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. 2016 Dec 13;13(12):e1002194.
doi: 10.1371/journal.pmed.1002194. eCollection 2016 Dec.

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

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Free PMC article

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

H Raza Ali et al. PLoS Med. .
Free PMC article

Abstract

Background: Immune infiltration of breast tumours is associated with clinical outcome. However, past work has not accounted for the diversity of functionally distinct cell types that make up the immune response. The aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in breast tumours influence survival and treatment response, and whether these effects differ by molecular subtype.

Methods and findings: We applied an established computational approach (CIBERSORT) to bulk gene expression profiles of almost 11,000 tumours to infer the proportions of 22 subsets of immune cells. We investigated associations between each cell type and survival and response to chemotherapy, modelling cellular proportions as quartiles. We found that tumours with little or no immune infiltration were associated with different survival patterns according to oestrogen receptor (ER) status. In ER-negative disease, tumours lacking immune infiltration were associated with the poorest prognosis, whereas in ER-positive disease, they were associated with intermediate prognosis. Of the cell subsets investigated, T regulatory cells and M0 and M2 macrophages emerged as the most strongly associated with poor outcome, regardless of ER status. Among ER-negative tumours, CD8+ T cells (hazard ratio [HR] = 0.89, 95% CI 0.80-0.98; p = 0.02) and activated memory T cells (HR 0.88, 95% CI 0.80-0.97; p = 0.01) were associated with favourable outcome. T follicular helper cells (odds ratio [OR] = 1.34, 95% CI 1.14-1.57; p < 0.001) and memory B cells (OR = 1.18, 95% CI 1.0-1.39; p = 0.04) were associated with pathological complete response to neoadjuvant chemotherapy in ER-negative disease, suggesting a role for humoral immunity in mediating response to cytotoxic therapy. Unsupervised clustering analysis using immune cell proportions revealed eight subgroups of tumours, largely defined by the balance between M0, M1, and M2 macrophages, with distinct survival patterns by ER status and associations with patient age at diagnosis. The main limitations of this study are the use of diverse platforms for measuring gene expression, including some not previously used with CIBERSORT, and the combined analysis of different forms of follow-up across studies.

Conclusions: Large differences in the cellular composition of the immune infiltrate in breast tumours appear to exist, and these differences are likely to be important determinants of both prognosis and response to treatment. In particular, macrophages emerge as a possible target for novel therapies. Detailed analysis of the cellular immune response in tumours has the potential to enhance clinical prediction and to identify candidates for immunotherapy.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study flowchart detailing the flow of samples at each stage of analysis.
*[15]. [16]. ER, oestrogen receptor; pCR, pathological complete response; MI, multiple imputation; TGCA, The Cancer Genome Atlas.
Fig 2
Fig 2. Summary of inferred immune cell subsets by study.
(A) Bar charts summarising immune cell subset proportions against ER status and CIBERSORT p-value by study. (B) Box plots depicting the association between immune cytolytic activity and CIBERSORT p-value (outliers are not shown; depicted chi-squared statistics and p-values are from Kruskal-Wallis tests); survival plots of groups defined by CIBERSORT p-value separately by ER status (depicted chi-squared statistics and p-values are from log-rank tests). *0.01 ≤ p < 0.05. a.u., arbitrary units; ER, oestrogen receptor; NK cells, natural killer cells; TGCA, The Cancer Genome Atlas.
Fig 3
Fig 3. Prognostic associations of subsets of immune cells.
(A) Unadjusted HRs (boxes) and 95% confidence intervals (horizontal lines) limited to cases with CIBERSORT p-value < 0.05. Box size is inversely proportional to the width of the confidence interval. Asterisks denote estimates with a q-value < 0.05. (B) Survival plots of quartiles of immune cell subsets. Depicted p-values are from log-rank tests. ER, oestrogen receptor; HR, hazard ratio; NK cells, natural killer cells.
Fig 4
Fig 4. Hazard ratios for three overlapping populations defined by CIBERSORT p-value.
Boxes represent hazard ratios, and vertical lines are 95% confidence intervals. Box size is inversely proportional to the width of the confidence interval. ER, oestrogen receptor; NK cells, natural killer cells.
Fig 5
Fig 5. Survival plots highlighting the patient subgroup with tumours containing little or no immune infiltration by CIBERSORT p-value.
Depicted p-values are from log-rank tests. ER, oestrogen receptor; T-regs, T regulatory cells.
Fig 6
Fig 6. Association between immune cell subsets and response to neoadjuvant chemotherapy.
(A) Boxes represent ORs from unadjusted logistic regression models. Horizontal lines are 95% confidence intervals. Box size is inversely proportional to the width of the confidence interval. Asterisks denote estimates with q-value < 0.05. (B) Spine plots depicting the association between quartiles of immune cell subsets and pCR. ER, oestrogen receptor; NK cells, natural killer cells; OR, odds ratio; pCR, pathological complete response.
Fig 7
Fig 7. Hierarchical clustering of all samples based on immune cell proportions.
Stacked bar charts of samples ordered by cluster assignment. NK cells, natural killer cells.
Fig 8
Fig 8. Survival plots by cluster separately for ER-positive and ER-negative disease.
Depicted p-values are from log-rank tests. ER, oestrogen receptor.

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