Breast Tumor Characteristics Research Articles

Abstract 5758: Regular aspirin use, breast tumor characteristics and long-term breast cancer survival

Abstract Compelling epidemiologic data, supported by experimental evidence, suggest aspirin may improve survival in breast cancer patients. However, recent clinical trials showed a lack of protective effect, though length of intervention (18 months to 4.7 years) and follow-up (20 months to 4.7 years) were limited. We sought to examine the association between post-diagnostic aspirin use (frequency, dose, and duration), timing and age of initiation on breast cancer-specific mortality. Our study included 10,493 women diagnosed with stage I, II, or III invasive breast cancer. Participants were enrolled in the large, prospective Nurses’ Health Study (NHS) and NHSII in 1980 and 1989 prior to diagnosis and followed up through June 1, 2017. We collected information on frequency, dose and duration of aspirin use. Regular aspirin use was defined as using aspirin (standard- and low-dose) ≥2 days per week, and non-regular aspirin users were those who reported use of aspirin <2 days per week. We used Cox proportional hazard models to calculate multivariable adjusted hazard ratios (HRs) for breast cancer-specific mortality. After a median follow-up of 10 years, there were 2,506 total deaths and 1,221 breast cancer-specific deaths among 10,493 stage I to III breast cancer patients over 32 years of follow-up. In multivariable models, regular use of aspirin (n=3,523; 34%) was associated with a 38% lower risk of death from breast cancer compared with non-regular users (including nonusers) (HR=0.62, 95% CI: 0.54-0.71). The association was independent of pre-diagnostic aspirin use. Associations between aspirin use and breast cancer-specific mortality were stronger with longer time since diagnosis (HRs for <5 years since diagnosis: 0.73 (95% CI: 0.59-0.90), 5-<10 years: 0.63 (95% CI: 0.49-0.80), and ≥10 years: 0.53 (95% CI: 0.41-0.69)). The relations between aspirin use and breast cancer survival were similar across categories of dose (compared to non-users, HRs for 0.5-5 tablets per week: 0.59 (95% CI: 0.51-0.68); ≥6 tablets per week: 0.60 (95% CI: 0.48-0.74)) and appeared stronger with longer duration (compared to non-users, HRs for <5 years: 0.80 (95% CI: 0.57, 1.10); ≥5 years: 0.61 (95% CI: 0.51, 0.72)). For women who initiated regular aspirin use after age 70, regular aspirin use was associated with worse survival (HR=1.74, 95% CI: 1.16-2.63); on the other hand, initiation of regular aspirin use at age ≤60 (HR=0.72, 95% CI: 0.54-0.95) or 60-≤70 (HR=0.69, 95% CI: 0.50-0.95) was associated with improved survival (p-interaction=0.02). Regular aspirin use after diagnosis of nonmetastatic breast cancer was associated with improved long-term survival over 32 years. Although the associations between post-diagnostic regular aspirin use and improved survival did not differ by BMI, smoking or tumor characteristics, women who initiated regular aspirin use >70 years of age had increased risk of death due to breast cancer. Citation Format: Cheng Peng, Michelle D. Holmes, Wendy Y. Chen, Tengteng Wang, Kristen D. Brantley, Yujing Jan Heng, Pepper J. Schedin, Bernard A. Rosner, Walter C. Willett, Meir J. Stampfer, Rulla M. Tamimi, A. Heather Eliassen. Regular aspirin use, breast tumor characteristics and long-term breast cancer survival. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5758.

Abstract 1937: Racial differences of epigenome in normal breast tissue reveal biological pathways implicated in racial disparities in breast cancer

Abstract Racial disparity in breast cancer is well recognized. Compared to European American (EA) women, African American (AA) women are more often diagnosed with breast cancer at younger ages, have a more advanced or aggressive disease, and have poorer outcomes. Increasing evidence suggests that biological differences between EA and AA women, particularly differences in epigenetic changes of DNA methylation (DNAm), may contribute to the observed cancer disparity. We hypothesize that racial differences in DNAm in normal breast tissue may reflect distinct genomic susceptibility to breast cancer in different racial groups. Therefore, we investigated epigenome-wide differences of DNAm in normal breast tissue between 178 EA and 272 AA women using the Illumina TruSeq Methyl Capture EPIC library and NGS technology. We identified 17,340 differentially methylated CpG loci (DMLs) between EA and AA women, of which 7,744 were hypermethylated in AA women and 9,596 were hypermethylated in EA women. Pathway analyses of genes annotated to these racial DMLs suggested enrichment of biological processes including cell movement, cell motility, and cell migration, features that are likely linked to the aggressiveness and metastatic potentials of tumor cells. We further examined racial differences in DNAm of breast cancer-related candidate genes and GWAS-identified breast cancer risk loci. Approximately 30% of candidate genes were found to have racial differences in DNAm at more than three CpG sites and these candidate genes were enriched for pathways related to response to stimulus and anatomic structure development. Most interestingly, racial DMLs were found to be enriched for breast cancer risk loci that are associated with risk of estrogen-receptor negative (ER-) breast cancer, treatment response to aromatase inhibitor, and breast cancer-specific morality, phenotypes of which we observed differences between EA and AA breast cancer patients. Our results suggest that racial differences in DNAm in normal breast tissue may induce different biological processes that lead to distinct clinical features of breast tumors observed in EA and AA women and provide an epigenetic mechanism underlying the observed racial disparities. Further research is need to validate the findings of this study. Citation Format: Nan Lin, James Castle, Jinpeng Liu, Chunyan He. Racial differences of epigenome in normal breast tissue reveal biological pathways implicated in racial disparities in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1937.

3D microgel with extensively adjustable stiffness and homogeneous microstructure for metastasis analysis of solid tumor

3D microgels with various mechanical properties have been important platforms tumor metastasis analysis, and widely adjustable stiffness is crucial for deeper researches. Herein, by mixing biodegradable polylactic acid (PLA) nanofibers in the modified alginate with different concentrations of Ca2+, we significantly enhance the stiffness range of microgels while retaining the pore size, which provides bionic microenvironment for tumor analysis. As a proof of concept, we simulated the mechanical characteristics of breast tumors by encapsulating cells in 3D microgels with diverse stiffness, and analyzed cellular behaviors of two typical breast cancer cell lines: MCF-7 and SUM-159. Results showed that with the addition of 2.0% (w/v) PLA short nanofibers, the Young's modulus of modified alginate increased more than three-fold. Besides preserving high survival and proliferation rates, both cells also displayed stronger migration ability in soft microgel spheres, where RT-qPCR analysis revealed the underlying changes at the genetic level. This systematic study demonstrated our method is powerful for creating widely adjustable 3D mechanical microenvironment, and the results of cellular behavior analysis shows its promising application prospects in tumorigenesis and progression.

Abstract P2-23-16: Clinico-Pathological and Molecular Characterization of HER2-Enriched Breast Tumors Independently of HER2 Status

Abstract INTRODUCTION: Breast cancer (BC) is a highly prevalent and heterogeneous disease, entailing different so-called intrinsic subtypes (IS) according to gene expression, namely Luminal A, Luminal B, HER2-Enriched (HER2E) and Basal-like, as well as a normal breast-like group. The HER2E is still a poorly understood entity, which needs further biologic characterization to improve therapeutic management. MATERIAL AND METHODS: Patients (pts) treated at Hospital Clinic (Barcelona, Spain) over 18 years with a diagnosis of metastatic BC, with available matched primary and metastatic tumor samples for gene expression analyses were retrospectively recruited. Using the nCounter® Breast Cancer 360 panel, we studied the expression of 776 genes pertaining to different tumor and immune pathway-related signatures, with a focus on HER2E vs. non-HER2E tumors. Significant changes were considered at a false discovery rate (FDR) < 5%. Main clinicopathological features were also compared. IS changes from primary to metastatic disease were assessed. RESULTS: Ninety-one pts with paired tumor samples were included. Briefly, primary tumors were 67.0% hormone receptor-positive (HR+)/HER2-negative (HER2-), 14.8% were HER2-positive (HER2+) and 18.2% were triple-negative (TNBC). IS distribution in primary tumors was the following: 25 Luminal A (28%), 22 Luminal B (24%), 24 HER2E (26%), 12 Basal-like (13%), and 8 Normal-like (9%). In contrast, IS distribution in metastatic tumors was the following: 13 Luminal A (14%), 22 Luminal B (24%), 34 HER2-E (37%), 16 Basal-like (18%), and 6 Normal-like (7%). The HER2E disease proved to be a relatively stable subtype, with 16 (66.7%) tumors not changing IS in the transition to metastatic disease (p=0.078). Particularly, within HR+/HER2-, HER2+ and TNBC, 8/12 (66.7%), 8/10 (80.0%) and 1/2 (50.0%) tumors remained HER2E. Conversely, an overall significant switch from Luminal to non-Luminal tumors at the metastatic progression was observed (p=0.031), with 14/19 (73.7%) new non-Luminal BC being HER2E. When considering all primary and metastatic tumors, HER2E were observed to be less frequently estrogen receptor (ER) positive than non-HER2E tumors (55.8% vs. 78.7%; p=0.002), with lower mean progesterone receptor (PR) levels (15.3% vs. 25.8%; p=0.027). Compared to the other subtypes (as a group), the PAM50 risk of recurrence score (ROR-P) was higher for HER2E tumors (59.1 vs. 41.7; p < 0.001), which were also more frequently HER2+ (36.5% vs. 5.8%) and less likely HR+/HER2- (50.0% vs. 73.6%) and TNBC (13.5% vs. 20.7%), compared to non-HER2E (p < 0.001). No significant differences in grade, TILs, Ki67 and histotype were observed. Overall, 140/776 genes were significantly downregulated in HER2E vs. non-HER2E tumors, including genes related to cell adhesion, migration, DNA damage repair, apoptosis, estrogen signalling pathway, senescence. Conversely, 178/776 genes were significantly upregulated, including the tyrosine-kinase receptor FGFR4, genes involved in nuclear activity, DNA transcription regulation, MAP-kinase signaling pathways, proliferation, cytokine response, epithelial-to-mesenchymal transition (EMT), cell cycle progression, and immune-related genes such as CD274 (PD-L1 gene). DISCUSSION: A switch towards more aggressive IS from primary to advanced disease was observed, with an increase in HER2E prevalence. HER2E tumors, which tend to maintain their IS at the metastatic disease, showed upregulation of genes related to proliferation, survival and EMT, coherently with their well-known worse prognosis. FGFR4 and CD274 upregulation, along with downregulation of genes involved in DNA damage repair suggest these tumors might benefit from targeted therapeutic approaches. Genomic higher risk was not convoyed by consistent clinicopathological features, except for lower PR levels and HER2 overexpression at IHC. Further characterization according to primary/metastatic and HR status is ongoing. Intrinsic subtype distribution across primary and metastatic breast tumors Citation Format: Francisco Javier Muñoz-Carrillo, Laia Paré, Benedetta Conte, Adela Rodríguez, Patricia Galván, Esther Sanfeliu, Blanca González-Farré, Claudette Falato, Isabel Garcia-Fructuoso, Barbara Adamo, Nuria Chic, Olga Martínez-Sáez, Tomás Pascual, Reinaldo Moreno, Montserrat Muñoz, Fara Brasó-Maristany, Aleix Prat, Francesco Schettini, Maria Vidal. Clinico-Pathological and Molecular Characterization of HER2-Enriched Breast Tumors Independently of HER2 Status [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-16.

Neutrophil extracellular traps (NETs)-related lncRNAs signature for predicting prognosis and the immune microenvironment in breast cancer.

Background: Neutrophil extracellular traps (NETs) are closely associated to tumorigenesis and development. However, the relationship between NETs-related long non-coding RNAs (lncRNAs) and the characteristics of breast tumor remains an enigma. This study aimed to explore the clinical prognostic value of NETs-related lncRNAs, their correlation with the tumor microenvironment (TME) and their predictive ability of drug sensitivity in patients with breast cancer (BC). Methods: The expression profiles of RNA-sequencing and relevant clinical data of BC patients were extracted from TCGA database. The co-expression network analysis, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were employed to construct the NETs-related lncRNAs signature. A nomogram was established and validated to explore the clinical application. Furthermore, the immune microenvironment and drug sensitivity for BC with different prognostic risks were explored. Finally, the expression pattern of lncRNAs was validated using qRT-PCR in BC tissues and their adjacent non-cancerous tissues. Results: Based on NETs-related lncRNAs, a prognostic risk model consisted of 10 lncRNAs (SFTA1P, ACTA2-AS1, AC004816.2, AC000067.1, LINC01235, LINC01010, AL133467.1, AC092919.1, AL591468.1, and MIR200CHG) was established. The Kaplan-Meier analysis showed that the overall survival (OS) was significantly better in low-risk BC patients than in high-risk BC patients (P training cohort < 0.001, P validation cohort = 0.009). The nomogram also showed good predictive accuracy for OS of BC individuals in both training and validation cohorts. The function enrichment analysis revealed that high-risk group was mainly enriched in immune-related functions and pathways, and the tumor mutation burden in this group was markedly higher than that in the low-risk group (p = 0.022). Moreover, significant differences were observed in immune cells, immune functions and immune checkpoint genes among BC patients at different risks (p < 0.05). The response to chemotherapeutic agents and immunotherapy were also closely related with the expression of NETs-related lncRNAs (p < 0.001). The expression of lncRNAs from experimental validation were generally consistent with the bioinformatics analysis results. Conclusion: Our study provided a novel prognostic model for BC and yielded strong scientific rationale for individualized treatment strategies, elucidating immunotherapy in BC patients.

Abstract A009: Association of neighborhood disadvantage with biomarkers of biological aging and chronic stress among breast cancer survivors. [R

Abstract Living in a disadvantaged neighborhood is associated with adverse outcomes among breast cancer patients, but the underlying pathway is still unclear. Limited evidence has suggested that chronic stress and accelerated biological aging may play an important role. Biological aging and chronic stress are closely related, and both associated with mitochondrial dysfunction, abnormal DNA methylation, telomere shortening, etc. For example, allostatic load (AL), a biomarker of chronic stress, increases with age as a result of the cumulative effects of allostasis across the lifespan. In this study, we attempted to take the first step by evaluating the association of neighborhood disadvantage with biomarkers of chronic stress and biological aging among breast cancer patients. Using a sub-sample of 906 women with newly diagnosed breast cancer at M.D. Anderson, we examined whether levels of AL and selected biological aging biomarkers (global DNA methylation, telomere length, etc.) were affected by neighborhood disadvantage. The Area Deprivation Index (ADI) was used to measure the levels of neighborhood disadvantage. Telomere length and global DNA methylation in leukocytes were measured by qPCR and ELISA, respectively. We used 17 factors that represent the activity of five physiological systems to construct the AL score. Based on the median ADI at the national level, the study population was divided into low and high ADI groups. Overall, breast cancer patients from the high ADI group were more likely to be younger and non-Hispanic Black than those from the low ADI group (P&amp;lt;0.001). They were also more likely to have higher stage and poorly differentiated breast tumors (P=0.029 and 0.019, respectively). For the relationship with markers, compared to the low ADI group, high ADI group had higher median levels of AL (P=0.046) and lower median levels of global DNA methylation (P&amp;lt;0.001). Higher AL was considered as an indicator of increased chronic stress. Compared to their counterparts, those from the high ADI group were 20% and 32% more likely to have increased AL and telomerase activity, respectively, and 51% less likely to have increased levels of global DNA methylation. We also found that ADI was inversely correlated with telomere length. Additionally, we found that the significant associations of AL and global DNA methylation with ADI group were observed in non-Hispanic Whites and Blacks, but not in Hispanics. Lastly, using the mediation analysis, we found that decreased global DNA methylation mediated 6.52 and 7.98% of the association between higher ADI group with stage III and poorly differentiated tumor (P=0.037 and 0.023), respectively. In summary, we observed that neighborhood disadvantage was associated with more aggressive breast tumor characteristics, and influenced the levels of AL and global DNA methylation among breast cancer patients. Our findings suggest that neighborhood disadvantage is biologically embedded in molecular level and associated with accelerated biological aging and increased chronic stress among breast cancer patients. Citation Format: Jie Shen, Hua Zhao, Bernard Fuemmeler, Vanessa Sheppard, Harry Bear. Association of neighborhood disadvantage with biomarkers of biological aging and chronic stress among breast cancer survivors. [R] [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr A009.

Abstract C015: Characterization of HER2-low breast tumors among a cohort of Colombian women

Abstract Introduction: HER2-low expression tumors, either with weak or incomplete membrane expression (immunohistochemistry (IHC) 1+ or 2+ with negative in situ hybridization (ISH) result), are considered and treated as HER2-negative tumors. However, recent clinical trials have shown promising results in the application of antibody-drug conjugates (ADCs) for patients with HER2-low breast tumors, highlighting the need to characterize the clinical phenotype of this group. This is the first study in Colombia aiming to explore the clinical-pathological features and survival of women affected with HER2-low breast cancer, compared with HER2-negative (0+) and HER2-positive groups (2+ with ISH+ or 3+). Methods: We included 516 breast cancer patients from different health institutions in Colombia, diagnosed at clinical stages that ranged between I-III. Protein expression of HER2, Ki67, and hormone receptors (estrogen (ER) and progesterone (PR) receptor) was evaluated by IHC in a paraffin block with tumoral tissue by a single pathologist. We defined HER2 expression as positive (3+ or 2+ with positive ISH test), low (1+/2+ with negative ISH result), and negative (0+). We applied a Chi-square or Fisher’s exact test to evaluate clinical-pathological features for HER2-low cases, compared with HER2-negative (low vs. negative) and HER2-positive tumors (low vs. positive). Differences in overall survival (OS) and risk of mortality were assessed using the Kaplan-Meier and log-rang test, and a Cox proportional hazard model. Results: 325 cases (63%) were classified as HER2-negative, 97 (18.8%) as HER2-low, and 94 (18.2%) as HER2-positive. We encountered more clinical-pathological significant differences between HER2-low and HER2-positive tumors than with HER2-negative cases. A higher proportion of patients diagnosed after the age of 50 (79.4% vs. 62.4%, p=0.018), with better-differentiated tumors (Bloom Richardson II: 58.8 vs. 41.2%, p=0.025), and a lower proliferation index (Ki67 &amp;lt;20%: 50.5 % vs. 21.2%, p&amp;lt;0.001) was observed for HER2-low tumors, compared to the HER2-positive group; these characteristics were not significantly different between HER2-low and HER2-negative tumors. The proportion of ER-positivity was significantly higher in the HER2-low group, compared to both HER2-positive (89.7% vs. 62.4%, p&amp;lt;0.001) and negative groups (89.7% vs. 79.7%, p=0.039). Moreover, although longer OS rates were found in HER2-low patients, compared to the negative group (5.5 vs. 5.0 years, p=0.0032), the multivariate Cox model adjusted by ER status showed no statistically significant differences between these groups (HR=0.62, 95% CI, 0.34 – 1.13, p=0.122). Conclusions: HER2-low tumors represent an important proportion of all breast cancer cases diagnosed in Colombian women. Our results are consistent with other reports that show differences in ER expression between HER2-low tumors and both HER2-positive and negative subtypes, although it seems that the prognosis of HER2-low patients is not different than of the other groups. Citation Format: Laura Rey Vargas, Lina Maria Bejarano, Juan Carlos Mejia Henao, Diego Felipe Ballen Lozano, Maria Carolina Sanabria Salas, Silvia Juliana Serrano Gomez. Characterization of HER2-low breast tumors among a cohort of Colombian women [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C015.

FGF/FGFR1 system in paired breast tumor-adjacent and tumor tissues, associations with mammographic breast density and tumor characteristics.

Mammographic breast density (MBD) is an established breast cancer risk factor, yet the underlying molecular mechanisms remain to be deciphered. Fibroblast growth factor receptor 1 (FGFR1) amplification is associated with breast cancer development and aberrant FGF signaling found in the biological processes related to both high mammographic density and breast cancer microenvironment. The aim of this study was to investigate the FGF/FGFR1 expression in-between paired tumor-adjacent and tumor tissues from the same patient, and its associations with MBD and tumor characteristics. FGFR1 expression in paired tissues from 426 breast cancer patients participating in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort study was analyzed by immunohistochemistry. FGF ligand expression was obtained from RNA-sequencing data for 327 of the included patients. FGFR1 levels were differently expressed in tumor-adjacent and tumor tissues, with increased FGFR1 levels detected in 58% of the tumors. High FGFR1 expression in tumor tissues was associated with less favorable tumor characteristics; high histological grade (OR=1.86, 95% CI 1.00-3.44), high Ki67 proliferative index (OR=2.18, 95% CI 1.18-4.02) as well as tumors of Luminal B-like subtype (OR=2.56, 95%CI 1.29-5.06). While no clear association between FGFR1 expression and MBD was found, FGF ligand (FGF1, FGF11, FGF18) expression was positively correlated with MBD. Taken together, these findings support a role of the FGF/FGFR1 system in early breast cancer which warrants further investigation in the MBD-breast cancer context.

Abstract C080: Neighborhood disadvantage and biological aging biomarkers among breast cancer survivors

Abstract Living in a disadvantaged neighborhood is associated with adverse outcomes among breast cancer patients, but the underlying pathway is still unclear. Limited evidence has suggested that accelerated biological aging may play an important role. In this study, we attempted to take the first step by evaluating the relationship between neighborhood disadvantage and biological aging biomarkers among breast cancer patients. Using a sub-sample of 906 women with newly diagnosed breast cancer at M.D. Anderson, we examined whether levels of selected biological aging biomarkers (allostatic load (AL), telomere length, and global DNA methylation) were affected by neighborhood disadvantage. The Area Deprivation Index (ADI) was used to measure the levels of neighborhood disadvantage. Telomere length and global DNA methylation in leukocytes were measured by qPCR and ELISA, respectively. We used 17 factors that represent the activity of five physiological systems to construct the AL score. Based on the median ADI at the national level, the study population was divided into low and high ADI groups. Overall, breast cancer patients from the high ADI group were more likely to be younger and non-Hispanic Black than those from the low ADI group (P&amp;lt;0.001, respectively). They were also more likely to have higher stage and poorly differentiated breast tumors (P=0.029 and 0.019, respectively). For the relationship with markers, compared to the low ADI group, high ADI group had higher median levels of AL (P=0.046) and lower median levels of global DNA methylation (P&amp;lt;0.001). Compared to their counterparts, those from the high ADI group were 20% more likely to have increased AL and 51% less likely to have increased levels of global DNA methylation. Additionally, we found that the significant associations of AL and global DNA methylation with ADI group were observed in non-Hispanic Whites and Blacks, but not in Hispanics. Lastly, using the mediation analysis, we found that decreased global DNA methylation mediated 6.52 and 7.98% of the association between higher ADI group with stage III and poorly differentiated tumor (P = 0.037 and 0.023), respectively. In summary, we observed that neighborhood disadvantage was associated with more aggressive breast tumor characteristics, and influenced the levels of AL and global DNA methylation among breast cancer patients. Our findings suggest that neighborhood disadvantage is biologically embedded in molecular level and associated with accelerated biological aging among breast cancer patients. Citation Format: Jie Shen, Hua Zhao, Bernard Fuemmeler, Vanessa Sheppard, Harry Bear. Neighborhood disadvantage and biological aging biomarkers among breast cancer survivors [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C080.

Abstract IA047: Adipokine and adipokine receptor expression in breast tumors: Associations with greater body fatness and more aggressive clinicopathologic features

Abstract The molecular mechanisms that underlie the relationship between adiposity and poorer breast cancer outcomes are not well understood but might be explained partly by the biological effects of the adipokines, adiponectin (ADIPOQ) and leptin (LEP), which are secreted by adipocytes, and their respective receptors (ADIPOR1, ADIPOR2, LEPR). Prior work has shown that circulating ADIPOQ and LEP are not well correlated with levels within histologically normal tissues, underscoring the need for research to characterize tissue-based and tumor-based adipokine and adipokine receptor expression and their relationship with cancer outcomes. There is a dearth of epidemiologic data on the relationship between body fatness and protein and gene expression of the adipokines and adipokine receptors. Furthermore, the relationship between protein and gene expression of the adipokines and adipokine receptors with cancer is also not well understood. This presentation will discuss recent findings from the Women’s Circle of Health Study supporting an association between increased body fatness – beyond body mass index (BMI) – and expression of the adipokines and adipokine receptors and between protein and gene expression of these markers with more aggressive breast tumor features. Clarification of the impact of adiposity-related adipokine and adipokine receptor expression in the breast tumor microenvironment on long-term breast cancer outcomes is a critical next step. Citation Format: Adana A.M. Llanos. Adipokine and adipokine receptor expression in breast tumors: Associations with greater body fatness and more aggressive clinicopathologic features [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr IA047.

Multiparametric MRI Features of Breast Cancer Molecular Subtypes.

Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.

Breast microbiome associations with breast tumor characteristics and neoadjuvant chemotherapy: A case-control study.

BackgroundCommensal microbiota have been proven to colonize the mammary gland, but whether their composition is altered in patients with breast cancer (BC) remains elusive. This study intends to explore the breast microbiome differences between benign and malignant diseases and to investigate the impact of neoadjuvant chemotherapy (NAC) on the breast microbiota in patients with BC.MethodsBreast normal adipose tissues (NATs) were collected from 79 patients with BC and 15 controls between July 2019 and November 2021. The BC group consisted of 29 patients who had received NAC and 50 who were non-NAC patients. Participants diagnosed with benign breast disease were recruited as controls. 16S rRNA gene sequencing was used to analyze the bacterial diversity of NATs.ResultsThe community structure of the NAT microbiome was significantly different between the BC and control groups. Proteobacteria decreased (47.40% versus 39.74%), whereas Firmicutes increased (15.71% versus 25.33%) in patients with BC when compared with that in control tissues. Nine genera were enriched in BC NATs, and four genera levels increased in the control group. The associations between differential bacterial genera and breast tumor grade were calculated by Spearman’s correlation. The results showed that tumor grade was positively associated with the relative abundance of Streptococcus and negatively related to Vibrio, Pseudoalteromonas, RB41, and Photobacterium. Moreover, menopause was associated with the microbiota composition change of non-NAC BC patients and related to the significant reduction in the abundance level of Pseudoalteromonas, Veillonella, and Alcaligenes. In addition, NAC was related to the beta diversity of patients with BC and associated with the decrease of Clostridium_sensu_stricto_7 and Clostridium_sensu_stricto_2 in postmenopausal patients. Of note, Tax4Fun functional prediction analysis revealed that the metabolic state was more exuberant in the BC group with upregulating of multiple metabolism-related pathways.ConclusionOur results offer new insight into the relationship between NAC and breast microbiota and help to better characterize the breast microbial dysbiosis that occurs in patients with BC. Further epidemiological studies with larger sample size and well-designed animal experiments are required to elucidate the role of breast microbiota in the therapeutic outcome of BC.

Mir-29b in Breast Cancer: A Promising Target for Therapeutic Approaches.

The miR-29 family comprises miR-29a, miR-29b, and miR-29c, and these molecules play crucial and partially overlapped functions in solid tumors, in which the different isoforms are variously de-regulated and mainly correlated with tumor suppression. miR-29b is the most expressed family member in cancer, in which it is involved in regulating gene expression at both transcriptional and post-transcriptional levels. This review focuses on the role of miR-29b in breast cancer, in which it plays a controversial role as tumor suppressor or onco-miRNA. Here we have highlighted the dual effect of miR-29b on breast tumor features, which depend on the prevailing function of this miRNA, on the mature miR-29b evaluated, and on the breast tumor characteristics. Remarkably, the analyzed miR-29b form emerged as a crucial element in the results obtained by various research groups, as the most abundant miR-29b-3p and the less expressed miR-29b1-5p seem to play distinct roles in breast tumors with different phenotypes. Of particular interest are the data showing that miR-29b1-5p counteracts cell proliferation and migration and reduces stemness in breast tumor cells with a triple negative phenotype. Even if further studies are required to define exactly the role of each miR-29b, our review highlights its possible implication in phenotype-specific management of breast tumors.

A Breast Cancer Contour Detection With Level Sets and SVM Model

Level sets have been widely used to isolate features of breast tumors in ultrasound images. However, region-based methods always produce multiple contours. Since tumors are regularly undefined from the shadows and muscular regions in breast ultrasound images, computerized tumors location and arrangement is significantly difficult. Therefore, the authors introduce a breast cancer contour detection model using support vector machine (SVM) as a binary classification. Features of the binary SVM model were extracted from level sets and FM method (the fusion of ultrasound, elasticity, and Doppler images). The model was accurately able to predict a correct breast tumor contour from false contours which were segmented by region-based level sets. The proposed method was evaluated on 60 datasets collected by professional radiologists at the Thammasat University Hospital of Thailand. From the experimental results, the breast cancer contours were detected correctly with high accuracy. The percentage of correct detection was 93%.

Aspects Regarding the Influence of Obesity on the Molecular Characteristics of Breast Tumors.

The influence of excess adipose tissue on the evolution and prognosis of breast cancer has been evaluated in numerous papers over the years. The ways in which obesity can influence the development, progression, and prognosis of this neoplasia are complex and requires the design of new studies, both clinical and preclinical. The aim of this study is to highlight a possible correlation between obesity-specific tumor microenvironment markers (adipokine or leptin) and the different histological subtypes and aggressive characteristics of breast tumors. We prospectively monitored the prognostic values of 39 patients diagnosed with breast cancer who received oncologic-specific treatment or are in follow-up regarding some obesity markers.Our analysis included parameters such as age, body mass index, immunohistochemical characteristics, and plasma concentration of leptin. The methodology was designed to reveal a possible correlation between obesity (quantified by measuring body mass index and waist circumference), the plasma level of leptin, and breast tumor immunohistochemical characteristics. The patients diagnosed with aggressive tumors subtypes (HER2-positive and triple-negative) had a significantly higher body mass index than patients diagnosed with luminal type tumors (32 kg/sqm versus 27 kg/sqm), the difference being 5 kg/sqm. In patients with non-luminal type breast tumors (HER2-positive and triple-negative), serum concentration of leptin is 55 pg/ml compared to 48 pg/ml in luminal type, statistically significant, p=0.0168. Leptin plays an important role in the connection of specific microenvironment tumors to breast cancer. An increased serum concentration of this adipokine was found in patients with HER2-positive and triple-negative breast tumors compared with luminal-type breast tumors, which could open new directions in the research of breast cancer prognosis in obese patients.

ROR1 and BMI-1 proteins as potential predictors of the effectiveness of hormone therapy in luminal breast cancer

The purpose of the study was to generalize information regarding the molecular and biological mechanisms involved in the resistance to endocrine therapy with aromatase inhibitors in patients with luminal breast cancer. Material and Methods. The literature search was conducted using Medline, Cochrane Library, Elibrary and PubMed databases. Results. The review highlights the results of international studies on molecular and biological characteristics of breast tumors and their relationship with the effectiveness of hormone therapy. Particular attention was paid to the description of modern studies on ROR1 and BMI-1 proteins and their contribution to the development of tumor resistance to treatment. Conclusion. The analysis of the world literature confrms the relevance of studying the molecular and genetic characteristics of tumor tissue in patients with luminal breast cancer. The data obtained were compared to the clinical course and response to hormone therapy in order to standardize them for implementation in everyday practice as the “gold standard of diagnosis”.

Lysyl Oxidases Expression and Breast Cancer Progression: A Bioinformatic Analysis.

LOX (Lysyl oxidase) and LOX like 1–4 (LOXL1–4) are amine oxidases that catalyse the cross-linking of elastin and collagen in the extracellular matrix (ECM). This activity can facilitate cell migration and the formation of metastases. Consequently, inhibition of these enzymes and, in particular of LOXL2, has been suggested as a therapeutic strategy to prevent breast cancer metastasis. Although medicinal chemistry studies have struggled to specifically inhibit LOXL2, the importance of selectivity in this context is not clear. To explore the role of each LOX in breast cancer and consequently their potential as biomarkers or therapeutic targets, a bioinformatic-based approach was followed. The expression profile of LOXs, the putative associations among mRNA expression from each LOX and clinical observations, the correlation between expression of LOX enzymes and other genes, and the association between expression of LOXs and the tumour infiltrates were assessed for breast cancer. Overall, the patient outcome and the characteristics of breast tumours with LOX, LOXL1 and LOXL2 upregulation is distinct from those with high expression of LOXL3 and LOXL4. Additionally, the expression correlation between LOXs and other genes involved in cellular processes relevant for cancer biology, also reveals a similar trend for LOX, LOXL1 and LOX2. This work further supports the relevance of LOXL2 as a breast cancer progression biomarker and therapeutic target. We speculate that while the impact of LOXL3 inhibition may vary with breast cancer subtype, the therapeutical inhibition of LOX, LOXL1 and LOXL2 but not of LOXL4 may be the most beneficial.

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It is more common in women over the age of 50. Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. Transgender women are more likely than cisgender men to acquire breast cancer. Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer. A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.

Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer.

e12612 Background: Computationally derived quantitative imaging (radiomic) features that describe tumor phenotypes at the pixel level have demonstrated associations with clinical characteristics in early investigations of other cancers. This implies that molecular differences among tumors may be reflected in their structure on the scales probed by 3D magnetic resonance imaging (MRI). We investigated whether radiomic features computed over tumor volumes from pre-treatment breast MRI could predict risk factors in triple-negative breast cancer (TNBC). Methods: We evaluated breast tumors on pre-treatment, post-contrast T1-weighted MRI from 156 patients with non-metastatic TNBC who underwent neoadjuvant chemotherapy. Tumor regions of interest were segmented by a convolutional neural network algorithm, with validation by breast radiologists. Features quantifying tumor shape and texture were extracted for the largest tumor present in each patient. We identified 23 principal components (PCs) describing these data within the original 364-dimensional feature space for further analysis. Tumor volume was also extracted for comparison with the shape and texture PCs, clinical variables and outcomes, but was kept separate from other radiomic features, since it directly correlates with clinical stage. We compiled for the cohort clinical variables including demographics, stage, grade, and, where available, absolute lymphocyte count (ALC) and Ki-67, a cellular proliferation index routinely used in clinical practice. We then performed a series of univariate and multivariate regression analyses to identify radiomic PCs and clinical variables that significantly predict patient outcomes, and radiomic PCs that predict established risk factors. Our multivariate analyses utilized 5-fold cross-validation and Monte-Carlo determination of p-values (based on 3000 random samplings from the null hypothesis), to ensure statistical rigor in identifying predictive relationships while correcting for multiple hypothesis testing. Results: Our univariate analyses confirmed expected correlations between: overall survival and pre-treatment tumor volume (p = 0.010); survival and ALC (p = 0.002); and clinical stage and tumor volume (p = 1.2⨉10-7). From our multivariate analysis, shape and texture radiomic features were predictive of: tumor volume (p &lt; 0.001); clinical stage (p &lt; 0.001); and Ki-67 (p = 0.02). We confirmed that Ki-67 was predictive of post-treatment residual cancer (p = 0.014), as has been previously reported. Conclusions: Radiomic features predict breast cancer risk factors that are significant for determining outcomes for TNBC patients. Combinations of radiomic shape and texture features track closely with tumor volumes, stage, and proliferative activity, potentially reflecting underlying molecular evolution.

Clinical and morphological features of breast tumors with PIK3CA mutations in Russian patients: Observational study

Background. By 2020, breast cancer (BC) has become the most frequent malignancy in the world. The most common type of BC is HR+/HER2-negative cancer,2540% of which harbors PIK3CA mutations that affect the catalytic subunit of the PI3K protein. PIK3CA alterations are actionable, as such neoplasms can be treated with a combination of fulvestrant and the PI3K inhibitor alpelisib. As PIK3CA mutations have an extremely versatile effect on the characteristics of a tumor cell, numerous associations of PIK3CA mutations and various clinico-pathological characteristics of BC can be traced.&#x0D; Aim. Our aim was to clarify the information on the frequency and spectrum of PIK3CA mutations in Russian patients with HR+/HER2- advanced BC, and to study the association of PIK3CA mutations with clinical and pathological parameters of BC.&#x0D; Materials and methods. Tissue samples from 694 patients with HR+/HER2- advanced BC (mixed population of primary metastatic and relapsed tumors) who received any line of anti-cancer treatment in Dec 2020 to June 2021 in Russian Federation were analyzed by high-resolution melting, allele-specific PCR, digital droplet PCR and Sanger sequencing (exons 7,9, and 20 of the PIK3CA gene). Mutation rates in different BC subgroups were compared using the Fishers exact test. The age at diagnosis in patients with different PIK3CA status was compared using the MannWhitney U-test. The relationship between the PIK3CA status and the degree of tumor differentiation was compared using the CochraneArmitage test for trends. Luminal A and B BC expression subtypes were distinguished with surrogate IHC markers according to St.-Gallen recommendations (2013).&#x0D; Results. Mutations were identified in 220/694 (32%) BC patients. The three most frequent missense substitutions in the PIK3CA gene (p.E542K, p.E545K, and p.H1047R) accounted for 190/220 (86%) mutations. Associations of PIK3CA mutations with luminal A subtype of BC, low proliferation index, small size of the primary tumor, and absence of signs of hereditary cancer were revealed. Associations of mutations in the kinase domain of PIK3CA (p.H1047R) with late recurrence of locally advanced BC and with non-Slavic ethnic origin of patients were found.&#x0D; Conclusion. PIK3CA mutation rate of 32% confirms high prevalence of mutation in Russian population, with some differences reflecting the ethnic origin of patients.