HER2 testing in metastatic breast cancer – is reflex in situ hybridization necessary in cases that are equivocal by immunohistochemistry?

Introduction/Objective Current guidelines recommend HER2 testing on all primary invasive breast cancers and at least one metastatic lesion. Typical HER2 testing involves immunohistochemistry (IHC), with reflex in situ hybridization (ISH) in the event of equivocal (2+) IHC. ISH testing is time consuming and resource intensive, and there may be situations where it is unnecessary. The incidence of discordance between HER2 negative primary tumors and HER2 IHC2+ metastases that are ISH positive is unknown. We hypothesize that the majority of such cases are non- amplified. Methods/Case Report A retrospective review of IHC2+ metastatic lesions further assessed with ISH at our center from 2013-2021 was undertaken. A total of 105 cases were identified after exclusion of cases missing HER2 results, with primaries of unconfirmed origin, and cases of synchronous primary and metastatic disease. IHC and ISH results were recorded, with a detailed slide review of discordant cases. Results (if a Case Study enter NA) 91/105 metastases had HER2 negative primaries (87%). A metastasis was significantly more likely to be HER2 negative when the primary was HER2 negative (93%) versus positive (43%) (p < 0.0001). 54/91 primaries were IHC2+/ISH negative, and 50/54 (93%) corresponding metastases had identical results. Of the 37 HER2 negative primaries that were IHC0/1+, 35 (95%) corresponding metastatic tumors were ISH negative. Six metastatic lesions in cases with HER2 negative primaries were discordant and slides were reviewed. Characteristics of metastatic tumors suggesting ISH testing was warranted to assess for discordance included IHC heterogeneity, morphological discordance, and increased staining of moderate intensity. These factors were present in all six discordant metastases. Conclusion Our results suggest that selective rather than reflex ISH testing on HER2 IHC2+ breast cancer metastases in the context of HER2 negative primary disease may be appropriate when there is careful review of the IHC. Validation of our findings await further studies with larger sample sizes.

HER2 Testing Characteristics Can Predict Residual Cancer Burden following Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Objectives. The response to HER2-targeted neoadjuvant chemotherapy (NAC) in HER2-positive (+) breast cancer can be quantified using residual cancer burden (RCB) pathologic evaluation to predict relapse free/overall survival. However, more information is needed to characterize the relationship between patterns of HER2 testing results and response to NAC. We evaluated clinicopathologic characteristics associated with RCB categories in HER2+ patients who underwent HER2-directed NAC. Methods. A retrospective chart review was conducted with Stage I–III HER2+ breast cancer cases following NAC and surgical resection. HER2 immunohistochemistry (IHC) staining and fluorescence in situ hybridization (FISH), histologic/clinical characteristics, hormone receptor status, and RCB scores (RCB-0, RCB-I, RCB-II, and RCB-III) were evaluated. Results. 64/151 (42.4%) patients with HER2+ disease had pathologic complete response (pCR). Tumors with suboptimal response (RCB-II and RCB-III) were more likely to demonstrate less than 100% HER2 IHC 3+ staining ( p < 0.0001 ), lower HER2 FISH copies ( p < 0.0001 ), and lower HER2/CEP17 ratios ( p = 0.0015 ) compared to RCB-I and RCB-II responses. Estrogen receptor classification using ≥10% versus ≥1% staining showed greater association with higher RCB categories. Conclusions. HER2+ characteristics show differing response to therapy despite all being categorized as positive; tumors with less than 100% IHC 3+ staining, lower HER2 FISH copies, and lower HER2/CEP17 ratios resulted in higher RCB scores.

Concordance of HER2+ status by IHC/ISH and ERBB2 status by NGS in a real-world clinicogenomic database and analysis of outcomes in patients (pts) with metastatic breast cancer (mBC).

1036 Background: HER2 overexpression/amplification measured by IHC or ISH is a predictive biomarker for HER2-targeted therapies. Next-generation sequencing (NGS) can identify ERBB2 amplification (amp) and mutations. We examined clinical characteristics, NGS testing patterns, and outcomes of pts treated with 1L HER2 therapy with HER2+ mBC based on ERBB2 amp status using a real-world (RW) clinico-genomic database (CGDB). Methods: Pts with mBC (HER2+ by IHC and/or ISH) treated with 1L HER2 therapy who had undergone NGS and were treated within the Flatiron Health (FH) network were eligible. Clinical characteristics and HER2 testing results were obtained via technology-enabled abstraction of clinician notes and radiology/pathology reports and linked to genomic data from Foundation Medicine (FMI) in the nationwide (US-based), de-identified FH-FMI CGDB. Demographic, clinical and genomic characteristics were summarized and stratified by concordance between HER2+ (IHC 3+ or ISH amp+) and ERBB2amp+ status [copy number (CN) ≥ 5]. NGS testing patterns and 1L HER2 therapy were characterized and stratified by concordance status. Concordance was assessed based on contemporaneous timing of paired test specimen collection dates (FMI NGS ≤ 30 days of HER2+ status). RW overall survival (rwOS) stratified by HER2+/ ERBB2 amp concordance was estimated with Kaplan-Meier analysis and adjusted Cox proportional hazards models. Results: Among 268 eligible pts, HER2+/ ERBB2amp+ concordance was 66% (176/268); concordance among contemporaneous paired specimens was 73% (106/145). Demographic and clinical features were overall well-balanced with most pts treated at community sites [94%, (252/268)]; the discordant (HER2+/ ERBB2amp-) group (95/268) had more pts with hormone receptor positive disease (73% vs 62%). Concordance by assay type varied; IHC+ only, IHC+/ISH+, and ISH+ only agreement was 72% (95/132), 76% (26/34) and 52% (50/96), respectively. A higher proportion of discordance (35% vs 19%) was seen in pts treated at community vs. academic sites. Median rwOS was 32.9 months (IQR 25.9-38.9) among concordant (HER2+/ ERBB2amp+) and 15.5 months (IQR 8.9-30.1) among discordant (HER2+/ ERBB2amp-) pts, aHR = 0.71 [95% CI: 0.48-1.03; p = 0.073]. Conclusions: Among RW pts with HER2+ mBC receiving 1L HER2 therapy, discordance between ERBB2amp and IHC/ISH HER2 testing methods was observed. Pts with tumors HER2+ by IHC and/or ISH but negative for ERBB2amp had a trend towards worse rwOS following receipt of HER2 therapy compared to concordant cases. Contemporaneous timing of specimen collection was associated with greater concordance. Future analyses on the additive value of ERBB2 CN as a predictive marker, and assessing factors that may affect discordance such as intratumor HER2 heterogeneity, tumor content, and biopsy site are warranted.

Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials.

3061 Background: Patient eligibility for HER2-targeting treatments is commonly informed by testing tumor HER2 expression using immunohistochemistry. As HER2 expression is visually assessed by pathologists, inter- and intra-rater variability might affect treatment decisions. Here, we report the development of an automated machine learning (ML)-based algorithm to quantify HER2 cell membrane expression across a diversity of breast cancer phenotypes as a clinical tool for monitoring HER2 testing quality. Methods: A total of 689 breast cancer tissue samples were either procured (Avaden Biosciences) or were anonymized samples from the AstraZeneca biobank comprising tissues from primary and metastatic tumors, core needle biopsies and surgical resections, lobular and ductal carcinomas, across tumor grades and HER2 expression levels. Samples were stained for HER2 detection (Ventana HER2 (4B5) Assay) and digitized (Leica Biosystems) across 5 laboratories in the US. Whole-slide images (WSIs) were stratified into training (n = 407), validation (n = 110), and test sets (n = 172). Multiple convolutional neural network based ML models (PathAI, Boston, MA) were trained using 190,000 manual annotations provided by 30 board-certified pathologists to identify artifacts, invasive tumor, identify individual cancer cells and measure tumor cell membrane HER2 expression as partial or complete, and negative, weak-or-moderate, or intense. Cell-level scores were validated against a consensus of manual cell counts from 5 independent pathologists in 320 representative regions of test set WSIs. HER2 scores were generated by automatically applying rules derived from 2018 ASCO/CAP guidelines and then compared in the test set with consensus scores from 3 independent pathologists. Results: Cell counts provided by the ML model were strongly consistent with cell counts obtained by pathologist consensus in all cell-types except for faintly positive HER2 cells where ML-based quantification identified more cells on average. Automatically generated ML-ASCO/CAP HER2 scores using WSI showed substantial consistency across IHC categories with the consensus of pathologists (ICC 0.88, 95%CI 0.82-0.92) in the test set and improved further when ML models were trained to agree with pathologists by adjusting cut offs (ICC 0.91, 95%CI 0.89-0.94). The ML-based model was deployed through the PathAI cloud platform to calculate HER2 testing quality control metrics in real-time in multicentric clinical trials. Conclusions: Automated image analysis of HER2-stained breast cancer tissues using ML-based models is consistent with pathologist consensus across breast cancer tissue types. The results support evidence that ML-based algorithms can help pathologists assess HER2 testing reproducibility in clinical trials.

HER2 in uterine serous carcinoma: Testing platforms and implications for targeted therapy.

5580 Background: HER2 is an emerging prognostic and therapeutic target in uterine serous carcinoma (USC). Testing algorithms and platforms in breast and gastric cancers are well studied and validated, but optimal HER2 testing in uterine cancer is not yet established. We aimed to assess the concordance of chromogenic in situ hybridization (CISH), immunohistochemistry (IHC), and next generation sequencing (NGS) platforms to aid in the development of USC specific testing guidelines. We also evaluated the rate of downstream mutations that may affect response to HER2 directed therapy. Methods: A total of 2,192 USC tumors were analyzed using NGS (NextSeq, 592 Genes and WES, NovaSEQ), a subset of 1,423 tumors were also tested by IHC and CISH (Caris Life Sciences, Phoenix, AZ). HER2 positivity through IHC (4B5, Ventana) and CISH (INFORM DUAL HER2 ISH Assay, Ventana) was determined based on 2007 and 2018 ASCO/CAP HER2 breast cancer guidelines. PD-L1 expression was tested by IHC using SP142 (Spring Biosciences) (positive cut-off >1%). Microsatellite instability (MSI) was tested by fragment analysis (FA), IHC and NGS. Tumor mutational burden (TMB) was measured by totaling somatic mutations per tumor (TMB-high cut-off > 10 mutations per Mb). Statistical significance was determined using chi-square. Results: Rates of HER2 positivity were comparable using the 2018 and 2007 breast cancer guidelines (19.5% vs 17.5%; p=0.25). Based on 2018 guidelines, the concordance between IHC and CISH was 98.9%. Specifically, 229/1423 patients (16%) were IHC+/CISH+, 5 patients (0.4%) were IHC+/CISH- and 11 patients (0.8%) were IHC-/CISH+ (Table). Common pathway alterations in HER2+ USC include TP53, RTK RAS, PI3K, NOTCH, chromatin remodeling and cell cycle genes. Single gene alterations in HER2+ tumors that may implicate HER2 therapy resistance (based on pathway analyses in other tumor types) included PI3K (36%), KRAS (2.6%), and PTEN (2.1%). HER2+ tumors had low immunotherapy biomarker profiles (0.3% MSI-H, 0.8% TMB, 17.1% PD-L1). Conclusions: High concordance rates were observed between CISH and IHC. Ultimately these testing platforms need to be validated by response to HER2 targeted therapies in order to develop USC specific HER2 testing guidelines.[Table: see text]

HER2 Status in High-Risk Endometrial Cancers (PORTEC-3): Relationship with Histotype, Molecular Classification, and Clinical Outcomes

HER2 status has not been investigated in the context of the molecular endometrial cancer (EC) classification. Here, we aimed to determine the clinicopathological features and prognostic significance of the HER2 status in the molecularly classified PORTEC-3 trial population of patients with high-risk EC (HREC). HER2 testing was performed on tumor tissues of 407 molecularly classified HREC. HER2 status was determined by HER2 immunohistochemistry (IHC; all cases) and subsequent HER2 dual in situ hybridization for cases with any (in) complete moderate to strong membranous HER2 IHC expression. The Χ2 test and Spearman’s Rho correlation coefficient were used to compare clinicopathological and molecular features. The Kaplan–Meier method, log-rank test, and Cox proportional hazards models were used for survival analysis. We identified 24 (5.9%) HER2-positive EC of various histological subtypes including serous (n = 9, 37.5%), endometrioid (n = 6, 25.0%), and clear cell (n = 5, 20.8%). HER2 positivity was highly associated with the p53-abnormal subgroup (p53abn, 23/24 cases; p < 0.0001). The correlation between p53abn and the HER2 status (ρ = 0.438; p < 0.0001) was significantly stronger (p < 0.0001) than between serous histology and the HER2 status (ρ = 0.154; p = 0.002). HER2 status did not have independent prognostic value for survival after correction for the molecular classification. Our study strongly suggests that molecular subclass-directed HER2 testing is superior to histotype-directed testing. This insight will be relevant for future trials targeting HER2.