The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies
Open accessAbstract
SUMMARY Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies which efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Concomitant suppression of three target genes can explain the impact of a microRNA on metastasisPreprint
Valastyan, S., Benaich, N., Chang, A., Reinhardt, F., & Weinberg, R. A. (2009) · Genes & Development · doi:10.1101/gad.1832709 · Cited by 105
RetractedJemal, A., Siegel, R., Xu, J., & Ward, E. (2010) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20073 · Cited by 12,596
Spencer, P. M., Cameron, J. L., & Lin, L. (2000) · Cited by 7,202
Genetic instabilities in human cancers
Lengauer, C., Kinzler, K. W., & Vogelstein, B. (1998) · Nature · doi:10.1038/25292 · Cited by 4,168 · cited 1×
“…of the mutation rate, 10 8 < u < 10 4; this choice was made since experimental evidence suggests that the mutation rate per base per cell division is about 10 8 in genetically stable cells and about 10 4 in cells with microsatellite instability (Lengauer et al., 1997, 1998; Seshadri et al., 1987).”
BackgroundCore Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses
Jones, S., Zhang, X., Parsons, D. W., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Kamiyama, H., Jimeno, A., et al. (2008) · Science · doi:10.1126/science.1164368 · Cited by 3,969
Self-citationTransitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits
Polyak, K., & Weinberg, R. A. (2009) · Nature Reviews Cancer · doi:10.1038/nrc2620 · Cited by 3,302
A Randomized Trial of Chemoradiotherapy and Chemotherapy after Resection of Pancreatic Cancer
Neoptolemos, J. P., Stocken, D. D., Friess, H., Bassi, C., Dunn, J. A., Hickey, H., Beger, H., Fernandez-Cruz, L., Dervenis, C., Lacaine, F., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa032295 · Cited by 2,716
Metastasis: from dissemination to organ-specific colonization
Nguyen, D. X., Bos, P. D., & Massagué, J. (2009) · Nature Reviews Cancer · doi:10.1038/nrc2622 · Cited by 2,649
Hidalgo, M. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0901557 · Cited by 2,570
Distant metastasis occurs late during the genetic evolution of pancreatic cancer
Yachida, S., Jones, S., Bozic, I., Antal, T., Leary, R., Fu, B., Kamiyama, M., Hruban, R. H., Eshleman, J. R., Nowak, M. A., et al. (2010) · Nature · doi:10.1038/nature09515 · Cited by 2,502
Self-citation
Cited by
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
Toward understanding and exploiting tumor heterogeneity
Alizadeh, A. A., Aranda, V., Bardelli, A., Blanpain, C., Bock, C., Borowski, C., Caldas, C., Califano, A., Doherty, M., Elsner, M., et al. (2015) · Nature Medicine · doi:10.1038/nm.3915 · Cited by 746
A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns
Notta, F., Chan-Seng-Yue, M., Lemire, M., Li, Y., Wilson, G. W., Connor, A. A., Denroche, R. E., Liang, S.-B., Brown, A. M. K., Kim, J. C., et al. (2016) · Nature · doi:10.1038/nature19823 · Cited by 528
Pancreatic cancer evolution and heterogeneity: integrating omics and clinical data
Connor, A. A., & Gallinger, S. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00418-1 · Cited by 369
Boone, B. A., Bahary, N., Zureikat, A. H., Moser, A. J., Normolle, D. P., Wu, W.-C., Singhi, A. D., Bao, P., Bartlett, D. L., Liotta, L. A., et al. (2015) · Annals of Surgical Oncology · doi:10.1245/s10434-015-4566-4 · Cited by 234
Metadata
- Authors
- Haeno, H., Gonen, M., Davis, M. B., Herman, J. M., Iacobuzio-Donahue, C. A., & Michor, F.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 148
- Issue
- 1-2
- Pages
- 362-375
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2150550712
- Cited by
- 409
- References
- 41
- FWCI
- 46.52
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies. https://app.cochat.ai/papers/10.1016/j.cell.2011.11.060
- The paper
- Haeno, H., Gonen, M., Davis, M. B., Herman, J. M., Iacobuzio-Donahue, C. A., & Michor, F. (2012). Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies. Cell. https://doi.org/10.1016/j.cell.2011.11.060
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
