Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer (SPLICE)

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.

Study Overview

• Status: Recruiting
• Conditions: Colorectal Cancer; Colorectal Cancer Stage II; Colorectal Cancer Stage III; Colorectal Cancer Recurrent
• Intervention / Treatment: Other: SPLICE

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.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Ajay Goel, PhD; Phone Number: 626-218-3452; Email: AJGOEL@COH.ORG

Study Locations

• United States
  • California
    • Duarte, California, United States, 91010
      • Recruiting
      • City of Hope Medical Center
      • Contact: Ajay Goel, PhD; Phone Number: 626-218-3452; Email: AJGOEL@COH.ORG

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Two independent cohorts of colorectal cancer patients treated with adjuvant chemotherapy after curative-intent surgery.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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	Other: SPLICE; A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
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	Other: SPLICE; A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
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	Other: SPLICE; A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
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	Other: SPLICE; A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Recurrence Free Survival	Time from disease treatment to development of recurrent colorectal cancer	from date of disease treatment to date of death or up to 60 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall survival	Time from disease treatment to death from any cause	from date of disease treatment to date of death or up to 60 months

Collaborators and Investigators

• Sponsor: City of Hope Medical Center
• Investigators: Principal Investigator: Ajay Goel, PhD, City of Hope Medical Center

Publications and helpful links

General 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.
• 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.
• 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 randomised, phase 3 trials. Lancet Oncol. 2020 Dec;21(12):1620-1629. doi: 10.1016/S1470-2045(20)30527-1.
• 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.
• 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.
• 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.
• 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.
• 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 7.
• Zhang 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.
• Reichling 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.

Study record dates

Study Major Dates

• Study Start (Actual): June 21, 2024
• Primary Completion (Estimated): June 18, 2026
• Study Completion (Estimated): June 18, 2026

Study Registration Dates

• First Submitted: November 3, 2025
• First Submitted That Met QC Criteria: November 5, 2025
• First Posted (Actual): November 10, 2025

Study Record Updates

• Last Update Posted (Actual): November 10, 2025
• Last Update Submitted That Met QC Criteria: November 5, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: Chemotherapy; Response; Adjuvant; Prediction; Splicing
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Intestinal Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colonic Diseases; Colorectal Neoplasms
• Other Study ID Numbers: 23228/SPLICE

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No