Background and introduction~Metastasis is the main cause of death in cancer patients and often epithelial-to-mesenchymal transition (EMT) is advocated as the basic mechanism, although some limitations have been identified [1,2] Recently Fang and colleagues described an EMT-independent process of metastasis in hepatocellular carcinoma (HCC): endothelium (highlighted by CD34 immunohistochemistry) covers small cluster of tumor cells allowing tumor dissemination [3]. This process of angiogenesis, named VETC (vessels that encapsulate tumor clusters) in HCC literature, has been described under different names in other cancer types [4]. Furthermore, the investigators confirmed the negative impact of VETC on patients' prognosis on a large multicenter cohort of HCCs [5]. Moreover, Fang et al demonstrated that patients affected by VETC-positive HCC benefit more from sorafenib therapy [6]. 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 [7-10, 34,35]. 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 [36].~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) [11-14].~Rationale of the study 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. Moreover, 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.~Objectives of the study General objectives~The systematic investigation of VETC in RCC and AC in order to depict the impact of this phenomenon.~To explore the possible role of TME and in particular of VETC in predicting a more beneficial response to VEGFR-TKIs, providing a new tool in guiding the therapeutic choice.~Study Design The study is monocentric, observational, and it will be performed on clinical and histological data collected in the course of study.~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.~Statistical considerations The project plans to collect data of 100 of patients who underwent surgery for RCC at our institution between 2005 and 2007 for the evaluation of VETC impact on prognosis, and data of 60 patients who received sunitinib or pazopanib as first-line treatment for RCC at our center to explore if patients with VETC vascular phenotype would benefit more from the treatment with TKIs. Furthermore, the investigators will collect data of 20 patients who underwent surgery for AC at our Institution between 2000 and 2018.~Bayesian Analysis Directed acyclic graphs will be constructed with available scientific information; adjustment sets and conditional independencies will be calculated [15-17]. Prior predictive simulations, when relevant, will be deployed to regularize the prior and reduce overfitting. Continuous variables will be standardized to facilitate sampling. Models will be fit using Stan (a probabilistic programming language) and R [18,19]. Stan runs a No U-Turn sampler, an extension to Hamiltonian Monte Carlo (HMC) sampling, which is itself a form of Markov Chain Monte Carlo [20-22]. Four chains for 4000 iterations, or 8000 whenever the bulk effective sample size will be low, will be generated. The final 2000 (or 4000) iterations of each chain converge as indicated by post-modeling diagnostics such as the number of effective Gelman-Rubin R ̂.[23] A satisfactory posterior predictive model performance will be ensured before using sample means (for estimates) and sample quantiles (for compatibility intervals (CI)) [23,24]. CI will be calculated as 89% of the highest posterior density interval (HDPI) [23]. Whenever more clusters of data would be present, the investigators will use varying effects multilevel (hierarchical) models [25]. To limit divergent transitions, the investigators will reparameterize the models with a non-centered equivalent form [26] Predictive accuracy will be measured trough widely applicable information criteria (WAIC) [27].~Withdrawal of subjects Missing data will be treated modeling the missingness process. [28-29]~Forms and procedures for collecting data and data managing To each subject will be assigned a sequential identification number. For each subject data will be collected in a case report form (CRF). CRF will include SIN, name, sex, date of birth, date of primary surgery, side , size, histotype, relevant grading, necrosis, lymphovascular invasion , R , stage, date of TKI therapy, date of disease progression, prognostic scores (IMDC score [30], MSKCC score [31]), Karnofsky score [32], first-last line data (type, dates of beginning and end, best response, progression date), last follow-up status, last contact, death, VETC. All data will be registered in Microsoft Excel spreadsheet format. Data are collected by the data manager and database base will be locked with a password. Spaces will be filled with NA whenever a characteristic was not explored or an item is not applicable to the individual case.~For AC, CRF will include SIN, name, sex, date of birth, date of primary surgery, side, size, and prognostic criteria for malignancy based on Weiss Classification mod [33].~Ethical considerations Patient protection The responsible investigator will ensure that this study will be conducted in agreement with either the Declaration of Helsinki (Tokyo, Venice, Hong Kong and Somerset West amendments) or the laws and regulations of the country.~The protocol has been written, and the study will be conducted according to the institutional (ICH) Guideline for Good Clinical Practice The protocol and its annexes were subject to review and approval by the competent Independent Ethics Committee(s) (IEC).~Subject identification - Personal Data protection All records identifying the subject must be kept confidential and, to the extent permitted by the applicable laws and/or regulations, not be made publicly available. The name of the patient will not be asked for nor recorded at the Data Center. A sequential identification number will be automatically attributed to each patient registered in the study. This number will identify the patient and must be included on all case report forms. In order to avoid identification errors, patient initials and date of birth will also be reported on the case report forms.~Any and all patient information or documentation pertaining to a clinical trial, to the extent permitting, through a key kept anywhere, regardless of whether such key is supplied along with the information or documentation or not, must be considered as containing sensitive personal data of the patient, and is therefore subjected to the provisions of applicable data protection (privacy) regulations. Breach of such regulations may result in administrative or even criminal sanctions.~Patient information or documentation may be considered anonymous, and as such not subject to privacy regulations, only when no key whatsoever, permitting the identification of the patient, is any longer available.~Informed consent All patients will be informed of the aims of the study. They will be informed as to the strict confidentiality of their patient data, but that their medical records may be reviewed for study purposes by authorized individuals other than their treating physician.It will be emphasized that the participation is voluntary and that the patient is allowed to refuse further participation in the protocol whenever he/she wants. This will not prejudice the patient's subsequent care. Documented informed consent must be obtained for all patients included in the study before they are registered at the Data Center. This must be done in accordance with the national and local regulatory requirements. For European Union member states, the informed consent procedure must conform to the ICH guidelines on Good Clinical Practice. This implies that the written informed consent form should be signed and personally dated by the patient or by the patient's legally acceptable representative.~Conflict of Interest Any investigator and/or research staff member who has a conflict of interest with this study (such as patent ownership, royalties, or financial gain greater than the minimum allowable by their institution) must fully disclose the nature of the conflict of interest.~Data ownership According to the ICH Guidelines on Good Clinical Practice the sponsor of a study (the Institution, should the investigator or study coordinators act as sponsor in the performance of her/his institutional duties under the employment or collaboration agreement with Humanitas) is the owner of the data resulting therefrom. All centers and investigators participating in the study should be made aware of such circumstance and invited not to disseminate information or data without the Institution's prior express consent.
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.