findings, identifying 26% of the IBC samples as basal and 42% as HER2-positive.
Biche et al used real-time polymerase chain reaction (RT-PCR) to quantify the mRNA expression level of 538 candidate genes in 8 IBC and 8 non-IBC samples; they demonstrated a more than two-fold change in a set of 48 genes between the two sample types. The investigators further identified a three-gene signature composed of epiregulin (EREG), v-myc myelocytomatosis viral-related oncogene (MYCN), and sonic hedgehog (SHH). This gene signature was shown to distinguish between three subgroups of women with IBC with different recurrence rates.
Nguyen et al, using Affymetrix U133A microarrays, defined the expression profiles of prechemotherapy samples from 13 patients with IBC and 12 patients with stage III non-IBC. Because of considerable heterogeneity across both sample types, a robust signature could not be identified; however, the investigators were able to demonstrate a higher expression of genes associated with increases in metabolic rate, lipid signaling, and cell turnover in the IBC samples than in the non-IBC samples. They also confirmed the presence of three molecular subtypes of IBC, including luminal, HER2-positive, and basal subtypes.
The most recent study comes from the group at the National Cancer Institute in Bethesda; these investigators used Affymetrix U133A microarrays to profile 15 IBC samples and 35 non-IBC samples. Eighty seven percent of the IBC samples and 23% of the non-IBC samples were obtained postchemotherapy, and each sample was obtained via laser capture microdissection. The investigators were unable to identify a significant signature in the epithelia that could distinguish between the two sample types, but they were able to identify a stromal signature that could distinguish IBC from non-IBC. They further demonstrated that the 109-gene signature originally identified by Bertucci et al performed better in classifying IBC when the tumor stroma was used instead of the tumor epithelial; this finding lends evidence to the hypothesis that gene expression in the stroma contributes to the IBC phenotype.
There are several drawbacks to these studies, including the facts that they involved very small numbers of IBC and non-IBC samples, that the definition of IBC varied across studies, that some studies used prechemotherapy samples while others used postchemotherapy samples, and that the methods used to collect samples also varied. Despite these drawbacks, we can derive valuable information from the data described. First, the transcriptional heterogeneity of IBC appears to be as extensive as that for non-IBC. Second, at the clinical level, the existence of molecular subtypes within IBC may in the future prove to have a prognostic significance similar to that seen in non-IBC subtypes. Third, the genetic signatures identified may also be used to divide patients with IBC into several prognostic groups. Bertucci et al identified an 85-gene set that divided patients with IBC into two groups with different pathological complete response rates (pCR), while the three-gene signature identified by Biche et al categorized patients with IBC into good, intermediate, and poor outcome groups. Fourth, a signature identified for IBC may reflect the aggressiveness of breast cancer generally and thus may play a prognostic role if also identified within non-IBC samples. Van Laere et al applied a gene signature predictive of IBC to a set of 1157 non-IBC samples and showed that non-IBC samples with an IBC-like phenotype had a significantly shorter relapse-free survival than samples that did not have an IBC-like phenotype.
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