Flour milling separates endosperm from bran through repeated roller milling and sifting, in which the size distribution of particles produced by the initial breakage of the wheat kernels critically affects the process. The double normalized Kumaraswamy breakage function (DNKBF), previously developed to describe wheat breakage during roller milling, was extended to refine the modeling of the effect of roll gap on breakage. The DNKBF describes two populations of particles arising from roller milling of wheat, a narrow peak of mid‐sized particles and a wider distribution of both small and very large particles. A new dataset was obtained from milling a set of wheat samples bred to give a range of shapes by cross‐breeding a conventional wheat, Cappelle, with an almost spherical wheat, Triticum sphaerococcum. A residual analysis showed a statistically significant effect of kernel shape on breakage using this new dataset. This analysis supports earlier suggestions that more elongated kernels break to give slightly larger particles than more spherical kernels of equivalent hardness, because of the relatively greater bran content of elongated kernels. The extended DNKBF was also used to model effects of moisture content, showing a distinct disjunction at around 16% moisture that aligns with commercial practice for wheat milling.

Background Mechanical interactions between tumor cells and microenvironments are frequent phenomena during breast cancer progression, however, it is not well understood how these interactions affect Epithelial-to-Mesenchymal Transition (EMT). EMT is associated with the progression of most carcinomas through induction of new transcriptional programs within affected epithelial cells, resulting in cells becoming more motile and adhesive to endothelial cells. Methods MDA-MB-231, SK-BR-3, BT-474, and MCF-7 cells and normal Human Mammary Epithelial Cells (HMECs) were exposed to fluid flow in a parallel-plate bioreactor system. Changes in expression were quantified using microarrays, qPCR, immunocytochemistry, and western blots. Gene–gene interactions were elucidated using network analysis, and key modified genes were examined in clinical datasets. Potential involvement of Smads was investigated using siRNA knockdown studies. Finally, the ability of flow-stimulated and unstimulated cancer cells to adhere to an endothelial monolayer, migrate and invade membrane pores was evaluated in flow and static adhesion experiments. Results Fluid flow stimulation resulted in upregulation of EMT inducers and downregulation of repressors. Specifically, Vimentin and Snail were upregulated both at the gene and protein expression levels in flow stimulated HMECs and MDA-MB-231 cells, suggesting progression towards an EMT phenotype. Flow-stimulated SNAI2 was abrogated with Smad3 siRNA. Flow-induced overexpression of a panel of cell adhesion genes was also observed. Network analysis revealed genes involved in cell flow responses including FN1, PLAU, and ALCAM. When evaluated in clinical datasets, overexpression of FN1, PLAU, and ALCAM was observed in patients with different subtypes of breast cancer. We also observed increased adhesion, migration and invasion of flow-stimulated breast cancer cells compared to unstimulated controls. Conclusions This study shows that fluid forces on the order of 1 Pa promote EMT and adhesion of breast cancer cells to an endothelial monolayer and identified biomarkers were distinctly expressed in patient populations. A better understanding of how biophysical forces such as shear stress affect cellular processes involved in metastatic progression of breast cancer is important for identifying new molecular markers for disease progression, and for predicting metastatic risk.

Introduction S100 proteins are intracellular calcium ion sensors that participate in cellular processes, some of which are involved in normal breast functioning and breast cancer development. Despite several S100 genes being overexpressed in breast cancer, their roles during disease development remain elusive. Human mammary epithelial cells (HMECs) can be exposed to fluid shear stresses and implications of such interactions have not been previously studied. The goal of this study was to analyze expression profiles of S100 genes upon exposing HMECs to fluid flow. Methods HMECs and breast cancer cell lines were exposed to fluid flow in a parallel-plate bioreactor system. Changes in gene expression were quantified using microarrays and qPCR, gene-gene interactions were elucidated using network analysis, and key modified genes were examined in three independent clinical datasets. Results S100 genes were among the most upregulated genes upon flow stimulation. Network analysis revealed interactions between upregulated transcripts, including interactions between S100P, S100PBP, S100A4, S100A7, S100A8 and S100A9. Overexpression of S100s was also observed in patients with early stage breast cancer compared to normal breast tissue, and in most breast cancer patients. Finally, survival analysis revealed reduced survival times for patients with elevated expression of S100A7 and S100P. Conclusion This study shows that exposing HMECs to fluid flow upregulates genes identified clinically to be overexpressed during breast cancer development, including S100A7 and S100P. These findings are the first to show that S100 genes are flow-responsive and might be participating in a fundamental adaptation pathway in normal tissue that is also active in breast cancer.

Background: Breast cancer is often detected at later stages, indicating a significant need for additional screening methods. Mammography has limitations in breast cancer detection, for example young age, mammographic high density (categories C and D), small tumors and breast cancer classifications such as invasive lobular carcinomas. Syantra DX Breast Cancer is a new whole blood test that detects the presence of breast cancer by evaluating the expression of 12 novel genes through a custom qPCR process with proprietary software that includes machine learning-derived algorithms. Methodology: Whole blood samples (2.5 ml) were collected and analyzed with Syantra DX Breast Cancer as part of the ongoing IDBC prospective international clinical study (NCT04495244). The study is designed to demonstrate test performance in 2,100 participants. Women aged 30 to 75 years with a normal screening mammogram or physical exam (for the controls), or a BI-RADs 3 – 5 score on a screening mammogram were enrolled. A total of 1,107 participants (240 asymptomatic breast cancer, 867 non-cancer) were recruited and evaluated. All blood samples were collected pre-biopsy. For this interim analysis, 383 samples (132 cancer, 251 non-cancer) were used for machine learning-based model development and initial testing using a cross-validation approach. A set of 724 samples, with 695 evaluable samples (blind test set: 96 cancer, 599 non-cancer) were used for independent testing. All samples in the test set were randomized and blinded by the Alberta Cancer Research Biobank. Clinical performance metrics are reported for the blind test set with 99.5% confidence intervals (CI) computed through an exact binomial test. Results: In the blind test set, 59% of breast cancer subjects were Stage 1 and 25% stage 2. For molecular subtype, 75% were hormone receptor positive, 10% were HER2 positive, and 5% were triple negative. For subjects with invasive breast cancer, the average tumor size was 29 mm (CI: 19 – 38 mm). For the entire test set, Syantra DX Breast Cancer demonstrated an inferred accuracy of 92.2% (CI: 88.9% – 94.6%) with a specificity of 94.3% (CI: 91.0% – 96.4%) and sensitivity of 79.2% (CI: 65.5% – 88.4%) for cancer detection (Table 1). Higher performance was observed in the group of study women under 50 with an inferred specificity of 99.0% and a sensitivity of 91.7% (Table 1). Evaluation of performance in women with extremely dense breast tissue (category D; n=52) revealed an inferred specificity of 95.3% (CI: 77.4% – 99.2%) and sensitivity of 88.9% (CI: 42.6% – 98.9%). This analysis also showed that small tumors less than 10 mm (n=19) were detected by the test, with a sensitivity of 68.4%. Conclusions: Interim data from the IDBC study demonstrated the clinical utility of the Syantra DX Breast Cancer test for use in early screening. Syantra DX Breast Cancer is the first blood test to show strong performance for women under 50, as well for those with very high breast density, and therefore provides a promising screening option to supplement current imaging approaches. Table 1. Performance Metrics of the Syantra DX Breast Cancer TestAgeNumber of participants (n)AccuracySpecificitySensitivity< 50Normal: 19298.5% (CI: 93.8% – 99.7%)99.0% (CI: 94.2% – 99.8%)91.7% (CI: 51.1% – 99.1%)Cancer: 12≥ 50Normal: 40789.6% (CI: 85.1% – 92.9%)92.1% (CI: 87.5% – 95.1%)77.4% (CI: 62.5% – 87.5%)Cancer: 84Entire cohortNormal: 59992.2% (CI: 88.9% – 94.6%)94.3% (CI: 91.0% – 96.4%)79.2% (CI: 65.5% – 88.4%)Cancer: 96 Citation Format: Nigel Bundred, Kenneth Fuh, Nasimeh Asgarian, Shannon Brown, Danielle Simonot, Xiuling Wang, Robert Shepherd, May Lynn Quan, Bobbi Jo Docktor, Anthony Maxwell, Cliona Kirwan, Alan Hollingsworth (retired), Donald Morris, Kristina Rinker. A whole blood assay to identify breast cancer: Interim analysis of the international identify breast cancer (IDBC) study evidence supporting the Syantra DX breast cancer test [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-02.

The market for therapeutic proteins is on the rise, plagued by several challenges related to production amounts and costs. Solutions to these problems are widely thought to come from academia, governments and production companies. This conference aimed to bring experts in the industry together under one roof, in order to demystify several novel technologies in therapeutic protein development. Key topics included analytical tools for protein stability and ligand interactions, measurement of protein aggregates as small as 30 nm and reducing production costs, just to name a few. The need to eliminate protein aggregates early during bioprocessing was emphasized. Finally, several companies presented novel technologies related to therapeutic protein development.