Citations

AbstractBackgroundIn treatment planning for intensity‐modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed‐integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose‐volume constraints. In this study, we incorporated dose‐volume constraints in an MIP model to generate treatment plans for IMPT.MethodsWe created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited‐memory Broyden–Fletcher–Goldfarb–Shanno (L‐BFGS) method while the other plan was derived with our MIP model with dose‐volume constraints. We then compared these two plans by dose‐volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose‐volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection.ResultsThe MIP model with dose‐volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial‐and‐error process. Some notable reduction in the mean doses of OARs is observed.ConclusionsThe treatment plans from our MIP model with dose‐volume constraints can meet all dose‐volume constraints for OARs and targets without any tedious trial‐and‐error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

Background: Anaplastic thyroid carcinoma (ATC) accounts for only 3% of thyroid cancers, yet strikingly, it accounts for almost 40% of thyroid cancer deaths. Currently, no effective therapies exist. In an effort to identify ATC-specific therapeutic targets, we analyzed global gene expression data from multiple studies to identify ATC-specific dysregulated genes. Methods: The National Center for Biotechnology Information Gene Expression Omnibus database was searched for high-throughput gene expression microarray studies from human ATC tissue along with normal thyroid and/or papillary thyroid cancer (PTC) tissue. Gene expression levels in ATC were compared with normal thyroid or PTC using seven separate comparisons, and an ATC-specific gene set common in all seven comparisons was identified. We investigated these genes for their biological functions and pathways. Results: There were three studies meeting inclusion criteria, (including 32 ATC patients, 69 PTC, and 75 normal). There were 259 upregulated genes and 286 downregulated genes in ATC with at least two-fold change in all seven comparisons. Using a five-fold filter, 36 genes were upregulated in ATC, while 40 genes were downregulated. Of the 10 top globally upregulated genes in ATC, 4/10 ( MMP1 , ANLN , CEP55 , and TFPI2 ) are known to play a role in ATC progression; however, 6/10 genes ( TMEM158 , CXCL5 , E2F7 , DLGAP5 , MME , and ASPM ) had not been specifically implicated in ATC. Similarly, 3/10 ( SFTA3 , LMO3 , and C2orf40 ) of the most globally downregulated genes were novel in this context, while 7/10 genes ( SLC26A7 , TG , TSHR , DUOX2 , CDH1 , PDE8B , and FOXE1 ) have been previously identified in ATC. We experimentally validated a significant correlation for seven transcription factors ( KLF16 , SP3 , ETV6 , FOXC1 , SP1 , EGFR1 , and MAFK ) with the ATC-specific genes using microarray analysis of ATC cell lines. Ontology clustering of globally altered genes revealed that “mitotic cell cycle” is highly enriched in the globally upregulated gene set (44% of top upregulated genes, p -value <10 −30 ). Conclusions: By focusing on globally altered genes, we have identified a set of consistently altered biological processes and pathways in ATC. Our data are consistent with an important role for M-phase cell cycle genes in ATC, and may provide direction for future studies to identify novel therapeutic targets for this disease.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

ABSTRACT Introduction: There are not uniform standards for nerve conduction testing across the United States. The objective of this study is to present a set of methodologically sound criteria to evaluate the literature for the purpose of identifying high‐quality normative nerve conduction studies (NCS) suitable for widespread use. Methods: The Normative Data Task Force (NDTF) was formed to review published studies on methodological issues related to NCS. A set of criteria was then developed to evaluate the literature. These criteria and their rationale are described. Results: We identified 7 key issues that reflect high quality in NCS. For each issue, specific review criteria were developed. Conclusion: Rigorous criteria enable identification of high‐quality studies dealing with nerve conduction reference values. This represents the first step toward the overarching goal of recommending NCS techniques and reference values for electrodiagnostic medicine. Muscle Nerve 54 : 366–370, 2016

ABSTRACT Introduction: To address the need for greater standardization within the field of electrodiagnostic medicine, the Normative Data Task Force (NDTF) was formed to identify nerve conduction studies (NCS) in the literature, evaluate them using consensus‐based methodological criteria derived by the NDTF, and identify those suitable as a resource for NCS metrics. Methods: A comprehensive literature search was conducted of published peer‐reviewed scientific articles for 11 routinely performed sensory and motor NCS from 1990 to 2012. Results : Over 7,500 articles were found. After review using consensus‐based methodological criteria, only 1 study each met all quality criteria for 10 nerves. Conclusion: The NDTF selected only those studies that met all quality criteria and were considered suitable as a clinical resource for NCS metrics. The literature is, however, limited and these findings should be confirmed by larger, multicenter collaborative efforts. Muscle Nerve 54 : 371–377, 2016

Abstract Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Abstract Purpose: Obesity promotes pancreatic and breast cancer progression via mechanisms that are poorly understood. Although obesity is associated with increased systemic levels of placental growth factor (PlGF), the role of PlGF in obesity-induced tumor progression is not known. PlGF and its receptor VEGFR-1 have been shown to modulate tumor angiogenesis and promote tumor-associated macrophage (TAM) recruitment and activity. Here, we hypothesized that increased activity of PlGF/VEGFR-1 signaling mediates obesity-induced tumor progression by augmenting tumor angiogenesis and TAM recruitment/activity. Experimental Design: We established diet-induced obese mouse models of wild-type C57BL/6, VEGFR-1 tyrosine kinase (TK)-null, or PlGF-null mice, and evaluated the role of PlGF/VEGFR-1 signaling in pancreatic and breast cancer mouse models and in human samples. Results: We found that obesity increased TAM infiltration, tumor growth, and metastasis in pancreatic cancers, without affecting vessel density. Ablation of VEGFR-1 signaling prevented obesity-induced tumor progression and shifted the tumor immune environment toward an antitumor phenotype. Similar findings were observed in a breast cancer model. Obesity was associated with increased systemic PlGF, but not VEGF-A or VEGF-B, in pancreatic and breast cancer patients and in various mouse models of these cancers. Ablation of PlGF phenocopied the effects of VEGFR-1-TK deletion on tumors in obese mice. PlGF/VEGFR-1-TK deletion prevented weight gain in mice fed a high-fat diet, but exacerbated hyperinsulinemia. Addition of metformin not only normalized insulin levels but also enhanced antitumor immunity. Conclusions: Targeting PlGF/VEGFR-1 signaling reprograms the tumor immune microenvironment and inhibits obesity-induced acceleration of tumor progression. Clin Cancer Res; 22(12); 2993–3004. ©2016 AACR.