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Prognostic and predictive immune gene signatures in breast cancer

Bedognetti, Davidea; Hendrickx, Woutera; Marincola, Francesco M.b; Miller, Lance D.c,d

doi: 10.1097/CCO.0000000000000234
BREAST CANCER: Edited by Giuseppe Curigliano
Editor's Choice

Purpose of review Here, we focus on molecular biomarkers derived from transcriptomic studies to summarize the recent advances in our understanding of the mechanisms associated with differential prognosis and treatment outcome in breast cancer.

Recent findings Breast cancer is certainly immunogenic; yet it has been historically resistant to immunotherapy. In the past few years, refined immunotherapeutic manipulations have been shown to be effective in a significant proportion of cancer patients. For example, drugs targeting the PD-1 immune checkpoint have been proven to be an effective therapeutic approach in several solid tumors including melanoma and lung cancer. Very recently, the activity of such therapeutics has also been demonstrated in breast cancer patients. Pari passu with the development of novel immune modulators, the transcriptomic analysis of human tumors unveiled unexpected and paradoxical relationships between cancer cells and immune cells.

Summary This review examines our understanding of the molecular pathways associated with intratumoral immune response, which represents a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of breast cancer.

aTumor Biology, Immunology and Therapy Section, Division of Translational Medicine, Research Branch

bOffice of the Chief Research Officer (CRO), Research Branch, Sidra Medical and Research Center, Doha, Qatar

cDepartment of Cancer Biology, Wake Forest School of Medicine

dThe Comprehensive Cancer Center of Wake Forest University, Winston Salem, North Carolina, USA

Correspondence to Davide Bedognetti, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar. Tel: +974 44042043; e-mail:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.

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The transcriptomic assessment of tumor specimens before and after administration of immunotherapeutic agents has defined molecular pathways implicated in the development of immune-mediated tumor rejection. These pathways summarize a process resulting in a T-helper 1 (Th1) intratumoral immune response typified by the coordinated activation of interferon stimulated genes (ISGs), the recruitment of cytotoxic cells through the production of specific CXCR3/CCR5 chemokine-receptor ligands (e.g. CCL5, CXCL9, and CXCL10), and the activation of immune effector functions by cytotoxic cells [e.g. granzymes (GZMs), perforin (PRF1), and granulysin (GNLY)] [1,2].

In our earlier studies employing interleukin (IL)-2-based therapy in metastatic melanoma or topical anti-TLR7 in basal cell carcinoma, activation of these pathways was found to precede tumor shrinkage [3–5]. These findings have been corroborated in the setting of immune-checkpoint inhibitors [6]. Investigations in the context of adoptive therapy [7], cytokine-based therapy [3,5], vaccination [8,9], and immune-checkpoint inhibitors (i.e. CTLA4 and PD-1 pathway blockades) [6,10] have proposed that tumors displaying a preactivation of these molecular pathways have a greater likelihood to respond to immunotherapy as compared to those lacking such as polarized inflammatory status [2,11].

Moreover, these pathways are critical for the development of other forms of immune-mediated tissue destruction such as allograft rejection, graft-vs.-host disease (GVHD), and autoimmunity [12–15]. This analogy prompted us to define them as the ‘immunologic constant of rejection’ (ICR) pathways [1,16]. The signal transducers and activator of transcription 1 (STAT1), and the intereferon-regulatory factor 1 (IRF1) are the master regulators of the ICR pathways [1,12,17,18].

Several investigations in melanoma, ovarian, colorectal, hepatocellular, lung, and breast cancers have shown that high expression of ICR genes correlate with favorable prognosis [14]. Recently, in a pan-cancer meta-analysis, leukocyte-associated gene signatures have been correlated with favorable survival across different types of cancers [19▪].

Here, we review studies assessing the prognostic and predictive value of the immune signatures in relationship with breast cancer clinicopathological and molecular parameters. We will discuss how these different immune gene signatures develop around dominant themes that are well captured by the ICR pathways. A particular emphasis is given on those reports published from 2013 onwards, as summarized in Table 1.

Table 1

Table 1

Table 1

Table 1

Table 1

Table 1

Box 1

Box 1

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In the adjuvant setting, robust data from more than 1300 triple negative breast cancer (TNBC) patients demonstrated a positive correlation between the density of tumor-infiltrating lymphocytes (TILs) and favorable prognosis, as recently reviewed by the TIL working group [20▪]. In HER2+ patients, TIL infiltration is associated with response to adjuvant anti-HER2 therapy (i.e. trastuzumab) [20▪,21]. In addition, a recent study assessing more than 12 000 tumor samples showed that the presence of CD8+ T cells corresponds to a reduction of breast cancer-specific mortality in ER− but also in ER+ tumors expressing HER2 [22]. A flurry of gene expression profiling studies has added novel functional dimensions to these clinically relevant findings.

In 2007, Teschendorff et al.[23], by analyzing 186 ER− primary mammary carcinomas from different microarray datasets, identified a cluster of tumors enriched in immune-related genes and characterized by good prognosis. Genes positively associated with decreased risk of relapse included B-cell transcripts (IGLC2), immune effector function genes (GZMB), and other genes linked with immune response (C1QA, IGL2, LY9, TNFRSF17, SPP1, XCL2, and HLA-F). The following year, Desmedt et al.[24], in a comprehensive meta-analysis of gene expression datasets from 946 early breast cancer patients not receiving adjuvant treatment, tested the prognostic value of gene modules (i.e. cluster of coregulated genes) indicative of preselected key biological functions. The authors showed that a metagene centered on STAT1, which is one of the main drivers of the ICR pathways, correlates with prolonged relapse-free survival (RFS), especially in ER−/HER2− and HER2+ patients. During 2009–2011, other studies employing multiple datasets of untreated or adjuvant-treated patients have corroborated the association between immune-related genes and decreased risk of recurrence. Specifically, in those years, the following immune markers were found to be associated with favorable prognosis: a T-cell metagene centered on lymphocytic kinase (LCK) in ER− and in ER+/HER2+ tumors (LCK metagene includes CD3A, TRA, TRB, LCK, and IL7R) [25] (mixed treated and untreated cohorts); a HER2-derived prognostic signature enriched in immune-related genes such as CD69, CD3D, and CD247 in HER2+ and also in ER− and basal-like specimens (untreated and mixed treated and untreated cohorts) [26]; a kinase metagene consisting of several immune kinases in ER+/HER2− and HER2+ samples (untreated cohort) [27]; a medullary breast cancer (MBC)-derived signature enriched in immune genes including CD8+ T and B-cell transcripts in basal-like carcinoma [28] (mixed treated and untreated cohorts); a TNBC-derived prognostic signature enriched in immune genes (i.e. IFN signaling, B-cell and T-cell transcripts) in TNBCs [29] (mixed treated and untreated cohorts); and a B/plasma cell gene signature in ER− cancers, and in high proliferative tumors expressing ER (untreated cohort) [30].

In 2012, the assessment of a small internal cohort of 17 patients (heterogeneous in terms of ER and HER2 expression) revealed that the genes discriminating the two categories were centered on the ICR pathways [31]. These genes include transcripts encoding for chemokines (CXCL9 and CXCL10), cytotoxic granules (GZMA and GZMB), T-cell surface markers (CD8A), and IFN-stimulated genes (STAT1 and GBP1). B-cell and natural killer (NK)-cell transcripts were also up-regulated in samples from patients experiencing prolonged RFS [31,32]. The ability to identify these immune signatures in such a small sample size emphasizes the biologic relevance of the phenomenon underlined by these pathways. In the same year, Curtis et al.[33] analyzed transcriptomic data of a new cohort of 2000 breast cancers (i.e. the METABRIC dataset) untreated or treated with adjuvant therapy. By using joined copy number and gene expression clustering analysis, the authors typified different breast cancer subtypes, of which one, enriched in immune-related genes including ICR genes and displaying a flat copy number landscape and deletion of T-cell receptor loci, was characterized by a favorable prognosis [33]. In 2014, Burstein et al.[34] defined different subclusters of TNBC including a basal-like immunosuppressed (BLIS) and a basal-like immunoactivated (BLIA) phenotype, characterized by a poor and good prognosis, respectively.

We recently delineated the prognostic features and the underlying biology of distinct immune metagenes through the analysis of an integrated large dataset of primary breast cancer patients, either untreated or treated with adjuvant systemic therapy [35,36]. Through an analytic pipeline encompassing transcriptomic data from sorted leukocytes and tumor samples, as well as ‘immunohistochemical’ staining of tumor biopsies, we defined immune metagene scores that quantify the relative abundance of distinct effector cells. Such populations include cytotoxic T and/or NK cells (the T/NK metagene), antibody-producing plasma B cells (the B/P metagene), and antigen-presenting myeloid/dendritic cells (the M/D metagene). By portioning breast cancers into discrete categories according to different metagene scores, it became evident that, for each metagene, high values correlated with prolonged distant metastasis-free survival (DMFS) and displayed additive prognostic information when considered in multivariate Cox regression models inclusive of conventional prognostic markers [35].

Proliferation is recognized as one of the strongest prognostic factors in breast cancer, which prompted us to investigate the relationship between markers of proliferation and immune metagenes. Overall, in tumors with elevated proliferative capacity, high immune metagene scores equated with prolonged DMFS compared to the poor DMFS associated with low immune metagene scores. By contrast, the immune metagenes cannot permit prognostic stratification of tumors with low proliferative capacity (Fig. 1). Stratification according to intrinsic molecular subtypes and ER status revealed differential performances of the immune metagenes in prognosticating the risk of recurrence. For example, in tumors with high proliferative capacity, all three immune metagenes retained their positive prognostic value independently of the ER status, and in HER2-enriched, basal-like, and luminal B subtypes, but not in luminal-A and claudin-low tumors.



Globally, these findings suggest that the prognostic relevance of antitumoral immune response is influenced by both proliferative status and intrinsic subtype, both of which may reflect the existence of underlying mechanisms that dictate immunogenic state.

We corroborated this observation by refining and expanding our previous analysis (preliminary analysis presented at the annual SITC meeting 2014) [36]. We observed that in those tumors in which the combined B/T, N/K, and M/D immune gene scores did not bear any predictive connotation, the immune infiltrates were not clearly polarized towards a Th1 phenotype. Vice versa, among tumors permitting prognostication by the integrated metagene score, those ones bearing high level of immune infiltration showed a coordinated up-regulation of the Th1/effector ICR pathways, stressing the importance of functional orientation as a critical determinant of a more effective antitumoral immune response [36,37].

Rather than profiling the bulk tumors, two studies have assessed the transcriptional program of tumor stroma or those of infiltrating lymphocytes. In 2008, Finak et al.[38], by profiling laser microdissected tumor stroma, observed that tumors with prolonged RFS were enriched in transcripts indicative of a Th1 immune response (e.g. CD8A, GZMA, CD52, and CD247). In 2013, Gu-Trantien et al.[39] found that polarization of CD4+ T cells differed among extensively and minimally infiltrated tumors. In highly infiltrated tumors, CD4+ T cells were enriched not only in Th1 but also in CXCL13-producing follicular helper T cells (T-fh). The authors next evaluated Th1 and T-fh signatures in a cohort of 794 systemically untreated early breast cancer patients from publicly available microarray datasets. The Th1 signature correlated with RFS survival in HER2+ tumors, whereas the T-fh one was associated with RFS in both HER2+ and ER+/HER−, with a nonsignificant trend in the ER−/HER2− subgroup.

This year, Bonsang-Kitzis et al., by mining data from 21 available gene expression datasets (557 TNBC), defined 167 gene signatures that could segregate TNBCs into six different subtypes. Among these genes, the authors defined six main gene clusters, or metagenes, and assessed their prognostic values in an additional cohort of 254 TNBCs from the METABRIC dataset [33]. Two of the metagenes (named Immunity1 and Immunity2) were centered on immune response. The strongest link with RFS, in both untreated and chemotherapy-treated patients, was observed for the Immunity2 metagene, which was associated with RFS even in multivariable models including clinicopathological information. This metagene includes T-cell and B-cell transcripts, chemokine genes, and immune effector genes (e.g. CD3D, CD2, CCL2, CCR7, ITGB2, HLA-DR, GZMA, and GZMB). The Immunity2 metagene correlated heavily with the immune suppressive molecules PD-1, PD-L1, and CTLA4 (Pearson’ correlation R > 0.9) and with the Immunity1 metagene (centered on STAT1; Pearson's correlation R = 0.6), although the latter was not statistically associated with RFS. Immunity2, however, was not predictive of response to chemotherapy when tested on 314 TNBCs extracted from the neoadjuvant datasets composed by Ignatiadis [40]. As a note, in the Ignatiadis cohort [40], the predictive value of the STAT1 signature originally tested by the authors [40], as well as the Th1/STAT1 signature by Gu-Trantien et al.[39], was weaker in HER2−/ER− as compared to that observed in HER2+ tumors [39,40]. Furthermore, Bonsang-Kitzis et al. assessed the performance of the 10 previously published immune gene signatures (described above) up front in the METABRIC dataset of Curtis et al.[33]. Strikingly, 8 out of the 10 signatures [23–25,28,34,39] predicted clinical outcome, including the two STAT1-centered signatures described by Desmedt et al.[24] (STAT1 module) and Gu-Trantien et al.[39] (Th1 signature). Only the B-cell metagene of Bianchini et al.[30] and the TNBC-derived immune-metagene of Karn et al.[29] did not perform well. Interestingly, the authors’ Immunity2 outperformed the other immune signatures when tested in a multivariate analysis [41▪]. Although all these signatures appear to capture similar biological process, the differential performance in distinct contexts (i.e. adjuvant vs. neoadjuvant, or triple negative vs. HER2+) emphasizes the need of harmonization efforts before implementing them in clinical setting.

A recent transcriptomic study on patients from a randomized adjuvant trastuzumab trial highlighted the critical importance of an active immune environment in modulating response to anti-HER2 monoclonal antibody [42▪▪]. Among women treated with chemotherapy and adjuvant trastuzumab, but not in those treated with chemotherapy alone, the most significant pathways associated with prolonged RFS were centered on immune response. These pathways include cytokine–cytokine receptor activation, T-cell receptor signaling in CD8+ T cells, tumor necrosis factor (TNF), and IFNG signaling. Once again, ICR genes such as CXCL9, CCL5, CXCR3, PRF1, and GZMB were central in the nodes identified by pathway analysis. Eight out of 10 biological processes were immune related and included not only T-cell signaling but also B-cell-specific response [42▪▪]. The authors then defined a 14-gene signature that could stratify patients in immune enriched vs. nonimmune-enriched subgroups. Immune-enriched patients treated with trastuzamb-based chemotherapy enjoyed better RFS survival as compared to the nonimmune-enriched ones, in which risk of relapse was superimposable with those of patients treated with chemotherapy alone.

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In the neoadjuvant setting, studies assessing collectively more than 3000 patients have conclusively shown that TIL infiltration is associated with pathological complete response (pCR) independent of ER/HER2 status and chemotherapeutic regimen [20▪].

A number of immune markers have been found to correlate with pCR in the neoadjuvant chemotherapy setting, including immune-kinase metagene in HER2+ tumors [27]; LCK and immunoglobulin (IgG) metagenes in ER− tumors; a B-cell metagene, especially when combined with a TNBC-derived signature enriched in immune-related genes in TNBC [28]; the expression of CD3D and CXCL9 transcripts [43▪▪]; and the expression of the immunoglobulin kappa C (IGKC) as surrogate of B-cell mutagen [44].

In 2012, the Ignatiadis meta-analysis of 996 gene expression arrays from eight publicly available datasets pointed out that two highly correlated modules, of which one (Immune1) centered on STAT1 [24], were predictive of pCR independent of HER2 and ER status [40]. The predictive value was, however, much stronger in HER2+ as compared with that in HER2−/ER− or HER2−/ER+ tumors [40].

In another recent meta-analysis by Stoll et al.[45], an immune-related metagene centered on the CXCL13 transcript demonstrated the highest reproducibility with the achievement of pCR among the other metagenes tested. Genes of the CXCL13 metagene largely overlap with those included in the ICR pathways, implying a coordinated Th1 (i.e. CXCL10, CCL5, PRF1, INFG, and CD8+), and also T-fh (CXCL13) response, in line with the observation of Gu-Trantien et al.[39,46]. CXCL13 drives the migration of high-affinity CXCR5+ T-fh cells and B cells into B-cell-concentrated areas. Several other studies have then listed CXCL13 among the predictive genes (Table 1. ).

Our meta-analysis on 701 breast cancer patients treated with neoadjuvant chemotherapy corroborated the predictive values of the T/NK, M/D, and B/P metagenes. Interestingly, whereas the prognostic role of these metagenes was proliferation-dependent, their predictive value was largely independent of the proliferative status [37].

Denkert et al. recently assessed the predictive connotation of intratumoral TILs and a 12-gene panel consisting of immune-activating (CCL5, CXCL9, CXCL13, CD80, CD21, CD8A, IGKC) and immunoregulatory markers [PD-1, CD274 (PD-L1), CTLA4, FOXP3, IDO1], in HER2+ or TNBC patients from the GeparSixto randomized neoadjuvant trial [47]. Strikingly, both TILs and all the transcripts assessed were associated with response to neoadjuvant chemotherapy, in both HER2+ and TNBC patients. Although the expression of these transcripts correlates with the extent of TIL infiltration [48], the expression of CCL5, PD-L1, and IDO1 provided additional predictive information even when controlled for the levels of TIL infiltration in a multivariate analysis [47]. Interestingly, TIL levels and immune genes seem to be particularly predictive of pCR to carboplatin-containing regimens.

Correlations between immune activation pathways centered on ICR genes, PD-L1 (expressed predominantly but not exclusively by cancer cells), and response to chemotherapy have been also found in the context of inflammatory breast cancer – a particularly rare and aggressive type of mammary carcinoma [49,50▪]. These observations corroborate notions derived from immune-checkpoint studies proposing that the most immune-responsive tumors display an inflammatory status accompanied by the concomitant counteractivation of immunosuppressive mechanisms, recapitulated by the expression of immune-regulatory molecules such as PD-L1 and IDO1 [6,10,51] (Fig. 2). In fact, in breast cancer, a high degree of correlation between pro-inflammatory and anti-inflammatory transcript exists (Fig. 3). This phenomenon can reconcile the paradoxical observation that the expression of immune-regulatory markers (such as FOXP3 and IDO1) is associated with a decreased risk of relapse [52,53].





As for FOXP3, it should be noted that this molecule can be expressed by T-regulatory cells, but also by activated T cells and breast cancer cells, adding complexity to the functional interpretation of gene expression studies [39,54,55].

Results from the NeoALTTO trial have recently shown that TIL density predicts response to neoadjuvant chemotherapeutic regimens containing the anti-HER2 monoclonal antibody trastuzumab or the HER1/HER2 inhibitor lapatinib [56]. Finally, preliminary results from two other clinical trials in patients treated with anti-HER2-based neoadjuvant chemotherapy found a positive correlation between pCR and the expression of plasma cell genes [57] or immune-function metagenes (i.e. PD-1 and STAT1) [58].

It is presently unclear how an immunologically active microenvironment can enhance the efficacy of antineoplastic drugs. It has been proposed that certain chemotherapeutic agents such as cyclophosphamide, doxorubicin, and oxaliplatin may act as immune adjuvants by inducing an immunogenic cell death through the stimulation of dendritic cell-mediated uptake of apoptotic corpses and consequent enhancement of antigen-specific T-cell response [59]. According to this model, the elicitation of a more effective antitumoral response following chemotherapy in tumors bearing a subacute inflammatory status could contribute to a more effective tumor clearance. Recently, Sistigu et al.[60▪] proved that anthracyclines can directly stimulate the rapid production of type I interferon by cancer cells with subsequent release of the CXCL10 chemokine through the activation of autocrine and paracrine loops. Vice versa, IFN-γ release by activated T cells could enhance the cytotoxic activity of chemotherapeutic agents by modulating IFN-inducible molecules involved in DNA-damage repair, even though no solid data exist to date to support this latter hypothesis. As for the findings from trastuzumab studies, it is plausible that an active immune microenvironment could potentiate the elimination of residual disease via antibody-dependent cell-mediated cytotoxicity.

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Robust evidence supports the prognostic and therapy-predictive roles of immune gene signatures in primary breast cancer. However, the further characterization of these biomarkers is clearly warranted. Studies that seek to disentangle the unique and overlapping genes comprising the numerous published signatures, and in treatment-specific contexts, are needed in order to optimize clinical implementation strategies. Moreover, research aimed at dissecting the phenotypic and biological attributes of tumors that statistically modify the immune signature–patient outcome associations (such as proliferation and subtype), could shed valuable light on immunogenic differences among patient populations, as well as on the underlying mechanisms that regulate antitumor immunity and its therapeutic manipulation in breast cancer. Furthermore, it is unknown whether the prognostic and predictive information captured by tumor immune signatures can be predicted by the assessment of peripheral blood markers. The identification of peripheral parameters that indicate the level of intratumoral immune response could enable the development of less invasive diagnostics for immune responsiveness. It should also be highlighted that no studies have assessed so far the predictive role of immune biomarkers in metastatic breast cancer.

As discussed, different performances among proposed immune signatures have been observed, hindering their development toward clinical application. Optimization and validation of immune gene signatures could have a dramatic impact on clinical practice. For example, in ER+ node-negative tumors classified at medium or high risk of relapse by conventional molecular prognostic indices centered on proliferation indexes (e.g. Oncotype-Dx ‘Recurrence Score’, Genomic Health, Inc., Redwood City, California, USA), immune gene scores could define a novel category of low-risk patients for whom chemotherapy could be omitted. In HER2+ tumors, it is plausible that immune signatures could be used to identify patients who will not benefit from trastuzumab and for whom other therapeutic approaches could be tested (e.g. combination of trastuzumab and PD-1 inhibitor [61] or the less immune-dependent anti-HER2 therapy as trastuzumab–emtansine) [42▪▪,62]. Similarly, they could be used to assign patients unlikely to benefit from conventional neoadjuvant chemotherapy to experimental approaches or to define those patients who will benefit from carboplatin-containing regimens. In the metastatic setting, it is plausible that immune gene signatures may better define patients likely to benefit from PD-1 blockade, particularly those signatures that dually incorporate measures of immunosuppressive markers, for which more research is needed.

In two early clinical trials presented last year at San Antonio Breast Cancer Symposium, anti-PD-1 and anti-PD-L1 have been tested in patients bearing TNBC expressing PD-L1 [63,64]. This inclusion criteria was mostly based on the observation that a higher proportion of TNBC, especially if expressing PD-L1, displays high levels of TIL.

Although the rate and duration of clinical responses in these two trials were encouraging, the majority of patients (70–80%) were completely refractory to treatment [63,64]. Hence, it is expected that immunogenomic studies will elucidate the mechanisms that prevent or promote the development of a favorable antitumoral immunity. Such mechanisms could then be targeted to reprogram the microenviroment toward an immune permissive one, resulting in an enhanced efficacy of immunotherapeutic approaches [65].

The role of cancer genomic instability in shaping immune response [66–72] and therefore influencing the response to immunotherapy [73–75] only now begins to be elucidated. Very little is known in breast cancer, in which clashing observation, for example, regarding TP53 mutational status and level of immune infiltration [76–78], has been reported.

Understanding how cancer genetics, host genetics, and environmental factors [79] collectively regulate the development of immune phenotypes of breast cancer will have valuable clinical implications.

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Financial support and sponsorship


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Conflicts of interest

There are no conflicts of interest.

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Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest
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1. Bedognetti D, Wang E, Sertoli MR, Marincola FM. Gene-expression profiling in vaccine therapy and immunotherapy for cancer. Expert Rev Vaccines 2010; 9:555–565.
2. Wang E, Bedognetti D, Marincola FM. Prediction of response to anticancer immunotherapy using gene signatures. J Clin Oncol 2013; 31:2369–2371.
3. Wang E, Miller LD, Ohnmacht GA, et al. Prospective molecular profiling of melanoma metastases suggests classifiers of immune responsiveness. Cancer research 2002; 62:3581–3586.
4. Panelli MC, Stashower ME, Slade HB, et al. Sequential gene profiling of basal cell carcinomas treated with imiquimod in a placebo-controlled study defines the requirements for tissue rejection. Genome Biol 2007; 8:R8.
5. Weiss GR, Grosh WW, Chianese-Bullock KA, et al. Molecular insights on the peripheral and intratumoral effects of systemic high-dose rIL-2 (aldesleukin) administration for the treatment of metastatic melanoma. Clin Cancer Res 2011; 17:7440–7450.
6. Ji RR, Chasalow SD, Wang L, et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol Immunother 2012; 61:1019–1031.
7. Bedognetti D, Spivey TL, Zhao Y, et al. CXCR3/CCR5 pathways in metastatic melanoma patients treated with adoptive therapy and interleukin-2. Br J Cancer 2013; 109:2412–2423.
8. Ulloa-Montoya F, Louahed J, Dizier B, et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J Clin Oncol 2013; 31:2388–2395.
9. Gajewski T, Meng Y, Harlin H. Chemokines expressed in melanoma metastases associated with T cell infiltration. J Clin Oncol 2007; (abstract); 25; 18S: 8501.
10. Herbst RS, Soria JC, Kowanetz M, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014; 515:563–567.
11. Wang E, Bedognetti D, Tomei S, Marincola FM. Common pathways to tumor rejection. Ann N Y Acad Sci 2013; 1284:75–79.
12. Spivey TL, Uccellini L, Ascierto ML, et al. Gene expression profiling in acute allograft rejection: challenging the immunologic constant of rejection hypothesis. J Transl Med 2011; 9:174.
13. Imanguli MM, Swaim WD, League SC, et al. Increased T-bet+ cytotoxic effectors and type I interferon-mediated processes in chronic graft-versus-host disease of the oral mucosa. Blood 2009; 113:3620–3630.
14. Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 2013; 39:11–26.
15. Uccellini L, De Giorgi V, Zhao Y, et al. IRF5 gene polymorphisms in melanoma. J Transl Med 2012; 10:170.
16. Wang E, Worschech A, Marincola FM. The immunologic constant of rejection. Trends Immunol 2008; 29:256–262.
17. Spivey TL, De Giorgi V, Zhao YD, et al. The stable traits of melanoma genetics: an alternate approach to target discovery. BMC Genomics 2012; 13:156.
18. Murtas D, Maric D, De Giorgi V, et al. IRF-1 responsiveness to IFN-gamma predicts different cancer immune phenotypes. Br J Cancer 2013; 109:76–82.
19▪. Gentles AJ, Newman AM, Liu CL, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature Med 2015; 21:938–945.

A pan-cancer meta-analysis that corroborates the prognostic role of immune genes reflecting immune infiltration across cancers.

20▪. Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group. Ann Oncol 2015; 26:259–271.

An important consensus paper addressing the clinical relevance of TIL annotation in breast cancer. Relevant studies are summarized and discussed.

21. Loi S, Michiels S, Salgado R, 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; 25:1544–1550.
22. Ali HR, Provenzano E, Dawson SJ, et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann Oncol 2014; 25:1536–1543.
23. Teschendorff AE, Miremadi A, Pinder SE, et al. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007; 8:R157.
24. Desmedt C, Haibe-Kains B, Wirapati P, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 2008; 14:5158–5165.
25. Rody A, Holtrich U, Pusztai L, et al. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res 2009; 11:R15.
26. Staaf J, Ringner M, Vallon-Christersson J, et al. Identification of subtypes in human epidermal growth factor receptor 2: positive breast cancer reveals a gene signature prognostic of outcome. J Clin Oncol 2010; 28:1813–1820.
27. Bianchini G, Iwamoto T, Qi Y, et al. Prognostic and therapeutic implications of distinct kinase expression patterns in different subtypes of breast cancer. Cancer Res 2010; 70:8852–8862.
28. Sabatier R, Finetti P, Cervera N, et al. A gene expression signature identifies two prognostic subgroups of basal breast cancer. Breast Cancer Res Treatment 2011; 126:407–420.
29. Karn T, Pusztai L, Holtrich U, et al. Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures. PloS One 2011; 6:e28403.
30. Bianchini G, Qi Y, Alvarez RH, et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol 2010; 28:4316–4323.
31. Ascierto ML, Kmieciak M, Idowu MO, et al. A signature of immune function genes associated with recurrence-free survival in breast cancer patients. Breast Cancer Res Treatment 2012; 131:871–880.
32. Ascierto ML, Idowu MO, Zhao Y, et al. Molecular signatures mostly associated with NK cells are predictive of relapse free survival in breast cancer patients. J Transl Med 2013; 11:145.
33. Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486:346–352.
34. Burstein MD, Tsimelzon A, Poage GM, et al. Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res 2015; 21:1688–1698.
35. Nagalla S, Chou JW, Willingham MC, et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol 2013; 14:R34.
36. Miller LD, Chou JW, Black MA, et al. Immune gene signatures and tumor intrinsic markers delineate novel immunogenic subtypes of breast cancer. J ImmunoTher Cancer 2014; 2 (Suppl 3):256.
37. Alistar A, Chou JW, Nagalla S, et al. Dual roles for immune metagenes in breast cancer prognosis and therapy prediction. Genome Med 2014; 6:80.
38. Finak G, Bertos N, Pepin F, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nature Med 2008; 14:518–527.
39. Gu-Trantien C, Loi S, Garaud S, et al. CD4(+) follicular helper T cell infiltration predicts breast cancer survival. J Clin Investig 2013; 123:2873–2892.
40. Ignatiadis M, Singhal SK, Desmedt C, et al. Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis. J Clin Oncol 2012; 30:1996–2004.
41▪. Bonsang-Kitzis H, Sadacca B, Hamy-Petit AS, et al. Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis. Oncoimmunology 2015; [Epub ahead of print].

In this study, a new immune gene signature is proposed as prognostic indicator in TNBC. Authors tested the prognostic performance of virtually all the previously published immune signatures. Authors’ signature outperformed previous published immune signatures.

42▪▪. Perez EA, Thompson EA, Ballman KV, et al. Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the North Central Cancer Treatment Group N9831 Adjuvant Trastuzumab Trial. J Clin Oncol 2015; 33:701–708.

This study from a randomized clinical trial is the first to demonstrate that immune gene signatures predict outcome to trastuzumab-based chemotherapy.

43▪▪. Denkert C, Loibl S, Noske A, et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010; 28:105–113.

This study from a randomized clinical trial is the first to demonstrate that gene expression markers add predictive information to TIL-based scores in HER2+ breast cancer.

44. Schmidt M, Hellwig B, Hammad S, et al. A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin kappa C as a compatible prognostic marker in human solid tumors. Clin Cancer Res 2012; 18:2695–2703.
45. Stoll G, Enot D, Mlecnik B, et al. Immune-related gene signatures predict the outcome of neoadjuvant chemotherapy. Oncoimmunology 2014; 3:e27884.
46. Bedognetti D, Wang E, Marincola FM. Meta-analysis and metagenes: CXCL-13-driven signature as a robust marker of intratumoral immune response and predictor of breast cancer chemotherapeutic outcome. Oncoimmunology 2014; 3:e28727.
47. Denkert C, von Minckwitz G, Brase JC, et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J Clin Oncol 2015; 33:983–991.
48. Lee HJ, Lee JJ, Song IH, et al. Prognostic and predictive value of NanoString-based immune-related gene signatures in a neoadjuvant setting of triple-negative breast cancer: relationship to tumor-infiltrating lymphocytes. Breast Cancer Res Treatment 2015; 151:619–627.
49. Bertucci F, Finetti P, Colpaert C, et al. PDL1 expression in inflammatory breast cancer is frequent and predicts for the pathological response to chemotherapy. Oncotarget 2015; 6:13506–13519.
50▪. Bertucci F, Ueno NT, Finetti P, et al. Gene expression profiles of inflammatory breast cancer: correlation with response to neoadjuvant chemotherapy and metastasis-free survival. Ann Oncol 2014; 25:358–365.

This investigation in inflammatory breast cancer neoadjuvant setting identifies a gene signature enriched in immune genes discriminating responder and nonresponder patients

51. Taube JM, Klein A, Brahmer JR, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 2014; 20:5064–5074.
52. Jacquemier J, Bertucci F, Finetti P, et al. High expression of indoleamine 2,3-dioxygenase in the tumour is associated with medullary features and favourable outcome in basal-like breast carcinoma. International journal of cancer. J Int Cancer 2012; 130:96–104.
53. West NR, Kost SE, Martin SD, et al. Tumour-infiltrating FOXP3(+) lymphocytes are associated with cytotoxic immune responses and good clinical outcome in oestrogen receptor-negative breast cancer. Br J Cancer 2013; 108:155–162.
54. Ladoire S, Martin F, Ghiringhelli F. Prognostic role of FOXP3+ regulatory T cells infiltrating human carcinomas: the paradox of colorectal cancer. Cancer Immunol Immunother 2011; 60:909–918.
55. Kmieciak M, Gowda M, Graham L, et al. Human T cells express CD25 and Foxp3 upon activation and exhibit effector/memory phenotypes without any regulatory/suppressor function. J Transl Med 2009; 7:89.
56. Salgado R, Denkert C, Campbell C, et al. Tumor-infiltrating lymphocytes and associations with pathological complete response and event-free survival in HER2-positive early-stage breast cancer treated with lapatinib and trastuzumab: a secondary analysis of the NeoALTTO trial. J Am Med Assoc Oncol 2015; 1:448–454.
57. Bianchini G, Prat A, Pickl M, et al. Response to neoadjuvant trastuzumab and chemotherapy in ER+ and ER- HER2-positive breast cancers: gene expression analysis. J Clin Oncol 2011; 29(Suppl; abstr 529).
58. Gianni L, Bianchini G, Valagussa P, et al. Adaptive immune system and immune checkpoints are associated with response to pertuzumab (P) and trastuzumab (H) in the NeoSphere study. Cancer Res 2012; 72 (Abstr S6-7).
59. Vacchelli E, Senovilla L, Eggermont A, et al. Trial watch: chemotherapy with immunogenic cell death inducers. Oncoimmunology 2013; 2:e23510.
60▪. Sistigu A, Yamazaki T, Vacchelli E, et al. Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nature Med 2014; 20:1301–1309.

This is an interesting research demonstrating that anthracyclines stimulate the rapid production of interferon by cancer cells.

61. Stagg J, Loi S, Divisekera U, et al. Anti-ErbB-2 mAb therapy requires type I and II interferons and synergizes with anti-PD-1 or anti-CD137 mAb therapy. Proc Natl Acad Sci U S A 2011; 108:7142–7147.
62. Foekens JA, Martens JW, Sleijfer S. Are immune signatures a worthwhile tool for decision making in early-stage human epidermal growth factor receptor 2-positive breast cancer? J Clin Oncol 2015; 33:673–675.
63. Nanda R, Chow LQ, Dees EC, et al. A phase Ib study of pembrolizumab (MK-3475) in patients with advanced triple-negative breast cancer. Cancer Res 2015; 75 (Abstr S1-09).
64. Emens LA, Braiteh FB, Cassier P, et al. Inhibition of PD-L1 by MPDL3280A leads to clinical activity in patients with metastatic triple negative breast cancer. Cancer Res 2015; 75 (Abstr PD1-6).
65. Tomei S, Wang E, Delogu LG, et al. Non-BRAF-targeted therapy, immunotherapy, and combination therapy for melanoma. Exp Opin Biol Ther 2014; 14:663–686.
66. Tomei S, Bedognetti D, De Giorgi V, et al. The immune-related role of BRAF in melanoma. Mol Oncol 2015; 9:93–104.
67. Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014; 158:929–944.
68. Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic beta-catenin signalling prevents antitumour immunity. Nature 2015; 523:231–235.
69. Mlecnik B, Bindea G, Angell HK, et al. Functional network pipeline reveals genetic determinants associated with in situ lymphocyte proliferation and survival of cancer patients. Sci Transl Med 2014; 6:228ra237.
70. Rooney MS, Shukla SA, Wu CJ, et al. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 2015; 160:48–61.
71. Linsley PS, Speake C, Whalen E, Chaussabel D. Copy number loss of the interferon gene cluster in melanomas is linked to reduced T cell infiltrate and poor patient prognosis. PloS One 2014; 9:e109760.
72. Simeone I, Hendricks W, Miller L, et al. Toward the identification of genetic determinants of breast cancer immune responsiveness. In Breast cancer immunotherapy symposium (BRECIS): Sidra Symposia Series, April 13–14, Doha, Qatar (JITC Suppl in press); 2015.
73. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014; 371:2189–2199.
74. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in nonsmall cell lung cancer. Science 2015; 348:124–128.
75. Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015; 372:2509–2520.
76. Quigley D, Silwal-Pandit L, Dannenfelser R, et al. Lymphocyte invasion in IC10/basal-like breast tumors is associated with wild-type TP53. Mol Cancer Res 2015; 13:493–501.
77. Gatalica Z, Snyder C, Maney T, et al. Programmed cell death 1 (PD-1) and its ligand (PD-L1) in common cancers and their correlation with molecular cancer type. Cancer Epidemiol Biomarkers Prevent 2014; 23:2965–2970.
78. Kriegsmann M, Endris V, Wolf T, et al. Mutational profiles in triple-negative breast cancer defined by ultradeep multigene sequencing show high rates of PI3K pathway alterations and clinically relevant entity subgroup specific differences. Oncotarget 2014; 5:9952–9965.
79. Trinchieri G. Cancer and inflammation: an old intuition with rapidly evolving new concepts. Annu Rev Immunol 2012; 30:677–706.

breast cancer; immune gene signatures; immune score; immunotherapy; transcriptomic studies

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