自拟乳痛方治疗乳腺增生69例疗效观察

目的：观察自拟乳痛方治疗乳腺增生的疗效。方法选取2013年1月~12月来我院就诊的69例乳腺增生患者,所有患者均采用自拟乳痛方进行治疗,治疗时间1~7个疗程不等,记录乳房肿块评分以及乳房疼痛评分。结果共治疗69例患者,其中治愈30例;好转33例;无效6例;总有效率91.30%。结论自拟乳痛方治疗乳腺增生效果明显,安全可靠,值得推广。     

Concurrent DNA Copy‐Number Alterations and Mutations in Genes Related to Maintenance of Genome Stability in Uninvolved Mammary Glandular Tissue from Breast Cancer Patients

Somatic mosaicism for DNA copy‐number alterations (SMC‐CNAs) is defined as gain or loss of chromosomal segments in somatic cells within a single organism. As cells harboring SMC‐CNAs can undergo clonal expansion, it has been proposed that SMC‐CNAs may contribute to the predisposition of these cells to genetic disease including cancer. Herein, the gross genomic alterations (>500 kbp) were characterized in uninvolved mammary glandular tissue from 59 breast cancer patients and matched samples of primary tumors and lymph node metastases. Array‐based comparative genomic hybridization showed 10% (6/59) of patients harbored one to 359 large SMC‐CNAs (mean: 1,328 kbp; median: 961 kbp) in a substantial portion of glandular tissue cells, distal from the primary tumor site. SMC‐CNAs were partially recurrent in tumors, albeit with considerable contribution of stochastic SMC‐CNAs indicating genomic destabilization. Targeted resequencing of 301 known predisposition and somatic driver loci revealed mutations and rare variants in genes related to maintenance of genomic integrity: BRCA1 (p.Gln1756Profs*74, p.Arg504Cys), BRCA2 (p.Asn3124Ile), NCOR1 (p.Pro1570Glnfs*45), PALB2 (p.Ser500Pro), and TP53 (p.Arg306*). Co‐occurrence of gross SMC‐CNAs along with point mutations or rare variants in genes responsible for safeguarding genomic integrity highlights the temporal and spatial neoplastic potential of uninvolved glandular tissue in breast cancer patients.     

Four types of Ipsilateral Breast Tumor Recurrence (IBTR) after breast‐conserving surgery: Classification of IBTR based on precise pathological examination

We classified ipsilateral breast tumor recurrences (IBTRs) based on strict pathological rules. Ninety‐six women who were surgically treated for IBTR were included. IBTRs were classified according to their origins and were distinguished based on strict pathological rules: relationship between the IBTR and the primary lumpectomy scar, surgical margin status of the primary cancer, and the presence of in situ lesions of IBTR. The prognosis of these subgroups were compared to that of new primary tumors (NP) in the narrow sense (NPn) that occurred far from the scar. Distant‐disease free survival of IBTR that occurred close to the scar with in situ lesions and a negative surgical margin of the primary cancer (NP occurred close to the scar, NPcs) was similar to that of NPn. In contrast, IBTR that occurred close to the scar without in situ lesions (true recurrence (TR) that arose from residual invasive carcinoma foci, TRinv) had significantly poorer prognosis than NPn. IBTR that occurred close to the scar with in situ lesions and a positive surgical margin of the primary cancer (TR arising from a residual in situ lesion, TRis) had more late recurrences than NPcs. Precise pathological examinations indicated four distinct IBTR subtypes with different characteristics.     

A humanised tissue‐engineered bone model allows species‐specific breast cancer‐related bone metastasis in vivo

Bone metastases frequently occur in the advanced stages of breast cancer. At this stage, the disease is deemed incurable. To date, the mechanisms of breast cancer‐related metastasis to bone are poorly understood. This may be attributed to the lack of appropriate animal models to investigate the complex cancer cell–bone interactions. In this study, two established tissue‐engineered bone constructs (TEBCs) were applied to a breast cancer‐related metastasis model. A cylindrical medical‐grade polycaprolactone‐tricalcium phosphate scaffold produced by fused deposition modelling (scaffold 1) was compared with a tubular calcium phosphate‐coated polycaprolactone scaffold fabricated by solution electrospinning (scaffold 2) for their potential to generate ectopic humanised bone in NOD/SCID mice. While scaffold 1 was found not suitable to generate a sufficient amount of ectopic bone tissue due to poor ectopic integration, scaffold 2 showed excellent integration into the host tissue, leading to bone formation. To mimic breast cancer cell colonisation to the bone, MDA‐MB‐231, SUM1315, and MDA‐MB‐231BO breast cancer cells were cultured in polyethylene glycol‐based hydrogels and implanted adjacent to the TEBCs. Histological analysis indicated that the breast cancer cells induced an osteoclastic reaction in the TEBCs, demonstrating analogies to breast cancer‐related bone metastasis seen in patients.     

Combination Immunotherapy of MUC1 mRNA Nano-vaccine and CTLA-4 Blockade Effectively Inhibits Growth of Triple Negative Breast Cancer

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哺乳动物雷帕霉素靶蛋白及其抑制剂在乳腺癌临床诊治中的应用进展

PI3K-Akt-mTOR信号通路异常激活在乳腺癌的发生发展中发挥重要作用,也与乳腺癌内分泌治疗及靶向治疗耐药密切相关。mTOR位于该细胞信号通路的下游,与肿瘤细胞的转录、翻译、增殖、代谢和转移等行为密切相关。mTOR抑制剂通过不同的靶点作用于PI3K-Akt-mTOR信号通路,恢复内分泌治疗的敏感性,从而发挥其抗癌作用。本文就mTOR、mTOR抑制剂及PI3K-Akt-mTOR信号通路在乳腺癌中的最新研究进展作一综述。     

A power series beta Weibull regression model for predicting breast carcinoma

The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had a disease, but they do not accurately predict what will happen in any particular patient's case. Pathologic explanatory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhanced lymph node staining are prognostically significant to predict these survival rates. We propose a new cure rate survival regression model for predicting breast carcinoma survival in women who underwent mastectomy. We assume that the unknown number of competing causes that can influence the survival time is given by a power series distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows the beta Weibull distribution. The new compounding regression model includes as special cases several well‐known cure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Further, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess local influences. The potentiality of the new regression model to predict accurately breast carcinoma mortality is illustrated by means of real data. Copyright © 2015 John Wiley & Sons, Ltd.     

LEAP: Biomarker Inference Through Learning and Evaluating Association Patterns

Single nucleotide polymorphism (SNP) high‐dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000‐SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high‐dimensional datasets and determining their probability.     

乳腺癌人表皮生长因子受体－2基因扩增和蛋白表达差异及其临床意义

目的：研究乳腺癌人表皮生长因子受体－2（ Her－2）基因扩增和蛋白表达的差异及其临床意义。方法乳腺癌手术患者87例分别采用荧光原位杂交（ FISH）技术和免疫组化（ IHC）法检测乳腺癌组织中Her－2基因及其蛋白表达,比较两者差异,分析其临床意义。结果87例乳腺癌组织中,IHC法检测Her－2蛋白阳性（＋＋＋）表达33例（37．9％）。FISH法检测基因扩增36例（41．38％）,无扩增51例（58．62％）。免疫组化法Her－2蛋白评分（0～＋）25例中基因扩增2例（20％）,（＋＋）30例中基因扩增8例（26．67％）,（＋＋＋）32例中26例基因扩增（81．25％）。结论 IHC法检测可作为Her－2表达状态的初筛,其检测Her－2蛋白评分（0～＋）、（＋＋＋）与Her－2基因扩增具有较高的一致性,可直接指导临床用药,而Her－2蛋白评分（＋＋）的病例则需行FISH检测确定有无基因扩增。