Overview of retrospective and prospective study design. The retrospective arm compared baseline tumor SLC31A1 gene expression in patients stratified by subsequent pathologic response to neoadjuvant therapy, and the prospective arm measured serum Cu changes (ΔCu) before and after neoadjuvant therapy.

Abstract Through a randomized phase 2 trial, we aim to overcome resistance and improve outcomes for triple-negative breast cancer (TNBC) patients (pts) with significant residual disease at surgery. We and others have demonstrated that copper (Cu) plays key roles in supporting TNBC resistance pathways [Ramchandani et al. Nat Comm 2021; Shanbhag et al PNAS 2019]. We completed a pilot phase 2 clinical trial of Cu-depletion with TM in 75 BC pts at a high-risk of relapse. We found that TM was safe and well tolerated. Event-free survival (EFS) for TNBC pts is 88% for high-risk adjuvants (stage 3 BC) and 59.3% for stage 4 NED TNBC at a median follow-up of 10.4 yrs. Three components of the tumor microenvironment were significantly affected: VEFGR2+ endothelial progenitor cells (EPCs) and Cu-dependent LOXL-2 were significantly reduced, whereas the collagen microenvironment was normalized in Cu-depleted pts [Chan et al Clin Cancer Res 2017, Liu NPJ Breast 2021], recapitulating our observations in pre-clinical models.These findings have led to the hypothesis that Cu contributes to 3 aspects of metastasis: (1) cancer cell intrinsic mitochondrial bioenergetics that mediate invasion/metastasis/chemoresistance; (2) the “pre-metastatic niche” that supports colonization, and outgrowth of disseminated metastatic tumor cells, and (3) stromal remodeling that promotes immune evasion and immunotherapy resistance. We expect that complementing standard chemo-immunotherapy with a Cu depletion strategy will overcome resistance and improve outcome. We will test this through a randomized phase 2 trial of TM with capecitabine (C) vs C alone +/- pembrolizumab (P), in TNBC pts with RCB 2, 3 residual disease after completion of neoadjuvant therapy and surgery. The primary endpoint is distant relapse free survival. Secondary endpoints are (i) safety, (ii) invasive disease-free survival (iDFS), and OS for both the entire cohort and those who complete 6 months of TM therapy, (iii) Pt-reported outcomes and (iv) effect of therapy on biomarkers. Prior to the randomized phase 2 study, a phase Ib clinical trial in 6 to 18 pts will be done to establish the safety of the combination of adjuvant TM, capecitabine and pembrolizumab. Robust scientific correlative and exploratory work will evaluate the effect of Cu depletion on serial blood-based biomarkers. We plan to enroll 186 pts in this study across 10 sites. The study has 8 pts accrued to the phase 1b portion at dose levels 1 and -1. TM appears safe and well tolerated in combination with C and P. Citation Format: Linda T. Vahdat, Nancy Chan, Ben H. Park, Jessica M. Sharpe, Kathy D. Miller, Bryan P. Schneider, Kevin M. Kalinsky, Neelima Vidula, Mark E. Robson, Peter A. Kauffman, Rebecca A. Shatsky, Joyce A. O'Shaughnessy, Sujata Patil, Eileen H. Shinn, James Talton, Raven M. Lavoie, Naomi T. Kornhauser, Christina A. Seymour, Rebecca E. Dabrowski, Vivek Mittal. Novel targeting of the microenvironment to decrease metastatic recurrence of high-risk TNBC: A randomized phase II study of tetrathiomolybdate (TM) plus capecitabine in patients with breast cancer at high risk of recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr CT072.

Abstract Copper (Cu) metabolism represents a unique vulnerability in cancer. CTR1 (SLC31A1) mediates cellular Cu uptake, but its relationship with tumor aggressiveness and neoadjuvant response is unclear. We tested whether tumor SLC31A1 expression and systemic Cu dynamics reflect a coordinated tumor-systemic Cu axis in breast cancer. Baseline tumor SLC31A1 gene expression was analyzed in a retrospective cohort of 1,632 neoadjuvant-treated patients with breast cancer, who were subsequently stratified by pathologic response across molecular subtypes and tumor grades. These findings were extended to an exploratory prospective neoadjuvant cohort, in which ΔCu, defined as the posttreatment to pretreatment change in serum Cu, was assessed by subtype, grade, and response, whereas baseline serum Cu was examined by tumor size. Baseline SLC31A1 expression was higher in triple-negative breast cancer (TNBC) nonresponders than in responders (P = 0.0021), particularly in grade 3 tumors (P = 0.0035), with no differences in luminal subtypes. In the prospective cohort, ΔCu changes were most pronounced in TNBC and were strongly grade-dependent: all grade 3 TNBCs showed posttherapy Cu elevation, whereas all grade 2 TNBCs showed decreases (P = 0.034). The relapsed TNBC nonresponder exhibited persistently positive ΔCu, whereas nonresponders from other subtypes showed near-zero or negative changes (P = 0.011). Baseline serum Cu was higher in patients with smaller (T1) versus larger (T2–T3) tumors (P = 0.033). Elevated baseline tumor SLC31A1 expression and posttherapy systemic Cu increases converge in high-grade TNBC, indicating a potential coordinated Cu mobilization program linked to aggressive biology and neoadjuvant resistance. Significance: SLC31A1 expression was evaluated in a large retrospective cohort, providing robust evidence that elevated baseline SLC31A1 in high-grade TNBC associates with nonresponse. ΔCu dynamics were assessed in a prospective cohort. Despite the limited size, the consistent ΔCu increase in grade 3 TNBC and a relapsing nonresponder supports a biologically meaningful resistance signal. Together, these datasets define a coordinated Cu axis warranting prospective validation and early Cu-targeted intervention.

Baseline ceruloplasmin activity was measured across breast cancer molecular subtypes and healthy volunteers.

e12629 Background: Copper is an essential nutrient required for energy production, antioxidant defense, and connective tissue maturation, yet has emerged as a metabolic vulnerability in cancer. CTR1 ( SLC31A1 ), the high-affinity copper importer, mediates cellular copper uptake, and its upregulation may signal increased copper demand in tumor cells. The dynamics of copper regulation across tumor growth, aggressiveness, and treatment resistance remain poorly defined in breast cancer. We investigated whether CTR1 expression and systemic copper changes reflect a coordinated tumor-systemic copper axis. Methods: A retrospective dataset of 1632 breast cancer patients receiving neoadjuvant chemotherapy was analyzed to compare CTR1 gene expression between responders and non-responders across molecular subtypes and tumor grades. Findings were extended to a prospective neoadjuvant cohort in which paired pre- and post-treatment serum copper levels were measured. △Copper (post–pre change) was correlated with subtype, grade, response, and tumor size. Results: CTR1 expression was significantly higher in triple-negative breast cancer (TNBC) non-responders than responders ( P = 0.0021), particularly in grade 3 tumors ( P = 0.0035), with no difference in luminal subtypes. In the prospective cohort, △Copper was positive predominantly in TNBC and strongly grade-dependent: all grade 3 TNBCs exhibited copper elevation post-therapy, whereas all grade 2 TNBCs showed negative △Copper ( P = 0.034). The only relapse in the cohort, a TNBC non-responder, exhibited persistently positive △Copper at follow-up and relapse, whereas non-responders from other subtypes showed near-zero or negative △Copper ( P = 0.011). Baseline serum copper was higher in patients with smaller (clinical T1) versus larger (T2–T3) tumors ( P = 0.033). Conclusions: Parallel CTR1 upregulation in tumors and systemic copper elevation post-therapy suggest a coordinated copper mobilization program in high-grade TNBC. These integrated retrospective and prospective findings link copper transport to therapy response and tumor aggressiveness, highlighting copper biology as a potential therapeutic axis in breast cancer.

This figure shows Kaplan–Meier survival analysis of grade 3 triple-negative breast cancer patients stratified by SLC31A1 expression.

This figure shows the relationship between baseline serum copper concentration and ceruloplasmin activity across breast cancer subtypes and healthy volunteers.

ΔCu levels in patients with breast cancer by molecular subtype. Box plots show the distribution of ΔCu values (after treatment − before treatment) across subtypes. The horizontal line within each box represents the median, box edges represent the IQR, and whiskers indicate the minimum and maximum values. The Kruskal–Wallis P value is shown on the plot.

This table lists baseline serum copper and ceruloplasmin activity values for healthy volunteers and breast cancer patients grouped by molecular subtype.

TPS12171 Background: Approximately half of breast cancer patients do not complete the standard 5-year course of adjuvant hormone therapy due to side effects, a finding associated with increased recurrence rates and breast cancer-specific mortality. In clinical practice, side effects are commonly addressed by switching to an alternative hormone therapy; however, this strategy has not been prospectively evaluated. In prior retrospective work, adherence was significantly lower among patients who switched therapies compared with those who remained on the same therapy and received treatment for side effects (62% vs 91%, p=0.013); however, interpretation is limited by the retrospective design. Therefore, a prospective randomized clinical trial was initiated to address this evidence gap by directly comparing medication switching with guideline-directed symptom management. Methods: SWIVEL is an ongoing phase II randomized trial comparing medication switching with guideline-directed interventions for the management of aromatase inhibitor side effects. Eligible patients are postmenopausal women or men with stage I–III ER-positive/HER2-negative breast cancer who are planning to initiate aromatase inhibitor monotherapy in the adjuvant setting. Upon enrollment, participants will be screened every 4 weeks with a validated single-item questionnaire (FACIT/GP5). Those who respond that they are ‘quite a bit’ or ‘very much’ bothered by side effects of treatment will be randomized to either (1) switch to a different hormone therapy, which may include an alternative aromatase inhibitor or tamoxifen, or (2) initiate an evidence-based intervention for side effects in accordance with NCCN guidelines and patient-provider preference. Participants may crossover between strategies at any time. The primary outcome is change in patient-reported symptom burden at 3 months following randomization or at crossover, whichever occurs first, measured by the FACT-ES survey. Secondary outcomes include patient-reported symptom burden at 6 months assessed in an intention-to-treat analysis using FACT-ES and PROMIS measures, and medication adherence assessed at 3, 6, 12, and 24 months using a multi-modal approach incorporating validated patient-reported measures (VOILS), pharmacy records, and urine testing for drug metabolites. Additional outcomes include patient-reported quality of life (FACT-G) and sexual function (FSFI), factors associated with adherence including access to care among rural communities, and evaluation of a novel screening tool to identify patients at higher risk for non-adherence. The study is open at Dartmouth Health and has enrolled 7 of 200 planned participants (NCT# 07071038). Clinical trial information: RCT07071038 .

CTR1 protein function and SLC31A1 expression by pathologic response. A, Illustration of CTR1 on the tumor cell surface mediating Cu uptake. B–D, Baseline tumor SLC31A1 transcript levels stratified by subsequent pathologic response across luminal A, luminal B, and TNBC. E and F,SLC31A1 expression in grade 2 and grade 3 TNBCs stratified by pathologic response. ns, not significant.

This figure shows pan-cancer SLC31A1 expression in tumor versus normal tissues using TCGA data accessed through GEPIA.

Abstract Introduction: Tumor purity, defined as the proportion of malignant cells within a tumor region, is a critical factor in cancer research and clinical practice. Accurate tumor purity estimates (TPEs) are crucial in triple-negative breast cancer (TNBC), where tumor heterogeneity complicates diagnosis, biomarker interpretation, and therapeutic decisions. Traditional pathological assessment of tumor purity is limited by observer variability and scalability. Spatial transcriptomics (ST) integrates whole-transcriptome data with spatial context, enabling high-resolution and scalable estimation of tumor purity directly from H&E-stained slides. This study develops and validates ST-supervised deep learning models for spatially resolved, reliable TPEs that support more precise clinical evaluation and treatment guidance in TNBC. Methods: Visium ST data and matched 40× H&E whole-slide images from 25 TNBC patients were collected from Dartmouth-Hitchcock Medical Center (DHMC), yielding 120,973 50-µm Visium spots with co-registered 512×512-pixel H&E patches. Tumor regions were segmented using a validated DeepLabv3 model. Cell-type reference profiles were derived by integrating an external breast single-cell atlas with in-house single-cell RNA-seq data from seven TNBC patients using SCANVI. The integrated single-cell data were mapped onto in-house Visium sections with Cell2Location to estimate spot-level tumor cell-type proportions, serving as supervisory labels. A deep learning model (VIDCellTyper), built on the Virchow 2 model, was trained and cross-validated to predict spot-level tumor proportions and produce spatially resolved tumor purity maps. HoVerNet-derived cell counts from each patch were used for slide-level purity computation via cell-count-weighted averaging. The workflow was validated on an independent TNBC cohort (n=29; DHMC and Cedars-Sinai Medical Center), where slide-level purity was derived from aggregated patch-level predictions by the ST-informed model and HoVerNet. Results: Across tumor regions, the ST-supervised deep learning model achieved a spot-level purity correlation of 0.88 (p < 0.001) with spot-level Hovernet-derived TPE. When aggregating across the internal and external cohorts (n=29), slide-level ST-informed TPE showed a Spearman correlation of 0.83 (p < 0.001) with Hovernet-derived TPE. Conclusion: This proof-of-concept study shows that ST can guide computational models to derive TPE directly from H&E slides, yielding accurate and spatially resolved results. The ST-guided model generalized across independent TNBC cohorts, demonstrating robustness across tissue workflows. Future work will refine and validate this approach in clinically relevant contexts, including therapy response, prognosis, and pre- versus post-chemotherapy evaluation, to advance precision in treatment assessment and biomarker development. Citation Format: Minh-Khang Le, Vivek Pujara, I-Chuang Liao, Yuan Yuan, Joseph Lownik, Graeme Murray, Jin Sun Bitar, Luxi Chen, Parisa Najafzadeh, Keeyon Dabirian, David Lin, Fred W Kolling IV, Parth S. Shah, Jonathan Marotti, Xiaoying Liu, Louis J. Vaickus, Keluo Yao, Linda T. Vahdat, Zarif L. Azher, Joshua Levy. Spatial transcriptomics informed tumor purity estimation from histology slides for triple negative breast cancer [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 1442.

Sample distribution and statistical comparison for SLC31A1 analysis across breast cancer subtypes.

This figure compares therapy-associated changes in serum copper and ceruloplasmin activity across breast cancer molecular subtypes.

ΔCu levels stratified by tumor grade within molecular subtypes. Box plots display ΔCu values (after treatment − before treatment) by tumor grade within each molecular subtype: (A) TNBC, (B) HR+/HER2+, (C) HR+/HER2−, and (D) HR−/HER2+. The horizontal line within each box represents the median, box edges indicate the IQR, and whiskers extend to the minimum and maximum values.

ΔCu levels in patients with breast cancer by molecular subtype (N = 21).

The estrogen receptor (ER) signaling pathway is a key driver of breast cancer, primarily through the activation of genes that promote tumor cell survival and growth. The recommended first-line treatment for ER-positive (ER+)/human epidermal growth factor receptor 2–negative (HER2−) advanced or metastatic breast cancer is endocrine therapy plus a cyclin-dependent kinase 4/6 (CDK4/6) inhibitor. However, most patients experience disease progression, and there is no clear standard of care in the second-line setting. Thus, novel treatments in the advanced setting are needed. In this narrative review, we describe the unique mechanisms of action of a new class of drugs called PROteolysis TArgeting Chimera (PROTAC) ER degraders. Unlike other ER-targeted therapies, these small molecules harness the body’s primary intracellular natural protein disposal machinery, the ubiquitin-proteasome system, to directly induce ER degradation. Vepdegestrant (ARV-471) is the furthest advanced PROTAC ER degrader currently in clinical development. Preclinical data demonstrate increased tumor growth inhibition with vepdegestrant alone or in combination with CDK4/6 inhibitors compared with the selective ER degrader fulvestrant. In a first-in-human phase 1/2 clinical study, vepdegestrant administered orally as monotherapy or in combination with palbociclib showed promising clinical activity and a favorable safety profile in patients with heavily pretreated ER+/HER2− advanced breast cancer. Several other PROTAC ER degraders (AC699, ERD-3111, ERD-4001, and HP568) are in early development and have demonstrated activity in preclinical breast cancer models, with some recently entering clinical trials. The data highlight the potential for PROTAC ER degraders to be a new backbone therapy in breast cancer. The most common subtype of breast cancer, estrogen receptor (ER)-positive (ER+)/human epidermal growth factor receptor 2–negative (HER2−), grows in response to the hormone estrogen. The primary treatment for ER+/HER2− advanced breast cancer is typically hormone therapy, also known as endocrine therapy, combined with medications called cyclin-dependent kinase 4/6 (CDK4/6) inhibitors. Hormone therapies block the body’s ability to produce estrogen or block estrogen activity in cancer cells, which may slow or stop cancer growth. CDK4/6 inhibitors block the CDK4 and CDK6 proteins that cause cancer cell growth. However, most cancers eventually stop responding to treatment and start to grow again; therefore, new therapies are needed. This review describes how new medicines called PROteolysis TArgeting Chimera (PROTAC) ER degraders work. These degraders directly eliminate ER, blocking estrogen activity and potentially shrinking or stopping ER+ breast cancer tumors from growing. The furthest advanced PROTAC ER degrader in development is called vepdegestrant. In animal studies, vepdegestrant, either alone or combined with CDK4/6 inhibitors, was better at stopping tumor growth than a selective estrogen degrader called fulvestrant. In a clinical trial involving people with advanced breast cancer who had already received several treatments, vepdegestrant taken as pills by mouth either alone or with a CDK4/6 inhibitor called palbociclib showed promising results with manageable side effects. Animal studies investigating several other PROTAC ER degraders in early-stage development also indicate potential for these therapies. Overall, PROTAC ER degraders may become an important part of breast cancer treatment in the future.

Abstract Essential trace elements are crucial in various biological processes, including tumor growth, migration, and metastasis. For instance, copper ions are involved in mitochondrial metabolism, cell proliferation, tumor migration, metastasis and proangiongenic pathways. Localizing where metal ion homeostasis is disrupted is vital for understanding these processes beyond what bulk analysis can reveal. Elemental imaging offers high-resolution, quantifiable multi-elemental distribution maps, providing a more detailed analysis compared to traditional bulk measurements. However, aligning findings with tissue structures identified in histopathological data can be challenging. To address this, we developed a web-based application for the co-registration and analysis of Whole Slide Images (WSI) and elemental imaging data at the cellular level. The application integrates WSI with elemental maps generated by technologies such as ultrafast laser ablation and inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS), as well as a range of other elemental imaging techniques. Hosted by the Biomedical National Elemental Imaging Resource, the current application allows for a streamlined data integration, co-registration, and analysis, including: 1) uploading WSI, elemental maps, and pathologist annotations, 2) automated preprocessing to identify tissue regions, 3) co-registration to merge WSI and elemental maps, 4) custom import, real-time overlay/editing of pathologist annotations transferred from WSI, 5) plotting, statistical tools and 6) data export. This application enables users to compare elemental abundance within/between tissue structures annotated on H&E or immunohistochemical slides. Derived data can pinpoint elements and mixtures whose abundance differs significantly across various pathological conditions. As an initial test, we applied the software on breast HER2/ER/PR tumors, varying in molecular subtype/histology. Preliminary results, using Bayesian lognormal regression models, adjusting for tumor subtypes, indicated a subtype-dependent variation in copper abundance. The relative difference in copper levels between tumor and stromal regions was notably less in HER2 tumors (p=0.026) compared to HR tumors (p<0.0001). Conversely, iron showed a consistently higher abundance in stromal areas across both subtypes (p<0.0001). Future enhancements include improved co-registration capabilities, hotspot analysis, machine learning, mixture effects, and cloud-based architecture. This method uniquely enables integration of elemental data with spatial transcriptomics, mapping gene transcripts on tissue slides. With current ST assays aligned with H&E slides, this tool facilitates co-registration, offering insights into biological pathways affected by metal homeostasis disruptions. Citation Format: Yunrui Lu, Ramsey Steiner, Serin Han, Matthew Chan, Tracy Punshon, Brian Jackson, Linda Vahdat, Jonathan Marotti, Joshua Levy. A web-based application to co-register elemental imaging with histopathology to enhance the study of metal bioaccumulation within tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7429.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.