Spatial transcriptomics–guided computational pathology model to stratify docetaxel benefit in metastatic hormone-sensitive prostate cancer: CHAARTED trial (ECOG-ACRIN E3805).

5002 Background: The CHAARTED trial demonstrated improved overall survival (OS) with the addition of docetaxel (DTX) to androgen deprivation therapy (ADT) in metastatic hormone-sensitive prostate cancer (mHSPC), but many patients do not benefit and experience significant toxicity. Clinical factors (e.g., metastatic burden) are suboptimal for predicting DTX benefit, indicating a need for better biomarkers. We developed ST-DoxPCa (Spatial Transcriptomics–Guided Docetaxel Therapy Stratification in Prostate Cancer), an artificial intelligence (AI)-driven biomarker that infers intratumoral gene expression from routine H&E slides (via virtual spatial transcriptomics) to stratify patients by likelihood of benefiting from DTX. Methods: ST-DoxPCa was developed using paired H&E and spatial transcriptomics from the HEST1K prostate cohort to train a Vision Transformer–based regressor to predict a 208-gene panel at spot/patch level; predictions were distilled into a biologically informed 26-gene signature and summarized into patient-level features capturing expression level and heterogeneity. In CHAARTED, a Cox model for overall survival (OS) was trained and internally validated in the ADT-only arm (n = 154; 50/50 split: n = 77 train, n = 77 validation) to derive an ST-DoxPCa risk score and fixed median threshold. This locked model was then applied to the ADT validation cohort (n = 77) and the held-out ADT+DTX arm (n = 129), and OS was compared between treatment arms within biomarker-defined risk groups using Kaplan–Meier estimates and Cox hazard ratios. Results: ST-DoxPCa stratified patients into biomarker-defined risk groups using aggregated spatial-expression features from a biologically informed gene panel; key genes included AR, PTEN, FASN, FOXA1, and ACPP. In ST-DoxPCa-positive (high-risk) patients, adding DTX to ADT significantly improved OS versus ADT alone (HR = 0.53, 95% CI 0.31–0.90; p = 0.018), with no benefit in ST-DoxPCa-negative (low-risk) patients (HR = 1.32, 95% CI 0.74–2.34; p = 0.34). Conclusions: ST-DoxPCa is a novel spatial transcriptomics guided histology biomarker that predicts differential benefit from docetaxel in mHSPC. ST-DoxPCa identified a subset of patients who achieve substantial survival gains from upfront DTX and another subset with no benefit, enabling personalized treatment intensification. ST-DoxPCa has the potential to spare low-risk patients unnecessary chemotherapy while directing therapy to those most likely to benefit. Clinical trial information: NCT00309985 .

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.5002
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial transcriptomics–guided computational pathology model to stratify docetaxel benefit in metastatic hormone-sensitive prostate cancer: CHAARTED trial (ECOG-ACRIN E3805).

Naoto Tokuyama, S Gupta, Fu P, Erik Meijering et al.
Journal of Clinical Oncology
Prostate Cancer Diagnosis and Treatment
article

Spatial transcriptomics–guided computational pathology model to stratify docetaxel benefit in metastatic hormone-sensitive prostate cancer: CHAARTED trial (ECOG-ACRIN E3805).

Naoto Tokuyama, S Gupta, Fu P, Erik Meijering, Tuomas Mirtti, Amritpal Singh, Kamal Hammouda, Md Mamunur Rahaman, Christopher Sweeney, Ewan KA MIllar, Priti Lal, Sebastian R. Medina, Anant Madabhushi
article en

Abstract

5002 Background: The CHAARTED trial demonstrated improved overall survival (OS) with the addition of docetaxel (DTX) to androgen deprivation therapy (ADT) in metastatic hormone-sensitive prostate cancer (mHSPC), but many patients do not benefit and experience significant toxicity. Clinical factors (e.g., metastatic burden) are suboptimal for predicting DTX benefit, indicating a need for better biomarkers. We developed ST-DoxPCa (Spatial Transcriptomics–Guided Docetaxel Therapy Stratification in Prostate Cancer), an artificial intelligence (AI)-driven biomarker that infers intratumoral gene expression from routine H&E slides (via virtual spatial transcriptomics) to stratify patients by likelihood of benefiting from DTX. Methods: ST-DoxPCa was developed using paired H&E and spatial transcriptomics from the HEST1K prostate cohort to train a Vision Transformer–based regressor to predict a 208-gene panel at spot/patch level; predictions were distilled into a biologically informed 26-gene signature and summarized into patient-level features capturing expression level and heterogeneity. In CHAARTED, a Cox model for overall survival (OS) was trained and internally validated in the ADT-only arm (n = 154; 50/50 split: n = 77 train, n = 77 validation) to derive an ST-DoxPCa risk score and fixed median threshold. This locked model was then applied to the ADT validation cohort (n = 77) and the held-out ADT+DTX arm (n = 129), and OS was compared between treatment arms within biomarker-defined risk groups using Kaplan–Meier estimates and Cox hazard ratios. Results: ST-DoxPCa stratified patients into biomarker-defined risk groups using aggregated spatial-expression features from a biologically informed gene panel; key genes included AR, PTEN, FASN, FOXA1, and ACPP. In ST-DoxPCa-positive (high-risk) patients, adding DTX to ADT significantly improved OS versus ADT alone (HR = 0.53, 95% CI 0.31–0.90; p = 0.018), with no benefit in ST-DoxPCa-negative (low-risk) patients (HR = 1.32, 95% CI 0.74–2.34; p = 0.34). Conclusions: ST-DoxPCa is a novel spatial transcriptomics guided histology biomarker that predicts differential benefit from docetaxel in mHSPC. ST-DoxPCa identified a subset of patients who achieve substantial survival gains from upfront DTX and another subset with no benefit, enabling personalized treatment intensification. ST-DoxPCa has the potential to spare low-risk patients unnecessary chemotherapy while directing therapy to those most likely to benefit. Clinical trial information: NCT00309985 .

Journal of Clinical OncologyVol. 44(16_suppl)
Georgia Institute of Technology (US), New South Wales Department of Health (AU), Cleveland Clinic (US), Emory University (US), Hospital of the University of Pennsylvania (US), Helsinki University Hospital (FI), The Wallace H. Coulter Department of Biomedical Engineering (US), UNSW Sydney (AU), St George Hospital (AU), The University of Adelaide (AU), Case Western Reserve University (US)
U.S. Department of Veterans Affairs
Openalex Percentile: Top 8%
Prostate Cancer Diagnosis and Treatment
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