Integrated scoring approach to assess radiotherapy plan quality for breast cancer treatment

BackgroundProposal of an integrated scoring approach assessing the quality of different treatment techniques in a radiotherapy planning comparison. This scoring method incorporates all dosimetric indices of planning target volumes (PTVs) as well as organs at risk (OARs) and provides a single quantitative measure to select an ideal plan.Materials and methodsThe radiotherapy planning techniques compared were field-in-field (FinF), intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), hybrid IMRT (H-IMRT), and hybrid VMAT (H-VMAT). These plans were generated for twenty-five locally advanced left-sided breast cancer patients. The PTVs were prescribed a hypofractionation dose of 40.5 Gy in 15 fractions. The integrated score for each planning technique was calculated using the proposed formula.ResultsAn integrated score value that is close to zero indicates a superior plan. The integrated score that incorporates all dosimetric indices (PTVs and OARs) were 1.37, 1.64, 1.72, 1.18, and 1.24 for FinF, IMRT, VMAT, H-IMRT, and H-VMAT plans, respectively.ConclusionThe proposed integrated scoring approach is scientific to select a better plan and flexible to incorporate the patient-specific clinical demands. This simple tool is useful to quantify the treatment techniques and able to differentiate the acceptable and unacceptable plans.     

An epigenomic approach to therapy for tamoxifen-resistant breast cancer

Tamoxifen has been a frontline treatment for estrogen receptor alpha (ERα)-positive breast tumors in premenopausal women. However, resistance to tamoxifen occurs in many patients. ER still plays a critical role in the growth of breast cancer cells with acquired tamoxifen resistance, suggesting that ERα remains a valid target for treatment of tamoxifen-resistant (Tam-R) breast cancer. In an effort to identify novel regulators of ERα signaling, through a small-scale siRNA screen against histone methyl modifiers, we found WHSC1, a histone H3K36 methyltransferase, as a positive regulator of ERα signaling in breast cancer cells. We demonstrated that WHSC1 is recruited to the ERα gene by the BET protein BRD3/4, and facilitates ERα gene expression. The small-molecule BET protein inhibitor JQ1 potently suppressed the classic ERα signaling pathway and the growth of Tam-R breast cancer cells in culture. Using a Tam-R breast cancer xenograft mouse model, we demonstrated in vivo anti-breast cancer activity by JQ1 and a strong long-lasting effect of combination therapy with JQ1 and the ER degrader fulvestrant. Taken together, we provide evidence that the epigenomic proteins BRD3/4 and WHSC1 are essential regulators of estrogen receptor signaling and are novel therapeutic targets for treatment of Tam-R breast cancer.     

Network-based integration method for potential breast cancer gene identification

Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.     

In silico evaluation for the potential naturally available drugs for breast cancer

Abstract

Breast cancer is one of the major causes of deaths in women. During the incidence of breast cancer, the HER-2 is amplified and over expressed. This transmembrane receptor is involved in the signal transduction pathway. The present article evaluates 14 naturally available breast cancer drugs, in silico and the ADMET studies were conducted. The HER-2, a validate breast cancer target was taken for the present study. The protein was prepared for docking on the Discovery Studio 2.5. About 14 ligand molecules were used to dock with HER-2 after they were prepared for docking. The ADMET assessment was also done. The dock results showed that the ligand 4′-epidoxorubicin to be the potential drug with the highest dock score of 49.386. Among the 14 naturally available breast cancer drugs, our results evaluated that 4′-epidoxorubicin as the best drug for breast cancer. Further, the ADMET studies give an idea about the drug molecules.     

T-cell receptors provide potential prognostic signatures for breast cancer

Although T-cell receptors (TCRs) are related to the progression of breast cancer (BC), their prognostic values remain unclear. We downloaded the messenger RNA (mRNA) profiles and corresponding clinical information of 1413 BC patients from the Cancer Genome Atlas and Gene Expression Omnibus database, respectively. The different expression analysis of 104 TCRs in BC samples was performed, and the consensus clustering based on 104 TCRs was performed by using the K-mean method of R language. Univariate cox regression analysis was used to screen TCRs significantly associated with the prognosis of BC, and LASSO Cox analysis was applied to optimize key TCRs. The risk score was calculated using the prognostic model constructed based on six optimal TCRs, and multivariate Cox regression analysis was used to determine whether it was an independent prognostic signature. Finally, the nomogram was constructed to predict the overall survival of BC patients. Six optimal TCRs (ZAP70, GRAP2, NFKBIE, IFNG, NFKBIA, and PAK5), which were favorable for the prognosis of BC patients, were screened. Risk score could reliably predict the prognosis of BC patients as an independent prognostic signature. In addition, when bringing into two independent prognostic signatures, age and risk score, the nomogram model could better predict the overall survival of BC patients. Our results suggested that the poor prognosis of BC patients with high risk might be due to an immunosuppressive microenvironment. In summary, a prognostic risk model based on six TCRs was established and could efficiently predict the prognosis of BC patients.     

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

In vitro vascularized tumor platform for modeling tumor-vasculature interactions of inflammatory breast cancer

Inflammatory breast cancer (IBC), a rare form of breast cancer associated with increased angiogenesis and metastasis, is largely driven by tumor-stromal interactions with the vasculature and the extracellular matrix (ECM). However, there is currently a lack of understanding of the role these interactions play in initiation and progression of the disease. In this study, we developed the first three-dimensional, in vitro, vascularized, microfluidic IBC platform to quantify the spatial and temporal dynamics of tumor-vasculature and tumor-ECM interactions specific to IBC. Platforms consisting of collagen type 1 ECM with an endothelialized blood vessel were cultured with IBC cells, MDA-IBC3 (HER2+) or SUM149 (triple negative), and for comparison to non-IBC cells, MDA-MB-231 (triple negative). Acellular collagen platforms with endothelialized blood vessels served as controls. SUM149 and MDA-MB-231 platforms exhibited a significantly (p < .05) higher vessel permeability and decreased endothelial coverage of the vessel lumen compared to the control. Both IBC platforms, MDA-IBC3 and SUM149, expressed higher levels of vascular endothelial growth factor (p < .05) and increased collagen ECM porosity compared to non-IBCMDA-MB-231 (p < .05) and control (p < .01) platforms. Additionally, unique to the MDA-IBC3 platform, we observed progressive sprouting of the endothelium over time resulting in viable vessels with lumen. The newly sprouted vessels encircled clusters of MDA-IBC3 cells replicating a key feature of in vivo IBC. The IBC in vitro vascularized platforms introduced in this study model well-described in vivo and clinical IBC phenotypes and provide an adaptable, high throughput tool for systematically and quantitatively investigating tumor-stromal mechanisms and dynamics of tumor progression.     

Sodium ditiocarb as adjuvant immunotherapy for high risk breast cancer: A randomized study

Sixty-four patients with non metastatic high risk breast cancer were randomized in a double blind trial of adjuvant immunotherapy with sodium ditiocarb (DDC) versus placebo. All patients underwent prior surgery (mammectomy according to Patey) then adjuvant FAC chemotherapy +/- DDC. With a median follow-up of 5 years we observed 6 relapses and 5 deaths in DDC group; 13 relapses and 12 deaths in control group. At 6 years, overall survival is 81% in DDC group versus 55%. Disease free survival (DFS) is 76% in DDC group versus 55%. DDC associated to chemotherapy and locoregional treatment can improve survival and probably DFS in this high risk breast cancer subgroup.Abbreviations DDC sodium ditiocarb - DFS disease free survival - OS overall survival - N node - FAC 5 Fluorouracil, Adrianycin, Cyclopho-pharmide     

Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer

Triple-negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non-TNBC and 405 TNBC samples were mined from Gene Expression Omnibus. The data sets were integrated by meta-analysis and identified 1075 differentially expressed genes. Protein-protein interaction network was constructed which consists of 486 nodes and 1932 edges, where 29 hub genes were obtained with high topological measures. Further, 16 features (hub genes), 12 upregulated (AURKB, CCNB2, CDC20, DDX18, EGFR, ENO1, MYC, NUP88, PLK1, PML, POLR2F, and SKP2) and four downregulated ( CCND1, GLI3, SKP1, and TGFB3) were selected through machine learning correlation based feature selection method on training data set. A naïve Bayes based classifier built using the expression profiles of 16 features (hub genes) accurately and reliably classify TNBC from non-TNBC samples in the validation test data set with a receiver operating curve of 0.93 to 0.98. Subsequently, Gene Ontology analysis revealed that the hub genes were enriched in mitotic cell cycle processes and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that they were enriched in cell cycle pathways. Thus, the identified key hub genes and pathways highlighted in the study would enhance the understanding of molecular mechanism of TNBC which may serve as potential therapeutic target.     

乳腺癌靶向治疗的分子基础

近年来,乳腺癌靶向治疗的研究取得了显著进展。分子靶向药物通过作用于乳腺癌细胞相关细胞的分子信号传导途径,特异性针对异常环节进行干预,控制细胞基因的表达,从而抑制或杀死肿瘤细胞,以此达到治疗乳腺癌的目的。本文将对乳腺癌靶向治疗的分子基础研究进展做一综述。     

Production of bioengineered cancer tissue constructs in vitro: epithelium-mesenchyme heterotypic interactions

A few models have been established to study cancer cells in vitro. However, the cellular interactions have rarely been studied specifically using bioengineered cancer constructs combining human carcinoma cells and tumor-associated fibroblasts. We developed an in vitro model of tridimensional bioengineered cancer tissue constructs (bCTC) by seeding mammary epithelial cancer cells or normal keratinocytes over a mesenchymal layer containing tumor-derived fibroblastic cells or normal skin fibroblasts. After the introduction of epithelial cells, each construct was cultured for another 10 d. Histologic analyses showed that carcinoma cell lines could invade the subjacent mesenchymal layer and that the capacity to migrate was related to the invasive potential of cancer cells and the type of fibroblasts used, while noninvasive populations did not. Of the tested epithelial cells, MDA-MB-231 and, to a lesser degree, HDQ-P1 cell lines were invasive, and the invasion was deeper into the mesenchymal component containing tumor-derived fibroblasts. However, with normal skin fibroblasts, the mesenchymal layer was degraded twice faster than with tumor-derived fibroblastic cells. MDA-MB-231 cells and normal keratinocytes induced the highest level of gelatinase B, and the level was lowest with the MCF-7 cell line. The activated form of gelatinase B was, however, induced to the highest levels in the keratinocyte-seeded bCTC containing tumor-derived but not normal fibroblasts. MDA-MB-231 was the only epithelial cancer cell line whose activity of gelatinase A was reduced when cocultured with tumor-derived fibroblasts but not under normal fibroblast stimulation. Finally, a 50/48-kDa gelatinase band has been observed in bCTCs with noninvasive epithelial cells only. Our study demonstrates the selective secretion of gelatinases according to the phenotype of the cells seeded in the various bCTCs.     

The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis

BackgroundSeveral studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT), including blood measurements of liver function and urate.MethodsWe analysed a cohort of 388,199 UK Biobank participants with 1873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 s - FEV₁), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51 % risk at 6 years.ResultsThe c-index was 0.805 (95 %CI:0.794–0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95 %CI: 0.804–0.826;p < 0.0001;FNI:0.06). Expanding to include FEV₁, alcohol status, and waist circumference increased the c-index to 0.811 (95 %CI: 0.800–0.822;p < 0.0001;FNI: 0.04). The c-index for the fully expanded model containing all variables was 0.819 (95 %CI:0.808–0.830;p < 0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15–21 %, and lung cancer cases detected by 7–8 %. The additional cost per lung cancer case detected relative to the conventional model was £ 1018 for adding blood tests and £ 9775 for the fully expanded model.ConclusionBlood measurements of liver function and urate made a modest improvement to lung cancer risk prediction compared with a model containing conventional risk factors. There was no evidence that model expansion would improve the cost per lung cancer case detected in UK healthcare settings.     

A multi‐wavelength,laser‐based optical spectroscopy device for breast density and breast cancer risk pre‐screening

Optical Breast Spectroscopy (OBS) has been shown to predict mammographic breast density, a strong breast cancer risk factor. OBS is a low‐cost technique applicable at any age. OBS information may be useful for personalizing breast cancer screening programs based on risk to improve consensus on and adherence to screening guidelines. To facilitate the use of OBS in population‐wide studies, a research prototype OBS device was modified to make it portable and cheaper and to require less operator interaction. Two major changes were made: (1) the broadband light source was replaced with a laser module with 13 individual wavelengths turned on sequentially, enabling the use of photodiode detectors instead of a spectrometer, and (2) the light sources and detectors were placed in fixed positions within 4 sizes of cup, eliminating the need for placement by the operator. Wavelengths were selected using data from two previous studies. The reduction in spectral content did not significantly reduce the ability to distinguish between different risk groups. Positions for the light sources and detectors were chosen based on Monte Carlo simulations to match the optically interrogated volumes of the original device. Two light sources and six detectors per cup were used in the final design.

     

Population prevalence of hereditary breast cancer phenotypes and implementation of a genetic cancer risk assessment program in southern Brazil

In 2004, a population-based cohort (the Núcleo Mama Porto Alegre - NMPOA Cohort) was started in Porto Alegre, southern Brazil and within that cohort, a hereditary breast cancer study was initiated, aiming to determine the prevalence of hereditary breast cancer phenotypes and evaluate acceptance of a genetic cancer risk assessment (GCRA) program. Women from that cohort who reported a positive family history of cancer were referred to GCRA. Of the 9218 women enrolled, 1286 (13.9%) reported a family history of cancer. Of the 902 women who attended GCRA, 55 (8%) had an estimated lifetime risk of breast cancer ≥ 20% and 214 (23.7%) had pedigrees suggestive of a breast cancer predisposition syndrome; an unexpectedly high number of these fulfilled criteria for Li-Fraumeni-like syndrome (122 families, 66.7%). The overall prevalence of a hereditary breast cancer phenotype was 6.2% (95%CI: 5.67-6.65). These findings identified a problem of significant magnitude in the region and indicate that genetic cancer risk evaluation should be undertaken in a considerable proportion of the women from this community. The large proportion of women who attended GCRA (72.3%) indicates that the program was well-accepted by the community, regardless of the potential cultural, economic and social barriers.     

A comparative study of the ability of recombinant oncolytic adenovirus,doxorubicin and tamoxifen to inhibit the proliferation of breast cancer cells

In this study, we compared the inhibitory effects of recombinant oncolytic adenovirus (Ad‐apoptin‐hTERTp‐E1a, Ad‐VT) with that of doxorubicin (DOX), a first‐line chemotherapy drug, and tamoxifen (TAM), an endocrine therapy drug, on the proliferation of breast cancer cells. We found that Ad‐VT could effectively inhibit the proliferation of breast cancer cells (p < 0.01); the inhibition rate of Ad‐VT on normal mammary epithelial MCF‐10A cells was less than 20%. DOX can effectively inhibit the proliferation of breast cancer cells and also has a strong inhibitory effect on MCF‐10A cells (p < 0.01). TAM also has a strong inhibitory effect on breast cancer cells, among which the oestrogen‐dependent MCF‐7 cell inhibition was stronger (p < 0.01), At higher concentrations, TAM also had a high rate of inhibition (>70%) on the proliferation of MCF‐10A cells. We also found that both recombinant adenovirus and both drugs could successfully induce tumour cell apoptosis. Further Western blot results showed that the recombinant adenovirus killed breast cancer cells through the endogenous apoptotic pathway. Analysis of the nude mouse subcutaneous breast cancer model showed that Ad‐VT significantly inhibited tumour growth (the luminescence rate of cancer cells was reduced by more than 90%) and improved the survival rate of tumour‐bearing mice (p < 0.01). Compared with DOX and TAM, Ad‐VT has a significant inhibitory effect on breast cancer cells, but almost no inhibitory effect on normal breast epithelial cells, and this inhibitory effect is mainly through the endogenous apoptotic pathway. These results indicate that Ad‐VT has significant potential as a drug for the treatment of breast cancer.