Jeffrey W. Miller
Research Subjects
Jeffrey W. Miller has a total of 129 co-authors. Here are the top co-authors by number of shared publications:
- Gaurav N. Joshi
Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut
23 shared publications - Johan Spetz
Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden
23 shared publications - Kristopher A. Sarosiek
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA
23 shared publications - Xingping Qin
John B. Little Center for Radiation Sciences, Harvard T.H. Chan School of Public Health, Boston, USA
23 shared publications - Zintis Inde
John B. Little Center for Radiation Sciences, Harvard T.H. Chan School of Public Health, Boston, MA
23 shared publications - Abigail H. Cleveland
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, USA
22 shared publications
Latest Publications
- Open Access
LOESS and DE-SWAN can induce artifactual “waves” of molecular aging
A growing body of literature has investigated the relationship between age and biomolecular changes, leading to conclusions that aging occurs in discrete molecular “waves.” Data summary tools such as LOESS and sliding window analyses like DE-SWAN are common approaches that have gained acceptance in recent years. We demonstrate via simple simulations that these tools can identify non-linear patterns of aging where they do not exist. Specifically, we show that (i) clustering of molecular trajectories using LOESS can lead to artifactual characteristic patterns of molecular aging, (ii) “waves” of aging identified using the combination of LOESS and DE-SWAN in real data are not robust to changes in the underlying age distribution and are not supported by valid permutation testing, and (iii) DE-SWAN alone can generate pronounced “waves” of nonlinear molecular aging in linear data due to differences in statistical power along the age continuum. Our results specifically challenge the statistical support for discrete aging crests inferred in the literature, but do not rule out nonlinear molecular aging or age-associated transitions that may be detectable using other cohorts and statistical models.
Open AccessLOESS and DE-SWAN can induce artifactual “waves” of molecular aging
A growing body of literature has investigated the relationship between age and biomolecular changes, leading to conclusions that aging occurs in discrete molecular “waves.” Data summary tools such as LOESS and sliding window analyses like DE-SWAN are common approaches that have gained acceptance in recent years. We demonstrate via simple simulations that these tools can identify non-linear patterns of aging where they do not exist. Specifically, we show that (i) clustering of molecular trajectories using LOESS can lead to artifactual characteristic patterns of molecular aging, (ii) “waves” of aging identified using the combination of LOESS and DE-SWAN in real data are not robust to changes in the underlying age distribution and are not supported by valid permutation testing, and (iii) DE-SWAN alone can generate pronounced “waves” of nonlinear molecular aging in linear data due to differences in statistical power along the age continuum. Our results specifically challenge the statistical support for discrete aging crests inferred in the literature, but do not rule out nonlinear molecular aging or age-associated transitions that may be detectable using other cohorts and statistical models.
- Open Access
- 24 November 2025
- Published by American Association for Cancer Research (AACR)
Data from Radiotherapy-Induced Neurocognitive Impairment Is Driven by Heightened Apoptotic Priming in Early Life and Prevented by Blocking BAX
Although external beam radiotherapy (xRT) is commonly used to treat central nervous system (CNS) tumors in patients of all ages, young children treated with xRT frequently experience life-altering and dose-limiting neurocognitive impairment (NI) while adults do not. The lack of understanding of mechanisms responsible for these differences has impeded the development of neuroprotective treatments. Using a newly developed mouse model of xRT-induced NI, we found that neurocognitive function is impaired by ionizing radiation in a dose- and age-dependent manner, with the youngest animals being most affected. Histologic analysis revealed xRT-driven neuronal degeneration and cell death in neurogenic brain regions in young animals but not adults. BH3 profiling showed that neural stem and progenitor cells, neurons, and astrocytes in young mice are highly primed for apoptosis, rendering them hypersensitive to genotoxic damage. Analysis of single-cell RNA sequencing data revealed that neural cell vulnerability stems from heightened expression of proapoptotic genes including BAX, which is associated with developmental and mitogenic signaling by MYC. xRT induced apoptosis in primed neural cells by triggering a p53- and PUMA-initiated, proapoptotic feedback loop requiring cleavage of BID and culminating in BAX oligomerization and caspase activation. Notably, loss of BAX protected against apoptosis induced by proapoptotic signaling in vitro and prevented xRT-induced apoptosis in neural cells in vivo as well as neurocognitive sequelae. On the basis of these findings, preventing xRT-induced apoptosis specifically in immature neural cells by blocking BAX, BIM, or BID via direct or upstream mechanisms is expected to ameliorate NI in pediatric patients with CNS tumor.Significance:Age- and differentiation-dependent apoptotic priming plays a pivotal role in driving radiotherapy-induced neurocognitive impairment and can be targeted for neuroprotection in pediatric patients.
- 24 November 2025
- Published by American Association for Cancer Research (AACR)
Open AccessData from Radiotherapy-Induced Neurocognitive Impairment Is Driven by Heightened Apoptotic Priming in Early Life and Prevented by Blocking BAX
Although external beam radiotherapy (xRT) is commonly used to treat central nervous system (CNS) tumors in patients of all ages, young children treated with xRT frequently experience life-altering and dose-limiting neurocognitive impairment (NI) while adults do not. The lack of understanding of mechanisms responsible for these differences has impeded the development of neuroprotective treatments. Using a newly developed mouse model of xRT-induced NI, we found that neurocognitive function is impaired by ionizing radiation in a dose- and age-dependent manner, with the youngest animals being most affected. Histologic analysis revealed xRT-driven neuronal degeneration and cell death in neurogenic brain regions in young animals but not adults. BH3 profiling showed that neural stem and progenitor cells, neurons, and astrocytes in young mice are highly primed for apoptosis, rendering them hypersensitive to genotoxic damage. Analysis of single-cell RNA sequencing data revealed that neural cell vulnerability stems from heightened expression of proapoptotic genes including BAX, which is associated with developmental and mitogenic signaling by MYC. xRT induced apoptosis in primed neural cells by triggering a p53- and PUMA-initiated, proapoptotic feedback loop requiring cleavage of BID and culminating in BAX oligomerization and caspase activation. Notably, loss of BAX protected against apoptosis induced by proapoptotic signaling in vitro and prevented xRT-induced apoptosis in neural cells in vivo as well as neurocognitive sequelae. On the basis of these findings, preventing xRT-induced apoptosis specifically in immature neural cells by blocking BAX, BIM, or BID via direct or upstream mechanisms is expected to ameliorate NI in pediatric patients with CNS tumor.Significance:Age- and differentiation-dependent apoptotic priming plays a pivotal role in driving radiotherapy-induced neurocognitive impairment and can be targeted for neuroprotection in pediatric patients.
- Open Access
A powerful goodness-of-fit test using the probability integral transform of order statistics
Goodness-of-fit (GoF) tests are a fundamental component of statistical practice, essential for checking model assumptions and testing scientific hypotheses. Despite their widespread use, popular GoF tests exhibit surprisingly low statistical power against substantial departures from the null hypothesis. To address this, we introduce PITOS, a novel GoF test based on applying the probability integral transform (PIT) to the $j$th order statistic (OS) given the $i$th order statistic for selected pairs $i,j$. Under the null, for any pair $i,j$, this yields a $\mathrm{Uniform}(0,1)$ random variable, which we map to a p-value via $u\mapsto 2\min(u, 1-u)$. We compute these p-values for a structured collection of pairs $i,j$ generated via a discretized transformed Halton sequence, and aggregate them using the Cauchy combination technique to obtain the PITOS p-value. Our method maintains approximately valid Type I error control, has an efficient $O(n \log n)$ runtime, and can be used with any null distribution via the Rosenblatt transform. In empirical demonstrations, we find that PITOS has much higher power than popular GoF tests on distributions characterized by local departures from the null, while maintaining competitive power across all distributions tested.Open AccessA powerful goodness-of-fit test using the probability integral transform of order statistics
Goodness-of-fit (GoF) tests are a fundamental component of statistical practice, essential for checking model assumptions and testing scientific hypotheses. Despite their widespread use, popular GoF tests exhibit surprisingly low statistical power against substantial departures from the null hypothesis. To address this, we introduce PITOS, a novel GoF test based on applying the probability integral transform (PIT) to the $j$th order statistic (OS) given the $i$th order statistic for selected pairs $i,j$. Under the null, for any pair $i,j$, this yields a $\mathrm{Uniform}(0,1)$ random variable, which we map to a p-value via $u\mapsto 2\min(u, 1-u)$. We compute these p-values for a structured collection of pairs $i,j$ generated via a discretized transformed Halton sequence, and aggregate them using the Cauchy combination technique to obtain the PITOS p-value. Our method maintains approximately valid Type I error control, has an efficient $O(n \log n)$ runtime, and can be used with any null distribution via the Rosenblatt transform. In empirical demonstrations, we find that PITOS has much higher power than popular GoF tests on distributions characterized by local departures from the null, while maintaining competitive power across all distributions tested.