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.

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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

Dr. Jingyu Zhang from Xing’s lab published a first-author paper in PLoS Computational Biology

Spatial clustering and common regulatory elements correlate with coordinated gene expression

Cellular responses to environmental stimulation are often accompanied by changes in gene expression patterns. Genes are linearly arranged along chromosomal DNA, which folds into a three-dimensional structure. The chromosome structure affects gene expression activities and is regulated by multiple events such as histone modifications and DNA binding of transcription factors. A basic question is how these mechanisms work together to regulate gene expression. In this study, we analyzed temporal gene expression patterns in the context of chromosome structure both in a human cell line under TGF-β treatment and during mouse nervous system development. In both cases, we observed that genes regulated by common transcription factors have an enhanced tendency to be spatially close. Our analysis suggests that spatial co-localization of genes may facilitate the concerted gene expression.

Zhang J, Chen H, Li R, Taft DA, Yao G, Bai F, Xing J. (2019) Spatial clustering and common regulatory elements correlate with coordinated gene expression. PLoS Computational Biology 15(3):e1006786

Dr. Robin Lee to receive collaborative R01 grant with PI Yuan Chang

Title: Role of a Novel Mitotic 4E-BP1 Protein Isoform in Cellular Transformation
PI: Yuan Chang
Co-I: Robin Lee and Patrick Moore

4E-BP1 is the primary gatekeeper for cancer cell cap dependent protein translation. It is directly targeted by mTOR kinase during interphase. We have found that CDK1/CYCB1 substitutes for mTOR during mitosis to phosphorylate 4E-BP1 generating a novel phosphorylation mark at serine (S) 83 that is not present when mTOR phosphorylates 4E-BP1. Unlike other 4E-BP1 phospho-isoforms, phospho-4E-BP1-S83 preferentially localizes to mitotic centrosomes as well as being diffusely distributed in a speckled pattern in the nucleo/cyto-plasm. A mutant form of 4E-BP1 that is unable to be phosphorylated at S83 partially reverses cell transformation caused by the Merkel cell polyomavirus (MCV) small T oncoprotein. This is particularly interesting since discovery of a novel pathway targeted by a tumor virus has always led to discovery of the same pathway being altered in non-infectious cancers. We developed a new phosphospecific antibody to p4E-BP1S83 that allows us to uniquely identify mitosis-related 4E-BP1 phosphorylation and determine its function in cancer cells. With this antibody, we will first survey phosphor-4E-BP-S83 expression in TCGA cancer tissues and anticipate that this approach will be a sensitive measure for activated CDK1 circuits and will provide unique data on cancer-type specific severity. We will next examine the biology of p4E-BP1S83 by identifying S83 phosphospecific effects on mitogenesis and by examining 4E-BP1’s potential role in regulating translation of specific transcripts during mitosis using ribosomal profiling and novel quantitative single cell imaging techniques. Finally, we will generate knock-in mutant mouse models of inactivated and phosphomimetic mitotic 4E-BP1 to determine its role in tumor susceptibility. Our specific aims will advance our fundamental understanding of how a mitosis-specific, hyperphosphorylated form of 4E-BP1 functions in normally cycling cells and how its dysregulation in cancer cells may contribute to human malignancies.

PI: Yuan Chang, MD Co-I: Robin Lee, PhD Co-I: Patrick Moore, MD, MPH

Congratulations to Drs. Nick Pabon and Qiuhong Zhang for their first author paper in Nature Comm.

Congratulations to Drs. Nick Pabon and Qiuhong Zhang for their first author paper in Nature Communications titled: A network-centric approach to drugging TNF-induced NF-kB signaling
with Profs. Carlos J. Camacho and Robin E.C. Lee.

Target-centric drug development strategies prioritize single-target potency in vitro and do not account for connectivity and multi-target effects within a signal transduction network. Here, we present a systems biology approach that combines transcriptomic and structural analyses with live-cell imaging to predict small molecule inhibitors of TNF-induced NF-κB signaling and elucidate the network response. We identify two first-in-class small molecules that inhibit the NF-κB signaling pathway by preventing the maturation of a rate-limiting multiprotein complex necessary for IKK activation. Our findings suggest that a network-centric drug discovery approach is a promising strategy to evaluate the impact of pharmacologic intervention in signaling.

 

Pabon NA, Zhang Q, Cruz JA, Schipper DL, Camacho CJ, Lee REC, (2019). A network-centric approach to drugging TNF-induced NF-κB signaling. Nature Communications. Vol 10, Article number: 860

Dr. Nathan Clark to receive 2019 Chancellor’s Distinguished Research Award

Dr. Nathan Clark has been selected for the 2019 Chancellor’s Distinguished Research Award which annually recognizes outstanding scholarly accomplishments of members of the University of Pittsburgh’s faculty.

Awarded in the Junior Scholar category, Dr. Clark was chosen by the selection committee for his demonstration of great potential by virtue of the quality of his early contributions. His accomplishments, information provided in support of his nomination, and letters of recommendation from well-known authorities in his field demonstrated that he has achieved national and international prominence as an outstanding scholar.

The award recognition ceremony will take place at the 2019 Honors Convocation.