Citations

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.

Importance Semaglutide, a glucagonlike peptide-1 receptor agonist (GLP-1RA), has recently been implicated in cases of nonarteritic anterior ischemic optic neuropathy (NAION), raising safety concerns in the treatment of type 2 diabetes (T2D). Objective To investigate the potential association between semaglutide and NAION in the Observational Health Data Sciences and Informatics (OHDSI) network. Design, Setting, and Participants This was a retrospective study across 14 databases (6 administrative claims and 8 electronic health records). Included were adults with T2D taking semaglutide, other GLP-1RA (dulaglutide, exenatide), or non–GLP-1RA medications (empagliflozin, sitagliptin, glipizide) from December 1, 2017, to December 31, 2023. The incidence proportion and rate of NAION were calculated. Association between semaglutide and NAION was assessed using 2 approaches: an active-comparator cohort design comparing new users of semaglutide with those taking other GLP-1RAs and non–GLP-1RA drugs, and a self-controlled case-series (SCCS) analysis to compare individuals’ risks during exposure and nonexposure periods for each drug. The cohort design used propensity score–adjusted Cox proportional hazards models to estimate hazard ratios (HRs). The SCCS used conditional Poisson regression models to estimate incidence rate ratios (IRRs). Network-wide HR and IRR estimates were generated using a random-effects meta-analysis model. Exposures GLP-1RA and non–GLP-1RAs. Main Outcomes and Measures NAION under 2 alternative definitions based on diagnosis codes: one more inclusive and sensitive, the other more restrictive and specific. Results The study included 37.1 million individuals with T2D, including 810 390 new semaglutide users. Of the 43 620 new users of semaglutide in the Optum’s deidentified Clinformatics Data Mart Database, 24 473 (56%) were aged 50 to 69 years, and 26 699 (61%) were female. The incidence rate of NAION was 14.5 per 100 000 person-years among semaglutide users. The HR for NAION among new users of semaglutide was not different compared with that of the non–GLP-1RAs using the sensitive NAION definition—empagliflozin (HR, 1.44; 95% CI, 0.78-2.68; P = .12), sitagliptin (HR, 1.30; 95% CI, 0.56-3.01; P = .27), and glipizide (HR, 1.23; 95% CI, 0.66-2.28; P = .25). The risk was higher only compared with patients taking empagliflozin (HR, 2.27; 95% CI, 1.16-4.46; P = .02) using the specific definition. SCCS analysis of semaglutide exposure showed an increased risk of NAION (meta-analysis IRR, 1.32; 95% CI, 1.14-1.54; P &amp;lt; .001). Conclusions and Relevance Results of this study suggest a modest increase in the risk of NAION among individuals with T2D associated with semaglutide use, smaller than that previously reported, and warranting further investigation into the clinical implications of this association.

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.

Inositol 1,4,5-trisphosphate (IP3) receptor type 1 ( ITPR1 ), 2 ( ITPR2 ), and 3 ( ITPR3 ) encode the IP3 receptor (IP3R), a key player in intracellular calcium release. In four unrelated patients, we report that an identical ITPR3 de novo variant—NM_002224.3:c.7570C>T, p.Arg2524Cys—causes, through a dominant-negative effect, a complex multisystemic disorder with immunodeficiency. This leads to defective calcium homeostasis, mitochondrial malfunction, CD4 + lymphopenia, a quasi-absence of naïve CD4 + and CD8 + cells, an increase in memory cells, and a distinct TCR repertoire. The calcium defect was recapitulated in Jurkat knock-in. Site-directed mutagenesis displayed the exquisite sensitivity of Arg 2524 to any amino acid change. Despite the fact that all patients had severe immunodeficiency, they also displayed variable multisystemic involvements, including ectodermal dysplasia, Charcot-Marie-Tooth disease, short stature, and bone marrow failure. In conclusion, unlike previously reported ITPR1-3 deficiencies leading to narrow, mainly neurological phenotypes, a recurrent dominant ITPR3 variant leads to a multisystemic disease, defining a unique role for IP3R3 in the tetrameric IP3R complex.

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 .