PaCIFiC-CUP Classifies Cancer of Unknown Primary

DNA Methylation Clinical Trial

Official title:

PaCIFiC-CUP：Pan-Cancer Integrated Fingerprinting Classifier for Identifying the Origin of Cancer of Unknown Primary: A Multi-Center Bidirectional Cohort Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06140992
• Other study ID #: PaCIFiC-CUP
• Status: Recruiting
• Start date: July 1, 2023
• Est. completion date: December 31, 2025

Study information

• Verified date: November 2023
• Source: Sun Yat-sen University
• Contact: Yiyang Zhang
• Phone: +8615652797092
• Email: zhangyy[@]sysucc.org.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This study aims to initially utilize machine learning on pan-cancer DNA methylation data from public databases to construct a DNA methylation classification model (PaCIFiC-CUP, pan-cancer integrated fingerprinting classifier of CUP) for diagnosing various types of cancer, particularly the primary site of cancer of unknown primary. The goal is to achieve diagnosis of cancer pathology type by analyzing the DNA methylation patterns of cancer specimens, thereby guiding subsequent precision treatment for cancer.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 160
• Est. completion date: December 31, 2025
• Est. primary completion date: December 31, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

1. The patient specimens were obtained from the Sun Yat-sen University Cancer Center and affiliated cooperating centers, with written consent from the patients authorizing the use of the specimens for research purposes.

2. Following standard assessments (medical history, physical examination, complete blood count, biochemistry, computed tomography scans of the neck, chest, abdomen, and pelvis, targeted evaluations of all symptomatic areas, pathology, and immunohistochemistry), the diagnosis was determined as a primary site unknown tumor (including adenocarcinoma, squamous cell carcinoma, undifferentiated carcinoma, neuroendocrine carcinoma, sarcoma, etc).

3. The diagnosis was confirmed at the participating institution and the patient had received systemic therapy.

4. Complete clinical, pathological, and follow-up data for the patients can be obtained.

5. ECOG performance status score: 0-2 points.

Exclusion Criteria:

1. Pregnant or lactating female patients.

2. Tumor tissue sample size is too small (tumor tissue accounts for <70% in the biopsy or slice tissue).

3. Organ transplant or history of non-autologous (allogeneic) bone marrow or stem cell transplantation.

4. History of previous tumors, with the current condition being a recurrent tumor.

5. Hematological malignancies (excluding lymphoma).

6. Other diseases that may severely impact patient survival, such as severe cardiovascular or cerebrovascular diseases, sepsis, severe trauma or burns, etc.

Related Conditions & MeSH terms

• DNA Methylation
• Neoplasms, Unknown Primary
• Unknown Primary Tumors

Locations

Country	Name	City	State
China	Sun Yat-sen university cancer center	Guangzhou	Guangdong

Sponsors (15)

Lead Sponsor

Collaborator

Sun Yat-sen University

Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Anhui Provincial Cancer Hospital, Anhui Provincial Hospital, Beijing Tongren Hospital, Dongguan People's Hospital, First Affiliated Hospital of Jinan University, First People's Hospital of Foshan, Ganzhou Cancer Hospital, Guangzhou Chest Hospital., Henan Cancer Hospital, Peking University Shenzhen Hospital, Peking University Third Hospital, The First Affiliated Hospital of Anhui Medical University, Third Affiliated Hospital, Sun Yat-Sen University

Country where clinical trial is conducted

China, 

References & Publications (16)

Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, Koelsche C, Sahm F, Chavez L, Reuss DE, Kratz A, Wefers AK, Huang K, Pajtler KW, Schweizer L, Stichel D, Olar A, Engel NW, Lindenberg K, Harter PN, Braczynski AK, Plate KH, Dohmen H, Garvalov — View Citation

Greco FA, Lennington WJ, Spigel DR, Hainsworth JD. Molecular profiling diagnosis in unknown primary cancer: accuracy and ability to complement standard pathology. J Natl Cancer Inst. 2013 Jun 5;105(11):782-90. doi: 10.1093/jnci/djt099. Epub 2013 May 2. — View Citation

Hovestadt V, Remke M, Kool M, Pietsch T, Northcott PA, Fischer R, Cavalli FM, Ramaswamy V, Zapatka M, Reifenberger G, Rutkowski S, Schick M, Bewerunge-Hudler M, Korshunov A, Lichter P, Taylor MD, Pfister SM, Jones DT. Robust molecular subgrouping and copy — View Citation

Koelsche C, Schrimpf D, Stichel D, Sill M, Sahm F, Reuss DE, Blattner M, Worst B, Heilig CE, Beck K, Horak P, Kreutzfeldt S, Paff E, Stark S, Johann P, Selt F, Ecker J, Sturm D, Pajtler KW, Reinhardt A, Wefers AK, Sievers P, Ebrahimi A, Suwala A, Fernande — View Citation

Kramer A, Bochtler T, Pauli C, Baciarello G, Delorme S, Hemminki K, Mileshkin L, Moch H, Oien K, Olivier T, Patrikidou A, Wasan H, Zarkavelis G, Pentheroudakis G, Fizazi K; ESMO Guidelines Committee. Electronic address: clinicalguidelines@esmo.org. Cancer — View Citation

Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV; CCGA Consortium. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020 Jun;31(6):745-759. doi: 10.1016/j.annonc.2020.02.011. Epub 20 — View Citation

Maros ME, Capper D, Jones DTW, Hovestadt V, von Deimling A, Pfister SM, Benner A, Zucknick M, Sill M. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Nat Protoc. 2020 Feb;15(2 — View Citation

Moran S, Martinez-Cardus A, Sayols S, Musulen E, Balana C, Estival-Gonzalez A, Moutinho C, Heyn H, Diaz-Lagares A, de Moura MC, Stella GM, Comoglio PM, Ruiz-Miro M, Matias-Guiu X, Pazo-Cid R, Anton A, Lopez-Lopez R, Soler G, Longo F, Guerra I, Fernandez S — View Citation

Muller D, Gyorffy B. DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer. Biochim Biophys Acta Rev Cancer. 2022 May;1877(3):188722. doi: 10.1016/j.bbcan.2022.188722. Epub 2022 Mar 17. — View Citation

Nunes SP, Moreira-Barbosa C, Salta S, Palma de Sousa S, Pousa I, Oliveira J, Soares M, Rego L, Dias T, Rodrigues J, Antunes L, Henrique R, Jeronimo C. Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women. Ca — View Citation

Pajtler KW, Witt H, Sill M, Jones DT, Hovestadt V, Kratochwil F, Wani K, Tatevossian R, Punchihewa C, Johann P, Reimand J, Warnatz HJ, Ryzhova M, Mack S, Ramaswamy V, Capper D, Schweizer L, Sieber L, Wittmann A, Huang Z, van Sluis P, Volckmann R, Koster J — View Citation

Paziewska A, Dabrowska M, Goryca K, Antoniewicz A, Dobruch J, Mikula M, Jarosz D, Zapala L, Borowka A, Ostrowski J. DNA methylation status is more reliable than gene expression at detecting cancer in prostate biopsy. Br J Cancer. 2014 Aug 12;111(4):781-9. — View Citation

Pisanic TR 2nd, Wang Y, Sun H, Considine M, Li L, Wang TH, Wang TL, Shih IM. Methylomic Landscapes of Ovarian Cancer Precursor Lesions. Clin Cancer Res. 2020 Dec 1;26(23):6310-6320. doi: 10.1158/1078-0432.CCR-20-0270. Epub 2020 Aug 17. — View Citation

Sturm D, Orr BA, Toprak UH, Hovestadt V, Jones DTW, Capper D, Sill M, Buchhalter I, Northcott PA, Leis I, Ryzhova M, Koelsche C, Pfaff E, Allen SJ, Balasubramanian G, Worst BC, Pajtler KW, Brabetz S, Johann PD, Sahm F, Reimand J, Mackay A, Carvalho DM, Re — View Citation

Wehmas LC, Hester SD, Wood CE. Direct formalin fixation induces widespread transcriptomic effects in archival tissue samples. Sci Rep. 2020 Sep 2;10(1):14497. doi: 10.1038/s41598-020-71521-w. — View Citation

Zhang Z, Lu Y, Vosoughi S, Levy JJ, Christensen BC, Salas LA. HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation. NAR Cancer. 2023 Apr 19;5(2):zcad0 — View Citation

* Note: There are 16 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Overall Survival	Overall survival refers to the length of time that a person lives after being diagnosed with cancer of unknown primary, without regard to any specific cause of death.	From date of diagnosis until the date of death from any cause, assessed up to 24 months
Secondary	Progression-Free Survival	Progression-Free Survival refers to the length of time during which a patient with cancer of unknown primary remains alive without any signs or symptoms of disease progression	From date of diagnosis until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 24 months