The recent development of tissue microarrays-composed of hundreds of tissue sections from different tumors arrayed on a single glass slide-facilitates rapid evaluation of large-scale outcome studies. Realization of this potential depends on the ability to rapidly and precisely quantify the protein expression within each tissue spot. We have developed a set of algorithms that allow the rapid, automated, continuous and quantitative analysis of tissue microarrays, including the separation of tumor from stromal elements and the sub-cellular localization of signals. Validation studies using estrogen receptor in breast carcinoma show that automated analysis matches or exceeds the results of conventional pathologist-based scoring. Automated analysis and sub-cellular localization of beta-catenin in colon cancer identifies two novel, prognostically significant tumor subsets, not detected by traditional pathologist-based scoring. Development of automated analysis technology empowers tissue microarrays for use in discovery-type experiments (more typical of cDNA microarrays), with the added advantage of inclusion of long-term demographic and patient outcome information.
Background: A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool. Patients and methods: This pre-specified substudy included 4077 participants in an independent validation set (cancer: n ¼ 2823; non-cancer: n ¼ 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured. Results: Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%). Conclusion:In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests. Clinical trial number: NCT02889978.
Background: High insulin and insulin-like growth factor-I (IGF-I) levels may be associated with an increased breast cancer risk and/or death. Given the need to identify modifiable factors that decrease insulin, IGF-I, and breast cancer risk and death, we investigated the effects of a 6-month randomized controlled aerobic exercise intervention versus usual care on fasting insulin, IGF-I, and its binding protein (IGFBP-3) in postmenopausal breast cancer survivors. Methods: Seventy-five postmenopausal breast cancer survivors were identified from the Yale-New Haven Hospital Tumor Registry and randomly assigned to an exercise (n = 37) or usual care (n = 38) group. The exercise group participated in 150 minutes per week of moderate-intensity aerobic exercise. The usual care group was instructed to maintain their current physical activity level. A fasting blood sample was collected on
After lactation, weaning causes mammary epithelial cell (MEC) apoptosis. MECs express the plasma membrane calcium-ATPase 2 (PMCA2), which transports calcium across the apical surface of the cells into milk. Here we show that PMCA2 is down-regulated early in mammary involution associated with changes in MEC shape. We demonstrate that loss of PMCA2 expression raises intracellular calcium levels and sensitizes MECs to apoptosis. In contrast, overexpression of PMCA2 in T47D breast cancer cells lowers intracellular calcium and protects them from apoptosis. Finally, we show that high PMCA2 expression in breast cancers is associated with poor outcome. We conclude that loss of PMCA2 expression at weaning triggers apoptosis by causing cellular calcium crisis. PMCA2 overexpression, on the other hand, may play a role in breast cancer progression by conferring resistance to apoptosis. intracellular calcium
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