ABSTRACT. Patterns of DNA methylation are established and maintained by a family of DNA methyltransferases (DNMTs). Aberrant promoter DNA methylation of tumor suppressor genes is found in breast cancer. Association studies between DNMT gene polymorphisms and breast cancer in various populations have reported inconsistent results. This study assessed the associations of single nucleotide polymorphisms (SNPs) in DNMT1, DNMT3A, DNMT3B, DNMT3L, and DNMT2 with breast cancer among Han Chinese women from South China. Sixteen SNPs (rs2114724, rs2228611, rs2228612, rs8101866, and rs16999593 in DNMT1; rs13420827, rs11887120, rs13428812, rs1550117, rs11695471, and rs6733301 in DNMT3A; rs2424908, rs2424913, and rs6087990 in DNMT3B; rs113593938 in DNMT3L, and rs11254413 in DNMT2) in 408 women with breast cancer and 469 controls were genotyped using a MassARRAY matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry platform. Two SNPs, Polymorphisms of DNMT1 and DNMT3B and breast cancer risk rs16999593 in DNMT1 and rs2424908 in DNMT3B, were significantly associated with breast cancer risk. The heterozygous genotype CT of rs16999593 was associated with increased breast cancer risk [odds ratio (OR) = 1.60; 95% confidence interval (95%CI) = 1.20-2.14; P = 0.0052], whereas rs2424908 was associated with decreased risk (OR = 0.62; 95%CI = 0.46-0.84; P = 0.0061). Other DNMT polymorphisms showed no significant associations with breast cancer risk in the study population. Haplotype CGTC of rs2114724, rs2228611, rs8101866, and rs16999593 in DNMT1 differed significantly as a risk factor between the case and control groups (OR = 1.51; 95%CI = 1.18-1.93; P = 0.0012). The heterozygous genotypes of rs16999593 in DNMT1 and rs2424908 in DNMT3B were strongly associated with breast cancer risk.

Background: The epidermal growth factor receptor (EGFR) is a potential therapeutic target for breast cancer treatment; however, its use does not lead to a marked clinical response. Studies of non-small cell lung cancer and colorectal cancer showed that mutations of genes in the PIK3CA/AKT and RAS/RAF/MEK pathways, two major signalling cascades downstream of EGFR, might predict resistance to EGFR-targeted agents. Therefore, we examined the frequencies of mutations in these key EGFR pathway genes in Chinese breast cancer patients. Methods: We used a high-throughput mass-spectrometric based cancer gene mutation profiling platform to detect 22 mutations of the PIK3CA, AKT1, BRAF, EGFR, HRAS, and KRAS genes in 120 Chinese women with breast cancer. Results: Thirteen mutations were detected in 12 (10%) of the samples, all of which were invasive ductal carcinomas (two stage I, six stage II, three stage III, and one stage IV). These included one mutation (0.83%) in the EGFR gene (rs121913445-rs121913432), three (2.50%) in the KRAS gene (rs121913530, rs112445441), and nine (7.50%) in the PIK3CA gene (rs121913273, rs104886003, and rs121913279). No mutations were found in the AKT1, BRAF, and HRAS genes. Six (27.27%) of the 22 genotyping assays called mutations in at least one sample and three (50%) of the six assays queried were found to be mutated more than once. Conclusions: Mutations in the EGFR pathway occurred in a small fraction of Chinese breast cancers. However, therapeutics targeting these potential predictive markers should be investigated in depth, especially in Oriental populations.

Exposure to endogenous sex hormones has been reported as a risk factor for breast cancer. The CYP11A1 gene encodes the key enzyme that catalyzes the initial and rate-limiting step in steroid hormone synthesis. In this study, the associations between single nucleotide polymorphisms (SNPs) in CYP11A1 and breast cancer susceptibility were examined. Six SNPs in CYP11A1 were genotyped using the MassARRAY IPLEX platform in 530 breast cancer patients and 546 healthy controls. Association analyses based on a χ2 test and binary logistic regression were performed to determine the odds ratio (OR) and 95% confidence interval (95% CI) for each SNP. Two loci (rs2959008 and rs2279357) showed evidence of associations with breast cancer risk. The variant genotype C/T-C/C of rs2959008 was significantly associated with a decreased risk (age-adjusted OR, 0.75; 95% CI, 0.58–0.96; P = 0.023) compared with the wild-type TT. However, the homozygous TT variant of rs2279357 exhibited increased susceptibility to breast cancer (age-adjusted OR, 1.44; 95% CI, 1.05–1.98; P = 0.022). The locus rs2959003 also showed an appreciable effect, but no associations were observed for three other SNPs. Our results suggest that polymorphisms of CYP11A1 are related to breast cancer susceptibility in Han Chinese women of South China.

Background: Genome-wide association studies (GWAS) have identified various genetic susceptibility loci for breast cancer based mainly on European-ancestry populations. Differing linkage disequilibrium patterns exist between European and Asian populations. Methods: Ten SNPs (rs2075555 in COL1A1, rs12652447 in FBXL17, rs10941679 in 5p12/MRPS30, rs11878583 in ZNF577, rs7166081 in SMAD3, rs16917302 in ZNF365, rs311499 in 20q13.3, rs1045485 in CASP8, rs12964873 in CDH1 and rs8170 in 19p13.1) were here genotyped in 1009 Chinese females (487 patients with breast cancer and 522 control subjects) using the Sequenom MassARRAY iPLEX platform. Association analysis based on unconditional logistic regression was carried out to determine the odds ratio (OR) and 95% confidence interval (95% CI) for each SNP. Stratification analyses were carried out based on the estrogen receptor (ER) and progesterone receptor (PR) status. Results: Among the 10 SNPs, rs10941679 showed significant association with breast cancer when differences between the case and control groups in this Han Chinese population were compared (30.09% GG, 45.4% GA and 23.7% AA; P = 0.012). Four SNPs (rs311499, rs1045485, rs12964873 and rs8170) showed no polymorphisms in our study. The remaining five SNPs showed no association with breast cancer in the present population. Immunohistochemical tests showed that rs2075555 was associated with ER status; the AA genotype showed greater association with ER negative than ER positive (OR = 0.54, 95% CI, 0.29-0.99; P = 0.046). AA of rs7166081 was also associated with ER status, but showed a greater association with ER positive than negative (OR = 1.59, 95% CI = 1.04-2.44; P = 0.031). However, no significant associations were found among the SNPs and PR status. Conclusion: In this study using a Han Chinese population, rs10941679 was the only SNP associated with breast cancer risk, indicating a difference between European and Chinese populations in susceptibility loci. Therefore, confirmation studies are necessary before utilization of these loci in Chinese.

Background Hydroxychloroquine (HCQ) and chloroquine (CQ) have been widely used for the treatment of the coronavirus disease 2019 (COVID-19), despite limited clinical evidence and controversial early reports. The aim of this report was to provide a systematic review of the literature and meta-analysis on the use of HCQ/CQ with respect to safety and clinical efficacy of these medications. Methods We performed a systematic search of the medical databases and included studies if they focused on patients with COVID-19 who received HCQ or CQ alone, or in combination with other treatments, and were compared with a control group. We analyzed two important clinical objectives; viral clearance rate by reverse transcription-polymerase chain reaction (RT-PCR) negativity and all-cause mortality. Results A total of 14 studies were included in the quantitative synthesis. The use of HCQ/CQ was associated with higher viral clearance rate compared with control group (OR: 3.12, 95% CI: 2.17–4.49 p < 0.0001). In the sensitivity analysis, the effect on viral clearance disappeared (OR 1.44, 95% CI: 0.87–2.37, p = 0.155). The use of HCQ/CQ was associated with a higher risk of mortality (OR 1.26, 95% CI: 1.05–1.51, p < 0.0001). Due to huge heterogeneity between the studies (I2 = 86%, p < 0.01), we performed a meta regression analysis. Both treatment within 24 hours (p = 0.047) and comorbidities [hypertension (p = 0.025), diabetes (p = 0.049) and chronic lung disease (p = 0.0064)] contributed to the heterogeneity. HCQ/CQ daily dose (p = 0.61) and age (p = 0.62) had no impact on effect size. Higher rate of comorbidities led to a higher risk of mortality by using HCQ/CQ. Overall, the use of HCQ/CQ resulted in longer QTc intervals. Conclusions Our meta-analysis did not reveal a clinical benefit of HCQ/CQ on in-hospital outcomes for patients with COVID-19. The use of HCQ/CQ did not result in rapid viral clearance on RT-PCR. Moreover, our results showed that HCQ/CQ treatment even increase in-hospital mortality, and higher rate of comorbidities led to a higher risk of mortality by using HCQ/CQ.

With rising life expectancy in cancer patients with bone metastases, the need for local treatment (LT) is expanding. Machine learning (ML) could create reasonable generalizations, the purpose of this article was to evaluate the use of ML model in LT strategies. Patients were treated by an interdisciplinary team in Shanghai Sixth People's Hospital. Visual analog scale (VAS) and Quality of Life (QoL) Questionnaire Bone Metastases Module scores were analyzed before, 1 week, 1, 3, and 6 months after treatments. ML models were used to calculate LT probability, and confusion matrix was used to calculate the accuracy, precision, recall and F1 score of models. ML models were further used to calculate pathological fracture (PF) probability in lung cancer patients. Of 386 patients enrolled between 2016 and 2017, 101 patients underwent LT. Significant improved VAS and pain domains scores were observed in 27 surgery patients at 1, 3 and 6m, while functional domains scores at 3 and 6m. All five scores improved significantly in 46 percutaneous osteoplasty patients at 1w, 1 and 3m. Significant improved VAS and pain domains scores were observed in 28 radiation patients at 1 and 3m, while functional domains scores at 3m. Compared with team decision-making, decision tree was superior to support vector machine and Bayesian neural networks in model building. The VAS scale, primary cancer, Frankel classification, Mirels score, C-terminal telopeptide of type I collagen (CTx), age, mid-fragment of osteocalcin (MID), character of bone metastases, CA153, and visceral metastases were included in the DT model. In 386 patients, the values of decision tree for the accuracy, precision, recall and F1 score were 86.53%, 78.44%, and 64.90% and 0.69 respectively. 124 lung cancer patients were used to calculate PF probability by decision tree. We also put driving gene mutation and five differentially expressed proteins into the model. The VAS scale, character of bone metastases, age, driving gene mutation, C-terminal telopeptide of type I collagen (CTx), mid-fragment of osteocalcin (MID) and enolase 1 (ENO1) were included in the DT model. The sensitivity, specificity and accuracy of DT model was 90.52%, 87.19% and 77.72%. Appropriate LT provided significant pain relief and improvement in QoL. The ML model is effective in helping physicians determine which patient may be the candidate for LT. Citation Format: Hui Zhao, zhiyu wang, jing Sun, yifeng Gu, mengdi Yang, guangyu Yao. Exploiting machine learning in local treatment strategies in cancer patients with bone metastases: A real world clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2059.