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- Block, Keith I., et al.
(författare)
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Designing a broad-spectrum integrative approach for cancer prevention and treatment
- 2015
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Ingår i: Seminars in Cancer Biology. - : Academic Press. - 1044-579X .- 1096-3650. ; 35, s. S276-S304
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Forskningsöversikt (refereegranskat)abstract
- Targeted therapies and the consequent adoption of "personalized" oncology have achieved notable successes in some cancers; however, significant problems remain with this approach. Many targeted therapies are highly toxic, costs are extremely high, and most patients experience relapse after a few disease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistant immortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are not reliant upon the same mechanisms as those which have been targeted). To address these limitations, an international task force of 180 scientists was assembled to explore the concept of a low-toxicity "broadspectrum" therapeutic approach that could simultaneously target many key pathways and mechanisms. Using cancer hallmark phenotypes and the tumor microenvironment to account for the various aspects of relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a wide range of high-priority targets (74 in total) that could be modified to improve patient outcomes. For these targets, corresponding low-toxicity therapeutic approaches were then suggested, many of which were phytochemicals. Proposed actions on each target and all of the approaches were further reviewed for known effects on other hallmark areas and the tumor microenvironment Potential contrary or procarcinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixed evidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of the relationships revealed potentially complementary effects, and the remainder had no known relationship. Among the approaches, 1.1% had contrary, 2.8% had mixed and 62.1% had complementary relationships. These results suggest that a broad-spectrum approach should be feasible from a safety standpoint. This novel approach has potential to be relatively inexpensive, it should help us address stages and types of cancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for future research is offered. (C) 2015 The Authors. Published by Elsevier Ltd.
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| 2. |
- Yeung, Edwina, et al.
(författare)
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Maternal age is related to offspring DNA methylation : a meta-analysis of results from the pace consortium
- 2024
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Ingår i: Aging Cell. - : John Wiley & Sons. - 1474-9718 .- 1474-9726.
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Tidskriftsartikel (refereegranskat)abstract
- Worldwide trends to delay childbearing have increased parental ages at birth. Older parental age may harm offspring health, but mechanisms remain unclear. Alterations in offspring DNA methylation (DNAm) patterns could play a role as aging has been associated with methylation changes in gametes of older individuals. We meta-analyzed epigenome-wide associations of parental age with offspring blood DNAm of over 9500 newborns and 2000 children (5–10 years old) from the Pregnancy and Childhood Epigenetics consortium. In newborns, we identified 33 CpG sites in 13 loci with DNAm associated with maternal age (PFDR < 0.05). Eight of these CpGs were located near/in the MTNR1B gene, coding for a melatonin receptor. Regional analysis identified them together as a differentially methylated region consisting of 9 CpGs in/near MTNR1B, at which higher DNAm was associated with greater maternal age (PFDR = 6.92 × 10−8) in newborns. In childhood blood samples, these differences in blood DNAm of MTNR1B CpGs were nominally significant (p < 0.05) and retained the same positive direction, suggesting persistence of associations. Maternal age was also positively associated with higher DNA methylation at three CpGs in RTEL1-TNFRSF6B at birth (PFDR < 0.05) and nominally in childhood (p < 0.0001). Of the remaining 10 CpGs also persistent in childhood, methylation at cg26709300 in YPEL3/BOLA2B in external data was associated with expression of ITGAL, an immune regulator. While further study is needed to establish causality, particularly due to the small effect sizes observed, our results potentially support offspring DNAm as a mechanism underlying associations of maternal age with child health.
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| 3. |
- Lozano, Manuel, et al.
(författare)
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DNA methylation changes associated with prenatal mercury exposure : A meta-analysis of prospective cohort studies from PACE consortium
- 2022
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Ingår i: Environmental Research. - : Elsevier BV. - 0013-9351 .- 1096-0953. ; 204
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Tidskriftsartikel (refereegranskat)abstract
- Mercury (Hg) is a ubiquitous heavy metal that originates from both natural and anthropogenic sources and is transformed in the environment to its most toxicant form, methylmercury (MeHg). Recent studies suggest that MeHg exposure can alter epigenetic modifications during embryogenesis. In this study, we examined associations between prenatal MeHg exposure and levels of cord blood DNA methylation (DNAm) by meta-analysis in up to seven independent studies (n = 1462) as well as persistence of those relationships in blood from 7 to 8 year-old children (n = 794). In cord blood, we found limited evidence of differential DNAm at cg24184221 in MED31 (β = 2.28 × 10−4, p-value = 5.87 × 10−5) in relation to prenatal MeHg exposure. In child blood, we identified differential DNAm at cg15288800 (β = 0.004, p-value = 4.97 × 10−5), also located in MED31. This repeated link to MED31, a gene involved in lipid metabolism and RNA Polymerase II transcription function, may suggest a DNAm perturbation related to MeHg exposure that persists into early childhood. Further, we found evidence for association between prenatal MeHg exposure and child blood DNAm levels at two additional CpGs: cg12204245 (β = 0.002, p-value = 4.81 × 10−7) in GRK1 and cg02212000 (β = −0.001, p-value = 8.13 × 10−7) in GGH. Prenatal MeHg exposure was associated with DNAm modifications that may influence health outcomes, such as cognitive or anthropometric development, in different populations.
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| 4. |
- Mehta, Raghav, et al.
(författare)
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QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
- 2022
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Ingår i: Journal of Machine Learning for Biomedical Imaging. - 2766-905X. ; , s. 1-54
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
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