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NCT Number: NCT06262386

Combined Relapse Prediction Model for Resectable Non-Small Cell Patients - a Prospective Clinical Feasibility Trial

For patients with lung cancer who have undergone tumor resection, early relapse significantly impacts survival. However, there are currently no reliable screening or imaging tools available to identify patients at risk of early relapse. To address this clinical challenge, many studies have focused on understanding the clinicopathologic characteristics associated with an increased risk of early relapse. Despite these efforts, we can identify patients at risk but cannot pinpoint which individuals will actually experience early relapse. Studies on adjuvant therapy have shown improved survival in cases of more advanced disease but have not demonstrated a reduction in early relapse rates.

In our preliminary analysis of previous study data, we observed that patients with a smaller reduction in circulating tumor cells (CTCs) within the first three days after surgery, followed by an increase on the third-day post-operation, are more likely to experience early relapse during regular monitoring. This pattern may be indicative of minimal residual disease. By combining trends in circulating tumor cell variations with pathologic characteristics, we aim to select patients for adjuvant therapy who are at high risk of developing early relapse.

The objective of our study is to employ screening based on circulating tumor cell dynamics and pathologic features to identify patients likely to experience early relapse and to assess the effectiveness of adjuvant therapy in these cases.

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Key information

Age range

20 year–90 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

About this study

For patients with resectable lung cancer, anatomic resection alongside mediastinal lymph node dissection is pivotal in removing all tumor tissue visible on imaging from the patient's body. Despite these efforts, early relapse remains a significant issue. Literature review shows that the early relapse rate varies between 8 to 10%, potentially due to undetectable occult metastasis by imaging modalities, suggesting the presence of minimal residual disease or tumor cells evading the primary site. Limitations in imaging, such as the slice thickness in computed tomography (CT) scans, which range from 0.375 to 0.5 centimeters, can render tumors smaller than the slice thickness invisible. Similarly, tumors smaller than 0.5 cm may not accumulate sufficient F18-Deoxyglucose to be detectable in positron emission tomography (PET) scans. Additionally, tumor cells may migrate to extrapulmonary sites via lymphatic drainage or circulation.

Survival studies have predominantly focused on the pathologic TNM stage, which aggregates different disease presentations with similar survival outcomes. However, the heterogeneity inherent in pathology may help in identifying patients prone to relapse. From a tumor biology perspective, tumor cells may detach from surrounding tissues, becoming more invasive and entering the bloodstream. Circulating tumor cells (CTCs) have been recognized early in cancer stages and are correlated with treatment response, tumor genetic alterations, and survival. Research has combined CT tumor size and CTCs in a malignancy prediction model for suspicious pulmonary lesions, highlighting that CTCs can rebound in patients experiencing early relapse, indicating occult metastases or minimal residual disease.

Systemic adjuvant therapy is considered the best approach to minimize disease relapse in resectable lung cancer patients. Although many studies have sought to identify patients at risk of relapse to improve survival, the presence of intrapulmonary (N1) or mediastinal (N2) lymph node invasion significantly affects survival in non-small cell lung cancer patients. Even tumors smaller than 1 cm carry a risk of lymph node metastases, with respective risks for cT1a, cT1b, and cT1c tumors reported as 3.8%, 16.3%, and 19.6%. Therefore, patients with tumors larger than 1 cm are recommended adjuvant therapy due to the high risk of lymph node involvement. Adjuvant chemotherapy is advised for patients with stages 1b to 3a, showing a 5.4% survival benefit by the fifth postoperative year, although this benefit diminishes in subsequent years. This could be due to adjuvant therapy being administered based on the pathologic stage rather than the likelihood of relapse. Tumor heterogeneity might also influence the response to different therapeutic regimens. Molecular profiling of tumors has identified mutations predicting responses to targeted therapies and elucidated drug resistance mechanisms, offering more precise treatments and improving survival. Targeted and immune therapies have shown improved survival in specific tumor subgroups.

This study aims to utilize trends in CTC variations as a screening tool to identify patients at risk of relapse and prescribe adjuvant therapy to evaluate the therapeutic efficacy and survival impact of CTCs.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients who presented with resectable disease ( Clinical stage 1a to 3a)
  • Patients who received tumor resection

Exclusion criteria

  • Pathologic stage greater than stage 3b or 4
  • Pathologic stage less than stage 1a1
  • Could not complete treatment course
  • Could not receive blood sampling for CTC (circulating tumor cell) or regular surveillance

Treatment and study plan

Cisplatin based chemottherapy

Drug

adjuvant therapy for high risk patient

Primary outcomes

  1. Accuracy of proposed relapse prediction model

    Time frame: follow up in 3 month-interval

    • Utilized the enrolled patients to testify proposed relapse prediction model
    • Calculated Positive prediction rate, Negative prediction rate, accuracy
    • Goal: high positive prediction rate, lower negative prediction rate, high accuracy
  2. early relapse rate

    Time frame: follow up in 3 month-interval

    • adjuvant therapy based on proposed relapse prediction model
    • calculate the early relapse rate (relapse within 3 years)
    • utilized historical cohort as historical control (cohort that utilized to establish proposed relapse prediction model
    • adjuvant therapy based on TNM stage
    • calculate the early relapse rate (relapse within 3 years)
    • follow up Chest CT/ CTC in 3-month interval

Secondary outcomes

  1. Overall surveival

    Time frame: follow up in 3 month-interval

    Goal: difference of overall survival among patients with relapse risk

    • treatment based on proposed relapse prediction model
    • calculate the overall survival
    • utilized historical cohort as historical control ( cohort that utilized to establish proposed relapse prediction model
    • treatment based on TNM stage
    • calculate the overall survival rate

Study contacts

Contact information is provided by the study sponsor or research team.

Chia-Tsun CH Hsieh

CONTACT

[email protected]

-886975366137

Ching-Yang Wu

CONTACT

[email protected]

+886975368204

Sponsors and collaborators

Lead sponsor

Chang Gung Memorial Hospital

Other

Collaborators

  • National Science and Technology Council

Registry information

Official study title

Utilizing Perioperative Variation Trends of Circulating Tumor Cells and Tumor Pathological Characteristics as a Combined Relapse Prediction Model for Resectable Non-Small Cell Patients - a Prospective Clinical Feasibility Trial

Important dates

Study start
2023
Primary completion
2028
Study completion
2028
First posted
Feb 16, 2024
Registry last updated
Feb 16, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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