Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

Rahman, Mumtahena; MacNeil, Shelley M.; Jenkins, D. Graham; Shrestha, Gajendra; Wyatt, Sydney Rose; McQuerry, Jasmine A.; Piccolo, Stephen R.; Heiser, Laura M.; Gray, Joe W.; Johnson, W. Evan; Bild, Andrea

doi:10.1186/s13073-017-0429-x

Cited by 16 publications

(23 citation statements)

References 98 publications

(116 reference statements)

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“…5c ). Further, our custom experimentally generated Akt and K-Ras signatures 35 showed that the Akt signature was increased in patients #2 and #3 and the K-Ras signature was increased in patients #1, #3, and #4 (Supplementary Fig. 21 ), indicating that increased post-treatment RTK expression may have promoted increased Akt and Ras signaling.…”

Section: Resultsmentioning

confidence: 97%

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Combating subclonal evolution of resistant cancer phenotypes

et al. 2017

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Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.

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Section: Resultsmentioning

confidence: 97%

“…EGFR, K-Ras G12V, and Akt pathway signatures were developed as described elsewhere 35 and can be found on GEO at GSE73628. RNA-Seq data was adjusted for batch effects using ComBat.…”

Section: Methodsmentioning

confidence: 99%

Combating subclonal evolution of resistant cancer phenotypes

et al. 2017

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“…The control genes are drawn from a N (0.5,10) distribution. We set up these simulation studies based on the design of real signature profiling studies [ 21 ], and selected parameters to capture the statistical properties of realistic gene expression distributions (Additional file 2 ). Simulation code for this dataset is available at https://github.com/zhangyuqing/meanonly_reference_combat .…”

Section: Methodsmentioning

confidence: 99%

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

et al. 2018

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BackgroundCombining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.ResultsHere we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios.ConclusionsWe demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2263-6) contains supplementary material, which is available to authorized users.

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“…We applied the proposed ComBat-Seq approach on an RNA-Seq data from a perturbation experiment using primary breast tissue attempting to profile the activity levels of growth factor receptor network (GFRN) pathways in relation to breast cancer progression (Rahman et al (2017); Mc-Querry et al (2019)). We took a subset of experiments, which consists of 3 batches.…”

Section: Real Data Applicationmentioning

confidence: 99%

“…We applied our ComBat-Seq approach to address batch effects in a real RNA-Seq dataset designed to develop pathway signatures for breast cancer progression and treatment response (Rahman et al, 2017) as described in the Methods section. Figure 5 shows the scatter plot of samples projected on the first two principle components in unadjusted data, and in data adjusted by RUV-Seq, ComBat-Seq, and using the original ComBat on logCPM.…”

Section: Application To the Gfrn Signature Datasetmentioning

confidence: 99%

ComBat-Seq: batch effect adjustment for RNA-Seq count data

Zhang

Parmigiani

Johnson

2020

Preprint

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The benefit of integrating batches of genomic data to increase statistical power in differential expression is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data. Many existing methods for batch effect adjustment assume continuous, bellshaped Gaussian distributions for data. However in RNA-Seq studies where data are skewed, over-dispersed counts, this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used to better capture the properties of counts. We developed a batch correction method, ComBat-Seq, using negative binomial regression. ComBat-Seq retains the integer nature of count data in RNA-Seq studies, making the batch adjusted data 1 compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-Seq adjusted data result in better statistical power and control of false positives in differential expression, compared to data adjusted by the other available methods. We further demonstrated in a real data example where ComBat-Seq successfully removes batch effects and recovers the biological signal in the data.

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Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

Cited by 16 publications

References 98 publications

Combating subclonal evolution of resistant cancer phenotypes

Combating subclonal evolution of resistant cancer phenotypes

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

ComBat-Seq: batch effect adjustment for RNA-Seq count data

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