Intrapatient Comparative Efficacy of Selective RET Inhibitors Using Growth Modulation Index in Patients with RET Aberrant Cancers.
JOURNAL OF CLINICAL ONCOLOGY(2023)
The University of Texas MD Anderson Cancer Center
Abstract
3086 Background: Selective RET inhibitors (RETi) have changed the paradigm for treatment of RET altered cancers. Current evidence on their efficacy comes from single-arm basket trials. In this study, we performed intrapatient efficacy comparison between approved selective RETi (selpercatinib and pralsetinib) and prior and/or subsequent therapies. We used growth modulation index (GMI) as a tool to compare efficacy, where a value of >1.33 is presumed a cutoff to establish superior efficacy. Methods: We included patients with RET-positive tumors who were treated at our institution as part of RETi clinical trials. We excluded patients with no other systemic therapy received before or after the RETi of interest. We also excluded patients who received both drugs to avoid overlapping efficacy bias, and patients who were treated for ≤ 30 days or had non-evaluable disease. Since some patients have been treated beyond progression or discontinued RETi due to intolerance, we used ratios for both time to treatment takeoff (TTT) and time to progression (TTP) to compare efficacy of RETi (at the RETi treatment line (n)) to prior therapy (n-1) and to subsequent therapy (n+1). GMI was defined as the ratio between TTT/TTT (n±1) or TTP to TTP (n±1). Results: We included 66 patients who received RETi and met our inclusion criteria [39 received selpercatinib and 27 received pralsetinib]. Most patients had GMI>1.33 using either TTT or TTP (61% (n=40) and 58% (n=38), respectively). The median GMI based on pre-RETi therapy was 2.1 and 1.6 (using TTT and TTP, respectively); while the median GMI based on post-RETi therapy was 4.9 and 4.4 (using TTT and TTP, respectively). Patients with GMI>1.33 were more likely to have PR as best response compared to patients with GMI<1.33 (85% vs 53%, p=0.005 using TTT; and 87% vs 54%, p=0.003 using TTP). GMI using TTT (n/n-1) was higher in patients with RET fusions compared to patients with RET mutations (3.7 vs 1.4, p=0.048). GMI using TTT and GMI using TTP(n/n+1) were lower in patients with WBCs <8 at baseline (2.6 vs 13.8, p=0.014; 2.6 vs 11.1, p=0.014; for TTT and TTP) and GI cancer diagnosis (0.4 vs 9.3, p=0.042; 0.4 vs 10.4, p=0.042; for TTT and TTP). Conclusions: Intrapatient efficacy comparisons are feasible using GMI calculations and provide a proof of concept on the favorability of selective RETi compared to other systemic therapies. [Table: see text]
MoreTranslated text
Key words
Tumor Heterogeneity
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper