Gene signature associated with resistance to fluvastatin chemoprevention for breast cancer.

BACKGROUND: Although targeting of the cholesterol pathway by statins prevents breast cancer development in mouse models, efficacy is not absolute. Therefore, the goal of this study is to investigate if the upregulation in the cholesterol biosynthesis pathway genes associates with response to statin chemoprevention and may potentially be used as response biomarkers. METHODS: Expression of cholesterol biosynthesis pathway genes was initially derived from the RNA sequencing of MCF10A cell line- based breast cancer progression model system and subsequently validated by quantitative PCR assay. Response to fluvastatin was assessed in vitro using the MCF10A cell line model system, including a statin resistant cell line that was generated (MCF10.AT1-R), and measured using colony forming assays. In vivo efficacy of statin for chemoprevention was assessed in the SV40C3 TAg mouse model. Mammary tumors were identified by histologic analysis of the mammary glands. Mammary glands without histologic evidence of high-grade lesions (in situ and/or invasive carcinoma) were considered responsive to statin treatment. RESULTS: We found more than 70% of a published multi-gene fluvastatin resistance signature to be significantly upregulated during breast cancer progression and inversely correlated with statin inhibition of cellular growth and proliferation. This inherent statin resistance gene signature was also largely shared with the signature of acquired resistance to fluvastatin in MCF10.AT1-R cell line model of acquired statin resistance. These inherent resistance genes and genes exclusive to acquired statin resistance map to steroid-, and terpenoid backbone- biosynthesis pathway. We found upregulation of ~ 80% of cholesterol biosynthesis pathway genes in the tumor bearing mammary glands of SV40 C3TAg transgenic mouse model of TNBC, suggesting the involvement of cholesterol biosynthesis pathway in resistance to statin chemoprevention in vivo. A panel of 13-genes from the pathway significantly associated with response to statin treatment, as did the expression level of HMGCR alone in a mouse model of breast cancer suggesting their utility to predict the efficacy of statin chemoprevention. CONCLUSIONS: High basal level, or restorative upregulation, in the cholesterol biosynthesis pathway genes appear to be strongly associated with resistance to statin chemoprevention for breast cancer and may serve as a biomarker to tailor statin treatment to individuals who are most likely to benefit.

Efficacy of fluvastatin and aspirin for prevention of hormonally insensitive breast cancer.

PURPOSE: Primary prevention of hormonally insensitive breast cancers remains an important clinical need and repurposing existing low-toxicity drugs represents a low-cost, efficient strategy for meeting this goal. This study targeted the cholesterol pathway using fluvastatin, a cholesterol-lowering drug, and aspirin, an AMPK activator that acts as a brake in the cholesterol pathway, in a transgenic mouse model of triple-negative breast cancer (TNBC). METHODS: Using SV40C3 TAg mice, the efficacy and mechanism of fluvastatin, aspirin, or both in combination were compared with vehicle alone. RESULTS: Sixteen-weeks of fluvastatin treatment resulted in significant delay in onset of tumors (20 weeks vs. 16.8 weeks in vehicle treatment, p = 0.01) and inhibited tumor incidence and tumor multiplicity by 50% relative to the vehicle control. In animals that developed tumors, fluvastatin treatment inhibited tumor weight by 75% relative to vehicle control. Aspirin alone did not significantly affect tumor latency, tumor incidence or tumor burden compared to vehicle control. Fluvastatin and aspirin in combination delayed the onset of tumors but failed to inhibit tumor incidence and tumor multiplicity. The growth-inhibitory effects of fluvastatin were mediated through increased FAS/FASL mediated apoptotic cell death that was characterized by increased cleaved PARP and driven in part by depletion of an isoprenoid, geranyl geranyl pyrophosphate (GGPP). CONCLUSIONS: In line with NCI's emphasis to repurpose low-toxicity drugs for prevention of cancer, fluvastatin was effective for prevention of TNBC and warrants further clinical testing. Aspirin did not provide chemopreventive benefit.

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect.

Purpose: In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. Approach: This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128×128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall's Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Results: Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Conclusions: Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.

Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.

The identification of women at risk for sporadic breast cancer remains a clinical challenge. We hypothesize that the temporal analysis of annual screening mammograms, using a long short-term memory (LSTM) network, could accurately identify women at risk of future breast cancer. Women with an imaging abnormality, which had been biopsy-confirmed to be cancer or benign, who also had antecedent imaging available were included in this case-control study. Sequences of antecedent mammograms were retrospectively collected under HIPAA-approved guidelines. Radiomic and deep-learning-based features were extracted on regions of interest placed posterior to the nipple in antecedent images. These features were input to LSTM recurrent networks to classify whether the future lesion would be malignant or benign. Classification performance was assessed using all available antecedent time-points and using a single antecedent time-point in the task of lesion classification. Classifiers incorporating multiple time-points with LSTM, based either on deep-learning-extracted features or on radiomic features, tended to perform statistically better than chance, whereas those using only a single time-point failed to show improved performance compared to chance, as judged by area under the receiver operating characteristic curves (AUC: 0.63 ± 0.05, 0.65 ± 0.05, 0.52 ± 0.06 and 0.54 ± 0.06, respectively). Lastly, similar classification performance was observed when using features extracted from the affected versus the contralateral breast in predicting future unilateral malignancy (AUC: 0.63 ± 0.05 vs. 0.59 ± 0.06 for deep-learning-extracted features; 0.65 ± 0.05 vs. 0.62 ± 0.06 for radiomic features). The results of this study suggest that the incorporation of temporal information into radiomic analyses may improve the overall classification performance through LSTM, as demonstrated by the improved discrimination of future lesions as malignant or benign. Further, our data suggest that a potential field effect, changes in the breast extending beyond the lesion itself, is present in both the affected and contralateral breasts in antecedent imaging, and, thus, the evaluation of either breast might inform on the future risk of breast cancer.

Erratum: Ju, Z., et al. Integrative Analyses of Multilevel Omics Reveal Preneoplastic Breast to Possess a Molecular Landscape That Is Globally Shared with Invasive Basal-Like Breast Cancer (Running Title: Molecular Landscape of Basal-Like Breast Cancer Progression). Cancers 2020, 12, 722.

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Integrative Analyses of Multilevel Omics Reveal Preneoplastic Breast to Possess a Molecular Landscape That is Globally Shared with Invasive Basal-Like Breast Cancer (Running Title: Molecular Landscape of Basal-Like Breast Cancer Progression).

To characterize molecular changes accompanying the stepwise progression to breast cancer and to identify functional target pathways, we performed miRNA and RNA sequencing using MCF10A cell lines based model system that replicates the multi-step progression involving normal, preneoplastic, ductal carcinoma in situ, and invasive carcinoma cells, where the carcinoma most resemble the basal-like subgroup of human breast cancers. These analyses suggest that 70% of miRNA alterations occurred during the initial progression from normal to a preneoplastic stage. Most of these early changes reflected a global upregulation of miRNAs. This was consistent with a global increase in the miRNA-processing enzyme DICER, which was upregulated as a direct result of loss of miRNA let-7b-5p. Several oncogenic and tumor suppressor pathways were also found to change early, prior to histologic stigmata of cancer. Our finding that most genomic changes in the progression to basal-like breast cancer occurred in the earliest stages of histologic progression has implications for breast cancer prevention and selection of appropriate control tissues in molecular studies. Furthermore, in support of a functional significance of let-7b-5p loss, we found its low levels to predict poor disease-free survival and overall survival in breast cancer patients.