Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions

Gonzalez-Reymundez, Agustin; Campos, Gustavo de los; Gutiérrez, Lucı́a; Lunt, Sophia Y.; Vázquez, Ana I.

doi:10.1038/ejhg.2017.12

Cited by 28 publications

(33 citation statements)

References 58 publications

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“…Similarly, in a maize diversity panel, genomic prediction models that combined transcript and marker data only outperformed models using markers alone for certain traits (Guo et al, 2016). Finally, efforts to integrate additional omics information to predict various traits in Drosophila melanogaster (Li et al, 2019) and human diseases, such as breast cancer (González-Reymúndez et al, 2017), and responses to treatment interventions, including acute kidney rejection and response to infliximab in ulcerative colitis (Kang et al, 2017;Zarringhalam et al, 2018), have demonstrated the potential usefulness of transcriptome data in the field of precision medicine.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-Based Prediction of Complex Traits in Maize

et al. 2019

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The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

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Section: Introductionmentioning

confidence: 99%

Transcriptome-Based Prediction of Complex Traits in Maize

et al. 2019

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“…Similarly, in a maize diversity panel, GP models that combined transcript and marker data only outperformed models using markers alone for certain traits 16 . Finally, efforts to integrate additional omic information to predict various traits in Drosophila melanogaster 17 , and human diseases, such as breast cancer 18 , and responses to treatment interventions, including acute kidney rejection and response to infliximab in ulcerative colitis 19,20 , have demonstrated the potential usefulness of transcriptome data in the field of precision medicine.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome-based prediction of complex traits in maize

Azodi

Pardo

VanBuren

et al. 2019

Preprint

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27!The ability to predict traits from genome-wide sequence information (Genomic 28! Prediction, GP), has improved our understanding of the genetic basis of complex traits and 29! 86! for trait prediction. However, GP-based approaches that trained on the entire transcriptome data 87! have not been used to better understand the genetic mechanisms for a trait. In addition, it is not 88! ! 4! known the degree to which transcriptomes obtained at a particular developmental stage can be 89! informative for predicting phenotypes scored at a different stage. To address these questions, we 90! used transcriptome data derived from maize whole seedling 22 to predict phenotypes (flowering 91! time, height, and grain yield) at much later developmental stages. In addition to comparing 92! prediction performance between genetic marker and transcriptome-based models, we also looked 93! at whether transcripts and genetic marker features important for the prediction models were 94! located in the same or adjacent regions. Finally, we determined how well our models were able 95! to identify a benchmark set of flowering time genes to explore the potential of using GP to better 96! understand the mechanistic basis of complex traits. 97! 98! Results and Discussion 99! Relationships between transcript levels, kinship, and phenotypes among maize lines 100!Before using the transcriptome data for GP, we first assessed properties of the 101! transcriptome data in three areas: (1) the quantity and distribution of transcript information 102! across the genome, (2) the amount of variation in transcript levels, and (3) the similarity in the 103! transcriptome profile between maize lines, with an emphasis on how these properties compared 104! to those based on the genotype data. After filtering out 16,898 transcripts that did not map to the 105! B73 reference genome or had zero or near zero variance across lines (see Methods), we had 106! 31,238 transcripts. While the number of transcripts was <10% of the number of genetic markers 107! used in this study (332,178), the distribution of transcripts along the genome was similar to the 108! genetic marker distribution (Fig. S1). The log2-transformed median transcript level across lines 109! ranged from 0 to 12.4 (median=2.2) and the variance ranged from 3x10 -30 to 14.5 (median= 110! 0.13), highlighting that a subset of transcripts had relatively high variation in transcript levels 111! across maize lines at the seedling stage. To determine how similar transcript levels were between 112! lines, we calculated the expression Correlation (eCor) between all pairs of lines using Pearson's 113! Correlation Coefficient (PCC). The eCor values ranged from 0.84 to 0.99 (mean=0.93). As 114! expected, lines with similar transcriptome profiles were also genetically similar as there was a 115! significant correlation between eCor values with values in the kinship matrix generated from the 116! genetic marker data (Spearman's Rank ρ = 0.27, p < 2.2x10 -16 ; Fig. 1A). As a result, we were 117! able to find clust...

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“…Samples correspond to biopsies from primary tumor (breast) conserved as frozen tissue, and taken before patients underwent treatment. A total of 284 deaths occurred within four years after diagnosis (based on the time of maximum prediction accuracy of survival time for this data set [14], across different cancer subtypes: 131 luminal (40% deceased at fourth year), 431 triple negative (28%) and 131 Her2 + subtype (10%)). In this study, tumor grade was grade one (n = 950), grade two (n = 775), and grade three (n = 169), while tumor sizes ranged from 0.17 to 1.82 cm of diameter.…”

Section: Study Population and Samplementioning

confidence: 99%

“…The final number of genes after quality controls was 19,535. More detail about cohort and edition criteria can be found elsewhere [12,14]. The current study uses anonymized data accessed through Synapse (https://www.…”

Section: Study Population and Samplementioning

confidence: 99%

Breast cancer survival and the expression of genes related to alcohol drinking

Cheng

Gonzalez-Reymundez

et al. 2020

PLoS ONE

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Breast cancer is the leading cause of cancer-related disease in women. Cumulative evidence supports a causal role of alcohol intake and breast cancer incidence. In this study, we explore the change on expression of genes involved in the biological pathways through which alcohol has been hypothesized to impact breast cancer risk, to shed new insights on possible mechanisms affecting the survival of breast cancer patients. Here, we performed differential expression analysis at individual genes and gene set levels, respectively, across survival and breast cancer subtype data. Information about postdiagnosis breast cancer survival was obtained from 1977 Caucasian female participants in the Molecular Taxonomy of Breast Cancer International Consortium. Expression of 16 genes that have been linked in the literature to the hypothesized alcohol-breast cancer pathways, were examined. We found that the expression of 9 out of 16 genes under study were associated with cancer survival within the first 4 years of diagnosis. Results from gene set analysis confirmed a significant differential expression of these genes as a whole too. Although alcohol consumption is not analyzed, nor available for this dataset, we believe that further study on these genes could provide important information for clinical recommendations about potential impact of alcohol drinking on breast cancer survival.

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Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions

Cited by 28 publications

References 58 publications

Transcriptome-Based Prediction of Complex Traits in Maize

Transcriptome-Based Prediction of Complex Traits in Maize

Transcriptome-based prediction of complex traits in maize

Breast cancer survival and the expression of genes related to alcohol drinking

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