OSCR

Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Single-cell tracking and morphology analysis ↔ analyzeCMSoutputv1_0.m, the whole file · a weak match · score 0.73 · minor axis lengths, random motility coefficient, written, fitted, MSD, cell
  2. [2] § Materials and Methods › Brownian dynamics tumor simulator ↔ TPM_3D_20190201_V1p9_20190911_1.m, lines 151–186 · score 0.57 · hit, dies, immune cells, Cancer cell, killing, radius
  3. [3] § Results › Migration phenotype is species- and tumor microenvironment– independent ↔ analyzeCMSoutputv1_0.m, the whole file · a weak match · score 0.53 · random motility coefficient, cell spreading, fitted

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 117 lines · 4.2 KB · MIT · 2 matches

  1. function [cellpos,trac,modnum,aflow,polyrate,gactin,trec,Forcec,t,msd,sem,gof,rmc,traction,rgflow,modulenum,modulel,csl,gact,area,aratio]=...
  2. analyzeCMSoutputv1_0(tint,tanalysis,texc,nucr,mc,trec,Forcec,cellpos,polyrate,gactin,trac,aflow,modnum,sdist,mdist)
  4. % Original code written by Benjamin Bangasser and Ghaidan Shamsan
  6. % Oct 2017 Brian Castle created analysis function from original code and
  7. % modified for efficiency and speed
  9. maxtind=find(trec,1,'last');
  10. exind=find(trec-texc*60>0,1,'first');
  12. cellpos = cellpos(:,exind+1:maxtind);
  13. trac = trac(exind+1:maxtind);
  14. modnum = modnum(exind+1:maxtind);
  15. aflow = aflow(exind+1:maxtind);
  16. polyrate = polyrate(exind+1:maxtind);
  17. gactin = gactin(exind+1:maxtind);
  18. trec = trec(exind+1:maxtind);
  19. Forcec = Forcec(:,exind+1:maxtind);
  21. % MSD and random motility coefficient (RMC) analysis
  22. celldisp = sqrt(diff(cellpos(1,:)).^2+diff(cellpos(2,:)).^2);
  23. jump_mask = celldisp>(1000*tint);
  24. if any(jump_mask(:))
  25. tmp1 = [0,diff(cellpos(1,:)).*jump_mask];tmp1=cumsum(tmp1);
  26. tmp2 = [0,diff(cellpos(2,:)).*jump_mask];tmp2=cumsum(tmp2);
  27. filter_cellpos = cellpos - [tmp1;tmp2];
  28. else
  29. filter_cellpos = cellpos;
  30. end
  32. cellpos = filter_cellpos(:,1:(tanalysis*(60/tint)):end);
  33. trac = trac(1:(tanalysis*(60/tint)):end);
  34. modnum = modnum(1:(tanalysis*(60/tint)):end);
  35. aflow = aflow(1:(tanalysis*(60/tint)):end);
  36. polyrate = polyrate(1:(tanalysis*(60/tint)):end);
  37. gactin = gactin(1:(tanalysis*(60/tint)):end);
  38. trec = trec(1:(tanalysis*(60/tint)):end);
  39. Forcec = Forcec(:,1:(tanalysis*(60/tint)):end);
  41. t = (trec-min(trec))/60+tanalysis; %convert to minutes
  42. [~,col] = size(cellpos);
  43. maxcol = 0;
  44. if col>maxcol;maxcol = col;end
  45. msd = zeros(1,col-1);
  46. sem = zeros(1,col-1);
  47. for i = 1:col-1
  48. xdif = cellpos(1,1:end-i)-cellpos(1,(1+i):end);
  49. ydif = cellpos(2,1:end-i)-cellpos(2,(1+i):end);
  51. sqdisp = xdif.^2+ydif.^2;
  52. sqdisp(isnan(sqdisp)) = [];
  53. msd(i) = mean(sqdisp);
  54. sem(i) = std(sqdisp)/sqrt(numel(sqdisp));
  55. end
  56. t = t(1:numel(msd));
  57. msd(sem==0 | isnan(sem)) = [];
  58. t(sem==0 | isnan(sem)) = [];
  59. sem(sem==0 | isnan(sem)) = [];
  61. if length(msd)>5;
  62. msd = msd*10^-6;
  63. sem = sem*10^-6;
  65. msdfun = fittype(@(D,x)4*D.*x);
  66. wght = 1./sqrt(sem);
  67. [fitresult,gof,~] = fit(t(1:ceil(end/2))',msd(1:ceil(end/2))',msdfun,'Weights',wght(1:ceil(end/2))',...
  68. 'Lower',0,'Upper',1000,'StartPoint',(msd(ceil(end/2))/msd(2))./(t(ceil(end/2))/t(2)));
  69. D = fitresult.D;
  70. else
  71. D = NaN;
  72. msd = [];sem = [];
  73. end
  75. rmc = D; %value in um^2/min
  76. traction = mean(trac);
  77. rgflow = mean(aflow);
  78. modulenum = mean(modnum);
  79. modulel = mean(sdist-mdist);
  80. csl = mean(mdist);
  81. gact = mean(gactin);
  83. % cell spread area calculations
  84. pix = 65.2;
  85. maxmodlength = max(sdist-mdist);
  86. numpix = round(maxmodlength/pix)*4;
  87. polymask = zeros(numpix,numpix);
  88. [q,p] = size(polymask);
  90. for i = 1:numel(mc)
  91. % polytheta = mc(i).theta;
  92. polyx = mc(i).xsub(1);
  93. polyy = mc(i).xsub(2);
  95. tang = real(sqrt((polyx-cellpos(1,end)).^2+(polyy-cellpos(2,end)).^2-nucr.^2))+1;
  96. [xout,yout] = circcirc(cellpos(1,end),cellpos(2,end),nucr,polyx,polyy,tang);
  98. if isfinite(sum([xout yout]))
  99. tmpmaskx = ([xout,polyx]-cellpos(1,end))/pix+p/2;
  100. tmpmasky = ([yout,polyy]-cellpos(2,end))/pix+q/2;
  101. trimask = poly2mask(tmpmaskx,tmpmasky,q,p);
  102. polymask = polymask+trimask;
  103. end
  104. end
  106. nucentx=p/2;
  107. nucenty=q/2;
  108. [x,y] = meshgrid(-(nucentx-1):(p-nucentx),-(nucenty-1):(q-nucenty));
  109. nucmask=((x.^2+y.^2)<=(nucr/pix)^2);
  110. cellmask = nucmask+polymask;
  111. cellmask = im2bw(cellmask,0);
  113. stats = regionprops(cellmask,'Area','MajorAxisLength','MinorAxisLength');
  114. area = max(stats.Area)*(pix^2)*(1/1000)^2;
  115. aratio = max(stats.MajorAxisLength)./max(stats.MinorAxisLength);
  116. end

analyzeCMSoutputv1_0.m at commit 72e9b46, under MIT · at the source

Overview

Authors: Ghaidan A Shamsan1, Chao J Liu1, Brooke C Braman1, Ruyi Li1, Susan K Rathe2, Aaron L Sarver2,3, Nima Ghaderi4, Mariah McMahon1, Rebecca L Klank1, Barbara R Tschida2, S Joseph McFarren2, Pamela C Rosato5, David Masopust5, Jann N Sarkaria6, H Brent Clark7, Steven Rosenfeld8, David A Largaespada2,9, David J Odde1
ORCID iDs: Ghaidan A Shamsan, Chao J Liu, Brooke C Braman, Ruyi Li, Susan K Rathe, Aaron L Sarver, Nima Ghaderi, Mariah McMahon, Rebecca L Klank, Barbara R Tschida, S Joseph McFarren, Pamela C Rosato, David Masopust, Jann N Sarkaria, H Brent Clark, Steven Rosenfeld, David A Largaespada, David J Odde
  1. Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
  2. Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
  3. Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota
  4. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota
  5. Department of Microbiology and Immunology, Center for Immunology, University of Minnesota, Minneapolis, Minnesota
  6. Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
  7. Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota
  8. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Jacksonville, Florida
  9. Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
Institutions: University of Minnesota (United States); Masonic Cancer Center; Mayo Clinic (United States); Mayo Clinic Comprehensive Cancer Center (Minnesota); Mayo Clinic in Florida (United States)
Journal: Cancer research communications, volume 6, issue 8, pages 1898-1915
Dates: received 21 September 2025; accepted 13 July 2026; published online 11 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1158/2767-9764.crc-25-0579 · PMID 42447444 · PMCID PMC13489672 · OpenAlex W7168294846
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population)
Methods: Statistics, Machine learning, Evoked potentials, Connectivity
MeSH: Brain Neoplasms*, Cell Movement*, Glioblastoma*, Animals, Cell Line, Tumor, Cell Proliferation, Humans, Mice (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: Children's Cancer Research Fund (CCRF); National Cancer Institute (NCI) (U54CA268069, P01CA254849, U54CA210180, U54CA210190); American Cancer Society (ACS); NCI NIH HHS (P01 CA254849, U54 CA268069, U54 CA210180, U54 CA210190)
Citations: not cited yet (Europe PMC); 64 references in the paper
Research resources: Both immunocompetent FVB/NJ WT RRID:IMSR_JAX:001800, RRID:IMSR_JAX:002216, Using a custom written MATLAB RRID:SCR_001622, RRID:SCR_003070

Abstract

Glioblastoma (GBM) remains a deadly cancer driven in part by invasion of tumor cells into the brain. Transcriptomic analyses have identified distinct molecular subtypes, but mechanistic differences that account for clinical differences are not clear. In this study, we show that, as predicted by the motor-clutch model of cell migration, mesenchymal glioma cells are more spread, generate larger traction forces, and migrate faster in brain tissue compared with proneural cells. Despite their rapid migration and comparable proliferation rates in vitro, mice with mesenchymal tumors survive longer than those with proneural tumors. This improved survival correlated with an immune response in mesenchymal tumors, including T cell–mediated. Consistently, inducing mesenchymal tumors in immunodeficient mice resulted in shorter survival, supporting a protective immune role in mesenchymal tumors. Thus, mesenchymal tumors have aggressive migration but are immunologically “hot,” which suppresses net proliferation. These two features counteract each other and may explain the lack of a strong survival difference between subtypes clinically, while also opening up new opportunities for subtype-specific therapies.

Significance: This study highlights new mechanical and immunologic insights into GBM molecular subtypes using an integrated modeling–genome engineering strategy, which can potentially facilitate GBM subtype-specific therapeutic strategies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

drive.google.com/file/d

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: drive.google.com/file/d/1T4FEdRsEchBCMSvWy8vhU4QvF7ABjVqn/view

davidodde/shamsan_etal_codes

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 72e9b467d58c28c328eef81857d9a025034babf2, 9 July 2026
Languages: MATLAB (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file), Optimization Toolbox (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
7 files
At the source: github.com/davidodde/shamsan_etal_codes

Code Availability

All data and codes are available on the Odde laboratory website (BDTS simulation and analysis codes are at https://drive.google.com/file/d/1T4FEdRsEchBCMSvWy8vhU4QvF7ABjVqn/view and https://drive.google.com/file/d/1MdPpuBp4HZFKuTBRAV028EHsrenII7-1/view, respectively). The CMS code is available at https://drive.google.com/file/d/1Qx0xvYhsWSrUPC8zXUmmtMW2ZTOzh-Tk/view. Both BDTS and CMS codes are also located at https://github.com/davidodde/shamsan_etal_codes. Codes are also available upon request to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

Fastq files and the Cuffnorm output were deposited at Gene Expression Omnibus (GSE161154 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161154)).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 8 MeSH terms, 4 funders, 64 references, 4 RRIDs.

Cite

This paper

Shamsan, G. A., Liu, C. J., Braman, B. C., Li, R., Rathe, S. K., Sarver, A. L., Ghaderi, N., McMahon, M., Klank, R. L., Tschida, B. R., McFarren, S. J., Rosato, P. C., Masopust, D., Sarkaria, J. N., Clark, H. B., Rosenfeld, S., Largaespada, D. A., & Odde, D. J. (2026). Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses. Cancer research communications, 6(8), 1898-1915. https://doi.org/10.1158/2767-9764.crc-25-0579

BibTeX

@article{shamsan2026glioblastoma,
author = {Shamsan, Ghaidan A and Liu, Chao J and Braman, Brooke C and Li, Ruyi and Rathe, Susan K and Sarver, Aaron L and Ghaderi, Nima and McMahon, Mariah and Klank, Rebecca L and Tschida, Barbara R and McFarren, S Joseph and Rosato, Pamela C and Masopust, David and Sarkaria, Jann N and Clark, H Brent and Rosenfeld, Steven and Largaespada, David A and Odde, David J},
title = {{Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses}},
journal = {Cancer research communications},
year = {2026},
month = aug,
volume = {6},
number = {8},
pages = {1898--1915},
publisher = {American Association for Cancer Research},
issn = {2767-9764},
doi = {10.1158/2767-9764.crc-25-0579},
url = {https://doi.org/10.1158/2767-9764.crc-25-0579},
pmid = {42447444},
pmcid = {PMC13489672}
}

RIS

TY - JOUR
AU - Shamsan, Ghaidan A
AU - Liu, Chao J
AU - Braman, Brooke C
AU - Li, Ruyi
AU - Rathe, Susan K
AU - Sarver, Aaron L
AU - Ghaderi, Nima
AU - McMahon, Mariah
AU - Klank, Rebecca L
AU - Tschida, Barbara R
AU - McFarren, S Joseph
AU - Rosato, Pamela C
AU - Masopust, David
AU - Sarkaria, Jann N
AU - Clark, H Brent
AU - Rosenfeld, Steven
AU - Largaespada, David A
AU - Odde, David J
TI - Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses
T2 - Cancer research communications
J2 - Cancer Res Commun
PY - 2026
DA - 2026/08/01
VL - 6
IS - 8
SP - 1898
EP - 1915
SN - 2767-9764
PB - American Association for Cancer Research
DO - 10.1158/2767-9764.crc-25-0579
UR - https://doi.org/10.1158/2767-9764.crc-25-0579
LA - en
ER -

CSL-JSON

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