Chemosensitivity Predicted by BluePrint 80-Gene Functional Subtype and MammaPrint in the Prospective Neoadjuvant Breast Registry Symphony Trial (NBRST).

PURPOSE: The purpose of the NBRST study is to compare a multigene classifier to conventional immunohistochemistry (IHC)/fluorescence in situ hybridization (FISH) subtyping to predict chemosensitivity as defined by pathological complete response (pCR) or endocrine sensitivity as defined by partial response. METHODS: The study includes women with histologically proven breast cancer, who will receive neoadjuvant chemotherapy (NCT) or neoadjuvant endocrine therapy. BluePrint in combination with MammaPrint classifies patients into four molecular subgroups: Luminal A, Luminal B, HER2, and Basal. RESULTS: A total of 426 patients had definitive surgery. Thirty-seven of 211 (18 %) IHC/FISH hormone receptor (HR)+/HER2- patients were reclassified by Blueprint as Basal (n = 35) or HER2 (n = 2). Fifty-three of 123 (43 %) IHC/FISH HER2+ patients were reclassified as Luminal (n = 36) or Basal (n = 17). Four of 92 (4 %) IHC/FISH triple-negative (TN) patients were reclassified as Luminal (n = 2) or HER2 (n = 2). NCT pCR rates were 2 % in Luminal A and 7 % Luminal B patients versus 10 % pCR in IHC/FISH HR+/HER2- patients. The NCT pCR rate was 53 % in BluePrint HER2 patients. This is significantly superior (p = 0.047) to the pCR rate in IHC/FISH HER2+ patients (38 %). The pCR rate of 36 of 75 IHC/FISH HER2+/HR+ patients reclassified as BPLuminal is 3 %. NCT pCR for BluePrint Basal patients was 49 of 140 (35 %), comparable to the 34 of 92 pCR rate (37 %) in IHC/FISH TN patients. CONCLUSIONS: BluePrint molecular subtyping reclassifies 22 % (94/426) of tumors, reassigning more responsive patients to the HER2 and Basal categories while reassigning less responsive patients to the Luminal category. These findings suggest that compared with IHC/FISH, BluePrint more accurately identifies patients likely to respond (or not respond) to NCT

DNA storage in thermoresponsive microcapsules for repeated random multiplexed data access

In support of the publication "DNA storage in thermoresponsive microcapsules for repeated random multiplexed data access" we share the following datasets and code: AutoCAD drawing of the microfluidic trapping device. Sequences of the DNA used to encode the 25 files used in the current study. FASTQ-files of the sequencing experiments of Figures 5b and d. Python scripts that allow for the reproduction of our sequencing data analysis. The code has been tested on MacOS 13.0.1, Python 3.7.13, samtools 1.16.1 and BWA 0.7.17

Oral History of Bichlien Nguyen

Oral History with Dr. BichLien Nguyen, born in Saigon, Vietnam in 1954. She is currently an oncologist practicing in Orange County, California. Her interview focused on her memories of going to pharmacy school in Saigon, being the eldest in her family and taking care of her mother who suffered from cancer. She left Vietnam by boat with her family and passed through Guam and the Philippines before resettling in Albuquerque, New Mexico through the sponsorship of a Lutheran Church. She met and married her husband, another Vietnamese refugee, in Albuquerque and then they moved to Orange County where she completed her undergraduate degree and medical school at UC Irvine. She has two children. She co-founded the Vietnamese American Cancer Foundation in 2002. At the time of interview she continues to participate actively in community life in Orange County.Recorded Digitall

Towards Sustainable Electrochemistry: Applications from Femtomolar to Preparative Scale

Electrochemistry provides a versatile method for a variety of synthetic transformations. It has been used in the Moeller lab to generate reactive intermediates to trigger intramolecular cyclization reactions, recycle chemical oxidants on microelectrode arrays, and to study binding interactions of molecules attached to the arrays with biological receptors in solution. Electrochemical techniques have also been demonstrated as a green chemical alternative to many traditional processes. While organic electrochemistry has been widely accepted by the electrochemical community, the synthetic chemistry community has failed to fully appreciate its utility. The goal of this thesis is to demonstrate the versatility and scope of electrochemical techniques from the femtomolar to preparative scale and to emphasize electrochemistry as a sustainable process. First, a series of electrochemically mediated chemical oxidations was explored. Since the selectivity of a chemical oxidant is not always based on oxidation potential and can be influenced by chirality, chelation, and other factors, electrochemical mediated oxidations are an important tool for organic synthesis. In addition, traditional stoichiometric metal oxidants generate a stoichiometric reduction metal waste product that can be avoided in an electrochemically mediated oxidation reaction. The chemical oxidants were recycled on a microelectrode array to functionalize the polymer surface and then scaled to the preparative scale. In doing so, chemical oxidation reactions were performed in an environmentally benign, sustainable manner. On the preparative scale, the electricity used to power the mediated oxidation reactions was supplied by a photovoltaic cell. This was done to emphasize that for electrochemistry to be a green alternative to traditional oxidation methods, it requires a green source of electricity. For electrochemical oxidations to be sustainable the whole process must be considered and that includes the source of the electricity consumed. In a similar vein, the origins of the chemicals used in any electrolysis reaction must be evaluated. Chemicals obtained from biorenewable resources would be an ideal alternative to chemicals derived from petroleum feedstocks. Towards that effort, lignin disassembly to small aromatic monomers was explored. Under solvolytic conditions, raw sawdust could be converted to electron-rich aromatic aldehyde- and cinammyl- monomers. These monomers were then electrochemically processed into synthetic precursors, and their synthetic utility is currently being further studied. Finally, the energy demands and economics of an electrochemical process were investigated. Every electrochemical oxidation is by nature paired with an electrochemical reduction. Only considering the oxidative half reaction is thereby energetically wasteful. For a more efficient electrochemical process, the anodic oxidation reaction must be paired with a useful cathodic reduction reaction. In preliminary studies, the oxidation of lignin-derived substrates was paired with H2 production for in situ generation of H2 for hydrogenation reactions. Currently, the paired electrochemical reduction of CO2 to CO is being explored for use in hydroformylation reactions. In this manner, chemical reagents can be generated on site thus bypassing the costs associated with shipping and storage

Photovoltaic-driven organic electrosynthesis and efforts toward more sustainable oxidation reactions

The combination of visible light, photovoltaics, and electrochemistry provides a convenient, inexpensive platform for conducting a wide variety of sustainable oxidation reactions. The approach presented in this article is compatible with both direct and indirect oxidation reactions, avoids the need for a stoichiometric oxidant, and leads to hydrogen gas as the only byproduct from the corresponding reduction reaction

High density DNA data storage library via dehydration with digital microfluidic retrieval.

DNA promises to be a high density data storage medium, but physical storage poses a challenge. To store large amounts of data, pools must be physically isolated so they can share the same addressing scheme. We propose the storage of dehydrated DNA spots on glass as an approach for scalable DNA data storage. The dried spots can then be retrieved by a water droplet using a digital microfluidic device. Here we show that this storage schema works with varying spot organization, spotted masses of DNA, and droplet retrieval dwell times. In all cases, the majority of the DNA was retrieved and successfully sequenced. We demonstrate that the spots can be densely arranged on a microfluidic device without significant contamination of the retrieval. We also demonstrate that 1 TB of data could be stored in a single spot of DNA and successfully retrieved using this method

Improved Environmental Chemistry Property Prediction of Molecules with Graph Machine Learning

Rapid prediction of environmental chemistry properties is critical towards the green and sustainable development of chemical industry and drug discovery. Machine learning methods can be applied to learn the relations between chemical structures and their environmental impact. Graph machine learning, by learning the representations directly from molecular graphs, may enable better predictive power than conventional feature-based models. In this work, we leveraged graph neural networks to predict environmental chemistry properties of molecules. To systematically evaluate the model performance, we selected a representative list of datasets, ranging from solubility to reactivity, and compare directly to commonly used methods. We found that the graph model achieved near state-of-the-art accuracy for all tasks and, for several, improved the accuracy by a large margin over conventional models that rely on human-designed chemical features. This demonstrates that graph machine learning can be a powerful tool to do representation learning for environmental chemistry. Further, we compared the data efficiency of conventional feature-based models and graph neural networks, providing guidance for model selection dependent on the size of datasets and feature requirements