Weiguang Mao and Dr. Maria Chikina Publish in Nature Methods

Pathway-level information extractor (PLIER): a new tool to quantify pathway level effects in gene expression data

A major challenge in gene expression analysis is to accurately infer relevant biological insights, such as variation in cell-type proportion or pathway activity, from global gene expression studies. We present pathway-level information extractor (PLIER), a broadly applicable solution for this problem that outperforms available cell proportion inference algorithms and can automatically identify specific pathways that regulate gene expression. Our method improves interstudy replicability and reveals biological insights when applied to trans-eQTL (expression quantitative trait loci) identification.

Mao W, Zaslavsky E, Hartmann BM, Sealfon SC, Chikina M. Pathway-level information extractor (PLIER) for gene expression data. Nature Methods; 16, 607–610 (2019)

 

Weiguang Mao Maria Chikina, PhD

Dr. Carlos Camacho Publishes in PNAS

 

 

A team of University of Pittsburgh neuroscientists and computational biologists have moved another step toward preventing brain cell death after an acute stroke event. In a paper published this week in the Proceedings of the National Academy of Sciences, they describe how first-in-class molecules discovered by student Zhaofeng Ye and Professor Carlos J. Camacho stops a key protein-protein interaction from opening the door to stroke-triggered damage to neurons.

 

 

 

 

Dr. Carlos Camacho

Yeh CY, Ye Z, Moutal A, Gaur S, Henton AM, Kouvaros S, Saloman J Hartnett-Scott KA, Tzounopoulos T, Khanna R, Aizenman E, Camacho C. Defining the Kv2.1-syntaxin molecular interaction identifies a first-in-class small molecule neuroprotectant. Proc Natl Acad Sci USA. 2019 Jul 15. pii: 201903401. doi: 10.1073/pnas.1903401116.

Carvunis Lab publishes review on De Novo Gene Birth in PLOS Genetics

Drs. Branden Van Oss and Anne-Ruxandra Carvunis review the field of de novo gene birth in their new PLOS Genetics Topic Page article. As part of the Topic Page initiative, the journal article also seeds a new Wikipedia page on the topic.

Abstract: De novo gene birth is the process by which new genes evolve from DNA sequences that were ancestrally non-genic. De novo genes represent a subset of novel genes, and may be protein-coding or instead act as RNA genes. The processes that govern de novo gene birth are not well understood, though several models exist that describe possible mechanisms by which de novo gene birth may occur. Although de novo gene birth may have occurred at any point in an organism’s evolutionary history, ancient de novo gene birth events are difficult to detect. Most studies of de novo genes to date have thus focused on young genes, typically taxonomically-restricted genes (TRGs) that are present in a single species or lineage, including so-called orphan genes, defined as genes that lack any identifiable homolog. It is important to note, however, that not all orphan genes arise de novo, and instead may emerge through fairly well-characterized mechanisms such as gene duplication (including retroposition) or horizontal gene transfer followed by sequence divergence, or by gene fission/fusion. Though de novo gene birth was once viewed as a highly unlikely occurrence [4], there are now several unequivocal examples of the phenomenon that have been described. It furthermore has been advanced that de novo gene birth plays a major role in the generation of evolutionary innovation.

Branden Van Oss, PhD Anne-Ruxandra Carvunis, PhD

To view the full article, please click here.

A collaboration betwen the labs of Drs. Xing and Sant (Pharmacy) leads to publication in Cancer Research

Targeting the temporal dynamics of hypoxia-induced tumor-secreted factors halts tumor migration

Targeting microenvironmental factors that foster migratory cell phenotypes is a promising strategy for halting tumor migration. However, lack of mechanistic understanding of the emergence of migratory phenotypes impedes pharmaceutical drug development. Using our 3D microtumor model with tight control over tumor size, we recapitulated the tumor size-induced hypoxic microenvironment and emergence of migratory phenotypes in microtumors from epithelial breast cells and patient-derived primary metastatic breast cancer cells, mesothelioma cells, and lung cancer xenograft cells (PDX). The microtumor models from various patient-derived tumor cells and PDX cells revealed upregulation of tumor-secreted factors including matrix metalloproteinase-9 (MMP9), fibronectin (FN), and soluble E-cadherin (sE-CAD), consistent with clinically reported elevated levels of FN and MMP9 in patient breast tumors compared to healthy mammary glands. Secreted factors in the conditioned media of large microtumors induced a migratory phenotype in non-hypoxic, non-migratory small microtumors. Subsequent mathematical analyses identified a two-stage microtumor progression and migration mechanism whereby hypoxia induces a migratory phenotype in the initialization stage which then becomes self-sustained through a positive feedback loop established among the tumor-secreted factors. Computational and experimental studies showed that inhibition of tumor-secreted factors effectively halts microtumor migration despite tumor-to-tumor variation in migration kinetics, while inhibition of hypoxia is effective only within a time window and is compromised by tumor-to-tumor variation, supporting our notion that hypoxia initiates migratory phenotypes but does not sustain it. In summary, we show that targeting temporal dynamics of evolving microenvironments, especially tumor-secreted factors during tumor progression, can halt tumor migration.

Singh M, Tian XJ, Donnenberg VS, Watson AM, Zhang JY, Stabile LP, Watkins SC, Xing J, Sant S. (2019) Targeting the temporal dynamics of hypoxia-induced tumor-secreted factors halts tumor migration. Cancer Res. DOI: 10.1158/0008-5472.CAN-18-3151

Shilpa Sant, PhD Jianhua Xing, PhD

Machine learning model can help reduce follow up screenings to 28% of people with benign nodules

A new machine learning model, developed in the Benos lab, can help reduce the unnecessary follow up screenings to people with benign nodules detected in low-dose CT scans by 28%.

Introduction Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives.

Methods In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort.

Results Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules.

Discussion LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial.

Press:

UPMC: https://www.upmc.com/media/news/031219-lung-ca-machine-learning

NPR: https://www.wesa.fm/post/artificial-intelligence-could-reduce-false-positives-lung-cancer-screenings

Vineet K. Raghu

 

 

 

 

Takis Benos   

 

 

 

 

      David Wilson


Raghu, …, Benos*, Wilson. Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal modelsThorax, 2019