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W Fraser Symmans

Publications and source records attributed to W Fraser Symmans.

4 recordsLinked to original sources
PubMed2026

Sensitivity to Endocrine Therapy Index Predicts Benefit from Weekly Adjuvant Paclitaxel for Hormone Receptor-Positive Breast Cancer in the GEICAM/9906 Trial.

PURPOSE: To independently validate that low endocrine transcriptional activity measured by the sensitivity to endocrine therapy (SETER/PR) index in hormone receptor-positive (HR+) breast cancer predicts benefit from dose-dense paclitaxel chemotherapy within a second prospective-retrospective biomarker study. EXPERIMENTAL DESIGN: We conducted a blinded, prospective-retrospective biomarker analysis within the GEICAM/9906 trial (NCT00129922), which compared adjuvant 5-fluorouracil, epirubicin, and cyclophosphamide (FEC) followed by weekly paclitaxel (P) versus six cycles of FEC in lymph node-positive breast cancer. The SETER/PR index was measured in all available HR+/HER2- tumor RNA samples using a prespecified cut point (<0.75). The primary endpoint was the distant recurrence-free interval (DRFI); secondary endpoints were overall survival (OS) and breast cancer-specific survival (BCSS). RESULTS: Of 647 HR+/HER2- tumors, 567 (87.6%) passed assay quality control (279 FEC + P; 288 FEC). A low SETER/PR index was identified in 92 tumors (16.2%). There was a significant interaction between SETER/PR status and treatment on DRFI (P = 0.046). Among patients with a low SETER/PR index, FEC + P significantly improved DRFI [hazard ratio (HR), 0.46; 95% confidence interval (CI), 0.22-0.95; P = 0.035], with similar results after adjustment (HR, 0.48; 95% CI, 0.24-1; P = 0.049). No treatment benefit was observed for SETER/PR &#x2265;0.75 (HR, 1.02; 95% CI, 0.70-1.47; P = 0.931). Differences in OS and BCSS did not reach significance. CONCLUSIONS: Low endocrine transcriptional activity predicts benefit from adding weekly paclitaxel to anthracycline-based adjuvant chemotherapy in HR+/HER2- breast cancer. These findings independently validate the SETER/PR index as a predictive biomarker for paclitaxel-based chemotherapy and support its potential role in guiding regimen selection.

Humans↗
PubMed2026

Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.

PURPOSE: To develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. METHODS: We developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrell's concordance index (C-index) and Kaplan-Meier analyses. RESULTS: A total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. CONCLUSION: Digital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

Journal Article↗
PubMed2025

An Annotated Biobank of Triple-Negative Breast Cancer Patient-Derived Xenografts Features Treatment-Na&#xef;ve and Longitudinal Samples during Neoadjuvant Chemotherapy.

UNLABELLED: Triple-negative breast cancer (TNBC) that fails to respond to neoadjuvant chemotherapy (NACT) can be lethal. Developing effective strategies to eradicate chemoresistant disease requires experimental models that recapitulate the heterogeneity characteristic of TNBC. To that end, we established a biobank of 92 orthotopic patient-derived xenograft (PDX) models of TNBC from the tumors of 75 patients enrolled in A Robust TNBC Evaluation fraMework to Improve Survival clinical trial (ARTEMIS, NCT02276443), including 12 longitudinal sets generated from serial patient biopsies collected throughout NACT treatment and from metastatic disease. Models were established from both chemosensitive and chemoresistant tumors, and nearly 30% of the PDX models were capable of metastasizing to the lungs. Comprehensive molecular profiling demonstrated conservation of genomes and transcriptomes between patient and corresponding PDX tumors, with representation of all major transcriptional subtypes. Transcriptional changes observed in the longitudinal PDX models highlighted dysregulation in pathways associated with DNA integrity, extracellular matrix interactions, the ubiquitin-proteasome system, epigenetics, and inflammatory signaling. These alterations revealed a complex network of adaptations associated with chemoresistance. Overall, this PDX biobank provides a valuable tool for tackling the most pressing issues facing the clinical management of TNBC. SIGNIFICANCE: The development of a patient-derived xenograft biobank that comprehensively captures the genomic and transcriptional diversity of triple-negative breast cancer promises to be a robust resource to investigate and overcome chemoresistance and metastasis.

Animals↗
PubMed2025

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago. Among HER2-negative patients, a transformer-based model trained using a signature composed of estrogen-regulated genes, proliferation, apoptosis, and interferon response genes predicted pCR with an AUC of 0.794, outperforming models based on clinical features alone (AUC 0.704, p = 0.001), pathologist TIL assessment, and a model trained directly to predict response from I-SPY2 cases. Tertiles of this signature stratify patients into clinically relevant groups with increasing likelihood of complete response, with pCR rates &#x2265;50% in the top tertile regardless of treatment or hormone receptor status. Additional transformer-based signature models predicted response to specific therapies (but not chemotherapy alone), including a HER2 signaling signature in IO-treated patients, and a claudin-low signature in bevacizumab treated patients. In HER2- cohorts with available gene expression data and histology, models trained on expression data performed similarly to digital histology predictions, but the combination of gene expression and histology outperformed histology alone. These findings suggest that histology-based GES provides additive information to RNA sequencing data and can inform precision treatment selection across breast cancer subtypes.

Journal Article↗