Dataset Information

Tissue Immune Profile: A Tool to Predict Response to Neoadjuvant Therapy in Triple Negative Breast Cancer.

ABSTRACT: Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) can predict better survival outcomes in patients with early triple negative breast cancer (TNBC). Tumor infiltrating lymphocytes (TILs), Programmed Death-Ligand 1 (PD-L1), and Cluster of Differentiation 73 (CD73) are immune-related biomarkers that can be evaluated in the tumor microenvironment. We investigated if the contemporary expression of these biomarkers combined in a tissue immune profile (TIP) can predict pCR better than single biomarkers in TNBC. Tumor infiltrating lymphocytes (TILs), CD73 expression by cancer cells (CC), and PD-L1 expression by immune cells (IC) were evaluated on pre-NACT biopsies. We defined TIP positive (TIP+) as the simultaneous presence of TILS ? 50%, PD-L1 ? 1%, and CD73 ? 40%. To consider the effects of all significant variables on the pCR, multivariate analysis was performed. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used for model selection. We retrospectively analyzed 60 biopsies from patients with TNBC who received standard NACT. Pathological complete response was achieved in 23 patients (38.0%). Twelve (20.0%) cases resulted to be TIP+. The pCR rate was significantly different between TIP+ (91.7%) and TIP- (25.0%) (p < 0.0001). Using a multivariate analysis, TIP was confirmed as an independent predictive factor of pCR (OR 49.7 (6.30-392.4), p < 0.0001). Finally, we compared the efficacy of TIP versus each single biomarker in predicting pCR by AIC and BIC. The combined immune profile is more accurate in predicting pCR (AIC 68.3; BIC 74.5) as compared to single biomarkers. The association between TIP+ and pCR can be proposed as a novel link between immune background and response to chemotherapy in TNBC, highlighting the need to consider an immunological patients' profile rather than single biomarkers.

SUBMITTER: Cerbelli B

PROVIDER: S-EPMC7565153 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tissue Immune Profile: A Tool to Predict Response to Neoadjuvant Therapy in Triple Negative Breast Cancer.

Cerbelli Bruna B Scagnoli Simone S Mezi Silvia S De Luca Alessandro A Pisegna Simona S Amabile Maria Ida MI Roberto Michela M Fortunato Lucio L Costarelli Leopoldo L Pernazza Angelina A Strigari Lidia L Della Rocca Carlo C Marchetti Paolo P d'Amati Giulia G Botticelli Andrea A

Cancers 20200916 9

Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) can predict better survival outcomes in patients with early triple negative breast cancer (TNBC). Tumor infiltrating lymphocytes (TILs), Programmed Death-Ligand 1 (PD-L1), and Cluster of Differentiation 73 (CD73) are immune-related biomarkers that can be evaluated in the tumor microenvironment. We investigated if the contemporary expression of these biomarkers combined in a tissue immune profile (TIP) can predict pCR bett ...[more]

PMID: 32947953

Similar Datasets

Project description:BackgroundNeoadjuvant chemotherapy (NACT) is considered a standard treatment strategy for locally advanced triple negative breast cancer (TNBC). TNBC patients who achieve a pathologic complete response (pCR) are predicted to have a better prognosis while unfavorable chemo-sensitivity is still associated with a higher risk of disease relapse. The objective of this study was to construct a nomogram to predict disease-free survival (DFS) for TNBC patients following NACT.MethodsA total of 165 TNBC patients who underwent standard NACT and surgery were retrospectively reviewed, and data on their clinicopathological factors before and after NACT were collected. Independent prognostic factors for DFS were identified by Cox regression based on lower Akaike information criteria (AIC) and Bayesian information criterion (BIC). A nomogram to predict the 2-year and 5-year DFS following NACT for TNBC was constructed based on training cohort (n = 132) and validated by a validation cohort (n = 33).ResultsEither limited or full pCR (breast-only pCR, node-only pCR, or both-pCR) indicated significantly improved DFS and overall survival (OS) (p < 0.001). Lager residual tumor size (hazard ratio [HR] 1.175, p = 0.011) and the presence of lymphatic vessel invasion (LVI) (HR 3.168, p = 0.001) were identified as independent predictors of disease relapse in the training cohort. Five variables, including age, primary tumor size, histological grade, residual tumor size, and LVI were used to establish the nomogram. The C-index of the nomogram was 0.815, and calibration curves showed an acceptable consistency between the actual and nomogram-predicted 2-year and 5-year DFS. The proposed nomogram demonstrated superior predictive performance compared with Residual Cancer Burden (RCB) classification and the 8th American Joint Committee on Cancer Post Neoadjuvant Therapy Classification (AJCC ypTNM) staging system (area under the curve [AUC] for 2-year DFS: 0.870 vs. 0.758 vs. 0.711, respectively; AUC for 5-year DFS: 0.794 vs. 0.731 vs. 0.702, respectively) in the validation cohort.ConclusionsThe nomogram proposed in our study enabled to quantify the risk of disease relapse and demonstrated superior predictive performance than a survival predict instrument. It was an easy-to-use tool for clinicians to guide individualized surveillance of TNBC patients following standard NACT.

Project description:BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

Project description:BACKGROUND:Triple-negative breast cancer (TNBC) is a uniquely aggressive cancer with high rates of relapse due to resistance to chemotherapy. TNBC expresses higher levels of programmed cell death-ligand 1 (PD-L1) compared to other breast cancers, providing the rationale for the recently approved immunotherapy with anti-PD-L1 monoclonal antibodies (mAbs). A huge effort is dedicated to identify actionable biomarkers allowing for combination therapies with immune-checkpoint blockade. Platelet-derived growth factor receptor ? (PDGFR?) is highly expressed in invasive TNBC, both on tumor cells and tumor microenvironment. We recently proved that tumor growth and lung metastases are impaired in mouse models of human TNBC by a high efficacious PDGFR? aptamer. Hence, we aimed at investigating the effectiveness of a novel combination treatment with the PDGFR? aptamer and anti-PD-L1 mAbs in TNBC. METHODS:The targeting ability of the anti-human PDGFR? aptamer toward the murine receptor was verified by streptavidin-biotin assays and confocal microscopy, and its inhibitory function by transwell migration assays. The anti-proliferative effects of the PDGFR? aptamer/anti-PD-L1 mAbs combination was assessed in human MDA-MB-231 and murine 4?T1 TNBC cells, both grown as monolayer or co-cultured with lymphocytes. Tumor cell lysis and cytokines secretion by lymphocytes were analyzed by LDH quantification and ELISA, respectively. Orthotopic 4?T1 xenografts in syngeneic mice were used for dissecting the effect of aptamer/mAb combination on tumor growth, metastasis and lymphocytes infiltration. Ex vivo analyses through immunohistochemistry, RT-qPCR and immunoblotting were performed. RESULTS:We show that the PDGFR? aptamer potentiates the anti-proliferative activity of anti-PD-L1 mAbs on both human and murine TNBC cells, according to its human/mouse cross-reactivity. Further, by binding to activated human and mouse lymphocytes, the aptamer enhances the anti-PD-L1 mAb-induced cytotoxicity of lymphocytes against tumor cells. Importantly, the aptamer heightens the antibody efficacy in inhibiting tumor growth and lung metastases in mice. It acts on both tumor cells, inhibiting Akt and ERK1/2 signaling pathways, and immune populations, increasing intratumoral CD8?+?T cells and reducing FOXP3?+?Treg cells. CONCLUSION:Co-treatment of PDGFR? aptamer with anti-PD-L1 mAbs is a viable strategy, thus providing for the first time an evidence of the efficacy of PDGFR?/PD-L1 co-targeting combination therapy in TNBC.

Dataset Information

Tissue Immune Profile: A Tool to Predict Response to Neoadjuvant Therapy in Triple Negative Breast Cancer.

Publications

Tissue Immune Profile: A Tool to Predict Response to Neoadjuvant Therapy in Triple Negative Breast Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets