Distinct profiles of proliferating CD8+/TCF1+ T cells and CD163+/PD-L1+ macrophages predict risk of relapse differently among treatment-naïve breast cancer subtypes.

Immunophenotypic analysis of breast cancer microenvironment is gaining attraction as a clinical tool improving breast cancer patient stratification. The aim of this study is to evaluate proliferating CD8 + including CD8 + TCF1 + Τ cells along with PD-L1 expressing tissue-associated macrophages among different breast cancer subtypes. A well-characterized cohort of 791 treatment-naïve breast cancer patients was included. The analysis demonstrated a distinct expression pattern among breast cancer subtypes characterized by increased CD8 + , CD163 + and CD163 + PD-L1 + cells along with high PD-L1 status and decreased fraction of CD8 + Ki67 + T cells in triple negative (TNBC) and HER2 + compared to luminal tumors. Kaplan-Meier and Cox univariate survival analysis revealed that breast cancer patients with high CD8 + , CD8 + Ki67 + , CD8 + TCF1 + cells, PD-L1 score and CD163 + PD-L1 + cells are likely to have a prolonged relapse free survival, while patients with high CD163 + cells have a worse prognosis. A differential impact of high CD8 + , CD8 + Ki67 + , CD8 + TCF1 + T cells, CD163 + PD-L1 + macrophages and PD-L1 status on prognosis was identified among the various breast cancer subtypes since only TNBC patients experience an improved prognosis compared to patients with luminal A tumors. Conversely, high infiltration by CD163 + cells is associated with worse prognosis only in patients with luminal A but not in TNBC tumors. Multivariate Cox regression analysis in TNBC patients revealed that increased CD8 + [hazard ratio (HR) = 0.542; 95% confidence interval (CI) 0.309-0.950; p = 0.032), CD8 + TCF1 + (HR = 0.280; 95% CI 0.101-0.779; p = 0.015), CD163 + PD-L1 + (HR: 0.312; 95% CI 0.112-0.870; p = 0.026) cells along with PD-L1 status employing two different scoring methods (HR: 0.362; 95% CI 0.162-0.812; p = 0.014 and HR: 0.395; 95% CI 0.176-0.884; p = 0.024) were independently linked with a lower relapse rate. Multivariate analysis in Luminal type A patients revealed that increased CD163 + was independently associated with a higher relapse rate (HR = 2.360; 95% CI 1.077-5.170; p = 0.032). This study demonstrates that the evaluation of the functional status of CD8 + T cells in combination with the analysis of immunosuppressive elements could provide clinically relevant information in different breast cancer subtypes.

Comparison of Breast Fine-Needle Aspiration Cytology and Tissue Sampling for High-Throughput Proteomic Analysis and Cancer Biomarker Detection.

INTRODUCTION: Fine-needle aspiration cytology (FNAC) specimens are widely utilized for the diagnosis and molecular testing of various cancers. We performed a comparative proteomic analysis of three different sample types, including breast FNAC, core needle biopsy (CNB), and surgical resection tissues. Our goal was to evaluate the suitability of FNAC for in-depth proteomic analysis and for identifying potential therapeutic biomarkers in breast cancer. METHODS: High-throughput proteomic analysis was conducted on matched FNAC, CNB, and surgical resection tissue samples obtained from breast cancer patients. The protein identification, including currently established or promising therapeutic targets, was compared among the three different sample types. Gene Ontology (GO) enrichment analysis was also performed on all matched samples. RESULTS: Compared to tissue samples, FNAC testing revealed a comparable number of proteins (7,179 in FNAC; 7,196 in CNB; and 7,190 in resection samples). Around 85% of proteins were mutually identified in all sample types. FNAC, along with CNB, showed a positive correlation between the number of enrolled tumor cells and identified proteins. In the GO analysis, the FNAC samples demonstrated a higher number of genes for each pathway and GO terms than tissue samples. CCND1, CDK6, HER2, and IGF1R were found in higher quantities in the FNAC compared to tissue samples, while TUBB2A was only detected in the former. CONCLUSION: FNAC is suitable for high-throughput proteomic analysis, in addition to an emerging source that could be used to identify and quantify novel cancer biomarkers.

Diagnostic Challenges during Inflammation and Cancer: Current Biomarkers and Future Perspectives in Navigating through the Minefield of Reactive versus Dysplastic and Cancerous Lesions in the Digestive System.

In the setting of pronounced inflammation, changes in the epithelium may overlap with neoplasia, often rendering it impossible to establish a diagnosis with certainty in daily clinical practice. Here, we discuss the underlying molecular mechanisms driving tissue response during persistent inflammatory signaling along with the potential association with cancer in the gastrointestinal tract, pancreas, extrahepatic bile ducts, and liver. We highlight the histopathological challenges encountered in the diagnosis of chronic inflammation in routine practice and pinpoint tissue-based biomarkers that could complement morphology to differentiate reactive from dysplastic or cancerous lesions. We refer to the advantages and limitations of existing biomarkers employing immunohistochemistry and point to promising new markers, including the generation of novel antibodies targeting mutant proteins, miRNAs, and array assays. Advancements in experimental models, including mouse and 3D models, have improved our understanding of tissue response. The integration of digital pathology along with artificial intelligence may also complement routine visual inspections. Navigating through tissue responses in various chronic inflammatory contexts will help us develop novel and reliable biomarkers that will improve diagnostic decisions and ultimately patient treatment.

HER2 amplification level by in situ hybridization predicts survival outcome in advanced HER2-positive breast cancer treated with pertuzumab, trastuzumab, and docetaxel regardless of HER2 IHC results.

BACKGROUND: The role of HER2 amplification level in predicting the effectiveness of HER2-directed therapies has been established. However, its association with survival outcomes in advanced HER2-positive breast cancer treated with dual HER2-blockade remains unexplored. METHODS: This is a single-center retrospective study of patients with advanced HER2-positive breast cancer treated with first-line pertuzumab, trastuzumab, and docetaxel. The primary objective was to ascertain the relationship between treatment outcomes and the level of HER2 amplification by in situ hybridization (ISH). RESULTS: A total of 152 patients were included with a median follow-up duration of 50.0 months. Among the 78 patients who received ISH, a higher HER2/CEP17 ratio correlated significantly with longer PFS (HR 0.50, p = 0.022) and OS (HR 0.28, p = 0.014) when dichotomized by the median. A higher HER2 copy number also correlated significantly with better PFS (HR 0.35, p < 0.001) and OS (HR 0.27, p = 0.009). In multivariate analysis, the HER2/CEP17 ratio was an independent predictive factor for PFS (HR 0.66, p = 0.004) and potentially for OS (HR 0.64, p = 0.054), along with HER2 copy number (PFS HR 0.85, p = 0.004; OS HR 0.84, p = 0.049). Furthermore, the correlation between HER2 amplification level by ISH with PFS and OS was consistent across the HER2 IHC 1+/2+ and 3+ categories. CONCLUSIONS: This is the first study to report that a higher level of HER2 amplification by ISH is associated with improved PFS and OS in advanced HER2-positive breast cancer treated with dual HER2-blockade. Notably, HER2 amplification level had a predictive role regardless of IHC results. Even in patients with HER2 protein expression of 3+, treatment outcome to HER2-directed therapy was dependent on the level of HER2 gene amplification.

The prognostic value of a combined immune score in tumor and immune cells assessed by immunohistochemistry in triple-negative breast cancer.

BACKGROUND: This study aimed to develop a novel combined immune score (CIS)-based model assessing prognosis in triple-negative breast cancer (TNBC). METHODS: The expression of eight immune markers (PD-1, PD-L1, PD-L2, IDO, TIM3, OX40, OX40L, and H7-H2) was assessed with immunohistochemistry on the tumor cells (TCs) and immune cells (ICs) of 227 TNBC cases, respectively, and subsequently associated with selected clinicopathological parameters and survival. Data retrieved from The Cancer Genome Atlas (TCGA) were further examined to validate our findings. RESULTS: All immune markers were often expressed in TCs and ICs, except for PD-1 which was not expressed in TCs. In ICs, the expression of all immune markers was positively correlated between one another, except between PD-L1 and OX40, also TIM3 and OX40. In ICs, PD-1, PD-L1, and OX40L positive expression was associated with a longer progression-free survival (PFS; p = 0.040, p = 0.020, and p = 0.020, respectively). In TCs, OX40 positive expression was associated with a shorter PFS (p = 0.025). Subsequently, the TNBC patients were classified into high and low combined immune score groups (CIS-H and CIS-L), based on the expression levels of a selection of biomarkers in TCs (TCIS-H or TCIS-L) and ICs (ICIS-H or ICIS-L). The TCIS-H group was significantly associated with a longer PFS (p < 0.001). Furthermore, the ICIS-H group was additionally associated with a longer PFS (p < 0.001) and overall survival (OS; p = 0.001), at significant levels. In the multivariate analysis, both TCIS-H and ICIS-H groups were identified as independent predictors of favorable PFS (p = 0.012 and p = 0.001, respectively). ICIS-H was also shown to be an independent predictor of favorable OS (p = 0.003). The analysis of the mRNA expression data from TCGA also validated our findings regarding TNBC. CONCLUSION: Our novel TCIS and ICIS exhibited a significant prognostic value in TNBC. Additional research would be needed to strengthen our findings and identify the most efficient prognostic and predictive biomarkers for TNBC patients.

Quantitative proteomics identifies TUBB6 as a biomarker of muscle-invasion and poor prognosis in bladder cancer.

Muscle-invasive urothelial carcinoma (MIUC) of the bladder shows highly aggressive tumor behavior, which has prompted the quest for robust biomarkers predicting invasion. To discover such biomarkers, we first employed high-throughput proteomic method and analyzed tissue biopsy cohorts from patients with bladder urothelial carcinoma (BUC), stratifying them according to their pT stage. Candidate biomarkers were selected through bioinformatic analysis, followed by validation. The latter comprised 2D and 3D invasion and migration assays, also a selection of external public datasets to evaluate mRNA expression and an in-house patient-derived tissue microarray (TMA) cohort to evaluate protein expression with immunohistochemistry (IHC). Our multilayered platform-based analysis identified tubulin beta 6 class V (TUBB6) as a promising prognostic biomarker predicting MIUC of the bladder. The in vitro 2D and 3D migration and invasion assays consistently showed that inhibition of TUBB6 mRNA significantly reduced cell migration and invasion ability in two BUC cell lines with aggressive phenotype (TUBB6 migration, P = .0509 and P < .0001; invasion, P = .0002 and P = .0044; TGFBI migration, P = .0214 and P = .0026; invasion, P < .0001 and P = .0001; T24 and J82, respectively). Validation through multiple public datasets, including The Cancer Genome Atlas (TCGA) and selected GSE (Genomic Spatial Event) databases, confirmed TUBB6 as a potential biomarker predicting MIUC. Further protein-based validation with our TMA cohort revealed concordant results, highlighting the clinical implication of TUBB6 expression in BUC patients (overall survival: P < .001). We propose TUBB6 as a novel IHC biomarker to predict invasion and poor prognosis, also select the optimal treatment in BUC patients.

Proteomics analysis of an individual formalin-fixed paraffin-embedded tissue section using isobaric-tag amplification.

RATIONALE: The comprehensive analysis of formalin-fixed paraffin-embedded (FFPE) tissues is essential for retrospective clinical studies. However, detecting low-abundance proteins and obtaining proteome-scale data from FFPE samples pose analytical challenges in mass spectrometry-based proteomics. To overcome this challenge, our study focuses on implementing an isobaric labeling approach to improve the detection of low-abundance target proteins in FFPE tissues, thereby enhancing the qualitative and quantitative analysis. METHODS: We employed an isobaric labeling approach utilizing synthetic peptides or proteins to enable the qualitative and quantitative measurement of target proteins in FFPE tissue samples. To achieve this, we incorporated tandem mass tag (TMT)-labeled recombinant proteins or synthetic peptides into TMT-labeled metastatic breast cancer FFPE tissues. Through this strategy, we successfully detect coexisting CD276 (B7-H3) and CD147 proteins while identifying over 6000 proteins using targeted analysis of individual FFPE tissue sections. RESULTS: Our findings provide compelling evidence that the incorporation of isobaric labeling, along with the inclusion of TMT-labeled peptides or proteins, greatly enhances the detection of target proteins in FFPE tissue samples. By employing this approach, we were able to obtain robust qualitative measurements of CD276 and CD147 proteins, showcasing its effectiveness in identifying more than 6000 proteins in FFPE samples. CONCLUSIONS: The integration of an isobaric labeling approach, in conjunction with synthetic peptides or proteins, presents a valuable strategy for enhancing the detection and validation of target proteins in FFPE tissue analysis. This technique holds immense potential in retrospective clinical studies, as it enables comprehensive analysis of low-abundance proteins and facilitating proteome-scale investigations in FFPE samples. By leveraging this methodology, researchers can unlock new insights into disease mechanisms and advance our understanding of complex biological processes.

Discrepancies in Hormone Receptor and HER2 Expression between Malignant Serous Effusions and Paired Tissues from Primary or Recurrent Breast Cancers.

OBJECTIVES: Immunohistochemistry (IHC) for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) biomarkers has prognostic and therapeutic value in breast cancer, while it facilitates molecular subtyping. This study aimed to identify subtype discordance and its clinical significance among different phases of breast cancer evolution, focusing on effusion cytology samples diagnosed with malignancy. METHODS: Our electronic archive was searched for all effusion cases diagnosed as breast carcinomas within a pre-defined period (January 2018-October 2021), and their cell blocks (CBs) were subjected to ER, PR, and HER2 IHC or in situ hybridization. Furthermore, information regarding the same biomarkers from previously obtained tissue specimens of these patients was extracted. RESULTS: Only 2/76 (2.6%) of the breast cancer patients analyzed showed a malignant effusion at their initial presentation. The triple negative breast cancer (TNBC) phenotype was found significantly more often at effusion CBs, compared to their paired biopsies received during initial diagnosis (30/70 vs 16/70; p<0.001). In addition, the presence of TNBC subtype was significantly associated with an earlier development of a malignant effusion, more specifically at initial diagnosis (P<0.001; log-rank test), at first recurrence/metastasis (either solid or effusion) (P=0.012; log-rank test), at effusion (P=0.007; log-rank test), and at any tumor evolution phase (P=0.009; log-rank test). CONCLUSION: Serous effusion cytology provides high-quality material for ancillary techniques, especially when CBs are prepared, reflecting cancer heterogeneity.

Prognostic role of tumor subtype and germline BRCA mutation in advanced breast cancer patients treated with palbociclib plus endocrine therapy.

PURPOSE: Palbociclib is a cyclin-dependent kinase 4 and 6 inhibitor which shows promising effect in hormone receptor-positive breast cancer. The purpose of this study is to evaluate the real-world efficacy and toxicity of palbociclib plus endocrine therapy. METHODS: This is a retrospective study performed in two tertiary referral hospitals in Korea. Advanced breast cancer patients who were treated with 1st-line palbociclib plus endocrine therapy were enrolled. RESULTS: A total of 216 patients were included between August 2016 and May 2019. Median age was 56 (29-89) years old and 75 patients (34.7%) were premenopausal. Median progression-free survival (PFS) was 33.0 months [95% confidence interval (CI) 24.7 to 41.3] and objective response rate was 59.3%. Luminal B patients had shorter PFS (33.0 months vs. Not reached, p = 0.019) and tendency of lower ORR (58.3 vs. 62.0%, p = 0.19) compared to luminal A patients. Multivariate analysis revealed luminal B (adjusted hazard ratio 1.90, p = 0.038) and germline BRCA mutation (adjusted hazard ratio 5.57, p = 0.002) as an independent poor prognostic factor for PFS. The most common grade 3 or 4 adverse event was neutropenia (86.7%). CONCLUSION: The efficacy and toxicity of palbociclib in the real world were comparable to those of clinical trials. In addition, palbociclib with endocrine therapy was an effective treatment option for young patients. Luminal B and germline BRCA mutation were associated with inferior outcome.

Diffusion-weighted Breast MRI in Prediction of Upstaging in Women with Biopsy-proven Ductal Carcinoma in Situ.

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue. An earlier incorrect version appeared online. This article was corrected on July 7, 2022.

HER2 expression, copy number variation and survival outcomes in HER2-low non-metastatic breast cancer: an international multicentre cohort study and TCGA-METABRIC analysis.

BACKGROUND: HER2-low breast cancer (BC) is currently an area of active interest. This study evaluated the impact of low expression of HER2 on survival outcomes in HER2-negative non-metastatic breast cancer (BC). METHODS: Patients with HER2-negative non-metastatic BC from 6 centres within the Asian Breast Cancer Cooperative Group (ABCCG) (n = 28,280) were analysed. HER2-low was defined as immunohistochemistry (IHC) 1+ or 2+ and in situ hybridization non-amplified (ISH-) and HER2-zero as IHC 0. Relapse-free survival (RFS) and overall survival (OS) by hormone receptor status and HER2 IHC 0, 1+ and 2+ ISH- status were the main outcomes. A combined TCGA-BRCA and METABRIC cohort (n = 1967) was also analysed to explore the association between HER2 expression, ERBB2 copy number variation (CNV) status and RFS. RESULTS: ABCCG cohort median follow-up was 6.6 years; there were 12,260 (43.4%) HER2-low BC and 16,020 (56.6%) HER2-zero BC. The outcomes were better in HER2-low BC than in HER2-zero BC (RFS: centre-adjusted hazard ratio (HR) 0.88, 95% CI 0.82-0.93, P < 0.001; OS: centre-adjusted HR 0.82, 95% CI 0.76-0.89, P < 0.001). On multivariable analysis, HER2-low status was prognostic (RFS: HR 0.90, 95% CI 0.85-0.96, P = 0.002; OS: HR 0.86, 95% CI 0.79-0.93, P < 0.001). These differences remained significant in hormone receptor-positive tumours and for OS in hormone receptor-negative tumours. Superior outcomes were observed for HER2 IHC1+ BC versus HER2-zero BC (RFS: HR 0.89, 95% CI 0.83-0.96, P = 0.001; OS: HR 0.85, 95% CI 0.78-0.93, P = 0.001). No significant differences were seen between HER2 IHC2+ ISH- and HER2-zero BCs. In the TCGA-BRCA and METABRIC cohorts, ERBB2 CNV status was an independent RFS prognostic factor (neutral versus non-neutral HR 0.71, 95% CI 0.59-0.86, P < 0.001); no differences in RFS by ERBB2 mRNA expression levels were found. CONCLUSIONS: HER2-low BC had a superior prognosis compared to HER2-zero BC in the non-metastatic setting, though absolute differences were modest and driven by HER2 IHC 1+ BC. ERBB2 CNV merits further investigation in HER2-negative BC.

Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study.

Accurate diagnosis and grading of needle biopsies are crucial for prostate cancer management. A uropathologist-level artificial intelligence (AI) system could help make unbiased decisions and improve pathologists' efficiency. We previously reported an artificial neural network-based, automated, diagnostic software for prostate biopsy, DeepDx® Prostate (DeepDx). Using an independent external dataset, we aimed to validate the performance of DeepDx at the levels of prostate cancer diagnosis and grading and evaluate its potential value to the general pathologist. A dataset composed of 593 whole-slide images of prostate biopsies (130 normal and 463 adenocarcinomas) was assembled, including their original pathology reports. The Gleason scores (GSs) and grade groups (GGs) determined by three uropathology experts were considered as the reference standard. A general pathologist conducted user validation by scoring the dataset with and without AI assistance. DeepDx was accurate for prostate cancer detection at a similar level to the original pathology report, whereas it was more concordant than the latter with the reference GGs and GSs (kappa/quadratic-weighted kappa = 0.713/0.922 vs. 0.619/0.873 for GGs and 0.654/0.904 vs. 0.576/0.858 for GSs). Notably, it outperformed the original report, especially in the detection of Gleason patterns 4/5, and achieved excellent agreement in quantifying the Gleason pattern 4. When the general pathologist used AI assistance, the concordance of GG between the user and the reference standard increased (kappa/quadratic-weighted kappa, 0.621/0.876 to 0.741/0.925), while the average slide examination time was substantially decreased (55.7 to 36.8 s/case). Overall, DeepDx was capable of making expert-level diagnosis in prostate core biopsies. In addition, its remarkable performance in detecting high-grade Gleason patterns and enhancing the general pathologist's diagnostic performance supports its potential value in routine practice.

Dual Oxidase 2 (DUOX2) as a Proteomic Biomarker for Predicting Treatment Response to Chemoradiation Therapy for Locally Advanced Rectal Cancer: Using High-Throughput Proteomic Analysis and Machine Learning Algorithm.

High-throughput mass-spectrometry-based quantitative proteomic analysis was performed using formalin-fixed, paraffin-embedded (FFPE) biopsy samples obtained before treatment from 13 patients with locally advanced rectal cancer (LARC), who were treated with concurrent chemoradiation therapy (CCRT) followed by surgery. Patients were divided into complete responder (CR) and non-complete responder (nCR) groups. Immunohistochemical (IHC) staining of 79 independent FFPE tissue samples was performed to validate the predictive ability of proteomic biomarker candidates. A total of 3637 proteins were identified, and the expression of 498 proteins was confirmed at significantly different levels (differentially expressed proteins-DEPs) between two groups. In Gene Ontology enrichment analyses, DEPs enriched in biological processes in the CR group included proteins linked to cytoskeletal organization, immune response processes, and vesicle-associated protein transport processes, whereas DEPs in the nCR group were associated with biosynthesis, transcription, and translation processes. Dual oxidase 2 (DUOX2) was selected as the most predictive biomarker in machine learning algorithm analysis. Further IHC validation ultimately confirmed DUOX2 as a potential biomarker for predicting the response of nCR to CCRT. In conclusion, this study suggests that the treatment response to RT may be affected by the pre-treatment tumor microenvironment. DUOX2 is a potential biomarker for the early prediction of nCR after CCRT.

Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis.

Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer.

Quantitative Proteomics Reveals Knockdown of CD44 Promotes Proliferation and Migration in Claudin-Low MDA-MB-231 and Hs 578T Breast Cancer Cell Lines.

CD44 is a transmembrane glycoprotein that can regulate the oncogenic process. This is known to be a marker of the claudin-low subtype of breast cancer, as well as a cancer stem cell marker. However, its functional regulatory roles are poorly understood in claudin-low breast cancer. To gain comprehensive insight into the function of CD44, we performed an in-depth tandem mass tag-based proteomic analysis of two claudin-low breast cancer cell lines (MDA-MB-231 and Hs 578T) transfected with CD44 siRNA. As a result, we observed that 2736 proteins were upregulated and 2172 proteins were downregulated in CD44-knockdown MDA-MB-231 cells. For Hs 578T CD44-knockdown cells, 412 proteins were upregulated and 443 were downregulated. Gene ontology and network analyses demonstrated that the suppression of this marker mediates significant functional alterations related to oncogenic cellular processes, including proliferation, metabolism, adhesion, and gene expression regulation. A functional study confirmed that CD44 knockdown inhibited proliferation by regulating the expression of genes related to cell cycle, translation, and transcription. Moreover, this promoted the expression of multiple cell adhesion-associated proteins and attenuated cancer cell migration. Finally, our proteomic study defines the landscape of the CD44-regulated proteome of claudin-low breast cancer cells, revealing changes that mediate cell proliferation and migration. Our proteomics data set has been deposited to the ProteomeXchange Consortium via the PRIDE repository with the data set identifier PXD015171.

Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US.

Background Breast density at mammography is an established risk factor for breast cancer, but it cannot be used to distinguish between glandular and fibrous tissue. Purpose To evaluate the association between the glandular tissue component (GTC) at screening breast US and the risk of future breast cancer in women with dense breasts and the association between the GTC and lobular involution. Materials and Methods Screening breast US examinations performed in women with no prior history of breast cancer and with dense breasts with negative findings from mammography from January 2012 to December 2015 were retrospectively identified. The GTC was reported as being minimal, mild, moderate, or marked at the time of the US examination. In women who had benign breast biopsy results, the degree of lobular involution in normal background tissue was categorized as not present, mild, moderate, or complete. The GTC-related breast cancer risk in women with a cancer diagnosis or follow-up after 6 months was estimated by using Cox proportional hazards regression. Cumulative logistic regression was used to evaluate the association between the GTC and lobular involution. Results Among 8483 women (mean age, 49 years ± 8 [standard deviation]), 137 developed breast cancer over a median follow-up time of 5.3 years. Compared with a minimal or mild GTC, a moderate or marked GTC was associated with an increased cancer risk (hazard ratio, 1.5; 95% CI: 1.05, 2.1; P = .03) after adjusting for age and breast density. The GTC had an inverse association with lobular involution; women with no, mild, or moderate involution had greater odds (odds ratios of 4.9 [95% CI: 1.5, 16.6], 2.6 [95% CI: 0.95, 7.2], and 1.8 [95% CI: 0.7, 4.6], respectively) of a moderate or marked GTC than those with complete involution (P = .004). Conclusion The glandular tissue component was independently associated with the future breast cancer risk in women with dense breasts and reflects the lobular involution. It should be considered for risk stratification during screening breast US. © RSNA, 2021 Online supplemental material is available for this article.

Genomic profile of metastatic breast cancer patient-derived xenografts established using percutaneous biopsy.

BACKGROUND: Metastatic breast cancer (mBC) is a complex and life-threatening disease and although it is difficult to cure, patients can benefit from sequential anticancer treatment, including endocrine therapy, targeted therapy and cytotoxic chemotherapy. The patient-derived xenograft (PDX) model is suggested as a practical tool to predict the clinical outcome of this disease as well as to screen novel drugs. This study aimed to establish PDX models in Korean patients and analyze their genomic profiles and utility for translational research. METHODS: Percutaneous core needle biopsy or punch biopsy samples were used for xenotransplantation. Whole exome sequencing and transcriptome analysis were performed to assess the genomic and RNA expression profiles, respectively. Copy number variation and mutational burden were analyzed and compared with other metastatic breast cancer genomic results. Mutational signatures were also analyzed. The antitumor effect of an ATR inhibitor was tested in the relevant PDX model. RESULTS: Of the 151 cases studied, 40 (26%) PDX models were established. Notably, the take rate of all subtypes, including the hormone receptor-positive (HR +) subtype, exceeded 20%. The PDX model had genomic fidelity and copy number variation that represented the pattern of its donor sample. TP53, PIK3CA, ESR1, and GATA3 mutations were frequently found in our samples, with TP53 being the most frequently mutated, and the somatic mutations in these genes strengthened their frequency in the PDX model. The ESR1 mutation, CCND1 amplification, and the APOBEC signature were significant features in our HR + HER2- PDX model. Fulvestrant in combination with palbociclib showed a partial response to the relevant patient's tumor harboring the ESR1 mutation, and CCND1 amplification was found in the PDX model. AZD6738, an ATR inhibitor, delayed tumor growth in a relevant PDX model. CONCLUSIONS: Our PDX model was established using core needle biopsy samples from primary and metastatic tissues. Genomic profiles of the samples reflected their original tissue characteristics and could be used for the interpretation of clinical outcomes.

Parallel Reaction Monitoring-Mass Spectrometry (PRM-MS)-Based Targeted Proteomic Surrogates for Intrinsic Subtypes in Breast Cancer: Comparative Analysis with Immunohistochemical Phenotypes.

Advances in targeted medications have improved the survival rate of breast cancer patients with molecular marker-positive tumors. To date, immunohistochemistry (IHC) has remained as the standard method for quantifying the markers including HER2, ER, and PR. Nevertheless, IHC-based grading is subjective, because the results depend on trained individuals' eye rather than numerical quantities. Thus, alternative methods that can account for quantitative levels of markers are gaining popularity, including targeted proteomics by mass spectrometry (MS). However, technical limitations have impeded the application of MS-based protein quantification to pathological FFPE slides that contain low amounts of cross-linked proteins. To challenge this, we developed a parallel reaction monitoring-mass spectrometry (PRM-MS) method to measure the expression levels of breast cancer markers. After developing the method using cell lines, we performed PRM-MS using 51 individuals' FFPE samples. As a result, we obtained numerical measures of targets, quantifying 13 peptides of 4 markers in a single analysis per sample. The results correlated well with the IHC readings of experienced pathologists. Moreover, the results distinguished a gray zone in HER2 classification, which IHC alone failed to do. This proof-of-concept study demonstrates the application of targeted proteomics in pathologic slides, further supporting the applicability of MS-based approaches in precision medicine.

Combined the SMAC mimetic and BCL2 inhibitor sensitizes neoadjuvant chemotherapy by targeting necrosome complexes in tyrosine aminoacyl-tRNA synthase-positive breast cancer.

BACKGROUND: Chemotherapy is the standard treatment for breast cancer; however, the response to chemotherapy is disappointingly low. Here, we investigated the alternative therapeutic efficacy of novel combination treatment with necroptosis-inducing small molecules to overcome chemotherapeutic resistance in tyrosine aminoacyl-tRNA synthetase (YARS)-positive breast cancer. METHODS: Pre-chemotherapeutic needle biopsy of 143 invasive ductal carcinomas undergoing the same chemotherapeutic regimen was subjected to proteomic analysis. Four different machine learning algorithms were employed to determine signature protein combinations. Immunoreactive markers were selected using three common candidate proteins from the machine-learning algorithms and verified by immunohistochemistry using 123 cases of independent needle biopsy FFPE samples. The regulation of chemotherapeutic response and necroptotic cell death was assessed using lentiviral YARS overexpression and depletion 3D spheroid formation assay, viability assays, LDH release assay, flow cytometry analysis, and transmission electron microscopy. The ROS-induced metabolic dysregulation and phosphorylation of necrosome complex by YARS were assessed using oxygen consumption rate analysis, flow cytometry analysis, and 3D cell viability assay. The therapeutic roles of SMAC mimetics (LCL161) and a pan-BCL2 inhibitor (ABT-263) were determined by 3D cell viability assay and flow cytometry analysis. Additional biologic process and protein-protein interaction pathway analysis were performed using Gene Ontology annotation and Cytoscape databases. RESULTS: YARS was selected as a potential biomarker by proteomics-based machine-learning algorithms and was exclusively associated with good response to chemotherapy by subsequent immunohistochemical validation. In 3D spheroid models of breast cancer cell lines, YARS overexpression significantly improved chemotherapy response via phosphorylation of the necrosome complex. YARS-induced necroptosis sequentially mediated mitochondrial dysfunction through the overproduction of ROS in breast cancer cell lines. Combination treatment with necroptosis-inducing small molecules, including a SMAC mimetic (LCL161) and a pan-BCL2 inhibitor (ABT-263), showed therapeutic efficacy in YARS-overexpressing breast cancer cells. CONCLUSIONS: Our results indicate that, before chemotherapy, an initial screening of YARS protein expression should be performed, and YARS-positive breast cancer patients might consider the combined treatment with LCL161 and ABT-263; this could be a novel stepwise clinical approach to apply new targeted therapy in breast cancer patients in the future.

Prediction of pathologic complete response using image-guided biopsy after neoadjuvant chemotherapy in breast cancer patients selected based on MRI findings: a prospective feasibility trial.

PURPOSE: Accurate prediction of pathologic complete response (pCR) in breast cancer using magnetic resonance imaging (MRI) and ultrasound (US)-guided biopsy may aid in selecting patients who forego surgery for breast cancer. We evaluated the accuracy of US-guided biopsy aided by MRI in predicting pCR in the breast after neoadjuvant chemotherapy (NAC). METHODS: After completion of NAC, 40 patients with near pCR (either tumor size ≤ 0.5 cm or lesion-to-background signal enhancement ratio (L-to-B SER) ≤ 1.6 on MRI) and no diffused residual microcalcifications were prospectively enrolled at a single institution. US-guided multiple core needle biopsy (CNB) or vacuum-assisted biopsy (VAB) of the tumor bed, followed by standard surgical excision, was performed. Matched biopsy and surgical specimens were compared to assess pCR. The negative predictive value (NPV), accuracy, and false-negative rate (FNR) were analyzed. RESULTS: pCR was confirmed in 27 (67.5%) surgical specimens. Preoperative biopsy had an NPV, accuracy, and FNR of 87.1%, 90.0%, and 30.8%, respectively. NPV for hormone receptor-negative and hormone receptor-positive tumors were 83.3% and 100%, respectively. Obtaining at least 5 biopsy cores based on tumor size ≤ 0.5 cm and an L-to-B SER of ≤ 1.6 on MRI (27 patients) resulted in 100% NPV and accuracy. No differences in accuracy were noted between CNB and VAB (90% vs. 90%). CONCLUSIONS: Investigation using stringent MRI criteria and ultrasound-guided biopsy could accurately predict patients with pCR after NAC. A larger prospective clinical trial evaluating the clinical safety of breast surgery omission after NAC in selected patients will be conducted based on these findings.