Immune response-associated gene profiling in Japanese melanoma patients using multi-omics analysis
- Authors:
- Published online on: December 21, 2017 https://doi.org/10.3892/or.2017.6173
- Pages: 1125-1131
Abstract
Introduction
Programmed death-ligand 1 (PD-L1) and PD-1 expression is variably regulated in immune cells and tumor cells to maintain immunological tolerance, which controls the occurrence of an autoimmune reaction against self-antigens (1,2). PD-L1-expressing antigen-presenting cells, such as monocytes, macrophages, dendritic and tumor cells regulate excess immune reactions and inhibit activated T-cell function (3,4). Meanwhile, PD-1, the receptor for PD-L1, is expressed on activated T, B and NK cells in the tumor microenvironment. Anti-PD-1 blockade therapy promotes exhaustive marker-positive T-cell expansion and survival (5), resulting in an antitumor response in vivo.
Since the recent success of immune checkpoint antibodies, such as ipilimumab and nivolumab, as reported for metastatic melanoma patients, many ongoing clinical trials have been underway to evaluate their efficacy in various solid cancers other than melanomas (6–8). Despite these promising results, the response rate associated with the single antibody treatment is ~20–40% while 60–70% of cancer patients belong to the non-responding group. Furthermore, it is still difficult to accurately predict the responders to antibody therapy based on the current preclinical studies (9,10).
In the present study, we used a previously reported immune response-associated gene panel consisting of 164 genes (56 antigen-presenting cells and T-cell-associated genes, 34 cytokine- and metabolism-associated genes, 47 TNF and TNF receptor superfamily genes and 27 regulatory T-cell-associated genes) (11). The present study investigated the association of the gene panel expression with immunological and clinical parameters, such as i) PD-L1 expression; ii) a high mutation load [single nucleotide variant (SNV) number]; iii) a driver gene mutation; iv) CD8 expression; and v) survival time, using the genomic data from 13 melanoma patients in the Project High-tech Omics-based Patient Evaluation (HOPE). Since 2014, 1,685 cancer patients have been enrolled in Project HOPE in which the simultaneous analyses of whole-exome sequencing (WES) and gene expression profiling (GEP) have been performed (12,13). We aimed to evaluate the immunological status in the tumor tissues using next-generation sequencing and to better obtain a prediction of the responders to immune checkpoint antibody treatment through suitable biomarker detection.
Materials and methods
Patient registration
Project HOPE uses comprehensive whole-exome sequencing and gene expression profiling of various tumor tissues and is conducted in accordance with the ‘Ethical Guidelines for Human Genome and Genetic Analysis Research’ in Japan. Informed consent was obtained from all the patients participating in Project HOPE, and the study was approved by the Institutional Review Board of Shizuoka Cancer Center (SCC), Japan. Tumor tissues, along with the surrounding normal tissues, were dissected from surgical specimens by trained pathologists. A total of 1,685 cancer patients were registered in Project HOPE from 2014 to 2015. Characteristics of the 13 melanoma patients listed are shown in Table I.
Comprehensive gene expression analysis using DNA microarray
Total RNA was extracted from ~10 mg of tissue samples using the miRNeasy Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The method of performing the DNA microarray analysis was previously described (13,14). Briefly, the ratio of the expression intensity between the tumor tissue (T) and the surrounding normal tissue (N) was calculated from the normalized values. The expression values for all probes were log (base 2) transformed before performing the statistical analysis.
Whole-exome sequencing (WES) analysis of the melanoma tissues using next-generation sequencing
WES analysis including mapping, variant calling and identification of somatic mutation were performed using the Ion Proton system with the Ion AmpliSeq™ Exome kit, Torrent Suite Software and Ion Reporter™ Server system (Thermo Fisher Scientific, Waltham, MA USA) as previously reported (12). Briefly, all the variants called by the variant caller were available. However, the data presented in SCC represent those variants considered to be of good quality, based on the filtering in which the sequences were discarded with a quality <30, variant allele frequency <10% or depth of coverage <20. Those mutations that were identified in tumor samples and not observed in matched normal samples were extracted as somatic mutations. Single-nucleotide variants (SNVs) of the total exonic mutations for each sequenced tumor included non-synonymous, synonymous, and indels/frameshift mutations. In the present study we focused on somatic SNVs. Additionally, Vogelstein driver gene mutation (15) profiling was investigated.
Immunohistochemistry
For the immune checkpoint protein staining, the anti-PD-L1 antibody (rabbit monoclonal, cat. 13684; 1:200 dilution) was purchased (Cell Signaling, Danvers, MA, USA). For the tumor-infiltrating lymphocyte (TIL) staining, anti-CD4 (mouse monoclonal, cat. MS-1528-S; 1:20 dilution) and anti-CD8 (mouse monoclonal, cat. MS-457-S; 1:50 dilution) antibodies (Thermo Fisher Scientific) were purchased and were used for the immunohistochemistry analysis. In each section stained with the various antibodies, 10 high-magnification (×200) fields were analyzed using WinROOF image-analyzing software (Mitani Corporation, Tokyo, Japan). The PD-L1 staining was evaluated as the percentage of tumor cells exhibiting positive membranous staining as follows: score 0, <1%; score 1, 1–5%; score 2, >5-50%; and score 3, >50% (16). The TIL level was assessed by a semi-quantitative estimation of the density of the CD8+ T cells inside the tumor site as follows: score 0, no or sporadic CD8+ T cells; score 1, moderate number of CD8+ T cells; score 2, abundant number of CD8+ T cells; and score 3, highly abundant number of CD8+ T cells (17). The score that was most frequent in entire sections was assigned.
Statistical analysis
The differentially expressed genes derived from the 164 immune response-associated gene panels between the immunological parameter-positive and parameter-negative groups were identified using the volcano plot method. Each microarray probe was considered significantly differentially expressed between two groups of samples if they satisfied the following criteria: i) corrected t-test P-value <0.05; ii) a Benjamini-Hochberg false discovery rate (FDR) <0.1; and iii) a fold change >2.0 or below 1/2. Correlations between the immune response-associated gene expression and the clinicopathological features, including the survival data, were analyzed using an unpaired two-tailed t-test or a Spearman coefficiency test. Values of P<0.05 were considered significant. The relapse-free survival (RFS) was calculated from the date of the diagnosis until the date of distant relapse. The overall survival (OS) was calculated from the date of the diagnosis to the date of death from cancer. Follow-up was assessed from the date of the diagnosis to the last contact date with the event-free patients.
Results
PD-L1 and CD8 expression, Vogelstein driver genes mutations, and SNV number in melanoma tumors
PD-L1 expression was evaluated according to the criteria of the staining score, such that the scores of 1 and 2 were positive and a score of 0 was negative. Five cases were positive and 8 were negative for PD-L1 expression. According to the Vogelstein driver mutation number, the WES analysis revealed that 5 cases had ≥2 mutations, and 8 had <2 mutations. For the SNV number, 4 cases had ≥100 SNVs and 9 had <100. The CD8 expression level was high in 5 (scores 3 and 4) and low in 8 cases (scores 0–2) based on the IHC scoring denotations (Table I).
Association of the immune response-associated gene expression with immunological parameters using a volcano plot
We previously established an immune response-associated gene panel, consisting of 164 genes (11). The association of the immune response-associated gene expression obtained by the GEP data from Project HOPE with PD-L1 expression, SNV number, Vogelstein driver gene mutation number and CD8 expression was investigated using a volcano plot analysis.
With regard to the PD-L1 expression, 12 immune response-associated genes were identified as upregulated genes in the PD-L1-positive melanomas, in which 6 genes were involved in T-cell suppression and 6 were related to T-cell activation (Fig. 1A and Table II). In addition, the VEGF gene alone was identified as an upregulated gene in high SNV number with >100 melanomas (Fig. 1B). Regarding the Vogelstein driver mutation number, in contrast, 18 immune response-associated genes were downregulated in the Vogelstein mutation high-number group. Notably, 9 genes involved in T-cell activation, such as CD3 (D, G and Z), CD40LG, STAT4, CCL5, TNFRSF4, TNFSF8 and TNFSF14, were identified (Fig. 1C and Table III). Notably, 14 immune response-associated genes were identified as upregulated genes in the TIL marker CD8-high melanomas, which were mostly correlated to T-cell activation favoring a Th1 response leading to tumor killing by CTLs (Fig. 1D).
Correlation of the immune response-associated gene expression with the survival time of the melanoma patients
The correlation of the expression of 164 immune response-associated genes with the overall and relapse-free survival time was investigated using a Spearman's rank-order correlation. Fourteen genes and 17 genes were significantly correlated with the overall and relapse-free survival time, respectively (Table IV). Eight genes, including CD27, CXCR6, IL17RB, PDCD1, TNFRSF11A, ADAM12, EDA2R and TREM1, were commonly identified in both the overall and relapse-free survival time groups, and 5 were positively correlated and 3 were negatively correlated.
Discussion
In the present study, we used a previously reported immune response-associated gene panel that consisted of 164 genes (11), and investigated the association of the expression of the gene panel with immunological and clinical parameters, such as: i) PD-L1 expression; ii) a high mutation load [single nucleotide variant (SNV) number]; iii) driver gene mutation; iv) CD8 expression; and v) survival time, using the genomic data from 13 melanoma patients registered in Project HOPE.
With advances in cancer genomic sequencing, specific gene signatures involved in the therapeutic response and prognosis have been reported, and their accuracy and efficiency have been investigated in various types of cancer, such as breast, stomach, non-small cell lung cancers and melanomas (18,19). However, few studies focusing on immune-related gene panels or signature identifications have been reported since the development of cancer genomic technologies such as next-generation sequencing. The identification of cancer-specific T-cell receptor (TCR) sequences has been attempted in immunological routine analyses (20), but not much success has been obtained. Small scale genetic studies focusing on renal cell cancer or polypoid precancerous colorectal lesions revealed that tumor-associated macrophage markers or TIL markers were involved in the prognosis or the progression of precancerous to cancerous lesions (21,22). However, Lee et al (23) obtained biopsy tissues from 55 triple-negative breast cancer patients treated with combined chemotherapy, and evaluated immune responses using the NanoString nCounter GX human immunology panel (579 immune-related genes), which demonstrated that a higher expression of cytotoxic molecules, TCR signaling pathway molecules, Th1 cytokines and B cell markers were associated with a pathological complete response (CR).
First, the association of the immune response-associated gene panel expression with the expression level of PD-L1 was investigated in the present study. Twelve immune response-associated genes were identified as upregulated in the PD-L1-positive melanomas; 6 of these genes were involved in T-cell suppression and 6 were related to T-cell activation. In particular, the 6 T-cell stimulation-related genes were: CCRL2 (attraction of TILs) (24); CD68 (M1 macrophage activation); CCR5 (T-cell migration); HLA-DMB (increase of CD8+ TIL and IFN-γ level, and improvement of survival) (25); STAT1 (IFN-γ signal activation in T cells) (26) and TNFRSF9 (T-cell activation) (27). However, the others were T-cell inhibition-related genes, including; CD274 (PD-L1), IDO-1, HAVCR2 (TIM-3), LGALS3 (galectin-3), MSR1 and TNFRSF12A. Taube et al (28) reported similar results using a volcano plot of 11 melanoma patients, which demonstrated 12 upregulated genes in PD-L1-positive melanomas including 4 immuno-regulatory genes, such as CD274, PDCD1 (PD-1), LAG3 and IL-10. The upregulated gene profile in the PD-L1-positive melanomas in our study was similar to their analysis.
Second, the association of the immune response-associated gene panel expression with Vogelstein driver mutation number was investigated. Eighteen immune response-associated genes were downregulated in the Vogelstein mutation high-number (>2) group. Notably, 9 genes involved in T-cell activation, including CD3 (D, G and Z), CD40LG, STAT4, CCL5, TNFRSF4, TNFSF8 and TNFSF14, were identified. Among these, TNFRSF4 (OX40), TNFSF8 (CD30-L) and TNFSE14 (HVEM-L) are TNF ligand superfamily members and trigger T-cell stimulating signals by binding to their specific receptors. A constitutive signal activation, such as MAPK, STAT3, NF-κB, and β-catenin, in cancer cells induces an immunosuppressive effect that is mediated by the TGF-β, IL-6, IL-10 and VEGF produced by the cancer cells, resulting in regulatory T-cell and myeloid derived suppressor cell (MDSC) induction (29). Specifically, an STK11 mutation and RAS/MAPK activation were linked to CD3 gene downregulation or a TIL reduction in the tumor (30,31) Additionally, Frederick et al (32) demonstrated that a BRAF inhibition was associated with an upregulation of melanoma antigen expression and a favorable tumor microenvironment through the reduction of immunosuppressive cytokines, such as IL-6 and IL-8. In the present study, an extensive immunosuppressive effect on the T-cell activation signal was ascertained, but the upregulation of melanoma antigens was not significant because of the small number of cases in the evaluation.
Third, the correlation of the expression of 164 immune response-associated genes with the overall and relapse-free survival time was investigated using a Spearman's rank-order correlation. Eventually, 8 genes, such as CD27, CXCR6, IL17RB, PDCD1, TNFRSF11A, ADAM12, EDA2R and TREM1, were commonly identified in both the overall and relapse-free survival time groups, of which 5 were positively correlated and 3 were negatively correlated. Briefly, the survival-correlated gene profiling was as follows: CD27 (expressed on CD8+ TIL was associated with a good prognosis) (33); CXCR6 (the CXCR6/CXCL16 axis in the tumor was associated with a TIL increase and a good prognosis) (34); IL17RB (a higher HOXB13-to-IL17RB ratio was linked to a worse outcome) (35); TNFRSF11A (RANK upregulation may be linked to mammary tumorigenesis in BRCA1-mutant carriers) (36); ADAM12 [an aggressive ovarian cancer marker was associated with a TGF-β-induced epithelial to mesenchymal transition (EMT)] (37); EDA2R (highly expressed in ovarian cancer and was associated with a poor prognosis); and TREM1 (induced a proinflammatory and protumor microenvironment and was associated with a poor prognosis) (38). Based on these observations, the protein expression of the 8 markers, using previously resected melanoma tissues, warrants future investigation, and the specific association of the protein expression with the survival data based on a Kaplan-Meier analysis should be precisely performed.
Finally, in the present study, we investigated the association of the expression of the immune response-associated gene panel with various parameters, mainly PD-L1 and CD8 expression, driver gene mutation and survival time, and several gene signatures involved in patient prognosis were identified. These results revealed that cancer genomic data may be associated with specific immunological gene signatures closely linked to the immunological status in the tumor microenvironment, which could contribute to the development of specific cancer immunotherapies for tailored medicine called precision immunotherapy (39).
Acknowledgements
The authors thank the staff at the Shizuoka Cancer Center Hospital for the clinical support and sample preparation.
Glossary
Abbreviations
Abbreviations:
WES |
whole-exome sequencing |
GEP |
gene expression profiling |
PD-1 |
programmed death-1 |
PD-L1 |
programmed death-ligand 1 |
NGS |
next-generation sequencing |
SNV |
single nucleotide variant |
TIL |
tumor-infiltrating lymphocyte |
OS |
overall survival |
RFS |
relapse-free survival |
References
Zhang B, Chikuma S, Hori S, Fagarasan S and Honjo T: Nonoverlapping roles of PD-1 and FoxP3 in maintaining immune tolerance in a novel autoimmune pancreatitis mouse model. Proc Natl Acad Sci USA. 113:pp. 8490–8495. 2016; View Article : Google Scholar : PubMed/NCBI | |
Wang J, Okazaki IM, Yoshida T, Chikuma S, Kato Y, Nakaki F, Hiai H, Honjo T and Okazaki T: PD-1 deficiency results in the development of fatal myocarditis in MRL mice. Int Immunol. 22:443–452. 2010. View Article : Google Scholar : PubMed/NCBI | |
Heeren AM, Koster BD, Samuels S, Ferns DM, Chondronasiou D, Kenter GG, Jordanova ES and de Gruijl TD: High and interrelated rates of PD-L1+CD14+ antigen-presenting cells and regulatory T cells mark the microenvironment of metastatic lymph nodes from patients with cervical cancer. Cancer Immunol Res. 3:48–58. 2015. View Article : Google Scholar : PubMed/NCBI | |
Yaguchi T and Kawakami Y: Cancer-induced heterogeneous immunosuppressive tumor microenvironments and their personalized modulation. Int Immunol. 28:393–399. 2016. View Article : Google Scholar : PubMed/NCBI | |
Fourcade J, Sun Z, Pagliano O, Chauvin JM, Sander C, Janjic B, Tarhini AA, Tawbi HA, Kirkwood JM, Moschos S, et al: PD-1 and Tim-3 regulate the expansion of tumor antigen-specific CD8+ T cells induced by melanoma vaccines. Cancer Res. 74:1045–1055. 2014. View Article : Google Scholar : PubMed/NCBI | |
Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, et al: Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 366:2443–2454. 2012. View Article : Google Scholar : PubMed/NCBI | |
Weber JS, O'Day S, Urba W, Powderly J, Nichol G, Yellin M, Snively J and Hersh E: Phase I/II study of ipilimumab for patients with metastatic melanoma. J Clin Oncol. 26:5950–5956. 2008. View Article : Google Scholar : PubMed/NCBI | |
Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM, Segal NH, Ariyan CE, Gordon RA, Reed K, et al: Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 369:122–133. 2013. View Article : Google Scholar : PubMed/NCBI | |
Okazaki T, Chikuma S, Iwai Y, Fagarasan S and Honjo T: A rheostat for immune responses: The unique properties of PD-1 and their advantages for clinical application. Nat Immunol. 14:1212–1218. 2013. View Article : Google Scholar : PubMed/NCBI | |
Weber JS, D'Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, Hoeller C, Khushalani NI, Miller WH Jr, Lao CD, et al: Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): A randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 16:375–384. 2015. View Article : Google Scholar : PubMed/NCBI | |
Akiyama Y, Kondou R, Iizuka A, Ohshima K, Urakami K, Nagashima T, Shimoda Y, Tanabe T, Ohnami S, Ohnami S, et al: Immune response-associated gene analysis of 1,000 cancer patients using whole-exome sequencing and gene expression profiling-Project HOPE. Biomed Res. 37:233–242. 2016. View Article : Google Scholar : PubMed/NCBI | |
Urakami K, Shimoda Y, Ohshima K, Nagashima T, Serizawa M, Tanabe T, Saito J, Usui T, Watanabe Y, Naruoka A, et al: Next generation sequencing approach for detecting 491 fusion genes from human cancer. Biomed Res. 37:51–62. 2016. View Article : Google Scholar : PubMed/NCBI | |
Yamaguchi K, Urakami K, Ohshima K, Mochizuki T, Akiyama Y, Uesaka K, Nakajima T, Takahashi M, Tamai S and Kusuhara M: Implementation of individualized medicine for cancer patients by multiomics-based analyses-the Project HOPE-. Biomed Res. 35:407–412. 2014. View Article : Google Scholar : PubMed/NCBI | |
Ohshima K, Hatakeyama K, Nagashima T, Watanabe Y, Kanto K, Doi Y, Ide T, Shimoda Y, Tanabe T, Ohnami S, et al: Integrated analysis of gene expression and copy number identified potential cancer driver genes with amplification-dependent overexpression in 1,454 solid tumors. Sci Rep. 7:6412017. View Article : Google Scholar : PubMed/NCBI | |
Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr and Kinzler KW: Cancer genome landscapes. Science. 339:1546–1558. 2013. View Article : Google Scholar : PubMed/NCBI | |
Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, et al: KEYNOTE-001 Investigators: Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 372:2018–2028. 2015. View Article : Google Scholar : PubMed/NCBI | |
Dahlin AM, Henriksson ML, Van Guelpen B, Stenling R, Oberg A, Rutegård J and Palmqvist R: Colorectal cancer prognosis depends on T-cell infiltration and molecular characteristics of the tumor. Mod Pathol. 24:671–682. 2011. View Article : Google Scholar : PubMed/NCBI | |
Hallett RM, Dvorkin-Gheva A, Bane A and Hassell JA: A gene signature for predicting outcome in patients with basal-like breast cancer. Sci Rep. 2:2272012. View Article : Google Scholar : PubMed/NCBI | |
Cristescu R, Lee J, Nebozhyn M, Kim KM, Ting JC, Wong SS, Liu J, Yue YG, Wang J, Yu K, et al: Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med. 21:449–456. 2015. View Article : Google Scholar : PubMed/NCBI | |
Munson DJ, Egelston CA, Chiotti KE, Parra ZE, Bruno TC, Moore BL, Nakano TA, Simons DL, Jimenez G, Yim JH, et al: Identification of shared TCR sequences from T cells in human breast cancer using emulsion RT-PCR. Proc Natl Acad Sci USA. 113:pp. 8272–8277. 2016; View Article : Google Scholar : PubMed/NCBI | |
Mickley A, Kovaleva O, Kzhyshkowska J and Gratchev A: Molecular and immunologic markers of kidney cancer-potential applications in predictive, preventive and personalized medicine. EPMA J. 6:202015. View Article : Google Scholar : PubMed/NCBI | |
Maglietta A, Maglietta R, Staiano T, Bertoni R, Ancona N, Marra G and Resta L: The immune landscapes of polypoid and nonpolypoid precancerous colorectal lesions. PLoS One. 11:e01593732016. View Article : Google Scholar : PubMed/NCBI | |
Lee HJ, Lee JJ, Song IH, Park IA, Kang J, Yu JH, Ahn JH and Gong G: 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 Treat. 151:619–627. 2015. View Article : Google Scholar : PubMed/NCBI | |
Wang LP, Cao J, Zhang J, Wang BY, Hu XC, Shao ZM, Wang ZH and Ou ZL: The human chemokine receptor CCRL2 suppresses chemotaxis and invasion by blocking CCL2-induced phosphorylation of p38 MAPK in human breast cancer cells. Med Oncol. 32:2542015. View Article : Google Scholar : PubMed/NCBI | |
Callahan MJ, Nagymanyoki Z, Bonome T, Johnson ME, Litkouhi B, Sullivan EH, Hirsch MS, Matulonis UA, Liu J, Birrer MJ, et al: Increased HLA-DMB expression in the tumor epithelium is associated with increased CTL infiltration and improved prognosis in advanced-stage serous ovarian cancer. Clin Cancer Res. 14:7667–7673. 2008. View Article : Google Scholar : PubMed/NCBI | |
Avalle L, Pensa S, Regis G, Novelli F and Poli V: STAT1 and STAT3 in tumorigenesis: A matter of balance. JAK-STAT. 1:65–72. 2012. View Article : Google Scholar : PubMed/NCBI | |
Nam KO, Kang WJ, Kwon BS, Kim SJ and Lee HW: The therapeutic potential of 4–1BB (CD137) in cancer. Curr Cancer Drug Targets. 5:357–363. 2005. View Article : Google Scholar : PubMed/NCBI | |
Taube JM, Young GD, McMiller TL, Chen S, Salas JT, Pritchard TS, Xu H, Meeker AK, Fan J, Cheadle C, et al: Differential expression of immune-regulatory genes associated with PD-L1 display in melanoma: Implications for PD-1 pathway blockade. Clin Cancer Res. 21:3969–3976. 2015. View Article : Google Scholar : PubMed/NCBI | |
Kawakami Y, Yaguchi T, Sumimoto H, Kudo-Saito C, Tsukamoto N, Iwata-Kajihara T, Nakamura S, Nishio H, Satomi R, Kobayashi A, et al: Roles of signaling pathways in cancer cells and immune cells in generation of immunosuppressive tumor-associated microenvironmentsThe Tumor Immunoenvironment. Shurin MR, Umansky V and Malyguine A: Springer Science+Buisiness Media B.V.; Dordrecht, The Netherlands: pp. 307–323. 2013, View Article : Google Scholar | |
Schabath MB, Welsh EA, Fulp WJ, Chen L, Teer JK, Thompson ZJ, Engel BE, Xie M, Berglund AE, Creelan BC, et al: Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene. 35:3209–3216. 2016. View Article : Google Scholar : PubMed/NCBI | |
Loi S, Dushyanthen S, Beavis PA, Salgado R, Denkert C, Savas P, Combs S, Rimm DL, Giltnane JM, Estrada MV, et al: RAS/MAPK activation is associated with reduced tumor-infiltrating lymphocytes in triple-negative breast cancer: Therapeutic cooperation between MEK and PD-1/PD-L1 immune checkpoint inhibitors. Clin Cancer Res. 22:1499–1509. 2016. View Article : Google Scholar : PubMed/NCBI | |
Frederick DT, Piris A, Cogdill AP, Cooper ZA, Lezcano C, Ferrone CR, Mitra D, Boni A, Newton LP, Liu C, et al: BRAF inhibition is associated with enhanced melanoma antigen expression and a move favorable tumor microenvironment in patients with metastatic melanoma. Clin Cancer Res. 19:1225–1231. 2013. View Article : Google Scholar : PubMed/NCBI | |
Wouters MC, Komdeur FL, Workel HH, Klip HG, Plat A, Kooi NM, Wisman GB, Mourits MJ, Arts HJ, Oonk MH, et al: Treatment regimen, surgical outcome, and T-cell differentiation influence prognostic benefit of tumor-infiltrating lymphocytes in high-grade serous ovarian cancer. Clin Cancer Res. 22:714–724. 2016. View Article : Google Scholar : PubMed/NCBI | |
Hojo S, Koizumi K, Tsuneyama K, Arita Y, Cui Z, Shinohara K, Minami T, Hashimoto I, Nakayama T, Sakurai H, et al: High-level expression of chemokine CXCL16 by tumor cells correlates with a good prognosis and increased tumor-infiltrating lymphocytes in colorectal cancer. Cancer Res. 67:4725–4731. 2007. View Article : Google Scholar : PubMed/NCBI | |
Zhao L, Zhu S, Gao Y and Wang Y: Two-gene expression ratio as predictor for breast cancer treated with tamoxifen: Evidence from meta-analysis. Tumour Biol. 35:3113–3117. 2014. View Article : Google Scholar : PubMed/NCBI | |
Nolan E, Vaillant F, Branstetter D, Pal B, Giner G, Whitehead L, Lok SW, Mann GB, Rohrbach K, Huang LY, et al Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), : RANK ligand as a potential target for breast cancer prevention in BRCA1-mutation carriers. Nat Med. 22:933–939. 2016. View Article : Google Scholar : PubMed/NCBI | |
Cheon DJ, Li AJ, Beach JA, Walts AE, Tran H, Lester J, Karlan BY and Orsulic S: ADAM12 is a prognostic factor associated with an aggressive molecular subtype of high-grade serous ovarian carcinoma. Carcinogenesis. 36:739–747. 2015. View Article : Google Scholar : PubMed/NCBI | |
Duan M, Wang ZC, Wang XY, Shi JY, Yang LX, Ding ZB, Gao Q, Zhou J and Fan J: TREM-1, an inflammatory modulator, is expressed in hepatocellular carcinoma cells and significantly promotes tumor progression. Ann Surg Oncol. 22:3121–3129. 2015. View Article : Google Scholar : PubMed/NCBI | |
Mandal R and Chan TA: Personalized oncology meets immunology: The path toward precision immunotherapy. Cancer Discov. 6:703–713. 2016. View Article : Google Scholar : PubMed/NCBI |