Colorectal CancerSPLICENCT07226115
Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer
An observational study of Colorectal Cancer and Colorectal Cancer Recurrent, run by City of Hope Medical Center at 1 site in United States.
Trial summary
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II
- What is being tested?
- SPLICE
- How many people
- 200 participants
- Where
- 1 siteUnited States
- Study dates
- 21 Jun 2024to 18 Jun 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Two independent cohorts of colorectal cancer patients treated with adjuvant chemotherapy after curative-intent surgery.
Who may be able to join
- Histologically confirmed stage II-III colorectal cancer (TNM classification, 8th edition)
- Received standard adjuvant chemotherapy after curative resection
- Availability of tumor tissue (FFPE or frozen) before chemotherapy
- Sufficient clinical data for outcome analysis (recurrence, survival)
- Age 18-80 years Stage
Who cannot join
- Inflammatory bowel disease
- Inadequate RNA quality or lack of consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
City of Hope Medical Center
Duarte, California, 91010
Ajay Goel, PhD, 626-218-3452, AJGOEL@COH.ORG
About this study
Official title: Splicing-Based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer (SPLICE)
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment.
This study aims to develop a machine learning-based predictive model for adjuvant chemotherapy response using tumor-derived alternative splicing signatures.
By integrating RNA-seq data, splicing isoform and clinical outcomes, this study seeks to identify molecular predictors of treatment response and recurrence risk after surgery.
Read the detailed description
Colorectal cancer (CRC) remains a major global health burden, with adjuvant chemotherapy representing the standard of care after curative resection. However, patient responses to therapy vary widely, and no validated molecular model currently guides adjuvant treatment selection.
Recent studies suggest that aberrant alternative splicing-rather than gene-level expression alone-plays a crucial role in shaping chemotherapy sensitivity and tumor recurrence. Yet, these complex transcriptomic variations are often missed by standard differential expression analyses.
The ASPAIRE framework (Alternative Splicing and Predictive mAchIne learnIng for Response Evaluation) applies advanced computational modeling to capture multidimensional splicing features from RNA-seq data and transform them into clinically actionable predictions.
In this research effort, the investigators will leverage machine learning to predict adjuvant chemotherapy response for CRC. The research plan will employ three phases:
- Identification of alternative splicing patterns associated with adjuvant chemotherapy response through RNA sequencing and computational feature extraction.
- The investigators will then develop an assay based on reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and train a machine-learning model to predict chemotherapy response.
- The investigators will independently validate the assay. This assay is provisionally termed " SPLICE " (Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer) and will be tested for disease free survival up to five years after treatment.
At the end of this study, this assay will have been developed and validated to help clinical decision-making by predicting both disease free survival.
- Conditions
- Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II, Colorectal Cancer Stage III
- Being tested
- SPLICE
- Keywords
- Chemotherapy, Adjuvant, Response, Splicing, Prediction
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SPLICEOther
A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
| Study group | Role | What they receive |
|---|---|---|
| Non-responders of colorectal cancer (Training Cohort) | Non-responders of colorectal cancer who developed recurrent CRC within 60 months from primary tumor treatment, in the first cohort Receives: Other: SPLICE | |
| Responders of colorectal cancer (Training Cohort) | Responders of colorectal cancer who did not develop recurrent CRC within 60 months from primary tumor treatment, in the first cohort Receives: Other: SPLICE | |
| Non-responders of colorectal cancer, with recurrent disease (Validation Cohort) | Non-responders of colorectal cancer who developed recurrent CRC within 60 months from primary tumor treatment, in the second, independent, validation cohort Receives: Other: SPLICE | |
| Responders of colorectal cancer (Validation Cohort) | Responders of colorectal cancer who did not develop recurrent CRC within 60 months from primary tumor treatment, in the second, independent, validation cohort Receives: Other: SPLICE |
What is being measured
The primary outcome is the main question the study is designed to answer.
Recurrence Free Survival
Time from disease treatment to development of recurrent colorectal cancer
Measured: from date of disease treatment to date of death or up to 60 months
Secondary outcomes (1)
Overall survival
Time from disease treatment to death from any cause
Measured: from date of disease treatment to date of death or up to 60 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Nov 2025
- Study start21 Jun 2024
- Primary completion18 Jun 2028 (estimated)
- Study completion18 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Colorectal CancerStudies of the same condition, open and under way.
- More trials run by City of Hope Medical CenterThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- City of Hope Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ajay Goel, PhD, Principal investigator, City of Hope Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07226115
- Sponsor's protocol ID
- 23228/SPLICE
- Acronym
- SPLICE
- Status last verified
- Jul 2026
- First submitted
- 3 Nov 2025
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data collected for the study will be made available to others, including de-identified participant data, at publication, via a signed data access agreement and at the discretion of the investigators' approval of the proposed use of such data.
Publications
Dienstmann R, Salazar R, Tabernero J. Personalizing colon cancer adjuvant therapy: selecting optimal treatments for individual patients. J Clin Oncol. 2015 Jun 1;33(16):1787-96. doi: 10.1200/JCO.2014.60.0213. Epub 2015 Apr 27.
PubMed 25918287Di Narzo AF, Tejpar S, Rossi S, Yan P, Popovici V, Wirapati P, Budinska E, Xie T, Estrella H, Pavlicek A, Mao M, Martin E, Scott W, Bosman FT, Roth A, Delorenzi M. Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data. J Natl Cancer Inst. 2014 Sep 22;106(10):dju247. doi: 10.1093/jnci/dju247. Print 2014 Oct.
PubMed 25246611Auclin E, Zaanan A, Vernerey D, Douard R, Gallois C, Laurent-Puig P, Bonnetain F, Taieb J. Subgroups and prognostication in stage III colon cancer: future perspectives for adjuvant therapy. Ann Oncol. 2017 May 1;28(5):958-968. doi: 10.1093/annonc/mdx030.
PubMed 28453690Andre T, Boni C, Navarro M, Tabernero J, Hickish T, Topham C, Bonetti A, Clingan P, Bridgewater J, Rivera F, de Gramont A. Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J Clin Oncol. 2009 Jul 1;27(19):3109-16. doi: 10.1200/JCO.2008.20.6771. Epub 2009 May 18.
PubMed 19451431Andre T, Meyerhardt J, Iveson T, Sobrero A, Yoshino T, Souglakos I, Grothey A, Niedzwiecki D, Saunders M, Labianca R, Yamanaka T, Boukovinas I, Vernerey D, Meyers J, Harkin A, Torri V, Oki E, Georgoulias V, Taieb J, Shields A, Shi Q. Effect of duration of adjuvant chemotherapy for patients with stage III colon cancer (IDEA collaboration): final results from a prospective, pooled analysis of six randomised, phase 3 trials. Lancet Oncol. 2020 Dec;21(12):1620-1629. doi: 10.1016/S1470-2045(20)30527-1.
PubMed 33271092Okuno K, Kandimalla R, Mendiola M, Balaguer F, Bujanda L, Fernandez-Martos C, Aparicio J, Feliu J, Tokunaga M, Kinugasa Y, Maurel J, Goel A. A microRNA signature for risk-stratification and response prediction to FOLFOX-based adjuvant therapy in stage II and III colorectal cancer. Mol Cancer. 2023 Jan 20;22(1):13. doi: 10.1186/s12943-022-01699-2. No abstract available.
PubMed 36670412Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, Hu M, Chen GZ, Liao B, Lu J, Zhao HW, Chen W, He YL, Wang HY, Xie D, Luo JH. Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol. 2013 Dec;14(13):1295-306. doi: 10.1016/S1470-2045(13)70491-1. Epub 2013 Nov 13.
PubMed 24239208Gray RG, Quirke P, Handley K, Lopatin M, Magill L, Baehner FL, Beaumont C, Clark-Langone KM, Yoshizawa CN, Lee M, Watson D, Shak S, Kerr DJ. Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J Clin Oncol. 2011 Dec 10;29(35):4611-9. doi: 10.1200/JCO.2010.32.8732. Epub 2011 Nov 7.
PubMed 22067390Zhang M, Chen C, Lu Z, Cai Y, Li Y, Zhang F, Liu Y, Chen S, Zhang H, Yang S, Gen H, Jiang Y, Ning C, Huang J, Wang W, Fan L, Zhang Y, Jin M, Han J, Xiong Z, Cai M, Liu J, Huang C, Yang X, Xu B, Li H, Li B, Zhu X, Wei Y, Zhu Y, Tian J, Miao X. Genetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms. Gastroenterology. 2023 Nov;165(5):1151-1167. doi: 10.1053/j.gastro.2023.07.019. Epub 2023 Aug 3.
PubMed 37541527Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet JM, Becheur H, Fein F, Cojocarasu O, Kaminsky MC, Lagasse JP, Luet D, Nguyen S, Etienne PL, Gasmi M, Vanoli A, Perrier H, Puig PL, Emile JF, Lepage C, Ghiringhelli F. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020 Apr;69(4):681-690. doi: 10.1136/gutjnl-2019-319292. Epub 2019 Nov 28.
PubMed 31780575
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Splicing-Based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer (SPLICE)
- ClinicalTrials.gov ID
- NCT07226115
- Acronym / sponsor ID
- SPLICE / 23228/SPLICE
- Conditions
- Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II, Colorectal Cancer Stage III
- Keywords
- Chemotherapy, Adjuvant, Response, Splicing, Prediction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- City of Hope Medical CenterUniversity, hospital or other
- Study start
- 21 Jun 2024 (actual)
- Primary completion
- 18 Jun 2028 (estimated)
- Study completion
- 18 Jun 2028 (estimated)
- First posted
- 10 Nov 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment.
This study aims to develop a machine learning-based predictive model for adjuvant chemotherapy response using tumor-derived alternative splicing signatures.
By integrating RNA-seq data, splicing isoform and clinical outcomes, this study seeks to identify molecular predictors of treatment response and recurrence risk after surgery.
Detailed description
Colorectal cancer (CRC) remains a major global health burden, with adjuvant chemotherapy representing the standard of care after curative resection. However, patient responses to therapy vary widely, and no validated molecular model currently guides adjuvant treatment selection.
Recent studies suggest that aberrant alternative splicing-rather than gene-level expression alone-plays a crucial role in shaping chemotherapy sensitivity and tumor recurrence. Yet, these complex transcriptomic variations are often missed by standard differential expression analyses.
The ASPAIRE framework (Alternative Splicing and Predictive mAchIne learnIng for Response Evaluation) applies advanced computational modeling to capture multidimensional splicing features from RNA-seq data and transform them into clinically actionable predictions.
In this research effort, the investigators will leverage machine learning to predict adjuvant chemotherapy response for CRC. The research plan will employ three phases:
- Identification of alternative splicing patterns associated with adjuvant chemotherapy response through RNA sequencing and computational feature extraction.
- The investigators will then develop an assay based on reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and train a machine-learning model to predict chemotherapy response.
- The investigators will independently validate the assay. This assay is provisionally termed " SPLICE " (Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer) and will be tested for disease free survival up to five years after treatment.
At the end of this study, this assay will have been developed and validated to help clinical decision-making by predicting both disease free survival.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Non-responders of colorectal cancer (Training Cohort) | – | Other: SPLICENon-responders of colorectal cancer who developed recurrent CRC within 60 months from primary tumor treatment, in the first cohort |
| Responders of colorectal cancer (Training Cohort) | – | Other: SPLICEResponders of colorectal cancer who did not develop recurrent CRC within 60 months from primary tumor treatment, in the first cohort |
| Non-responders of colorectal cancer, with recurrent disease (Validation Cohort) | – | Other: SPLICENon-responders of colorectal cancer who developed recurrent CRC within 60 months from primary tumor treatment, in the second, independent, validation cohort |
| Responders of colorectal cancer (Validation Cohort) | – | Other: SPLICEResponders of colorectal cancer who did not develop recurrent CRC within 60 months from primary tumor treatment, in the second, independent, validation cohort |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Two independent cohorts of colorectal cancer patients treated with adjuvant chemotherapy after curative-intent surgery.
Key inclusion criteria (5 listed)
- Histologically confirmed stage II-III colorectal cancer (TNM classification, 8th edition)
- Received standard adjuvant chemotherapy after curative resection
- Availability of tumor tissue (FFPE or frozen) before chemotherapy
- Sufficient clinical data for outcome analysis (recurrence, survival)
- Age 18-80 years Stage
Key exclusion criteria (2 listed)
- Inflammatory bowel disease
- Inadequate RNA quality or lack of consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SPLICEA panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor. | Other | Non-responders of colorectal cancer (Training Cohort), Non-responders of colorectal cancer, with recurrent disease (Validation Cohort), Responders of colorectal cancer (Training Cohort), Responders of colorectal cancer (Validation Cohort) |
Primary outcome
Recurrence Free Survival
Time frame: from date of disease treatment to date of death or up to 60 months
Time from disease treatment to development of recurrent colorectal cancer
1 secondary outcome
Overall survival
Time frame: from date of disease treatment to date of death or up to 60 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2026.
- Background: PubMed 25918287Dienstmann R, Salazar R, Tabernero J. Personalizing colon cancer adjuvant therapy: selecting optimal treatments for individual patients. J Clin Oncol. 2015 Jun 1;33(16):1787-96. doi: 10.1200/JCO.2014.60.0213. Epub 2015 Apr 27.
- Background: PubMed 25246611Di Narzo AF, Tejpar S, Rossi S, Yan P, Popovici V, Wirapati P, Budinska E, Xie T, Estrella H, Pavlicek A, Mao M, Martin E, Scott W, Bosman FT, Roth A, Delorenzi M. Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data. J Natl Cancer Inst. 2014 Sep 22;106(10):dju247. doi: 10.1093/jnci/dju247. Print 2014 Oct.
- Background: PubMed 28453690Auclin E, Zaanan A, Vernerey D, Douard R, Gallois C, Laurent-Puig P, Bonnetain F, Taieb J. Subgroups and prognostication in stage III colon cancer: future perspectives for adjuvant therapy. Ann Oncol. 2017 May 1;28(5):958-968. doi: 10.1093/annonc/mdx030.
- Background: PubMed 19451431Andre T, Boni C, Navarro M, Tabernero J, Hickish T, Topham C, Bonetti A, Clingan P, Bridgewater J, Rivera F, de Gramont A. Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J Clin Oncol. 2009 Jul 1;27(19):3109-16. doi: 10.1200/JCO.2008.20.6771. Epub 2009 May 18.
- Background: PubMed 33271092Andre T, Meyerhardt J, Iveson T, Sobrero A, Yoshino T, Souglakos I, Grothey A, Niedzwiecki D, Saunders M, Labianca R, Yamanaka T, Boukovinas I, Vernerey D, Meyers J, Harkin A, Torri V, Oki E, Georgoulias V, Taieb J, Shields A, Shi Q. Effect of duration of adjuvant chemotherapy for patients with stage III colon cancer (IDEA collaboration): final results from a prospective, pooled analysis of six randomised, phase 3 trials. Lancet Oncol. 2020 Dec;21(12):1620-1629. doi: 10.1016/S1470-2045(20)30527-1.
- Background: PubMed 36670412Okuno K, Kandimalla R, Mendiola M, Balaguer F, Bujanda L, Fernandez-Martos C, Aparicio J, Feliu J, Tokunaga M, Kinugasa Y, Maurel J, Goel A. A microRNA signature for risk-stratification and response prediction to FOLFOX-based adjuvant therapy in stage II and III colorectal cancer. Mol Cancer. 2023 Jan 20;22(1):13. doi: 10.1186/s12943-022-01699-2. No abstract available.
- Background: PubMed 24239208Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, Hu M, Chen GZ, Liao B, Lu J, Zhao HW, Chen W, He YL, Wang HY, Xie D, Luo JH. Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol. 2013 Dec;14(13):1295-306. doi: 10.1016/S1470-2045(13)70491-1. Epub 2013 Nov 13.
- Background: PubMed 22067390Gray RG, Quirke P, Handley K, Lopatin M, Magill L, Baehner FL, Beaumont C, Clark-Langone KM, Yoshizawa CN, Lee M, Watson D, Shak S, Kerr DJ. Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J Clin Oncol. 2011 Dec 10;29(35):4611-9. doi: 10.1200/JCO.2010.32.8732. Epub 2011 Nov 7.
- Background: PubMed 37541527Zhang M, Chen C, Lu Z, Cai Y, Li Y, Zhang F, Liu Y, Chen S, Zhang H, Yang S, Gen H, Jiang Y, Ning C, Huang J, Wang W, Fan L, Zhang Y, Jin M, Han J, Xiong Z, Cai M, Liu J, Huang C, Yang X, Xu B, Li H, Li B, Zhu X, Wei Y, Zhu Y, Tian J, Miao X. Genetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms. Gastroenterology. 2023 Nov;165(5):1151-1167. doi: 10.1053/j.gastro.2023.07.019. Epub 2023 Aug 3.
- Results publication: PubMed 31780575Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet JM, Becheur H, Fein F, Cojocarasu O, Kaminsky MC, Lagasse JP, Luet D, Nguyen S, Etienne PL, Gasmi M, Vanoli A, Perrier H, Puig PL, Emile JF, Lepage C, Ghiringhelli F. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020 Apr;69(4):681-690. doi: 10.1136/gutjnl-2019-319292. Epub 2019 Nov 28.
- Individual participant dataSharing planned: No. Data collected for the study will be made available to others, including de-identified participant data, at publication, via a signed data access agreement and at the discretion of the investigators' approval of the proposed use of such data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ajay Goel, PhD principal investigator
City of Hope Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Cancer and Colorectal Cancer Recurrent, run by City of Hope Medical Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 7 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ajay Goel, PhD (626-218-3452, AJGOEL@COH.ORG), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Recurrence Free Survival (time frame: from date of disease treatment to date of death or up to 60 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07226115, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2026.