A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in <i>HER2</i>-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways

<div><p>Introduction</p><p><i>HER2</i>-positive breast cancer (BC) is a heterogeneous group of aggressive breast cancers, the prognosis of which has greatly improved since the introduction of treatments targeting <i>HER2</i>. However, these tumors may display intrinsic or acquired resistance to treatment, and classifiers of <i>HER2</i>-positive tumors are required to improve the prediction of prognosis and to develop novel therapeutic interventions.</p><p>Methods</p><p>We analyzed 2893 primary human breast cancer samples from 21 publicly available datasets and developed a six-metagene signature on a training set of 448 <i>HER2</i>-positive BC. We then used external public datasets to assess the ability of these metagenes to predict the response to chemotherapy (Ignatiadis dataset), and prognosis (METABRIC dataset).</p><p>Results</p><p>We identified a six-metagene signature (138 genes) containing metagenes enriched in different gene ontologies. The gene clusters were named as follows: Immunity, Tumor suppressors/proliferation, Interferon, Signal transduction, Hormone/survival and Matrix clusters. In all datasets, the Immunity metagene was less strongly expressed in ER-positive than in ER-negative tumors, and was inversely correlated with the Hormonal/survival metagene. Within the signature, multivariate analyses showed that strong expression of the “Immunity” metagene was associated with higher pCR rates after NAC (OR = 3.71[1.28–11.91], <i>p</i> = 0.019) than weak expression, and with a better prognosis in <i>HER2</i>-positive/ER-negative breast cancers (HR = 0.58 [0.36–0.94], <i>p</i> = 0.026). Immunity metagene expression was associated with the presence of tumor-infiltrating lymphocytes (TILs).</p><p>Conclusion</p><p>The identification of a predictive and prognostic immune module in <i>HER2</i>-positive BC confirms the need for clinical testing for immune checkpoint modulators and vaccines for this specific subtype. The inverse correlation between Immunity and hormone pathways opens research perspectives and deserves further investigation.</p></div

Association between tumor-infiltrating lymphocyte levels and Immunity metagene expression in the REMAGUS dataset.

<p><b>A:</b> Percentage of intratumoral TILs according to Immunity metagene status (low <i>versus</i> high). <b>B</b> Percentage of stromal TILs according to Immunity metagene status (low <i>versus</i> high). <b>C</b>: Correlation between metagene expression and the percentages of intratumoral TILs. <b>D</b>: Correlation between metagene expression and the percentage of stromal TILs.</p

Gene selection process.

<p><b>A</b> Heatmap showing the 616 most variable genes in the 448 <i>HER2</i>-positive samples (training set). <b>B</b> String database software confidence view of the Matrix genes cluster. Stronger associations between genes are represented by thicker lines. <b>C</b> Cytoscape View for the Immunity gene cluster. GE correlations between genes are indicated by edges (edge color varies from green to red and edge size increases with increasing correlation) and gene expression variance is represented by node color (node color varies from green to red and node size increases with increasing variance). <b>D</b> Heatmap showing the relative expression of 138 selected genes in 448 <i>HER2</i>-positive samples from the training set. <b>E</b> Table of Pearson’s correlation coefficient values for the correlations between the 6 metagenes. <b>F</b> Heatmap showing the anticorrelation between the Immunity and the Hormone/Survival metagene.</p

Survival analysis (disease-specific survival) in the METABRIC dataset (univariate and multivariate analysis); whole population and ER-negative population.

<p>Survival analysis (disease-specific survival) in the METABRIC dataset (univariate and multivariate analysis); whole population and ER-negative population.</p

Lymphocytic infiltration in breast tumors.

<p><b>A</b> and <b>B:</b> Tumor specimen with weak lymphocytic infiltration (A: zoom x10 B: zoom x 40). Abbreviations: S = stroma, T = tumor, L = lymphocytes. Intratumoral TILs are indicated by a black star. <b>C</b> and <b>D</b>: Tumor specimen with prominent lymphocytic infiltration. (C: zoom x10 D: zoom x 40). Abbreviations: S = stroma, T = tumor, L = lymphocytes. Intratumoral TILs are indicated by a black star; stromal TILs are indicated by a blue star.</p

pCR and DSS outcomes in the Ignatiadis and the METABRIC dataset.

<p><b>A:</b> pCR rates by ER and Immunity metagene status (low <i>versus</i> high in the Ignatiadis dataset). <b>B:</b> Kaplan-Meier plots. Disease-specific survival of the ER-negative population (<i>n</i> = 138) according to Immunity metagene expression (low/high) and nodal status in the METABRIC dataset.</p

Prognostic Impact of Time to Ipsilateral Breast Tumor Recurrence after Breast Conserving Surgery

<div><p>Background</p><p>The poor prognosis of patients who experience ipsilateral breast tumor recurrence (IBTR) after breast conserving surgery (BCS) is established. A short time between primary cancer and IBTR is a prognostic factor but no clinically relevant threshold was determined. Classification of IBTR may help tailor treatment strategies.</p><p>Purpose</p><p>We determined a specific time frame, which differentiates IBTR into early and late recurrence, and identified prognostic factors for patients with IBTR at time of the recurrence.</p><p>Methods</p><p>We analyzed 2209 patients with IBTR after BCS. We applied the optimal cut-points method for survival data to determine the cut-off times to IBTR. A subgroup analysis was performed by hormone receptor (HR) status. Survival analyses were performed using a Cox proportional hazard model to determine clinical features associated with distant-disease-free survival (DDFS) after IBTR. We therefor built decision trees.</p><p>Results</p><p>On the 828 metastatic events observed, the majority occurred within the first 3 months after IBTR: 157 in the HR positive group, 98 in the HR negative group. We found different prognostic times to IBTR: 49 months in the HR positive group, 33 in the HR negative group. After multivariate analysis, time to IBTR was the first discriminant prognostic factor in both groups (HR 0.65 CI95% [0.54–0.79] and 0.42 [0.30–0.57] respectively). The other following variables were significantly correlated with the DDFS: the initial number of positive lymph nodes for both groups, the initial tumor size and grade for HR positive tumors.</p><p>Conclusion</p><p>A short interval time to IBTR is the strongest factor of poor prognosis and reflects occult distant disease. It would appear that prognosis after IBTR depends more on clinical and histological parameters than on surgical treatment. A prospective trial in a low-risk group of patients to validate the safety of salvage BCS instead of mastectomy in IBTR is needed.</p></div

Decision tree algorithm HR = hazard ratio for distant disease at 10 years.

<p>(A) Patients with hormone receptor positive tumors. (B) Patients with hormone receptor negative tumors.</p

Distant disease free survival curves according prognostic subgroup.

<p>(A) Patients with hormone receptor positive tumors. (B) Patients with hormone receptor negative tumors.</p

Cutpoint determination.

<p>(larger values indicate cut point more likely here). (A) Patients with hormone receptor positive tumors: 49 months. (B) Patients with hormone receptor negative tumors: 33 months.</p