Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. RESULTS: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. CONCLUSIONS: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Reproductive characteristics, menopausal status, race and ethnicity, and risk of breast cancer subtypes defined by ER, PR and HER2 status: the Breast Cancer Etiology in Minorities study.

BACKGROUND: Associations between reproductive factors and risk of breast cancer differ by subtype defined by joint estrogen receptor (ER), progesterone receptor (PR), and HER2 expression status. Racial and ethnic differences in the incidence of breast cancer subtypes suggest etiologic heterogeneity, yet data are limited because most studies have included non-Hispanic White women only. METHODS: We analyzed harmonized data for 2,794 breast cancer cases and 4,579 controls, of whom 90% self-identified as African American, Asian American or Hispanic. Questionnaire data were pooled from three population-based studies conducted in California and data on tumor characteristics were obtained from the California Cancer Registry. The study sample included 1,530 luminal A (ER-positive and/or PR-positive, HER2-negative), 442 luminal B (ER-positive and/or PR-positive, HER2-positive), 578 triple-negative (TN; ER-negative, PR-negative, HER2-negative), and 244 HER2-enriched (ER-negative, PR-negative, HER2-positive) cases. We used multivariable unconditional logistic regression models to estimate subtype-specific ORs and 95% confidence intervals associated with parity, breast-feeding, and other reproductive characteristics by menopausal status and race and ethnicity. RESULTS: Subtype-specific associations with reproductive factors revealed some notable differences by menopausal status and race and ethnicity. Specifically, higher parity without breast-feeding was associated with higher risk of luminal A and TN subtypes among premenopausal African American women. In contrast, among Asian American and Hispanic women, regardless of menopausal status, higher parity with a breast-feeding history was associated with lower risk of luminal A subtype. Among premenopausal women only, luminal A subtype was associated with older age at first full-term pregnancy (FTP), longer interval between menarche and first FTP, and shorter interval since last FTP, with similar OR estimates across the three racial and ethnic groups. CONCLUSIONS: Subtype-specific associations with reproductive factors overall and by menopausal status, and race and ethnicity, showed some differences, underscoring that understanding etiologic heterogeneity in racially and ethnically diverse study samples is essential. Breast-feeding is likely the only reproductive factor that is potentially modifiable. Targeted efforts to promote and facilitate breast-feeding could help mitigate the adverse effects of higher parity among premenopausal African American women.

Trajectories of body mass index in adulthood and risk of subtypes of postmenopausal breast cancer.

BACKGROUND: Body fatness is a dynamic exposure throughout life. To provide more insight into the association between body mass index (BMI) and postmenopausal breast cancer, we aimed to examine the age at onset, duration, intensity, and trajectories of body fatness in adulthood in relation to risk of breast cancer subtypes. METHODS: Based on self-reported anthropometry in the prospective Norwegian Women and Cancer Study, we calculated the age at onset, duration, and intensity of overweight and obesity using linear mixed-effects models. BMI trajectories in adulthood were modeled using group-based trajectory modeling. We used Cox proportional hazards models to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BMI exposures and breast cancer subtypes in 148,866 postmenopausal women. RESULTS: A total of 7223 incident invasive postmenopausal breast cancer cases occurred during follow-up. Increased overweight duration and age at the onset of overweight or obesity were associated with luminal A-like breast cancer. Significant heterogeneity was observed in the association between age at overweight and overweight duration and the intrinsic-like subtypes (pheterogeneity 0.03). Compared with women who remained at normal weight throughout adulthood, women with a descending BMI trajectory had a reduced risk of luminal A-like breast cancer (HR 0.54, 95% CI 0.33-0.90), whereas women with ascending BMI trajectories were at increased risk (HR 1.09; 95% CI 1.01-1.17 for "Normal-overweight"; HR 1.20; 95% CI 1.07-1.33 for "Normal-obesity"). Overweight duration and weighted cumulative years of overweight and obesity were inversely associated with luminal B-like breast cancer. CONCLUSIONS: In this exploratory analysis, decreasing body fatness from obesity in adulthood was inversely associated with overall, hormone receptor-positive and luminal A-like breast cancer in postmenopausal women. This study highlights the potential health benefits of reducing weight in adulthood and the health risks associated with increasing weight throughout adult life. Moreover, our data provide evidence of intrinsic-like tumor heterogeneity with regard to age at onset and duration of overweight.

Clinical evaluation of molecular surrogate subtypes in patients with ipsilateral multifocal primary breast cancer.

BACKGROUND: When ipsilateral multifocal primary breast cancer (IMBC) is detected, standard routine is to evaluate the largest tumor with immunohistochemistry (IHC). As all foci are not routinely characterized, many patients may not receive optimal adjuvant treatment. Here, we assess the clinical relevance of examining at least two foci present in patients with IMBC. METHODS: Patients diagnosed and treated for IMBC at Sahlgrenska University Hospital (Gothenburg, Sweden) between 2012 and 2017 were screened. In total, 180 patients with ≥ 2 invasive foci (183 specimens) were assessed with IHC and included in this study. Expression of the estrogen (ER) and progesterone (PR) receptors, Ki67, HER2, and tumor grade were used to determine the molecular surrogate subtypes and discordance among the foci was recorded. An additional multidisciplinary team board was then held to re-assess whether treatment recommendations changed due to discordances in molecular surrogate subtype between the different foci. RESULTS: Discordance in ER, PR, HER2, and Ki67 was found in 2.7%, 19.1%, 7.7%, and 16.9% of invasive foci, respectively. Discordance in the molecular surrogate subtypes was found in 48 of 180 (26.7%) patients, which resulted in therapy changes for 11 patients (6.1%). These patients received additional endocrine therapy (n = 2), chemotherapy (n = 3), and combined chemotherapy and trastuzumab (n = 6). CONCLUSION: Taken together, when assessing at least two tumor foci with IHC, regardless of shared morphology or tumor grade between the different foci, 6.1% of patients with IMBC were recommended additional adjuvant treatment. A pathologic assessment using IHC of all foci is therefore recommended to assist in individualized treatment decision making.

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues.

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

BCLncRDB: a comprehensive database of LncRNAs associated with breast cancer.

Breast cancer, the most common cancer in women, is characterized by high morbidity and mortality worldwide. Recent evidence has shown that long non-coding RNAs (lncRNAs) play a crucial role in the development and progression of breast cancer. However, despite increasing data and evidence indicating the implication of lncRNAs in breast cancer, no web resource or database exists primarily for lncRNAs associated with only breast cancer. Therefore, we developed a manually curated, comprehensive database, "BCLncRDB," for lncRNAs associated with breast cancer. For this, we collected, processed, and analyzed available data on breast cancer-associated lncRNAs from different sources, including previously published research articles, the Gene Expression Omnibus (GEO) Database of the National Centre for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), and the Ensembl database; subsequently, these data were hosted at BCLncRDB for public access. Currently, the database contains 5324 unique breast cancer-lncRNA associations and has the following features: (i) a user-friendly, easy-to-use web interface for searching and browsing about lncRNAs of the user's interest, (ii) differentially expressed and methylated lncRNAs, (iii) stage- and subtype-specific lncRNAs, and (iv) drugs, subcellular localization, sequence, and chromosome information of these lncRNAs. Thus, the BCLncRDB provides a one-stop dedicated platform for exploring breast cancer-related lncRNAs to advance and support the ongoing research on this disease. The BCLncRDB is publicly available for use at http://sls.uohyd.ac.in/new/bclncrdb_v1 .

Characterizing breast cancer incidence and trends among Asian American, Native Hawaiian, and non-Hispanic White women in Hawai'i, 1990-2014.

PURPOSE: To characterize breast cancer (BC) incidence by age at diagnosis and BC subtype among disaggregated Asian American, Native Hawaiian, and Pacific Islander (AANHPI) women and non-Hispanic White (NHW) women in Hawai'i. METHODS: Using 1990-2014 data from the Hawai'i tumor registry, we estimated age-adjusted incidence rates (AAIR) of BC and the annual percent change in BC incidence by age (<50 and ≥50 years) and BC subtype (hormone receptor [HR]+/human epidermal growth factor receptor 2 [HER2]-, HR+/HER2+, HR-/HER2+, triple negative BC) for Filipino American (FA), Japanese American (JA), Native Hawaiian (NH), and NHW women. RESULTS: Among young (<50 years) women, annual BC incidence increased 2.9% (1994-2014) among JA and 1.0% (1990-2014) among NHW women. Incidence was highest among young JA women (2010-2014 AAIR 52.0 per 100,000; 95% confidence interval [CI] 45.6, 58.9). HR+/HER2- BC, the major BC subtype, was similarly highest among young JA women (AAIR 39.5; 95% CI 33.9, 45.4). Among older (≥50 years) women, annual BC incidence increased 1.6% (1990-2014) among FA and 4.2% (2006-2014) for JA women. BC incidence was highest among older NH women (AAIR 137.6, 95% CI 128.2, 147.4), who also displayed highest incidence of two subtypes: HR+/HER2- (AAIR 106.9; 95% CI 98.6, 115.5) and HR+/HER2+ (AAIR 12.1; 95% CI 9.4, 15.1). CONCLUSION: We observed high and increasing BC incidence among JA women ages <50 years and high incidence among NH women ages ≥50 years. These results highlight racial and ethnic differences in BC incidence among disaggregated AANHPI populations in Hawai'i by age and BC subtype.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment.

Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

Incidence of breast cancer subtypes in immigrant and non-immigrant women in Norway.

BACKGROUND: Breast cancer incidence differs between non-immigrants and immigrants from low- and middle-income countries. This study investigates whether immigrants also have different subtype-specific incidences. METHODS: We used national health registries in Norway and calculated subtype-specific incidence rate ratios (IRRs) for invasive breast cancer among women aged 20-75 and 20-49 years between 2005 and 2015. Immigrant groups were classified by country of birth broadly defined based on WHO regional groupings. Subtype was defined using estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (HER2) status as luminal A-like (ER+ PR+ HER2-), luminal B-like/HER2- (ER+ PR- HER2-), luminal B-like/HER2+ (ER+ PR any HER2+), HER2+ (ER-PR-HER2+) and triple-negative breast cancer (TNBC) (ER-PR-HER2-). RESULTS: Compared to non-immigrants, incidence of the luminal A-like subtype was lower in immigrants from Sub-Saharan Africa (IRR 0.43 95% CI 0.28-0.66), South East Asia (IRR 0.63 95% CI 0.51-0.79), South Asia (IRR 0.67 95% CI 0.52-0.86) and Eastern Europe (IRR 0.86 95% CI 0.76-0.99). Immigrants from South Asia had higher rates of HER2 + tumors (IRR 2.02 95% CI 1.26-3.23). The rates of TNBC tended to be similar regardless of region of birth, except that women from South East Asia had an IRR of 0.54 (95% CI 0.32-0.91). CONCLUSIONS: Women from Eastern Europe, Sub-Saharan Africa and Asia had different subtype-specific incidences compared to women from high-income countries (including non-immigrants). These differences in tumor characteristics between immigrant groups should be taken into consideration when planning preventive or screening strategies.

Postmenopausal overweight and breast cancer risk; results from the KARMA cohort.

PURPOSE: To study the risk of incident breast cancer and subtype-specific breast cancer in relation to excess body weight in a contemporary Swedish prospective cohort study, The Karolinska Mammography Project for Risk Prediction of Breast Cancer, KARMA. METHODS: A total of 35,412 postmenopausal women attending mammography and included in the KARMA study provided baseline data on body mass index (BMI) and potential confounders. During eight years of follow-up, 822 incident invasive breast cancer cases were identified. RESULTS: Women with overweight (BMI ≥ 25-< 30 kg/m2) constituting 34% of the study cohort had an increased risk of incident breast cancer with an adjusted Hazard Ratio (HRadj) 1.19 (95% CI 1.01-1.4). A similar, however, non-significant, association was found for women with obesity (BMI ≥ 30 kg/m2) conferring 13% of the cohort, with a HRadj of 1.19 (95% CI 0.94-1.5). Overweight was associated with risk of node-negative disease (HRadj 1.29, 95% CI 1.06-1.58), whereas obesity was associated with node-positive disease (HRadj 1.64, 95% CI 1.09-2.48). Both overweight and obesity were associated with risk of estrogen receptor positive (ER+) disease (HRadj 1.20, 95% CI 1.00-1.44 and HRadj 1.33, 95% CI 1.03-1.71, respectively), and low-grade tumors (HRadj 1.25, 95% CI 1.02-1.54, and HRadj 1.40, 95% CI 1.05-1.86, respectively). Finally, obesity was associated with ER+HER2 negative disease (HRadj 1.37, 95% CI 1.05-1.78) and similarly luminal A tumors (HRadj 1.43, 95% CI 1.02-2.01). CONCLUSION: Overweight and obesity are associated with an increased risk of developing breast cancer, specifically ER+, low-grade, and for obesity, node-positive, high-risk breast cancer indicating a further need for risk communication and preventive programs.

A pooled case-only analysis of obesity and breast cancer subtype among Black women in the southeastern United States.

PURPOSE: To evaluate the association between obesity and the relative prevalence of tumor subtypes among Black women with breast cancer (BC). METHODS: We conducted a pooled case-only analysis of 1,793 Black women with invasive BC recruited through three existing studies in the southeastern US. Multivariable case-only polytomous logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between obesity, measured by pre-diagnostic body mass index (BMI), and human epidermal growth factor receptor 2 + (HER2 +) and triple negative BC (TNBC) subtype relative to hormone receptor (HR) + /HER2- status (referent). RESULTS: Among 359 premenopausal women, 55.4% of cases were HR + /HER2 -, 20.1% were HER2 + , and 24.5% were TNBC; corresponding percentages among 1,434 postmenopausal women were 59.3%, 17.0%, and 23.6%. Approximately, 50-60% of both pre- and postmenopausal women were obese (BMI > 30 kg/m2), regardless of BC subtype. We did not observe a significant association between obesity and BC subtype. Among postmenopausal women, class I obesity (BMI 35 + kg/m2) was not associated with the development of HER2 + BC (OR 0.69; 95% CI 0.42-1.14) or TNBC (OR 0.93; 95% CI 0.60-1.45) relative to HR + /HER2- tumors. Corresponding estimates among premenopausal women were 1.03 (95% CI 0.43-2.48) and 1.13 (95% CI 0.48-2.64). CONCLUSION: In this large study of Black women with BC, there was no evidence of heterogeneity of BMI by BC subtype.

The Effect of Age on Outcomes After Neoadjuvant Chemotherapy for Breast Cancer.

BACKGROUND: Younger women (age ≤ 40 years) with breast cancer undergoing neoadjuvant chemotherapy (NAC) have higher rates of pathologic complete response (pCR); however, it is unknown whether axillary or breast downstaging rates differ by age. In this study, we compared pCR incidence and surgical downstaging rates of the breast and axilla post NAC, between patients aged ≤ 40, 41-60, and ≥ 61 years. METHODS: We identified 1383 women with stage I-III breast cancer treated with NAC and subsequent surgery from November 2013 to December 2018. pCR and breast/axillary downstaging rates were assessed and compared across age groups. RESULTS: Younger women were significantly more likely to have ductal histology, poorly differentiated tumors, and BRCA mutations; 35% of tumors were hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-), 36% were HER2-positive (HER2+), and 29% were triple negative (TN), with similar subtype distribution across age groups (p = 0.6). Overall, pCR rates did not differ by age, however among patients with TN tumors (n = 394), younger women had higher pCR rates (52% vs. 35% among those aged 41-60 years and 29% among those aged ≥61 years; p = 0.007) and were more likely to have tumors with high tumor-infiltrating lymphocyte (TIL) concentrations (p < 0.001). Downstaging to breast-conserving surgery (BCS) eligibility post NAC among initially BCS-ineligible patients was similar across age groups; younger women chose BCS less often (p < 0.001). Among cN1 patients (n = 813), 52% of women ≤40 years of age avoided axillary lymph node dissection (ALND) with NAC, versus 39% and 37% in the older groups (p < 0.001). CONCLUSIONS: Younger women undergoing NAC for axillary downstaging were more likely to avoid ALND across all subtypes; however, overall pCR rates did not differ by age. Despite equivalent breast downstaging and BCS eligibility rates across age groups, younger women were less likely to undergo BCS.

Body size in early life and the risk of postmenopausal breast cancer.

BACKGROUND: Greater early life adiposity has been reported to reduce postmenopausal breast cancer risk but it is unclear whether this association varies by tumour characteristics. We aimed to assess associations of early life body size with postmenopausal breast cancer and its subtypes, allowing for body size at other ages. METHODS: A total of 342,079 postmenopausal UK women who reported their body size at age 10, clothes size at age 20, and body mass index (BMI) at baseline (around age 60) were followed by record linkage to national databases for cancers and deaths. Cox regression yielded adjusted relative risks (RRs) of breast cancer, overall and by tumour subtype, in relation to body size at different ages. RESULTS: During an average follow-up of 14 years, 15,506 breast cancers were diagnosed. After adjustment for 15 potential confounders, greater BMI at age 60 was associated with an increased risk of postmenopausal breast cancer (RR per 5 kg/m2=1.20, 95%CI 1.18-1.22) whereas greater adiposity in childhood and, to a lesser extent, early adulthood, was associated with a reduced risk (0.70, 0.66-0.74, and 0.92, 0.89-0.96, respectively). Additional adjustment for midlife BMI strengthened associations with BMI at both age 10 (0.63, 0.60-0.68) and at age 20 (0.78, 0.75-0.81). The association with midlife adiposity was confined to hormone sensitive subtypes but early life adiposity had a similar impact on the risk of all subtypes. CONCLUSION: Early life and midlife adiposity have opposite effects on postmenopausal breast cancer risk and the biological mechanisms underlying these associations are likely to differ.

Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes.

BACKGROUND: The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification. METHODS: Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures. RESULTS: On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment. CONCLUSIONS: Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

Conditional local recurrence risk: the effect of event-free years in different subtypes of breast cancer.

BACKGROUND: After breast cancer treatment, follow-up consists of physical examination and mammography for at least 5 years, to detect local and regional recurrence. The risk of recurrence may decrease after event-free time. This study aims to determine the risk of local recurrence (LR) as a first event until 5 years after diagnosis, conditional on being event-free for 1, 2, 3 and 4 years. METHODS: From the Netherlands Cancer Registry, all M0 breast cancers diagnosed between 2005 and 2008 were included. LR risk was calculated with Kaplan-Meier analysis, overall and for different subtypes. Conditional LR (assuming x event-free years) was determined by selecting event-free patients at x years, and calculating their LR risk within 5 years after diagnosis. RESULTS: Five-year follow-up was available for 34,453 patients. Overall, five-year LR as a first event occurred in 3.0%. This risk varied for different subtypes and was highest for triple negative (6.8%) and lowest for ER+PR+Her2- (2.2%) tumors. After 1, 2, 3 and 4 event-free years, the average risk of LR before 5 years after diagnosis decreased from 3.0 to 2.4, 1.6, 1.0, and 0.6%. The risk decreased in all subtypes, the effect was most pronounced in subtypes with the highest baseline risk (ER-Her2+ and triple negative breast cancer). After three event-free years, LR risk in the next 2 years was 1% or less in all subtypes except triple negative (1.6%). CONCLUSION: The risk of 5-year LR as a first event was low and decreased with the number of event-free years. After three event-free years, the overall risk was 1%. This is reassuring to patients and also suggests that follow-up beyond 3 years may produce low yield of LR, both for individual patients and studies using LR as primary outcome. This can be used as a starting point to tailor follow-up to individual needs.

Rare Breast Cancer Subtypes.

PURPOSE OF REVIEW: Breast cancer is a collection of diseases including the more common invasive ductal and lobular carcinomas and rarer subtypes of breast cancer. This review summarizes the features of rare breast cancers. RECENT FINDINGS: Each of the rare tumors has defined pathological and clinical features that impact treatment recommendations. In this review, we summarize these for each rare type of breast cancer and where available we include molecular features of each tumor. Rare subtypes of breast cancer each have unique features. In many cases, data is limited for the optimal treatment approaches.

Metabolomic Profiling of Blood-Derived Microvesicles in Breast Cancer Patients.

Malignant cells differ from benign ones in their metabolome and it is largely unknown whether this difference is reflected in the metabolic profile of their microvesicles (MV), which are secreted into the blood of cancer patients. Here, they are present together with MV from the various blood and endothelial cells. Harvesting MV from 78 breast cancer patients (BC) and 30 controls, we characterized the whole blood MV metabolome using targeted and untargeted mass spectrometry. Especially (lyso)-phosphatidylcholines and sphingomyelins were detected in a relevant abundance. Eight metabolites showed a significant discriminatory power between BC and controls. High concentrations of lysoPCaC26:0 and PCaaC38:5 were associated with shorter overall survival. Comparing BC subtype-specific metabolome profiles, 24 metabolites were differentially expressed between luminal A and luminal B. Pathway analysis revealed alterations in the glycerophospholipid metabolism for the whole cancer cohort and in the ether lipid metabolism for the molecular subtype luminal B. Although this mixture of blood-derived MV contains only a minor number of tumor MV, a combination of metabolites was identified that distinguished between BC and controls as well as between molecular subtypes, and was predictive for overall survival. This suggests that these metabolites represent promising biomarkers and, moreover, that they may be functionally relevant for tumor progression.

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles.

Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

New generation breast cancer cell lines developed from patient-derived xenografts.

BACKGROUND: Breast cancer is a highly heterogeneous disease characterized by multiple histologic and molecular subtypes. While a myriad of breast cancer cell lines have been developed over the past 60 years, estrogen receptor alpha (ER)+ disease and some mutations associated with this subtype remain underrepresented. Here we describe six breast cancer cell lines derived from patient-derived xenografts (PDX) and their general characteristics. METHODS: Established breast cancer PDX were processed into cell suspensions and placed into standard 2D cell culture; six emerged into long-term passageable cell lines. Cell lines were assessed for protein expression of common luminal, basal, and mesenchymal markers, growth assessed in response to estrogens and endocrine therapies, and RNA-seq and oncogenomics testing performed to compare relative transcript levels and identify putative oncogenic drivers. RESULTS: Three cell lines express ER and two are also progesterone receptor (PR) positive; PAM50 subtyping identified one line as luminal A. One of the ER+PR+ lines harbors a D538G mutation in the gene for ER (ESR1), providing a natural model that contains this endocrine-resistant genotype. The third ER+PR-/low cell line has mucinous features, a rare histologic type of breast cancer. The three other lines are ER- and represent two basal-like and a mixed ductal/lobular breast cancer. The cell lines show varied responses to tamoxifen and fulvestrant, and three were demonstrated to regrow tumors in vivo. RNA sequencing confirms all cell lines are human and epithelial. Targeted oncogenomics testing confirmed the noted ESR1 mutation in addition to other mutations (i.e., PIK3CA, BRCA2, CCND1, NF1, TP53, MYC) and amplifications (i.e., FGFR1, FGFR3) frequently found in breast cancers. CONCLUSIONS: These new generation breast cancer cell lines add to the existing repository of breast cancer models, increase the number of ER+ lines, and provide a resource that can be genetically modified for studying several important clinical breast cancer features.