HER2 Status In Breast Cancer Research Articles

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

The impact of HER2-low status on pathological complete response and disease-free survival in early-stage breast cancer

BackgroundThe HER-2 status of breast cancer (BC) has been classified as negative or positive for a long time. Given the efficacy of novel anti-HER2-targeted antibody drug conjugates (ADCs) in HER2-low BC, a distinct subgroup of HER2-low tumors has emerged within BC. The biology and prognostic impact of HER2-low expression are not yet well defined, and inconsistent results were reported. This study aims to evaluate the impact of low HER-2 status on the response to neoadjuvant chemotherapy (NACT) and disease- free survival (DFS) rates.MethodsWe retrospectively analyzed BC patients treated with NACT from 2017 to 2023 in two cancer centers. HER2-negative patients were included. HER-2 low status was defined by IHC + 1 or + 2/ISH non-amplified, and HER2-zero was defined by IHC 0. Pathological complete response (pCR) rates and DFS between HER2-low and HER2-zero populations were compared.Results170 patients were identified. 122 (72%) of these patients were HER2- zero BC, whereas 48 (28%) were HER2-low BC. Overall, pCR was achieved in 35 (20.5%) patients. Of these, pCR was observed in 30 patients (44.6%) from the HER2- zero group, compared to 5 patients (10.4%) from the HER2-low group (p = 0.046), but significance was lost in multivariate analysis. Among the hormone receptor (HR) positive subtype, pCR was achieved 19.8% of HER2-zero tumors and 7.5% of HER2-low tumors (p = 0.08). For HR-negative subtype 34.1% HER2-zero tumors had pCR and 25% of the HER2-low tumors had pCR (p = 0.614). There was no association between DFS and HER2-low status.ConclusionsOur study indicates that HER2-low status had no impact on pCR or DFS.

Abstract PS06-07: Liquid biopsy determination of HER2 status in breast cancer: results from a novel epigenomic platform

Abstract Background: Tumor HER2 status remains a critical predictive biomarker for patients with breast cancer given the availability of highly effective anti-HER2 therapies. However, determination of HER2 status relies on a complex, tissue-dependent algorithm optimized for extremes of the phenotype – HER2+ vs HER2- status. Given the challenges in reliably obtaining informative tissue biopsies as disease progresses, the new therapeutic relevance of HER2-low breast cancer (IHC 1+ and FISH-negative IHC 2+), and the undependability of biomarkers in this range, better approaches are needed. Epigenomic signatures detectable in cell-free tumor DNA (cfDNA) may provide a minimally invasive, reliable determination of HER2 status. Here, we present a multimodal epigenomic liquid biopsy platform that could offer a minimally invasive alternative to tissue-based determination of transcriptionally regulated targets such as HER2. Methods: We identified patients with metastatic breast cancer (n=20) that were HER2-positive with IHC 3+ (n=6) or HER2-negative with IHC 0 (n=14) based on ASCO/CAP guidelines who underwent research biopsies at the Dana-Farber Cancer Institute from 2012-2023. Study participants had undergone concurrent blood sampling collected in Streck Tubes, processed as plasma, and stored at -80ºC. We applied a novel, multimodal epigenomic assay to profile tumor-derived gene regulatory programs from 1mL plasma. This genome-wide approach analyzed histone modifications associated with active gene promoters and enhancers, and DNA methylation. Classifier feature selection was derived from epigenomically profiled breast cancer cell lines. Final classifier performance was cross validated using a standard Leave one Out (LoO) scheme. Results: Our cross-validated classifier yielded an area under the ROC curve (AUC) of 0.91 in the 20 samples tested. When including only samples with detectable ctDNA as assessed by low pass whole genome sequencing (ichorCNA algorithm) (N=13), our classifier had an AUC of 0.98. Using regularized regression approach, we identified 33 loci with the highest predictive value; 8 loci were within the ERBB2 amplicon on chromosome 17, while the remaining 25 loci were in other genomic regions. We will present results from this and an expanded cohort, including participants with HER2 IHC 1+ and 2+ disease. Conclusions: We present a novel, minimally invasive HER2 classifier leveraging epigenomic information from cell-free tumor DNA from 1 mL plasma. If this approach remains robust in accurately classifying additional HER2 states, it could address the limitations of current tissue-based testing. Integration of this epigenomic platform in prospective interventional studies could support the development of novel and improved predictive classifiers for response to HER2-directed therapy. Citation Format: Heather Parsons, Sylvan Baca, Anthony D'Ippolito, Hyun-Hwan Jeong, Kalie Smith, Paolo Tarantino, Melissa Hughes, Kerry Sendrick, Corrie Painter, Ji-Heui Seo, Elizabeth Grant, Jamey Guess, Matthew Davidsohn, Gitanjali Lakshminarayanan, Sarah Strauss, Hunter Savignano, John Canniff, Brad Fortunato, Matthew Eaton, Toni Choueiri, Sara Tolaney, Matthew Freedman, Nancy Lin. Liquid biopsy determination of HER2 status in breast cancer: results from a novel epigenomic platform [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-07.

Abstract PO5-06-05: DISCORDANCE BETWEEN HORMONE RECEPTORS AND HER2 STATUS IN BREAST CANCER PATIENTS RELAPSE

Abstract Introduction: Breast cancer (BC) is a heterogeneous disease, and during its progression, the tumor phenotype can undergo changes that are often associated with a poor prognosis. In this study, we aim to compare the discordance in estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression between primary and recurrent/metastatic lesions. Methods: This study is a retrospective analysis of 132 non-advanced breast cancer patients who experienced confirmed recurrence/metastasis at the Instituto Nacional de Enfermedades Neoplasicas between 2011 and 2022. Descriptive analysis was conducted to assess the expressions of ER, PgR, HER-2, and Ki-67 in primary and metastatic breast cancer, as well as clinical variables. Normality tests, including Kolmogorov-Smirnov and Shapiro-Wilk, were performed. The paired samples Wilcoxon test was used to determine significant differences between primary and recurrent/metastatic tumors. Additionally, Spearman's rank correlation coefficient was employed to evaluate the relationships of expression discordance. Results: Fifty-nine percent of patients presented locoregional recurrence, while 41% presented visceral metastasis. In the primary tumor, ER and PR expression were found in 63.6% and 55.3% of cases, respectively. HER2 status was determined as 0 (36.3%), +1 (22%), +2 (14.4%), +3 (26.5%), and not determined (0.8%) by IHC. For the recurrent/metastatic tumor, ER and PR expression were found in 59% and 34% of cases, respectively. HER2 status was determined as 0 (27.3%), +1 (19.7%), +2 (25%), +3 (25%), and not determined (3%). The median Ki-67 levels were 37% in the primary tumor and 40% in the recurrent/metastatic tumor. Significant differences were found for ER (p=0.014), PR (p< 0.001), and HER2 (p=0.019), but not for Ki-67 percentage (p=0.139). The discordance rates of ER, PR, HER2, and Ki-67 expression were 27.9%, 47.7%, and 26.3%, respectively. Conclussions: Discordance in hormone receptor expression between primary and recurrent/metastatic tumors is a common phenomenon, with ER showing a discordance rate of 27.9% and PR showing a higher rate of 47.7%. HER2 status also displayed significant discordance, with a 26.3% rate of discordant expression between primary and recurrent/metastatic tumors. The findings from this study emphasize the dynamic nature of breast cancer and the importance of comprehensive evaluation of biomarker expression in both primary and recurrent/metastatic tumors, to choose the best treatment options. Citation Format: Katia Roque, Iris Otoya, Natalia Valdivieso, Zaida Morante, Hugo Fuentes, Silvia Neciosup, Henry Gómez, Jorge Cotito, Gonzalo Ziegler, Estefania Peña, Tatiana Vidaurre, Ramon Andrade de Mello, Carlos Castañeda. DISCORDANCE BETWEEN HORMONE RECEPTORS AND HER2 STATUS IN BREAST CANCER PATIENTS RELAPSE [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-06-05.

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660–947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

Characterisation ofIGF-1 (rs7136446) and IL-6 (rs1800795) polymorphisms among breast cancer patients in western Algeria.

Breast cancer (BC) is themost frequently diagnosed cancer among women worldwide, including Algeria. Certain single nucleotide polymorphisms (SNPs) have been linked to higher risk ofBC. Several studies have been performed on theinsulin- like growth factor 1 (IGF-1) T > C (rs7136446) and theinterleukin 6 (IL-6) 174 G > C (rs1800795) SNPs to explore their role in BC. Theaim ofthis study is to investigate theassociation between the2 SNPs, IL-6 (rs1800795) and IGF-1 (rs7136446) with BC in thepopulation ofwestern Algeria. This study was carried out for thefirst time among theAlgerian population. This case-control study included 109 BC patients and 112 healthy controls. Genomic DNA was extracted from peripheral blood samples. DNA concentration was determined using theQubit 2.0 fluorometer. Thegenotyping ofselected SNPs was performed by real-time polymerase chain reaction followed by statistical analysis to assess genotypic frequencies and genetic association with BC. Theresults showed that IGF-1 rs7136446 was positively associated with BC (p = 0.00001) and that thedistribution ofgenotype frequencies ofrs7136446 showed astatistically difference between human epidermal growth factor receptor 2 (HER-2) positive and HER-2 negative BC (p = 0.04), but this association did not last after thecorrection ofBonferroni. No association was found in genotype distribution oftheIL-6 (rs1800795) among controls and BC patients or with clinicopathological parameters including HER-2 status in BC (p > 0.05). In summary, our findings indicate that IGF-1 rs7136446 is associated with BC in our population ofwestern Algeria.

Changes of HER2 low expression status in primary and recurrent/metastatic breast cancer

Objective: To investigate the evolution and clinical significance of HER2 low expression status in HER2 negative patients in primary and recurrent/metastatic breast cancers. Methods: The data and archived sections of 259 breast cancer patients with recurrence/metastasis and HER2-negative primary foci were collected from January 2015 to January 2022 at the Fourth Hospital of Hebei Medical University, and the HER2 status of primary and recurrence/metastasis foci was determined by immunohistochemistry (IHC), among which IHC 2+patients were subject to fluorescence in situ hybridization (FISH). The HER2 status was classified as HER2-0 group; patients with IHC 1+, IHC 2+and no FISH amplification were classified as HER2 low expression group; and patients with IHC 3+, IHC 2+and FISH amplified were classified as HER2-positive group. The changes of HER2 status in patients with HER2 low expression in primary versus recurrent/metastatic breast cancer foci were compared, and their clinicopathologic characteristics and prognosis were analyzed. Results: The overall concordance rate between primary and recurrent/metastatic HER2 status in breast cancer was 60.6% (157/259, κ=0.178). A total of 102 patients (102/259, 39.4%) had inconsistent primary and recurrent/metastatic HER2 status; 37 patients (37/259, 14.3%) had HER2-0 at the primary foci and HER2-low expression at the recurrent/metastatic; and 56 patients (56/259, 21.6%) had HER2-low expression in the primary foci and HER2-0 in the recurrent/metastatic. The recurrent/metastatic foci became low-expressing compared with the recurrent/metastatic foci which remained HER2-0 patients, with longer overall survival time, higher ER and PR positivity, lower Ki-67 positivity index, and lower tumor histological grade; all with statistically significant differences (all P<0.05). In the primary HER2-low group, patients with recurrent/metastatic foci became HER2-0 while those with recurrent/metastatic foci remained low expression; there were no statistically significant differences in clinicopathological features and overall survival time (all P>0.05). Conclusions: Unstable HER2 status in patients with HER2-0 and low expression in primary versus recurrent/metastatic breast cancer foci, and HER2-0 in the primary foci but low HER2 expression status in recurrence/metastasis is associated with favourable prognosis, and testing HER2 status in recurrence/metastasis can provide more treatment options for such patients.

Radiomic Signatures Based on Mammography and Magnetic Resonance Imaging as New Markers for Estimation of Ki-67 and HER-2 Status in Breast Cancer.

The aim of the study is to investigate the values of intratumoral and peritumoral regions based on mammography and magnetic resonance imaging for the prediction of Ki-67 and human epidermal growth factor (HER-2) status in breast cancer (BC). Two hundred BC patients were consecutively enrolled between January 2017 and March 2021 and divided into training (n = 133) and validation (n = 67) groups. All the patients underwent breast mammography and magnetic resonance imaging screening. Features were derived from intratumoral and peritumoral regions of the tumor and selected using the least absolute shrinkage and selection operator regression to build radiomic signatures (RSs). Receiver operating characteristic curve analysis and the DeLong test were performed to assess and compare each RS. For each modality, the combined RSs integrating features from intratumoral and peritumoral regions always showed better prediction performance for predicting Ki-67 and HER-2 status compared with the RSs derived from intratumoral or peritumoral regions separately. The multimodality and multiregional combined RSs achieved the best prediction performance for predicting the Ki-67 and HER-2 status with an area under the receiver operating characteristic curve of 0.888 and 0.868 in the training cohort and 0.800 and 0.848 in the validation cohort, respectively. Peritumoral areas provide complementary information to intratumoral regions of BC. The developed multimodality and multiregional combined RSs have good potential for noninvasive evaluation of Ki-67 and HER-2 status in BC.

HER2-low metastases of HER2-negative primary tumors: a single institution analysis of intertumoral and internodal heterogeneity in node-positive breast cancer.

HER2 status in breast cancer is an essential parameter in individual therapeutic decision-making and is routinely assessed in primary tumors in accordance with international recommendations. Reports of HER2 heterogeneity raise the question of basing treatment decisions on HER2 status in metastases, if present. We investigated the degree and clinical implications of HER2 heterogeneity in lymph node-positive breast cancer. Because of recent recognition of therapeutic opportunities in this group of tumors, we especially focused on cases involving low-level HER2 expression. The HER2 status of primary tumors and of corresponding lymph node metastases was determined in archived material at the protein and gene levels using the gene- protein assay and interpreted in accordance with 2018 ASCO/CAP criteria. HER2-low status was defined as protein expression levels 1+ or 2+ with negative amplification status. We analyzed a series of 43 cases of primary infiltrating breast cancer, each with at least two axillary nodes harboring macrometastases (>2 mm), in total 206 such nodes. In 7% of cases, we detected intertumoral HER2 heterogeneity. Three of nine HER2-positive primary tumors were associated with HER2-negative metastases. No cases with HER2-negative primary tumors had HER2-positive metastases, but 55% (6/11) of HER2 0 primary tumors had HER2 1+ and/or 2+ metastases, and 19% (3/16) HER2 1+ cases had exclusively HER2 0 metastases. All metastases in HER2 2+ cases showed HER2-low protein expression levels. Internodal HER2 heterogeneity at low protein expression levels (presence of HER2 0, HER2 1+, and/or HER2 2+ metastatic deposits within the same axilla) was seen in 40% (17/43) of cases. We found no statistically significant association between HER2 heterogeneity and other tumor-related parameters. Survival data indicated worse outcomes in the HER2-low group compared with the rest of the cohort. Our results indicate a substantial instability of HER2 protein expression, leading to considerable intertumoral and internodal HER2 heterogeneity in lymph node-positive breast carcinomas. This heterogeneity is particularly relevant in HER2-low tumors in which the corrective effects of HER2 gene copy number analysis definitionally is absent. Our findings suggest that determining HER2 status in metastatic lymph nodes may generate relevant information for therapeutic decision-making.

The incidence and risk factors predictive of bone metastases at initial diagnosis of malignancy.

e18816 Background: Bone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. We report here the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older and with solid cancers of all stages diagnosed from 2010 to 2019. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics. Results: Among the 1,132,154 patients identified, 1,075,069 (94.96%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 (4.9%) patients. Among patients with bone metastases, the most common primary tumors were lung (44.4%), prostate (19.3%), breast (12.4%), kidney (4.0%), colorectal (2.24%) and pancreas (2.23%). The percentage of bone metastases in patients with non-small cell lung cancer (NSCLC), prostate adenocarcinoma, infiltrating ductal carcinoma of the breast (IDC), and colorectal adenocarcinoma (CRC) was 18.04% (18,123 out of 100,447), 5.92% (9,071 out of 153,325), 2.9% (4,490 out of 154,626), and 1.17% (1,043 out of 89,510) respectively. In addition to T and N stage, independent risk factors by multivariable analysis for bone metastases included female sex and non-squamous histology in NSCLC, black race, hormonal and HER2 status in breast cancer, Gleason score and PSA in prostate cancer, male sex, black race, rectal location, liver metastases and lung metastases in CRC. In univariate analysis, the strongest predictors for bone metastases were N2/3 (28.3%) and T3/4 (24.9%) in NSCLC, T3/4 (15.8%), N2/3 (10.6%), N1 (5.9%) and HER2 positive (4.9%) in IDC, N1 (38.1%), Gleason score 9/10 (22.8%), PSA 98 or higher (19.5%) and PSA 20-97 (16.8%) in prostate cancer, and rectal or rectosigmoid location (1.43% vs 1.05% in colon cancer, p &lt; 0.001) in CRC. Conclusions: Approximately 5% of patients with solid tumors have bone metastases at presentation. In addition to T and N stage, there are several risk factors for bone metastases according to the primary tumor site.

The association between CTSZ methylation in peripheral blood and breast cancer in Chinese women.

Previous studies have shown that DNA methylation in peripheral blood may be associated with breast cancer (BC). To explore the association between the methylation level of the Cathepsin Z (CTSZ) gene in peripheral blood and BC, we conducted a case-control study in the Chinese population. Peripheral blood samples were collected from 567 BC cases, 635 healthy controls, and 303 benign breast disease (BBD) cases. DNA extraction and bisulfite-specific PCR amplification were performed for all samples. The methylation levels of seven sites of the CTSZ gene were quantitatively determined by Mass spectrometry. The odds ratios (ORs) of CpG sites were evaluated for BC risk using per 10% reduction and quartiles analyses by logistic regression. Our analysis showed that five out of the seven CpG sites exhibited significant associations with hypomethylation of CTSZ and BC, compared to healthy controls. The highest OR was for Q2 of CTSZ_CpG_1 (OR: 1.62, P=0.006), particularly for early-stage breast cancer in the case of per 10% reduction of CTSZ_CpG_1 (OR: 1.20, P=0.003). We also found that per 10% reduction of CTSZ_CpG_5 (OR: 1.39, P=0.004) and CTSZ_CpG_7,8 (OR: 1.35, P=0.005) were associated with increased BC risk. Our study also revealed that four out of seven CpG sites were linked to increased BC risk in women under 50 years of age, compared to healthy controls. The highest OR was for per 10% reduction of CTSZ_CpG_1 (OR: 1.47, P<0.001). Additionally, we found that BC exhibited lower methylation levels than BBD at CTSZ_CpG_4 (OR for Q1: 2.18, P<0.001) and CTSZ_CpG_7,8 (OR for Q1: 2.01, P=0.001). Furthermore, we observed a correlation between methylation levels and tumor stage, ER, and HER2 status in BC patients. Overall, our findings suggest that altered CTSZ methylation levels in peripheral blood may be associated with breast cancer, particularly in young women, and may serve as a potential biomarker for early-stage BC.

Abstract P5-02-33: Deep learning-based assessment of HER2-low expression on breast cancer H&amp;E digital whole slide images

Abstract Background: Antibody drug conjugates (ADCs) against HER2 have shown meaningful clinical activity in HER2 low breast cancers, defined as 1+ or 2+ staining on immunohistochemistry (IHC) without gene amplification by in situ hybridization (ISH) techniques. Given that these methods were originally developed for an accurate detection of HER2 3+, their sensitivity and robustness for the detection of low and ultra-low levels of HER2 are questionable. We have recently described a deep learning algorithm that can detect signatures of HER2 expression based on training utilizing scanned H&amp;E whole slide images (WSI) of breast cancers for which IHC and mRNA expression levels of HER2 were available. Here, we report the application of our algorithm to two independent breast cancer cohorts. Methods: A model was developed based on recognition of invasive breast cancer in whole slide images of H&amp;E staining, and then trained via computational neural network with multiple instance learning for binary classification of cases as HER2 “negative” and HER2 “expressed” (low). For training, true negatives were defined as having HER2 IHC-0 and mRNA level &amp;lt; 7.6. HER2-low cases were defined as IHC-1+/2+ and mRNA &amp;gt;9. IHC-0 cases with mRNA &amp;gt;7.6 were excluded from the training cohorts. The resulting model (HER2Complete) was able to distinguish HER2-negatives from HER2-low cases with an AUC of 0.91 (+/- 0.08). Here we use Her2Complete to assess HER2 in two additional cohorts that include 901 ER+/HER2 IHC-0 and 52 HER2 IHC 0+ breast cancers from MSK and TCGA cohorts, respectively. For the TCGA cohort, concomitant transcriptomics data (RNASeq) as a reference for HER2 mRNA expression were retrieved and “HER2 expressed” defined as RNASeq expression of HER2 greater than the 90th percentile of the geometric mean of expression of three reference genes not expressed in breast tissues (TTN, MUC13, OR10A6). Values less than this reference cut-off in the TCGA cohort were considered “HER2 not expressed.” Results: Among the 901 IHC-0 test cases from the MSK cohort, the model identified 82 as ‘negative’, whereas 819 were found to have features of HER2 expression (HER2-Low). Of the 82 negative cases in the MSK cohort, all except 13 cases expressed mRNA levels &amp;lt; 9, and 786/819 of the HER2-low cases expressed mRNA levels &amp;gt;8. Of the 52 IHC 0+ cases in the TCGA cohort, 33 also had “HER2 not expressed” by our reference based RNASeq expression cut-off. Our model identified 15 of these 33 as ‘negative’, while 15 of the 19 TCGA cases with IHC 0+ and HER2 ‘expressed’ by our cut-off were identified as ‘HER2-Low’ by our model. Conclusions: AI tools based on the analysis of WSIs of routinely prepared H&amp;E sections may predict HER2 status in breast cancer. This work requires further investigation using treatment response data to demonstrate that cases with morphologic features of low level HER2 expression will respond to ADCs. Citation Format: Gerard Oakley III, Jorge Reis-Filho, David Klimstra, Marc Goldfinger, Yikan Wang, Antonio Marra. Deep learning-based assessment of HER2-low expression on breast cancer H&amp;E digital whole slide images [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-33.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions

BackgroundHuman epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer.MethodsThis retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer.ResultsEight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI).ConclusionWe searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.

BREAST CANCER SURVIVAL: A HOSPITAL-BASED STUDY IN REGIONAL CANCER CENTRE, BENGALURU

Introduction: Breast cancer is one of the most common cancer among Indian Women. The age adjusted rate (AAR) of Breast cancer is 42.1/100000 women in Bengaluru and it is the leading site of cancer in Bengalurian females. The hospital based cancer registry was established in 1983 and is registering around 800 Breast cancer cases per year. Most of them were diagnosed with late stage. The objective of this study is to evaluate Pattern of Care and estimate the Survival rate with respect to different stages, Treatment modalities, ER, PR, HER-2 Status of breast cancer treated only at Kidwai Cancer Institute. A retrospective cohort study was Methodology carried out with 191 Carcinoma Breast cases at a Regional Cancer Centre (Kidwai Memorial Institute of Oncology), Bengaluru. The study started from 2006-2007 and was followed-up until the end of 2020. Five-year survival (60 Months) probabilities were estimated using the Kaplan-Meier and loss adjusted survival analysis method for less than 80% follow-up group for ten years. The log rank test was used to compare the survival between the factors. The overall observed survival rates at 5years Results (60 months) was 72.7%. When analysis was conducted for specic stages, Stage II, Stage III and Stage IV survival rates are 95%, 67% and 17% respectively and is statistically signicant (p&lt;0.001), 39 Patients were dead and 13 Patients were lost for follow-up. The overall survival rate at 10 years was 62.4% and lost for follow-up was 26.5% and death was 27.7%, the Kaplan Meier analysis was unable to be carried out as more than 20% censored cases were seen, hence loss adjusted survival analysis was carried out for 10 years. 89 Patients were alive, 52 patients were dead and 50 patients were lost for follow-up at 10 years. Conclusion: In surgical histopathological nding Estrogen receptor, Progesterone receptor and HER-2 status did not have signicant impact on survival of patients. The post-operative Radiotherapy and combination of modalities of treatment had signicant effect on the long term survival of the patients after adjusting for disease stage.

The Revelation of Continuously Organized, Co-Overexpressed Protein-Coding Genes with Roles in Cellular Communications in Breast Cancer

Many human cancers, including breast cancer, are polygenic and involve the co-dysregulation of multiple regulatory molecules and pathways. Though the overexpression of genes and amplified chromosomal regions have been closely linked in breast cancer, the notion of the co-upregulation of genes at a single locus remains poorly described. Here, we describe the co-overexpression of 34 continuously organized protein-coding genes with diverse functions at 8q.24.3(143437655-144326919) in breast and other cancer types, the CanCord34 genes. In total, 10 out of 34 genes have not been reported to be overexpressed in breast cancer. Interestingly, the overexpression of CanCord34 genes is not necessarily associated with genomic amplification and is independent of hormonal or HER2 status in breast cancer. CanCord34 genes exhibit diverse known and predicted functions, including enzymatic activities, cell viability, multipotency, cancer stem cells, and secretory activities, including extracellular vesicles. The co-overexpression of 33 of the CanCord34 genes in a multivariant analysis was correlated with poor survival among patients with breast cancer. The analysis of the genome-wide RNAi functional screening, cell dependency fitness, and breast cancer stem cell databases indicated that three diverse overexpressed CanCord34 genes, including a component of spliceosome PUF60, a component of exosome complex EXOSC4, and a ribosomal biogenesis factor BOP1, shared roles in cell viability, cell fitness, and stem cell phenotypes. In addition, 17 of the CanCord34 genes were found in the microvesicles (MVs) secreted from the mesenchymal stem cells that were primed with MDA-MB-231 breast cancer cells. Since these MVs were important in the chemoresistance and dedifferentiation of breast cancer cells into cancer stem cells, these findings highlight the significance of the CanCord34 genes in cellular communications. In brief, the persistent co-overexpression of CanCord34 genes with diverse functions can lead to the dysregulation of complementary functions in breast cancer. In brief, the present study provides new insights into the polygenic nature of breast cancer and opens new research avenues for basic, preclinical, and therapeutic studies in human cancer.

Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population

PurposeThe status of human epidermal growth factor receptor 2 (HER2) is important for treatment decision-making of breast cancer and was commonly determined by core needle biopsy (CNB). The concordance of CNB with surgical excision biopsy (SEB) has been verified, but remain unclear according to the newly developed classification of HER2 status. Our study aimed to re-evaluate the diagnostic value of CNB for determining HER2 status in breast cancer, especially in the HER2-low population.MethodsEligible breast cancer patients in West China Hospital between January 1, 2007 and December 31, 2021 were enrolled consecutively and data were extracted from the Hospital Information System. The agreement of HER2 status between CNB and SEB was calculated by concordance rate and κ statistics, as well as the sensitivity, specificity, positive, and negative predictive values (PPV & NPV). Logistic models were used to explore potential factors associated with the discordance between both tests.ResultsOf 1829 eligible patients, 1097 (60.0%) and 1358 (74.2%) were consistent between CNB and SEB by pathological and clinical classifications, respectively, with κ value being 0.46 (0.43–0.49) and 0.57 (0.53–0.60). The sensitivity (50.9%–52.7%) and PPV (50.5%-55.2%) of CNB were especially low among IHC 1+ and 2+/ISH - subgroups by pathological classifications; however, it showed the highest sensitivity (77.5%) and the lowest specificity (73.9%) in HER2-low population by clinical classifications. Advanced N stages might be a stable indicator for the discordance between both tests.ConclusionThe diagnostic value of CNB was limited for determining HER2 status in breast cancer, especially in HER2-low population.

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.

HER2 Low, Ultra-low, and Novel Complementary Biomarkers: Expanding the Spectrum of HER2 Positivity in Breast Cancer.

HER2 status in breast cancer is assessed to select patients eligible for targeted therapy with anti-HER2 therapies. According to the American Society of Clinical Oncology (ASCO) and College of American Pathologists (CAP), the HER2 test positivity is defined by protein overexpression (score 3+) at immunohistochemistry (IHC) and/or gene amplification at in situ hybridization (ISH). The introduction of novel anti-HER2 compounds, however, is changing this paradigm because some breast cancers with lower levels of protein expression (i.e. score 1+/2+ with no gene amplification) benefited from HER2 antibody-drug conjugates (ADC). Recently, a potential for HER2 targeting in HER2 “ultra-low” (i.e. score 0 with incomplete and faint staining in ≤10% of tumor cells) and MutL-deficient estrogen receptor (estrogen receptor)-positive/HER2-negative breast cancers has been highlighted. All these novel findings are transforming the traditional dichotomy of HER2 status and have dramatically raised the expectations in this field. Still, a more aware HER2 status assessment coupled with the comprehensive characterization of the clinical and molecular features of these tumors is required. Here, we seek to provide an overview of the current state of HER2 targeting in breast cancers beyond the canonical HER2 positivity and to discuss the practical implications for pathologists and oncologists.