Invasive Breast Cancer Patients Research Articles

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes(TILs). We developed and evaluated our method on slides from n=721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n=126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC). In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs). Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set. ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer

BackgroundPreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. Materials and MethodsUsing hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. ResultsAnalytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. ConclusionIn summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.

High pathological grade might discourage early invasive breast cancer patients who have a pathologic complete response to neoadjuvant systemic therapy from eliminating breast surgery.

High pathological grade might discourage early invasive breast cancer patients who have a pathologic complete response to neoadjuvant systemic therapy from eliminating breast surgery.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study

BackgroundSeveral studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.MethodsIn this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.ResultsThe RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.ConclusionsThis study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Analysis of Turkish Breast Cancer Patients With ATM-Heterozygous Germline Mutation According to Clinicopathological Features.

The ATM gene is one of the most common breast cancer (BC) susceptibility genes after BRCA1/2 and has been shown to be a moderate BC susceptibility gene. The association between ATM germline mutation and clinical features of BC is now unknown. In this article, clinicopathological features of BC patients with ATM germline heterozygous mutation were investigated. Patients admitted to the Medical Genetics departmentof a tertiary hospital between January 2020 and December 2022 were examined. Only invasive BC patients with pathogenic mutation, likely pathogenic mutation, or variants of uncertain significance (VUS) were included in the study. In all, 121 patients were included in the study. The median age at the first cancer diagnosis of the patients was 44 years. Of the total number of patients, 75.2% (91) had the histological subtype of infiltrating ductal carcinoma, and 43% (52) had Luminal B molecular subtype features. At a median follow-up of 16 months, 5.8% (7) of patients developed cancer in the contralateral breast. In addition, 7.4% (9) of the patients developed a second primary cancer during follow-up. When the patients were compared according to ATM variant classification, the localization, histologic types, and molecular subtypes of the BC were not different between all groups (respectively; p=0.68, p=0.65, p=0.32). To the best of our knowledge, this is the first publication that evaluates the clinical and pathological characteristics of BC patients with germline heterozygous ATM mutations in the Turkish population. When patients were compared according to variant classifications of ATM mutation, patients' histological and molecular subtypes were similar.

Delayed Nipple-Areola Complex Radiotherapy after Nipple Sparing Mastectomy and Immediate Reconstruction for Invasive Breast Cancer or DCIS: Long-Term Results of a Phase I Study

To report long-term outcomes of a novel nipple-sparing approach in patients with DCIS or early-stage breast cancer that would not be eligible for nipple sparing mastectomy due to multifocality/multicentricity or tumor less than 2 cm from the nipple-areola complex (NAC). We conducted a phase I pilot study approved by Institutional IRB. Eligible patients had a biopsy confirming DCIS or invasive breast cancer, and a surgical recommendation for mastectomy due to extent of disease, with tumor at least 1 cm from the NAC. Nipple sparing mastectomy (NSM) was performed after negative shave biopsy behind the nipple was obtained at time of surgery, followed by immediate reconstruction per plastic surgeon's preference. NAC RT was administered 5 to 8 weeks postoperative. A 3+3 dose-escalation design was used to test RT doses of 25, 30, 35 Gy in 10 fractions, given BID, 6 hours apart with electrons. Dose-limiting toxicity (DLT) was specified as RT-related NAC loss or any grade 4/5 AE up to 1-month from end of RT. Adverse events were assessed using CTCAE. v 4.0. Patients completed cosmesis and satisfaction assessment at standard follow up visits. From 2010-2017, 18 women were treated on the study, 6 per dose level. Median age was 45.5 (range 30-63). 100% of patients were White, of those 44.5% were Hispanic. 17% had DCIS and 83% had invasive breast cancer (all ER+, 14 pStage T1N0, 1 pStage T1N1mi). Endocrine therapy was given to all ER+ invasive breast cancer patients. With a median F/U of 80.5 months, there was no RT-related DLT or grade 4/5 AEs (73 G1, 15 G2, 6 G3). All grade 3 AEs were related to the surgery (infection, pain, allergy to tape). There was no local recurrence in the NAC, with 100% of nipple preservation with longer follow up. Two patients developed metastases 2 years after diagnosis (bone, liver). 1 DCIS patient developed a second ipsilateral breast cancer away from the NAC, later found to have BRCA2+ mutation. Patient reported cosmesis at last visit was 72% excellent, 16.5% good, and 5.5% poor. Physicians score worse cosmesis compared to patients with 39% scored excellent and 61% good. Patient reported satisfaction with nipple sparing treatment was high, with 94% of patients choosing this treatment again. This novel approach of nipple preserving therapy using delayed adjuvant NAC RT after nipple sparing mastectomy, resulted in 100% nipple preservation without compromise of local control, in this specific patient population not eligible for nipple sparing mastectomy alone. This study supports further exploration of nipple preservation with a broader inclusion criterion.

A Biosignature Integrating Immune and Metabolic Signaling Axes to Assess Limited Radiation Therapy Response in Early-Stage Breast Cancer from a Low-Risk Cohort

Radiation therapy (RT) post breast conserving surgery (BCS) reduces in-breast recurrence (IBR) rate in early-stage invasive breast cancer (BC) patients. The RT treatment recommendation is often driven by clinicopathological (CP) factors; however, CP factors alone have limited ability to identify which women significantly benefit from RT, or those with higher IBR risk after BCS plus RT. Biologic factors driving unique phenotypes, in addition to CP, may improve prediction of RT response. In this study we evaluated the role of immune and metabolic signaling axes in predicting RT response in hormone receptor positive, HER2 negative, early-stage BC patients. Biomarkers from immune and metabolic signaling axes were studied in a cohort of 939 women from Sweden, at a CLIA certified lab (Laguna Hills, CA). Formalin fixed paraffin embedded tissues were assayed for protein expression using multiplex immunofluorescence and multi-spectral imaging. Immune and metabolic axes were assessed using biomarkers combined with a non-linear model, adjusting for patient age. RT prediction by the model was assessed, along and adjusted for CP factors and also among patients over 50-yrs. The model defined patient risk groups that were analyzed for IBR rate using Kaplan Meier analyses and Cox proportional hazards to test for RT-risk group interaction. Within the cohort, 440 patients had hormone receptor positive, HER2 negative BC treated with BCS (negative margins) and +/- RT without chemotherapy, where 296 patients had complete biomarker data. CP factors individually were not predictive for RT benefit, but grade was prognostic for IBR rate (p = 0.02) after BCS without RT. In multivariable analysis, adjusting for CP factors (grade, palpability, continuous size and age), the model was predictive for RT benefit (p-interaction = 0.046), identifying patients (n = 129) with worse RT benefit (HR = 7.8) compared to baseline RT benefit. The model was not prognostic for IBR rate in patients treated with BCS without RT (16% 10-yr IBR rate) but identified patients with increased IBR rates after BCS plus RT (HR = 3.9, p<0.001), where corresponding 10-yr IBR rates increased from 3% to 15%. The model was also predictive for RT benefit in women over 50-yrs (p-interaction = 0.05). The model identified 28% of women over 50-yrs who had increased IBR rates after BCS plus RT (HR = 4.0, p = 0.004), where corresponding 10-yr IBR rates increased from 3% to 12%. The model incorporating metabolic and immune signaling axes assessed in the study was predictive for RT benefit among women with early-stage hormone receptor positive, HER2 negative BC. While CP factors were not predictive of RT benefit, the inclusion of metabolic and immune signaling axes improved identification of patients with high residual risk after BCS plus RT and can potentially aid in personalized treatment of early-stage breast cancer based on individualized risk.

Utility of Stereotactic Body Radiation Therapy (SBRT) in Establishing Local Control for Invasive Breast Cancer Patients Not Undergoing Definitive Surgery

Breast cancer (BC) treatment involves a multi-modal approach where surgical resection is the backbone of therapy. However, due to medical comorbidities precluding surgery, unresectable disease, or patient preference, some patients do not undergo surgery. In the absence of resection, it is paramount to improve local control (LC) of the primary tumor. To that end, this study explored the role of ablative RT dose delivered with SBRT. Between 2015-2022, 21 patients (10 metastatic, 11 stage IA-IIIC) received 23 SBRT courses to primary BC lesions without planned surgery at our institution. One patient had undergone whole breast RT 20 years prior to SBRT. 7 patients were analyzed retrospectively; 15 are currently enrolled in a prospective study. SBRT dose was 40 Gy/5 fractions delivered every other day. Follow-up imaging (PET, CT, MRI, MMG/US) was reviewed to evaluate the response. CTCAE v.3 was used to document any acute (≤3 months) and late toxicities. Kaplan-Meier curves were used to estimate local control (LC) and overall survival (OS). Median age was 78.4 (45.9-97.3) years. Median follow-up was 14.7 (3.3-70.3) months after RT completion. Initial post-treatment imaging was available for all cases and was performed at a median of 4.0 (0.6-11.9) months after SBRT. The median pre-SBRT index lesion size was 3.1 (0.5-14.5) cm and PTV was 32.4 (11.5-522.4) cc. At a median 4 months post-SBRT, the median decrease in index lesion size was 20.8 (0-100) % and SUV reduction was 65.2 (20.8-100) %. Further improvement was seen on the 2nd follow-up scans at a median 7.8 months post-SBRT, with 62 (0-100) % and 88 (33.3-100) % median reduction in the tumor size and SUV, respectively, compared to pre-SBRT values. The estimated LC rate was 100% at 6 months and 93.3% at 12 months. The sole case of local progression occurred 9.5 months after SBRT, after an initial interval decrease in the primary lesion. Among the patients with available systemic follow-up scans, regional progression occurred in 4 (17.4%) cases at a median 18.6 (5.2-22.7) months post-SBRT. Six (37.5%) patients developed distant progression at a median 4.8 (0.9-16.2) months, all of whom had metastatic disease at diagnosis. The estimated OS was 85.7% at 6 months and 63.8% at 12 months. Most patients tolerated the treatment well. There was no grade 4 toxicity. Complete acute toxicity data is presented in Table 1. Only one patient experienced late grade 2 breast edema. Based on our preliminary results, definitive SBRT to the primary BC appears to have a role in providing LC in the treatment of non-surgical patients and is well-tolerated. Since the disease progression tends to occur outside the treated primary tumor, additional approaches to improve regional and distant control should be investigated.

Discordance of Oncotype DX scores in synchronous bilateral and unilateral multifocal breast cancers.

Oncotype DX, a 21-gene expression profiling test, has become standard of care in the management of estrogen receptor (ER)-positive breast cancer. In multifocal tumors, it is unclear whether testing of the different foci is necessary. We evaluated the concordance of Oncotype DX recurrence scores (RS) between 2 tumor foci in synchronous bilateral or unilateral multifocal tumors and characterized pathological predictors of discordance. We reviewed 713 ER+, HER2- primary invasive breast cancer patients with Oncotype RS and identified 17 bilateral synchronous patients (34 tumors) and 13 unilateral multifocal patients (26 tumors) with available Oncotype RS on all foci. Discordance in Oncotype RS between synchronous tumors was recorded and associations with clinicopathologic features including tumor size, histology, Nottingham histologic grade, progesterone receptor staining, and Ki67 index were analyzed. Bilateral synchronous tumors were present in older patients (median age 59years) and had larger tumor (median size 17mm) and more discordant histology (10/17, 59%) as compared to unilateral multifocal tumors (median age 49years, p < 0.01; median tumor size 12mm, p = 0.01; discordant histology 2/13, 15%, p = 0.03). Oncotype RS were discordant in 47% (8/17) of bilateral and 54% (7/13) of unilateral multifocal tumors. Concordant Oncotype RS was associated with similar histologic grade and Ki67 index in 78% (7/9) of bilateral and 100% (6/6) of multifocal tumors. In contrast, only 25% (2/8) of bilateral (p = 0.06) and 14% (1/7) of unilateral multifocal (p < 0.01) cases with discordant Oncotype RS had concordant histology grades and Ki67 levels. In synchronous tumors with discordant Oncotype RS and Ki67 index, all (4/4) foci with higher RS had higher Ki67 index. Discordance of Oncotype RS is common in both bilateral and unilateral multifocal breast cancer and is likely associated with discordant histologic grade or Ki67.

Enhancing the invasive traits of breast cancers by CYP1B1 via regulation of p53 to promote uPAR expression

Human cytochrome P450 1B1 (CYP1B1) catalyzes estrogen metabolism to produce metabolites that promote the progression of breast cancer. Since the invasive properties of cancer cells cause cancer relapse, which dramatically reduces patient survival, we investigated the new pro-invasive mechanism involving CYP1B1 in breast cancer. Exploring clinical data from invasive breast cancer patients revealed that CYP1B1 exhibits a potential correlation with urokinase-type plasminogen activator receptor (uPAR). Interestingly, uPAR mRNA expression was elevated in invasive breast cancer patients carrying TP53 genes with driver mutations, and our results showed that CYP1B1 activates the uPAR pathway following regulation of p53 according to its mutant status. CYP1B1 suppressed wild-type (WT) p53 whereas it induced the oncogenic gain-of-function mutant p53R280K, not only via transcriptional regulation but also the protein stabilization and activation following phosphorylation on Ser15 residue of p53R280K. Intriguingly, results from CYP1B1 polymorphic gene study and 4-hydroxyestradiol (4-OHE2) treatment showed that CYP1B1 regulates p53s and uPAR through its enzymatic activity. Furthermore, effects of DMBA and TMS on uPAR expression disappeared in HCT116p53−/− cells, indicating that p53 is critical for uPAR induction by CYP1B1. Collectively, our results demonstrate that CYP1B1 may reduce the relapse-free survival rate of breast cancer patients by inducing invasive traits in cancer cells via p53 regulation based on the mutation status of TP53 genes and further activation of the uPAR pathway. The elucidation of the previously unknown molecular mechanism of CYP1B1 may provide evidence for the development of effective anti-cancer therapeutic strategies that target the progression of cancer invasion.

CD105 expression in cancer-associated fibroblasts: a biomarker for bone metastasis in early invasive ductal breast cancer patients.

Introduction: Bone metastasis is one of the causes that mainly decrease survival in patients with advanced breast cancer. Therefore, it is essential to find prognostic markers for the occurrence of this type of metastasis during the early stage of the disease. Currently, cancer-associated fibroblasts, which represent 80% of the fibroblasts present in the tumor microenvironment, are an interesting target for studying new biomarkers and developing alternative therapies. This study evaluated the prognostic significance of the CD105 expression in cancer-associated fibroblasts in early breast cancer patients. Methods: Immunohistochemistry was used to assess CD105 expression in invasive ductal breast carcinomas (n = 342), analyzing its association with clinical and pathological characteristics. Results: High CD105 expression in cancer-associated fibroblasts was associated with an increased risk of metastatic occurrence (p = 0.0003), particularly bone metastasis (p = 0.0005). Furthermore, high CD105 expression was associated with shorter metastasis-free survival, bone metastasis-free survival, and overall survival (p = 0.0002, 0.0006, and 0.0002, respectively). CD105 expression also constituted an independent prognostic factor for metastasis-free survival, bone metastasis-free survival, and overall survival (p = 0.0003, 0.0006, and 0.0001, respectively). Discussion: The high CD105 expression in cancer-associated fibroblasts is an independent prognostic marker for bone metastasis in early breast cancer patients. Therefore, the evaluation of CD105(+) CAFs could be crucial to stratify BCPs based on their individual risk profile for the development of BM, enhancing treatment strategies and outcomes.

The Ongoing Necessity of Sentinel Lymph Node Biopsy for cT1–2N0 Breast Cancer Patients

Background: Recent clinical trials attempt to determine whether it is appropriate to omit axillary lymph node surgery in patients with cT1–2N0 breast cancer. The study aimed to investigate the true extent of axillary node disease in patients with clinically negative nodes and explore the differences between negative axillary ultrasound (AUS-cN0) and suspicious axillary ultrasound with negative fine-needle aspiration (FNA-cN0). Methods: Pathologically identified T1–2 invasive breast cancer patients with clinically negative nodes were retrospectively analyzed at our center between January 2019 and December 2022. Patients who received any systematic treatment before surgery were excluded from this study. Results: A total of 538 patients were enrolled in this study. 134 (24.9%) patients had pathologically positive nodes, and 404 (75.1%) patients had negative nodes. Univariate analysis revealed that tumor size, T stage, Ki67 level, and vascular invasion (VI) were strongly associated with pathological axillary lymph node positivity. In multivariate analysis, VI was the only independent risk factor for node positivity in patients with cT1–2N0 disease (OR: 3.723, confidence interval [CI]: 2.380–5.824, p < 0.001). Otherwise, pathological node positivity was not significantly different between AUS-cN0 and FNA-cN0 groups (23.4% vs. 28.8%, p = 0.193). However, the rate of high nodal burden (≥3 positive nodes) was significantly higher in FNA-cN0 group. Further investigation revealed that FNA-cN0 and VI were independently associated with a high nodal burden (OR: 2.650, CI: 1.081–6.496, p = 0.033; OR: 3.521, CI: 1.249–9.931, p = 0.017, respectively). Conclusions: cT1–2 breast cancer patients with clinically negative axillary lymph nodes may have pathologically positive lymph nodes and even a high nodal burden. False negatives in AUS and AUS-guided FNA should not be ignored, and sentinel lymph node biopsy remains an ongoing necessity for cT1–2N0 breast cancer patients.

Validation and Clinical Utility of a Prediction Model for the Risk of Upstaging to Invasive Breast Cancer After a Biopsy Diagnosis Ductal Carcinoma In Situ

BackgroundThis study aimed to validate the DCIS-upstage model, a previously developed model to predict the risk of upstaging to invasive breast cancer in patients with biopsy-proven ductal carcinoma in situ (DCIS) in a more recent cohort and to assess the model’s clinical utility.MethodsThe model was validated in a registry cohort (n = 2269) and in an institution cohort (n = 302). A calibration plot was made, followed by a decision curve analysis (DCA). The model’s area under the curve (AUC) was compared with the AUC of another published model and with the AUCs of new models using the risk factors of the DCIS-upstage model and additional risk factors.ResultsThe DCIS-upstage model had an AUC of 0.67 at development; in the validation, the AUC was 0.65 in the registry cohort and 0.73 in the institution cohort. The DCA showed that the model has clinical utility. The other published model had an AUC of 0.66 in the institution cohort. Adding risk factors to the DCIS-upstage model slightly increased the AUC.ConclusionsThe DCIS-upstage prediction model is valid in other cohorts. The model has clinical utility and may be used to select patients with biopsy-proven DCIS for sentinel lymph node biopsy.

Clinicopathological characteristics of HER2-low breast cancer: a retrospective study

Human Epidermal Growth Factor Receptor-2 (HER2)-negative breast cancers (BCs) contain HER2-low and HER2-zero ones. HER2-low breast cancer has been receiving wide-spread concerns as the marvelous effect of novel anti-HER2 antibody-drug conjugates, however, the characteristic remains unknown. Our aim was to explore the differences of clinicopathological indicators and survival outcomes between HER2-low and HER2-0 breast cancers. We retrospectively analyzed 501 invasive breast cancer patients with complete data on HER2 status from 2017 to 2021 in our single center, of whom 415 HER2 negative patients were included for subsequent analysis. Each cohort was further divided into hormone receptor (HR) positive and HR negative subgroup. Clinicopathological factors and survival outcomes were collected and compared between HER2-low BCs and HER2-0 BCs. HER2-low BCs was obviously higher in HR positive BCs, with 277 (90.5%) HER2-low HR positive patients, 29 (9.5%) HER2-low HR negative patients, 68 (62.4%) HER2-0 HR positive patients and 41 (37.6%) HER2-0 HR negative patients (P < 0.001). Significant differences between HER2-low BCs and Her2-0 BCs were observed in lymph node ratio (LNR) (mean rank, 215 vs. 188 P = 0.014), estrogen receptor (ER)expression (90.5% vs. 62.4% P < 0.001), progesterone receptor (PR) expression (84.3% vs. 56.9% P < 0.001), Ki-67 expression (46.4% vs. 61.5% P < 0.001), androgen receptor (AR) expression (68% vs. 50.5% P < 0.001), adjuvant chemotherapy (69% vs. 79.8% P = 0.03). HER2-low BCs had lower histological grade than HER2-0 BCs, with grade I–II (68.7% vs. 43.1%) and grade III (22.2% vs. 43.1%) P < 0.01. No statistical differences were detected between the two groups for DFS and DDFS. Our results demonstrated that HR and AR status was closely related to HER2-low breast cancers. Further exploration about survival prognosis of HER2-low breast cancer is badly needed.

Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value—an analysis of 2473 invasive breast cancer patients

BackgroundThe value and utility of axillary lymph node (ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The aim of the current study is to test the potential of combining breast MRI and clinicopathologic factors to identify low-risk groups of ALN metastasis and improve diagnostic performance.Material and methodsPatients with primary operable invasive breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected from January 2009 to December 2021 in a single institute. The concordance of MRI and pathology of ALN status were determined, and also analyzed in different intrinsic subtypes. A stepwise strategy was designed to improve MRI-negative predictive value (NPV) on ALN metastasis.Results2473 patients were enrolled. The diagnostic performance of MRI in detecting metastatic ALN was significantly different between intrinsic subtypes (p = 0.007). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with HER-2 (MRI tumor size ≤ 2 cm), or TNBC (MRI tumor size ≤ 2 cm) were found to have MRI–ALN-NPV higher than 90%, and these false cases were limited to low axillary tumor burden.ConclusionThe diagnostic performance of MRI to predict ALN metastasis varied according to the intrinsic subtype. Combined pre-operative clinicopathologic factors and intrinsic subtypes may increase ALN MRI NPV, and further identify some groups of patients with low risks of ALN metastasis, high NPV, and low burdens of axillary disease even in false-negative cases.

An Australian-based nomogram predicting nodal positivity for breast cancer in older patients

IntroductionRegional nodal status is one of the most powerful prognosticators in breast cancer. The sentinel lymph node biopsy (SLNB) evaluates the first node in the axillary lymphatic basin theorised to drain the anatomical region of breast cancer. Recent literature has appropriately raised the query of the need for SLNB for breast cancer in older patients (BCOP). Though some early-stage older patients may safely have SLNB omitted, we are potentially missing the under-represented aggressive cancers. No sentinel lymph node metastases nomogram has been developed solely from BCOP data to date. This study aimed to identify older patients with breast cancer at risk for nodal involvement using a nomogram developed from their data alone. MethodsA retrospective analysis of prospectively collected data on BCOP (aged ≥70 years) was performed using the Breast Surgery Quality Audit (BQA). Inclusion criteria were T1-2 invasive breast cancer patients that underwent a SLNB from 1st January 2001 to 31st December 2019. The primary outcome was nodal involvement. Data obtained from the dataset included: age, tumour type, tumour size in millimeters, histological grade, lymphovascular invasion, oestrogen receptor status, progesterone receptor status, HER2 status and referral source. Binary logistic regression was used to develop a nomogram. The model was internally validated by splitting the data set (80% for training and 20% for testing). A receiver operating characteristic curve was developed, with the area under the curve (AUC) and a calibration plot. ResultsThere were 22,313 patients of which 14,856 (66.6%) were symptomatic presentations and 7457 (33.4%) were screen-detected. Invasive tumour type, tumour size, tumour grade, lymphovascular invasion, oestrogen receptors, and referral source indicated a statistically significant effect on predicting a nodal positivity event (Table 1). The AUC was 0.782 (95% CI 0.776–0.789) (Fig. 1a) and demonstrated good calibration (Fig. 1b). The negative predictive value established was 85%. ConclusionWe have developed an Australian sentinel lymph node metastasis nomogram for BCOP using routine histopathological data obtained pre-operatively (Fig. 2). This is the first Australian nomogram, as well as the first nomogram developed solely for BCOP – and maintains a superior AUC compared to other well-established nomograms.

Pathologic complete response of ductal carcinoma in situ to neoadjuvant systemic therapy in HER2-positive invasive breast cancer patients: a nationwide analysis

PurposeDuctal carcinoma in situ (DCIS) is present in more than half of HER2-positive invasive breast cancer (IBC). Recent studies show that DCIS accompanying HER2-positive IBC can be completely eradicated by neoadjuvant systemic therapy (NST). Our aim was to determine the percentage of pathologic complete response of the DCIS component in a nationwide cohort and to assess associated clinicopathologic variables. Furthermore, the impact on surgical treatment after NST was investigated.MethodsWomen diagnosed with HER2-positive IBC, treated with NST and surgery, between 2010 and 2020, were selected from the Netherlands Cancer Registry. Pre-NST biopsy and postoperative pathology reports were obtained from the Dutch Nationwide Pathology Databank and assessed for the presence of DCIS. Clinicopathologic factors associated with DCIS response were assessed using logistic regression analyses.ResultsA DCIS component was present in the pre-NST biopsy in 1403 (25.1%) of 5598 included patients. Pathologic complete response of the DCIS component was achieved in 730 patients (52.0%). Complete response of DCIS occurred more frequently in case of complete response of IBC (63.4% versus 33.8%, p < 0.001). ER-negative IBC (OR 1.79; 95%CI 1.33–2.42) and more recent years of diagnosis (2014–2016 OR 1.60; 95%CI 1.17–2.19, 2017–2019 OR 1.76; 95%CI 1.34–2.34) were associated with DCIS response. Mastectomy rates were higher in IBC+DCIS compared to IBC (53.6% versus 41.0%, p < 0.001).ConclusionPathologic complete response of DCIS occurred in 52.0% of HER2-positive IBC patients and was associated with ER-negative IBC and more recent years of diagnosis. Future studies should investigate imaging evaluation of DCIS response to improve surgical decision making.

Cytokine-induced apoptosis inhibitor 1: a comprehensive analysis of potential diagnostic, prognosis, and immune biomarkers in invasive breast cancer.

Cytokine-induced apoptosis inhibitor 1 (CIAPIN1) is strictly associated with the incidence and progress of several malignant tumors, but its effect on invasive breast cancer (IBC) remains unclear. We directed to research the potential diagnostic and prognostic significance of CIAPIN1 in IBC. The Cancer Genome Atlas (TCGA) database and Tumor Immune Estimation Resource (TIMER) database were utilized to examine CIAPIN1 expression level in IBC and its relationship with clinicopathological features. The diagnostic value and prognostic importance of CIAPIN1 in IBC were assessed by Kaplan-Meier analysis, Cox regression analysis, receiver operating characteristic (ROC) curve and nomogram model. The STRING database and enrichment analysis were utilized to discover the interacting proteins, biological roles and possible cellular mechanisms related to CIAPIN1. The methylation status of CIAPIN1 was analyzed using MethSurv database and the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN). By using Spearman correlation assessment, how the expression of CIAPIN1 was related to TP53, immune checkpoint genes and immune cell infiltration was determined. CIAPIN1 mRNA and protein levels were overexpressed in IBC, and significantly correlated with T stage, histological type, age, ER status, PR status and PAM50 (P<0.001). CIAPIN1 overexpression significantly decreased overall survival, distant metastasis free survival (DMFS) and relapse free survival in IBC patients (P<0.001). Similarly, hypermethylation of CIAPIN1 was associated with adverse outcomes in IBC patients. Multivariate Cox analysis identified CIAPIN1 as a potential risk factor for disease specific survival (DSS) and progression free survival (PFS) in individuals with IBC. The outcomes of the ROC curve showed that CIAPIN1 had a better accuracy in predicting ER(-), PR(-) and Asian breast cancer subtypes. Furthermore, there was a substantial correlation between the CIAPIN1 expression level in IBC and immune cell infiltration, TP53, and immune checkpoint genes. The high expression of CIAPIN1 in IBC is significantly related to the infiltration status of various tumor immune cells and the poor prognosis of IBC patients. According to this current study, CIAPIN1 is a promising diagnostic and prognostic marker for IBC.

Stromal Tumor-Infiltrating Lymphocytes Associated with Immunohistopathology and Molecular Subtypes of Breast Cancer in Vietnam.

Breast cancer is a heterogeneous disease with varied symptoms and pathogenesis, as well as variable prognosis and therapeutic outcomes. Stromal tumor-infiltrating lymphocytes, one of the tumor microenvironment factors, has been recognized as an important immunological biomarker that reflected the antitumor immune response in breast cancer. We analyzed 207 invasive breast cancer patients who had lumpectomy or mastectomy and have not received any pre-operative treatment. Clinicopathological characteristics, immunohistochemistry characteristics, molecular subtypes classification and stromal TILs evaluation were investigated. Stromal TILs correlated with well-established prognostic markers. Tumor grade showed significantly higher sTILs percentages in high-grade tumors than in low-grade tumors (p<0.001). There was a statistically significant association between intermediate and high levels of sTILs and a high Ki-67 index (p< 0.001). ER/PR negative was significantly related to high sTILs. Mean sTILs score was significantly higher in TNBC (40.1±31.6%) compared to others, statistically significant (p<0.001). In HER2-negative breast cancer, sTILs were significantly associated with histologic grade, ER status, PR status, and Ki67 index. sTILs played an important role, associated with unfavorable factors in breast cancer. Our findings support the use of stromal sTILs to identify a more aggressive phenotype of tumors.