Dr. Joseph Ayoob Named NRMN Mentor of the Month

Dr. Joeseph Ayoob was named as the National Research Mentoring Network Mentor of the Month for July 2019, where he is also one of only four NRMN’s Master Mentors.  Dr. Ayoob is an Associate Professor in the Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh.  Additionally, he is the Founding Program Director of our NSF-funded Training and Experimentation in Computational Biology (TECBio) Research Experience for Undergraduates (REU) Program and the UPMC Hillman Cancer Center and University of Pittsburgh’s Computational Biology Research Academy (CoBRA) for outstanding high-school scholars.  Dr. Ayoob is also the Co-Founding Program Director for our CPCB MetaSchool Graduate Student Professional Development Series and the Course Director for Laboratory Methods for Computational Biologists, part of our Joint Carnegie Mellon/Pitt, Ph.D. Program in Computational Biology (CPCB), as well as the Co-Founding Director for the Computational Biomedicine & Biotechnology Masters Program (COBB). Click here to read more about his story as a scientist and mentor.

About the program:

Despite several decades of efforts to increase diversity in the U.S. biomedical workforce, the issue of the under-representation of many populations remains.  Scholars from non-majority backgrounds–whether by race, ethnicity, socioeconomic status, sexual orientation, or disability–have overcome many barriers yet still carry the burdens of disadvantaged and discrimination.  NRMN is funded by NIH and is a part of the NIH Diversity Program Consortium (DPC), which is a national collaborative that develops, implements, and determines the effectiveness of innovative approaches to strengthen institutional capacity to sustain mentor-mentee relationships. The NRMN is a nationwide consortium of biomedical professionals and institutions collaborating to provide enhanced networking and mentorship experiences in support of the training and career development of individuals from under represented backgrounds who are pursuing biomedical, behavioral, clinical, and social science research careers (collectively termed biomedical research careers). The NRMN is intended to enable mentees across career stages to find effective mentors who will engage in productive, supportive, and culturally responsive mentoring relationships.  The NRMN monthly newsletter serves over 15,000 researchers around the country across all career stages in the biomedical sciences.

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

Dr. Ivet Bahar receives Kadir Has Outstanding Achievement Award in Turkey

Mr. Nuri Has (left), Dr. Ivet Bahar (middle), and Dr. Sondan Durukanoglu Feyiz (right)

The 15th Annual Kadir Has Awards were held on Friday, March 22nd in Istanbul, Turkey. Member of the Science Academy, Dr. Ivet Bahar, received the Kadir Has “Outstanding Achievement Award” for her contributions to the development of theoretical and computational models for explaining the functional dynamics of biomolecular systems as well as mentoring and teaching a new wave of scientists. She was presented with the award by the Chairman of the Board of Trustees of Kadir Has University, Mr. Nuri Has, the Kadir Has Foundation President, Mr. Can Has, and the President of Kadir Has University, Dr. Sondan Durukanoglu Feyiz.

The Kadir Has Awards seek to recognize the outstanding accomplishments that Turkish scientists have made at the national and international level and to promote people and institutions that have contributed to the development of society.

For more information, please visit http://kadirhasvakfi.org/en/.

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.


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