Remote physiologic and behavioral monitoring to predict early treatment response in metastatic cancer: High-Definition Oncology study (HDOs) preliminary results.

1651 Background: Emerging evidence suggests that behavioral, physiologic or emotional factors may act as real-time indicators of treatment response, with potential as modifiable factors. Advances in remote monitoring technologies provide passive (e.g., heart rate, sleep patterns) and active (e.g., self-reported emotions) data. HDOs collects such data and serial -omics from 300 women with metastatic cancer to identify novel markers, understand disease trajectories and develop a digital twin for individualized care. We present data from 25% accrual. Methods: Women receiving first-line treatment for metastatic colorectal, lung, or hormone-positive breast cancer were eligible. Patients continuously wore a smartwatch and used the EB2 App to capture step count (SC), sleep duration (SL), phone usage (PU), time at home (TH), location clusters (LC), mean (MHR) and minimum (mHR) heart rate, mean (MSHR) and minimum (mSHR) sleeping heart rate and sleeping oxygen saturation (SOS). Emotional valence was self-reported from a list of 20 emotions and classified as negative (-1), neutral (0) or positive (+1). Aim 1: to explore the relationship between the variables and response (CB: CR+PR+SD vs. PD) at the first CT scan at day +90 analyzing data from days 1-15 and 75-90 (Mann-Whitney U). Aim 2: to find Response-Associated Behavioral Patterns (RABPs) associated with CB or PD. First, Daily Behavioral Profiles (DBPs) are obtained using unsupervised learning models from > 2 million days of smartwatch and App data (external set). After identifying 256 DBPs with the VQ-VAE model, Latent Dirichlet Allocation defined RABPS based on the frequency and abundance of DBPs per patient. RABPs were compared among classes (response type, age group) using X 2 . Bilateral P values < 0.01 were deemed significant. Results: from May 2023 to April 2024, 77 female patients (median age 61; 28-80) were accrued (46 Breast, 23 Lung, 8 Colorectal). At first CT, 72 (93.5%) achieved CB while 5 (6.5%) had PD. During days 1-15, CB patients showed lower PU (2.4 vs. 3.9 hours; P = 0.002), TH (18.5 vs. 22 hours; P = 2* 10 ^-7 ), MHR (78 vs. 88 bpm), mHR (59 vs 70 bpm), MSHR (75 vs 88 bpm) mSHR (66 vs 78 bpm) (all Ps < 10^ -10 ) and reported more negative EV. The trends persisted in days 76-90 in addition to SC (7235 vs 4038 steps/day; P = 0.00001) and decreased SOS (90.1% vs. 92.6%; P = 1.5*10^ -8 ). Six RABPS were identified. Patients < 60 yo displayed more often RABPs 1, 2 and 5 (84% vs. 16%; P = 0.02). RABP1 breast cancer and RABP5 lung cancer patients were more likely to experience PD vs. CB (75% vs. 24%; P = 0.08; and 69% vs.19%, P = 0.07, respectively). Conclusions: Behavioral and physiologic data in days 1-15 and 76-90 were strongly associated with treatment response, independent of tumor type, age or treatment. RABPS identifying patients at high risk of PD can be detected, highlighting their value as markers for early intervention.

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Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.1651
Primary Topic
Advanced Biosensing Techniques and Applications
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article

Remote physiologic and behavioral monitoring to predict early treatment response in metastatic cancer: High-Definition Oncology study (HDOs) preliminary results.

Miguel Quintela-Fandiño, David Vicente, Begoña Bermejo, Josefa Terrasa et al.
Journal of Clinical Oncology
Advanced Biosensing Techniques and Applications
article

Remote physiologic and behavioral monitoring to predict early treatment response in metastatic cancer: High-Definition Oncology study (HDOs) preliminary results.

Miguel Quintela-Fandiño, David Vicente, Begoña Bermejo, Josefa Terrasa, Sònia Pernas, Rámón Colomer, M.L. Álvarez Sauras, Rosario García-Campelo, Leonardo D. Garma, Ruth Vera, Pablo M. Olmos, Santiago González Santiago, Antonio Artés-Rodrı́guez, Leire Paz-Arbaizar, Desirée Jiménez, Antonio López‐Alonso, Berta Nasarre
article en

Abstract

1651 Background: Emerging evidence suggests that behavioral, physiologic or emotional factors may act as real-time indicators of treatment response, with potential as modifiable factors. Advances in remote monitoring technologies provide passive (e.g., heart rate, sleep patterns) and active (e.g., self-reported emotions) data. HDOs collects such data and serial -omics from 300 women with metastatic cancer to identify novel markers, understand disease trajectories and develop a digital twin for individualized care. We present data from 25% accrual. Methods: Women receiving first-line treatment for metastatic colorectal, lung, or hormone-positive breast cancer were eligible. Patients continuously wore a smartwatch and used the EB2 App to capture step count (SC), sleep duration (SL), phone usage (PU), time at home (TH), location clusters (LC), mean (MHR) and minimum (mHR) heart rate, mean (MSHR) and minimum (mSHR) sleeping heart rate and sleeping oxygen saturation (SOS). Emotional valence was self-reported from a list of 20 emotions and classified as negative (-1), neutral (0) or positive (+1). Aim 1: to explore the relationship between the variables and response (CB: CR+PR+SD vs. PD) at the first CT scan at day +90 analyzing data from days 1-15 and 75-90 (Mann-Whitney U). Aim 2: to find Response-Associated Behavioral Patterns (RABPs) associated with CB or PD. First, Daily Behavioral Profiles (DBPs) are obtained using unsupervised learning models from > 2 million days of smartwatch and App data (external set). After identifying 256 DBPs with the VQ-VAE model, Latent Dirichlet Allocation defined RABPS based on the frequency and abundance of DBPs per patient. RABPs were compared among classes (response type, age group) using X 2 . Bilateral P values < 0.01 were deemed significant. Results: from May 2023 to April 2024, 77 female patients (median age 61; 28-80) were accrued (46 Breast, 23 Lung, 8 Colorectal). At first CT, 72 (93.5%) achieved CB while 5 (6.5%) had PD. During days 1-15, CB patients showed lower PU (2.4 vs. 3.9 hours; P = 0.002), TH (18.5 vs. 22 hours; P = 2* 10 ^-7 ), MHR (78 vs. 88 bpm), mHR (59 vs 70 bpm), MSHR (75 vs 88 bpm) mSHR (66 vs 78 bpm) (all Ps < 10^ -10 ) and reported more negative EV. The trends persisted in days 76-90 in addition to SC (7235 vs 4038 steps/day; P = 0.00001) and decreased SOS (90.1% vs. 92.6%; P = 1.5*10^ -8 ). Six RABPS were identified. Patients < 60 yo displayed more often RABPs 1, 2 and 5 (84% vs. 16%; P = 0.02). RABP1 breast cancer and RABP5 lung cancer patients were more likely to experience PD vs. CB (75% vs. 24%; P = 0.08; and 69% vs.19%, P = 0.07, respectively). Conclusions: Behavioral and physiologic data in days 1-15 and 76-90 were strongly associated with treatment response, independent of tumor type, age or treatment. RABPS identifying patients at high risk of PD can be detected, highlighting their value as markers for early intervention.

Journal of Clinical OncologyVol. 43(16_suppl)
Universidade da Coruña (ES), Institut d'Investigació Biomédica de Bellvitge (ES), Spanish National Cancer Research Centre (ES), Hospital Universitario Virgen Macarena (ES), Hospital Clínico Universitario de Valencia (ES), Institut Català d'Oncologia (ES), Hospital San Pedro de Alcántara (ES), Hospital Universitario de La Princesa (ES), Centro de Investigación del Cáncer (ES), Hospital Universitario de Fuenlabrada (ES), Hospital Universitario Son Espases (ES), Universidad Carlos III de Madrid (ES), Universidad de Navarra (ES)
Openalex Percentile: Top 89%
Advanced Biosensing Techniques and Applications
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