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Cancer results from alterations at essential genomic sites and is characterized by uncontrolled cell proliferation, invasion and metastasis. Identification of driver genes of metastatic progression is essential, as metastases, not primary tumors, are fatal. To gain insight into the mutational concordance between different steps of malignant progression we performed exome sequencing and validation with targeted deep sequencing of successive steps of malignant progression from pre-invasive stages to asynchronous distant metastases in six breast cancer patients. Using the ratio of non-synonymous to synonymous mutations, a surprisingly large number of cancer driver genes, ranging between 3 and 145, were estimated to confer a selective advantage in the studied primary tumors. We report a substantial amount of metastasis specific mutations and a number of novel putative metastasis driver genes. Most notable are the DCC, ABCA13, TIAM2, CREBBP, BCL6B and ZNF185 genes, mainly mutated exclusively in metastases and highly likely driver genes of metastatic progression. We find different genes and pathways to be affected at different steps of malignant progression. The Adherens junction pathway is affected in four of the six studied patients and this pathway most likely plays a vital role in the metastatic process. [ABSTRACT FROM AUTHOR]

Expression of HOX transcript antisense intergenic RNA ( HOTAIR)—a long non-coding RNA—has been examined in a variety of human cancers, and overexpression of HOTAIR is correlated with poor survival among breast, colon, and liver cancer patients. In this retrospective study, we examine HOTAIR expression in 164 primary breast tumors, from patients who do not receive adjuvant treatment, in a design that is paired with respect to the traditional prognostic markers. We show that HOTAIR expression differs between patients with or without a metastatic endpoint, respectively. Survival analysis shows that high HOTAIR expression in primary tumors is significantly associated with worse prognosis independent of prognostic markers ( P = 0.012, hazard ratio (HR) 1.747). This association is even stronger when looking only at estrogen receptor (ER)-positive tumor samples ( P = 0.0086, HR 1.985). In ER-negative tumor samples, we are not able to detect a prognostic value of HOTAIR expression, probably due to the limited sample size. These results are successfully validated in an independent dataset with similar associations ( P = 0.018, HR 1.825). In conclusion, our findings suggest that HOTAIR expression may serve as an independent biomarker for the prediction of the risk of metastasis in ER-positive breast cancer patients. [ABSTRACT FROM AUTHOR]

Simple Summary: To support health care providers in clinical decision-making for breast cancer (BC) patients, profiles of gene activity patterns have previously been developed, where the majority have been based on messenger RNAs (mRNAs), molecules coding for proteins. However, we and others have recently developed profiles based on functional molecules that do not code for proteins—e.g., long non-coding RNAs (lncRNAs)—demonstrating great prognostic potential. Unfortunately, studies comparing such profiles for predicting relapse in BC patients are very scarce. Therefore, we aimed to compare these two types of molecules (mRNAs and lncRNAs) to forecast relapse in low-risk BC patients using advanced machine learning methods with two different approaches. Regardless of approach, our data suggested that profiles based on lncRNAs improved prediction of relapse and demonstrated potential advantages for future profile development. Several gene expression signatures based on mRNAs and a few based on long non-coding RNAs (lncRNAs) have been developed to provide prognostic information beyond clinical evaluation in breast cancer (BC). However, the comparison of such signatures for predicting recurrence is very scarce. Therefore, we compared the prognostic utility of mRNAs and lncRNAs in low-risk BC patients using two different classification strategies. Frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients were included; 80 developed recurrence—i.e., regional or distant metastasis while 80 remained recurrence-free (mean follow-up of 20.9 years). Patients were pairwise matched for clinicopathological characteristics. Classification based on differential mRNA or lncRNA expression using seven individual machine learning methods and a voting scheme classified patients into risk-subgroups. Classification by the seven methods with a fixed sensitivity of ≥90% resulted in specificities ranging from 16–40% for mRNA and 38–58% for lncRNA, and after voting, specificities of 38% and 60% respectively. Classifier performance based on an alternative classification approach of balanced accuracy optimization also provided higher specificities for lncRNA than mRNA at comparable sensitivities. Thus, our results suggested that classification followed by voting improved prognostic power using lncRNAs compared to mRNAs regardless of classification strategy. [ABSTRACT FROM AUTHOR]

Background: Endocrine resistance in estrogen receptor-positive (ER+) breast cancer is a major clinical problem and is associated with accelerated cancer cell growth, increased motility and acquisition of mesenchymal characteristics. However, the specific molecules and pathways involved in these altered features remain to be detailed, and may be promising therapeutic targets to overcome endocrine resistance.Methods: In the present study, we evaluated altered expression of epithelial-mesenchymal transition (EMT) regulators in ER+ breast cancer cell models of tamoxifen or fulvestrant resistance, by gene expression profiling. We investigated the specific role of increased SNAI2 expression in fulvestrant-resistant cells by gene knockdown and treatment with a SNAIL-p53 binding inhibitor, and evaluated the effect on cell growth, migration and expression of EMT markers. Furthermore, we evaluated SNAI2 expression by immunohistochemical analysis in metastatic samples from two cohorts of patients with breast cancer treated with endocrine therapy in the advanced setting.Results: SNAI2 was found to be significantly upregulated in all endocrine-resistant cells compared to parental cell lines, while no changes were observed in the expression of other EMT-associated transcription factors. SNAI2 knockdown with specific small interfering RNA (siRNA) converted the mesenchymal-like fulvestrant-resistant cells into an epithelial-like phenotype and reduced cell motility. Furthermore, inhibition of SNAI2 with specific siRNA or a SNAIL-p53 binding inhibitor reduced growth of cells resistant to fulvestrant treatment. Clinical evaluation of SNAI2 expression in two independent cohorts of patients with ER+ metastatic breast cancer treated with endocrine therapy in the advanced setting (N = 86 and N = 67) showed that high SNAI2 expression in the metastasis correlated significantly with shorter progression-free survival on endocrine treatment (p = 0.0003 and p = 0.004).Conclusions: Our results suggest that SNAI2 is a key regulator of the aggressive phenotype observed in endocrine-resistant breast cancer cells, an independent prognostic biomarker in ER+ advanced breast cancer treated with endocrine therapy, and may be a promising therapeutic target in combination with endocrine therapies in ER+ metastatic breast cancer exhibiting high SNAI2 levels. [ABSTRACT FROM AUTHOR]