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- 2019
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Journal article (peer-reviewed)
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- Wan, Guihong, et al.
(author)
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Prediction of early-stage melanoma recurrence using clinical and histopathologic features
- 2022
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In: NPJ precision oncology. - : Springer Science and Business Media LLC. - 2397-768X. ; 6:1
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Journal article (peer-reviewed)abstract
- Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.
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- Gusev, A, et al.
(author)
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Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation
- 2016
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7, s. 10979-
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Journal article (peer-reviewed)abstract
- Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.
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- Lappalainen, Tuuli, et al.
(author)
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Genetic and molecular architecture of complex traits
- 2024
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In: Cell. - : Elsevier BV. - 0092-8674 .- 1097-4172. ; 187:5, s. 1059-1075
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Research review (peer-reviewed)abstract
- Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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- Litvinov, D. A., et al.
(author)
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RadioAstron gravitational redshift experiment: Status update
- 2018
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In: 14th Marcel Grossman Meeting On Recent Developments in Theoretical and Experimental General Relativity, Astrophysics and Relativistic Field Theories, Proceedings. - : WORLD SCIENTIFIC. - 9789813226593 ; , s. 3569-3575
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Conference paper (peer-reviewed)abstract
- A test of a cornerstone of general relativity, the gravitational redshift effect, is currently being conducted with the RadioAstron spacecraft, which is on a highly eccentric orbit around Earth. Using ground radio telescopes to record the spacecraft signal, synchronized to its ultra-stable on-board H-maser, we can probe the varying flow of time on board with unprecedented accuracy. The observations performed so far, currently being analyzed, have already allowed us to measure the effect with a relative accuracy of 4 × 10−4. We expect to reach 2.5 × 10−5 with additional observations in 2016, an improvement of almost a magnitude over the 40-year old result of the GP-A mission.
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- Litvinov, Dmitry, et al.
(author)
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Probing the gravitational redshift with an Earth-orbiting satellite
- 2018
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In: Physics Letters, Section A: General, Atomic and Solid State Physics. - : Elsevier BV. - 0375-9601. ; 382:33, s. 2192-2198
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Journal article (peer-reviewed)abstract
- We present an approach to testing the gravitational redshift effect using the RadioAstron satellite. The experiment is based on a modification of the Gravity Probe A scheme of nonrelativistic Doppler compensation and benefits from the highly eccentric orbit and ultra-stable atomic hydrogen maser frequency standard of the RadioAstron satellite. Using the presented techniques we expect to reach an accuracy of the gravitational redshift test of order 10−5, a magnitude better than that of Gravity Probe A. Data processing is ongoing, our preliminary results agree with the validity of the Einstein Equivalence Principle.
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8. |
- Mancuso, Nicholas, et al.
(author)
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Large-scale transcriptome-wide association study identifies new prostate cancer risk regions
- 2018
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In: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 9
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Journal article (peer-reviewed)abstract
- Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.
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- Rutter, Nick, et al.
(author)
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Evaluation of forest snow processes models (SnowMIP2)
- 2009
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In: Journal of Geophysical Research. - : American Geophysical Union (AGU). - 0148-0227 .- 2156-2202. ; 114:6
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Journal article (peer-reviewed)abstract
- Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal "best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites shows that there is less consistency at forest sites than open sites, and even less consistency between forest and open sites in the same year. A good performance by a model at a forest site is therefore unlikely to mean a good model performance by the same model at an open site (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.
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10. |
- Wan, Guihong, et al.
(author)
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Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma
- 2024
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In: Journal of the American Academy of Dermatology. - 0190-9622. ; 90:2, s. 288-298
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Journal article (peer-reviewed)abstract
- Background: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. Objective: To develop time-to-event risk prediction models for melanoma metastatic recurrence. Methods: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. Results: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. Limitations: Retrospective nature and cohort from one geography. Conclusions: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.
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