Citations

Abstract Purpose: While an addition of immune checkpoint inhibitor (ICI), pembrolizumab, has improved outcomes of patients with TNBC, early-stage TNBC patients with residual cancer burden (RCB) II/III after neoadjuvant chemotherapy in combination with pembrolizumab (NAP) still have poor outcomes. Previously, we demonstrated that intratumoral HLA-DRA expression and interferon-alpha (IFNα) responses were associated with immune-inflamed phenotype and better outcomes in TNBC patients receiving chemotherapy. This study utilized the CosMx™ SMI to investigate in early-stage TNBC treated with NAP. Methods: Of 151 samples from TNBC patients treated with NAP at Mayo Clinic, 8 pre-treatment biopsy samples (5 pCR and 3 RCB II/III were analyzed using the CosMx™ SMI platform. Linear mixed models to assess differential expression (DE), adjusting for hierarchical data structures. DE analyses were expressed as log2-fold changes (logFC), with the Benjamini-Yekutieli method. GSEA and IPA for signaling pathway exploration. Results: 1.7 million cells were identified across 8 samples after stringent quality control. Immune cell profiling revealed higher B cells, plasma cells, plasmablasts, and T cells (CD4 and CD8) in responders, whereas fibroblasts and myeloid cells were enriched in non-responders. DE analysis showed significantly elevated expression of HLA-DRA (logFC 1.955, p<0.001), other MHC class I and II components, and IFNα in responders. Tumor cells in responders exhibited the highest IFNα expression across cell types. GSEA and IPA analysis revealed enrichment in interferon signaling, antigen presentation, and the cGAS-STING pathway, which drives IFNα production. Spatial analysis categorized cells by their proximity to tumor cells: within 50 µm, 50-100 µm, and over 100 µm. T cells, particularly CD8 T cells, showed a distance-dependent distribution, with a higher abundance near tumor cells in responders. CD8 T cell activation scores declined with increasing distance from IFNα-positive tumor cells but remained consistently low near IFNα-negative tumor cells. IFNγ-positive CD8 T cells exhibited higher activation compared to IFNγ-negative cells, with activation levels influenced by proximity to tumor cells. Lastly, MHC expression in tumor cells was examined relative to CD8 T cell proximity. HLA-DRA and CIITA expression decreased as tumor cells were farther from CD8 T cells. Conclusions: This spatially resolved single-cell analysis highlights the pivotal role of tumor-immune interactions in response to pembrolizumab in TNBC. Our data suggest that tumor cells secrete IFNα through the activation of the cGAS-STING pathway, which activates T cells within close proximity, highlighting the potential role of cGAS-STING agonist as well as IFNα signaling and antigen presentation in augmenting TNBC responses to ICIs. Citation Format: Yi Liu, Saranya Chumsri, Yaohua Ma, Jodi M. Carter, Aziza Nassar, Edith Perez, Roberto A. Leon-Ferre, David Zahrieh, David W. Hillman, Judy C. Boughey, James Ingle, Krishna R. Kalari, Fergus J. Couch, Matthew P. Goetz, Keith L. Knutson, E. Aubrey Thompson. Spatially resolved single-cell analysis reveals cGAS-STING-mediated tumor-immune interactions via IFNα as determinants of pembrolizumab sensitivity in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7130.

Breast and ovarian cancer represent the most common and lethal cancers in women. Despite chemotherapy and targeted agents, recurrence is frequent and outcomes for patients with advanced disease are dismal, underscoring the need for novel therapeutic strategies. Using an unbiased approach with DepMap CRISPR screens across more than 1000 cancer cell lines, we identified six genes (CTPS1, RHOA, PRKRA, RAD9A, HUS1, and RAD1) that were essential for triple negative breast cancer (TNBC) cells, but not ERα+ breast cancer cells, that were also associated with poor outcomes. Validation of these effects by siRNA knockdown studies revealed that suppression of CTPS1 elicited the most potent inhibitory effects, characterized by S phase cell cycle arrest and apoptosis. Similar effects were also found in ovarian cancer cells which share many molecular features. CTPS1, along with CTPS2, is responsible for enzymatic conversion of UTP to CTP, an essential nucleoside triphosphate required for multiple cellular processes including DNA/RNA synthesis, phospholipid synthesis and glycosylation. CTPS1 was found to be highly expressed in tumor tissues, for both breast and ovarian cancer. RT-PCR assessment of CTPS1 expression in a panel of over 40 breast and ovarian cancer lines confirmed variable, but elevated expression relative to normal models. Notably, chemotherapy-resistant and PARP inhibitor (PARPi)-resistant models exhibited the highest levels of CTPS1 expression among all cells analyzed. In partnership with Step Pharma, which has developed a first-in-class highly selective CTPS1 inhibitor (STP938), we have demonstrated that STP938 elicits potent anti-proliferative effects at nM concentrations across many cell line and patient derived models, both in 2D and 3D culture systems. Furthermore, STP938 exhibited strong anti-cancer activity in chemotherapy- and PARPi-resistant ovarian cancer models, both as a monotherapy and in combination with standard-of-care treatments. RNA-seq analysis in cell models following STP938 treatment or CTPS1 knockdown/knockout showed significant dysregulation of DNA replication and cell cycle related genes, suggesting potential synergy with drugs targeting replication stress or DNA damage response pathways. Single-cell sequencing further revealed specific cell-cell interactions/cross talk/pathways that are disrupted by STP938 in vivo using ovarian cancer patient-derived xenograft. The first-in-human clinical trial for STP938 began in 2022 for relapsed/refractory B- and T-cell lymphomas (NCT05463263). In August 2024, a second phase I trial opened for advanced solid tumors, including an expansion cohort for CTPS2-null ovarian cancer (NCT06297525) at Mayo Clinic. These laboratory discoveries, and ongoing clinical studies, are expected to pave the way for a broader uptake of this novel therapeutic approach aimed at improving outcomes in patients with highly aggressive forms of these diseases. Citation Format: Xiyin Wang, Michael Emch, Lauren Voll, Rebecca Epp, Esther Rodman, Noa Odell, Nicole Pearson, Xiaonan Hou, Matthew Goetz, Scott Kaufmann, John Weroha, Philip Beer, John Hawse. Exploiting CTPS1 dependency for the treatment of breast and ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6388.

Abstract Breast cancer (BC) is the most common cancer diagnosed in American women. The majority of breast cancers express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER2). However, 15-20% lack expression of all three and are called triple-negative breast cancer (TNBC). TNBC accounts for a disproportionately large amount of morbidity and mortality due to its aggressive nature and the paucity of effective adjuvant maintenance therapy, driving an urgent need for novel therapeutics. Z-endoxifen (ENDX) is a potent tamoxifen metabolite that dually targets ERα and protein kinase C beta I (PKCβI). We recently described next-generation molecules, the first-in-class endoxifen-PROTAC (ENDX-PROTAC) compounds which incorporate ENDX as the warhead and cereblon (CRBN) as the E3 ubiquitin ligase recruitment moiety (Jayaraman 2024, SABCS). We used the PRISM screen of over 900 cancer cell lines to determine that ENDX-PROTACs have broad antitumor activity in both TNBC and ER+ breast cancer in addition to, leukemia, pancreatic, lymphoma, lung, melanoma, and ovarian cancer cells, among others. ENDX-PROTACs primarily target a key translation termination factor, G to S phase transition 1 (GSPT1). Here, we demonstrate for the first time that these compounds also cause marked DNA damage. The lead compounds of this series, EPrC-007 and EPrC-009 demonstrated potent anti-proliferative effects in MDA-MB-231, BT549, and MDA-MB-436 TNBC cell lines. EPrC-007 induced substantial DNA double strand breaks in MDA-MB-231 cells as assessed by γ-H2AX Western blotting. Interrogation of DNA repair pathways revealed activation of ATM/CHK2 and downregulation of ATR/CHK1, suggesting inhibition of DNA repair as a potential mechanism. Additionally, EPrC-007 also induced γ-H2AX foci in MDA-MB-231 and BT549 cells, but not in the MCF10A normal breast tissue cell line, suggesting selective anti-tumor activity. Since we suspect that EPrC-007 blocks DNA repair machinery, we next sought to determine if these molecules would synergize with DNA damaging agents or other DNA repair inhibitors. Promisingly, EPrC-007 demonstrated remarkable synergy with olaparib (poly-(ADP-ribose) polymerase (PARP) inhibitor), hydroxyurea (ribonucleotide reductase inhibitor), and cisplatin (direct DNA damaging agent), but not camptothecin (topoisomerase I inhibitor). Importantly, only olaparib co-treatment demonstrated synergy in the PARP- and PROTAC-resistant HCC1143 TNBC cell line. Furthermore, the combination of EPrC-007 with olaparib induced markedly more γ-H2AX foci than either agent alone, confirming synergy at the DNA damage level. Taken together, our studies demonstrate the remarkable potential of ENDX-PROTACs in the single-agent setting as well as in combination with PARP inhibitors. Citation Format: Aamod S. Dekhne, Swaathi Jayaraman, Lucy Rai Thulung, Becky Bruinsma, Baustin M. Welch, Samuel Wyatt, Esther P. Rodman, Sayantani Bhattacharya, Thomas R. Caulfield, John R. Hawse, Matthew J. Schellenberg, Matthew P. Goetz. Novel endoxifen-based proteolysis targeting chimera (PROTAC) small molecules synergize with olaparib and exhibit potent antitumor activity in triple negative breast cancer through induction of DNA damage [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1666.

Abstract Background: Despite recent advances in the treatment of triple negative breast cancer (TNBC) many patients are still suffering from early recurrence, therapy resistance, and organ metastasis 1 to 3 years post-chemotherapy. TNBC cells with stem cell-like properties are resistant to standard of care chemotherapy and are responsible for early tumor relapse and poor outcome. Up-regulation of PD-L1 expression in Cancer Stem Cells (CSCs) has been associated with immune evasion capacity and promotion of their stem-like properties. In this study we identify two druggable stemness target, Aurora-A Kinase (AURKA) and NOTCH3, with positive feedback on intra-tumoral PD-L1 expression and cancer cell plasticity. Methods: MDA-MB 231, MDA-MB 231 LM and SUM149-PT TNBC cells were used both in 2D and 3D environment. Unique TNBC cells were established from metastatic PDX models. TNBC cells were treated with Alisertib (AURKA inhibitor, 50nM) or AV353 (NOTCH3 inhibitor, 200ng). Immunofluorescence and immunoblot were used to compare different biomarker expression. ALDH was measured using Aldefluor kit. Apoptotic cells were quantified in real-time using IncuCyte S3. In vivo studies; 1x106 MDA MB 231/LM cells were injected into the mammary fat pad of NSG-female mice and treated with Alisertib or AV353. Control groups were treated with pacebo. Results: Our study shows that AURKA and NOTCH3 pharmacological blockade reduced nuclear/undruggable PD-L1, that is not achieved clinically by using FDA-approved immune checkpoint inhibitors (ICIs). Significantly, following stemness-targeted therapy with alisertib or AV353, reduction of intra-tumoral PD-L1 resulted in inhibition of tumor growth, decreased levels of CD44 expression, increased CD8+ T-cell tumor infiltration, impairment in the enrichment of ALDHhigh CSCs and enhanced chemosensitivity. In vivo pharmacological blockade of AURKA and NOTCH3 signaling pathways induced inhibition of tumor growth, apoptosis, and impairment of organ metastases. Conclusions: This study demonstrates that selective AURKA and NOTCH3 pharmacological blockade results in the inhibition of cancer cell plasticity that is linked to reduction of undruggable nuclear PD-L1 in TNBC cells. Our findings provide the strong preclinical rationale to develop novel clinical trials for patients with advanced TNBC that are refractory to chemotherapy and show limited response to FDA-approved ICIs. Citation Format: Antonino Bonaventura D'Assoro, Matthew Goetz, Carol Lange, Roberto Leon-Ferre, Tufia Haddad, James Ingle, Annapaola Montagner, Andrew Takchi, Ellie Babadi, Lucio Miele, Urban Lendahl, Urban Lendahl. Pharmacological blockade of AURKA and NOTCH3 oncogenic pathways to inhibit cancer cell plasticity in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5978.

Abstract Deep learning has advanced digital pathology and multi-omics data analysis. However, there is still a critical need to integrate these modalities to better understand complex diseases. Deep learning models, such as Prov-Gigapath, a state-of-the-art pathology foundation model, have been developed for analyzing whole-slide images (WSI). Our previously published deep-learning framework, OmicsFootPrint (OFP), transforms high-dimensional multi-omics data into intuitive genomically-arranged circular images and utilizes transfer learning to overcome the challenges of high-dimensional, low-sample-size data. In this study, we aimed to combine WSI and multi-omics data to develop a framework capable of distinguishing between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which is clinically significant as it may help explain differential responses to therapies, such as tamoxifen versus letrozole. We enhanced the performance of our OFP model by dividing each multi-omics circular image (1024x1024x3 pixels) into 16 equal-sized patches (256x256x3 pixels). Each patch was trained using a separate EfficientNetV2 architecture with pre-trained ImageNet weights. The probability outcomes from these 16 models were subsequently combined through a meta-learner model using the AutoGluon framework. Concurrently, WSI images corresponding to the same patients were independently trained, maintaining consistent train/validation/test splits. The Prov-Gigapath model, which was trained using a dataset from the Providence Health System, was employed to extract patch-level embeddings from the WSI data. These embeddings were fine-tuned using a LongNet-based masked autoencoder to predict outcomes. Finally, predictions from the OFP model and the WSI-based Prov-Gigapath model were integrated through a meta-learner, preserving the original train/validation/test split. Model performances (AUC) were evaluated using the held-out test set. The gold standard used for the analysis was histological identification of IDC versus ILC, against which the performance of omics data alone, WSI data alone, and the multi-modal integration approach were compared. We evaluated our integrated model as a proof-of-concept using The Cancer Genome Atlas (TCGA) breast cancer cohort, comprising 92 ILC and 391 IDC samples. Multi-omics data, including gene expression, microRNA, copy number variation, and RPPA, were analyzed using the OFP framework, while WSI data were processed using the Prov-Gigapath method. The results demonstrated that the multi-modal integration approach significantly outperformed individual modalities, achieving an AUC of 0.98 compared to 0.87 for OFP (original), 0.95 for OFP (16-patches), and 0.92 for WSI. Integrating WSI and multi-omics data improves predictive accuracy, significantly improving the classification of breast cancer subtypes over histology alone. Citation Format: Naresh Prodduturi, Xiaojia Tang, Hamid R. Tizhoosh, Kevin J. Thompson, Richard Weinshilboum, Karthik V. Giridhar, Judy C. Boughey, Eric W. Klee, Liewei Wang, Matthew P. Goetz, Vera Suman, Krishna R. Kalari. A multi-modal learning framework to integrate digital pathology image and multi-omics data in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2429.

PURPOSE Adjuvant endocrine therapy (ET) improves overall survival (OS) in estrogen receptor (ER)–positive early-stage breast cancer (BC). However, the benefit of ET for those with ER-low BC (ER 1%-10%) is unclear. METHODS Using the National Cancer Database, we studied patients with high-risk stage I to III, ER-low BC (defined as immunohistochemistry 1%-10%) who received (neo)adjuvant chemotherapy and did or did not initiate ET. OS was analyzed with ET initiation as a time-dependent covariate using Cox proportional hazards regression. RESULTS Of 10,362 patients with stage I to III ER-low BC, 7,018 received chemotherapy and met inclusion criteria. ET omission was 42% at 12 months and more common in patients with tumors that were progesterone receptor–negative, human epidermal growth factor receptor 2–negative, higher-grade (grade 2/3) and higher Ki-67 (≥20%; all P < .001) and those who received neoadjuvant chemotherapy (NAC; P < .001). With a median follow-up of 3 years, 586 deaths were observed. In a multivariable analysis, ET omission was associated with a higher risk of death (hazard ratio [HR], 1.23 [95% CI, 1.04 to 1.46]; P = .02), with a greater impact in those with higher ER levels: ER 1%-5% (HR, 1.15 [95% CI, 0.91 to 1.45]; P = .24) versus ER 6%-10% (HR, 1.42 [95% CI, 1.00 to 2.02]; P = .048). Among patients treated with NAC (n = 4,377, 62%), ET omission was associated with worse OS in those with residual disease (RD; HR, 1.26 [95% CI, 1.00 to 1.57]; P = .046) but not in those who achieved a pathologic complete response (HR, 1.06 [95% CI, 0.62 to 1.80]; P = .84). CONCLUSION In ER-low, early-stage BC, ET omission is associated with significantly worse OS, especially in patients with RD after NAC and those with higher (6%-10%) ER levels. Until prospective data are available, patients with ER-low BC should be counseled regarding the potential benefit of ET.

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

Abstract The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores—0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Breast cancer is treated with a multidisciplinary approach involving surgical oncology, radiation oncology, and medical oncology. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Breast Cancer include recommendations for clinical management of patients with carcinoma in situ, invasive breast cancer, Paget’s disease, Phyllodes tumor, inflammatory breast cancer, and management of breast cancer during pregnancy. The content featured in this issue focuses on the recommendations for overall management of systemic therapy (preoperative and adjuvant) options for nonmetastatic breast cancer. For the full version of the NCCN Guidelines for Breast Cancer, visit NCCN.org .