DOI: 10.5061/dryad.612jm64jr
title: Nectin-4 PET for predicting enfortumab vedotin dose-response in urothelial carcinoma

description: Optimizing dosing strategies is critical to balance effectiveness and
 toxicity, especially for drugs with narrow therapeutic windows such as
 antibody-drug conjugates (ADCs). This study evaluates whether positron
 emission tomography (PET) imaging targeting Nectin-4 can non-invasively
 quantify the real-time interaction of the ADC enfortumab vedotin (EV) with
 tumors in urothelial carcinoma (UC). Using the imaging agent [68Ga]AJ647,
 dynamic changes in interaction of EV with Necin-4 were measured across
 preclinical models and correlated with therapeutic responses. PET imaging
 identified dose-dependent variations in Nectin-4 engagement, with
 suboptimal EV doses resulting in incomplete Nectin-4 engagement and
 increased tumor growth. Crucially, PET measured target engagement
 predicted therapeutic outcomes more reliably than either drug dose or
 baseline target expression. By defining effective target engagement levels
 needed for optimal therapeutic outcomes, PET imaging provides a clear
 benchmark for dosing decisions, maximizing efficacy while potentially
 reducing exposure to higher, toxic doses and thereby enhancing patient
 safety.
description: # Nectin-4 PET for predicting enfortumab vedotin dose-response in
 urothelial carcinoma Dataset DOI:
 [10.5061/dryad.612jm64jr](https://doi.org/10.5061/dryad.612jm64jr) ##
 **Overview** This README outlines the contents of files uploaded to Dryad
 for the study titled "Nectin-4 PET for predicting enfortumab vedotin
 dose-response in urothelial carcinoma." All required files are
 contained within the zip file
 “ROC_Curves_and_Response_prediction.zip". ### **1. Data File:
 20240502_responder_prediction.xlsx** * **Description:** This file contains
 tumor volume (mm3) measurements for preclinical mouse models over time,
 structured to predict response using the modified RECIST criteria. *
 **File Structure:** * Each column (except the first) represents an
 individual mouse. * The first column contains day numbers, and the first
 row identifies mouse IDs. * **Variables:** * **Day (Column 1):** Time
 points for tumor volume measurements. Unit for tumor volume is mm3. *
 **Mouse IDs (Row 1):** Identifiers for individual mice. ### **2. Script:
 20240429 Responders mRECIST prediction.ipynb** * **Description:** This
 Python script processes longitudinal tumor volume data and classifies
 responses according to modified RECIST-like criteria. * **Date Created:**
 2025-05-02 * **Input File:** **`20240502_responder_prediction.xlsx`** *
 **Note:** The script automatically implements the criteria summarized from
 **[PMID: 26479923](https://doi.org/10.1038/nm.3954)**. #### Requirements *
 **Python Version:** 3.8+ * **Packages:** * pandas * openpyxl (for Excel
 I/O) The script is designed to run smoothly without any additional
 requirements. #### Output Categories and Criteria: This Python script
 processes longitudinal tumor volume measurements in preclinical (mouse)
 models and automatically classifies each animal into one of four standard
 response categories using modified RECIST-like criteria. The criteria were
 summarized below: ΔVol is calculated as: ΔVol (%) = 100 × (V_day – V_day0)
 / V_day0 (relative to baseline, Day 0) Only measurements from Day ≥ 10
 onward are considered for average ΔVol response evaluation (to avoid early
 transient fluctuations). For each time t, the average ΔVolt from t = 0 to
 t was calculated, with the ‘Best average ΔVol’ defined as the lowest
 average ΔVolt for t ≥ 10 days. ‘Best ΔVol’ is defined as minimum ΔVol ever
 achieved by a mice. | Output Category | Criteria Applied in This Script |
 | :----------------------- | :--------------------------------------------
 | | CR (Complete Response) | Best ΔVol ≤ −95% and Best average ΔVol ≤ −40%
 | | PR (Partial Response) | Best ΔVol ≤ −50% and Best average ΔVol ≤ −20%
 | | SD (Stable Disease) | Best ΔVol &lt; +35% and Best average ΔVol &lt;
 +30% | | PD (Progressive Disease) | All other cases | 
 
 ### **3. Text File: 20240429 Responders mRECIST prediction.txt** *
 **Description:** This text file contains the code corresponding to the
 **`20240429 Responders mRECIST prediction.ipynb`** script that performs
 longitudinal data processing and response classification. The contents and
 structure are similar to the Jupyter notebook, providing an alternative
 format for users. ### **4. Data File: 20240501_ROC_Curve.xlsx** *
 **Description:** This file serves as the input dataset for the ROC Curve
 Analysis. * **File Structure:** * Each row corresponds to a mouse, with
 columns representing different attributes as detailed below. ####
 Variables: | Column | Description | **Type** | | :------------- |
 :------------------------------------------------------------- |
 :---------- | | M# | **Mice number (identifier)** | **integer** | |
 **dose** | **Pre-EV \[68Ga]AJ647, unit is SUV** | **float** | | **pre** |
 **Pre-intervention predictor score, unit is SUV** | **float** | | **post**
 | **Post-EV \[68Ga]AJ647, unit is SUV** | **float** | | **predelpost** |
 **Difference or change score (post Ð pre), unit is SUV** | **float** | |
 **engagement** | **Engagement metric (primary predictor), no unit,
 percentage** | **float** | | **response** | **Response metric based on
 tumor volumes** | integer | 
 
 ### **5. Script: 20241210_ROC_Curves.ipynb** * **Description:** This
 script generates Receiver Operating Characteristic (ROC) curves and
 calculates optimal classification thresholds for predictive features based
 on tumor responses. * **Date:** 2024-12-10 * **Data File:**
 **`20240501_ROC_Curve.xlsx`** #### Features Analyzed: * **pre:** Pre-EV
 [68Ga]AJ647 SUV * **post:** Post-EV [68Ga]AJ647 SUV * **predelpost:**
 Change score (Post–Pre) [68Ga]AJ647 SUV * **engagement:** -(Pre–Post)/Pre
 * 100 #### Requirements * **Python Version:** 3.8+ * **Packages:** *
 pandas * numpy * matplotlib * scikit-learn * plotly * scipy 
 
 The script is designed to run smoothly without any additional
 requirements. ### **6. Text File: 20241210 ROC Curves.txt** *
 **Description:** This text file contains the code corresponding to the
 **`20241210_ROC_Curves.ipynb`** script for generating ROC curves and
 threshold calculations. The contents are structured similarly to the
 Jupyter notebook, providing an alternative format for users.