Validation of the prognostic gene portfolio,ClinicoMolecular Triad Classification,using an independent prospective breast cancer cohort and external patient populations

Introduction

Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts.

Methods

An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147).

Results

The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups.

Conclusions

Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.     

Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification,prognostication and prediction

Breast cancer comprises a collection of diseases with distinctive clinical, histopathological, and molecular features. Importantly, tumors with similar histological features may display disparate clinical behaviors. Gene expression profiling using microarray technologies has improved our understanding of breast cancer biology and has led to the development of a breast cancer molecular taxonomy and of multigene 'signatures' to predict outcome and response to systemic therapies. The use of these prognostic and predictive signatures in routine clinical decision-making remains controversial. Here, we review the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.     

Combining method of detection and 70-gene signature for enhanced prognostication of breast cancer

Breast Cancer Research and Treatment - Achieving a higher chemotherapy completion rate is associated with better outcomes in breast cancer patients. We examined the role of exercise and...     

A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer.

PURPOSE: The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. EXPERIMENTAL DESIGN: A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. RESULTS: We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. CONCLUSION: This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.     

A long non-coding RNA signature to improve prognosis prediction of colorectal cancer

Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed to develop a lncRNA signature to improve prognosis prediction of colorectal cancer (CRC). Using a lncRNA-mining approach, we performed lncRNA expression profiling in large CRC cohorts from Gene Expression Ominus (GEO), including {"type":"entrez-geo","attrs":{"text":"GSE39582","term_id":"39582"}}GSE39582 test series(N=436), internal validation series (N=117); and two independent validation series {"type":"entrez-geo","attrs":{"text":"GSE14333","term_id":"14333"}}GSE14333 (N=197) and {"type":"entrez-geo","attrs":{"text":"GSE17536","term_id":"17536"}}GSE17536(N=145). We established a set of six lncRNAs that were significantly correlated with the disease free survival (DFS) in the test series. Based on this six-lncRNA signature, the test series patients could be classified into high-risk and low-risk subgroups with significantly different DFS (HR=2.670; P<0.0001). The prognostic value of this six-lncRNA signature was confirmed in the internal validation series and another two independent CRC sets. Gene set enrichment analysis (GSEA) analysis suggested that risk score positively correlated with several cancer metastasis related pathways. Functional experiments demonstrated three dysregulated lncRNAs, {"type":"entrez-nucleotide","attrs":{"text":"AK123657","term_id":"34529258"}}AK123657, {"type":"entrez-nucleotide","attrs":{"text":"BX648207","term_id":"34367366"}}BX648207 and {"type":"entrez-nucleotide","attrs":{"text":"BX649059","term_id":"34368231"}}BX649059 were required for efficient invasion and proliferation suppression in CRC cell lines. Our results might provide an efficient classification tool for clinical prognosis evaluation of CRC.     

A gene expression signature that predicts the therapeutic response of the basal-like breast cancer to neoadjuvant chemotherapy

Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described “intrinsic” signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23-gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen.     

Identification and meta-analysis of a small gene expression signature for the diagnosis of estrogen receptor status in invasive ductal breast cancer

In breast cancer, the determination of estrogen receptor (ER) expression is crucial for the decision on therapeutic strategies. Current ER expression analysis is based on immunohistochemical (IHC) staining of ER on formalin fixed tissue sections. However, low levels of ER expression frequently escape detection because of varying sensitivities of routine histopathological laboratories. Moreover, in estimating ER by IHC the receptor protein only is tested instead of the complex underlying ER pathway, which reflects its biological activity. To overcome this limitation, we have used the microarray technology to study 56 samples of invasive ductal carcinoma. We infer a robust and reliable signature of 10 genes, which is associated with ER expression and presumably therapeutically relevant biological processes. In a meta-analysis, the signature was tested on 3 further independent microarray gene expression data sets, covering different laboratories, array platforms, and clinics. The classification based on the signature showed a very low misclassification rate. In summary, the expression of few genes is sufficient to determine ER status. Future decisions on antiestrogen based therapy in breast cancer could be based on this signature rather than on immunostaining alone.     

A gene expression signature of genetic instability in colon cancer

Genetic instability plays a central role in the development and progression of human cancer. Two major classes of genetic instability, microsatellite instability (MSI) and chromosome instability (microsatellite stable; MSS), are best understood in the context of colon cancer, where MSI tumors represent approximately 15% of cases, and compared with MSS tumors, more often arise in the proximal colon and display favorable clinical outcome. To further explore molecular differences, we profiled gene expression in a set of 18 colon cancer cell lines using cDNA microarrays representing approximately 21,000 different genes. Supervised analysis identified a robust expression signature distinguishing MSI and MSS samples. As few as eight genes predicted with high accuracy the underlying genetic instability in the original and in three independent sample sets, comprising 13 colon cancer cell lines, 61 colorectal tumors, and 87 gastric tumors. Notably, the MSI signature was retained despite genetically correcting the underlying instability, suggesting the signature reflects a legacy of the tumor having arisen from MSI, rather than sensing the ongoing state of MSI. Our findings support a model in which MSI and MSS preferentially target different genes and pathways in cancer. Further, among the MSI signature genes, our findings implicate a role of elevated metallothionein expression in the clinical behavior of MSI cancers.     

A 16 Yin Yang gene expression ratio signature for ER+/node− breast cancer

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16‐gene signature capable of stratifying breast cancer patients into four risk levels with intention that low‐risk patients would not undergo adjuvant systemic therapy, intermediate‐low‐risk patients will be treated with hormonal therapy only, and intermediate‐high‐ and high‐risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16‐gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low‐risk group (n = 51) of 205 estrogen receptor‐positive and node negative (ER+/node?) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15‐year disease‐specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e‐12) in stratifying ER+/node? subgroup than OncotypeDx (HR = 2.7, p = 1.3e‐7), MammaPrint (HR = 2.5, p = 5.8e‐7), rorS (HR = 2.4, p = 1.4e‐6), and NPI (HR = 2.6, p = 1.2e‐6). YMR signature may be developed as a clinical tool to select a subgroup of low‐risk ER+/node? patients who do not require any adjuvant hormonal therapy (AHT).     

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

Introduction

Current 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.

Methods

We 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.

Results

Seventy-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.

Conclusions

We 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.     

A new era in the diagnosis of breast cancer

The regular use of high-quality mammography on asymptomatic women enables most breast cancers to be detected in the preclinical phase. Earlier detection dramatically rearranges the spectrum of breast cancer outcomes, resulting in better control of breast cancer. The new era requires a shift in thinking and a re-evaluation of the traditional diagnostic and therapeutic approaches to breast diseases. Tumors are smaller, less often node-positive, and have a more favorable malignancy grade. The challenge for diagnosticians is to find as many breast cancers as possible in the preclinical phase. The challenge for therapists is to adapt the treatment guidelines accordingly, in order to avoid over-treatment.     

Hemorheologic variables in breast cancer patients at the time of diagnosis and during treatment

The increased risk of thrombosis seen in patients with malignancy also was recently confirmed in breast cancer patients undergoing hormonal or chemotherapeutic treatment. Besides changes within the coagulation system, alterations of hemorheologic variables have been implicated in the genesis of thrombosis. We evaluated plasma viscosity, erythrocyte aggregation, fibrinogen level, sedimentation rate, hematocrit concentration, and protein concentration in patients with breast cancer at the time of primary diagnosis and during follow-up with or without treatment. We then compared the results to a control group without malignant or infectious disease. Plasma viscosity and erythrocyte aggregation were significantly higher in patients with malignant disease, with a further increase at the time of dissemination. Plasma fibrinogen level was significantly higher only at the time of dissemination. The influence of therapy on hemorheologic variables was minor. Tumor volume was the most important factor. As individual values vary considerably and form a continuous spectrum, no cutoff line between normal and pathologic values can be defined. However, high values should induce further measures to diagnose metastatic disease. Second, these factors could explain the relative inefficiency of thrombosis prophylaxis in this patient group and suggest the addition of rheologically active drugs to the treatment regimen.