Dataset Information

Dynamic evaluation of the immune infiltrate and immune function genes as predictive markers for neoadjuvant chemotherapy in hormone receptor positive, HER2 negative breast cancer.

ABSTRACT: Gene expression (GE) signatures and Tumor Infiltrating Lymphocytes (TIL) enumeration are predictive for response to neoadjuvant chemotherapy in HR- and in HER2+ breast cancer, but data are conflicting in HR+/HER2- disease. This study aimed to explore their predictive value in this subset, measured both at baseline and after short exposure to chemotherapy. Specifically, the PROMIX phase 2 trial enrolled patients with locally advanced HER2- BC to receive six cycles of epirubicin and docetaxel, plus bevacizumab during cycles 3-6. Patients underwent tumor biopsies at baseline and after cycle 2 for GE profiling and enumeration of TIL, FOXP3+ T-cells and CD163+ macrophages. An immune related gene module and the quantification of the immune infiltrate were analyzed for association with pathologic complete response (pCR), decrease in tumor size and disease-free survival (DFS). Of the 150 patients enrolled in PROMIX, 113 were HR+/HER2-. Baseline GE and immune cell enumeration data were available from 71 patients, while data after 2 cycles of chemotherapy were available from 41. At baseline, only GE was statistically significantly associated with higher pCR rates (OR 2.29, 95% CI 1.05 - 5.38, p = 0.037) and decrease in tumor size (r = 0.25, p = 0.047). In contrast, longitudinal data indicate that both GE (r = 0.54, p<0.001) and TIL abundance (p = 0.009) are stronger predictors for the reduction of tumor size, while low FOXP3+ was statistically significantly associated with an improved DFS (p = 0.027). In conclusion, GE analysis, TIL and FOXP3+ enumeration after short-term exposure to chemotherapy carry important predictive information in HR+/HER2- breast cancer at the neoadjuvant setting.

SUBMITTER: Matikas A

PROVIDER: S-EPMC6140817 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Dynamic evaluation of the immune infiltrate and immune function genes as predictive markers for neoadjuvant chemotherapy in hormone receptor positive, HER2 negative breast cancer.

Matikas Alexios A Lövrot John J Ramberg Anna A Eriksson Margareta M Lindsten Therese T Lekberg Tobias T Hedenfalk Ingrid I Loman Niklas N Bergh Jonas J Hatschek Thomas T Erlandsson Ann A Foukakis Theodoros T

Oncoimmunology 20180726 9

Gene expression (GE) signatures and Tumor Infiltrating Lymphocytes (TIL) enumeration are predictive for response to neoadjuvant chemotherapy in HR- and in HER2+ breast cancer, but data are conflicting in HR+/HER2- disease. This study aimed to explore their predictive value in this subset, measured both at baseline and after short exposure to chemotherapy. Specifically, the PROMIX phase 2 trial enrolled patients with locally advanced HER2- BC to receive six cycles of epirubicin and docetaxel, plu ...[more]

PMID: 30228933

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Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (M-bM-^@M-^XNKI datasetM-bM-^@M-^Y). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.

Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (‘NKI dataset’). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.

Dataset Information

Dynamic evaluation of the immune infiltrate and immune function genes as predictive markers for neoadjuvant chemotherapy in hormone receptor positive, HER2 negative breast cancer.

Publications

Dynamic evaluation of the immune infiltrate and immune function genes as predictive markers for neoadjuvant chemotherapy in hormone receptor positive, HER2 negative breast cancer.

Similar Datasets

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