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

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

Who can join

As published by the study on ClinicalTrials.gov. "Not stated" means the study hasn't said — ask the study team.

Age
18 Years to 80 Years
Sex
All sexes
Healthy volunteers
Not accepted
Compensation
Not stated

Other requirements are listed under Eligibility below. Only the study team can confirm whether you can join.

What to expect

No one can promise a trial will help you personally — that is what the research is trying to find out.

What participation involves

  • Time periods mentioned: 5 years

Only what the study's summary and detailed description on ClinicalTrials.gov say. Time periods may describe the whole study or a part of it — ask the study team what applies to you.

Show the study's own words
  • "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."
Compensation
Not stated
  • Study-related exams and procedures are typically provided at no cost. This is how many trials work in general — confirm the details for this study with the study team.
  • Participants are monitored closely by the research team throughout the study.
  • Some participants may receive access to an investigational treatment, which is still being studied.
  • Your participation helps researchers learn whether this approach could help future patients.

About this study

In plain language

This study aims to build a computer tool to predict how well chemotherapy after surgery works for people with colon or rectal cancer. It will look at patterns in how tumors make RNA, which carries instructions from genes, to predict treatment results and the risk of cancer coming back. It is for adults ages 18–80 with stage II or III cancer who had surgery and chemotherapy.

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.

Compensation is read from the study's own description and may be missing — always confirm with the study team.

Eligibility

Who can join

  • Age 18–80.
  • Stage II or III colon or rectal cancer confirmed by testing a tissue sample.
  • Had surgery to remove the cancer, followed by standard chemotherapy.
  • Have a stored tumor tissue sample taken before chemotherapy.
  • Have enough medical records to track survival and whether the cancer came back.

Who can't join

  • Have inflammatory bowel disease, which causes long-term swelling in the digestive tract.
  • The RNA in the tumor sample is not good enough for testing.
  • Have not given consent to take part.

Simplified for readability. The official criteria below are what the study team uses.

Official 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

Study sites (1)

CA