Thermal Characteristics of Lithium Indium Diselenide and Lithium Indium Gallium Diselenide Neutron Detection Crystals

Tracking special nuclear materials (SNM) has never been more important than in the 21st century where information is transferred rapidly around the globe. Tracking SNM is important to nuclear power, weapons, medicine, and science. Neutron and gamma ray detection are the primary methods of detecting SNM. Increased movement and availability of SNM have increased the demand for radiation detection systems beyond the capacity of traditional neutron detection technologies (3He) [Helium three]. Many alternative neutron detection materials are being considered, including 6LiInSe2 [Lithium Indium Diselenide grown with lithium enriched in lithium six] and its derivative 6LiIn1-xGaxSe2 [Lithium Indium Gallium Diselenide: where the x represents varying concentrations of each constituent]. The research herein describes thermal transport and expansion properties of these materials to help better inform both crystal growers and detection designers. While preliminary reports indicate promising detection properties, improvements in crystal size and quality are required to improve neutron detection charge collection efficiency for nonproliferation applications

RAS/MAPK activation is associated with reduced Tumor-infiltrating lymphocytes in Triple-Negative Breast Cancer: Therapeutic Cooperation Between MEK and PD-1/PD-L1 Immune Checkpoint Inhibitors

PURPOSE: Tumor-infiltrating lymphocytes (TIL) in the residual disease (RD) of triple-negative breast cancers (TNBC) after neoadjuvant chemotherapy (NAC) are associated with improved survival, but insight into tumor cell-autonomous molecular pathways affecting these features are lacking. EXPERIMENTAL DESIGN: We analyzed TILs in the RD of clinically and molecularly characterized TNBCs after NAC and explored therapeutic strategies targeting combinations of MEK inhibitors with PD-1/PD-L1-targeted immunotherapy in mouse models of breast cancer. RESULTS: Presence of TILs in the RD was significantly associated with improved prognosis. Genetic or transcriptomic alterations in Ras-MAPK signaling were significantly correlated with lower TILs. MEK inhibition upregulated cell surface MHC expression and PD-L1 in TNBC cells both in vivo and in vitro. Moreover, combined MEK and PD-L1/PD-1 inhibition enhanced antitumor immune responses in mouse models of breast cancer. CONCLUSIONS: These data suggest the possibility that Ras-MAPK pathway activation promotes immune-evasion in TNBC, and support clinical trials combining MEK- and PD-L1-targeted therapies. Furthermore, Ras/MAPK activation and MHC expression may be predictive biomarkers of response to immune checkpoint inhibitors

Annotation and query of tissue microarray data using the NCI Thesaurus

<p>Abstract</p> <p>Background</p> <p>The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.</p> <p>Results</p> <p>We developed methods to map these annotations to the NCI thesaurus. Using the NCI-T we can effectively represent annotations for about 86% of the samples. We demonstrate how this mapping enables ontology driven integration and querying of tissue microarray data. We have deployed the mapping and ontology driven querying tools at the TMAD site for general use.</p> <p>Conclusion</p> <p>We have demonstrated that we can effectively map the diagnosis-related terms describing a sample in TMAD to the NCI-T. The NCI thesaurus terms have a wide coverage and provide terms for about 86% of the samples. In our opinion the NCI thesaurus can facilitate integration of this resource with other biological data.</p

Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer

"Background Gene expression profiling of breast cancer has identified two biologically distinct estrogen receptor (ER)-positive subtypes of breast cancer: luminal A and luminal B. Luminal B tumors have higher proliferation and poorer prognosis than luminal A tumors. In this study, we developed a clinically practical immunohistochemistry assay to distinguish luminal B from luminal A tumors and investigated its ability to separate tumors according to breast cancer recurrence-free and disease-specific survival. Methods Tumors from a cohort of 357 patients with invasive breast carcinomas were subtyped by gene expression profile. Hormone receptor status, HER2 status, and the Ki67 index (percentage of Ki67-positive cancer nuclei) were determined immunohistochemically. Receiver operating characteristic curves were used to determine the Ki67 cut point to distinguish luminal B from luminal A tumors. The prognostic value of the immunohistochemical assignment for breast cancer recurrence-free and disease-specific survival was investigated with an independent tissue microarray series of 4046 breast cancers by use of Kaplan–Meier curves and multivariable Cox regression. Results Gene expression profiling classified 101 (28%) of the 357 tumors as luminal A and 69 (19%) as luminal B. The best Ki67 index cut point to distinguish luminal B from luminal A tumors was 13.25%. In an independent cohort of 4046 patients with breast cancer, 2847 had hormone receptor–positive tumors. When HER2 immunohistochemistry and the Ki67 index were used to subtype these 2847 tumors, we classified 1530 (59%, 95% confidence interval [CI] = 57% to 61%) as luminal A, 846 (33%, 95% CI = 31% to 34%) as luminal B, and 222 (9%, 95% CI = 7% to 10%) as luminal–HER2 positive. Luminal B and luminal–HER2-positive breast cancers were statistically significantly associated with poor breast cancer recurrence-free and disease-specific survival in all adjuvant systemic treatment categories. Of particular relevance are women who received tamoxifen as their sole adjuvant systemic therapy, among whom the 10-year breast cancer–specific survival was 79% (95% CI = 76% to 83%) for luminal A, 64% (95% CI = 59% to 70%) for luminal B, and 57% (95% CI = 47% to 69%) for luminal–HER2 subtypes. Conclusion Expression of ER, progesterone receptor, and HER2 proteins and the Ki67 index appear to distinguish luminal A from luminal B breast cancer subtypes.

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Introduction: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. Methods: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curves and compared it to nested Cox models obtained by robust backward selection procedures. Results: A prognostic index derived from of a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), the most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. Conclusions: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.This work was supported by a grant from the Susan G Komen Foundation (to YK)

Breast cancer in neurofibromatosis type 1 : overrepresentation of unfavourable prognostic factors

Background: An increased breast cancer incidence and poor survival have been reported for women with neurofibromatosis 1 (NF1). To explain the poor survival, we aimed to link the histopathology and clinical characteristics of NF1-associated breast cancers. Methods: The Finnish Cancer Registry and the Finnish NF Registry were cross-referenced to identify the NF1 patients with breast cancer. Archival NF1 breast cancer specimens were retrieved for histopathological typing and compared with matched controls. Results: A total of 32 breast cancers were diagnosed in 1404 NF1 patients during the follow-up. Women with NF1 had an estimated lifetime risk of 18.0% for breast cancer, and this is nearly two-fold compared with that of the general Finnish female population (9.74%). The 26 successfully retrieved archival NF1 breast tumours were more often associated with unfavourable prognostic factors, such as oestrogen and progesterone receptor negativity and HER2 amplification. However, survival was worse in the NF1 group (P = 0.053) even when compared with the control group matched for age, diagnosis year, gender and oestrogen receptor status. Scrutiny of The Cancer Genome Atlas data set showed that NF1 mutations and deletions were associated with similar characteristics in the breast cancers of the general population. Conclusions: These results emphasise the role of the NF1 gene in the pathogenesis of breast cancer and a need for active follow-up for breast cancer in women with NF1.Peer reviewe