Evidence map›Paper›PMID 39803494›Full record

ArticlebioRxiv : the preprint server for biology2025

Modeling tumor dynamics and predicting response to chemo-, targeted-, and immune-therapies in a murine model of pancreatic cancer.

Krithik Vishwanath, Hoon Choi, Mamta Gupta, Rong Zhou, Anna G Sorace, Thomas E Yankeelov, Ernesto A B F Lima

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Krithik VishwanathDepartment of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas, 78712.ORCID 0009-0000-6894-1796
Hoon ChoiDepartment of Radiology, Institute of Regenerative Medicine, Institute of Translational Medicine and Therapeutics, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Mamta GuptaDepartment of Radiology, Institute of Regenerative Medicine, Institute of Translational Medicine and Therapeutics, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Rong ZhouDepartment of Radiology, Institute of Regenerative Medicine, Institute of Translational Medicine and Therapeutics, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.ORCID 0000-0003-4018-381X
Anna G SoraceDepartment of Radiology, Department of Biomedical Engineering The University of Alabama, Birmingham, Birmingham, Alabama, 35223.ORCID 0000-0002-3640-3966
Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712.
Ernesto A B F LimaOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, 78712.

Funding

INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Penn Quantitative MRI Resource for Pancreatic CancerU24CA231858 · NCI · UNIVERSITY OF PENNSYLVANIA · PI ODWYER, PETER J, ROSEN, MARK ALAN · 2018 to 2022
$3.0M
Personalizing immunotherapy in HER2+ breast cancer through quantitative imagingR01CA240589 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C · 2020 to 2023
$1.6M
Mathematical modeling and molecular imaging to maximize response while minimizing toxicities from systemic therapies in preclinical models of breast cancerR01CA276540 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C, YANKEELOV, THOMAS E · 2023 to 2025
$1.4M
NCI NIH HHS R01 CA240589NCI NIH HHS R01 CA276540NCI NIH HHS U24 CA226110NCI NIH HHS U24 CA231858
6 · The paper itself

Abstract

We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size (growth and regression) under the influence of five cocktails of treatments. Model calibration relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model (KrasLSLG12D-Trp53LSLR172H-Pdx1-Cre). Our model reproduces tumor growth in the control and treatment scenarios with an average concordance correlation coefficient (CCC) of 0.99±0.01. We conduct leave-one-out predictions (average CCC=0.74±0.06), mouse-specific predictions (average CCC=0.75±0.02), and hybrid, group-informed, mouse-specific predictions (average CCC=0.85±0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.

Indexed as

Bayesiancisplatincombination therapygemcitabinemathematical oncologymNab-paclitaxelmodel calibrationtumor growth model

Identifiers

PMID39803494
PMCPMC11722293

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.