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.


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. Xing (PI) and Dr. Simon Watkins (co-I) receive R01 award from NIDDK

Dr. Xing (PI) and Dr. Simon Watkins (co-I) received a R01 award from NIDDK titled Role of the Snail1-Twist-p21 axis on cell cycle arrest and renal fibrosis development.

Kidney fibrosis is slowly-developing, largely irreversible process that accompanies all chronic kidney diseases. The team will use mathematical modeling and quantitative imaging approaches to study how the epithelial-to-mesenchymal transition and cell cycle program are coupled in kidney epithelial cells that lead to cell cycle arrest under acute kidney injury and subsequent fibrosis development.

Dr. Jianhua Xing Dr. Simon Watkins

Healthcare Startup SpIntellx Officially Launches

SpIntellx has officially launched as a computational pathology company applying its proprietary AI technologies to analyze whole slide images based on spatial analytics. Their mission is to develop tools that will improve the accuracy and efficiency of pathologists and to generate predictive data to optimize patient outcomes.

D. Lansing Taylor, Ph.D.
Chairman, Co-Founder

Chakra Chennubhotla, Ph.D.
President, Co-Founder

Michael Becich, M.D., Ph.D.

Jeffrey Fine, M.D.

Visit the website for more information: spintellx.com

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Jintao Liu receives 2018 Outstanding Young Scholar Award at Qiu Shi Awards Ceremony

Jintao Liu (Tsinghua University) received a 2018 Outstanding Young Scholar Award at the 2018 Annual Qiu Shi Awards Ceremony this past weekend at the University of Science and Technology of China. A total of twelve young scholars were presented with the award for having made outstanding achievements in areas such as new condensed theoretical physics, new energy materials, algebraic geometry, intestinal immunity and microorganisms.

Jintao Liu received his PhD in Physics under the joint supervision of Drs. Carlos Camacho and James Faeder on the Department of Computational and Systems Biology. His PhD research involved developing functional models of disordered proteins and modeling the kinetics of bacterial spore germination.

Founded in 1994, the primary mission of the Qiu Shi Science and Technology Foundation is to promote scientific and technological progress in China by recognizing and rewarding successful Chinese scientists and scholars.