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"Link, John S"
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A breast cancer care report card. An assessment of performance and a pursuit of value
The transition to managed care raises concerns about the resulting quality of care. The report card, a publicly released, standardized report on quality, has received widespread acceptance as a method to evaluate physician performance. Current report cards provide insufficient information to allow purchasers of health care to assess accurately the performance of professionals who provide breast care. To overcome these limitations, we propose an expanded report card on breast cancer care. Mammographers and general surgeons would assess an independent series of at least 100 consecutive cases of newly diagnosed breast cancer. Mammographers would determine the percentage of invasive cancers < 15 mm detected on screening mammograms in asymptomatic women aged 50 to 74 years. Surgeons would determine the percentage of combined stages 0 and 1 breast cancers detected and the percentage of patients receiving breast-conserving surgical therapy. Performance targets are set at 60% for invasive cancers < 15 mm detected on screening mammography, 60% for combined stage 0 and 1 breast cancers, and 50% for patients receiving breast-conserving therapy.
Journal Article
On the Reliability of N-Mixture Models for Count Data
N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the \"constant p\" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.
Journal Article
Cellular stressors contribute to the expansion of hematopoietic clones of varying leukemic potential
Hematopoietic clones harboring specific mutations may expand over time. However, it remains unclear how different cellular stressors influence this expansion. Here we characterize clonal hematopoiesis after two different cellular stressors: cytotoxic therapy and hematopoietic transplantation. Cytotoxic therapy results in the expansion of clones carrying mutations in DNA damage response genes, including
TP53
and
PPM1D
. Analyses of sorted populations show that these clones are typically multilineage and myeloid-biased. Following autologous transplantation, most clones persist with stable chimerism. However,
DNMT3A
mutant clones often expand, while
PPM1D
mutant clones often decrease in size. To assess the leukemic potential of these expanded clones, we genotyped 134 t-AML/t-MDS samples. Mutations in non-
TP53
DNA damage response genes are infrequent in t-AML/t-MDS despite several being commonly identified after cytotoxic therapy. These data suggest that different hematopoietic stressors promote the expansion of distinct long-lived clones, carrying specific mutations, whose leukemic potential depends partially on the mutations they harbor.
Cellular stressors can impact clonal hematopoiesis. Here, the authors explore the impact of cytotoxic therapy and hematopoietic transplantation on clonal expansion, suggesting different stressors can promote expansion of distinct long-lived clones.
Journal Article
On the robustness of N-mixture models
N-mixture models provide an appealing alternative to mark–recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model’s assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.
Journal Article
Genome Sequencing as an Alternative to Cytogenetic Analysis in Myeloid Cancers
In this study, investigators compared genome sequencing with cytogenetic analysis in 263 patients with acute myeloid leukemia or myelodysplastic syndromes. Prospective sequencing detected new genetic information that was not revealed by cytogenetic analysis in nearly 25% of the patients, which altered the risk category for most of these patients.
Journal Article
Clonal Architecture of Secondary Acute Myeloid Leukemia
Whole-genome sequencing of samples from seven subjects with secondary acute myeloid leukemia identified somatic mutations. These data, together with genotype analysis of the antecedent myelodysplastic syndromes (MDS), revealed the clonal evolution of MDS and secondary AML.
The myelodysplastic syndromes, a heterogeneous group of diseases characterized by ineffective hematopoiesis, are the most common cause of acquired bone marrow failure in adults.
1
Secondary acute myeloid leukemia (AML) develops in approximately one third of persons with myelodysplastic syndromes.
2
Clinical discrimination between the myelodysplastic syndromes and secondary AML currently rests predominantly on cytomorphologic analysis, since patients with myelodysplastic syndromes have dysplastic hematopoiesis and a myeloblast count of less than 20%, whereas those with a myeloblast count of 20% or more have AML. Although considerable overlap exists between the spectrum of cytogenetic and molecular lesions seen in the two disorders, there . . .
Journal Article
Clonal Architecture of Secondary Acute Myeloid Leukemia Defined by Single-Cell Sequencing
Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions-the population frequency of individual clones, their genetic composition, and their evolutionary relationships-which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.
Journal Article
Age-related mutations associated with clonal hematopoietic expansion and malignancies
Systematic analysis of cancer-associated mutations in the blood cells of healthy individuals.
Several genetic alterations characteristic of leukemia and lymphoma have been detected in the blood of individuals without apparent hematological malignancies. The Cancer Genome Atlas (TCGA) provides a unique resource for comprehensive discovery of mutations and genes in blood that may contribute to the clonal expansion of hematopoietic stem/progenitor cells. Here, we analyzed blood-derived sequence data from 2,728 individuals from TCGA and discovered 77 blood-specific mutations in cancer-associated genes, the majority being associated with advanced age. Remarkably, 83% of these mutations were from 19 leukemia and/or lymphoma-associated genes, and nine were recurrently mutated (
DNMT3A
,
TET2
,
JAK2
,
ASXL1
,
TP53
,
GNAS
,
PPM1D
,
BCORL1
and
SF3B1).
We identified 14 additional mutations in a very small fraction of blood cells, possibly representing the earliest stages of clonal expansion in hematopoietic stem cells. Comparison of these findings to mutations in hematological malignancies identified several recurrently mutated genes that may be disease initiators. Our analyses show that the blood cells of more than 2% of individuals (5–6% of people older than 70 years) contain mutations that may represent premalignant events that cause clonal hematopoietic expansion.
Journal Article
Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices
Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics-derived data.This Perspective, from a large group of metabolomics experts, provides best practices and simplified reporting guidelines for practitioners of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics.
Journal Article