Ethnic disparities relative to disease features and outcomes in children with acute myeloid leukemia.

Hispanics with acute leukemias have poorer outcomes than non-Hispanic whites (NHWs), despite an increased likelihood of favorable prognostic features. We reviewed medical records from 167 children ages 0-18 years diagnosed with de novo AML over an 18-year period at Texas Children's Cancer Center, among whom 129 self-identified as Hispanic or NHW. Although Hispanics were significantly more likely to have the favorable prognostic cytogenetic feature t(8;21) (P = 0.04), the expected survival benefit was not observed. This lack of survival benefit was primarily due to significantly poorer event-free and overall survival among Hispanics treated with upfront stem cell transplantation after achieving first clinical remission (P = 0.008).

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes.

BACKGROUND: RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. RESULTS: We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. CONCLUSIONS: We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.