570 Background: Detection of molecular residual disease (MRD), using circulating tumor DNA (ctDNA), following treatment for early-stage TNBC is associated with a high risk of recurrence. ctDNA clearance in response to NAC has shown potential for predicting pathologic compete response (pCR) and improving the prognostic ability of pCR status. PARTNER is a prospective, phase II-III, randomized controlled clinical trial, which recruited early-stage basal TNBC BRCA1/2 wild-type patients (Nature April 2024). Control regimen was neoadjuvant carboplatin–paclitaxel followed by anthracycline-based NAC. Experimental arms added olaparib to the platinum-taxane backbone. A sub-study of serial blood samples for ctDNA analysis during NAC and post-op were analyzed with 2 tumor informed MRD assays using primary tumor and germline sequencing: 1) whole-exome sequencing (WES) to select ≤200 variants and 2) whole-genome sequencing (WGS) to select 400-5000 variants for the two bespoke MRD assays, respectively. Both assays were independently used to assess available plasma samples collected for the presence or absence of ctDNA. Methods: This prospective sub study included TNBC patients enrolled within PARTNER with serial blood collections at baseline (prior to NAC), mid-NAC, post-NAC, 2-4 weeks post-op, 3 months post-op and 12 months post-op. Germline and somatic DNA were provided via the Personalised Breast Cancer Program. The primary objective was to determine the association of ctDNA positivity post-op with distant recurrence-free interval (DRFI). The distribution of DRFI by ctDNA status was compared using the log-rank test. The Cox proportional hazards regression model was used to estimate the strength of the relationship between ctDNA positivity and DRFI. Results: Median WES MRD assay panel size was 159 variants (range 47 – 200). At baseline ctDNA was detected in 50 of 55 patients (91%). After NAC, 4 of 63 patients had detectable ctDNA. 24 of 28 non-pCR patients were ctDNA negative and 0 of 35 pCR patients were ctDNA positive. Of 61 post-op patients available to assess DRFI distant recurrences developed in 8 patients (13.1%) within a median follow up of 5 years. Post-op 5 patients were ctDNA positive and 3 developed distant recurrences (log-rank p < 0.0001, HR = 20.2, 95% CI = 4.3, 95.1). In addition, 56 were ctDNA negative and 51 (91%) were distant recurrence free. Median WGS MRD assay panel size was 2962 variants (range 552 - 5000). At baseline ctDNA was detected in 46 of 47 patients (98%). Additional WGS MRD results are being generated and will be presented at the meeting. Conclusions: Post-op detection of ctDNA using a WES MRD assay was highly prognostic for distant recurrence in TNBC patients following NAC. WGS MRD improved baseline detection.

Abstract Introduction: Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods: We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results: Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion: This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Citation Format: Keli S. Santos-Parker, Jessica R. Santos-Parker, W. Fraser Symmans, Laura J. Esserman, Christina Yau, Angie DeMichele, Laura van't Veer, Doug Yee, Fabien Reyal, Helena Earl, Jean Abraham, David Cameron, Peter Hall, Judy Boughey, Matthew Goetz, Gabe Sonke, Miguel Martín, Sara López-Tarruella, Priyanka Sharma, Rachel Freiberg, Jane Perlmutter, Aditya Bardia, Martin Eklund, Rachel Freiberg, Lajos Pusztai. A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1401.

Supplementary Figures 1-20 and Supplementary Tables 1-10 Legends

ABSTRACT Emerging multi-omic profiling has made it feasible to subtype disease using multiple molecular layers. However, inconsistent preprocessing, heterogeneous implementations, variable evaluation, and limited reproducibility often constrain method selection. Here, we systematically benchmark 22 publicly available unsupervised approaches for bulk data on the TCGA-BRCA cohort across five modalities (RNA-seq, miRNA, DNA methylation, copy numbers, single nucleotide polymorphisms) and validate findings in two independent datasets, enabling a multi-layered comparison of performance, heterogeneous data support and interpretability. Most approaches fuse multi-omic data to produce a two-cluster solution largely aligned with ER status, with higher-resolution approaches further refining these into four coherent subclasses (angiogenic luminal, oxidative-phosphorylation/HER2-low luminal, immune-inflamed basal-like, and hyper-proliferative basal-like). Our benchmarking results indicate that methods based on similarity networks can efficiently produce stable, reliable partitions. Matrix factorisation and Bayesian factorisation algorithms produce rich latent representations, allowing quantification of feature and modality contributions, albeit at higher computational cost. Consensus clustering can be used on a case-by-case basis and refine partitions into more robust and generalisable findings. We aggregate our insights into a decision workflow that aligns with study goals, data characteristics, and computational resources, enabling optimal analytic strategies. This comprehensive assessment provides a practical roadmap for investigators seeking to extract reproducible, biologically meaningful subtypes from complex multi-omic datasets. We higlight the different technical and practical benefits and trade-offs that shape the selection and development of multi-omic approaches applied in precision oncology.

A coclinical trial framework reveals concordant patient–PDTX drug responses. A, Experimental framework (consisting of two trial designs) and associated analytical approach, with modeling metrics used to assess drug response. B and C, TV growth curves displaying linear mixed model fits of trial designs 1 (B) and 2 (C) over treatment duration. Treatment arm for each PDTX model corresponds to the clinical treatment of the matched patient. D and E, Analytical metrics derived from mathematical modeling (as in A). Change in growth rate (top) and estimated difference in the AUC (bottom) for trial design 1 (D) and growth rate under treatment (top) and predicted volume at treatment end (bottom) for trial design 2 (E). F, Box plots displaying growth rate under treatment (top) and predicted volume at treatment end (bottom) for trial design 2 between pCR and non-pCR models. Statistical significance is calculated using the Wilcoxon test.

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Data from clinical trials (CTs) drive advancements in clinical practice. Despite most CTs now incorporating extensive translational portfolios, the diverse modalities of clinical and sample data they generate often remain disconnected and underutilised. SYNERGIA is a resource designed to integrate multi-modal data from multiple trials in a comparable format, that will be appropriately accessible to clinicians and researchers. The aims of SYNERGIA are to:1) Develop a comprehensive, multi-modal data repository that integrates longitudinal CT data (>5 years), with genomics (for example, whole-genome sequencing, genome wide association data, circulating tumour DNA, transcriptomics, and spatial- and single-cell- omics), radiomics (for example, mammograms, MRIs, and ultrasounds), and digitalised pathology images from five UK-based breast cancer CTs/studies involving up to 5,000 patients. 2) Facilitate the development and validation of multi-modal machine learning tools, including models predicting response to neo-adjuvant treatment, risk of disease recurrence or death, radiology segmentation tools, and pathology tools for assessing residual cancer burden or cellular biomarkers. 3) Inform future trial designs, support research applications and grants, and establish standard operating procedures for the collection, storage, and management of extensive datasets, samples, and imaging resources. The repository will enable integration across data modalities, ensuring that legacy research data continues to have meaningful impact in future research. SYNERGIA’s tools may support the personalisation of treatment regimens for individual patients, particularly for higher risk sub-types such as human epidermal growth factor receptor 2 positive and triple negative breast cancers. The platform offers the potential to differentiate high risk from low-risk breast cancers and assess in detail which data features contribute to risk. It may enable identification of the most critical data modalities/features at each timepoint in a patient’s cancer journey. The multi-modal approach to cancer biomarker discovery and predictive/prognostic tool development promises to enhance patient stratification to the most appropriate care pathways and advance precision breast cancer medicine. Integrating diverse datasets generated from individual patients may provide new insights into long-standing clinical questions such as identifying early indicators of relapse and distinguishing between lethal and non-lethal breast cancers. By enabling data-driven insights, the SYNERGIA platform aims to support the development of rational data-informed clinical research and trials that improve outcomes while reducing unnecessary toxicities and costs. Amy Riddell, Joanna R. Worley, Fatima Begum-Miah, Steven Bell, Samuel Casford, Alexander J. Fulton, Melis O. Irfan, Justine Kane, Ollie Kane, Charlotte King, Zac Kinsella, Jonathan Lay, Bin Liu, Zoe Matthews, Meena Murthy, Claudia Pallucca, Karen Pinilla, Leah Prowse, Nikola Simidjievski, Aris Sionakidis, Deborah Whitehorn, Katrina Xian, Elena Provenzano, Philip C. Schouten, Mateja Jamnik, Pietro Liò, Silvia Tarantino, Akanksha Anand, Kui Hua, Clare A. Rebbeck, Ramona Woitek, Iris Allajbeu, Gregory J. Hannon, Jean E. Abraham. SYNERGIA Breast Cancer - Revolutionizing breast cancer care with multi-modal data integration for personalised treatment and future trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB339.

Residual Cancer Burden (RCB) after neoadjuvant chemotherapy (NAC) is validated to predict event-free survival (EFS) in breast cancer but has not been studied for invasive lobular carcinoma (ILC). We studied patient-level data from a pooled cohort across 12 institutions. Associations between RCB index, class, and EFS were assessed in ILC and non-ILC with mixed effect Cox models and multivariable analyses. Recursive partitioning was used in an exploratory model to stratify prognosis by RCB components. Of 5106 patients, the diagnosis was ILC in 216 and non-ILC in 4890. Increased RCB index was associated with worse EFS in both ILC and non-ILC ( p = 0.002 and p < 0.001, respectively) and remained prognostic when stratified by receptor subtype and adjusted for age, grade, T category, and nodal status. Recursive partitioning demonstrated residual invasive cancer cellularity as most prognostic in ILC. These results underscore the utility of RCB for evaluating NAC response in those with ILC.

Biomarkers for specific cytotoxic chemotherapy sensitivity could better inform drug selection. Both CEP17 duplication and abnormal Topoisomerase 2 copy number appear associated with anthracycline sensitivity in the adjuvant setting (Bartlett et al 2015). This data is however not currently applied clinically. Taxanes are a routine component of adjuvant and neoadjuvant chemotherapy either in combination or sequenced with anthracyclines, or increasingly used in the absence of anthracyclines. ROSCO: ISRCTN15094808 was designed to prospectively evaluate the clinical utility of these two biomarkers for initial neoadjuvant chemotherapy selection. Between November 2015 and May 2023, 990 consenting patients with early breast cancer considered suitable for neoadjuvant chemotherapy were randomised to four cycles of either Epirubicin and Cyclophosphamide with optional 5 Fluorouracil ((F)EC), or Docetaxel and Cyclophosphamide (TC). Patients with Grade 1 or 2 ER Rich, PR Rich, HER-2 negative tumours and all T1 N0 tumours were excluded. All HER-2 positive cancers were treated with concurrent anti HER-2 antibodies. Participants were stratified by centrally assessed biomarker status as biomarker normal (BM normal) with both CEP17 and TOP2A normal, or biomarker abnormal (BM abnormal) with CEP17 duplication and/or TOP2A abnormal. Surgery was performed after 4 cycles of chemotherapy; where pathological complete response (pCR) was not achieved, crossover to the alternative treatment arm for a further 4 cycles was given in an adjuvant setting. Crossover before surgery was permitted where interim biopsy after 4 cycles confirmed viable residual disease. The primary endpoint of the study is pCR ypT0/Tis ypN0 after initial neoadjuvant chemotherapy. Of the 990 patients consented, 496 were randomised to TC and 494 to (F)EC. 24 patients with no cancer seen in interim biopsy received further neoadjuvant chemotherapy and also had pCR at final surgery, these are considered in the primary analysis as non pCR. Data from 950 patients are evaluable for the primary endpoint. Overall pCR was 245 (26%): the TC arm was 131 (27%) and (F)EC arm was 114(24%). Overall BM was normal in 233 (24%) with BM abnormal in 756 (76%). With TC the pCR percentages for BM normal and abnormal were very similar: 30% and 27% respectively. For (F)EC BM normal, pCR is 17% and for FEC BM abnormal, it is 26%. Final data cleaning is ongoing, testing for treatment by biomarker interaction will be presented. Higher response to (F)EC in the biomarker abnormal group was observed across all pathological subtypes tested. Sensitivity analysis excluding 35 TC and 22 (F)EC patients where crossover chemotherapy was given off protocol neoadjuvantly despite a negative core biopsy or where patients withdrew prior to the primary endpoint was conducted. The pCR proportions observed in the treatment by biomarker groups were not impacted. Preliminary analysis of this large prospective evaluation of CEP17 and TOP2A as potential predictors of anthracycline sensitivity conducted in a neoadjuvant context shows that evaluation of these biomarkers shows no predictive value for sensitivity to TC but demonstrates differential pCR to (F)EC. Suggesting women with BM abnormal cancers are likely to benefit more from inclusion of anthracyclines. Anthracycline-free chemotherapy may be considered as an option for women with BM normal cancers. This work was supported by CRUK [CRUK/12/046/ A15756] and Bristol Myers Squibb. Citation Format: Daniel Rea, S. Pirrie, L. Hayward, S. Chan, M. Varughese, S. Spensley, U. Barthakur, M. MacKenzie, S. J. Bowden, C. Gaunt, E. Southgate, N. Nicholson, P. Wetherell, M. Soden, L. Billingham, C. Brookes, D. Cameron, J. Starczynski, J. Dowds. H. Earl, R. Ste. ROSCO: Response to Optimal Selection of neoadjuvant Chemotherapy in Operable breast cancer: Randomised phase III, stratified biomarker trial of neoadjuvant 5-Fluorouracil,Epirubicin & Cyclophosphamide vs Docetaxel & Cyclophosphamide chemotherapy [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 RF3-07.

BACKGROUND AND PURPOSE:Radiomics analysis has emerged as a promising approach to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on acquisition and pre-processing techniques used. In this study, we aim to investigate the effect of various image normalization techniques on robustness of radiomics features extracted from breast cancer patient MRI scans. MATERIALS AND METHODS:MRI scans from the publicly available MAMA-MIA dataset and an internal breast MRI test set depicting triple negative breast cancer (TNBC) were used. We compared the effect of commonly used image normalization techniques on radiomics feature robustnessusing Concordance-Correlation-Coefficient (CCC) between multiple combinations of normalization approaches. We also trained machine learning-based prediction models of pathologic complete response (pCR) on radiomics after different normalization techniques were used and compared their areas under the receiver operating characteristic curve (ROC-AUC). RESULTS:For predicting complete pathological response from pre-treatment breast cancer MRI radiomics, the highest overall ROC-AUC was achieved by using a combination of three different normalization techniques indicating their potentially powerful role when working with heterogeneous imaging data. The effect of normalization was more pronounced with smaller training data and normalization may be less important with increasing abundance of training data. Additionally, we observed considerable differences between MRI data sets and their feature robustness towards normalization. CONCLUSION:Overall, we were able to demonstrate the importance of selecting and standardizing normalization methods for accurate and reliable radiomics analysis in breast MRI scans especially with small training data sets.

OBJECTIVE: To evaluate the implementation of a digitally based diabetes management program by a large, self- insured employer in Minnesota from May 2021 to April 2022. STUDY DESIGN: Descriptive analysis. METHODS: We described the development, implementation, and effectiveness of a communications strategy to promote program enrollment in the initialyear. Using administrative claims data, we analyzed the demographic and clinical attributes associated with an eligible member's enrollment. Finally, we empirically assessed whether expanding the choice of modalities through which enrollees accessed diabetes self-management education and support (DSMES) increased overall utilization and addressed geographic disparities. RESULTS: Although digital health program applications responded to the timing of the communications campaigns, overall program enrollment in absolute terms was low compared with the size of the eligible population. Among those eligible, female and employee subscribers were more likely to enroll. Overall, DSMES use increased slightly during the initial year, but we did not observe significantly higher rates of use among members in rural areas following the digital health program launch. CONCLUSIONS: This study offers new insights to employers and health plans related to supporting digitally based disease management program implementation and enrollee engagement.

ObjectiveTo understand US hospitals' initial strategic responses to the federal price transparency rule that took effect January 2021.Data Sources and Study SettingPrimary interview data collected from 12 not-for-profit hospital organizations in six US metropolitan markets. All but one organization were multihospital systems; the 12 organizations represent a total of 81 hospitals.Study DesignExploratory, cross-sectional, qualitative interview study of a convenience sample of hospital organizations across six geographically and compliance diverse markets.Data Collection/Extraction MethodsIn-depth, semi-structured, qualitative interviews with 16 key informants across sampled organizations between November 2021 and March 2022. Interviews solicited data about internal organizational factors and external market factors affecting strategic responses. Transcribed interviews were de-identified, coded, and analyzed using the constant comparative method.Principal FindingsHospitals' strategic responses were influenced internally by the degree of the regulation's alignment with organizational values and goals, and task complexity vis-a-vis available resources. We found extensive variation in organizational capabilities to comply, and all but one organization relied on consultants and vendors to some degree. Key external factors driving strategic responses were hospitals' variable perceptions about how available price information would affect their competitive position, bottom line, and reputation. Organizations with more confidence in their interpretation of the environment, including how peers or purchasers would behave, and greater clarity in their own organization's position and goals, had more definitive initial strategic responses. In the first year, organizations' strategic responses skewed toward compliance, especially for the rule's consumer shopping requirements.ConclusionsA deeper understanding of the realities of operationalizing price transparency policy for hospitals is needed to improve its impact.

This paper is concerned with sample size determination methodology for prediction models. We propose combining the individual calculations via a learning-type curve. We suggest two distinct ways of doing so, a deterministic skeleton of a learning curve and a Gaussian process centred upon its deterministic counterpart. We employ several learning algorithms for modelling the primary endpoint and distinct measures for trial efficacy. We find that the performance may vary with the sample size, but borrowing information across sample size universally improves the performance of such calculations. The Gaussian process-based learning curve appears more robust and statistically efficient, while computational efficiency is comparable. We suggest that anchoring against historical evidence when extrapolating sample sizes should be adopted when such data are available. The methods are illustrated on binary and survival endpoints.

Goal:As of January 1, 2021, the Centers for Medicare & Medicaid Services requires most U.S. hospitals to publish pricing information on their website to help consumers make decisions regarding services and to transform negotiations with health insurers. For this study, we evaluated changes in hospitals' compliance with the federal price transparency rule after the first year of enactment, during which the Centers for Medicare & Medicaid Services increased the penalty for noncompliance.Methods:Using a nationally representative random sample of 470 hospitals, we assessed compliance with both parts of the hospital transparency rule (publishing a machine-readable price database and a consumer shopping tool) in the first quarter of 2022 and compared its baseline level in the first quarter of 2021. Using data from the American Hospital Association and Clarivate, we next assessed how compliance varied by hospital factors (ownership, number of beds, system membership, teaching status, type of electronic health record system), market factors (hospital and insurer market concentration), and the estimated change in penalty for noncompliance.Principal Findings:By early 2022, 46% of hospitals had posted both machine-readable and consumer-shoppable data, an increase of 24% from the prior year. Almost 9 in 10 hospitals had complied with the consumer-shoppable data requirement by early 2022. Larger hospitals and public hospitals had lower probabilities of baseline compliance with the machine-readable and consumer-shoppable requirements, respectively, although public hospitals were significantly more likely to become compliant with the consumer-shoppable requirement by 2022. Higher hospital market concentration was also associated with higher baseline compliance for both the machine-readable and consumer-shoppable requirements. Furthermore, our analyses found that hospitals with certain electronic health record systems were more likely to comply with the consumer-shoppable requirement in 2021 and became increasingly compliant with the machine-readable requirement in 2022. Finally, we found that hospitals with a larger estimated penalty were more likely to become compliant with the machine-readable requirement.Practical Applications:Longitudinal analyses of compliance with the federal price transparency rule are valuable for monitoring changes in hospitals' behavior and assessing whether compliance changes vary systematically for specific types of hospitals and/or market structures. Our results suggest a trend toward increased hospital compliance between 2021 and 2022. Although hospitals perceive the consumer-shopping tools as being the most impactful, the value of this information depends on whether it is comprehensible and comparable across hospitals. The new price transparency rule has facilitated the creation of new data that have the potential to significantly alter the competitive landscape for hospitals and may require hospital leaders to consider how their organizational strategies change concerning their engagement with payers and patients. Finally, greater price transparency is likely to bolster national policy discussions related to price variation, affordability, and the role of regulation in healthcare markets.

Abstract Background: Pre-clinical data suggest that combining anti-estrogen treatment with a progesterone receptor agonist leads to greater inhibition of tumor proliferation, due to molecular interactions between ER and PR [1]. A high dose of the PR agonist megestrol (160mg daily) is approved as monotherapy for the treatment of ER positive metastatic breast cancer. A lower dose of megestrol (20-40mg daily) can be an effective treatment for severe hot flashes associated with endocrine therapy [2] but whether this dose has anti-tumor activity is unknown. The PIONEER trial evaluated the potential anti-proliferative effect of low and high dose megestrol in combination with letrozole, relative to letrozole alone, using a short-term preoperative ‘window’ trial design assessing the direct effects of the trial treatment on tumor tissue before and after treatment. Methods: Eligible patients were post-menopausal women with histologically confirmed ER+ (Allred ≥ 3) HER2 negative breast cancer at least 10mm in size, with an ECOG performance status ≤ 2, planned for primary surgery or endocrine therapy. Enrolled patients were randomised 2:3:3 to Arm A: letrozole alone, Arm B: letrozole + lower-dose megestrol (40mg) or Arm C: letrozole + higher-dose megestrol (160mg). Treatment was given for 15 (13-19) days prior to surgery or end of treatment (EOT) core biopsy. The primary endpoint was change in tumor proliferation between baseline and EOT in Arm A vs (Arms B+C combined), measured by Ki67 immunohistochemistry (IHC). Secondary endpoints were comparison of Ki67 change in high versus low dose megestrol arms, absolute Ki67 at EOT, and change in tumor apoptosis (cleaved caspase 3 IHC), proliferation (Aurora Kinase A IHC), PR and androgen receptor expression. Exploratory analysis of ER chromatin binding (ChIP-Seq) was conducted on paired fresh-frozen samples from a subset of patients. Results: A total of 243 patients were randomised from July 2017 to October 2022 with recruitment paused for 3 months at onset of the COVID pandemic. 198 patients completed treatment and had evaluable tissue samples at baseline and EOT (Arm A: n = 51, Arm B: n = 74, Arm C: n = 73). Baseline mean Ki67 values were well balanced. Therapy was well tolerated and adverse events ≥ grade 3 were similarly rare across arms (A: 3.3%, B+C: 3.5%). The mean % reduction in Ki67 for each arm was: Arm A (letrozole): 71%, Arm B (letrozole + 40mg megestrol): 79%, Arm C (letrozole + 160mg megestrol): 80%. There was a statistically significantly greater reduction in Ki67 with megestrol combinations (B+C) versus letrozole alone (A) (P = 0.013). Analyses of secondary IHC endpoints and ER ChIP-Seq are ongoing and will be presented. Conclusion: Addition of the PR agonist megestrol enhanced the anti-proliferative effect of letrozole in this window-of opportunity trial. Megestrol combinations were well tolerated, and the anti-proliferative effect was observed in both low and high dose arms. These data support the potential use of low-dose megestrol as an inexpensive and well-tolerated means of improving aromatase inhibitor efficacy. Low dose megestrol can also ameliorate hot flashes and therefore might be a strategy to improve both treatment adherence and clinical outcomes for patients taking adjuvant endocrine therapy. References 1. Mohammed et al., Nature 523: 313–317 (2015) 2. Loprinzi et al., NEJM 331: 347-352 (1994) Citation Format: Rebecca Burrell, Sanjeev Kumar, Stuart McIntosh, Vassilis Pitsinis, Polly King, Beatrix Elsberger, Sasirekha Govindarajulu, Lucy Satherley, Sirwan Hadad, Peter Schmid, Jean Abraham, Amit Agrawal, John Benson, Danya Cheeseman, Igor Chernukhin, Parto Forouhi, Eleftheria Kleidi, Cleopatra Pike, Karen Pinilla, Elena Provenzano, Wendi Qian, Jason Carroll, Richard Baird. Results of the window-of-opportunity PIONEER trial evaluating addition of the progesterone receptor (PR) agonist megestrol to letrozole for early stage estrogen receptor (ER) positive breast cancer: exploiting ER-PR interaction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS15-06.

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England's 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an "FFPEImpact" score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many. Formalin fixation is commonly used in tissue storage; however, this process has traditionally limited downstream whole genome sequencing usage. Here, the authors identify artefactual signatures in FFPE-derived sequencing data and demonstrate the preservation of clinical utility, thus enabling FFPE whole genome sequencing when required.

PARTNER is a prospective, phase II-III, randomized controlled clinical trial that recruited patients with triple-negative breast cancer1,2, who were germline BRCA1 and BRCA2 wild type3. Here we report the results of the trial. Patients (n = 559) were randomized on a 1:1 basis to receive neoadjuvant carboplatin-paclitaxel with or without 150 mg olaparib twice daily, on days 3 to 14, of each of four cycles (gap schedule olaparib, research arm) followed by three cycles of anthracycline-based chemotherapy before surgery. The primary end point was pathologic complete response (pCR)4, and secondary end points included event-free survival (EFS) and overall survival (OS)5. pCR was achieved in 51% of patients in the research arm and 52% in the control arm (P = 0.753). Estimated EFS at 36 months in the research and control arms was 80% and 79% (log-rank P > 0.9), respectively; OS was 90% and 87.2% (log-rank P = 0.8), respectively. In patients with pCR, estimated EFS at 36 months was 90%, and in those with non-pCR it was 70% (log-rank P < 0.001), and OS was 96% and 83% (log-rank P < 0.001), respectively. Neoadjuvant olaparib did not improve pCR rates, EFS or OS when added to carboplatin-paclitaxel and anthracycline-based chemotherapy in patients with triple-negative breast cancer who were germline BRCA1 and BRCA2 wild type. ClinicalTrials.gov ID: NCT03150576 .

The Lancet Breast Cancer Commission—a diverse, multidisciplinary international group—are unanimous in our determination to improve the lives of all people who live with or are at risk of breast cancer. We came together in July, 2021, and are committed to raising the standard of breast cancer care to close the equity gap that exists between and within countries. Over a 2-year period, we brainstormed ideas, scoped the literature, obtained funding for dedicated pilot research that provided new data, and produced this Commission report to reduce the effects that breast cancer has on society. The role of racial and ethnic discrimination in breast cancer disparitiesThe Lancet Breast Cancer Commission report encompasses prevention, personalised treatment, inclusive management of metastatic breast cancer, identifying the hidden costs of breast cancer, tackling breast cancer gaps and inequities through global collaboration, and communication and empowerment.1 It is also important to recognise and address discrimination as a key determinant of breast cancer disparities. Here we elucidate mechanisms through which racial and ethnic discrimination affects breast cancer outcomes, and provide a few promising examples to address discrimination via the implementation of anti-discrimination health-care policies and interventions and the mobilisation of minority groups. Full-Text PDF