Skip to main content
  • AACR Publications
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

  • Register
  • Log in
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Focus on Computer Resources
    • 75th Anniversary
    • Meeting Abstracts
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citations
    • Author/Keyword
  • News
    • Cancer Discovery News
  • AACR Publications
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in

Search

  • Advanced search
Cancer Research
Cancer Research

Advanced Search

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Focus on Computer Resources
    • 75th Anniversary
    • Meeting Abstracts
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • OnlineFirst
    • Editors' Picks
    • Citations
    • Author/Keyword
  • News
    • Cancer Discovery News
Microenvironment and Immunology

Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

Anton Safonov, Tingting Jiang, Giampaolo Bianchini, Balázs Győrffy, Thomas Karn, Christos Hatzis and Lajos Pusztai
Anton Safonov
Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tingting Jiang
Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Giampaolo Bianchini
Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Balázs Győrffy
MTA TTK Lendület Cancer Biomarker Research Group & Semmelweis University Second Department of Pediatrics, Budapest, Hungary.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas Karn
Department of Obstetrics and Gynecology, Goethe-University Frankfurt, Frankfurt, Germany.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christos Hatzis
Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lajos Pusztai
Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: lajos.pusztai@yale.edu
DOI: 10.1158/0008-5472.CAN-16-3478 Published June 2017
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

The presence of tumor-infiltrating lymphocytes (TIL) is a favorable prognostic factor in breast cancer, but what drives immune infiltration remains unknown. Here we examine if clonal heterogeneity, total mutation load, neoantigen load, copy number variations (CNV), gene- or pathway-level somatic mutations, or germline polymorphisms (SNP) are associated with immune metagene expression in breast cancer subtypes. Thirteen published immune metagenes correlated separately with genomic metrics in the three major breast cancer subtypes. We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data of 627 ER+, 207 HER2+, and 191 triple-negative (TNBC) cancers from The Cancer Genome Atlas. P-values were adjusted for multiple comparisons, and permutation testing was used to assess false discovery rates. Increased immune metagene expression associated significantly with lower clonal heterogeneity estimated by MATH score in all subtypes and with a trend for lower overall mutation, neoantigen, and CNV loads in TNBC and HER2+ cancers. In ER+ cancers, mutation load, neoantigen load, and CNV load weakly but positively associated with immune infiltration, which reached significance for overall mutation load only. No highly recurrent single gene or pathway level mutations associated with immune infiltration. High immune gene expression and lower clonal heterogeneity in TNBC and HER2+ cancers suggest an immune pruning effect and equilibrium between immune surveillance and clonal expansion. Thus, immune checkpoint inhibitors may tip the balance in favor of immune surveillance in these cancers. Cancer Res; 77(12); 3317–24. ©2017 AACR.

Introduction

The presence of immune infiltration in the breast cancer microenvironment is a favorable prognostic marker particularly among triple-negative (TNBC), HER2+ and highly proliferative estrogen receptor (ER) positive cancers (1). High levels of immune infiltration, measured as either TIL count or expression of immune-cell related genes, predicts for better survival with or without systemic adjuvant therapy in early stage disease (2–6). Additionally, breast cancers that are rich in immune cells, regardless of subtype, have higher rates of pathologic complete response (pCR) to neoadjuvant chemotherapy (7, 8). The extent of immune infiltration is higher in TNBC and HER2+ cancers than in ER+ disease (7). However, within each subtype there is great variability in TIL counts ranging from no TILs in 10% to 20% of cancers to lymphocyte predominant cancers (i.e., >50% of stromal cells are lymphocytes) in 5% to 10% of cases (4, 7). The biological mechanisms underlying the variable TIL infiltration are unknown.

In a pooled analysis of solid tumors in The Cancer Genome Atlas (TCGA) database, the total number of somatic mutations and the number of new antigen epitopes (i.e., neoantigen load) correlated with immune infiltration (9–11). In hepatocellular, squamous cell lung cancer, and colorectal carcinomas greater number of copy number alterations were associated with higher immunogenicity (12–14). On the basis of these observations one can hypothesize that the more genomic alterations a cancer has, the greater the immune infiltration is, due to more immunogenic neoantigens in these cancers. Somatic mutations in the PI3KCA and MAPK genes were also shown to affect the immune microenvironment (15–17). Germline polymorphisms influence predisposition to immune disorders and response to infectious agents (18–20) and one could therefore speculate that they may also influence antitumor immune response.

The goal of this study was to systematically examine what DNA-level genomic alterations are associated with immune cell infiltration, measured by immune metagene expression, and if these associations differ by breast cancer subtype. We tested if either (i) total mutation load, (ii) neoantigen load, (iii) copy number variations (CNV), (iv) intratumor genomic heterogeneity, (v) gene-level or (vi) biological pathway level somatic mutations, or (vii) germline single-nucleotide variants (SNV) are associated with immune gene expression. Although associations do not imply a cause and effect relationship, they could lead to testable hypotheses in the laboratory and in the clinic.

Materials and Methods

Data sources

We obtained gene-level RNA-Seq expression (n = 1,066), level-4 copy number (n = 1,080), and germline SNV data (n = 501) and corresponding clinical information from TCGA public access portal. Supplementary Table S1 lists the TCGA samples included in this study. Gene-level somatic mutation data (n = 817 cases, n = 14,440 mutations) were obtained from Ciriello and colleagues (21). DNA segments were assigned to copy number categories based on GISTIC threshold scores. We filtered SNVs using the Duplicated Genes Database (DGD), removed rare variants and variants deviating from Hardy–Weinberg Equilibrium, and retained only SNPs with moderate or high functional impact (n = 8,861) using Variant Effect Predictor (22) in the final analysis.

Breast cancer subtypes were defined as (i) ER+/HER− (hereafter referred to as ER+), (ii) HER+ with any ER status (HER2+), and (iii) ER and HER2− (TNBC) based on the routine clinical information available for the samples (n = 1003 for ER and n = 892 for HER2). This routine clinical classification was chosen over PAM50 subtyping because of its more direct clinical applicability and to maintain consistency with previous immune marker studies in breast cancer. When clinical receptor status was unavailable or equivocal (n = 63), HER2 and ER status was assigned on the basis of mRNA expression of ERBB2 and ESR1, respectively. The final sample size for this study was n = 627 ER+ cases, n = 207 HER2+ cases, and n = 191 TNBC cases.

Analysis plan

The expression levels of 13 previously reported immune metagenes were calculated as the mean of the log2-transformed expression of the member genes (5–23). These metagenes correspond to various immune cell types and reflect various immune functions (Supplementary Table S2). The prognostic and chemotherapy response predictive value of each of these metagenes were previously assessed in the TCGA and also independent data sets (5, 23). In some analysis we selected the LCK metagene that showed a high average coexpression with other immune signatures and also correlated significantly with histologic tumor infiltration lymphocyte count, as the single representative measure of immune infiltration for correlation with global genomic metrics.

Neoantigen load data were taken from a previous publication (23). Overall deletion load was defined as the number of genes with GISTIC value of “−2,” and amplification load was defined as the number of genes with GISTIC value of “+2,” indicating definite deletion or amplification of a given segment, respectively. Somatic mutations in TCGA whole exome sequencing samples were detected using MuTech, as previously described (24). Mutation load was calculated as the number of somatic mutations in a sample, normalized by the total length of sequences with adequate read coverage. Mutational heterogeneity was measured using the MATH score, which uses the variance of the variant allele frequency distribution to approximate clonal heterogeneity, however this metric is influenced by the combined effect of clonality and copy number alterations (25). Correlation between immune metagene expression as continuous variable and the genomic metrics were assessed using the Spearman rank correlation coefficient. Significance was assessed by using the upper tail probabilities of Spearman's rho (26).

The association between nonsynonymous somatic mutations or high/intermediate functional impact germline SNVs and immune infiltration was assessed with linear regression after variants were collapsed at gene level. Histologic subtype (infiltrating ductal vs. lobular carcinoma) and the mutation load were included as additional covariates. The P-values were adjusted by calculating empirical FDRs (≤10%). Associations between somatic mutations and immune metagene expression were assessed in a “discovery” analysis, which included all genes with mutation frequency >3%, and a “candidate gene” analysis that included genes from biological pathways related to antigen presenting, cytokines, chemokines, angiogenesis-related signaling (27, 28), the MAPK pathway (29, 30), cell adhesion, and epithelial-to-mesenchymal transition (31). These pathways contained a total of 910 unique genes (Supplementary Table S3).

The association between copy number alterations and immune infiltration was assessed using linear regression of metagene expression as a function of either amplifications or deletions, with histologic subtype and the background rate of copy number alterations included as covariates. Contiguous regions with significant copy number effects (P < 0.05) were defined as copy number peaks. To obtain a null distribution for significance testing, the immune metagene expression value was permuted 500 times and copy number peak significance scores were generated for each permutation. The quantile of each true peak within this null distribution was assigned as the adjusted P value.

For pathway level analysis, we assembled 714 biological pathways from the NCI Pathway Interaction and BioCarta Pathway databases that correspond to most known biological functions (32). For each pathway, we defined an “aberration ratio score” calculated as the number of genes affected by either mutation or copy number change (GISTIC score of +2 or −2), divided by the total number of genes in the pathway. We examined the association between immune metagene expression and pathway aberration scores using linear regression including the histological diagnosis as covariate. To calculate significance, we constructed random gene sets with the same number of genes as a given pathway from our pathway gene pool and calculated aberration scores and their correlation with immune gene expression for these random sets in 1,000 iterations. The coefficients were compiled into a null distribution for each pathway. An observed coefficient from the unperturbed data was considered significant if it was >95% percentile of the null distribution.

Results

Correlation between immune metagenes and global genomic metrics

The expression distribution of 13 immune metagenes in the three breast cancer subtypes is shown in Supplementary Fig. S1 and the correlations between metagene expressions are presented in Fig. 1. The lymphocyte-specific kinase (LCK) metagene showed high average correlation with other immune metagenes across all subtypes and this metagene has also showed a strong correlation with histologic TIL counts in breast cancer samples in a previous study (5), which we have also observed in our data (Supplementary Fig. S2), therefore we selected this metagene as the best single measure of immune infiltration. When compared across breast cancer subtypes, the LCK metagene expression, mutation count, neoantigen load and amplification, and deletion loads were all higher in TNBC compared to the other breast cancer subtypes (Supplementary Fig. S3). When all breast cancers are analyzed together, these genomic metrics correlate closely with TIL and immune gene expression. This is because TNBCs are higher, and ER+ cancers are lower, in both measures. Supplementary Fig. S4 shows the correlations between the 13 immune metagenes and 5 exome-wide genomic features including all breast cancers combined.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Correlation between immune metagene expression and mutation, neoantigen, amplification, and deletion loads, and tumor genomic heterogeneity. ER+ (A); triple-negative (B); HER2+ (C) cancers. Spearman correlation coefficients are shown color-coded to illustrate positive (red) or negative (blue) associations. The top portion shows correlation between immune metagenes, and the lower part between the metagenes and genomic aberration metrics. Significant correlations at P < 0.0001 are outlined in bold.

Next, we examined the correlation between the 13 immune metagene expression levels and five different measures of global genomic aberrations in the three distinct breast cancer subtypes separately. In the subtypes, correlations were weak and in TNBC and HER2+ cancers tended to show an overall negative association between immune signatures and the five different types of genomic aberrations (Fig. 1). Correlation analysis revealed statistically significant negative associations between mutational heterogeneity, measured by MATH score, and the several immune metagenes in each breast cancer subtype. A significant positive association was only seen in ER+ cancers for the STAT1 metagene expression and overall mutation load. Supplementary Fig. S5 shows the correlation between the LCK metagene expression and the five genomic features with the corresponding R2 values for each subtype.

A potential confounder in mutation load and copy number analysis is the variable tumor cellularity of the TCGA samples and that cancers rich in TILs may have a higher normal to cancer cell ratio. To assess if tumor cellularity influenced our results, we applied computationally estimated tumor cellularity using the ASCAT tool (33) to adjust mutation load for each sample and have also performed immune gene signature correlation with the somatic copy number alteration (SCNA) score from Davoli and colleagues (34). The SCNA score is tumor aneuploidy measure that is adjusted for tumor cellularity. Adjusting for tumor cellularity did not substantially alter the associations we observed (Supplementary Fig. S6).

It is important to point out that the correlation coefficients between various immune metagenes and genomic metrics are small, which reflect that many other important variables, which are not captured by these genomic metrics, influence the extent of lymphocytic infiltration.

Correlation between LCK metagene expression and somatic mutations, CNVs and germline SNVs

After filtering somatic mutations to include only mutations with >3% frequency, a total of 188, 104, and 37 mutated genes were present in the ER+, HER2+, and TNBC cohorts, respectively. In ER+ cancers, mutations in six genes were nominally significantly associated with LCK metagene expression, but only two remained significant after adjusting for multiple hypothesis testing (FDR < 10%). Mutations in MAP2K4, which affected 5.3% of cases, were associated with lower, and mutations in TP53 (17.5% of cases) with higher LCK metagene expression (Table 1). In TNBC, mutations in seven genes had nominally significant association but only two remained significant at FDR < 10%. Mutations in MYH9 (4.1% of cases) and HERC2 (3.4% of cases) were both associated with lower LCK metagene expression (Table 1). There were no gene-level mutations significantly associated with immune infiltration in HER2+ cancers. When we restricted analysis only to genes that are involved in regulating the immune system, no additional gene level mutations were identified as significant. These results suggest that the primary driver of immune infiltration in breast cancers is not recurrent somatic mutations.

View this table:
  • View inline
  • View popup
Table 1.

Genomic alterations significantly associated with either higher or lower LCK immune metagene expression by breast cancer subtype

We performed similar analysis for germline polymorphisms. No SNP was significantly associated with higher immune infiltration in any subtype. In TNBC, we could identify three SNPs that were significantly associated with lower immune infiltration after adjusting for multiple hypothesis testing. These included rs425757 and rs410232, both in the coding regions of the CFHR1 gene, and rs470797 in the coding region of MLP (Table 1). These results suggest minimal contribution from germline polymorphisms reported in the TCGA data, to immune infiltration in breast cancer.

Next, we examined associations between amplifications or deletions and LCK metagene expression. In TNBC, two amplicons 5p12-14.3 and 17q11-241 showed significant association with decreased LCK metagene expression (Table 1). In HER2+ cancers, we found four significant amplifications (1q21-23.1, 1q24-32.1, 17q21.2, 17q21.32) associated with decreased immune infiltration, and one deletion (1p13.2-36.33) associated with increased immune infiltration (Table 1). In ER+ cancers, no copy number alterations were significantly associated with immune infiltration after multiple testing adjustment.

Taken together, these results indicate that there are no recurrent mutations, germline polymorphisms or copy number alterations that account for the majority of between-cancer variability in immune gene expression.

Association between LCK metagene expression and biological pathway-level alterations

In ER+ cancers, aberrations in 11 pathways showed association with immune gene expression at FDR < 10%, 10 of which were associated with lower immune infiltration (Table 1). Eight of the 10 pathways included members of the MAP-kinase family (MAP3K1, MAPK8, MAP2K4, MAPK1, MAPK3, MAP2K1, MAPK14, MAP2K3), suggesting that alterations in MAPK signaling may lead to lower cancer immunogenicity. In TNBC, aberrations in nine pathways showed association with immune infiltration at FDR <10% and in HER2+ cancers, aberrations in seven pathways had FDR < 10% (Table 1). An overview of all significant genomic aberrations at the level of individual cases in each breast cancer subtype are presented in Fig. 2. The results illustrate that the extent of immune cell infiltration is not associated with highly recurrent genomic events but rather with unique combinations of genomic alterations in each cancer.

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Genomic alterations associated with LCK immune metagene expression in ER+ (A), triple-negative (B), and HER2+ (C) cancers. Each column represents a sample ordered in ascending order by LCK metagene expression. Each row indicates a type of genomic abnormality that is statistically significantly associated with immune infiltration. Somatic mutations and germline SNPs are shown as binary (i.e., present or absent) variables. Mutation load, neoantigen load, total copy number alteration count, and intratumor heterogeneity (MATH score) and pathway alterations (i.e., higher proportion of mutated genes in the pathway is indicated by deeper shade) are displayed as continuous variables. Pathway alterations are displayed as combined alterations and also as amplifications or deletions only. White, normal genotype; gray, missing data.

Discussion

We examined associations between immune metagene expression and a broad range of DNA-level alterations in breast cancer subtypes. In all subtypes, higher immune metagene expression was statistically significantly associated with lower clonal heterogeneity. In TNBC and HER2+ cancers, higher overall mutation, neoantigen, and CNV loads were also consistently, but not statistically significantly associated with lower expression of a broad range of immune metagenes. These observations support an immune pruning/immune editing effect that is particularly apparent in TNBC. Although cancer neoantigenes are required for mounting an anticancer immune response (35) and a more disturbed cancer genome is more likely to produce more immunogenic epitopes, a robust local antitumor immune response is expected to continuously eliminate highly immunogenic clones and slow the genomic diversification of the cancer or could even lead to complete elimination before it becomes clinically apparent. In the case of clinically apparent, immune-rich cancers, immune surveillance does not completely control the growth but may impose a precarious balance (i.e., near-equilibrium) for a variable length of time, which could be tipped in favor of immune elimination (of microscopic residual cancer) with interventions such as surgery, chemotherapy, or immune checkpoint therapy (36). This model could explain the better prognosis of immune rich cancers and also raise the possibility that immune therapy may have a chemo-preventive effect. In this framework, TNBC with no, or very low, immune infiltration represent cancers that have escaped immune surveillance and are no longer subject to clonal elimination by immune cells, which explains their greater clonal heterogeneity higher mutation load and worse prognosis (Fig. 3).

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

Schema of tumor evolution under immune editing. A, Neoantigenes are required for mounting an initial anticancer immune response and genomic heterogeneity can foster this. B, A subsequent antitumor immune response may eliminate many of the immunogenic clones and lead to lower clonal heterogeneity and a near-equilibrium. C, With the emergence of immune escape mechanisms, the cancer becomes clonally heterogeneous again, because it is no longer subject to clonal elimination by immune cells. Tumors may progress through these phases at different rate depending on proliferation rate and other variables.

In contrast, in ER+ cancers, we detected a positive but weak association of mutation, neoantigen, and CNV loads with immune infiltration, which reached significance for the overall mutation load (i.e., higher mutation load correlated with higher immune infiltration). These results suggest a different dynamic between immune surveillance and subsequent immune editing in ER+ breast cancer. One might speculate that this difference may reflect the different proliferation rate of these cancers. Most ER+ cancers have a slower growth rate and may spend a longer time in the various phases of “immune struggle,” whereas TNBC has a higher proliferation rate, which could accelerate, reaching either a state of immune escape or near-equilibrium with immune surveillance.

Our original goal was to identify DNA level alterations that are associated with low or high immune gene expression and could therefore suggest possible molecular causes for the variable levels of immune infiltration. We could not identify any high frequency, recurrent, gene-level DNA alterations that are significantly associated with immune metagene expression in breast cancer. This is consistent with a previous report that showed no recurrent neoantigens in cancers but rather a broad distribution of individually rare tumor neoantigens (37). However, in all breast cancer subtypes we observed a few genomic alterations that were significantly associated with immune metagene expression even after adjusting for multiple comparisons (Table 1). Each individual alteration was rare and accounted for only a small portion of variability in immune gene expression. Overall, our analysis indicates that immune infiltration in breast cancer subtypes is not associated with a few highly recurrent genomic events but rather by a broad spectrum of gene and pathway level alterations that each affect small subsets of patients within each subtype. It is tempting to speculate that at least some of the alterations may mechanistically contribute to determining immune infiltration. For examples, in TNBC, two missense SNVs (rs425757 and rs410232) in the CFHR1 (Complement Factor H related 1) gene, an inhibitor of the complement cascade (38, 39), and the stop-gain variant in MBP (Myelin Basic Protein) gene (rs470797) that can regulate Th2 cells (40, 41) were associated with lower immune infiltration. Amplifications at the 17q11-241 region were also associated with lower immune infiltration in TNBC. This amplicon includes the Chemokine (C-C Motif) Receptor 7 (CCR7) gene and high expression of CCR7 was previously shown to cause decreased T-cell presence in the melanoma (42). Deletion in the 1p13-36 region was associated with increased immune infiltration in HER2+ cancers and this amplicon contains the immune checkpoint genes tumor necrosis factor receptor superfamily member 18 and 25 (TNFRSF18 and TNFRSF25). In ER+ cancers, mutations in MAPK kinase 4 (MAP2K4) were associated with low immune infiltration. Pathway-level analysis also identified several biological pathways that had alterations significantly more frequently in cancers with lower immune infiltration, and nine of these pathways included MAPK genes. This pathway was previously linked regulation of the tumor microenvironment. Although these associations do not prove a cause-and-effect relationship, they raise experimentally testable hypotheses and suggest a multiplicity of potential biological mechanisms that influence local antitumor immunity.

In summary, our data suggest that immune surveillance has an impact on sculpting the breast cancer genome. Cancers that have minimal or no immune infiltration have greater clonal heterogeneity, likely suggesting an escape from immune surveillance. However, cancers with high immune infiltration may be in near-equilibrium. These observations suggest that immune checkpoint inhibitors may be the most effective to tilt the balance in favor of immune surveillance in the immune-rich, breast cancers. For breast cancers with little immune infiltration, more complex immunotherapy strategies may be needed to rekindle immune response against a clonally diverse neoplastic population.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors' Contributions

Conception and design: A. Safonov, G. Bianchini, C. Hatzis, L. Pusztai

Development of methodology: A. Safonov, T. Jiang, B. Győrffy, T. Karn, C. Hatzis

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Safonov, T. Jiang, T. Karn, L. Pusztai

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Safonov, T. Jiang, G. Bianchini, B. Győrffy, T. Karn, C. Hatzis, L. Pusztai

Writing, review, and/or revision of the manuscript: A. Safonov, G. Bianchini, T. Karn, C. Hatzis, L. Pusztai

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A. Safonov, L. Pusztai

Study supervision: L. Pusztai

Grant Support

This work was supported in part by grants from the Breast Cancer Research Foundation (L. Pusztai and C. Hatzis) and Susan G. Komen Leadership Award (L. Pusztai), the H.W. & J. Hector-Stiftung, Mannheim (M67; T. Karn), and the Associazione Italiana per la Ricerca sul Cancro (AIRC; MFGA 13428; G. Bianchini).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

  • Received December 29, 2016.
  • Revision received March 13, 2017.
  • Accepted April 14, 2017.
  • ©2017 American Association for Cancer Research.

References

  1. 1.↵
    1. Bianchini G,
    2. Qi Y,
    3. Alvarez RH,
    4. Iwamoto T,
    5. Coutant C,
    6. Ibrahim NK,
    7. et al.
    Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol 2010;28:4316–23.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    1. Loi S,
    2. Sirtaine N,
    3. Piette F,
    4. Salgado R,
    5. Viale G,
    6. Van Eenoo F,
    7. et al.
    Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol 2013;31:860–7.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    1. Loi S,
    2. Michiels S,
    3. Salgado R,
    4. Sirtaine N,
    5. Jose V,
    6. Fumagalli D,
    7. et al.
    Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann Oncol 2014;25:1544–50.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Adams S,
    2. Gray RJ,
    3. Demaria S,
    4. Goldstein L,
    5. Perez EA,
    6. Shulman LN,
    7. et al.
    Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J Clin Oncol 2014;32:2959–66.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Rody A,
    2. Holtrich U,
    3. Pusztai L,
    4. Liedtke C,
    5. Gaetje R,
    6. Ruckhaeberle E,
    7. et al.
    T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res 2009;11:R15.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Callari M,
    2. Cappelletti V,
    3. D'Aiuto F,
    4. Musella V,
    5. Lembo A,
    6. Petel F,
    7. et al.
    Subtype-specific metagene-based prediction of outcome after neoadjuvant and adjuvant treatment in breast cancer. Clin Cancer Res 2016;22:337–45.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Denkert C,
    2. Loibl S,
    3. Noske A,
    4. Roller M,
    5. Muller BM,
    6. Komor M,
    7. et al.
    Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010;28:105–13.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Esteva FJ,
    2. Wang J,
    3. Lin F,
    4. Mejia JA,
    5. Yan K,
    6. Altundag K,
    7. et al.
    CD40 signaling predicts response to preoperative trastuzumab and concomitant paclitaxel followed by 5-fluorouracil, epiribucin, and cyclophosphamide in HER-2-overexpressing breast cancer. Breast Cancer Res 2007;9:R87.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Lennerz V,
    2. Fatho M,
    3. Gentilini C,
    4. Frye RA,
    5. Lifke A,
    6. Ferel D,
    7. et al.
    The response of autologous T cells to a human melanoma is dominated by mutated neoantigens. Proc Natl Acad Sci U S A 2005;102:16013–8.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Heemskerk B,
    2. Kvistborg P,
    3. Schumacher TN
    . The cancer antigenome. EMBO J 2013;32:194–203.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Brown SD,
    2. Warren RL,
    3. Gibb EA,
    4. Martin SD,
    5. Spinelli JJ,
    6. Nelson BH,
    7. et al.
    Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res 2014;24:743–50.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Hsiao TH,
    2. Chen HI,
    3. Roessler S,
    4. Wang XW,
    5. Chen Y
    . Identification of genomic functional hotspots with copy number alteration in liver cancer. EURASIP J Bioinform Syst Biol 2013;2013:14.
    OpenUrl
  13. 13.↵
    1. Yang Z,
    2. Zhuan B,
    3. Yan Y,
    4. Jiang S,
    5. Wang T
    . Integrated analyses of copy number variations and gene differential expression in lung squamous-cell carcinoma. Biol Res 2015;48:47.
    OpenUrl
  14. 14.↵
    1. Madhavan S,
    2. Gusev Y,
    3. Natarajan TG,
    4. Song L,
    5. Bhuvaneshwar K,
    6. Gauba R,
    7. et al.
    Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse. Front Genet 2013;4:236.
    OpenUrl
  15. 15.↵
    1. Smith MP,
    2. Sanchez-Laorden B,
    3. O'Brien K,
    4. Brunton H,
    5. Ferguson J,
    6. Young H,
    7. et al.
    The immune microenvironment confers resistance to MAPK pathway inhibitors through macrophage-derived TNFalpha. Cancer Discov 2014;4:1214–29.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Atefi M,
    2. Avramis E,
    3. Lassen A,
    4. Wong DJ,
    5. Robert L,
    6. Foulad D,
    7. et al.
    Effects of MAPK and PI3K pathways on PD-L1 expression in melanoma. Clin Cancer Res 2014;20:3446–57.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Dituri F,
    2. Mazzocca A,
    3. Giannelli G,
    4. Antonaci S
    . PI3K functions in cancer progression, anticancer immunity and immune evasion by tumors. Clin Dev Immunol 2011;2011:947858.
    OpenUrlPubMed
  18. 18.↵
    1. Dhiman N,
    2. Ovsyannikova IG,
    3. Vierkant RA,
    4. Ryan JE,
    5. Pankratz VS,
    6. Jacobson RM,
    7. et al.
    Associations between SNPs in toll-like receptors and related intracellular signaling molecules and immune responses to measles vaccine: preliminary results. Vaccine 2008;26:1731–6.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Schott E,
    2. Witt H,
    3. Neumann K,
    4. Bergk A,
    5. Halangk J,
    6. Weich V,
    7. et al.
    Association of TLR7 single nucleotide polymorphisms with chronic HCV-infection and response to interferon-a-based therapy. J Viral Hepat 2008;15:71–8.
    OpenUrlPubMed
  20. 20.↵
    1. Lee JC,
    2. Espeli M,
    3. Anderson CA,
    4. Linterman MA,
    5. Pocock JM,
    6. Williams NJ,
    7. et al.
    Human SNP links differential outcomes in inflammatory and infectious disease to a FOXO3-regulated pathway. Cell 2013;155:57–69.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Ciriello G,
    2. Gatza ML,
    3. Beck AH,
    4. Wilkerson MD,
    5. Rhie SK,
    6. Pastore A,
    7. et al.
    Comprehensive molecular portraits of invasive lobular breast cancer. Cell 2015;163:506–19.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. McLaren W,
    2. Pritchard B,
    3. Rios D,
    4. Chen Y,
    5. Flicek P,
    6. Cunningham F
    . Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 2010;26:2069–70.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    1. Rooney MS,
    2. Shukla SA,
    3. Wu CJ,
    4. Getz G,
    5. Hacohen N
    . Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 2015;160:48–61.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Jiang T,
    2. Shi W,
    3. Wali VB,
    4. Pongor LS,
    5. Li C,
    6. Lau R,
    7. et al.
    Predictors of chemosensitivity in triple negative breast cancer: an integrated genomic analysis. PLOS Med 2016;13:e1002193.
    OpenUrl
  25. 25.↵
    1. Mroz EA,
    2. Rocco JW
    . MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral Oncol 2013;49:211–5.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Best DJ,
    2. Roberts DE
    . Algorithm AS 89: The Upper Tail Probabilities of Spearman's rho. Appl Stat 1975;24:377–9.
    OpenUrlCrossRef
  27. 27.↵
    1. Allavena P,
    2. Sica A,
    3. Solinas G,
    4. Porta C,
    5. Mantovani A
    . The inflammatory micro-environment in tumor progression: the role of tumor-associated macrophages. Crit Rev Oncol Hematol 2008;66:1–9.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Facciabene A,
    2. Motz GT,
    3. Coukos G
    . T-regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res 2012;72:2162–71.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Sumimoto H,
    2. Imabayashi F,
    3. Iwata T,
    4. Kawakami Y
    . The BRAF-MAPK signaling pathway is essential for cancer-immune evasion in human melanoma cells. J Exp Med 2006;203:1651–6.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Liu Y,
    2. Shepherd EG,
    3. Nelin LD
    . MAPK phosphatases–regulating the immune response. Nat Rev Immunol 2007;7:202–12.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Santisteban M,
    2. Reiman JM,
    3. Asiedu MK,
    4. Behrens MD,
    5. Nassar A,
    6. Kalli KR,
    7. et al.
    Immune-induced epithelial to mesenchymal transition in vivo generates breast cancer stem cells. Cancer Res 2009;69:2887–95.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    1. Shi W,
    2. Jiang T,
    3. Nuciforo P,
    4. Hatzis C,
    5. Holmes E,
    6. Harbeck N,
    7. et al.
    Pathway level alterations rather than mutations in single genes predict response to HER2 targeted therapies in the neo-ALTTO trial. Ann Oncol 2017;28:128–35.
    OpenUrl
  33. 33.↵
    1. Van Loo P,
    2. Nordgard S,
    3. Lingjrde O,
    4. Russnes H,
    5. Rye I,
    6. Sun W,
    7. et al.
    Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A 2010;107:16910–5.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Davoli T,
    2. Uno H,
    3. Wooten EC,
    4. Elledge SJ
    . Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 2017;355:eaaf8399.
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    1. McGranahan N,
    2. Furness AJ,
    3. Rosenthal R,
    4. Ramskov S,
    5. Lyngaa R,
    6. Saini SK,
    7. et al.
    Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–9.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Pusztai L,
    2. Karn T,
    3. Safonov A,
    4. Abu-Khalad MM,
    5. Bianchini G
    . New strategies in breast cancer: immunotherapy. Clin Cancer Res 2016;22:2105–10.
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. Schumacher TN,
    2. Schreiber RD
    . Neoantigens in cancer immunotherapy. Science 2015;348:69–74.
    OpenUrlAbstract/FREE Full Text
  38. 38.↵
    1. Fritsche LG,
    2. Lauer N,
    3. Hartmann A,
    4. Stippa S,
    5. Keilhauer CN,
    6. Oppermann M,
    7. et al.
    An imbalance of human complement regulatory proteins CFHR1, CFHR3 and factor H influences risk for age-related macular degeneration (AMD). Hum Mol Genet 2010;19:4694–704.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    1. Goicoechea de Jorge E,
    2. Caesar JJ,
    3. Malik TH,
    4. Patel M,
    5. Colledge M,
    6. Johnson S,
    7. et al.
    Dimerization of complement factor H-related proteins modulates complement activation in vivo. Proc Natl Acad Sci U S A 2013;110:4685–90.
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    1. Thomson DM,
    2. Halliday WJ,
    3. Phelan K
    . Leukocyte adherence inhibition to myelin basic protein by cancer patients' T-lymphocytes in association with class II major histocompatibility antigens on monocytes. J Natl Cancer Inst 1985;75:995–1003.
    OpenUrlAbstract/FREE Full Text
  41. 41.↵
    1. Katsara M,
    2. Yuriev E,
    3. Ramsland PA,
    4. Tselios T,
    5. Deraos G,
    6. Lourbopoulos A,
    7. et al.
    Altered peptide ligands of myelin basic protein (MBP87-99) conjugated to reduced mannan modulate immune responses in mice. Immunology 2009;128:521–33.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Fang L,
    2. Lee VC,
    3. Cha E,
    4. Zhang H,
    5. Hwang ST
    . CCR7 regulates B16 murine melanoma cell tumorigenesis in skin. J Leukocyte Biol 2008;84:965–72.
    OpenUrlAbstract/FREE Full Text
View Abstract
PreviousNext
Back to top
Cancer Research: 77 (12)
June 2017
Volume 77, Issue 12
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by Author
  • Editorial Board (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Research article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer
(Your Name) has forwarded a page to you from Cancer Research
(Your Name) thought you would be interested in this article in Cancer Research.
Citation Tools
Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer
Anton Safonov, Tingting Jiang, Giampaolo Bianchini, Balázs Győrffy, Thomas Karn, Christos Hatzis and Lajos Pusztai
Cancer Res June 15 2017 (77) (12) 3317-3324; DOI: 10.1158/0008-5472.CAN-16-3478

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer
Anton Safonov, Tingting Jiang, Giampaolo Bianchini, Balázs Győrffy, Thomas Karn, Christos Hatzis and Lajos Pusztai
Cancer Res June 15 2017 (77) (12) 3317-3324; DOI: 10.1158/0008-5472.CAN-16-3478
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Grant Support
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • TLR4-Mediated Inflammation Promotes Cellular Transformation
  • CD103 Signaling in Human TRM Cells
  • Expansion of Neoclonotypes and Anti–PD-1 Clinical Efficiency
Show more Microenvironment and Immunology
  • Home
  • Alerts
  • Feedback
Facebook  Twitter  LinkedIn  YouTube  RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Meeting Abstracts

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians
  • Reviewers

About Cancer Research

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2018 by the American Association for Cancer Research.

Cancer Research Online ISSN: 1538-7445
Cancer Research Print ISSN: 0008-5472
Journal of Cancer Research ISSN: 0099-7013
American Journal of Cancer ISSN: 0099-7374

Advertisement