Quantitative fate mapping: A general framework for analyzing progenitor state dynamics via retrospective lineage barcoding.

Natural and induced somatic mutations that accumulate in the genome during development record the phylogenetic relationships of cells; whether these lineage barcodes capture the complex dynamics of progenitor states remains unclear. We introduce quantitative fate mapping, an approach to reconstruct the hierarchy, commitment times, population sizes, and commitment biases of intermediate progenitor states during development based on a time-scaled phylogeny of their descendants. To reconstruct time-scaled phylogenies from lineage barcodes, we introduce Phylotime, a scalable maximum likelihood clustering approach based on a general barcoding mutagenesis model. We validate these approaches using realistic in silico and in vitro barcoding experiments. We further establish criteria for the number of cells that must be analyzed for robust quantitative fate mapping and a progenitor state coverage statistic to assess the robustness. This work demonstrates how lineage barcodes, natural or synthetic, enable analyzing progenitor fate and dynamics long after embryonic development in any organism.

Geographic disparities in liver supply/demand ratio within fixed-distance and fixed-population circles.

Recent OPTN proposals to address geographic disparity in liver allocation have involved circular boundaries: the policy selected 12/17 allocated to 150-mile circles in addition to DSAs/regions, and the policy selected 12/18 allocated to 150-mile circles eliminating DSA/region boundaries. However, methods to reduce geographic disparity remain controversial, within the OPTN and the transplant community. To inform ongoing discussions, we studied center-level supply/demand ratios using SRTR data (07/2013-06/2017) for 27 334 transplanted deceased donor livers and 44 652 incident waitlist candidates. Supply was the number of donors from an allocation unit (DSA or circle), allocated proportionally (by waitlist size) to the centers drawing on these donors. We measured geographic disparity as variance in log-transformed supply/demand ratio, comparing allocation based on DSAs, fixed-distance circles (150- or 400-mile radius), and fixed-population (12- or 50-million) circles. The recently proposed 150-mile radius circles (variance = 0.11, P = .9) or 12-million-population circles (variance = 0.08, P = .1) did not reduce the geographic disparity compared to DSA-based allocation (variance = 0.11). However, geographic disparity decreased substantially to 0.02 in both larger fixed-distance (400-mile, P < .001) and larger fixed-population (50-million, P < .001) circles (P = .9 comparing fixed distance and fixed population). For allocation circles to reduce geographic disparities, they must be larger than a 150-mile radius; additionally, fixed-population circles are not superior to fixed-distance circles.

Toward Expanded Access to Cancer Care With Cost Awareness: An Optimization Modeling Analysis of Rwanda.

PURPOSE: Cancers are a growing cause of mortality especially in low- and middle-income countries in Africa. Rwanda is no exception. Two cancer centers currently provide care to the public, but there are both political and human interest in expanding access to tertiary cancer care. Improved geographic access could lead to both better patient outcomes and a better understanding of the existing cancer burden across Rwanda. METHODS: To identify cost-aware ways of expanding geographic access, we adopt an optimization approach and identify expansion plans that minimize the average travel time to a cancer center across the country while remaining under a given monetary budget. RESULTS: Three additional hospitals could reduce average travel times by 40%, with the largest decrease in travel times observed in populations with long travel times. However, such an expansion would require a 50% increase in the number of in-country oncologists. We find that oncologist scarcity, as opposed to monetary constraints, is likely to be a limiting factor for improved access to cancer care. CONCLUSION: We present an array of expansion plans and suggest that further modeling approaches that incorporate oncologist scarcity can help deliver better policy recommendations.