This paper describes a procedure that makes it possible to design and fabricate (including sealing) microfluidic systems in an elastomeric materialpoly(dimethylsiloxane) (PDMS)in less than 24 h. A network of microfluidic channels (with width >20 μm) is designed in a CAD program. This design is converted into a transparency by a high-resolution printer; this transparency is used as a mask in photolithography to create a master in positive relief photoresist. PDMS cast against the master yields a polymeric replica containing a network of channels. The surface of this replica, and that of a flat slab of PDMS, are oxidized in an oxygen plasma. These oxidized surfaces seal tightly and irreversibly when brought into conformal contact. Oxidized PDMS also seals irreversibly to other materials used in microfluidic systems, such as glass, silicon, silicon oxide, and oxidized polystyrene; a number of substrates for devices are, therefore, practical options. Oxidation of the PDMS has the additional advantage that it ...

Rapid prototyping of microfluidic systems in poly(dimethylsiloxane)

Nanotechnology is a multidisciplinary field, which covers a vast and diverse array of devices derived from engineering, biology, physics and chemistry. These devices include nanovectors for the targeted delivery of anticancer drugs and imaging contrast agents. Nanowires and nanocantilever arrays are among the leading approaches under development for the early detection of precancerous and malignant lesions from biological fluids. These and other nanodevices can provide essential breakthroughs in the fight against cancer.

Cancer nanotechnology: opportunities and challenges.

Microfabricated integrated circuits revolutionized computation by vastly reducing the space, labor, and time required for calculations. Microfluidic systems hold similar promise for the large-scale automation of chemistry and biology, suggesting the possibility of numerous experiments performed rapidly and in parallel, while consuming little reagent. While it is too early to tell whether such a vision will be realized, significant progress has been achieved, and various applications of significant scientific and practical interest have been developed. Here a review of the physics of small volumes (nanoliters) of fluids is presented, as parametrized by a series of dimensionless numbers expressing the relative importance of various physical phenomena. Specifically, this review explores the Reynolds number Re, addressing inertial effects; the Peclet number Pe, which concerns convective and diffusive transport; the capillary number Ca expressing the importance of interfacial tension; the Deborah, Weissenberg, and elasticity numbers De, Wi, and El, describing elastic effects due to deformable microstructural elements like polymers; the Grashof and Rayleigh numbers Gr and Ra, describing density-driven flows; and the Knudsen number, describing the importance of noncontinuum molecular effects. Furthermore, the long-range nature of viscous flows and the small device dimensions inherent in microfluidics mean that the influence of boundaries is typically significant. A variety of strategies have been developed to manipulate fluids by exploiting boundary effects; among these are electrokinetic effects, acoustic streaming, and fluid-structure interactions. The goal is to describe the physics behind the rich variety of fluid phenomena occurring on the nanoliter scale using simple scaling arguments, with the hopes of developing an intuitive sense for this occasionally counterintuitive world.

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Microfluidics: Fluid physics at the nanoliter scale

Microfluidic devices are finding increasing application as analytical systems, biomedical devices, tools for chemistry and biochemistry, and systems for fundamental research. Conventional methods of fabricating microfluidic devices have centered on etching in glass and silicon. Fabrication of microfluidic devices in poly(dimethylsiloxane) (PDMS) by soft lithography provides faster, less expensive routes than these conventional methods to devices that handle aqueous solutions. These soft-lithographic methods are based on rapid prototyping and replica molding and are more accessible to chemists and biologists working under benchtop conditions than are the microelectronics-derived methods because, in soft lithography, devices do not need to be fabricated in a cleanroom. This paper describes devices fabricated in PDMS for separations, patterning of biological and nonbiological material, and components for integrated systems.

Fabrication of microfluidic systems in poly(dimethylsiloxane)

Microfluidic devices for manipulating fluids are widespread and finding uses in many scientific and industrial contexts. Their design often requires unusual geometries and the interplay of multiple physical effects such as pressure gradients, electrokinetics, and capillarity. These circumstances lead to interesting variants of well-studied fluid dynamical problems and some new fluid responses. We provide an overview of flows in microdevices with focus on electrokinetics, mixing and dispersion, and multiphase flows. We highlight topics important for the description of the fluid dynamics: driving forces, geometry, and the chemical characteristics of surfaces.

Engineering flows in small devices

Ovarian cancers are curable by surgical resection when discovered early. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk. This opinion article summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

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Genetic Risk Scores and Missing Heritability in Ovarian Cancer

Abstract Introduction The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV). Methods We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without. Results We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results. Conclusion In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer. 

https://humgenomics.biomedcentral.com/counter/pdf/10.1186/s40246-023-00482-8

Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer.
Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations.
We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile.
The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

/pdf/a-genetic-risk-score-for-glioblastoma-multiforme-based-on-1xz5awg1f7.pdf

A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations.


 Genetic risk scores predict future outcomes based on germline genetics. Human germline genetics are usually characterized by a series of millions of SNP's. Polygenic risk scores estimate risk from a linear combination of these SNPs, but non-linear may also contribute. Modern machine learning algorithms excel at identifying differences in these non-linear combinations between two groups. However, these algorithms require many more subjects than features (SNPs). Thus, these algorithms can't be used to recognize genetic differences between two populations, unless one has millions of subjects. We developed a representation of the human genome that requires only dozens of numbers, each representing a measure of the length of a chromosome. We used this representation and machine learning methods to test two hypotheses related to breast cancer. We test whether any distinguishable genetic differences exist between (1) women who develop breast cancer and women who do not develop it and (2) women who have a recurrent breast tumor and those who do not. To test our hypotheses, we used data from UK Biobank, for recurrence, and the NIH All of Us datasets, for occurrence. We computed a set of numbers representing the chromosome scale length variation from germline DNA for each woman in the dataset. For each test we constructed a dataset that consisted of women with the desired trait (TRUE) and an equal number of age-matched participants that did not have the desired trait (FALSE). We used the H2O AI platform in conjunction with R statistical computing environment to train and test machine learning models to distinguish between the two classes in each dataset. To quantify the performance of the developed models, we calculated evaluation metrices of the best ML model on an unseen test data set. We compared the AUC of these models to a control for each dataset, in which we randomly scrambled the TRUE/FALSE labels. The UK Biobank has 488,377 patients with genetic data. There are 13968 patients within the dataset that have been diagnosed with breast cancer at least once through the study time. Among them, 489 patients have had breast cancer recurrence. Our machine learning model assigns a score to each patient based on their germ line genetics. We found that patients ranked by this score in the highest quintile are approximately 2.36 times as likely to have breast cancer recurrence compared to the lowest quantile. For the occurrence model, we assessed ALL of US genetic data which consists of 98,600 participants with whole genome sequencing data. There are 8260 female participants that have been diagnosed with malignant neoplasm of breast.
 This work lay basis for future investigations on big genetic data. We found a small genetic difference between women who have recurrent breast tumors and those who do not have recurrent tumors.
 Citation Format: Yasaman Fatapour, James Brody. Using chromosomal-scale length variation to predict breast cancer occurrence and recurrence with machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 772.

Abstract 772: Using chromosomal-scale length variation to predict breast cancer occurrence and recurrence with machine learning

Objective : Despite diagnostic advancements, development of reliable prognostic systems for assessing risk of cancer recurrence still remains a challenge. In this study, we developed a novel framework to leverage the expansive Surveillance, Epidemiology, and End Results (SEER) database to generate highly representative machine learning prediction models for oral tongue squamous cell carcinoma (OTSCC) cancer recurrence. Materials and Methods : Using our framework, we identified cases of 5- and 10-year OTSCC recurrence from the SEER database. Cases were split into training (80%) and test (20%) sets for model development and testing. Four classification models were trained by using the H2O artificial intelligence platform, whose performances were assessed according to their accuracy, recall, precision, and the area under the curve (AUC) of their receiver operating characteristic (ROC) curves. By evaluating Shapley additive explanations contribution plots, feature importance was studied. Results: Of 130,979 patients, 36,042 (27.5%) were female and the mean (SD) age was 58.2 (13.7) years. The Gradient Boosting Machine model performed the best, achieving 81.8% accuracy, 0.75 AUC, 83.0% recall, and 97.7% precision for 5-year prediction. Moreover, 10-year predictions demonstrated 80.0% accuracy, and 94.0% precision. The number of prior tumors, patient age, site of cancer recurrence, and tumor histology were the most significant predictors. Conclusion : Implementation of our novel SEER framework enabled successful identification of patients with OTSCC recurrence, with which highly accurate and sensitive prediction models were generated. Thus, we demonstrated our framework’s potential to be applied to various cancers for building generalizable screening tools to predict tumor recurrence.

James P. Brody

Papers

Genetic Risk Scores and Missing Heritability in Ovarian Cancer

Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer

A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations.

Abstract 772: Using chromosomal-scale length variation to predict breast cancer occurrence and recurrence with machine learning

Development of a supervised machine learning model to predict recurrence of oral tongue squamous cell carcinoma