Mise à jour 2014 des recommandations du GEFPICS pour l’évaluation du statut HER2 dans les cancers du sein en France

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

An immunological signature to predict outcome in patients with triple-negative breast cancer with residual disease after neoadjuvant chemotherapy

Background

When triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post-NACT samples to predict relapse.

Materials and methods

We identified 115 samples of residual tumors from post-NACT TNBC patients. We profiled the expression of 770 genes related to cancer microenvironment using the NanoString PanCancer IO360 panel to develop a prognostic transcriptomic signature, and we describe the immune microenvironments of the residual tumors.

Results

Thirty-eight (33%) patients experienced metastatic relapse. Hierarchical clustering separated patients into five clusters with distinct prognosis based on pathways linked to immune activation, epithelial-to-mesenchymal transition and cell cycle. The immune microenvironment of the residual disease was significantly different between patients who experienced relapse compared to those who did not, the latter having significantly more effector antitumoral immune cells, with significant differences in lymphoid subpopulations. We selected eight genes linked to immunity (BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7) to develop a transcriptomic signature which could predict relapse in our cohort. This signature was validated in two external cohorts (KMplot and METABRIC).

Conclusions

Lack of immune activation after NACT is associated with a high risk of distant relapse. We propose a prognostic signature based on immune infiltrate that could lead to targeted therapeutic strategies to improve patient prognosis.

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Infiltrate of cytotoxic cells is higher in the residual disease of TNBC patients who will not experience metastatic relapse.

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Underexpression of immune-related pathways is associated with metastatic relapse in residual disease of TNBC patients.

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An immune gene-based signature can predict metastatic relapse in TNBC patients after NACT.

Impact of lobular versus ductal histology on overall survival in metastatic breast cancer: a French retrospective multicentre cohort study

BACKGROUND

The impact of the histological lobular subtype on overall survival (OS) in metastatic breast cancer (MBC) is still under debate, with very few data available.

PATIENTS AND METHODS

Using the French national multicentre Epidemiological Strategy and Medico Economics [ESME]) data platform, the primary objective was to compare the OS of patients with invasive lobular carcinoma (ILC) versus invasive ductal carcinoma (IDC) MBC, with adjustment on the main prognostic factors using two approaches: multivariable analysis and matching with a propensity score. Secondary objectives were to compare first-line progression-free survival (PFS1) and describe patients and tumour characteristics.

RESULTS

Of the 16,703 patients with MBC in the ESME database, 13,111 met all inclusion criteria for the present analysis. One-thousand eight-hundred and four (13.8%) patients had ILC and 11.307 (86.2%) IDC. In the multivariable analysis, patients with ILC had a worse OS [hazard ratio (HR): 1.31; 95%CI 1.20-1.42; p < 0.0001] and a worse PFS1 (HR: 1.15; 95%CI 1.07-1.22; p < 0.0001) as compared with those with IDC, independently of hormone receptor and HER2 status. Interestingly, OS was better (HR 0.79; 95% confidence interval [CI] 0.64-0.98; p = 0.0302), worse (HR: 1.17; 95%CI 1.08-1.27; p = 0.0001) or similar (HR: 0.88; 95%CI 0.67-1.15; p = 0.3455) in patients with ILC with triple-negative, hormone receptor-positive/HER2-negative and HER2-positive MBC, respectively, compared with patients with IDC.

CONCLUSION

Lobular histology is an independent adverse prognostic factor among women with MBC. ILC MBC could be considered a specific entity. Dedicated prospective studies are needed to tailor the management of these patients.

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours

Objective

To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).

Methods

This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour.

Results

In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower.

Conclusion

Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status.

Key Points

• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography.

• The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors.

• Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08538-4.

L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2

HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negative categories is well implemented and reproducible, the introduction of a new "HER2-low" category could raise some concerns about its scoring and reproducibility. We herein described the current HER2 testing methods and the application of innovative machine learning techniques to improve these determinations, as well as the main challenges and opportunities related to the implementation of digital pathology in the up-and-coming AI era.

[2021 update of the GEFPICS' recommendations for HER2 status assessment in invasive breast cancer in France]

The last international guidelines on HER2 determination in breast cancer have been updated in 2018 by the American Society of Clinical Oncology and College of American Pathologists, on the basis of a twenty-year practice and results of numerous clinical trials. Moreover, the emerging HER2-low concept for 1+ and 2+ non amplified breast cancers lead to refine French practices for HER2 status assessment. The GEFPICS group, composed of expert pathologists, herein presents the latest French recommendations for HER2 status evaluation in breast cancer, taking into account the ASCO/CAP guidelines and introducing the HER2-low concept. In the era of personalized medicine, HER2 status assessment remains one of the most important biomarkers in breast cancer and its quality guaranties the optimal patients' care. French pathologists' commitment in theranostic biomarker quality is more than ever required to provide the most efficient cares in oncology.

Phenotypic discordance between primary and metastatic breast cancer in the large-scale real-life multicenter French ESME cohort

Expression of hormone receptor (HR) for estrogens (ER) and progesterone (PR) and HER2 remains the cornerstone to define the therapeutic strategy for breast cancer patients. We aimed to compare phenotypic profiles between matched primary and metastatic breast cancer (MBC) in the ESME database, a National real-life multicenter cohort of MBC patients. Patients with results available on both primary tumour and metastatic disease within 6 months of MBC diagnosis and before any tumour progression were eligible for the main analysis. Among the 16,703 patients included in the database, 1677 (10.0%) had available biopsy results at MBC diagnosis and on matched primary tumour. The change rate of either HR or HER2 was 27.0%. Global HR status changed (from positive = either ER or PR positive, to negative = both negative; and reverse) in 14.2% of the cases (expression loss in 72.5% and gain in 27.5%). HER2 status changed in 7.8% (amplification loss in 45.2%). The discordance rate appeared similar across different biopsy sites. Metastasis to bone, HER2+ and RH+/HER2- subtypes and previous adjuvant endocrine therapy, but not relapse interval were associated with an HR discordance in multivariable analysis. Loss of HR status was significantly associated with a risk of death (HR adjusted = 1.51, p = 0.002) while gain of HR and HER2 discordance was not. In conclusion, discordance of HR and HER2 expression between primary and metastatic breast cancer cannot be neglected. In addition, HR loss is associated with worse survival. Sampling metastatic sites is essential for treatment adjustment.

[CD10 expression in stromal cells of patients with breast cancer: a poor prognostic marker]

Introduction

markers in breast cancer stem cells, such as cluster of differentiation 10 (CD10), would be correlated with invasive and metastatic potential of several types of cancer, contributing to tumor growth and metastases. In patients with breast cancer, its prognostic value is still controversial, given the discrepancy of results. The purpose of the study was to study CD10 expression in stromal cells of patients with breast cancer as well as to evaluate the prognostic value of this expression.

Method

we conducted a retrospective, descriptive and prognostic study. It involved 57 patients with invasive cancer of no special type, whose data were collected in the Department of Pathological Anatomy at the Mongi Slim Hospital over a 38-month period. CD10 expression was studied immunohistochemically and interpreted by semi-quantitative scoring system based on three categories with thresholds of 10 and 30%.

Results

the average age of patients was 56.4 years. Twenty-eight patients (49%) had labeled CD10 stromal, half of patients had a score of 1 (low) and the other half had 2 (high). This labeling significantly reduced recurrence-free survival (p=0.001). However, it had no influence on overall survival (p=0.84). The correlation study showed that CD10 expression in stromal cells was significantly correlated with 12 poor prognostic factors in patients with breast cancer.

Conclusion

CD10 expression in stromal cells of invasive breast cancer is a poor prognostic factor, predictive of poor survival without recurrence and associated with a high invasive and metastatic potential.

Analysis of Molecular Pretreated Tumor Profiles as Predictive Biomarkers of Therapeutic Response and Survival Outcomes after Neoadjuvant Therapy for Rectal Cancer in Moroccan Population

Pathologic features depending on tumor response to preoperative chemoradiotherapy are important to determine the outcomes in patients with rectal cancer. Evaluating the potential predictive roles of biomarker expression and their prognostic impact is a promising challenge. We reported here the immunohistochemical staining of a panel marker of mismatch repair protein (MMR), Ki67, HER-2, and p53. Additionally, identification of somatic mutations of KRAS, NRAS, and BRAF genes were performed by direct sequencing and pyrosequencing in pretreated biopsy tissues from 57 patients diagnosed for rectal cancer. Clinical features and pathological criteria for postneoadjuvant treatment surgical resection specimen's data were collected. Immunohistochemical expression and mutational status were correlated with therapeutic response, overall survival, and disease progression. The mean age of patients was 56 years. Seven (12.3%) out of 57 patients had a complete therapeutic response. Our analysis showed that when using complete therapeutic response (Dworak 4) and incomplete therapeutic response (Dworak 3, 2, and 1) as grouping factor, high p53 expression at the pretreatment biopsy was significantly associated to an incomplete response (p = 0.002). For 20 and 2 out of 57, KRAS and NRAS mutations were detected, respectively. The majority of these mutations affected codon 12. KRAS mutations detected at codon 146 (A146T, A146V) was associated with the appearance of recurrence and distant metastasis (p = 0.019). A high expression of HER-2 corresponding to score 3+ was observed in 3 pretreatment biopsy specimens. This class was significantly associated with a short relapse-free survival (p = 0.002). Furthermore, the high expression of Ki67 was moderately correlated with an older age (p = 0.016, r = 0.319). In addition, this shows that high p53 expression in the pretreatment biopsy was associated with an incomplete response in surgical resection specimens after neoadjuvant treatment, and a HER-2 score 3+ can be a predictive factor of distant metastasis and local recurrence. Larger, prospective, and more studies are needed.

Residual cancer burden index and tumor-infiltrating lymphocyte subtypes in triple-negative breast cancer after neoadjuvant chemotherapy

PURPOSE

There is a need to refine the prognosis of triple-negative breast cancer (TNBC) patients after neoadjuvant chemotherapy (NAC) and to study the influence of the tumor microenvironment. We evaluated the prognostic value of pathological and immune markers in TNBC with residual disease (RD) after NAC.

METHODS

In a series of 186 TNBC patients treated by NAC, we assessed the prognostic value of the Residual Cancer Burden (RCB) index. In 109 patients with RD, we studied the impact of clinicopathological features and tumor immune response in the residual tumor on overall survival (OS) and distant recurrence-free interval (DRFI).

RESULTS

In the whole group, the OS and DRFI, at 3 years, were statistically different between the different classes of RCB (P = 0.0004 and P < 0.0001, respectively). In univariate analysis of the RD group, low RCB index and high ratios of stromal tumor-infiltrating lymphocytes (TILs), CD3 + TILs, CD4 + TILs, CD8 + TILs, and IDO1-positive cells were significant favorable prognostic factors for DRFI at 3 years. In the final multivariate model, CD4 + TILs and RCB index showed a statistically independent prognostic significance for DRFI [Hazard Ratio (HR) 2.88 (95%CI 1.34-6.17), P = 0.007 and HR 12.04 (95%CI 2.78-52.23, P < 0.0001), respectively]. The CD4 + TIL levels influenced survival in the different RCB classes with a significant effect observed in RCB-II and RCB-III classes (P = 0.05 and P = 0.05, respectively).

CONCLUSIONS

These results suggest that the combination of pathological (RCB index) and tumor micro-environmental features (CD4 + TILs) help refining the prognosis of TNBC patients with RD following NAC.

ERBB2 mutations associated with solid variant of high-grade invasive lobular breast carcinomas

ERBB2 and ERBB3 somatic gain-of-function mutations, which may be targeted by anti-ERBB2 therapies, were reported by high-throughput sequencing studies in 1% and 2% of invasive breast cancers respectively. Our study aims to determine ERBB2 and ERBB3 mutations frequencies in grade 3 and/or ERBB2-positive invasive lobular breast carcinomas (ILC). All the 529 ILC surgically-excised registered at Institut Curie in the years 2005 to 2008 were reviewed. Thirty-nine grade 3 ERBB2-negative ILC and 16 ERBB2-positive ILC were retrieved and subjected to Sanger sequencing of the ERBB2 and ERBB3 activation mutation hotspots (ERBB2: exons 8, 17, 19, 20, 21; ERBB3: exons 3, 6, 7, 8). Among the 39 grade 3 ERBB2-negative ILC, six tumors were found to have at least one detectable ERBB2 activating mutation (incidence rate: 15%, 95%CI [4%-27%]). No ERBB2 mutation was found among the 16 ERBB2-positive ILC. No ERBB3 mutation was found in any of the 55 ILC. ERBB2 mutations were statistically associated with solid ILC features (p=0.01). Survival analyses showed no significant prognostic impact of ERBB2 mutations. Our study demonstrates that high grade ERBB2-negative ILC display a high frequency of ERBB2 mutations, and should be subjected to systematic genetic screening.

Microfluidics-assisted fluorescence in situ hybridization for advantageous human epidermal growth factor receptor 2 assessment in breast cancer

Fluorescence in situ hybridization (FISH) is one of the recommended techniques for human epidermal growth factor receptor 2 (HER2) status assessment on cancer tissues. Here we develop microfluidics-assisted FISH (MA-FISH), in which hybridization of the FISH probes with their target DNA strands is obtained by applying square-wave oscillatory flows of diluted probe solutions in a thin microfluidic chamber of 5 μl volume. By optimizing the experimental parameters, MA-FISH decreases the consumption of the expensive probe solution by a factor 5 with respect to the standard technique, and reduces the hybridization time to 4 h, which is four times faster than in the standard protocol. To validate the method, we blindly conducted HER2 MA-FISH on 51 formalin-fixed paraffin-embedded tissue slides of 17 breast cancer samples, and compared the results with standard HER2 FISH testing. HER2 status classification was determined according to published guidelines, based on average number of HER2 copies per cell and average HER2/CEP17 ratio. Excellent agreement was observed between the two methods, supporting the validity of MA-FISH and further promoting its short hybridization time and reduced reagent consumption.

Axillary lymph node micrometastases decrease triple-negative early breast cancer survival

BACKGROUND

Triple-negative breast cancers (TNBCs) are the most deadly form of breast cancer (BC) subtypes. Axillary lymph node involvement (ALNI) has been described to be prognostic in BC taken as a whole, but its prognostic value in each subtype is unclear. We explored the prognostic impact of ALNI and especially of small size axillary metastases in early TNBCs.

METHODS

We analysed in this multicentre study all patients treated for early TNBC in 12 French cancer centres. We explored the correlation between clinicopathological data and ALNI, with a specific focus on the dichotomisation between macrometastases and occult metastases, which is defined as the presence of isolated tumour cells or micrometastases. The prognostic value of ALNI both in terms of disease-free survival (DFS) and overall survival (OS) was also explored.

RESULTS

We included 1237 TNBC patients. Five-year DFS and OS were 83.7% and 88.5%, respectively. The identified independent prognostic features for DFS were tumour size >20 mm (hazard ratio (HR)=1.86; 95% CI: 1.11-3.10, P=0.018), lymphovascular invasion (HR=1.69; 95% CI: 1.21-2.34, P=0.002) and ALNI both in case of macrometastases (HR=1.97; 95% CI: 1.38-2.81, P<0.0001) and occult metastases (HR=1.72; 95% CI: 1.1-2.71, P=0.019). DFS and OS were similar between tumours with occult metastases and macrometastases. Tumours presenting at least two pejorative features (out of ALNI, lymphovascular invasion and large tumour size) displayed a significantly poorer DFS in both the training set and validation set, independently of chemotherapy administration. Tumours with no more than one of the above-cited pejorative features had a 5-year OS of ⩾90% vs 70% for other cases (P<0.0001).

CONCLUSIONS

Axillary lymph node involvement is a key prognostic feature for early TNBC when isolated tumour cells were identified in lymph nodes. This impact is independent of chemotherapy use.