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 . 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  or immune-function metagenes (i.e. PD-1 and STAT1) .
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 . 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.
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  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.
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 .
Papers of particular interest, published within the annual period of review, have been highlighted as:
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.