Impact of the Human Genome Project on the clinical management of sporadic cancers

Machine learning meets genome assembly

MOTIVATION

With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale.

RESULTS

This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers-particularly the ones that use machine learning-to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.

Successful recruitment of healthy African American men to genomic studies from high-volume community health fairs: implications for future genomic research in minority populations

BACKGROUND

Study of genomic data obtained from patient biospecimens is frequent in research of subjects with prostate and other epithelial malignancies. Understanding of the characteristics of healthy men who participate in genomic research is limited.

METHODS

Patients were identified through the Prostate Cancer Genetic Risk Evaluation of SNPs Study and the Indiana University Cancer Biomarker Study, 2 population-based biomarker and cohort studies. Between 2006 and 2010, healthy Caucasian (n = 774) and healthy African American (n = 381) men were recruited and enrolled at high-volume free community health fairs. Each participant completed a demographic questionnaire and provided a blood sample for genomic research investigations. Frequency differences between demographic features of healthy African American and Caucasian men were compared and analyzed by 2-sample t test and multivariate logistic regression after adjusting potential confounding variables with significance at the P < .05 level. Features examined included: age, body mass index (BMI), income, education, marital status, tobacco, alcohol, family history, prostate-specific antigen (PSA) level, and prior prostate cancer screening history.

RESULTS

Significant differences between healthy Caucasian and African American men participating in genomic research included: marital status (married, 69% Caucasian vs 46% African American, P< < .001), mean age (years, 58 Caucasian vs 54 African American, P < .001), mean BMI (kg/m(2), 30.9 Caucasian vs 32.3 African American, P = .004), annual income (P = .038), education (P = .002), and mean PSA (ng/mL, 1.2 Caucasian vs 2.0 African American, P = .005).

CONCLUSIONS

Significant demographic differences exist between healthy Caucasian and African American men choosing to participate in genomic research. These differences may be important in designing genomic research study recruitment strategies.

Modification of poly(methyl methacrylate) microchannels for highly efficient and reproducible electrophoretic separations of double-stranded DNA

This paper deals with dynamic coating of the microchannels fabricated on poly(methyl methacrylate) (PMMA) chips and DNA separation by microchip electrophoresis (MCE). After testing a number of polymers, including 2-hydroxyethyl cellulose, hydroxypropylmethyl cellulose, different sizes of poly(ethylene oxide) (PEO), and poly(vinyl pyrrolidone) (PVP), we found that coating of the PMMA microchannels with PEO(Mr = 6.0 x 10(5) g/mol) on the first layer is essential to minimize the interaction of DNA with PMMA surface. To achieve high efficiency, multilayer coating of PMMA chips with PEO, PVP, and PEO containing gold nanoparticles [PEO(GNP)] is important. A 2-(PEO-PVP)-PEO(GNP) PMMA chip, which was repeatedly coated with 1.0% PEO and 5.0% PVP twice, and then coated with 0.75% PEO(GNP) each for 30 min, provided a high efficiency (up to 1.7 x 10(6) plates/m) for the separation of DNA markers V (pBR 322/HaeIII digest) and VI (pBR 328/BgiI digest and pBR 328/HinfI digest) when using 0.75% PEO(GNP). With such a high efficiency, we demonstrated the separation of hsp65 gene fragments of Mycobacterium HaeIII digests by MCE within 90 s. The advantages of this approach to DNA analysis include ease of filling the microchannel with 0.75% PEO(GNP), rapidity, and reproducibility.

A multiplex microsphere bead assay for comparative RNA expression analysis using flow cytometry

Comparative gene-expression profiling is an important tool in understanding molecular signatures of complex diseases as well as the responses of cells and tissues to external factors. With increasing microarray data, disease-specific molecular patterns are emerging but the acquisition of these data is expensive, difficult to customize and not well standardized. Once genome-wide scans identify differentially expressed genes in a given disease, cheaper, more easily customized methods will be needed for evaluating the expression of these genes in large population samples. Here we describe a novel multiplex microsphere bead assay (MBA) to compare gene expression levels. To test this assay we evaluated the expression levels of four transcripts (BRCA1, MGB1, DLG1 and ACT1) in normal and cancerous mammary tissue. The results were consistent with those generated by quantitative real-time PCR.