Purpose: Despite its enormous successes, the overall response rate of cancer immunotherapy remain suboptimal, especially in breast cancer. There is an increased interest in combining immune checkpoint inhibitor with targeted agents to enhance antitumor effect. Anti-angiogenic drugs have been shown to synergize with immune checkpoint blockades, but the optimal setting for combining these two modalities and the underlying mechanisms of synergistic responses are not fully understood. Experimental Design: We tested the combination of anti-PD-1 and different doses of VEGFR2-targeting agents in syngeneic breast cancer mouse models. Tumor infiltrated immune cell subsets were profiled by flow cytometry. A cytokine array was carried out to identify inflammatory changes in different treatment conditions. The efficacy of combined anti-angiogenic and anti-PD-1 therapy was further evaluated in advanced triple-negative breast cancer (TNBC) patients. Results: Blockade of VEGFR2 sensitizes breast tumors to PD-1 blockade in a dose-dependent manner. Although both conventional and low-dose anti-VEGFR2 antibody treatments normalize tumor vessels, low-dose VEGFR2 blockade results in more robust immune cell infiltration and activation and promotes the secretion of osteopontin (OPN) by CD8⁺ T cells. OPN subsequently induces tumor cell production of TGF-β, which in turn upregulates PD-1 expression on immune cells. In advanced TNBC patients, combined treatment with low-dose anti-VEGFR2 inhibitor and anti-PD-1 demonstrated excellent tolerability and efficacy. Higher OPN and TGF-β expressions correlated with improved treatment responses. Conclusions: Together, these results demonstrate a dose-dependent synergism between anti-angiogenic therapy and immune checkpoint blockade, thus providing important insights into the optimal strategies for combining immunotherapy with molecular targeted agents.

Background Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC). Methods Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis. Results Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype. Conclusions This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.

Triple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management. Nomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012. On multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts. We have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

We aimed to demonstrate the prognostic value of TGF-β1 in triple negative breast cancer (TNBC) and its association with clinicopathological characteristics of TNBC. A total of 180 women were randomly selected from non-metastatic invasive TNBC patients diagnosed at two hospitals between 2003 and 2012. Lmmunohistochemistry was performed to semi-quantify the expression of TGF-β1. Relationship between TGF-β1 expression and clinicopathological features was performed by Chi-square test. Univariate and multivariate survival analyses were performed to identify the prognostic role of TGF-β1 expression on survival outcomes. Of the 180 women included in this study, 67 (37.2%) patients expressed high level of TGF-β1. High expression of cytoplasmic TGF-β1 was correlated with higher histologic tumor grade (P < 0.001) and lymph node status (P < 0.001), and more axillary lymph node dissection (P = 0.029). High cytoplasmic TGF-β1 expression was associated with reduced disease-free survival (DFS) and overall survival (OS) by log-rank test (PDFS<0.001, POS = 0.045). However, multivariate survival analyses showed that high TGF-β1 was marginally correlated with unfavorable DFS (hazard ratio (HR) 1.796, 95% CI 0.995-3.242, P = 0.052), while it was not significantly associated with OS (HR 0.747, 95% CI 0.367-1.522, P = 0.422). This multi-centered retrospective study highlights the high expression of cytoplasmic TGF-β1 in TNBC is associated with higher histologic grade and lymph node status, more axillary lymph node dissection, as well as reduced DFS. Our observation that the prognostic role of TGF-β1 in TNBC suggests potential rationale for using therapeutic strategies based on targeting TGF-β1 in advanced tumors.

Purpose: Our understanding of breast cancer in very young women (≤35 years old) remains limited. We aimed to assess the clinicopathological characteristics, molecular subtype, and treatment distribution and prognosis of these young patients compared with patients over 35 years.Methods: We retrospectively analyzed non-metastatic female breast cancer cases treated at three Chinese academic hospitals between January 1, 2008, and December 31, 2018. Local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS) were compared between different age groups and stratified with distinct molecular subtypes.Results: A total of 11,671 women were eligible for the final analyses, and 1,207 women (10.3%) were ≤35 years at disease onset. Very young breast cancer women were more likely to be single or childless, have higher-grade disease, have more probability of lymphovascular invasion (LVI) in tumor and triple-negative subtype, and be treated by lumpectomy, chemotherapy especially more anthracycline- and paclitaxel-based chemotherapy, endocrine therapy plus ovarian function suppression (OFS), anti-HER2 therapy, and/or radiotherapy than older women (P < 0.05 for all). Very young women had the lowest 5-year LRFS and DFS among all age groups (P < 0.001 for all). When stratified by molecular subtype, very young women had the worst outcomes vs. women from the 35~50-year-old group or those from >50-year-old group for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) subtype, including LRFS, DFS, and OS (P < 0.05 for all). In terms of LRFS and DFS, multivariate analyses showed similar results among the different age groups.Conclusion: Our study demonstrated that very young women with breast cancer had higher-grade tumors, more probability of LVI in tumor, and more triple-negative subtype, when compared with older patients. They had less favorable survival outcomes, especially for patients with the HR+/HER2− subtype.