Heterogeneity Of Breast Cancer Research Articles

The N6-methyladenosine methylation landscape stratifies breast cancer into two subtypes with distinct immunological characteristics.

N6-methyladenosine (m6A) methylation modification affects the tumorigenesis and metastasis of breast cancer (BC). This study investigated the association between m6A regulator-mediated methylation modification patterns and characterization of the tumour microenvironment in BC, as well as their prognostic importance. Public gene expression data and clinical annotations were collected from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus website and the METABRIC program. We analysed the genetic expression, gene-gene interactions, gene mutations and copy number variations using R software. The data were screened for risk genes using the Cox risk regression model, and we developed an algorithm for risk score and its predictive value. Compared to adjacent normal tissue, we identified 16 differentially expressed m6A regulators in BC, including six writers and 10 readers. Under unsupervised clustering, two distinguished modification patterns were identified, cluster C1 and C2. Compared to m6A cluster C2, cluster C1 was found to be more involved in immune-related pathways, with a relatively higher immune score and stromal score (P < 0.05). Patients were divided into two groups based on their risk scores for survival analysis. The patients in the high-risk score group had significantly worse overall survival than patients in the low-risk score group, (P < 0.0001). The TCGA database validation revealed the same prognostic tendency. In summary, our study showed distinct m6A regulator modification patterns contribute to the immunological heterogeneity and diversity of BC. The development of m6A gene signatures and the m6A score aid in the prognostic prediction of patients with BC.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

BackgroundEarly prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients.MethodsThe DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways.ResultsA 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635–0.741 and AUC = 0.650, 95%CI: 0.595–0.705) and tested (AUC = 0.686, 95%CI: 0.594–0.778 and AUC = 0.626, 95%CI: 0.529–0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722–0.816 and test: 0.762, 95%CI: 0.679–0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665–0.767 and test AUC = 0.695, 95%CI: 0.656–0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system.ConclusionDynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

A β-Carboline Derivate PAD4 Inhibitor Reshapes Neutrophil Phenotype and Improves the Tumor Immune Microenvironment against Triple-Negative Breast Cancer.

Triple-negative breast cancer is a highly aggressive and heterogeneous breast cancer subtype characterized by early metastasis, poor prognosis, and high recurrence. Targeting histone citrullination-mediated chromatin dysregulation to induce epigenetic alterations shows great promise in TNBC therapy. We report the synthesis, optimization, and evaluation of a novel series of β-carboline-derived peptidyl arginine deiminase 4 inhibitors that exhibited potent inhibition of TNBC cell proliferation. The most outstanding PAD4 inhibitor, compound 28, hindered the PAD4-H3cit-NET signaling pathway and inhibited the growth of solid tumors and pulmonary metastatic nodules in the 4T1 in situ mouse model. Furthermore, 28 improved the tumor immune microenvironment by reshaping neutrophil phenotype, upregulating the proportions of dendritic cells and M1 macrophages, and reducing the amount of myeloid-derived suppressor cells. In conclusion, our work offered 28 as an efficacious PAD4 inhibitor that exerts a combination of conventional chemotherapy and immune-boosting effects, which represents a potential therapy strategy for TNBC.

Abstract PO1-06-05: Heterogeneity and prognostic characteristics of HER2-low breast cancer: A retrospective analysis of patients with HER2-negative metastatic breast cancer

Abstract Background: It remains uncertain as to whether low levels of HER2 positivity have any prognostic implications in breast cancer patients, whether HER2 levels can be inconsistent between primary tumors and metastatic lesions in individuals with advanced HER2-negative breast cancer, and how such inconsistencies may impact patient prognosis. Methods: A retrospective analysis of advanced breast cancer patients admitted to our hospital from from January 1st, 2010 to January 1st, 2019 was performed, with all patients that underwent at least one metastatic lesion biopsy being screened. The hormone receptor (HR) and HER2 profiles of both primary and metastatic lesions from these patients were confirmed, and HER2-positive patients (HER2 3+ immunohistochemistry [IHC] results or HER2 2+ IHC results with positive in situ hybridization [ISH] results) were excluded from further analyses. To examine the prevalence of HER2-low status across primary and metastatic cancers, the current study set out to detect inconsistencies between these two tumor compartments with respect to HER2-low status and to examine the prognostic implications of these findings in patients. Results: The median follow-up duration for this study at the cutoff date (December 31, 2021) was 49.3 (45.8-52.8) months. The current study examined 1,031 participants with HER2 and HR status data from both primary and metastatic tumors. The proportion of HER2-low expression was significantly higher in metastatic lesions than primary tumors (34.7% vs 25.7%), with a corresponding drop in the proportion of HER2-zero metastatic lesions (FIGURE 1). In a multivariate analysis, HER-2 upregulation was linked to HR status, which was established as an independent influencing factor, with such upregulation being most common in HR-positive individuals (P= 0.004). With respect to HER2 status of primary lesions, the medianl survival of patients in the HER2-zero was similar to HER2-low patients, regardless of HR status [44.8 months (95% CI 38.2-54.1) vs 41.5 months (95% CI 35.0-48.0)] (P = 0.954)(FIGURE 2A). However, when the HER2 status of metastatic lesions was examined, patients with HER2-low expression had a greater median OS than HER2-zero patients [47.6 months (95% CI 39.6-48.0) vs. 32.2 months (95% CI 24.6-40.0)] (P &amp;lt; 0.001)(FIGURE 2B). The median OS of patients that exhibited HER2 upregulation (zero→low) was longer than that of patients without such upregulation (zero-zero) (55.8 months (95% CI 45.0-66.6) vs. 32.4 months (95% CI 23.8-41.0)) (P &amp;lt; 0.001)(FIGURE 3A). Among HR-positive patients, HER2 upregulation was associated with significantly prolonged OS relative to patients without such upregulation [56.6 months (95% CI 44.6-68.6) vs. 41.7 months (95% CI 31.1-52.3)] (P=0.006)(FIGURE 3B), whereas survival outcomes were similar in these two groups in the HR-negative subgroup [44.8 months (95% CI 25.6-64.0) and 25.3 months (95% CI 20.4-30.3)] (P=0.103)(FIGURE 3C). However, independent of HR status, the median OS of patients who had HER2 downregulation (low→zero) was similar to those without downregulation (low→low)(FIGURE 3 D, E, F). Conclusion: These results support inconsistencies in HER2-low expression status between primary and metastatic lesions, with low HER2 levels in metastatic tumors being associated with improved survival outcomes. HR-positive patients are more likely to exhibit HER2 upregulation within metastatic lesions and experience corresponding prognostic benefits. Keywords: HER2-low, inconsistency, survival, prognosis, advanced breast cancer Citation Format: Huimin Lv, Min Yan, Mengwei Zhang, Limin Niu, Huiai Zeng, Huihui Sun, Shengnan Zhao, Jing Wang. Heterogeneity and prognostic characteristics of HER2-low breast cancer: A retrospective analysis of patients with HER2-negative metastatic breast cancer [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 PO1-06-05.

Abstract PO2-14-03: Novel Metrics of HER2 Heterogeneity in HER2-Positive and HER2-Low Breast Cancer via High Dimensional Multiplexed Immunofluorescence Spatial Profiling

Abstract Background: Categorizing breast cancer HER2/ERBB2 expression as “positive” or “negative” is no longer sufficient, with evidence that treatment response and outcomes are associated with HER2 “low” status and HER2 intratumoral heterogeneity. We hypothesized that interrogating HER2 heterogeneity (HER2het) across multiple spatial resolutions would more accurately capture HER2 diversity and be associated with clinical outcomes. Methods: We interrogated tumor cell and microenvironmental features by profiling 1,113,204 single cells in tissue sections from 171 HER2+/HER2low cancers via custom 25-marker high dimensional multiplexed immunofluorescence (HDmIF) using NeoGenomics MultiOmyx, with adjacent section HER2 immunohistochemistry (IHC). We developed novel metrics to concurrently: 1) interrogate HER2 heterogeneity at four spatial resolutions; 2) use machine learning to translate HER2 IF to IHC, termed ‘HAIQu’ (HER2 Automated Immunofluorescence Quantification), scoring HER2 IF expression according to ASCO/CAP guidelines; 3) delineate HER2 signaling phenotypes at the tumor cell level based on six HER2-related proteins; 4) evaluate immunophenotype of 23 immune cell types. We evaluated the association of these novel HER2het metrics with patient clinicopathologic features, recurrence-free survival (RFS), overall survival (OS), and diverse antibody markers representing tumor cell intrinsic processes and tumor-immune microenvironment (TME). Results: 1166 regions of interest were analyzed from 208 unique tumors profiled. Median follow-up from diagnosis was 143 months and 98.9% (n=183/185) received HER2-directed therapy in the (neo)adjuvant or metastatic setting. Our HAIQu scoring system effectively translated adjacent section HER2 IF to IHC with 97.9% concordance between HAIQu and clinical IHC scoring. Single-cell phenotypic analyses of 392,984 HER2+/PanCK+ tumor cells’ concurrent expression of six HER2-positive breast cancer related proteins (HER2, HER3, EGFR, pAKT, ER, KI67) using an unsupervised neural network-based self-organizing map approach resulted in 7 HER2 signaling cell phenotypes. Most patient samples are dominated by a single cell cluster but, intriguingly, Cluster 1 cells (EGFR-low) predominate in tumors with high HER2 cell membrane heterogeneity (ANOVA p=0.003). Evaluation of immunophenotype in hormone receptor-negative, HER2+ tumors, demonstrated significant association with immune cluster and recurrence-free survival (RFS; log-rank p=0.024) with zero RFS events among immune-high versus median survival of only 53.5mo among immune low-PDL1 low tumors. A multivariable Cox proportional hazards model including single cell HER2-heterogeneity (only significant metric on univariate), receptor subtype, and immunophenotype cluster demonstrated significant association with RFS (overall model log-rank p=0.005) and each significantly contributed to the model (all p&amp;lt; 0.05). Conclusions: We present novel metrics of HER2 heterogeneity via HDmIF, which offer detailed characterization of the diversity of HER2 expression in a large, clinically-annotated cohort with long-term follow-up. Identification of a strong association between immunophenotype and RFS supports further investigation of the highly immune activated subsets of ER-/HER2+ breast cancer. Strong correspondence of HER2 IF and IHC and our HAIQu methodology offers a pathway to translation of HER2het metrics to clinical practice. Table 1. Association of HER2 Heterogeneity Metrics with Recurrence-Free Survival Citation Format: David Tallman, Anna Juncker-Jensen, Harry Nunns, Kevin Gallagher, Heather LeFebvre, Karen Yamamoto, Katharine Collier, Mark Vater, Ava Strahan, Mathew Cherian, Ashley Pariser, Preeti Sudheendra, Bhuvaneswari Ramaswamy, Margaret Gatti-Mays, Ainura Kyshtoobayeva, Zaibo Li, Daniel Stover, Kai Johnson. Novel Metrics of HER2 Heterogeneity in HER2-Positive and HER2-Low Breast Cancer via High Dimensional Multiplexed Immunofluorescence Spatial Profiling [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 PO2-14-03.

Abstract PO5-01-09: Exploring cellular heterogeneity of localised breast cancers

Abstract Breast cancer is a heterogeneous disease at multiple levels, ranging from subtype differences between patients (inter-patient heterogeneity) to the diverse composition of malignant cells, heterogeneity of hormone receptor (HR) expression and cellular makeup within single breast cancer samples (intra-tumour heterogeneity). Despite an increase in the number of effective therapies available, many patients will experience an incomplete treatment response and subsequent relapse. These adverse treatment outcomes may be attributed to the often overlooked but critical factor of cellular heterogeneity. In order to gain deeper insights into intra-tumour heterogeneity, we have applied single-cell technologies on a cohort comprising 250 primary, untreated breast cancers. We have optimized methods for tissue cryopreservation, eliminating the need for fresh sample processing, and multiplex tissue profiling. Together, these are cost-efficient processes that allow for improved handling of small tissue sizes, such as biopsies, and reduce batch effect. To ensure accurate and reliable data processing, we have developed a scalable computational workflow that includes benchmarked methods for sample SNP-demultiplexing, doublet detection, high-resolution cell annotation and cellular integration. Finally, we are extending our existing methods1 to study the cellular heterogeneity of breast cancers. Our method, scSubtyper, explores the phenotypic differences between malignant cells within tumours, by comparing each single cell to distinct features associated with different molecular subtypes and assigning each cell to one of these subtypes. Our previous study1 and preliminary results of this project revealed that over 90% of the samples exhibit a mix of malignant cells of different subtypes, and 50% of samples contain cells that have characteristics of all subtypes, demonstrating cellular heterogeneity exists not only exists between malignant cells, but also within malignant cells of a tumour. Our second approach, known as ecotyping, assesses patterns of cell type frequencies across samples and groups them based on similarity of these co-occurences. Our preliminary results have revealed the existence of 5 ecotypes that lack significant associations with samples clinical subtypes. Applying the same approach exclusively within the HR-positive samples identified 4 ecotypes characterized by distinct abundances of immune and stromal cells. This analysis revealed that ecotypes are not a simple surrogate for clinical and molecular subtypes, but their presence could drive different response to treatment. Together, our high-throughput tissue processing and computational approaches to studying intra-tumour heterogeneity are now being applied to our large, well annotated, clinical cohort. Supported by the preliminary results, we hypothesize that this study will play a vital role in optimizing breast cancer patient stratification to improve treatment management and outcome. 1. Wu, Sunny Z., et al. "A single-cell and spatially resolved atlas of human breast cancers." Nature genetics 53.9 (2021): 1334-1347. Citation Format: Beata Kiedik, Daniel Roden, Kate Harvey, Ghamdan Al-Eryani, Sunny Wu, Mun Hui, Sandra O'Toole, Elgene Lim, Charles Perou, Alex Swarbrick. Exploring cellular heterogeneity of localised breast cancers [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 PO5-01-09.

Abstract PL-01: Breast Cancer Evolution, Immune Evasion and Metastasis Driven by Chromosomal Instability

Abstract Chromosomal instability (CIN) is a poor prognostic feature with high prevalence in breast cancer and is responsible for driving Somatic Copy Number Aberrations and Intratumour Heterogeneity (ITH) within subclones during breast cancer evolution. We have observed that SCNAs are enriched in metastatic samples, including gains in chromosome 11q13.3 (encompassing CCND1) in HER2+ breast cancer. However, there have thus far been no prospective, comprehensive studies of intratumor heterogeneity in breast cancer. We will present data from TRACERx Breast/SCANDARE a prospective, multicentre study of triple-negative breast cancer (TNBC) in which multi-region biopsies have been collected from primary TNBC prior to neoadjuvant treatment, and at longitudinal time points including surgery and relapse. We find prevalent intratumor heterogeneity in somatic mutations and SCNAs in the primary tumor prior to treatment. We have leveraged the rich clinical annotation in this cohort to correlate intratumor heterogeneity scores with key clinical outcomes, including treatment response (pathological complete response to neoadjuvant chemotherapy) and survival. We find that CIN manifests as oncogenic amplification on extrachromosomal DNA (ecDNA), which plays a pivotal role in driving drug resistance and tumor evolution. Through analysis of Whole Genome Sequencing data from 2936 breast tumors from the Genomics England breast cancer cohort, we have identified focal amplifications driven by ecDNA in 46.4% of HER2+ breast cancers. EcDNA were enriched in metastatic tumors and was associated with poor clinical outcomes. In addition to oncogenic amplifications such as HER2 derived from ecDNA during cancer evolution, ecDNAs contained immunomodulatory genes associated with reduced T cell infiltration. Further Immune-modulation by CIN results from loss of heterozygosity of the HLA locus (HLA LOH) and is a prevalent subclonal event in breast cancer. CIN is further drives haploid LOH, resulting in the high prevalence of whole genome doubling and further CIN in TNBC. Tumors are heterogeneous compositions of distinct clones with different compliments of SCNAs and varying levels of fitness, hence measuring the proliferation of individual clones to predict future evolutionary outcomes is likely to be critical. We have developed SPRINTER (Single-cell Proliferation Rate Inference in Non-homogeneous Tumours through Evolutionary Routes), a novel computational method to measure proliferation rates in individual tumour clones using single-cell whole-genome DNA sequencing data. We applied SPRINTER to 42,009 triple negative breast cancer cells, demonstrating significant proliferative heterogeneity between distinct tumor clones. Importantly, we show a correlation between higher proliferation rates and increased rates of somatic variants, suggesting proliferation links to clone evolvability. Taken together, these complimentary analyses provide evidence for the importance of CIN in driving ITH, selection, metastasis and immune evasion and provide a framework to determine recurrent evolutionary patterns in breast cancer evolution. Citation Format: C. Swanton. Breast Cancer Evolution, Immune Evasion and Metastasis Driven by Chromosomal Instability [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 PL-01.

Abstract PS09-09: Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer

Abstract Background: Anti-HER2 targeted therapy has achieved a series of breakthroughs. However, the current treatment strategy regarding HER2-positive breast cancer remains indiscriminate and lacks specificity, which limits the further improvement of overall treatment response and may lead to overtreatment and extra cost for some patients. Our study aims to reveal the molecular heterogeneity of HER2-positive breast cancer to guide a more precise treatment. Patients and methods: We selected HER2-positive breast cancer patients treated at Fudan University Shanghai Cancer Center between 2013 and 2014 and conducted genomic, transcriptomic, proteomic and metabolomic profiling. We then applied a non-negative matrix factorization algorithm on transcriptomic data to obtain an unsupervised classification. And we further studied the correlation between subtypes and corresponding treatment strategies in multiple cohorts of adjuvant and neoadjuvant therapy. For clinical accessibility, we developed convolutional neural network models through deep learning algorithm based on digital pathology to identify different subtypes. Additionally, we explored novel treatment strategies using the patient-derived organoids (PDOs) models. Results: We established a novel multiomics cohort of 180 HER2- breast cancer patients and classified them into four clinically significant molecular subtypes: (1) A classical HER2-enriched (HER2-CLA, N=51) subtype characterized by strong ERBB2 signaling and remarkable sensitivity to anti-HER2-targeted therapy (pathologic complete response with dual-targeted therapy: 93%). (2) an immunomodulatory (HER2-IM, N=36) subtype characterized by an immune-activated microenvironment and excellent prognosis with current treatment (no relapse in 97% of patients with a median follow-up of 86 months). Tumors of this subtype were therefore candidates for de-escalatory treatment. (3) A luminal-like (HER2-LUM, N=55) subtype distinguished by activated estrogen receptor signaling and (4) a basal/mesenchymal-like (HER2-BM, N=38) subtype enriched in activated receptor tyrosine kinase pathways. HER2-LUM and HER2- BM showed limited benefit from anti-HER2 therapy, and thus, add-on therapies might be needed. The overall area under the curve (AUC) of the convolutional neural network model based on digital pathology for identifying different subtypes is 0.77. In the exploration of novel treatment strategies, we found in the PDO model that the HER2-LUM subtype is more sensitive to a treatment regimen combining standard (chemotherapy and targeted therapy) with subsequent endocrine therapy and CDK4/6 inhibitors compared to other subtypes. Additionally, the HER2-BM subtype demonstrated greater sensitivity to treatment with a combination of EGFR inhibitors, PDGFR inhibitors or VEGFR inhibitors. Conclusion: We uncovered a high degree of molecular heterogeneity in HER2-positive breast cancer and illustrated its impact on treatment response. More precise treatment can be given according to the characteristics of different subtypes, which may achieve good efficacy and simultaneously reduce overtreatment and extra cost. The comprehensive profiling of HER2-positive breast cancers could also serve as an important resource for further exploration. Key Words: HER2-positive breast cancer cohort; molecular classification; targeted therapy; precision treatment; de-escalatory treatment. Citation Format: Yu-Wei Li, Ding Ma, Xiang-Rong Wu, Lei-Jie Dai, Shen Zhao, Yu-Zheng Xu, Xi Jin, Xiao Yi, Ying Wang, Cai-Jin Lin, Yi-Fan Zhou, Tong Fu, Wen-Tao Yang, Ming Li, Hong Lv, Yi-Zhou Jiang, Zhi-Ming Shao. Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer [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 PS09-09.

Abstract PO5-07-01: A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study

Abstract Introduction: Breast cancer (BC) in women below the age of 40 accounts for approximately 7% of all BC cases. According to studies, BC in younger women is more likely to have negative tumor characteristics and outcomes compared to older women. The rate of early-onset breast cancer in the United Arab Emirates (UAE) is higher than in the Western population (24%, previously reported). Young breast cancer patients in the UAE have worse adverse features, including a higher grade, a larger tumor size, and lymph node involvement, as we reported previously. Therefore, combining several clinical indicators to predict recurrence risk in our unique, heterogeneous young breast cancer group is needed. Aim:The aim of this study is to develop a statistical machine learning model that can predict survival and the risk of recurrence in a young breast cancer population seen and treated in a large-volume tertiary cancer center in the United Arab Emirates (Tawam Hospital). Methods: Early-onset breast cancer patients from a retrospective observational cohort study were included in this study (N = 904). A broad range of clinical data was collected. A multivariate ridge Cox regression model was performed using age, histology, molecular type (ER,PR,HER-2 status), T-stage, N-Stage, M-stage, grade, and BMI, and the interaction term age*BMI at the time of diagnosis. The primary outcome was time-to-death. A novel breast cancer risk score (BCRS) was developed as the linear predictor of the fitted-ridge Cox regression model. Breast cancer patients were categorized into low, medium, and high risk using the 33th and 66th centiles of the novel BCRS. A univariate Cox regression model was performed with a categorized BCRS as a predictor of time-to-death. Hazard ratios (HR) with a 95% confidence interval (95% CI) were estimated. Kaplan-Meier (KM) curves were plotted and compared using the log-rank test. Time-dependent sensitivity and specificity were computed at five- and ten-year follow-ups, respectively. Statistical analyses were performed in R version 4.2.3. Results: The median age was 36 years (IQR: 32–38), and 64% were Arabic. Fifteen percent had an event with a median duration of follow-up of 15.4 years (95% CI: 13.6–17.1). The estimated HR of the categorized novel BCRS in a univariate Cox regression model was 1.968 (95% CI: 1.075- 3.601) and 8.040 (95% CI: 4.695–13.769) for the intermediate and high-risk groups as compared to the low-risk group, respectively. KM curves show a clear separation in terms of survival between the three risk groups, p&amp;lt; 0.0001 (Figure 1). A cutoff value of -1.733 gives a sensitivity of 93.8 and 90.6 with corresponding specificities of 31.42 and 31.31 at five- and ten-year follow-up, respectively. Conclusion: This is the first study in the region that uses clinical data that combines several clinical characteristics and aims to design a statistical machine learning model to predict the risk of death from breast cancer. Our novel model can be used in daily clinical practice to identify high risk breast cancer patients and aid clinical decision-making in patients with breast cancer. However, further validation studies are needed. Figure 1:KM curves of breast-cancer survival for patients with low-risk (BCRS in [-2.32, -1.63], black curve), intermediate-risk (BCRS in [-1.64, -1.25], red curve), and high-risk BCRS in [-1.26, 1.41], green curve). Citation Format: Aydah Al-Awadhi, Mohammed Hourani, Mawada Hussein, Fatima Alkindi, Lina Wahba, Abla AlAgha, Alaa Shoqeir, Mouza AlShebli, Atlal Abusanad, Amar Ahmad. A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study [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 PO5-07-01.

Abstract PO1-08-12: Genomic Characterization of the Carolina Breast Cancer Study

Abstract Background: Breast cancer is a heterogeneous disease defined by distinct subtypes, mutational profiles, and genomic characteristics. Previous analyses in The Cancer Genome Atlas (TCGA) have been instrumental in understanding the genomic landscape of breast cancer, including identification of key driver mutations, mutational signatures, and distinguishing features of breast cancer subtypes. However, TCGA may not be representative of other population-based breast cancer cohorts, particularly those of earlier stage and including a more diverse set of patients. Our aim was to characterize the genomic landscape of breast cancer in the Carolina Breast Cancer Study (CBCS), a cohort which oversamples Black and young women. Methods: We used targeted sequencing to profile somatic alterations in 1,175 genes from 275 formalin fixed paraffin embedded primary breast tumors in CBCS, 52% of which came from Black women, 41% from women under age 50, and 29% of which were Basal-like based on bulk RNA profiling. We also evaluated TP53 mutation as a source of breast cancer heterogeneity, comparing the type and location of TP53 mutations in each cohort. We assessed chromosome arm-level gains and losses using GISTIC and defined genomic instability as the total number of aneuploid chromosome arms. Intratumoral heterogeneity was assessed with PyClone-VI and defined as the total number and Shannon’s Diversity Index of tumor-specific subclones. For genes mutated in more than 5% of samples and chromosomal aberrations significantly enriched in GISTIC, we estimated the prevalence of alterations and compared to previous distributions in 981 TCGA tumors. Results: Seven genes [TP53 (N=101, 37%), PIK3CA (N=76, 28%), GATA3 (N=32, 12%), CDH1 (N=31, 11%), MAP3K1 (N=22, 8%), KMT2C (N=20, 7%), and CBFB (N =19, 7%)] were mutated in more than 5% of CBCS tumors; all but CBFB which were also highly prevalent in TCGA. While most TP53 hotspot mutations observed in CBCS were previously reported in TCGA, CBCS had twice as many Y220C mutations, one-third fewer R175H mutations, and a non-significantly higher proportion of nonsense mutations than TCGA (20% vs 12%, p = 0.05). Hotspot mutation prevalence in other Luminal-associated driver genes (e.g. PIK3CA, GATA3, CDH1) did not differ by dataset. CBCS tumors showed a higher degree of genomic instability (8 arms vs. 5 arms, p &amp;lt; 0.001) and subclonal diversity than TCGA (48% vs 12% comprised of two or more subclones, p &amp;lt; 0.05). Both datasets showed significant amplifications of 1q and 8q, and deletions of 13q and 17p (where BRCA2 and TP53, respectively, are located), with CBCS having non-significantly higher prevalence of each of these changes. Conclusions: While comparisons should be interpreted in light of technical and population differences between TCGA and CBCS, the overall results show that the suite of commonly altered genes and chromosome arms were highly consistent between TCGA and the diverse CBCS cohort. CBCS tumors tended to display higher genomic instability, intratumoral heterogeneity, and number and diversity of TP53 mutations. This may reflect distinctions in tumor evolutionary state between CBCS and TCGA tumors, and/or could reflect differences in the study populations. Results underscore the importance of considering population characteristics – particularly stage and race – in large-scale genomic contexts, and highlight the importance of diverse cohorts in genomic research. Citation Format: Sarah Van Alsten, Ebonee Butler, Benjamin Calhoun, Michael Love, Charles Perou, Katherine A Hoadley, Melissa Troester. Genomic Characterization of the Carolina Breast Cancer Study [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 PO1-08-12.

A Nanorobotics-Based Approach of Breast Cancer in the Nanotechnology Era.

We are living in an era of advanced nanoscience and nanotechnology. Numerous nanomaterials, culminating in nanorobots, have demonstrated ingenious applications in biomedicine, including breast cancer (BC) nano-theranostics. To solve the complicated problem of BC heterogeneity, non-targeted drug distribution, invasive diagnostics or surgery, resistance to classic onco-therapies and real-time monitoring of tumors, nanorobots are designed to perform multiple tasks at a small scale, even at the organelles or molecular level. Over the last few years, most nanorobots have been bioengineered as biomimetic and biocompatible nano(bio)structures, resembling different organisms and cells, such as urchin, spider, octopus, fish, spermatozoon, flagellar bacterium or helicoidal cyanobacterium. In this review, readers will be able to deepen their knowledge of the structure, behavior and role of several types of nanorobots, among other nanomaterials, in BC theranostics. We summarized here the characteristics of many functionalized nanodevices designed to counteract the main neoplastic hallmark features of BC, from sustaining proliferation and evading anti-growth signaling and resisting programmed cell death to inducing angiogenesis, activating invasion and metastasis, preventing genomic instability, avoiding immune destruction and deregulating autophagy. Most of these nanorobots function as targeted and self-propelled smart nano-carriers or nano-drug delivery systems (nano-DDSs), enhancing the efficiency and safety of chemo-, radio- or photodynamic therapy, or the current imagistic techniques used in BC diagnosis. Most of these nanorobots have been tested in vitro, using various BC cell lines, as well as in vivo, mainly based on mice models. We are still waiting for nanorobots that are low-cost, as well as for a wider transition of these favorable effects from laboratory to clinical practice.

Increased prevalence of hybrid epithelial mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data – bulk, single-cell and spatial transcriptomics – from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity – two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify interventions to restrict it.

Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration.

The high heterogeneity of breast cancer (BC) caused by pathogenic gene mutations poses a challenge to immunotherapy, but the underlying mechanism remains unknown. The difference in the infiltration of M1 macrophages induced by TP53 mutations has a significant impact on BC immunotherapy. The aim of this study was to develop a TP53-related M1 macrophage infiltration molecular typing risk signature in BC and evaluate the biological functions of the key gene to find new immunotherapy biomarkers. Weighted correlation network analysis (WGCNA) and negative matrix factorization (NMF) were used for distinguishing BC subtypes. The signature and the nomogram were both constructed and evaluated. Biological functions of the novel signature gene SLC2A6 were confirmed through in vitro and in vivo experiments. RNA-Sequencing and protein profiling were used for detecting the possible mechanism of SLC2A6. The results suggested that four BC subtypes were distinguished by TP53-related genes that affect M1 macrophage infiltration. The signature constructed by molecular typing characteristics could evaluate BC's clinical features and tumor microenvironment. The nomogram could accurately predict the prognosis. The signature gene SLC2A6 was found to have an abnormally low expression in tumor tissues. Overexpression of SLC2A6 could inhibit proliferation, promote mitochondrial damage, and result in apoptosis of tumor cells. The HSP70 family member protein HSPA6 could bind with SLC2A6 and increase with the increased expression of SLC2A6. In summary, the risk signature provides a reference for BC risk assessment, and the signature gene SLC2A6 could act as a tumor suppressor in BC.

Utility of human epidermal growth factor 2 heterogeneity as a prognostic factor in triple-negative breast cancer.

In some cases of human epidermal growth factor 2 (HER2)-negative breast cancer, including triple-negative breast cancer, HER2 expression is sporadically and strongly upregulated, a condition known as HER2 heterogeneity. We investigated the clinicopathological features of patients with HER2 heterogeneity in triple-negative breast cancers treated with neoadjuvant chemotherapy. Thirty-nine patients with triple-negative breast cancer who had undergone preoperative chemotherapy participated in this study. To assess for HER2 heterogeneity, we used dual in situ hybridization slides. We evaluated the association between HER2 heterogeneity and clinicopathological factors such as rates of pathologic complete response (pCR) and of recurrence-free survival. Of the 39 patients, 15 (38.5%) had cancers with HER2 heterogeneity. The pCR rates were 13.3% among patients with HER2 heterogeneity and 20.8% among those with HER2 nonheterogeneity, but the difference was not significant. The recurrence-free survival rate was significantly lower in patients with HER2 heterogeneity than in those without (P = 0.025). HER2 heterogeneity is a significant predictor of poor prognosis in patients with triple-negative breast cancer treated with neoadjuvant chemotherapy.

The VEGF-Hypoxia Signature is Upregulated in Basal like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer.

Black women experience the highest breast cancer mortality rate compared to women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry. We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n=182) and the University of Chicago (n=161). We also analyzed RNA sequencing data from Nigeria (n=84) and TCGA datasets (n=863). Patient prognosis was analyzed using multiple datasets. The VEGF-hypoxia signature was highest in the basal-like subtype compared to other subtypes, with greater expression in Black women compared to White women. In the TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared to non-necrosis tumors (p<0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation and necrosis. T cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in the TCGA dataset. Lastly, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (p<0.0001) and SCAN-B datasets (p=0.002). These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Halofuginone-guided nano-local therapy: Nano-thermosensitive hydrogels for postoperative metastatic canine mammary carcinoma with scar removal

In female dogs, the highest morbidity and mortality rates cancer are the result of mammary adenocarcinoma, which presents with metastases in the lung. Other than early surgical removal, however, no special methods are available to treat mammary adenocarcinoma. Because human breast cancer and canine mammary carcinoma share clinical characteristics and heterogeneity, the canine model is a suitable spontaneous tumor model for breast cancer in humans. In this study, the physical swelling method was used to prepare halofuginone-loaded D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) polymer micelles nano-thermosensitive hydrogels (HTPM-gel). Furthermore, HTPM-gel was investigated via characterization, morphology, properties such as swelling experiment and in vitro release with reflecting its splendid nature. Moreover, HTPM-gel was further examined its capability to anti-proliferation, anti-migration, and anti-invasion. Ultimately, HTPM-gel was investigated for its in vivo anticancer activity in the post-operative metastatic and angiogenic canine mammary carcinoma. HTPM-gel presented spherical under transmission electron microscope (TEM) and represented grid structure under scanning electron microscope (SEM), with hydrodynamic diameter (HD) of 20.25 ± 2.5 nm and zeta potential (ZP) of 15.10 ± 1.82 mV. Additionally, HTPM-gel own excellent properties comprised of pH-dependent swelling behavior, sustained release behavior. To impede the migration, invasion, and proliferation of CMT-U27 cells, we tested the efficacy of HTPM-gel. Evaluation of in vivo anti-tumor efficacy demonstrates HTPM-gel exhibit a splendid anti-metastasis and anti-angiogenic ability, with exhibiting ideal biocompatibility. Notably, HTPM-gel also inhibited the scar formation in the healing process after surgery. In summary, HTPM-gel exhibited anti-metastasis and anti-angiogenic and scar repair features. According to the results of this study, HTPM-gel has encouraging clinical potential to treat tumors with multifunctional hydrogel.

Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach.

Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein-protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. The t-test was used through R programming to generate the P values for a test of the significance of expressed genes. A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.

Abstract 6628: Mapping the spatial and molecular determinants of macrophage heterogeneity in breast cancer

Abstract Tumor-infiltrating macrophages support critical steps in tumor progression, and their accumulation in the tumor microenvironment (TME) is associated with adverse outcomes and therapeutic resistance across human cancers. In the TME, macrophages adopt diverse phenotypic alterations, giving rise to heterogeneous immune activation states and induction of cell cycle. While the transcriptional profiles of these activation states are well-annotated across human cancers, the underlying signals that regulate macrophage heterogeneity and accumulation remain incompletely understood. Here, we leveraged a novel ex vivo organotypic TME (oTME) model of breast cancer, in vivo murine models, and human samples to map the determinants of functional heterogeneity of TME macrophages. We identified a subset of F4/80high Sca-1+ self-renewing macrophages maintained by type-I interferon (IFN) signaling and requiring physical contact with cancer-associated fibroblasts. We discovered that the contact-dependent self-renewal of TME macrophages is mediated via Notch4, and its inhibition abrogated tumor growth of breast and ovarian carcinomas in vivo, as well as lung dissemination in a PDX model of triple-negative breast cancer (TNBC). Through spatial multi-omic profiling of protein markers and transcriptomes, we found that the localization of macrophages further dictates functionally distinct but reversible phenotypes, regardless of their ontogeny. Whereas immune-stimulatory macrophages (CD11C+CD86+) populated the tumor epithelial nests, the stroma-associated macrophages (SAMs) were proliferative, immunosuppressive (Sca-1+CD206+PD-L1+), resistant to CSF-1R depletion, and associated with worse patient outcomes. Notably, following cessation of CSF-1R depletion, macrophages rebounded primarily to the SAM phenotype, which was associated with accelerated growth of mammary tumors. Our work reveals the spatial determinants of macrophage heterogeneity in breast cancer and highlights the disruption of macrophage self-renewal as a potential new therapeutic strategy. Citation Format: Nir Ben Chetrit, Xiang Niu, Jesus Sotelo, Ariel Swett, Vinagolu Rajasekhar, Maria Jiao, Caitlin Stewart, Priya Bhardwaj, Sanjay Kottapalli, Saravanan Ganesan, Pierre-Louis Loyher, Catherine Potenski, Kristy Brown, Neil M. Iyengar, Dilip D. Giri, Scott W. Lowe, John H. Healey, Frederic Geissmann, Irit Sagi, Johanna A. Joyce, Dan A. Landau. Mapping the spatial and molecular determinants of macrophage heterogeneity in breast cancer [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 6628.

Abstract 5483: Macrospatial ecotype heterogeneity in early-stage breast cancer identified by spatial nucleus barcoding

Abstract Ductal carcinoma in situ (DCIS), the predominant form of early-stage breast cancer and a precursor to invasive breast cancer, remains inadequately understood in terms of spatial microenvironment reprogramming. In this study, we developed a Spatial Nucleus Barcoding (SNuBar) approach for single-nucleus RNA sequencing (SNuBar-RNA) that utilizes a single oligonucleotide adaptor to preserve spatial information. The accuracy and scalability of SNuBar was validated using cell line mixture experiments. We then applied SNuBar-RNA to investigate the macrospatial ecotype heterogeneity in early-stage breast cancer. By analyzing single cells from different macrodissected spatial regions in normal breast tissues, matched normal and DCIS tissues, we identified 13 major cell types and 43 distinct cell states, co-localizing in diverse combinations across four primary topographic areas (adipose-enriched, normal epithelial, activated stroma and tumor). Spatial analysis revealed the reprogramming of vascular and myoepithelial cells in localized zones near premalignant cells, while fibroblasts, T-cells, and myeloid cells exhibited reprogramming across broader fields. Validation using the MERSCOPE assay confirmed stromal heterogeneity between tumor and normal areas. Collectively, our data shows that many diverse cell types are reprogrammed in spatially-defined areas in the early-stage breast cancer microenvironment, relative to the normal breast tissue regions. Citation Format: Kaile Wang, Zhenna Xiao, Yiyun Lin, Runmin Wei, Shanshan Bai, Jie Ye, Rui Ye, Min Hu, Aatish Thennavan, Emi Sei, Alastair Thompson, Savitri Krishnamurthy, Nicholas Navin. Macrospatial ecotype heterogeneity in early-stage breast cancer identified by spatial nucleus barcoding [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 5483.

Abstract 7353: Exploring intrapatient heterogeneity in metastatic breast cancer: Insights into tumor evolution from a warm procurement trial

Abstract Metastatic breast cancers show variable clinical responses due to inherent heterogeneity, both within tumors and among patients. Studies mainly examine molecular diversity across different patients' tumors and genetic variance within a single tumor. Yet, the variability among multiple tumors or metastases in a single patient remains underexplored. Our study investigates this intrapatient heterogeneity, examining cell and tumor evolution from a single genetic source in metastatic breast cancer. In a warm procurement trial involving 6 patients with multi-site tumors, samples were collected for detailed molecular analysis. Whole exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq) were used to analyze genetic similarities and disparities. WES data revealed mutational patterns and copy number alterations, showing homogeneity and heterogeneity in tumors within patients. Further, phylogenetic analysis with scRNA-seq variant calls paired with transcriptional phenotypes uncovered a MYC-enriched subclonal population in a metastatic site. Integrating WES and scRNA-seq data provided a clearer understanding of the genomic and phenotypic features in and between patients. Our WES study revealed significant interpatient variations in genetic drivers and CNA profiles. Among five patients, two showed APOBEC and HR mutations, linked to increased tumor mutations. Despite general genetic homogeneity, there were exceptions. Tumors in Patient 3's upper lymph nodes had distinct genetics compared to abdominal tumors. Unique genetic patterns were also noted in the pancreas and mesentery of Patients 3 and 7, respectively. Genetic findings were supported by transcriptional analysis, showing distinct expression patterns in specific sites. Phylogenetic analysis highlighted intratumor heterogeneity, as shown by variations in phylogenetic composition and gene expression across samples. Notably, in patients with the most samples (Patients 3 and 7), a conserved cell clade, evolved early and primarily located in a single site, displayed a high proliferation gene expression signature closely associated with Myc activity. This underscores the importance of early clonal expansions in tumor evolution and suggests potential therapeutic targets in these early, proliferative cells. Our study highlights the vital role of intrapatient heterogeneity in understanding cancer complexity and treatment resistance. By analyzing genetic and phenotypic variability in multi-site tumors within patients, and Myc's key role in tumor growth and evolution, our research offers new insights into tumor evolution. This underscores the need for personalized oncology treatments based on each patient's tumor genetics. Citation Format: Isaac Bishara, Xuan Liu, Jason Griffiths, Jiayi Liu, Patrick Cosgrove, Jasmine R. McQuerry, Feng Chi, Pierre Wallet, Vince K. Grolmusz, Benjamin Copeland, Lance Pflieger, Jinfeng Chen, Sumana Majumdar, Grace Ronquillo, Terron Crowder, Rena Emond, Rachel Factor, Eliza Barragan, David Bowtell, Adam Cohen, Daniel D. Schmolze, Peter Lee, Kena Ihle, Lusine Tumyan, Aritro Nath, James Waisman, Andrea Bild, Jeff Chang. Exploring intrapatient heterogeneity in metastatic breast cancer: Insights into tumor evolution from a warm procurement trial [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 7353.