A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA.

Interrogating plasma cell-free DNA (cfDNA) to detect cancer offers promise; however, no current tests scan structural variants (SVs) throughout the genome. Here, we report a simple molecular workflow to enrich a tumorigenic SV (DNA palindromes/fold-back inversions) that often demarcates genomic amplification and its feasibility for cancer detection by combining low-throughput next-generation sequencing with automated machine learning (Genome-wide Analysis of Palindrome Formation, GAPF-seq). Tumor DNA signal manifested as skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), differentiating 39 matched breast tumor DNA from normal DNA with an average AUC of 0.9819. In a proof-of-concept liquid biopsy study, cfDNA from 0.5 mL plasma from prostate cancer patients was sufficient for binary classification against matched buffy coat DNA with an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with AR amplification. GAPF-seq could generate unique cancer-specific SV profiles in an agnostic liquid biopsy setting.

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA.

Plasma cell-free DNA (cfDNA) is a promising source of gene mutations for cancer detection by liquid biopsy. However, no current tests interrogate chromosomal structural variants (SVs) genome-wide. Here, we report a simple molecular and sequencing workflow called Genome-wide Analysis of Palindrome Formation (GAPF-seq) to probe DNA palindromes, a type of SV that often demarcates gene amplification. With low-throughput next-generation sequencing and automated machine learning, tumor DNA showed skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), which differentiated 39 breast tumors from matched normal DNA with an average Area Under the Curve (AUC) of 0.9819. A proof-of-concept liquid biopsy study using cfDNA from prostate cancer patients and healthy individuals yielded an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with androgen receptor gene amplification. As a novel agnostic liquid biopsy approach, GAPF-seq could fill the technological gap offering unique cancer-specific SV profiles.

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors.

PURPOSE: Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions. METHODS: Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter. RESULTS: Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner). CONCLUSION: We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.

Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.

BACKGROUND AND OBJECTIVE: Detecting complex patterns of association between genetic or environmental risk factors and disease risk has become an important target for epidemiological research. In particular, strategies that provide multifactor interactions or heterogeneous patterns of association can offer new insights into association studies for which traditional analytic tools have had limited success. MATERIALS AND METHODS: To concurrently examine these phenomena, previous work has successfully considered the application of learning classifier systems (LCSs), a flexible class of evolutionary algorithms that distributes learned associations over a population of rules. Subsequent work dealt with the inherent problems of knowledge discovery and interpretation within these algorithms, allowing for the characterization of heterogeneous patterns of association. Whereas these previous advancements were evaluated using complex simulation studies, this study applied these collective works to a 'real-world' genetic epidemiology study of bladder cancer susceptibility. RESULTS AND DISCUSSION: We replicated the identification of previously characterized factors that modify bladder cancer risk--namely, single nucleotide polymorphisms from a DNA repair gene, and smoking. Furthermore, we identified potentially heterogeneous groups of subjects characterized by distinct patterns of association. Cox proportional hazard models comparing clinical outcome variables between the cases of the two largest groups yielded a significant, meaningful difference in survival time in years (survivorship). A marginally significant difference in recurrence time was also noted. These results support the hypothesis that an LCS approach can offer greater insight into complex patterns of association. CONCLUSIONS: This methodology appears to be well suited to the dissection of disease heterogeneity, a key component in the advancement of personalized medicine.