Shuang George Zhao

Purpose: Disease recurrence after definitive therapy remains a major cause of morbidity and mortality in early-stage breast cancer, with limited options for treatment intensification. Cell surface–targeted (CST) therapies, including antibody-drug conjugates (ADCs), radiopharmaceuticals, and immune-based approaches, have demonstrated success in treating malignancies. However, the known CST repertoire in breast cancer is narrow, largely restricted to HER2 and TROP2, highlighting an unmet need for novel targets in treatment-resistant disease. Methods: We profiled CST expression using large-scale gene expression datasets annotated with clinical outcomes, including SCAN-B ( n ≈ 10,000), aggregated neoadjuvant chemotherapy cohorts ( n ≈ 4,000), and the randomized SWEBCG91RT trial ( n = 765). CSTs with therapies in clinical trials were prioritized. RNA-protein correlation was assessed using CPTAC and SCMP datasets. Associations with recurrence-free survival (RFS) and treatment response were analyzed using Cox regression and Fisher’s exact test with Benjamini-Hochberg correction. Results: Of 78 CSTs under active clinical investigation, RNA expression strongly predicted protein expression (median accuracy 80–94%). In ER+ breast cancer treated with endocrine therapy, nine CSTs (including CEACAM5/6 , EPCAM , NECTIN4 ) were associated with significantly worse RFS. Of these, EPCAM and CEACAM5 were also associated with poor outcomes specifically after radiotherapy in the SWEBCG91RT trial. In HER2 + disease, high expression of FOLH1 and TNFRSF10B predicted relapse despite anti-HER2 therapy. In triple-negative breast cancer, six CSTs were associated with residual disease after neoadjuvant therapy. Conclusions: These analyses identify potential candidates for repositioning CST drugs, and single-cell analysis revealed substantial intratumoral heterogeneity for several targets, informing payload selection strategies. CSTs associated with treatment resistance across breast cancer subtypes represent actionable opportunities for therapeutic repositioning. These findings provide a framework for rational integration of CST therapies in early-stage breast cancer.

Purpose: The Decipher Prostate Genomic Classifier (GC) and ArteraAI Multimodal Artificial Intelligence (MMAI) platform are widely used prognostic tools for localized prostate cancer, yet no direct comparison has been performed within the same patient cohort. Experimental Design: We sought to evaluate the concordance and prognostic value of GC and MMAI across multi-institutional cohorts totaling 688 patients with localized and oligometastatic prostate cancer. These included populations enriched for African American and East Asian patients. GC and MMAI scores were stratified into risk groups based on specimen type [biopsy or radical prostatectomy (RP)]. The primary endpoint was distant metastasis–free survival, measured from diagnosis to last follow-up or event. Results: GC and MMAI scores were significantly correlated across multiple cohorts. In Moffitt RP, biopsy, and National Cancer Centre Singapore cohorts, both MMAI and GC were prognostic alone and after adjusting for clinical variables. Multivariable analysis, including both biomarkers and clinical variables, showed that MMAI was significant even when accounting for GC in Moffitt RP and borderline in Moffitt biopsy. Analysis of concordant/discordant cases showed that patients who were high risk for both GC and MMAI did qualitatively worse. In pathway analysis, concordance with GC and MMAI was high for Moffitt RP and oligometastatic patients. Across both biopsy cohorts, although most biological pathways were concordant, there were consistent discordant signaling, metabolic, and DNA repair pathways. Conclusions: In the first direct patient-level comparison of GC and MMAI in prostate cancer, these biomarkers were found to demonstrate moderate correlation, and both biomarkers demonstrated prognostic value across diverse populations and disease states.

Purpose:Circulating tumor cells (CTC) provide a minimally invasive window into metastatic disease and treatment response, but their clinical utility has been constrained by manual, subjective, low-throughput identification in multichannel fluorescence microscopy data. Experimental Design:To address this limitation, we developed and clinically validated the System for Enhanced Evaluation of Tumor Cells (SEE-TC), a deep learning approach for scalable, reproducible phenotyping of individual circulating cells. SEE-TC was developed and evaluated on more than 8.5 million cells from 3,386 blood samples spanning six cancer types, enabling generalization across heterogeneous imaging conditions, staining panels, and acquisition platforms. Results:SEE-TC achieved single-cell segmentation accuracy on par with humans, learned biologically relevant latent cellular representations that correlate with established morphologic and immunofluorescent biomarkers, and reliably distinguished CTCs from background populations without reliance on arbitrary thresholds. When applied longitudinally, SEE-TC provides a quantitative, patient-level readout of CTC burden over time, which was significantly associated with worse overall survival across multiple cancer types. Conclusions:To our knowledge, this is the first fully automated artificial intelligence approach to single-cell segmentation and CTC phenotyping. By transforming CTC analysis from a human-dependent task into a scalable and reproducible digital assay, SEE-TC enables high-fidelity longitudinal monitoring of tumor burden and supports broader clinical deployment of CTC-based liquid biopsies in precision oncology. It is currently being deployed to identify CTCs and quantify target expression for both prognostic and predictive biomarker evaluation in multiple prospective clinical trials on a commercial platform.

All supplementary Figures and tables.

Purpose:PrOstate cancer Radiation Therapy Outcomes Score (PORTOS) is a 24-gene radiation response signature developed and validated in multiple randomized trials in prostate cancer. Given its basis in general radiation and DNA damage response pathways, we sought to evaluate whether PORTOS could also predict radiotherapy (RT) benefit in breast cancer. Experimental Design:PORTOS scores were calculated from gene expression profiles of 765 tumor samples from SweBCG91RT, a randomized trial of adjuvant whole-breast RT versus observation following breast-conserving surgery in node-negative stages I to IIA breast cancer. The primary endpoint was 10-year cumulative incidence (CI) of any recurrence, which closely mirrored the prostate cancer endpoints PORTOS was validated on. Patient-reported breast pain and tumor-infiltrating leukocytes (TIL) were also analyzed. Results:A significant interaction (P = 0.049) between PORTOS and RT was observed for 10-year CI of any recurrence. Patients in the top 75% of PORTOS scores derived substantial benefit from RT [subdistribution hazard ratio (SHR) = 0.46, P < 0.001] compared with those in the bottom 25% (SHR = 0.73, P = 0.24). Higher PORTOS scores were also associated with increased risk of RT-related breast pain, though the interaction P value was not significant (interaction P = 0.07). PORTOS was weakly anticorrelated with tumor size, histologic grade, and TILs and provided orthogonal information to previously validated signatures ARTIC and POLAR. Conclusions:PORTOS predicts benefit from RT for any recurrence in early-stage breast cancer, mirroring its performance in prostate cancer. It is the first radiation response biomarker to demonstrate predictive value for both efficacy and toxicity across randomized trials in multiple tumor types.

Purpose: Circulating tumor cells (CTC) provide a minimally invasive window into metastatic disease and treatment response, but their clinical utility has been constrained by manual, subjective, low-throughput identification in multichannel fluorescence microscopy data. Experimental Design: To address this limitation, we developed and clinically validated the System for Enhanced Evaluation of Tumor Cells (SEE-TC), a deep learning approach for scalable, reproducible phenotyping of individual circulating cells. SEE-TC was developed and evaluated on more than 8.5 million cells from 3,386 blood samples spanning six cancer types, enabling generalization across heterogeneous imaging conditions, staining panels, and acquisition platforms. Results: SEE-TC achieved single-cell segmentation accuracy on par with humans, learned biologically relevant latent cellular representations that correlate with established morphologic and immunofluorescent biomarkers, and reliably distinguished CTCs from background populations without reliance on arbitrary thresholds. When applied longitudinally, SEE-TC provides a quantitative, patient-level readout of CTC burden over time, which was significantly associated with worse overall survival across multiple cancer types. Conclusions: To our knowledge, this is the first fully automated artificial intelligence approach to single-cell segmentation and CTC phenotyping. By transforming CTC analysis from a human-dependent task into a scalable and reproducible digital assay, SEE-TC enables high-fidelity longitudinal monitoring of tumor burden and supports broader clinical deployment of CTC-based liquid biopsies in precision oncology. It is currently being deployed to identify CTCs and quantify target expression for both prognostic and predictive biomarker evaluation in multiple prospective clinical trials on a commercial platform.

Purpose: PrOstate cancer Radiation Therapy Outcomes Score (PORTOS) is a 24-gene radiation response signature developed and validated in multiple randomized trials in prostate cancer. Given its basis in general radiation and DNA damage response pathways, we sought to evaluate whether PORTOS could also predict radiotherapy (RT) benefit in breast cancer. Experimental Design: PORTOS scores were calculated from gene expression profiles of 765 tumor samples from SweBCG91RT, a randomized trial of adjuvant whole-breast RT versus observation following breast-conserving surgery in node-negative stages I to IIA breast cancer. The primary endpoint was 10-year cumulative incidence (CI) of any recurrence, which closely mirrored the prostate cancer endpoints PORTOS was validated on. Patient-reported breast pain and tumor-infiltrating leukocytes (TIL) were also analyzed. Results: A significant interaction (P = 0.049) between PORTOS and RT was observed for 10-year CI of any recurrence. Patients in the top 75% of PORTOS scores derived substantial benefit from RT [subdistribution hazard ratio (SHR) = 0.46, P < 0.001] compared with those in the bottom 25% (SHR = 0.73, P = 0.24). Higher PORTOS scores were also associated with increased risk of RT-related breast pain, though the interaction P value was not significant (interaction P = 0.07). PORTOS was weakly anticorrelated with tumor size, histologic grade, and TILs and provided orthogonal information to previously validated signatures ARTIC and POLAR. Conclusions: PORTOS predicts benefit from RT for any recurrence in early-stage breast cancer, mirroring its performance in prostate cancer. It is the first radiation response biomarker to demonstrate predictive value for both efficacy and toxicity across randomized trials in multiple tumor types.

Table S1. Clinical characteristics of the 177LuPSMA-treated cohort

Supplementary Figure S3. OGDHL Loss in CRPC cells reduces abundance of key nucleotide synthesis precursors but does not alter glucose or glutamine incorporation into TCA cycle intermediates