• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Monday, November 17, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Mathematical modeling could help with personalized cancer care

Bioengineer by Bioengineer
July 12, 2017
in Science News
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

A new study from the University of Southern California could pave the way for improving personalised lung cancer care and treatment. The research used mathematical modelling to examine if there was a link between the molecular and anatomical properties of lung cancer metastases, and whether this has an influence on how they spread through the body.

The team's findings, published today in the journal Convergent Science Physical Oncology, show a pattern in some patients with non-small cell lung cancer who develop lung metastases, which then in turn spread to the brain.

Tumour spread in lung cancer typically involves the opposite lung, as well as the liver, bone, brain and lymph nodes. This is a seemingly random process, which has not yet been well quantified.

However, lung cancer is not primarily defined by its pattern of anatomical distribution. Instead, subsets of this disease are more commonly defined by oncogenic drivers such as EGFR (epidermal growth factor receptor), ALK (anaplastic lymphoma kinase), and ROS1 (ROS1 proto-oncogene receptor tyrosine kinase). These driver mutations are both clinically predictive and prognostic, as these subtypes respond to targeted therapeutic agents.

Co-author Professor Peter Kuhn, from USC, said: "We used a retrospective database of 664 patients with non-small cell lung cancer, and employed Markov mathematical modelling to assess metastatic sites in a spatiotemporal manner, through every time point in the progression of disease. Lead author Dr. Gino K. In, from USC, said: "Our results showed that there was a preferential pattern of primary lung disease progressing through lung metastases to the brain among non-small cell lung cancer with EGFR exon 19 deletions and exon 21 L858R mutations. In these cases, the brain could be classified as an anatomical sponge, with a higher ratio of incoming to outgoing tumour spread.

"In patients with EGFR wild type, however, we found a propensity for the tumours to spread from the lungs to bone."

Senior author Dr Jorge Nieva, from USC, said: "Current guidelines for non-small cell lung cancer, such as those from the National Cancer Comprehensive Network (NCCN) do not differentiate by EGFR mutation status, with respect to the frequency and timing of brain imaging, yet brain tumours are a significant cause of mortality and morbidity for patients with EGFR exon 19 and 21 aberrations.

"If the presence of these EGFR mutations, particularly in the setting of progressive lung disease, points to increased risk of brain metastases, these patients may benefit from intensified CNS specific imaging. Multi-modality strategies to treat limited metastatic disease and limited relapse have shown the potential to improve survival in advanced disease, and may directly impact the biology of EGFR exon 19 and 21 mutation harbouring lung cancer when combined with targeted therapy."

Professor Kuhn said: "Our results indicate that the molecular and anatomical characterisations of metastatic cancer are inherently connected. Further investigation is needed to reveal the underlying mechanism of these anatomic differences in metastatic progression. Doing so may have predictive and prognostic use in the management of personalized lung cancer.

"Additionally, while there are currently no standard tools for predicting metastatic spread, we anticipate that Markov modelling could provide a vehicle for driving this approach forward in personalized lung cancer care."

###

Media Contact

Simon Davies
[email protected]
44-011-793-01110
@IOPPublishing

Homepage

http://dx.doi.org/10.1088/2057-1739/aa7a8d

Share12Tweet8Share2ShareShareShare2

Related Posts

Unraveling ARPC1B Deficiency: Founder Mutation Insights

November 17, 2025

Assessing Cortisol Levels in Adrenal Incidentaloma Patients

November 17, 2025

Mapping Splicing Events in Cows’ β-Casein Genotypes

November 17, 2025

Linking Emotion Regulation to Eating Disorders in Young Women

November 17, 2025
Please login to join discussion

POPULAR NEWS

  • ESMO 2025: mRNA COVID Vaccines Enhance Efficacy of Cancer Immunotherapy

    211 shares
    Share 84 Tweet 53
  • New Research Unveils the Pathway for CEOs to Achieve Social Media Stardom

    201 shares
    Share 80 Tweet 50
  • Scientists Uncover Chameleon’s Telephone-Cord-Like Optic Nerves, A Feature Missed by Aristotle and Newton

    116 shares
    Share 46 Tweet 29
  • Neurological Impacts of COVID and MIS-C in Children

    89 shares
    Share 36 Tweet 22

About

BIOENGINEER.ORG

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Unraveling ARPC1B Deficiency: Founder Mutation Insights

Assessing Cortisol Levels in Adrenal Incidentaloma Patients

Mapping Splicing Events in Cows’ β-Casein Genotypes

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 69 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.