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Updated: Oct 31, 2025
Comparative Lesions Analysis Through a Targeted Sequencing Approach
Published on: November 5, 2019
Utilization of target lesion heterogeneity for treatment efficacy assessment in late stage lung cancer
Dung-Tsa Chen1, Wenyaw Chan2, Zachary J Thompson1
1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America.
Rationale:
Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogeneity defined as diversity of tumor growth profiles in RECIST target lesions. Furthermore, many cancer clinical trials have been activated to evaluate various treatment options for heterogeneity-related subgroups (e.g., 29 trials so far listed in clinicaltrials.gov for cancer patients with oligometastasis). Some of the trials have shown survival benefit by tailored treatment strategies. This evidence presents the unmet need to incorporate lesion heterogeneity to improve RECIST response classification.
Method:
An approach for Lesion Heterogeneity Classification (LeHeC) was developed using a contemporary statistical approach to assess target lesion variation, characterize patient treatment response, and translate informative evidence to improving treatment strategy. A mixed effect linear model was used to determine lesion heterogeneity. Further analysis was conducted to classify various types of lesion variation and incorporate with RECIST to enhance response classification. A study cohort of 110 target lesions from 36 lung cancer patients was used for evaluation.
Results:
Due to small sample size issue, the result was exploratory in nature. By analyzing RECIST target lesion data, the LeHeC approach detected a high prevalence (n = 21; 58%) of lesion heterogeneity. Subgroup classification revealed several informative distinct subsets in a descending order of lesion heterogeneity: mix of progression and regression (n = 7), mix of progression and stability (n = 9), mix of regression and stability (n = 5), and non-heterogeneity (n = 15). Evaluation for association of lesion heterogeneity and RECIST best response classification showed lesion heterogeneity commonly occurred in each response group (stable disease: 16/27; 59%; partial response: 3/5; 60%; progression disease: 2/4; 50%). Survival analysis showed a differential trend of overall survival between heterogeneity and non-heterogeneity in RECIST response groups.
Conclusion:
This is the first study to evaluate lesion heterogeneity, an underappreciated metric, for RECIST application in oncology clinical trials. Results indicated lesion heterogeneity is not an uncommon event. The LeHeC approach could enhance RECIST response classification by utilizing granular lesion level discovery of heterogeneity.
Insights
Lesion heterogeneity, a measure of diverse tumor growth, is common in cancer patients and can improve tumor response classification beyond RECIST criteria. This approach offers new insights for clinical trial strategies.
Area of Science:
- Oncology
- Clinical Trial Methodology
- Statistical Modeling
Background:
- Tumor response classification using Response Evaluation Criteria for Solid Tumors (RECIST) has limitations.
- Unique tumor response subgroups, like mixed response and oligometastasis, highlight the need for refined classification methods.
- Lesion heterogeneity, defined as diverse tumor growth profiles in target lesions, is a key property of these subgroups.
Purpose of the Study:
- To develop and evaluate an approach for Lesion Heterogeneity Classification (LeHeC).
- To assess the prevalence and characteristics of lesion heterogeneity in cancer patients.
- To explore the potential of incorporating lesion heterogeneity into RECIST for improved response classification and treatment strategies.
Main Methods:
- Developed the Lesion Heterogeneity Classification (LeHeC) approach using a mixed-effect linear model.
- Analyzed target lesion variation to characterize patient treatment response.
- Evaluated the LeHeC approach on a cohort of 110 target lesions from 36 lung cancer patients.
Main Results:
- The LeHeC approach detected a high prevalence (58%) of lesion heterogeneity.
- Identified distinct subgroups based on lesion heterogeneity, including mixed progression/regression, mixed progression/stability, and mixed regression/stability.
- Found lesion heterogeneity occurred across all RECIST response groups (stable disease, partial response, progressive disease).
- Observed differential trends in overall survival between heterogeneous and non-heterogeneous groups.
Conclusions:
- Lesion heterogeneity is an underappreciated but common metric in oncology.
- The LeHeC approach can enhance RECIST response classification by identifying granular lesion-level heterogeneity.
- This methodology holds promise for improving treatment strategies and clinical trial design in oncology.
