Designing an optimal digital pathology workspace is essential to ensure diagnostic accuracy and safeguard the long-term well-being of pathologists. While digital pathology improves reproducibility, facilitates multidisciplinary collaboration, and supports data-driven precision medicine, its clinical effectiveness depends not only on computational performance but also on the physical and ergonomic environment in which pathologists operate. Inadequate workstation design may impair visual perception, increase cognitive and musculoskeletal strain, and potentially affect diagnostic consistency. Moreover, the progressive integration of artificial intelligence (AI) into routine diagnostics introduces additional requirements related to display performance, visualization interfaces, and human–machine interaction. Despite the rapid global adoption of digital pathology systems, standardized recommendations addressing ergonomic, environmental, and technological aspects of the digital workspace remain limited. In this work, we propose a clinically oriented framework for the design of digital pathology workspaces suitable for AI-assisted diagnostics. Key elements include the selection and calibration of medical-grade displays, ergonomic furniture and input devices, optimized ambient lighting conditions, and institutional quality assurance procedures. Emerging developments, such as intelligent ergonomic monitoring, advanced visualization interfaces, and adaptive AI-assisted workflows, may further support safe, sustainable, and high-performance digital diagnostic environments.

BACKGROUND:The molecular concordance between primary lung adenocarcinoma and metastatic lesions remains incompletely characterized despite its direct implications for precision oncology and biopsy-driven therapeutic decision-making. This prospective monocentric paired-sample study evaluated genomic concordance between primary lung adenocarcinoma and synchronous thoracic metastatic lesions using targeted next-generation sequencing (NGS). METHODS:We identified 27 treatment-naïve patients with histologically confirmed lung adenocarcinoma who underwent paired molecular profiling of the primary tumor and a synchronous thoracic metastatic site (pleural or intrapulmonary). DNA and RNA were analyzed using validated institutional NGS platforms. Genomic alterations, including clinically actionable oncogenic drivers consistently covered by the sequencing panel used in each pair, were compared across matched samples. Concordance was assessed using exact binomial confidence intervals, Cohen's κ statistics, McNemar tests, and paired Wilcoxon signed-rank tests. RESULTS:Actionable driver alterations were identified in 17 of 27 patients (63.0%; 95% CI 42.4-80.6), including EGFR mutations (40.7%), KRAS alterations (18.5%), and one ALK gene rearrangement (3.7%). TP53 concurrent mutations were detected in 14 cases (51.9%). Across all 27 paired samples, driver-level concordance was 100% (95% CI 87.2-100), with perfect agreement for EGFR, KRAS, and ALK alterations (κ = 1.00). TP53 mutations showed high concordance (92.9%; κ = 0.85), while CNVs were concordant in 88.0% of evaluable pairs. Variant allele frequency (VAF) comparisons, adjusted for tumor cellularity, further supported the apparent clonal stability of driver alterations across paired samples. CONCLUSIONS:This study demonstrates very high molecular concordance between primary lung adenocarcinomas and their synchronous pleural or intrapulmonary metastases. The observed 100% concordance of actionable driver alterations across paired specimens supports the clinical reliability of thoracic metastatic biopsies for baseline molecular profiling in treatment-naïve disease. Although limited by sample size, these findings support the biological stability of actionable driver alterations during early thoracic metastatic dissemination.

Background:Radiomics and liquid biopsy represent minimally invasive approaches to assess disease characteristics in solid tumors. We integrated computed tomography (CT) radiomics and circulating tumor DNA (ctDNA) analysis to enhance prognostic stratification and longitudinal monitoring in patients with advanced non-small cell lung cancer (NSCLC). Methods:This study prospectively enrolled 91 patients with advanced NSCLC. Baseline molecular profiling was performed on both tumor tissue and plasma ctDNA using targeted next-generation sequencing. Radiomic features were extracted from baseline CT lung lesions using PyRadiomics, and radiomic scores (RS) were developed using LASSO-regularized Cox models. A subgroup of 21 patients with actionable molecular alterations underwent longitudinal CT scans and liquid biopsies during targeted therapy. Clinical, radiomic, and molecular associations with overall survival (OS) and disease-free survival (DFS) were evaluated using log-rank tests and included in multivariable models. Results:Overall concordance between tissue and ctDNA (n = 67 patients) was 85%. In the combined clinical-radiomic-genetic model (C-index: 0.73), the RS (p < 0.001) and the presence of actionable alterations (p = 0.041) were independent OS predictors. For DFS, the integrated model achieved a cross-validated C-index of 0.77, outperforming the clinical-only model (0.59). In patients with EGFR-mutant NSCLC, detectable baseline ctDNA was significantly associated with a higher risk of disease progression (p = 0.018). In this subgroup, the combined clinical-radiogenomic model achieved a cross-validated C-index of 0.80 for DFS. Longitudinal analysis showed that 17 of 21 patients achieved molecular clearance of ctDNA at the first follow-up, correlating with treatment response. Conclusions:Integrating radiomics with liquid biopsy provides a more robust prognostic assessment of advanced NSCLC than clinical or molecular data alone. This multi-modal approach may offer a minimally invasive strategy for personalized risk stratification and monitoring of treatment response in patients with NSCLC.Clinicaltrials.gov identifier: NCT06331975.

Activating ESR1 mutations are a major mechanism of resistance to aromatase inhibitors in hormone receptor-positive, HER2-negative metastatic breast cancer (mBC). International guidelines, including those from ASCO, NCCN, and ESMO, recommend liquid biopsy as the preferred approach for ESR1 mutation testing at progression on endocrine therapy, with digital PCR (dPCR) and next-generation sequencing (NGS) as the preferred analytical platforms. Although elacestrant was approved by the U.S. Food and Drug Administration together with Guardant360® Dx as its companion diagnostic, European regulatory frameworks allow the use of validated in-house assays for ESR1 testing, which are increasingly being implemented across clinical laboratories. To support the clinical implementation of ESR1 testing and improve analytical standardization in routine practice, we performed a European multicentre analytical verification study using dPCR- and NGS-based liquid biopsy workflows. Six referral institutions participated in this study. All laboratories verified dPCR workflows and four also verified NGS-based assays using standardized reference materials containing clinically relevant ESR1 mutations. Limit of detection (LoD) and limit of blank (LoB) were determined in each laboratory according to locally validated workflows following CLSI-based verification procedures. Analytical sensitivity and specificity were assessed across platforms, focusing on the two most frequently tested ESR1 hotspot mutations, p.Y537S and p.D538G. Total DNA input ranged from 10 to 30 ng per reaction for dPCR assays, while NGS input followed platform-specific requirements. LoD values ranged from 0.01

Histopathologic assessment of the tumor bed following neoadjuvant therapy in non-small cell lung cancer (NSCLC) is increasingly relevant, but comparative data across treatment modalities remain limited. We evaluated tumor bed features and artificial intelligence (AI)-assisted stromal quantification in advanced NSCLC, comparing patients treated with immunotherapy, tyrosine kinase inhibitors (TKIs), and chemotherapy. This multicenter retrospective study included 71 patients with stage IIIB-IV NSCLC who underwent salvage surgery after neoadjuvant therapy at five Italian centers between 2018 and 2022. Thirty-eight patients received immune-based therapy, 16 received TKIs, and 17 received chemotherapy alone. Tumor bed response was assessed according to International Association for the Study of Lung Cancer recommendations, including residual viable tumor, necrosis, fibrosis, and inflammation. Immune-related features were recorded. AI-assisted morphometric analysis quantified fibrosis on Azan-Mallory staining and inflammatory burden on CD45 immunohistochemistry. The immunotherapy group showed the most favorable regression profile, with higher pathological complete response rates than the TKI and chemotherapy groups, respectively (40.5% vs 21.4% vs 11.8%), and lower residual viable tumor burden (median, 5% vs 45% vs 45%). This group also showed a more immune-reactive tumor bed phenotype and significantly higher AI-quantified inflammatory burden (p = 0.022). In the overall cohort, multivariable analysis identified residual viable tumor percentage (HR, 1.02; p = 0.018) and AI-derived fibrosis (HR, 0.97; p = 0.013) as independent predictors of recurrence. Tumor bed evaluation after neoadjuvant therapy provides prognostically relevant information beyond residual viable tumor alone. AI-assisted fibrosis quantification may complement viable tumor assessment and refine post-surgical risk stratification.

e13002 Background: Selecting the most effective anticancer drugs remains challenging. Comprehensive molecular profiling identifies alterations actionable with targeted agents, but genomic and clinical data may also predict benefit across different drug classes. We developed machine learning–based models (Support Vector Machines - SVM and Random Forest - RF) to predict treatment outcomes using real-world clinical, pathological, genomic, and prior-therapy data at the treatment line level. Genomic alterations were summarized at the pathway level. Methods: We retrospectively analyzed 46 homogeneous patients with breast cancer who received PARP inhibitors (PARPi) discussed at the IEO Molecular Tumor Board between 2019 and 2023. Outcomes were progression-free survival (PFS) and objective response rate (ORR). As a quality control measure, models were trained to predict overall survival in first-line setting to assess data reliability. The most important predictors were expected (hormone receptor (HR), Ki-67 and age), thus supporting data reliability. We then trained outcome models for the four most represented treatment classes: PARPi, taxanes (Tx), endocrine therapies (ET), and pyrimidine analogues (Pyr). Results: RF models outperformed linear SVMs, particularly for PFS prediction, suggesting non linear interactions among predictors. Overall discrimination was modest (Table 1), reflecting limited sample size and the scarcity of true negative controls, especially for targeted treatments. Prior treatments emerged as the strongest predictor: longer PFS of previous platinum therapy (pPFS) was among the most important features to predict longer PFS and better ORR for PARPi, but similar results were obtained for all the treatment classes. Features such as Ki-67, performance status (PS), age, HR status and treatment history added predictive value, while genomic pathway alterations provided weaker but detectable signals. Conclusions: Our study demonstrates the feasibility of integrating data to predict treatment benefit and prioritize therapeutic options. Although current limited performance, the approach highlights the dominant yet still underestimated importance of prior treatment response on subsequent therapies’ outcome prediction, establishing a foundation for a methodological refinement, expansion across tumors, and external validation. It could evolve into a clinical decision support tool optimizing both personalized and standard treatments, which is especially relevant for low-income countries without access to expensive drugs. Drug PFS(RMSE) ORR(ROC) Top featuresORR Direction RF SVM RF SVM PARPi46 9 11.5 0.63 0.59 RAS wt + pPFS platinum + BRCA1, BRCA2 and PALB2 wt - Lobular - ET35 14.4 15.9 0.48 0.45 PGR% + pPFS ET + RAS altered - NOTCH wt - Tx35 8.2 10.6 0.75 0.58 pPFS anthracyclines + pPFS TROP2 ADC + PS - pPFS PARPi - Pyr34 15.5 22.8 0.6 0.58 pPFS alkylating + Lobular + Ki-67 - pPFS HER2 ADC -

The increasing complexity of oncology diagnostics requires advanced Clinical Decision Support Systems (CDSS) capable of integrating multimodal data. Traditional discriminative models often struggle with missing data and cross-modal dependencies. This review provides a novel, systematic analysis of conditional generative artificial intelligence (AI), including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), diffusion models and Multimodal Large Language Models (MLLMs), specifically tailored for oncological CDSS. We examine how these architectures move beyond simple prediction to learn joint data distributions, enabling robust data imputation, virtual staining, and automated clinical reporting. A central focus of this work is the assessment of translational application, identifying the gaps between experimental proof-of-concepts and clinical deployment. We address critical hurdles such as model hallucinations, domain shift, and demographic bias, providing a roadmap for biological consistency and regulatory compliance. This review highlights the transition from task-specific generators to multimodal reasoning systems. Ultimately, we argue that the integration of generative AI into diagnostic workflows is essential for precision oncology, provided that human-in-the-loop validation and uncertainty-aware inference remain central to their implementation.

OBJECTIVE:Homologous recombination deficiency predicts response to platinum-based chemotherapy and poly(adenosine diphosphate-ribose) polymerase inhibitors in advanced high-grade serous ovarian carcinoma. However, the optimal timing of homologous recombination deficiency testing remains unclear for patients receiving neoadjuvant chemotherapy, as it may affect test informativity and results. We evaluated the concordance of genomic and functional homologous recombination deficiency testing before and after neoadjuvant chemotherapy in patients with high-grade serous ovarian carcinoma. METHODS:Matched tumor samples collected before and after neoadjuvant chemotherapy from patients with high-grade serous ovarian carcinoma treated at the European Institute of Oncology (Milan, Italy, July 2018-December 2021) were analyzed. Genomic homologous recombination deficiency assessment included the Genomic Instability Score and tumor BRCA1/2 mutation testing using SOPHiA DDM Homologous Recombination Deficiency Solution. Functional homologous recombination deficiency assessment was performed through RAD51 foci formation immunofluorescence. Cohen's kappa coefficients (κ) assessed genomic and functional homologous recombination deficiency testing concordance for matched pre- versus post-neoadjuvant chemotherapy results, and functional versus genomic homologous recombination deficiency testing concordance at all time points. RESULTS:Samples collected before and after neoadjuvant chemotherapy from 23 patients with high-grade serous ovarian carcinoma were analyzed. Homologous recombination deficiency informativity was higher before neoadjuvant chemotherapy (87%, 20/23) than in samples collected afterward (65%, 15/23), whereas Genomic Instability Score informativity was 91% (21/23) and 78% (18/23), respectively. Concordance of the Genomic Instability Score was moderate (κ = 0.52), while homologous recombination deficiency concordance was substantial (κ = 0.67) in matched samples collected before and after neoadjuvant chemotherapy. Two of 15 matched informative samples (13%) lost Genomic Instability Score positivity after chemotherapy, but their homologous recombination deficiency test remained positive due to BRCA mutations. Functional homologous recombination deficiency assessment showed poor concordance between time points and with homologous recombination deficiency testing at each time point. CONCLUSIONS:Genomic homologous recombination deficiency tests were concordant in matched tumor samples collected before and after neoadjuvant chemotherapy for high-grade serous ovarian carcinoma, but tissue collection before chemotherapy should be prioritized due to higher informativity. Loss of informativity may result in missed opportunities for poly(adenosine diphosphate-ribose) polymerase inhibitor therapy.

Introduction: Oncotype Dx (ODX) is the most widely used genomic test for adjuvant treatment decision-making in patients with hormone receptor (HR)+ early breast cancer (EBC). Risk stratification in this clinical setting is crucial and often challenging, thus additional molecular information could significantly enhance patients’ management. We hypothesized that Prosigna, a prognostic assay estimating distant relapse-free survival in postmenopausal women with HR+ EBC, could serve as a complementary test to ODX for risk stratification. This study aims to evaluate the concordance between ODX and Prosigna in a real-world scenario. Methods: A total of 30 postmenopausal HR+ EBC patients, who were previously tested with ODX and classified as low (n=12), intermediate (n=5), and high (n=13) genomic risk, were included. The three risk categories were assigned considering the recurrence score (RS) and lymph node status: low (RS 0-10), intermediate (RS 11-25), and high risk (RS 26-100) for node-negative (pN0), as reported in TAILORx trial; low (RS ≤ 25) and high risk (RS>25) for 1-3 node-positive (N1-3), as reported in RxPONDER trial. For each case, RNA was extracted from the same formalin-fixed paraffin-embedded (FFPE) tumor block that was used for ODX, and subjected to Prosigna testing on a NanoString nCounter® DX Analysis. The Prosigna risk of recurrence (ROR) score also considers lymph node status: for pN0, 0-40 indicates low, 41-60 intermediate, and 61-100 high risk; for N1-3, 0-15 indicates low, 16-40 intermediate, and 41-100 high risk. Descriptive analyses of clinicopathological features were performed. Cohen's Kappa and Spearman (rs) correlation analyses were calculated between RS, ROR, and clinicopathological factors. Results: The overall agreement between the two platforms was the same as in the TransATAC study (κ=0.32; p<0.001). Considering ODX as the gold standard, a low-high disagreement was found in n=6 (20.0%) cases, while a low-intermediate or intermediate-high disagreement was observed in n=7 (23.3%) cases. The lowest concordance was seen in the ODX low (1/12) and intermediate (3/5) risk groups, with Prosigna tending to assign a higher risk. This tendency was confirmed by the 100% concordance (13/13) in the high-risk group. Prosigna assigned luminal A, luminal B, and HER2-enriched molecular subtypes to n=7, 23.3%; n=22, 73.3%; and n=1, 3.3% cases, respectively. Most luminal B (n=20; 90.9%) cases had high ROR scores, whereas n=12 (54.5%) cases were classified as high RS. A significant proportion of luminal A (6/7; 85.7%) cases were classified as intermediate ROR, while ODX classified n=1 (14.3%) of them as intermediate and n=6 (85.7%) as low RS. Tumor samples with high ROR were more likely G3 (p=0.002), pT2 (p=0.021), and Ki67>20% (p=0.01) in comparison with the intermediate risk ones. ROR showed a strong correlation with Ki67 expression (rs=0.72, p<0.001), whereas RS demonstrated a moderate correlation (rs=0.57, p<0.001). According to available follow-up (F/U) data (median 6 months; range 1-36), recurrence was detected in only one case (G2; Ki67=40%; F/U=14 months), which both assays classified as intermediate risk. Conclusions: Our study confirms that ODX and Prosigna provide different types of clinical information for postmenopausal HR+ EBC patients, as they analyze distinct genes and pathways. In our real-world cohort, Prosigna generally assigned a higher risk category compared to ODX. Therefore, integrating multiple molecular assays could potentially refine risk assessment and enhance personalized treatment strategies in this patient population. However, it remains to be defined which specific subpopulation might benefit from this integrated approach for molecular testing. Further investigations with larger cohorts and long-term F/U data are needed to validate these preliminary findings. Citation Format: Nicola Fusco, Giulia Cursano, Konstantinos Venetis, Elisabetta Munzone, Eltjona Mane, Mariia Ivanova, Chiara Frascarelli, Elisa De Camilli, Oriana Pala, Giovanni Mazzarol, Silvia Dellapasqua, Antonio Marra, Carmen Criscitiello, Giuseppe Viale, Giuseppe Curigliano, Elena Guerini-Rocco. Enhancing Risk Stratification in HR+ Early Breast Cancer: A Real-World Evaluation of Oncotype Dx and Prosigna [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-11-15.

OBJECTIVE:The optimal treatment for patients with cervical stromal invasion (CSI) in endometrial cancer (EC) remains unclear. We aimed to test the prognostic role of molecular classification in EC patients with CSI. METHODS:A retrospective, multicenter review of EC patients with CSI was performed. EC cases were assigned to one of the molecular classes: POLE mutated (POLEmut), MMR deficient (MMRd), p53 abnormal (p53abn), or no specific molecular profile (NSMP). Three-year recurrence-free survival (RFS) from surgery was estimated using the Kaplan-Meier method. Cox proportional hazards regression models were fit to adjust for confounders. RESULTS:Overall, 162 EC patients with CSI were identified: 70 (43.2 %) NSMP, 49 (30.2 %) p53abn, 40 (24.7 %) MMRd, 3 (1.9 %) POLEmut. POLEmut cases were excluded from further analysis, because of the small number of patients identified. At univariate analysis, molecular class was significantly associated with recurrence within 3 years after surgery (p = 0.04). Three-year RFS was 59.9 % (95 % confidence interval [CI], 46.1-77.8 %) for NSMP, 50.6 % (95 % CI, 34.9-73.2 %) for MMRd, and 33.1 % (95 % CI, 19.7-55.3 %) for p53abn. After adjusting for stage and grade, molecular class was no longer significantly associated with recurrence within three years (p = 0.28). CONCLUSIONS:Traditional risk factors such as grade and stage remain critical in determining the prognosis of endometrial cancer with cervical stromal invasion. This study highlights the importance of integrating both molecular and morphological features in determining the prognosis of endometrial cancer, with particular emphasis on endometrioid histotypes.

INTRODUCTION:In resected non-small cell lung cancer (NSCLC), molecular testing is currently limited to EGFR mutations and ALK rearrangements, as these guide approved adjuvant therapies. The role of next-generation sequencing (NGS), routinely used in metastatic NSCLC, remains unclear in earlier stages. The prevalence of other driver mutations and their impact on outcomes is not well established. METHODS:We retrospectively analyzed clinical, molecular, and survival data from patients (pts) with stage IA-IIIB NSCLC (AJCC 8th) who underwent surgery and NGS at our Institute from January 2020 to December 2023. The primary endpoint was the prevalence of driver alterations. Exploratory analyses assessed recurrence, disease-free survival (DFS), overall survival (OS), and correlation with mutation status. RESULTS:Of 221 pts, 216 were eligible. Oncogenic alterations were found in 71% (73% in stage I), most commonly KRAS (30%) and EGFR (26%), followed by MET exon 14 skipping (6%), BRAF (4%), HER2 exon 20 mutations (3%), and ALK and RET rearrangements (1%). Alteration distribution differed by sex and smoking status. With a median follow-up of 20 months, 36% of pts experienced recurrence, including 10 stage I cases. Median time to recurrence was 14 months. Recurrence rates were higher in pts with driver mutant NSCLC (39.6%) versus wild-type (29.6%). Highest recurrence was seen in pts with oncogenic fusion positive NSCLC and EGFR exon 20 insertions. CONCLUSIONS:Driver mutations were detected in 70% of resected NSCLC, including stage I. The recurrence patterns observed support integrating NGS into early-stage management and exploring tailored adjuvant therapies, even for stage I tumors.

The Oncotype DX® Recurrence Score (RS) is an assay for hormone receptor-positive early breast cancer with extensively validated predictive and prognostic value. However, its cost and lag time have limited global adoption, and previous attempts to estimate it using clinicopathologic variables have had limited success. To address this, we assembled 6172 cases across three institutions and developed Orpheus, a multimodal deep learning tool to infer the RS from H&E whole-slide images. Our model identifies TAILORx high-risk cases (RS > 25) with an area under the curve (AUC) of 0.89, compared to a leading clinicopathologic nomogram with 0.73. Furthermore, in patients with RS ≤ 25, Orpheus ascertains risk of metastatic recurrence more accurately than the RS itself (0.75 vs 0.49 mean time-dependent AUC). These findings have the potential to guide adjuvant therapy for high-risk cases and tailor surveillance for patients at elevated metastatic recurrence risk.

Background: Approximately 25.0% of metastatic prostate cancer patients harbour DNA damage repair mutations, including BRCA1 and BRCA2, which are actionable targets for poly(ADP-ribose) polymerase (PARP) inhibitors. Accurate detection of BRCA1/2 mutations is critical for guiding targeted therapies, but crucial pre-analytical factors, such as tissue storage duration and DNA fragmentation, drastically affect the reliability of next-generation sequencing (NGS) using real-world diagnostic specimens. Methods: This multicentre study analysed 954 formalin-fixed paraffin-embedded tissue samples from 11 centres, including 559 biopsies and 395 surgical specimens. This study examined the impact of storage duration (<1 year, 1–2 years, and >2 years) and DNA parameters (concentration and fragmentation index) on NGS success rates. Logistic regression and Cox regression analyses were used to assess correlations between these factors and sequencing outcomes. Results: NGS success rates decreased significantly with longer storage, from 87.8% (<1 year) to 69.1% (>2 years). Samples with higher DNA concentrations and fragmentation indexes had higher success rates (p < 0.001). Surgical specimens had superior success rates (83.3%) compared with biopsies (72.8%) due to better DNA quality. The DNA degradation rate was more pronounced in older samples, underscoring the negative impact of extended storage. Conclusions: Timely testing of BRCA1/2 mutations is critical for optimizing the identification of prostate cancer patients eligible for PARP inhibitors. Surgical specimens provide more reliable results than biopsies and minimizing the storage duration significantly enhances testing outcomes. Standardizing pre-analytical and laboratory procedures across centres is essential to ensure personalized treatments and improve patient outcomes.

559 Background: Germline pathogenic variants (PVs) in the BRCA1 and BRCA2 (g BRCA1/2 ) genes increase the risk for breast cancer (BC) development.The prognostic significance of gBRCA1/2 in patients with hormone receptor-positive/HER2-negative (HR+/HER2) early BC is still controversial. Methods: This cohort study derived from a prospectively-maintained institutional database of all consecutive patients with BC who underwent germline testing, including BRCA1 , BRCA2 and PALB2 , at the European Institute of Oncology (May 2002-Jan 2024). The study population comprised patients with stage I-III HR+/HER2- (estrogen receptor expression >1%) invasive BC who underwent surgery and (neo)adjuvant treatment, as endocrine therapy (ET) +/- chemotherapy (CT) (Jan 2000-Dec 2022). Primary endpoints were distant relapse-free interval (DRFI) and invasive disease-free survival (iDFS) by STEEP 2.0. Univariate and multivariate Cox proportional-hazard models were employed for survival analyses, with left-truncated models to account for the time from BC diagnosis to germline testing. Results: A total of 1,730 patients were included in the analyses, with 52 (3%) BRCA1 , 180 (10%) BRCA2 , and 9 (0.5%) PALB2 PV carriers. Compared to non-carriers, patients with gBRCA 1/2 and gPALB2 PVs were younger (median age: 39 vs 42 yrs, p<.001), had advanced disease stage (stage II-III: 71% vs 58%, p<.001), higher tumor grade (G3: 54% vs 26%, p<.001) and Ki-67 expression (median: 26% vs 20%, p<.001). Patients with gBRCA 1/2 and gPALB2 PVs were also more likely to receive neoadjuvant (13% vs 6%, p<.001) and/or adjuvant CT (56% vs 36%, p<.001) and mastectomy (56% vs 45%, p=.002). All patients received adjuvant ET, as tamoxifen or aromatase inhibitor +/- GnRH analogue. No patient received adjuvant olaparib or CDK4/6 inhibitor. At a median follow-up of 9.7 (IQR 6-13.9) years, 335 (19%) patients experienced local relapse, 316 (18%) distant metastasis, and 124 (7.2%) died due to BC. At multivariate analyses, gBRCA2 P/LPVs were independently associated with shorter DRFI (HR 1.46, 95%CI 1.04–2.06, p=.028) and iDFS (HR 1.34, 95 CI 1.01–1.78, p=.045), regardless of stage, nodal status, (neo)adjuvant CT, type of surgery and adjuvant ET, whereas gBRCA1 were not. Exploratory analyses showed that among 232 gBRCA1/2 carriers, 47 (20%) and 96 (41%) were eligible for adjuvant olaparib or abemaciclib therapy per OlympiA and monarchE criteria, respectively, with 37 (16%) eligible for both therapies. Additional analyses to unravel interaction of gBRCA status with adjuvant treatment are underway. Conclusions: Patients with HR+/HER2- early BC harboring gBRCA2 PVs had a significantly increased risk of recurrence, with a potentially distinct impact of BRCA2 vs BRCA1 . Only a small proportion of this population currently qualify to adjuvant treatment escalation with targeted therapies, underscoring the need of expanding the therapeutic options in this setting.

Mutations in ESR1 play a critical role in resistance to endocrine therapy (ET) in hormone receptor-positive (HR +)/HER2- metastatic breast cancer (MBC). Testing for ESR1 mutations is essential for guiding treatment with novel oral selective estrogen receptor degraders (SERDs) like elacestrant or camizestrant. While most studies have utilized liquid biopsy (LB) for mutation detection, the role of formalin-fixed paraffin-embedded (FFPE) tissue biopsy in this context remains unclear. In this study, we analyzed a cohort of HR + /HER2- MBC patients who experienced resistance to ET and CDK4/6 inhibitors. Next-generation sequencing (NGS) was performed on FFPE biopsy samples obtained from metastatic sites at the time of disease progression. ESR1 mutations were detected in 24 out of 38 patients (63.2