Deciphering the Clinical Behaviour of Invasive Lobular Carcinoma of the Breast Defines an Aggressive Subtype.

BACKGROUND: Invasive lobular carcinoma (ILC), the most common special type of breast cancer (BC), has unique clinical behaviour and is different from invasive ductal carcinoma of no special type (IDC-NST). However, ILC further comprises a diverse group of tumours with distinct features. This study aims to examine the clinicopathological and prognostic features of different variants of ILC, with a particular focus on characterising aggressive subtypes. METHODS: A large (n = 7140) well-characterised and histologically reviewed BC cohort with treatment and long-term follow-up data was investigated. The cohort was classified based on the WHO classification of tumours into main histological subtypes, including ILC and IDC-NST. ILCs were further classified into variants. Clinicopathological parameters and patient outcomes in terms of BC-specific survival (BCSS) and disease-free survival (DFS) were evaluated. RESULTS: ILC constituted 11% of the cohort. The most common non-classic ILC variants were pleomorphic (pILC) and solid (sILC), constituting 19% of ILC. Compared to classic and related variants (alveolar, trabecular, papillary, and tubulolobular; cILC), pILC and sILC variants were associated with aggressive tumour characteristics. The histologic grade of ILC was an important prognostic variable. The survival patterns identified an aggressive ILC subtype encompassing pILC and high-grade sILC. These tumours, which comprised 14% of the cases, were associated with clinicopathological characteristics of poor prognosis and had high BC-specific death and recurrence rates compared not only to cILC (p < 0.001) but also to IDC-NST (p = 0.02) patients. Contrasting this, cILC patients had significantly longer BCSS and DFS than IDC-NST patients in the first 10 to 15 years of follow-up. Adjuvant chemotherapy did not improve the outcome of patients with aggressive ILC subtypes. CONCLUSIONS: pILC and high-grade sILC variants comprise an aggressive ILC subtype associated with poor prognostic characteristics and a poor response to chemotherapy. These results warrant confirmation in randomised clinical trials.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Quantitative expression of oestrogen receptor in breast cancer: Clinical and molecular significance.

BACKGROUND: Oestrogen receptor (ER) positive breast cancer (BC) patients are eligible for endocrine therapy (ET), regardless of ER immunohistochemical expression level. There is a wide spectrum of ER expression and the response to ET is not uniform. This study aimed to assess the clinical and molecular consequences of ER heterogeneity with respect to ET-response. METHODS: ER expression, categorised by percentage and staining intensity in a large BC cohort (n = 7559) was correlated with clinicopathological parameters and patient ET response. The Cancer Genome Atlas Data BC cohort (n = 1047) was stratified by ER expression and transcriptomic analysis completed to better understand the molecular basis of ER heterogeneity. RESULTS: The quantitative proportional increase in ER expression was positively associated with favourable prognostic parameters. Tumours with 1-9% ER expression were characteristically similar to ER-negative (<1%) tumours. Maximum ET-response was observed in tumours with 100% ER expression, with responses significantly different to tumours exhibiting ER at < 100% and significantly decreased survival rates were observed in tumours with 50% and 10% of ER expression. The Histochemical-score (H-score), which considers both staining intensity and percentage, added significant prognostic value over ER percentage alone with significant outcome differences observed at H-scores of 30, 100 and 200. There was a positive correlation between ER expression and ESR1 mRNA expression and expression of ER-regulated genes. Pathway analysis identified differential expression in key cancer-related pathways in different ER-positive groups. CONCLUSION: ET-response is statistically proportionally related to ER expression with significant differences observed at 10%, 50% and 100%. The H-score adds prognostic and predictive information.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Characterisation of luminal and triple-negative breast cancer with HER2 Low protein expression.

BACKGROUND: Breast cancer (BC) expressing low levels of human epidermal growth factor receptor 2 (HER2 Low) is an emerging category that needs further refining. This study aims to provide a comprehensive clinico-pathological and molecular profile of HER2 Low BC including response to therapy and patient outcome in the adjuvant and neoadjuvant settings. METHODS: Two different independent and well-characterised BC cohorts were included. Nottingham cohort (A) (n = 5744) and The Cancer Genome Atlas (TCGA) BC cohort (B) (n = 854). The clinical, molecular, biological and immunological profile of HER2 Low BC was investigated. Transcriptomic and pathway enrichment analyses were performed on the TCGA BC cohort and validated through next-generation sequencing in a subset of Nottingham cases. RESULTS: Ninety percent of HER2 Low tumours were hormone receptor (HR) positive (HR+), enriched with luminal intrinsic molecular subtype, lacking significant expression of HER2 oncogenic signalling genes and of favourable clinical behaviour compared to HER2 negative (HER2-) BC. In HR+ BC, no significant prognostic differences were detected between HER2 Low and HER2- tumours. However, in HR- BC, HER2 Low tumours were less aggressive with longer patient survival. Transcriptomic data showed that the majority of HR- /HER2 Low tumours were of luminal androgen receptor (LAR) intrinsic subtype, enriched with T-helper lymphocytes, activated dendritic cells and tumour associated neutrophils, while most HR-/HER2- tumours were basal-like, enriched with tumour associated macrophages. CONCLUSION: HER2 Low BC is mainly driven by HR signalling in HR+ tumours. HR-/HER2 Low tumours tend to be enriched with LAR genes with a unique immune profile.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

Concordance of HER2-low scoring in breast carcinoma among expert pathologists in the United Kingdom and the republic of Ireland -on behalf of the UK national coordinating committee for breast pathology.

BACKGROUND: Recent clinical evidence showed that breast cancer with low HER2 expression levels responded to trastuzumab deruxtecan therapy. The HER2-low cancers comprise immunohistochemistry (IHC) score 1+ and 2+ ISH non-amplified tumours, currently classified as HER2 negative. Little data exists on the reproducibility of pathologists reporting of HER2-low cancer. PATIENT AND METHODS: Sixteen expert pathologists of the UK National Coordinating Committee for Breast Pathology scored 50 digitally scanned HER2 IHC slides. The overall level of agreement, Fleiss multiple-rater kappa statistics and Cohen's Kappa were calculated. Cases with low concordance were re-scored by the same pathologists after a washout period. RESULTS: Absolute agreement was achieved in 6% of cases, all of which scored 3+. Poor agreement was found in 5/50 (10%) of cases. This was due to heterogeneous HER2 expression, cytoplasmic staining and low expression spanning the 10% cut-off value. Highest concordance (86%) was achieved when scores were clustered as 0 versus others. Improvement in kappa of overall agreement was achieved when scores 1+ and 2+ were combined. Inter-observer agreement was moderate to substantial in the whole cohort but fair to moderate in the HER2-low group. Similarly, consensus-observer agreement was substantial to almost perfect in the whole cohort and moderate to substantial in the HER2-low group. CONCLUSION: HER2-low breast cancer suffers from lower concordance among expert pathologists. While most cases can reproducibly be classified, a small proportion (10%) remained challenging. Refining the criteria for reporting and consensus scoring will help select appropriate patients for targeted therapy.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence.

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

Combined proliferation and apoptosis index provides better risk stratification in breast cancer.

AIMS: Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision-making. Despite the well-established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in-vivo growth rate measure and a precise prognostic predictor. METHODS AND RESULTS: Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 BC cases derived from two well-defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BC patients with a high combined AI and MI had the shortest survival. Replacing the mitosis score with the mitosis-apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC-specific survival with a higher hazard ratio. CONCLUSION: Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.

Refining the definition of HER2-low class in invasive breast cancer.

BACKGROUND: Emerging evidence indicates that breast cancer (BC) patients whose tumours express HER2 protein without HER2 gene amplification (HER2-low), can benefit from antibody-drug conjugates (ADC). However, the current definition of HER2-low BC remains incomplete with low rates of concordance. This study aims to refine HER2-low definition with emphasis on distinguishing HER2 score 0 from score 1+ to identify patients who are eligible for ADC. METHODS: A BC cohort (n = 363) with HER2 IHC scores 0, 1+ and 2+ (without HER2 gene amplification) and available HER2 mRNA was included. HER2 staining intensity, pattern and subcellular localisation were reassessed. Artificial neural network analysis was applied to cluster the cohort and to distinguish HER2 score 0 from 1+. Reproducibility and reliability of the refined criteria were tested. RESULTS: HER2 IHC score 1+ was refined as membranous staining in invasive cells as either: (1) faint intensity in ≥ 20% of cells regardless the circumferential completeness, (2) weak complete staining in ≤ 10%, (3) weak incomplete staining in > 10% and (4) moderate incomplete staining in ≤ 10%. Based on this, 63% of the HER2-negative cases were reclassified as positive (HER2-low). The refined score showed perfect observer agreement compared to the moderate agreement in the original clinical scores. Similar results were generated when the refined score was applied on the independent BC cohorts. A proposal to refine the definition of other HER2 classes is presented. CONCLUSION: This study refined the definition of HER2-low BC based on correlation with HER2 mRNA and distinguished between HER2 IHC score 1+ and score 0 tumours.

Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice.

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.