Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.

Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method's accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

Circulating Tumor DNA Is Prognostic in Intermediate-Risk Rhabdomyosarcoma: A Report From the Children's Oncology Group.

PURPOSE: Novel biomarkers are needed to differentiate outcomes in intermediate-risk rhabdomyosarcoma (IR RMS). We sought to evaluate strategies for identifying circulating tumor DNA (ctDNA) in IR RMS and to determine whether ctDNA detection before therapy is associated with outcome. PATIENTS AND METHODS: Pretreatment serum and tumor samples were available from 124 patients with newly diagnosed IR RMS from the Children's Oncology Group biorepository, including 75 patients with fusion-negative rhabdomyosarcoma (FN-RMS) and 49 with fusion-positive rhabdomyosarcoma (FP-RMS) disease. We used ultralow passage whole-genome sequencing to detect copy number alterations and a new custom sequencing assay, Rhabdo-Seq, to detect rearrangements and single-nucleotide variants. RESULTS: We found that ultralow passage whole-genome sequencing was a method applicable to ctDNA detection in all patients with FN-RMS and that ctDNA was detectable in 13 of 75 serum samples (17%). However, the use of Rhabdo-Seq in FN-RMS samples also identified single-nucleotide variants, such as MYOD1L122R, previously associated with prognosis. Identification of pathognomonic translocations between PAX3 or PAX7 and FOXO1 by Rhabdo-Seq was the best method for measuring ctDNA in FP-RMS and detected ctDNA in 27 of 49 cases (55%). Patients with FN-RMS with detectable ctDNA at diagnosis had significantly worse outcomes than patients without detectable ctDNA (event-free survival, 33.3% v 68.9%; P = .0028; overall survival, 33.3% v 83.2%; P < .0001) as did patients with FP-RMS (event-free survival, 37% v 70%; P = .045; overall survival, 39.2% v 75%; P = .023). In multivariable analysis, ctDNA was independently associated with worse prognosis in FN-RMS but not in the smaller FP-RMS cohort. CONCLUSION: Our study demonstrates that baseline ctDNA detection is feasible and is prognostic in IR RMS.