Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: SPLICE.
- Who it may be relevant to
- Registry conditions: Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II, Colorectal Cancer Stage III. Basic parameters: 18 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Splicing-Based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer (SPLICE)
Overview
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:
1. Identification of alternative splicing patterns associated with adjuvant chemotherapy response through RNA sequencing and computational feature extraction. 2. 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. 3. 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.
Interventions
- Other SPLICE
A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
Primary outcome measures
- Recurrence Free Survival [Time frame: from date of disease treatment to date of death or up to 60 months]
Secondary outcome measures (1)
- Overall survival [Time frame: from date of disease treatment to date of death or up to 60 months]
Eligibility criteria
Inclusion criteria
- 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
Exclusion criteria
- Inflammatory bowel disease
- Inadequate RNA quality or lack of consent
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-control
Study locations
United States · 1 center
- City of Hope Medical Center — Duarte
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. PMID 25918287
- Di 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. PMID 25246611
- Auclin 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. PMID 28453690
- Andre 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. PMID 19451431
- Andre 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 ra PMID 33271092
- Okuno 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. PMID 36670412
- Zhang 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. PMID 24239208
- Gray 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 PMID 22067390
Identifiers
NCT: NCT07226115 · 23228/SPLICE