Breast cancer classification by proteomic technologies: Current state of knowledge

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

BackgroundBreast cancers can be classified by hierarchical clustering using an “intrinsic” gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis.MethodsThree publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset.ResultsNone of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81).ConclusionsAssignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required.

Background: Accurate status determination of breast cancer (BC) biomarkers, including estrogen receptor (ER/ESR1), progesterone receptor (PR/PGR), human epidermal growth factor receptor 2 (HER2/ERBB2), and marker of proliferation Ki67 (MKI67), is crucial for guiding therapeutic decisions. However, current “gold standard” methods such as immunohistochemistry (IHC) for assessing these biomarkers in formalin-fixed paraffin-embedded (FFPE) tissues face challenges in standardization and exhibit substantial inter- and intra-laboratory variability, particularly for Ki67. To address these limitations, APIS Breast Cancer Subtyping Kit has been developed, utilizing reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) to evaluate the status of ER, PR, HER2, KI67, and a novel proliferative signature in breast cancer core needle biopsy (CNB) and resected specimens. The APIS Breast Cancer Subtyping Kit offers enhanced accuracy, improved reproducibility, and the ability to provide results in a timely manner. Herein we report the development and validation of the APIS Breast Cancer Subtyping Kit, assessing clinical performance using retrospective and prospective sample collection. Furthermore, reproducibility and reliability across three testing sites were evaluated. By comparing the results of the kit with those obtained through standard IHC, we demonstrate the reliability and utility of the APIS Breast Cancer Subtyping Kit for precise breast cancer subtyping and guiding personalized patient management decisions. Methods: FFPE tissue sections (n=652) taken by CNB or resection underwent histological analysis in accordance with laboratory’s standard of care methods. Samples with a HER2 score of 2+ were referred to fluorescence in situ hybridization (FISH) to determine ERBB2 amplification, with the FISH result replacing the IHC result in determining the HER2 status (positive/negative) of the sample. Tumors with greater than 1% positive stained nuclei for ER/PR were scored positive. A cut-off of 20% Ki67 staining was used for positive/negative status. The performance was assessed as the agreement with IHC/ISH status and reported as OPA (overall percent agreement) with corresponding 95% confidence intervals (95% CI). To assess the precision of the assay, a panel of RNA pools (samples) was repeatedly measured on different days across three independent laboratories. Reproducibility was evaluated using variance components analysis. Results: The APIS Breast Cancer Subtyping Kit showed high concordance (OPA) with IHC: 93.1% (95% CI: 90.9-94.8) for ER, 86.8% (95% CI: 84.0-89.2) for PR, 94.2% (95% CI: 92.2-95.8) for HER2 (IHC/FISH), 78.3% (95% CI: 75.0-81.3) for Ki67, and 80.1% (95% CI: 76.8-83.1) for proliferative signature (assessed against MKI67 IHC status). Inter-site reproducibility demonstrated excellent agreement in quantitative measurements, with a total SD ranging from 0.00 to 0.22. Binary single-marker status (positive/negative) calling achieved 100% reproducibility for ER, PR, and HER2 for all tested samples (negative/low positive/medium positive), as well as for most Ki67 samples (87% agreement for negative samples). Conclusions: APIS Breast Cancer Subtyping Kit demonstrated a high level of concordance with standard of care IHC/FISH in assessing breast cancer biomarker status. These findings suggest that the APIS Breast Cancer Subtyping Kit provides highly precise and reproducible quantitative assessment of BC biomarkers and molecular subtypes. Inclusion and exclusion criteria Citation Format: Anna Gasior, Joanna Gorniak, Sara Rollinson, Leanne Gough, Anne-Sophie Wegscheider, Axel Niendorf. Comprehensive Molecular Profiling of Breast Cancer: A real-time PCR assay for identifying ESR1, PGR, ERBB2, MKI67 and a novel proliferative signature in core needle biopsies and resected invasive breast cancer tissues [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 PO1-02-08.

ความเป็นมา การตอบสนองทางพยาธิวิทยาภายหลังการได้รับยาเคมีบำบัดก่อนผ่าตัดมีความสัมพันธ์กับผลลัพธ์ทางคลินิก ปัจจุบันไม่มีปัจจัยใดที่สามารถทำนายการตอบสนองต่อยาเคมีบำบัดในมะเร็งเต้านมชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูได้อย่างแม่นยำ เช่นเดียวกับ Ki-67 การแสดงออกของโปรตีน MCM2 ซึ่งมีหน้าที่ควบคุมการสังเคราะห์ดีเอ็นเอในระดับสูงบ่งชี้ถึงการแบ่งตัวของเซลล์ที่รวดเร็ว และมีความสัมพันธ์กับการพยากรณ์โรคที่ไม่ดีในมะเร็งหลายชนิดรวมถึงมะเร็งเต้านม การศึกษานี้มีวัตถุประสงค์เพื่อศึกษาการแสดงออกของ MCM2 ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดเพื่อทำนายการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ในมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู วิธีการศึกษา ทบทวนเวชระเบียนอิเล็กทรอนิกส์ของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูที่ได้รับยาเคมีบำบัดก่อนผ่าตัดในโรงพยาบาลจุฬาลงกรณ์ ระหว่าง 1 มกราคม 2558 - 31 ธันวาคม 2563 นำชิ้นเนื้อก่อนได้รับยาเคมีบำบัดตรวจการแสดงออกของ MCM2 ด้วยการย้อมอิมมูโนฮิสโตเคมีและทบทวนลักณะทางพยาธิวิทยาเพื่อคำนวน RCB index ในชิ้นเนื้อหลังได้รับยาเคมีบำบัด คำนวนการติดสีของ MCM2 ด้วยโปรแกรมวิเคราะห์ภาพ Aperio Imagescope ผลการวิจัย ผู้ป่วยทั้งหมด 88 คน มีค่ามัธยฐานอายุ 49 ปี (พิสัย 27-80 ปี) 61.4% เป็นวัยก่อนหมดประจำเดือน มีระยะโรคมะเร็งทางคลินิก cT4 และ cN1 40% และ 66.7% ตามลำดับ เป็นมะเร็งเต้านมชนิด invasive ductal carcinoma 91.1% มีระดับทางพยาธิวิทยาระดับที่ 2 58.9% มีการแสดงออกของ ER >10% 92.2% และสถานะ PR เป็นลบ 22.2% ในชิ้นเนื้อมะเร็งเต้านมทั้งหมด 72 ชิ้นที่สามารถย้อม MCM2 และประเมิน RCB index ได้ พบว่าการแสดงออกของ MCM2 >=40% มีความสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I อย่างมีนัยสำคัญทางสถิติจากการวิเคราะห์แบบ univariate analysis (OR = 18.33 ช่วงความเชื่อมั่น 95% = 1.88-178.98 p-value = 0.012) ปัจจัยทางพยาธิวิทยาคลินิกที่พบว่ามีความสัมพันธ์กับ MCM2 >=40% คือ ระดับของพยาธิวิทยาระดับที่ 3 การแสดงออกของ ER <=10% สถานะ PR เป็นลบ และ Ki-67 >=20% แต่ในการวิเคราะห์แบบ multivariate analysis ไม่มีปัจจัยทางพยาธิวิทยาคลินิกใดสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ลักษณะทางอิมมูโนฮิสโตเคมีก่อนและหลังให้ยาเคมีบำบัดมีลักษณะโดยรวมใกล้เคียงกัน ค่ามัธยฐานของการปลอดโรค และอัตราการปลอดโรคที่ 3 ปี ไม่มีความแตกต่างกันอย่างมีนัยสำคัญทางสถิติระหว่างผู้ป่วยที่มีการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I และ RCB II/III สรุปผลการวิจัย การแสดงออกของ MCM2 >=40% ด้วยการตรวจทางอิมมูโนฮิสโตเคมี ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู มีความสัมพันธ์กับการตอบสนองแบบ RCB 0/I การตรวจการแสดงออกของ MCM2 อาจช่วยทำนายประโยชน์ของยาเคมีบำบัดก่อนผ่าตัดในผู้ป่วยกลุ่มนี้ได้ อย่างไรก็ตามควรมีการศึกษาเพิ่มเติมในประชากรที่มีขนาดใหญ่ขึ้นเพื่อยืนยันผลของการศึกษานี้

Simple SummaryTriple-negative breast cancer (TNBC) is one of the most challenging tumors with aggressive behavior, low recovery rate, poor prognosis, high metastatic potential, and rapid relapse compared to other breast cancer subtypes. Conventional therapies currently have minimal effect on TNBC; thus, using combination therapies is a valid strategy to enhance drug activity and minimize the overall adverse effect. Therefore, combining drugs with a different mechanism of actions such as apoptosis inducers and JAK/STAT3 inhibitors improved TNBC cell lines killing activity in vitro and in vivo. To further improve the hydrophobic drug activity, CD44 targeted polymeric nanoparticles (CD44-T-PNPs) were utilized by encapsulating hydrophobic drug (CFM-4.16) in CD44-T-PNPs to enhance the drug solubility, tumor accumulation, and most importantly, enhance drug potency. Tagging our PNPs with Hyaluronic acid (HA) enhanced tumor accumulation, reduced off-target distribution, and improved therapeutic efficacy.Identified as the second leading cause of cancer-related deaths among American women after lung cancer, breast cancer of all types has been the focus of numerous research studies. Even though triple-negative breast cancer (TNBC) represents 15–20% of the number of breast cancer cases worldwide, its existing therapeutic options are fairly limited. Due to the pivotal role of the presence/absence of specific receptors to luminal A, luminal B, HER-2+, and TNBC in the molecular classification of breast cancer, the lack of these receptors has accounted for the aforementioned limitation. Thereupon, in an attempt to participate in the ongoing research endeavors to overcome such a limitation, the conducted study adopts a combination strategy as a therapeutic paradigm for TNBC, which has proven notable results with respect to both: improving patient outcomes and survivability rates. The study hinges upon an investigation of a promising NPs platform for CD44 mediated theranostic that can be combined with JAK/STAT inhibitors for the treatment of TNBC. The ability of momelotinib (MMB), which is a JAK/STAT inhibitor, to sensitize the TNBC to apoptosis inducer (CFM-4.16) has been evaluated in MDA-MB-231 and MDA-MB-468. MMB + CFM-4.16 combination with a combination index (CI) ≤0.5, has been selected for in vitro and in vivo studies. MMB has been combined with CD44 directed polymeric nanoparticles (PNPs) loaded with CFM-4.16, namely CD44-T-PNPs, which selectively delivered the payload to CD44 overexpressing TNBC with a significant decrease in cell viability associated with a high dose reduction index (DRI). The mechanism underlying their synergism is based on the simultaneous downregulation of P-STAT3 and the up-regulation of CARP-1, which has induced ROS-dependent apoptosis leading to caspase 3/7 elevation, cell shrinkage, DNA damage, and suppressed migration. CD44-T-PNPs showed a remarkable cellular internalization, demonstrated by uptake of a Rhodamine B dye in vitro and S0456 (NIR dye) in vivo. S0456 was conjugated to PNPs to form CD44-T-PNPs/S0456 that simultaneously delivered CFM-4.16 and S0456 parenterally with selective tumor targeting, prolonged circulation, minimized off-target distribution.

Breast cancer is a highly heterogeneous disease with different subtypes presenting distinct clinical characteristics. During the last decade has been shown that molecular classification of breast cancer holds the promise for the development of novel prognostic tools and the identification of novel treatment targets. However, proteins are the main mediators of biological processes and the molecular targets of the majority of drugs. Moreover, the proteome integrates the cellular genetic information and environmental influences. Hence coupling proteomic and transcriptomic studies may augment the development of targeted therapies and the identification of disease-relevant proteins networks. Here, we report an extensive mass spectrometry-based (MS) secretome analysis of eight breast cancer cell lines representing, the three main breast cancer subtypes (luminal, basal and HER2-neu amplified) in search of subtype-specific proteomic signatures. Following a bottom-up proteomic approach coupled to tandem mass spectrometry the conditioned media of 8 breast cancer cell lines were analyzed. More than 5,200 non-redundant proteins were identified in the conditioned media of the 8 cell lines with a false positive rate less than 1%. The mass spectrometry-based identification of 5 proteins was successfully verified by ELISA. For the generation of subtype-specific proteomic signatures, proteins common to all cell lines of the same subtype but not present in the other two subtypes were identified. Twenty-three, four and four proteins were found uniquely in basal, HER2-neu amplified and luminal breast cancer cells. Notably, ERBB2 was one of the proteins uniquely identified in the HER2-neu amplified subtype verifying the validity of our approach. Given that most of these proteins have no quantitative methods available at present, we opted to preliminarily examine the relationship of these candidates with breast cancer subtypes by using an in silico approach, based on transcriptomic data. We performed an in silico mRNA expression analysis using publicly available data from four independent experiments containing a total of 1039 patients with primary breast cancer. The expression profiles of 15 of 31 proteins investigated showed significant correlation with estrogen receptor expression and power to distinguish subtypes. The development of multiplex quantitative mass spectrometry-based assays for the quantification of the proteomic signatures in relevant biological specimens along with immunohistochemical studies for two of the 31 candidates are currently ongoing. The significance of these subtype-specific proteins in breast cancer prognosis and disease progression warrants further investigation. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-05-11.

The human epidermal growth factor 2 (HER2) is a tyrosine kinase belonging to the Human Epidermal Receptor family [1]. HER2 is amplified leading to HER2 overexpression in 15% of breast cancers (BC) [2]. HER2 overexpression leads to poor prognosis, but offers the unique possibility to use a targeted therapy with monoclonal antibodies. This therapeutic option has radically changed the history of HER2 BC [3]. HER2 overexpression represents at present the most powerful predictive factor for likeliness of response to anti-HER2 agents [5]. The HER2 status is routinely assessed either using immunohistochemistry (HER2 protein expression level) or in situ hybridization (HER2 gene status) [2,5,6]. For the purpose of HER2 assessment, international guidelines have been developed by experts in the field, and have changed over the years according to new data. HER2 heterogeneity is linked to the complexity of equivocal results, HER2 mutations and the upcoming category of HER2-low BC [7]. Intratumor genetic heterogeneity interests various types of human cancers, including BC and HER2 overexpression and amplification, and it is demonstrated to be significantly more common in cases with HER2 equivocal status [8]. Equivocal cases frequently feature low levels of HER2 amplification. Several studies showed that HER2 heterogeneity is related to poor outcome, shorter disease-free survival and overall survival. Patients with HER2 heterogeneity are less responsive to target therapies as confirmed by a lower achievement of pathologic complete response following neoadjuvant treatment [9]. In spite of the scarcity of data still available on HER2-low BC, different intrinsic subtypes seem to influence different clinical behavior and response to treatment, as this has already been proved for other BC subtypes. HER2-low/HR- BC showed a higher frequency of HER2-enriched instrinsic subtype than HER2-/HR- BC. This might mean that, despite in clinical practice HER2-low/HR- BC is considered as HER2-/HR, these subtypes of BCs might reflect a different clinical behavior and a different response to targeted therapy [10]. Recent studies underlined that HER2-enriched instrinsic subtype are the most beneficial in reduction of risk of progression and death after CDK6/6-inhibitors treatment, compared to the other subtypes [11]. Traditionally HER2-positive BCs were separated from HER2-negative BCs, based on solid clinical data stating that only tumors driven by HER2 oncogene addition benefit from targeted therapies [12]. However in recent years new therapies have been developed, in particular the antibody drug conjugates which deliver chemotherapy inside the BC. Some of these agents - such as trastuzumab duocarmazine and trastuzumab deruxtecan showed encouraging response rates also in the so called HER2-low BC. For example, the phase 1 study of DS-8201 showed an overall response rate of 37% in pretreated HER2-low metastatic cancer patients, with a median duration of response of 10.4 months [13]. These data suggests a paradigm shift in the definition of HER2 status, which may be based on 3 groups: 1.HER2-positive, 2.HER2- negative, 3.HER2-low BCs, that may potentially benefit from a targeted therapy, even in absence of the addition of the tumor cells to the HER2 oncogene [7]. At present there are no formal definitions of HER2-low BC, but it seems reasonable to define as HER2-low all BCs displaying a score of 1+ or 2+ HER2 expression and no HER2 amplification. This assumption would categorize up to 55% of BCs as HER2-low. For this subtype there are no targeted therapies approved [7]. The majority of HER2 somatic mutations found in BCs have not been associated with HER2 gene amplification. However preclinical data suggest that functionally active HER2 mutations may lead to HER2 targeted therapies sensitivity [14]. Other preclinical models suggest that pertuzumab inhibits tumor proliferation even in absence of HER2 overexpression [15].So, HER2 mutation-positive but HER2-negative BC may benefit from targeted drugs, but further studies are needed. For example, trastuzumab, deruxtecan, a novel enzyme-cleavable linker, showed significant objective response and disease control in a group of patients including 25% HER2-low BCs, and therefore is worthy of further studies in this subgroup of patients [16]. The new agents being tested for HER2-low BC patients are antibody drug conjugates deploying anti-HER2 epitopes in their antibody component, but with different cytotoxic warheads and a bi-specific antibody targeting both HER2 and HER3 [17]. In conclusion, the concept of HER2-positive versus HER2-negative disease is changing, including the definition of “HER2-low” BC, against which new therapies such as antibody drug conjugates may be extremely effective.

Summary of United States Human Proteome Organisation (HUPO) Symposium Entitled “Standardized Clinical Proteomics Platforms”

PAM50 gene expression subtypes represent a cornerstone in molecular classification of breast cancer and are included in risk prediction models to guide therapy. We aimed to illustrate the impact of included genes and biological processes on subtyping while considering a tumor’s underlying clinical subgroup defined by ER, PR and HER2 status. To do this we used a population-representative and clinically annotated primary breast tumor cohort of 6233 samples profiled by RNA sequencing and applied a perturbation strategy of excluding co-expressed genes (gene sets). We demonstrate how PAM50 nearest centroid classification depends on biological processes present across, but also within, ER/PR/HER2 subgroups and PAM50 subtypes themselves. Our analysis highlights several key aspects of PAM50 classification. Firstly, we observed a tight connection between a tumor’s nearest and second nearest PAM50 centroid. Additionally, we show that second-best subtype is associated with overall survival in ER-positive, HER2-negative, and node negative disease. We also note that ERBB2 has little impact on PAM50 classification in HER2-positive disease regardless of ER-status, and that the Basal subtype is highly stable in contrast to the Normal subtype. Improved consciousness of the commonly used PAM50 subtyping scheme will aid in our understanding and interpretation of breast tumors that have seemingly conflicting PAM50 classification when compared to clinical biomarkers. Finally, our study adds further support in challenging the common misconception that PAM50 subtypes are distinct classes by illustrating that PAM50 subtypes in tumors represent a continuum that may have clinical implications. Citation Format: Srinivas Veerla, Lennart Hohmann, Deborah F. Nacer, Johan Vallon-Christersson, Johan Staaf. Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A017.

12049 Background: Over the last few decades, improvements in healthcare have increased life expectancy by nearly 10 years resulting in a rise in America’s older population. As age is one of the most significant risk factors for the development of cancer, there is a projected increase in the incidence of cancer in the elderly population, with the largest absolute and relative increases in breast cancer (BC). Although treatment advances have improved outcomes for patients with metastatic breast cancer (MBC), patients in clinical trials are often younger and there is limited data for the older population. The goals of this study were to use the National Cancer Database (NCDB) to assess overall survival (OS) in older patients over 75 years of age with MBC and evaluate the impact of tumor features and demographic factors. Methods: Women with a diagnosis of BC at age ≥75 years and with metastases at time of initial diagnosis from 2010 to 2019 were identified from the NCDB. Patients were stratified into age subgroups of 75-79, 80-84, and ≥85 years. Chi-square tests were used to compare categorical variables. Kaplan Meier curves were used to determine OS distributions for patients by age, BC subtype and year of diagnosis. Log-rank tests and multivariable cox proportional hazards modeling was performed to assess the difference in OS by BC subtype. Results: Of 17,325 eligible women included in the final analysis, 39.4% were 75-79, 30.1% 80-84, and 30.4% ≥85 years of age. We observed an improvement in OS over time, with 5-year OS rates improving from 13.7% in 2010 to 15.2% in 2015, p = 0.017. Three-year OS rates were higher in patients aged 75-79 (34.2%) compared with patients aged 80-84 (28.7%) and ≥85 years (18.6%), p < 0.001. Outcomes varied by BC subtype with better prognosis in patients with hormone receptor (HR)+, HER2-negative BC (3-year OS 35.1%), followed by HR+, HER2+ (32.8%), HER2+, HR-negative (20.6%) and lastly, HR-negative, HER2-negative BC (9.7%), p < 0.001. Based on multivariable analyses, a higher comorbidity index was associated with worse outcomes (HR 1.654 [1.48-1.849], p < 0.0001). Higher income (≥$63,000) was associated with decreased mortality (HR 0.859 [0.784-0.941], p = 0.0011) compared with lower income ( < $38,000). While the type of cancer facility (academic, community, etc) did not impact mortality, older patients living in metropolitan areas ( > 1 million) had improved outcomes (HR 0.829 [0.689-0.996], p = 0.046), compared to those in rural areas. Conclusions: We observed a modest though significant improvement in survival outcomes over time in older patients with MBC. Prognosis was better in patients with HR+ BC compared with HR-negative, HER2+, and HR-negative, HER2-negative subtypes. However, outcomes in patients ≥85 years remain poor. Given the lack of clinical trial data in older patients with cancer, future studies should specifically focus on improving outcomes in this population.

6583 Background: Breast cancer (BC) mortality in South Africa (SA) is double that of the United States. The aggressive triple-negative breast cancer (TNBC) subtype comprises 20.9% of BC cases among Black women in Southern Africa. SA has the second highest HIV prevalence worldwide, but if and how HIV affects the risk of developing TNBC is not well understood. Methods: Using the South African Breast Cancer and HIV Outcomes (SABCHO) prospective cohort of newly diagnosed BC patients from six public hospitals in SA, we evaluated the association of HIV with risk of TNBC relative to other BC subtypes. We developed multivariable logistic regression models to test for this association while adjusting for key demographic and reproductive risk factors. Results: Of 3,883 women with BC, 16.4% had TNBC and 23.0% were HIV-positive. The overall median age of BC diagnosis was 55 years. Among HIV positive patients, the median age of BC diagnosis was 45 years, without a difference between TNBC and non-TNBC cases (p = 0.244). Only 7.2% of women were nulliparous (4.6% in TNBC and 7.6% in non-TNBC subgroups). Of women who had at least 1 full-term pregnancy, 11.2% did not breastfeed, without a difference between TNBC and non-TNBC cases (p = 0.292). In our final multivariable logistic regression model, HIV-positive status was associated with increased risk of TNBC relative to non-TNBC (OR: 1.40; 95% CI: 1.12-1.74, compared to HIV-negative with BC). Parity without breastfeeding was also associated with increased risk of TNBC relative to non-TNBC (OR: 1.72; 95% CI: 1.07-2.84, compared to nulliparous with BC). HIV status and the combined parity/breastfeeding variable had no statistically significant interaction. In exploratory analyses, measures of HIV control at time of BC diagnosis were not associated with BC subtype. Conclusions: In this large prospective case-only study conducted in SA, we found that women living with HIV were more likely than HIV-negative women to have TNBC (relative to other BC subtypes), regardless of the extent of HIV control. Whether this relationship is mediated by immune mechanisms or other factors should be the focus of future research. Our findings highlight the importance of rigorous BC screening and early detection among HIV-positive women given their increased relative risk of developing TNBC. [Table: see text]

Breast cancer (BC) is the most common cancer among women. It is known that the prolactin receptor (PRLR) may play a role in breast carcinogenesis, but the available data are often contradictory. To get a more complete picture of the relationship between the receptor and mammary gland carcinogenesis, we examined the association between changes in PRLR expression level and tumor subtype (and its main characteristics). To do this, using real-time PCR, we evaluated the level of PRLR mRNA in BC tissue samples and untransformed adjoining tissue samples (89 pairs). Since the androgen receptor (AR) has begun to be seen as a prognostic marker in breast cancer, we also evaluated the association between mRNA levels of AR and PRLR. We found a significant increase in PRLR expression in luminal subtypes; the highest level of PRLR mRNA was detected in luminal A subtype. In HER2-positive ER-, PR-negative BC, the PRLR mRNA level decreases in tumor tissues compared with untransformed tissues. High PRLR expression is also associated with smaller tumor size in luminal B HER2-negative subtype. In ER-, PR-negative tumors, PRLR expression is associated with AR expression: PRLR mRNA level is increased when AR mRNA level is reduced by more than 8 times in triple-negative tumors; in contrast, in HER2-positive subtype it decreases more significantly when AR expression is reduced by more than 3 times. A tendency towards an increase in PRLR expression with an increase in the AR mRNA level was also discovered in luminal subtypes. The level of PRLR expression depends on the age of patients. In luminal A, PRLR expression is higher in patients under 65 years. In contrast, in luminal B HER2-negative and triple-negative BC, reduced PRLR expression was observed in patients under the age of 40 years and under the age of 50 years, respectively. In this group of patients under the age of 40 years with luminal B HER2-negative BC, ER expression was also reduced (0-4 score according to the IHC assay). Thus, PRLR probably plays a different role in the development and progression of BC: in luminal A and luminal B HER2-positive subtypes PRLR may act as an oncogen, and in luminal B HER2-negative and ER-, PR-negative subtypes can play a tumor suppressor role.

Analysis of the patterns of gene expression in breast cancer suggests that it is not a single entity, but is comprised of several biologically distinct subtypes with characteristic molecular profiles. These molecular profiles confirm the clinical impression that estrogen receptor (ER)-negative differs from ER-positive, and expands our understanding by identifying breast cancer subtypes, including the basal-like and human epidermal growth factor receptor (HER)2/ER subtypes within the ER-negative subset, and the luminal A and B subtypes within ER-positive disease. The basal-like subtype is characterized by the low expression levels of the ER-related and the HER2-related group of genes, and therefore is often 'triple negative' on clinical assays for these proteins. This review discusses the molecular profiles of breast cancer with a focus on the clinical characteristics of, and treatment options for, the basal-like subtype.

Molecular classification of breast cancer

Introduction: Breast cancer has heterogeneous tumor biology. Therefore, breast cancer is divided into molecular subtypes. However, molecular subtypes also show heterogeneity within themselves. In our study, we compared whether there were radiological and pathological differences between breast cancer molecular subtypes. Methods: The data of 569 patients operated on for breast cancer in our General Surgery clinic during an 8-year period were analyzed retrospectively. After dividing the patients into groups consisting of molecular subtypes, their radiological and pathological findings were compared. Results: According to the molecular classification of breast cancer, 47.6% of the patients were in the luminal A group, 23.9% in luminal B, 7.7% in HER2-enriched, and 12% in TN, and 8.8% in normal-like groups. DCIS and LCIS were significantly less common in the TN group than in the others (p=0.015). Tumor diameter and grade were considerably higher in the normal-like, HER2-enriched, and TN groups than in the other groups (p<0.001, p<0.001). LVI was significantly lower in luminal A and significantly higher in luminal B (p<0.001). Mortality was significantly higher in the luminal B and TN groups than in the other groups (p = 0.029). Conclusion: The addition of the basal-like group when classifying breast cancer into molecular subtypes showed histopathological differences. More studies are needed to investigate its effects on treatment and prognosis. Keywords: Breast cancer, Molecular subtype, Genotype, Immunohistochemistry, Survival