PD-L1 Expression Correlates with Tumor-Infiltrating Lymphocytes and Response to Neoadjuvant Chemotherapy in Breast Cancer

Wimberly, Hallie; Brown, Jennifer N.; Schalper, Kurt A.; Haack, Herbert; Silver, Matthew R.; Nixon, Christian P.; Bossuyt, Veerle; Pusztai, Lajos; Lannin, Donald R.; Rimm, David L.

doi:10.1158/2326-6066.cir-14-0133

Cited by 342 publications

(298 citation statements)

References 33 publications

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“…In 2014, 46 they showed that "PDL1 mRNA expression high" (57%) patients presented higher rates of pCR than "PDL1 mRNA expression low" (43%) patients (p < 0.001). Wimberley et al 47 showed that PDL1 protein levels in the epithelium and stroma were correlated with pCR only in hormone receptor-positive and HER2-amplified breast cancers. Denkert et al 44 demonstrated the importance of TIL and immune GE signatures for predicting pCR in breast carcinoma.…”

Section: Discussionmentioning

confidence: 99%

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

et al. 2015

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Triple-negative breast cancer (TNBC) is a heterogeneous group of aggressive breast cancers for which no targeted treatment is available. Robust tools for TNBC classification are required, to improve the prediction of prognosis and to develop novel therapeutic interventions. We analyzed 3,247 primary human breast cancer samples from 21 publicly available datasets, using a five-step method: (1) selection of TNBC samples by bimodal filtering on ER-HER2 and PR, (2) normalization of the selected TNBC samples, (3) selection of the most variant genes, (4) identification of gene clusters and biological gene selection within gene clusters on the basis of String© database connections and gene-expression correlations, (5) summarization of each gene cluster in a metagene. We then assessed the ability of these metagenes to predict prognosis, on an external public dataset (METABRIC). Our analysis of gene expression (GE) in 557 TNBCs from 21 public datasets identified a six-metagene signature (167 genes) in which the metagenes were enriched in different gene ontologies. The gene clusters were named as follows: Immunity1, Immunity2, Proliferation/DNA damage, AR-like, Matrix/Invasion1 and Matrix2 clusters respectively. This signature was particularly robust for the identification of TNBC subtypes across many datasets (n D 1,125 samples), despite technology differences (Affymetrix© A, Plus2 and Illumina©). Weak Immunity two metagene expression was associated with a poor prognosis (disease-specific survival; HR D 2.68 [1.59-4.52], p D 0.0002). The six-metagene signature (167 genes) was validated over 1,125 TNBC samples. The Immunity two metagene had strong prognostic value. These findings open up interesting possibilities for the development of new therapeutic interventions.

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Section: Discussionmentioning

confidence: 99%

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

et al. 2015

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“…However, in contrast to CD8, PD-L1 expression has been ascribed ambivalent prognostic significance. It has been correlated with decreased survival in esophageal, gastric, ovarian, pancreatic, and renal cell carcinomas (20)(21)(22)(23)(24)(25), and has been associated with an improved survival in patients with Merkel cell (26), breast (27), and cervical carcinomas (28). Conflicting reports also exist regarding its significance within the same tumor type (e.g., melanoma and NSCLC) (6,(29)(30)(31).…”

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confidence: 99%

Association of PD-1/PD-L axis expression with cytolytic activity, mutational load, and prognosis in melanoma and other solid tumors

Danilova

Wang

Sunshine

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

151

120

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Programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint blockade has led to remarkable and durable objective responses in a number of different tumor types. A better understanding of factors associated with the PD-1/PD-L axis expression is desirable, as it informs their potential role as prognostic and predictive biomarkers and may suggest rational treatment combinations. In the current study, we analyzedPD-L1,PD-L2,PD-1, and cytolytic activity (CYT) expression, as well as mutational density from melanoma and eight other solid tumor types using The Cancer Genome Atlas database. We found that in some tumor types,PD-L2expression is more closely linked toTh1/IFNGexpression and PD-1 and CD8 signaling thanPD-L1. In contrast, mutational load was not correlated with aTh1/IFNGgene signature in any tumor type.PD-L1,PD-L2,PD-1,CYTexpression, and mutational density are all positive prognostic features in melanoma, and conditional inference modeling revealedPD-1/CYTexpression (i.e., an inflamed tumor microenvironment) as the most impactful feature, followed by mutational density. This study elucidates the highly interdependent nature of these parameters, and also indicates that future biomarkers for anti–PD-1/PD-L1 will benefit from tumor-type–specific, integrated, mRNA, protein, and genomic approaches.

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“…In another neo-adjuvant study of triple-negative patients who received chemotherapy with or without the anti-EGFR antibody panitumumab, patients with higher CD8+ TILs (defined in this study as a TILs count >118) had an 84% pCR rate as compared to 10% in patients with low TILs (37). In addition, a positive correlation of TILs with PD-L1 expression in cancer cells and stroma and with complete pathologic response (pCR) following neo-adjuvant chemotherapy was shown in another study (38). Specific sub-sets predicting for a good pCR rate included CD8+ infiltrates and FOXP3+ but persistence of FOXP3+ TILs after neo-adjuvant treatment was associated with an inferior complete pathologic response rate (34).…”

Section: Tils As a Prognostic Marker And Possible Predictive Marker Omentioning

confidence: 50%

Immune Blockade Inhibition in Breast Cancer

Voutsadakis

2016

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PD-L1 Expression Correlates with Tumor-Infiltrating Lymphocytes and Response to Neoadjuvant Chemotherapy in Breast Cancer

Cited by 342 publications

References 33 publications

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

Association of PD-1/PD-L axis expression with cytolytic activity, mutational load, and prognosis in melanoma and other solid tumors

Immune Blockade Inhibition in Breast Cancer

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