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Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
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A Joint Model Considering Measurement Errors for Optimally Identifying Tumor Mutation Burden Threshold.

Yixuan Wang1, Xin Lai1, Jiayin Wang1,2,3

  • 1School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Genetics
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-endpoint joint model to standardize tumor mutation burden (TMB) thresholds for immunotherapy. The new method accounts for measurement errors and combines multiple clinical outcomes for more precise patient stratification.

Keywords:
clinical immunologyjoint modelingmeasurement errormultiple endpointsstratification biomarkertumor mutation burden

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Area of Science:

  • Oncology
  • Biostatistics
  • Immunotherapy

Background:

  • Tumor mutation burden (TMB) is a key biomarker for predicting immunotherapy response.
  • Current TMB thresholds lack standardization due to inconsistent criteria and imprecise measurements, leading to clinical controversies.
  • Existing methods fail to account for TMB measurement errors and often consider only single clinical benefit dimensions.

Purpose of the Study:

  • To develop an optimal method for determining TMB thresholds that accounts for measurement errors and multifaceted clinical efficacies.
  • To establish a generalized method for inferring TMB thresholds using a multi-endpoint joint model.
  • To improve patient stratification for immunotherapy by providing a robust TMB screening threshold.

Main Methods:

  • Developed a multi-endpoint joint model incorporating subject-specific random effects to link distinct clinical outcomes (objective response rate, time-to-event).
  • Employed an iterative numerical estimation procedure to handle mis-specified covariates and ensure consistent statistical inference.
  • Validated the model using simulations and pooled clinical data from non-small-cell lung cancer and nasopharyngeal carcinoma patient cohorts.

Main Results:

  • The proposed multi-endpoint joint model demonstrated superior precision and stability in parameter estimation and TMB threshold determination compared to standard models.
  • Simulations confirmed the advantages of the new model in handling measurement errors and integrating multiple efficacy endpoints.
  • Clinical validation in non-small-cell lung cancer and nasopharyngeal carcinoma cohorts confirmed the model's ability to provide a holistic efficacy assessment.

Conclusions:

  • The developed multi-endpoint joint model offers a robust and standardized approach to determining TMB thresholds for immunotherapy.
  • This methodology facilitates a more accurate assessment of clinical efficacy, leading to improved patient selection for cancer treatments.
  • The findings support the potential for innovative therapeutic selection and advance precision immuno-oncology.