Metastasis is the main cause of death in cancer patients and often epithelial-to-mesenchymal transition (EMT) is advocated as the basic mechanism. Recently Fang and colleagues described an EMT-independent process of metastasis in hepatocellular carcinoma (HCC): endothelium covers small cluster of tumor cells allowing tumor dissemination. This process of angiogenesis, named VETC (vessels that encapsulate tumor clusters) in HCC literature, has been described under different names in other cancer types. Furthermore, the investigators confirmed the negative impact of VETC on patients' prognosis on a large multicenter cohort of HCCs. Moreover, Fang et al demonstrated that patients affected by VETC-positive HCC benefit more from sorafenib therapy. Interestingly, this type of angiogenesis was also found in renal cell carcinoma, adrenal gland pheochromocytoma, thyroid follicular carcinoma and alveolar soft part sarcoma (ASPS) and associated to prognosis. Moreover, the distinction between benign and malignant neoplasms of the adrenal gland is a complex matter, being the established criteria still lacking a strong reproducibility.
Several tyrosine kinase inhibitors are available for different cancer types; among them, HCC, RCC, ASPS, and TC may benefit from the so-called antiangiogenic tyrosine kinase inhibitors (aTKI) (such as sunitinib, sorafenib, pazopanib). A general (histotype-independent) validation of the prognostic role of VETC is missing. Moreover, inhibitors of tyrosine-kinase vascular endothelial growth factor receptors (VEGFR-TKI), represent an effective treatment for different cancer types, but predictive markers are still needed. In addition, novel systemic immunotherapy agents are being approved in many cancer types, as alternative to angiogenesis inhibitors. A broader frame including metastatic mechanisms, tumor microenvironment (TME, i.e. angiogenesis and immune infiltrate) and treatment response could answer to several needs currently unmet. Bayesian networks and causal models can be employed to effectively draw conclusions from retrospective data.
The aim of the present study is to investigate in patients with RCC and adrenal carcinoma (AC) the VETC-expression on tumor tissue, correlating the results with clinical data, patients characteristics, and outcome.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
For all series, clinical and epidemiological features will be recorded, all available histological slides will be reviewed and, on the primary tumor slides, histological characteristics will be re-assessed.
Whenever multiple samples of tumors would be present, those having the tumor-surrounding tissue interface will be selected and stained with CD34 antibody.
VETC will be evaluated independently by, at least, two pathologists, blinded to clinical data. VETC will be recorded as positive or negative, being VETC defined as CD34 unequivocal immunoreactivity of a continuous lining of endothelial cells around tumor clusters. VETC will be considered alternative to the common capillary pattern, consisting in small circular or linear blood vessels.
interventionNames
Other: VETC evaluation
label
Adrenal carcinoma
description
see (RCC)
interventionNames
Other: VETC evaluation
Primary outcomes (1)
measure
Eligibility
Eligibility (as posted)
Sex
All
Show eligibility criteria text
Inclusion Criteria:
1. Histological diagnosis of Renal Cell Carcinoma;
2. Histological diagnosis of Carcinoma of the adrenal gland;
3. Availability of histological material;
4. For the evaluation of the prognostic role: no systemic treatment with TKI administered before surgery.
Exclusion Criteria:
1. Unavailable histological material;
2. For RCC: histological diagnosis different from Clear Cell histotype.
References
Publications (36)
BACKGROUNDKalluri R, Weinberg RA. The basics of epithelial-mesenchymal transition. J Clin Invest. 2009 Jun;119(6):1420-8. doi: 10.1172/JCI39104. PMID 19487818
BACKGROUNDLedford H. Cancer theory faces doubts. Nature. 2011 Apr 21;472(7343):273. doi: 10.1038/472273a. No abstract available. PMID 21512545
BACKGROUNDFang JH, Zhou HC, Zhang C, Shang LR, Zhang L, Xu J, Zheng L, Yuan Y, Guo RP, Jia WH, Yun JP, Chen MS, Zhang Y, Zhuang SM. A novel vascular pattern promotes metastasis of hepatocellular carcinoma in an epithelial-mesenchymal transition-independent manner. Hepatology. 2015 Aug;62(2):452-65. doi: 10.1002/hep.27760. Epub 2015 Apr 22. PMID 25711742
BACKGROUNDCarmeliet P, Jain RK. Molecular mechanisms and clinical applications of angiogenesis. Nature. 2011 May 19;473(7347):298-307. doi: 10.1038/nature10144. PMID 21593862
BACKGROUNDRenne SL, Woo HY, Allegra S, Rudini N, Yano H, Donadon M, Vigano L, Akiba J, Lee HS, Rhee H, Park YN, Roncalli M, Di Tommaso L. Vessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma. Hepatology. 2020 Jan;71(1):183-195. doi: 10.1002/hep.30814. Epub 2019 Aug 9. PMID 31206715
BACKGROUNDFang JH, Xu L, Shang LR, Pan CZ, Ding J, Tang YQ, Liu H, Liu CX, Zheng JL, Zhang YJ, Zhou ZG, Xu J, Zheng L, Chen MS, Zhuang SM. Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Predictor of Sorafenib Benefit in Patients with Hepatocellular Carcinoma. Hepatology. 2019 Sep;70(3):824-839. doi: 10.1002/hep.30366. Epub 2019 Mar 15. PMID 30506570
BACKGROUNDSugino T, Yamaguchi T, Hoshi N, Kusakabe T, Ogura G, Goodison S, Suzuki T. Sinusoidal tumor angiogenesis is a key component in hepatocellular carcinoma metastasis. Clin Exp Metastasis. 2008;25(7):835-41. doi: 10.1007/s10585-008-9199-6. Epub 2008 Aug 20.
VETC in RCC and AC.
timeFrame
2-3 months
description
To identify the expression of VETC in Renal Cell Carcinoma and Adrenal Carcinoma.
BACKGROUNDLopez JI, Erramuzpe A, Guarch R, Cortes JM, Pulido R, Llarena R, Angulo JC. CD34 immunostaining enhances a distinct pattern of intratumor angiogenesis with prognostic implications in clear cell renal cell carcinoma. APMIS. 2017 Feb;125(2):128-133. doi: 10.1111/apm.12649. PMID 28120493
BACKGROUNDSetsu N, Yoshida A, Takahashi F, Chuman H, Kushima R. Histological analysis suggests an invasion-independent metastatic mechanism in alveolar soft part sarcoma. Hum Pathol. 2014 Jan;45(1):137-42. doi: 10.1016/j.humpath.2013.07.045. PMID 24321522
BACKGROUNDBrose MS, Nutting CM, Jarzab B, Elisei R, Siena S, Bastholt L, de la Fouchardiere C, Pacini F, Paschke R, Shong YK, Sherman SI, Smit JW, Chung J, Kappeler C, Pena C, Molnar I, Schlumberger MJ; DECISION investigators. Sorafenib in radioactive iodine-refractory, locally advanced or metastatic differentiated thyroid cancer: a randomised, double-blind, phase 3 trial. Lancet. 2014 Jul 26;384(9940):319-28. doi: 10.1016/S0140-6736(14)60421-9. Epub 2014 Apr 24. PMID 24768112
BACKGROUNDMotzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Rixe O, Oudard S, Negrier S, Szczylik C, Kim ST, Chen I, Bycott PW, Baum CM, Figlin RA. Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med. 2007 Jan 11;356(2):115-24. doi: 10.1056/NEJMoa065044. PMID 17215529
BACKGROUNDPaoluzzi L, Maki RG. Diagnosis, Prognosis, and Treatment of Alveolar Soft-Part Sarcoma: A Review. JAMA Oncol. 2019 Feb 1;5(2):254-260. doi: 10.1001/jamaoncol.2018.4490. PMID 30347044
BACKGROUNDSternberg CN, Davis ID, Mardiak J, Szczylik C, Lee E, Wagstaff J, Barrios CH, Salman P, Gladkov OA, Kavina A, Zarba JJ, Chen M, McCann L, Pandite L, Roychowdhury DF, Hawkins RE. Pazopanib in locally advanced or metastatic renal cell carcinoma: results of a randomized phase III trial. J Clin Oncol. 2010 Feb 20;28(6):1061-8. doi: 10.1200/JCO.2009.23.9764. Epub 2010 Jan 25. PMID 20100962
BACKGROUNDPearl, J. Causality. (Cambridge university press, 2009).
BACKGROUNDvan der Zander, B., Liskiewicz, M. & Textor, J. Constructing Separators and Adjustment Sets in Ancestral Graphs. in Proceedings of UAI 11-24 (2014).
BACKGROUNDPerković, E., Textor, J., Kalisch, M. & Maathuis, M. H. A complete generalized adjustment criterion. arXiv Prepr. arXiv1507.01524 (2015).
BACKGROUNDR Core Team. R: A Language and Environment for Statistical Computing. (2019)
BACKGROUNDCarpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker MA, Guo J, Li P, Riddell A. Stan: A Probabilistic Programming Language. J Stat Softw. 2017;76:1. doi: 10.18637/jss.v076.i01. Epub 2017 Jan 11. PMID 36568334
BACKGROUNDDuane, S., Kennedy, A. D., Pendleton, B. J. & Roweth, D. Hybrid Monte Carlo. Phys. Lett. B 195, 216-222 (1987).
BACKGROUNDMaginn EJ, Siepmann JI, Fichthorn KA, Frenkel D, Okamoto Y, Krauth W, Filippi C. Monte Carlo methods, 70 years after "Equation of state calculations by fast computing machines" by Nicholas Metropolis, Arianna Rosenbluth, Marshall Rosenbluth, Augusta Teller, and Edward Teller (1953). J Chem Phys. 2025 Dec 7;163(21):210402. doi: 10.1063/5.0309018. No abstract available. PMID 41342507
BACKGROUNDHoffman, M. D. & Gelman, A. The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593-1623 (2014).
BACKGROUNDGelman, A. et al. Bayesian data analysis. (CRC press, 2013).
BACKGROUNDMcElreath, R. Statistical rethinking: A Bayesian course with examples in R and Stan. (CRC press, 2020).
BACKGROUNDGelman, A. Discussion paper analysis of variance - Why it is more important than ever. Ann. Stat. 33, 1-53 (2005).
BACKGROUNDPapaspiliopoulos, O., Roberts, G. O. & Sköld, M. A general framework for the parametrization of hierarchical models. Stat. Sci. 22, 59-73 (2007).
BACKGROUNDWatanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571-3594 (2010).
BACKGROUNDRubin, D. B. Inference and missing data. Biometrika 63, 581-592 (1976).
BACKGROUNDLittle, R. J. A. & Rubin, D. B. Statistical Analysis with Missing Data. (Wiley, 2019).
BACKGROUNDHeng DY, Xie W, Regan MM, Warren MA, Golshayan AR, Sahi C, Eigl BJ, Ruether JD, Cheng T, North S, Venner P, Knox JJ, Chi KN, Kollmannsberger C, McDermott DF, Oh WK, Atkins MB, Bukowski RM, Rini BI, Choueiri TK. Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study. J Clin Oncol. 2009 Dec 1;27(34):5794-9. doi: 10.1200/JCO.2008.21.4809. Epub 2009 Oct 13. PMID 19826129
BACKGROUNDMekhail TM, Abou-Jawde RM, Boumerhi G, Malhi S, Wood L, Elson P, Bukowski R. Validation and extension of the Memorial Sloan-Kettering prognostic factors model for survival in patients with previously untreated metastatic renal cell carcinoma. J Clin Oncol. 2005 Feb 1;23(4):832-41. doi: 10.1200/JCO.2005.05.179. PMID 15681528
BACKGROUNDKarnofsky DA Burchenal JH. (1949). 'The Clinical Evaluation of Chemotherapeutic Agents in Cancer.' In: MacLeod CM (Ed), Evaluation of Chemotherapeutic Agents. Columbia Univ Press. Page 196.
BACKGROUNDAubert S, Wacrenier A, Leroy X, Devos P, Carnaille B, Proye C, Wemeau JL, Lecomte-Houcke M, Leteurtre E. Weiss system revisited: a clinicopathologic and immunohistochemical study of 49 adrenocortical tumors. Am J Surg Pathol. 2002 Dec;26(12):1612-9. doi: 10.1097/00000478-200212000-00009. PMID 12459628
BACKGROUNDOudijk L, van Nederveen F, Badoual C, Tissier F, Tischler AS, Smid M, Gaal J, Lepoutre-Lussey C, Gimenez-Roqueplo AP, Dinjens WN, Korpershoek E, de Krijger R, Favier J. Vascular pattern analysis for the prediction of clinical behaviour in pheochromocytomas and paragangliomas. PLoS One. 2015 Mar 20;10(3):e0121361. doi: 10.1371/journal.pone.0121361. eCollection 2015. PMID 25794004
BACKGROUNDFavier J, Plouin PF, Corvol P, Gasc JM. Angiogenesis and vascular architecture in pheochromocytomas: distinctive traits in malignant tumors. Am J Pathol. 2002 Oct;161(4):1235-46. doi: 10.1016/S0002-9440(10)64400-8. PMID 12368197
BACKGROUNDLau SK, Weiss LM. The Weiss system for evaluating adrenocortical neoplasms: 25 years later. Hum Pathol. 2009 Jun;40(6):757-68. doi: 10.1016/j.humpath.2009.03.010. PMID 19442788