Alternative Polyadenylation Regulates Patient-specific Tumor Growth by Individualizing the MicroRNA Target Site Landscape

AbstractBackgroundAlternative polyadenylation (APA) shortens or lengthens the 3’-untranslated region (3’-UTR) of hundreds of genes in cancer. While APA genes modify microRNA target sites in the 3’-UTRs to promote tumorigenesis, previous studies have focused on a subset of the modification landscape.MethodFor comprehensive understanding of the function of global APA events, we consider the total target site landscape of microRNAs that are significantly and collectively modified by global APA genes. To identify such microRNAs in spite of complex interactions between microRNAs and the APA genes, we developedProbabilisticInference ofMicroRNATarget Site Modification throughAPA(PRIMATA-APA).ResultsRunning PRIMATA-APA on TCGA breast cancer data, we identified that global APA events concentrate to modify target sites of particular microRNAs (target-site-modified-miRNAor tamoMiRNA). TamoMiRNAs are enriched for microRNAs known to regulate cancer etiology and treatments. Also, their target genes are enriched in cancer-associated pathways, suggesting that APA modifies target sites of tamoMiRNAs to progress tumors. Knockdown of NUDT21, a master 3’-UTR regulator in HeLa cells, confirmed the causal role of tamoMiRNAs for tumor growth.ConclusionsFurther, the expressions of tamoMiRNA target genes, enriched in cancer-associated pathways, vary across tumor samples as a function of patient-specific APA events, suggesting that APA is a novel regulatory axis for interpatient tumor heterogeneity.

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The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer

Briefings in Bioinformatics ◽

10.1093/bib/bbaa191 ◽

2020 ◽

Author(s):

Soyeon Kim ◽

YuLong Bai ◽

Zhenjiang Fan ◽

Brenda Diergaarde ◽

George C Tseng ◽

...

Keyword(s):

Breast Cancer ◽

Mirna Target ◽

Alternative Polyadenylation ◽

Regulatory Elements ◽

Target Site ◽

Mirna Target Site ◽

Breast Cancer Data ◽

Microrna Target ◽

Cancer Data ◽

Target Sites

Abstract Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis-regulatory elements such as microRNA (miRNA) target sites in the 3′-untranslated region (3′-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3′-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker.

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Multimodal Image Driven Patient Specific Tumor Growth Modeling

Advanced Information Systems Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40760-4_36 ◽

2013 ◽

pp. 283-290 ◽

Cited By ~ 1

Author(s):

Yixun Liu ◽

Samira M. Sadowski ◽

Allison B. Weisbrod ◽

Electron Kebebew ◽

Ronald M. Summers ◽

...

Keyword(s):

Tumor Growth ◽

Growth Modeling ◽

Patient Specific ◽

Specific Tumor ◽

Tumor Growth Modeling

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Patient specific tumor growth prediction using multimodal images

Medical Image Analysis ◽

10.1016/j.media.2014.02.005 ◽

2014 ◽

Vol 18 (3) ◽

pp. 555-566 ◽

Cited By ~ 37

Author(s):

Yixun Liu ◽

Samira M. Sadowski ◽

Allison B. Weisbrod ◽

Electron Kebebew ◽

Ronald M. Summers ◽

...

Keyword(s):

Tumor Growth ◽

Patient Specific ◽

Growth Prediction ◽

Specific Tumor ◽

Multimodal Images

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Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies

Cancer Research ◽

10.1158/0008-5472.can-18-3652 ◽

2019 ◽

Vol 79 (14) ◽

pp. 3776-3788 ◽

Cited By ~ 11

Author(s):

Clemens Grassberger ◽

David McClatchy ◽

Changran Geng ◽

Sophia C. Kamran ◽

Florian Fintelmann ◽

...

Keyword(s):

Tumor Growth ◽

Cancer Cell ◽

Targeted Therapies ◽

Patient Specific ◽

Cell Populations ◽

Growth Trajectories ◽

Specific Tumor

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Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data

10.1101/2020.06.08.140723 ◽

2020 ◽

Author(s):

Cristian Axenie ◽

Daria Kurz

Keyword(s):

Breast Cancer ◽

Tumor Growth ◽

Unsupervised Learning ◽

Growth Curve ◽

Tumor Volume ◽

Patient Specific ◽

Breast Cancer Data ◽

Surgery Planning ◽

Cancer Data ◽

Mathematical Relations

AbstractDespite the variety of imaging, genetic and histopathological data used to assess tumors, there is still an unmet need for patient-specific tumor growth profile extraction and tumor volume prediction, for use in surgery planning. Models of tumor growth predict tumor size and require tumor biology-dependent parametrization, which hardly generalizes to cope with tumor variability among patients. In addition, the datasets are limited in size, owing to the restricted or single-time measurements. In this work, we address the shortcomings that incomplete biological specifications, the inter-patient variability of tumors, and the limited size of the data bring to mechanistic tumor growth models and introduce a machine learning model capable of characterizing a tumor, namely its growth pattern, phenotypical transitions, and volume. The model learns without supervision, from different types of breast cancer data the underlying mathematical relations describing tumor growth curves more accurate than three state-of-the-art models on three publicly available clinical breast cancer datasets, being versatile among breast cancer types. Moreover, the model can also, without modification, learn the mathematical relations among, for instance, histopathological and morphological parameters of the tumor and, combined with the growth curve, capture the (phenotypical) growth transitions of the tumor from a small amount of data. Finally, given the tumor growth curve and its transitions, our model can learn the relation among tumor proliferation-to-apoptosis ratio, tumor radius, and tumor nutrient diffusion length to estimate tumor volume, which can be readily incorporated within current clinical practice, for surgery planning. We demonstrate the broad unsupervised learning and prediction capabilities of our model through a series of experiments on publicly available clinical datasets.

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Non-Target Site Mechanisms of Fungicide Resistance in Crop Pathogens: A Review

Microorganisms ◽

10.3390/microorganisms9030502 ◽

2021 ◽

Vol 9 (3) ◽

pp. 502

Author(s):

Mengjun Hu ◽

Shuning Chen

Keyword(s):

Fungicide Resistance ◽

Crop Production ◽

Large Scale ◽

Plant Pathogens ◽

Target Genes ◽

Target Site ◽

Fungal Genome ◽

Nucleotide Polymorphisms ◽

Human Pathogens ◽

Target Sites

The rapid emergence of resistance in plant pathogens to the limited number of chemical classes of fungicides challenges sustainability and profitability of crop production worldwide. Understanding mechanisms underlying fungicide resistance facilitates monitoring of resistant populations at large-scale, and can guide and accelerate the development of novel fungicides. A majority of modern fungicides act to disrupt a biochemical function via binding a specific target protein in the pathway. While target-site based mechanisms such as alternation and overexpression of target genes have been commonly found to confer resistance across many fungal species, it is not uncommon to encounter resistant phenotypes without altered or overexpressed target sites. However, such non-target site mechanisms are relatively understudied, due in part to the complexity of the fungal genome network. This type of resistance can oftentimes be transient and noninheritable, further hindering research efforts. In this review, we focused on crop pathogens and summarized reported mechanisms of resistance that are otherwise related to target-sites, including increased activity of efflux pumps, metabolic circumvention, detoxification, standing genetic variations, regulation of stress response pathways, and single nucleotide polymorphisms (SNPs) or mutations. In addition, novel mechanisms of drug resistance recently characterized in human pathogens are reviewed in the context of nontarget-directed resistance.

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