Model-based inference of metastatic seeding rates in de novo metastatic breast cancer reveals the impact of secondary seeding and molecular subtype.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 06 2022
08 06 2022
Historique:
received:
09
09
2021
accepted:
08
03
2022
entrez:
8
6
2022
pubmed:
9
6
2022
medline:
11
6
2022
Statut:
epublish
Résumé
We present a stochastic network model of metastasis spread for de novo metastatic breast cancer, composed of tumor to metastasis (primary seeding) and metastasis to metastasis spread (secondary seeding), parameterized using the SEER (Surveillance, Epidemiology, and End Results) database. The model provides a quantification of tumor cell dissemination rates between the tumor and metastasis sites. These rates were used to estimate the probability of developing a metastasis for untreated patients. The model was validated using tenfold cross-validation. We also investigated the effect of HER2 (Human Epidermal Growth Factor Receptor 2) status, estrogen receptor (ER) status and progesterone receptor (PR) status on the probability of metastatic spread. We found that dissemination rate through secondary seeding is up to 300 times higher than through primary seeding. Hormone receptor positivity promotes seeding to the bone and reduces seeding to the lungs and primary seeding to the liver, while HER2 expression increases dissemination to the bone, lungs and primary seeding to the liver. Secondary seeding from the lungs to the liver seems to be hormone receptor-independent, while that from the lungs to the brain appears HER2-independent.
Identifiants
pubmed: 35676303
doi: 10.1038/s41598-022-12500-1
pii: 10.1038/s41598-022-12500-1
pmc: PMC9177582
doi:
Substances chimiques
Biomarkers, Tumor
0
Hormones
0
Receptors, Estrogen
0
Receptors, Progesterone
0
Receptor, ErbB-2
EC 2.7.10.1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9455Informations de copyright
© 2022. The Author(s).
Références
Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).
pubmed: 33433946
doi: 10.3322/caac.21654
Valastyan, S. & Weinberg, R. A. Tumor metastasis: Molecular insights and evolving paradigms. Cell 147, 275–292 (2011).
pubmed: 22000009
pmcid: 3261217
doi: 10.1016/j.cell.2011.09.024
Meng, S. et al. Circulating tumor cells in patients with breast cancer dormancy. Clin. Cancer Res. 10, 8152–8162 (2004).
pubmed: 15623589
doi: 10.1158/1078-0432.CCR-04-1110
Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J. & Clarke, M. F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. 100, 3983–3988 (2003).
pubmed: 12629218
pmcid: 153034
doi: 10.1073/pnas.0530291100
Benzekry, S., Sentis, C., Coze, C., Tessonnier, L. & Andre, N. Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma. MedRxiv (2020).
Newton, P. K. et al. A stochastic Markov chain model to describe lung cancer growth and metastasis. PLoS ONE 7, e34637 (2012).
pubmed: 22558094
pmcid: 3338733
doi: 10.1371/journal.pone.0034637
Scott, J., Kuhn, P. & Anderson, A. R. Unifying metastasis-integrating intravasation, circulation and end-organ colonization. Nat. Rev. Cancer 12, 445–446 (2012).
pubmed: 22912952
pmcid: 4533867
doi: 10.1038/nrc3287
Gerlee, P. & Johansson, M. Inferring rates of metastatic dissemination using stochastic network models. PLoS Comput. Biol. 15, e1006868 (2019).
pubmed: 30933969
pmcid: 6459558
doi: 10.1371/journal.pcbi.1006868
Disibio, G. & French, S. W. Metastatic patterns of cancers: Results from a large autopsy study. Arch. Pathol. Labor. Med. 132, 931–939 (2008).
doi: 10.5858/2008-132-931-MPOCRF
Hoadley, K. A. et al. Tumor evolution in two patients with basal-like breast cancer: A retrospective genomics study of multiple metastases. PLoS Med. 13, e1002174 (2016).
pubmed: 27923045
pmcid: 5140046
doi: 10.1371/journal.pmed.1002174
Seer*stat software. https://seer.cancer.gov/seerstat/ . Updated: 2021-07-23.
Sharma, A. et al. Patterns of lymphadenopathy in thoracic malignancies. Radiographics 24, 419–434 (2004).
pubmed: 15026591
doi: 10.1148/rg.242035075
Fregnani, J. H. T. G. & Macéa, J. R. Lymphatic drainage of the breast: From theory to surgical practice. Int. J. Morphol. 27, 873–878 (2009).
doi: 10.4067/S0717-95022009000300038
Suami, H., Pan, W.-R., Mann, G. B. & Taylor, G. I. The lymphatic anatomy of the breast and its implications for sentinel lymph node biopsy: A human cadaver study. Ann. Surg. Oncol. 15, 863–871 (2008).
pubmed: 18043970
doi: 10.1245/s10434-007-9709-9
Byrd, D. R. et al. Internal mammary lymph node drainage patterns in patients with breast cancer documented by breast lymphoscintigraphy. Ann. Surg. Oncol. 8, 234–240 (2001).
pubmed: 11314940
doi: 10.1007/s10434-001-0234-y
Urano, M. et al. Internal mammary lymph node metastases in breast cancer: What should radiologists know?. Jpn. J. Radiol. 36, 629–640 (2018).
pubmed: 30194586
doi: 10.1007/s11604-018-0773-9
de la Pared Torácica, A. & y Mama, A. Anatomy of the thoracic wall, axilla and breast. Int. J. Morphol. 24, 691–704 (2006).
Batson, O. V. The role of the vertebral veins in metastatic processes. Ann. Internal Med. 16, 38–45 (1942).
doi: 10.7326/0003-4819-16-1-38
Panikkath, R. et al. Metastasis of lung cancer through Batson’s plexus: Very rare but possible. Southwest Respir. Crit. Care Chron. 1, 45–49 (2013).
doi: 10.12746/swrccc.v1i4.84
Maccauro, G. et al. Physiopathology of spine metastasis. Int. J. Surg. Oncol. 2011, 107969 (2011).
pubmed: 22312491
pmcid: 3265280
Thomas, J., Redding, W. H. & Sloane, J. The spread of breast cancer: Importance of the intrathoracic lymphatic route and its relevance to treatment. Brit. J. Cancer 40, 540–547 (1979).
pubmed: 497105
pmcid: 2010083
doi: 10.1038/bjc.1979.219
Cresswell, G. D. et al. Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking. Nat. Commun. 11, 1–12 (2020).
doi: 10.1038/s41467-020-15047-9
El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Nat. Genet. 50, 718–726 (2018).
pubmed: 29700472
pmcid: 6103651
doi: 10.1038/s41588-018-0106-z
Echeverria, G. V. et al. High-resolution clonal mapping of multi-organ metastasis in triple negative breast cancer. Nat. Commun. 9, 1–17 (2018).
doi: 10.1038/s41467-018-07406-4
Stutte, H. Soft-tissue sonography in the follow-up care of breast cancer: Indications of liver metastases caused by lymphatic spread. Ultraschall in der Medizin (Stuttgart, Germany: 1980) 20, 150–157 (1999).
Wilson, M. A. & Calhoun, F. W. The distribution of skeletal metastases in breast and pulmonary cancer: Concise communication. J. Nucl. Med. Off. Publ. Soc. Nucl. Med. 22, 594–597 (1981).
Macedo, F. et al. Bone metastases: An overview. Oncol. Rev. 11, 321 (2017).
pubmed: 28584570
pmcid: 5444408
Achrol, A. S. et al. Brain metastases. Nat. Rev. Disease Prim. 5, 1–26 (2019).
Tsukada, Y., Fouad, A., Pickren, J. W. & Lane, W. W. Central nervous system metastasis from breast carcinoma autopsy study. Cancer 52, 2349–2354 (1983).
pubmed: 6640506
doi: 10.1002/1097-0142(19831215)52:12<2349::AID-CNCR2820521231>3.0.CO;2-B
Viadana, E., Bross, I. & Pickren, J. An autopsy study of some routes of dissemination of cancer of the breast. Brit. J. Cancer 27, 336–340 (1973).
pubmed: 4701705
pmcid: 2008793
doi: 10.1038/bjc.1973.40
Ullah, I. et al. Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes. J. Clin. Investig. 128, 1355–1370 (2018).
pubmed: 29480816
pmcid: 5873882
doi: 10.1172/JCI96149
Carter, C. L., Allen, C. & Henson, D. E. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 63, 181–187 (1989).
pubmed: 2910416
doi: 10.1002/1097-0142(19890101)63:1<181::AID-CNCR2820630129>3.0.CO;2-H
Hartkopf, A. D. et al. Bone marrow versus sentinel lymph node involvement in breast cancer: A comparison of early hematogenous and early lymphatic tumor spread. Breast Cancer Res. Treatment 131, 501–508 (2012).
doi: 10.1007/s10549-011-1802-x
Friberg, S. & Mattson, S. On the growth rates of human malignant tumors: Implications for medical decision making. J. Surg. Oncol. 65, 284–297 (1997).
pubmed: 9274795
doi: 10.1002/(SICI)1096-9098(199708)65:4<284::AID-JSO11>3.0.CO;2-2
Tyuryumina, E. Y. & Neznanov, A. A. Consolidated mathematical growth model of the primary tumor and secondary distant metastases of breast cancer (compas). PLoS One 13, e0200148 (2018).
pubmed: 29979733
pmcid: 6034839
doi: 10.1371/journal.pone.0200148
Gerlee, P. The model muddle: In search of tumor growth laws. Cancer Res. 73, 2407–2411 (2013).
pubmed: 23393201
doi: 10.1158/0008-5472.CAN-12-4355
Vaghi, C. et al. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Comput. Biol. 16, e1007178 (2020).
pubmed: 32097421
pmcid: 7059968
doi: 10.1371/journal.pcbi.1007178
Benzekry, S. et al. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput. Biol. 10, e1003800 (2014).
pubmed: 25167199
pmcid: 4148196
doi: 10.1371/journal.pcbi.1003800
Brunton, G. & Wheldon, T. Characteristic species dependent growth patterns of mammalian neoplasms. Cell Prolif. 11, 161–175 (1978).
doi: 10.1111/j.1365-2184.1978.tb00884.x
Norton, L. A Gompertzian model of human breast cancer growth. Cancer Res. 48, 7067–7071 (1988).
pubmed: 3191483
Ryu, E. B. et al. Tumour volume doubling time of molecular breast cancer subtypes assessed by serial breast ultrasound. Eur. Radiol. 24, 2227–2235 (2014).
pubmed: 24895040
doi: 10.1007/s00330-014-3256-0
Mooney, C. F., Mooney, C. Z., Mooney, C. L., Duval, R. D. & Duvall, R. Bootstrapping: A Nonparametric Approach to Statistical Inference (Sage, 1993).
doi: 10.4135/9781412983532
Scott, J. G., Basanta, D., Anderson, A. R. & Gerlee, P. A mathematical model of tumour self-seeding reveals secondary metastatic deposits as drivers of primary tumour growth. J. R. Soc. Interface 10, 20130011 (2013).
pubmed: 23427099
pmcid: 3627086
doi: 10.1098/rsif.2013.0011
Newton, P. K. et al. Spreaders and sponges define metastasis in lung cancer: A Markov chain Monte Carlo mathematical model. Cancer Res. 73, 2760–2769 (2013).
pubmed: 23447576
pmcid: 3644026
doi: 10.1158/0008-5472.CAN-12-4488
Leggett, R., Eckerman, K. & Williams, L. A blood circulation model for reference man. Tech. Rep., Oak Ridge National Lab., TN (United States) (1996).
Arriagada, R., Rutqvist, L.-E., Johansson, H., Kramar, A. & Rotstein, S. Predicting distant dissemination in patients with early breast cancer. Acta Oncol. 47, 1113–1121 (2008).
pubmed: 18607866
doi: 10.1080/02841860701829661
Seltzer, S., Corrigan, M. & O’Reilly, S. The clinicomolecular landscape of de novo versus relapsed stage IV metastatic breast cancer. Exp. Mol. Pathol. 114, 104404 (2020).
pubmed: 32067942
doi: 10.1016/j.yexmp.2020.104404