Correction: Evaluation of the cost and effectiveness of diverse recruitment methods for a genetic screening study.

The original version of this Article contained an error in the undergraduate degree awarded to the author Ian Halim, which was incorrectly given as BS. This has now been corrected to BA in both the PDF and HTML versions of the Article.

Evaluation of the cost and effectiveness of diverse recruitment methods for a genetic screening study.

PURPOSE: Recruitment of participants from diverse backgrounds is crucial to the generalizability of genetic research, but has proven challenging. We retrospectively evaluated recruitment methods used for a study on return of genetic results. METHODS: The costs of study design, development, and participant enrollment were calculated, and the characteristics of the participants enrolled through the seven recruitment methods were examined. RESULTS: A total of 1118 participants provided consent, a blood sample, and questionnaire data. The estimated cost across recruitment methods ranged from $579 to $1666 per participant and required a large recruitment team. Recruitment methods using flyers and staff networks were the most cost-efficient and resulted in the highest completion rate. Targeted sampling that emphasized the importance of Latino/a participation, utilization of translated materials, and in-person recruitments contributed to enrolling a demographically diverse sample. CONCLUSIONS: Although all methods were deployed in the same hospital or neighborhood and shared the same staff, each recruitment method was different in terms of cost and characteristics of the enrolled participants, suggesting the importance of carefully choosing the recruitment methods based on the desired composition of the final study sample. This analysis provides information about the effectiveness and cost of different methods to recruit adults for genetic research.

Multimodal machine learning models identify chemotherapy drugs with prospective clinical efficacy in dogs with relapsed B-cell lymphoma.

Dogs with B-cell lymphoma typically respond well to first-line CHOP-based chemotherapy, but there is no standard of care for relapsed patients. To help veterinary oncologists select effective drugs for dogs with lymphoid malignancies such as B-cell lymphoma, we have developed multimodal machine learning models that integrate data from multiple tumor profiling modalities and predict the likelihood of a positive clinical response for 10 commonly used chemotherapy drugs. Here we report on clinical outcomes that occurred after oncologists received a prediction report generated by our models. Remarkably, we found that dogs that received drugs predicted to be effective by the models experienced better clinical outcomes by every metric we analyzed (overall response rate, complete response rate, duration of complete response, patient survival times) relative to other dogs in the study and relative to historical controls.

Prognostic Utility of the Flow Cytometry and Clonality Analysis Results for Feline Lymphomas.

Feline lymphoma, a prevalent cancer in cats, exhibits varied prognoses influenced by anatomical site and cellular characteristics. In this study, we investigated the utility of flow cytometry and clonality analysis via PCR for antigen receptor rearrangement (PARR) with respect to characterizing the disease and predicting prognosis. For this purpose, we received fine needle aspirates and/or blood from 438 feline patients, which were subjected to flow cytometry analysis and PARR. We used a subset of the results from patients with confirmed B- or T-cell lymphomas for comparison to cytological or histological evaluation (n = 53). Using them as a training set, we identified the optimal set of flow cytometry parameters, namely forward scatter thresholds, for cell size categorization by correlating with cytology-defined sizes. Concordance with cytological sizing among this training set was 82%. Furthermore, 90% concordance was observed when the proposed cell sizing was tested on an independent test set (n = 24), underscoring the reliability of the proposed approach. Additionally, lymphoma subtypes defined by flow cytometry and PARR demonstrated significant survival differences, validating the prognostic utility of these methods. The proposed methodology achieves high concordance with cytological evaluations and provides an additional tool for the characterization and management of feline lymphoproliferative diseases.

The diagnostic yield of exome sequencing in liver diseases from a curated gene panel.

Exome sequencing (ES) has been used in a variety of clinical settings but there are limited data on its utility for diagnosis and/or prediction of monogenic liver diseases. We developed a curated list of 502 genes for monogenic disorders associated with liver phenotypes and analyzed ES data for these genes in 758 patients with chronic liver diseases (CLD). For comparison, we examined ES data in 7856 self-declared healthy controls (HC), and 2187 patients with chronic kidney disease (CKD). Candidate pathogenic (P) or likely pathogenic (LP) variants were initially identified in 19.9% of participants, most of which were attributable to previously reported pathogenic variants with implausibly high allele frequencies. After variant annotation and filtering based on population minor allele frequency (MAF ≤ 10-4 for dominant disorders and MAF ≤ 10-3 for recessive disorders), we detected a significant enrichment of P/LP variants in the CLD cohort compared to the HC cohort (X2 test OR 5.00, 95% CI 3.06-8.18, p value = 4.5e-12). A second-level manual annotation was necessary to capture true pathogenic variants that were removed by stringent allele frequency and quality filters. After these sequential steps, the diagnostic rate of monogenic disorders was 5.7% in the CLD cohort, attributable to P/LP variants in 25 genes. We also identified concordant liver disease phenotypes for 15/22 kidney disease patients with P/LP variants in liver genes, mostly associated with cystic liver disease phenotypes. Sequencing results had many implications for clinical management, including familial testing for early diagnosis and management, preventative screening for associated comorbidities, and in some cases for therapy. Exome sequencing provided a 5.7% diagnostic rate in CLD patients and required multiple rounds of review to reduce both false positive and false negative findings. The identification of concordant phenotypes in many patients with P/LP variants and no known liver disease also indicates a potential for predictive testing for selected monogenic liver disorders.