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Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma

Subjects

Abstract

While mutations in the KRAS oncogene are among the most prevalent in human cancer, there are few successful treatments to target these tumors. It is also likely that heterogeneity in KRAS-mutant tumor biology significantly contributes to the response to therapy. We hypothesized that the presence of commonly co-occurring mutations in STK11 and TP53 tumor suppressors may represent a significant source of heterogeneity in KRAS-mutant tumors. To address this, we utilized a large cohort of resected tumors from 442 lung adenocarcinoma patients with data including annotation of prevalent driver mutations (KRAS and EGFR) and tumor suppressor mutations (STK11 and TP53), microarray-based gene expression and clinical covariates, including overall survival (OS). Specifically, we determined impact of STK11 and TP53 mutations on a new KRAS mutation-associated gene expression signature as well as previously defined signatures of tumor cell proliferation and immune surveillance responses. Interestingly, STK11, but not TP53 mutations, were associated with highly elevated expression of KRAS mutation-associated genes. Mutations in TP53 and STK11 also impacted tumor biology regardless of KRAS status, with TP53 strongly associated with enhanced proliferation and STK11 with suppression of immune surveillance. These findings illustrate the remarkably distinct ways through which tumor suppressor mutations may contribute to heterogeneity in KRAS-mutant tumor biology. In addition, these studies point to novel associations between gene mutations and immune surveillance that could impact the response to immunotherapy.

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite therapeutic advances over the last several decades, the overall 5-year survival remains only 16%.1 Mutations in the KRAS gene occur frequently in non-small cell lung cancer (NSCLC), especially in adenocarcinoma (~30%) though less common in squamous cell carcinoma (about 7%).2, 3, 4 Although mutationally activated KRAS tumors were originally identified in 1982,5 to date there are no successful treatment strategies that target these mutations.3 However, key pathways activated by KRAS and mutation-associated vulnerabilities may be therapeutically targetable including the MEK, phosphoinositide 3-kinase, GSK-3α and RAL/TBK1 pathways.6, 7, 8, 9, 10, 11 Mutations in tumor suppressor genes TP53 and STK11 are also common in lung adenocarcinoma and often co-occur with KRAS mutations.2, 3, 4, 12 While much is known about tumor promotion mechanisms of TP53, less is known about STK11 function and impact on disease progression and patient survival. The STK11 gene encodes a serine/threonine protein kinase known as liver kinase β1.13 The most common STK11 mutations are deletion or inactivating mutations,14, 15, 16, 17, 18, 19, 20, 21 which, along with murine studies provide strong evidence for a tumor suppressor function for this gene.19

Recent studies have defined gene expression changes triggered by RAS.22, 23, 24 For example, an RAS signature associated with MEK pathway activation is also associated with sensitivity to MEK inhibitors.24 Gene expression studies have also defined signatures associated with enhanced tumor progression and reduced patient survival. Examples of such signatures include the malignancy risk (MR) signature reported by our group, which is rich in cell-cycle regulating genes and therefore associated with highly proliferative tumors.25, 26 It is now well established that a functional immune system is crucial in controlling tumor growth.27, 28, 29 Consequently, T-cell presence in tumors is associated with immune surveillance and improved patient survival.28, 30, 31, 32, 33, 34, 35 It is important to note that benefit from immunotherapy, including T-cell checkpoint blockade, is also commonly associated with high tumor expression of immuno-stimulatory genes and T-cell infiltration.36, 37, 38 Thus, activation of key immune regulatory pathways such as JAK-STAT and NF-κB pathways38, 39, 40, 41 in tumor cells or tumor infiltrating non-malignant cells likely enhances the response to immunotherapy. We recently identified a gene expression signature of NF-κB regulated genes that is associated with an immune-active tumor microenvironment.42 The role and potential association between common lung cancer mutations and the immune surveillance response has however not been investigated. The goal of studies described here was to better define molecular heterogeneity in KRAS-mutant tumors, especially as it relates to effects of co-occurring mutations in STK11 and TP53 tumor suppressors in shaping KRAS-mutant tumor biology, proliferative and immune surveillance responses in tumors.

Results and Discussion

Study population and prevalence of mutations

Study population characteristics and mutational status of the 442 adenocarcinoma lung cancer patients are summarized in Supplementary Table 1. Overall, the majority of patients were over 70 years of age (53.2%), female (54.3%), White (95.6%), self-reported ever smokers (91%) and early stage (stage I: 64%). In comparison of individual gene mutations with their wild-type counterpart, the overall prevalence was 34.8% for KRAS, 10.6% for EGFR, 15.3% for STK11 and 25.1% for TP53 (Supplementary Table 1). Of the 442 tumors, 159 did not harbor a mutation in any of these four genes (Figure 1a). Key variables including demographic and clinical information associated with mutations in these four genes are provided in Supplementary Table 1. As expected,4 KRAS and EGFR mutations were mutually exclusive while STK11 mutations were significantly associated with KRAS mutations (P<0.0001) (Figure 1a and Supplementary Table 1). In addition, co-occurrence of STK11 and TP53 mutations in KRAS-mutant tumors was rare (n=4; Figure 1a).

Figure 1
figure1

Co-occurring and mutually exclusive mutations in KRAS, STK11, TP53 and EGFR in 442 human lung adenocarcinoma samples. (a) Tumors with specific mutations are indicted in red while tumors without mutations are in green. To demonstrate co-occurring and exclusive mutations, the samples were sorted by KRAS mutations, then SKT11 mutations, then TP53 mutations, and then EGFR mutations. (be) Association of oncogene and tumor suppressor gene mutations with OS among stage I lung adenocarcinoma patients (n=265). Kaplan–Meier survival curves by mutation status of (b) KRAS, (c) STK11, (d) TP53 and (e) EGFR are shown. A two-sided log-rank test was used to assess statistically significant differences by mutational status. The number of patients at risk is listed below the survival curves. Additional methodology is provided in Supplementary Information.

Overall the results of sequencing analysis yielded findings similar to those in the literature and in public data sets (COSMIC).43 Briefly, KRAS alterations occurred at codon positions 12, 13 and 61, which are each well-characterized gain-of-function positions.43 The most common mutations in EGFR have also been well characterized.3 Although the focus of the present study was not on EGFR mutations, we found 23 L858R point mutations and 21 in-frame indels in codon 19 in this cohort. Consistent with a loss-of-function mutation pattern, deletions or inactivating mutations were commonly found in TP53 and STK11. Criteria used to identify and remove germline variants in TP53 and STK11 are described in Supplementary Information, which included filtering against the 1000 Genomes Project and the Exome Sequencing Project data set. In addition, we ensured that mutational events detected in these genes were previously reported in TCGA (The Cancer Genome Atlas) data set, and if not, that they resulted in frame-shifted/truncated proteins (Supplementary Information). The role of KRAS mutations as a prognostic factor in NSCLC is presently unclear. However, a recent meta-analysis of 41 studies concluded that KRAS mutations are associated with poor prognosis in patients with NSCLC, especially in patients with adenocarcinoma and early-stage NSCLCs.44 In the present cohort, we also found that KRAS mutations were associated with poor survival compared with wild type among stage I patients (Figure 1b). Conversely, EGFR mutations were associated with significantly better overall survival (OS) compared with wild type, while STK11 and TP53 were not significantly associated with OS (Figures 1c–e).

Impact of STK11 and TP53 mutations on a novel KRAS mutation-associated gene expression signature

With the goal of determining impact of STK11 and TP53 mutations on KRAS-associated gene expression responses, we generated a de novo signature of differentially expressed genes in KRAS-mutant versus KRAS wild-type tumors. We identified 58 probe sets encoding for 43 distinct genes that were differentially expressed in KRAS-mutant tumors (P<0.05 with a 1.5-fold change) (Supplementary Table 2 and Supplementary Information). Principal component analysis was used to evaluate activity of this signature as previously described.42 As expected, our KRAS de novo signature activity was highly elevated in KRAS-mutant tumors (Figure 2a; P<0.0001). Interestingly, activity of this signature was also substantially elevated in STK11 (Figure 2b; P<0.0001) but not in TP53-mutant tumors (Figure 2c; P=0.832). Importantly, signature activity was not only enhanced in KRASmut/STK11mut versus KRASmut/STK11wt tumors (Figures 2d and e; P=0.0015), but also in KRASwt/STK11mut tumors compared with KRASwt/STK11wt tumors (Figures 2d and e; P=0.02). Thus, STK11 mutations not only further elevate expression of these genes in KRAS-mutant tumors but can also independently increase their expression.

Figure 2
figure2

Impact of STK11 and TP53 mutations on KRAS mutation-associated gene expression. (ac) Boxplots indicating KRAS mutation-associated (RAS de novo) signature activity (PC1) within each gene group (a) KRAS, (b) STK11 and (c) TP53. T-test was used to determine significance in difference in signature activity between mut and wt groups indicated. Sample size (n), mean and s.d. are indicated on top of each figure. (d) Boxplots and (e) pairwise comparison plots indicating RAS de novo signature activity in indicated co-occurring and exclusive mutations in KRAS and STK11. ANOVA was used to determine overall significant difference in RAS de novo signature activity among indicated groups and Tukey honest significant difference method was used to adjust for P-value for pairwise comparison. (f) Boxplots and (g) pairwise comparison plots indicating RAS de novo signature activity in indicated co-occurring and exclusive mutations in KRAS and TP53. Additional methodology is provided in Supplementary Information.

To provide independent validation of KRAS signature association with the above gene mutations, we performed studies on TCGA data set. The results shown here are based upon data generated by the TCGA Research Network at http://cancergenome.nih.gov/. Normalized RNAseq data were utilized using this data set of 483 lung adenocarcinoma. Importantly, not only KRASmut (n=145) but also STK11mut (n=75) were very significantly associated with high KRAS signature activity (Supplementary Figure 1; P<0.0001). In contrast, activity of this signature was not associated with TP53 mutations (Supplementary Figure 1). Remarkably, and in complete concordance with our 442 data set, KRAS signature activity was not only significantly enhanced in KRASmut but also in KRASwt/STK11mut tumors compared with KRASwt/STK11wt tumors (Supplementary Figure 1; P=0.0015). Therefore, mutations in KRAS and STK11 are independently associated with upregulation of KRAS signature genes. To define the underlying biology of the KRAS signature, three analyses were performed using the following: Gene Ontology Biological process enrichment, GeneGO Pathway Map enrichment and MSigDB pathway/signature enrichment. However, we were not able to reproducibly associate genes in this signature with a specific biological pathway. Nonetheless, several of these genes have been previously shown to be involved in RAS pathway function, including DUSP4, RASGRF1/CDC25 and HRASLS5. Interestingly, DUSP4 expression was also reported to be associated with STK11 mutations,45 suggesting that mutations in KRAS and STK11 may result in the expression of at least some common genes.

Distinct association of TP53 mutations with tumor proliferative responses

Increased tumor cell proliferation is a main driver of malignancy and known to be strongly associated with poor patient survival in multiple tumor types.25, 26 We next determined whether proliferative responses were impacted by KRAS and tumor suppressor gene mutations. To this end, we used a previously defined MR (gene list in Supplementary Table 3) signature that is significantly correlated with tumor cell proliferation.25, 26 Importantly, no significant difference in MR activity was observed in KRAS-mutant or STK11-mutant tumors compared with wild-type tumors (Figures 3a, b, d and e). In contrast, TP53 mutations, either individually or with KRAS mutations, were significantly associated with higher MR activity (Figures 3c, f and g). These findings therefore indicate that TP53 and STK11 tumor suppressor mutations have distinct association with tumor cell proliferation.

Figure 3
figure3

Distinct association of TP53 mutations with tumor proliferative responses. (a, b) Boxplots indicating MR signature activity (PC1) within each gene group (a) KRAS, (b) STK11 and (c) TP53. T-test was used to determine significance in difference in MR activity between mut and wt groups indicated. Sample size (n), mean and s.d. are indicated on top of each figure. (d) Boxplots and (e) pairwise comparison plots indicating MR signature activity in indicated co-occurring and exclusive mutations in KRAS and STK11. ANOVA was used to determine overall significant difference in MR activity among indicated groups and Tukey honest significant difference method was used to adjust the P-value for pairwise comparison. (f) Boxplots and (g) pairwise comparison plots indicating MR signature activity in indicated co-occurring and exclusive mutations in KRAS and TP53.

Suppression of immune surveillance in STK11-mutant tumors

T-cell mediated immune surveillance is crucial in controlling tumor growth.27, 28, 29 We recently identified a gene expression signature of NF-κB regulated genes (gene list in Supplementary Table 3) that is associated with an immune-active tumor microenvironment and T-cell presence.42 Using this signature, we next determined potential association of different mutations with an immune-active tumor microenvironment. Intriguingly, only STK11 mutations were associated with significantly lower activity of this signature (Figures 4a and c; P<0.0001). Furthermore, STK11 mutations either individually or with KRAS mutations were strongly associated with lower NF-κB signature activity (Figures 4d and e), while no such association was seen with TP53 mutations (Figures 4f and g). To determine more directly the impact of STK11 mutations on T-cell immune surveillance, we examined T-cell infiltration by using T-cell receptor α and β chain expression as previously described.42 Importantly, STK11 mutations were the most significantly associated with reduced T-cell presence in tumors (Figure 4h; P=0.002), although KRAS and TP53 also showed reduced T-cell presence. Overall, these findings indicate that TP53 and STK11 tumor suppresser genes may promote tumor progression by different mechanisms: while TP53 mutations lead to greater proliferative responses, STK11 mutations appear to be associated with suppression of the tumor immune surveillance response. To the best of our knowledge, these findings provide among the first evidence of a potential association between a common cancer gene mutation and the immune surveillance response.

Figure 4
figure4

Suppression of immune surveillance in STK11-mutant tumors. (ac) Boxplots indicating NF-κB signature activity (PC1) within each gene group (a) KRAS, (b) STK11 and (c) TP53. T-test was used to determine significance in difference in NF-κB activity between mut and wt groups indicated. Sample size (n), mean and s.d. are indicated on top of each figure. (d) Boxplots and (e) pairwise comparison plots indicating NF-κB signature activity in indicated co-occurring and exclusive mutations in KRAS and STK11. ANOVA was used to determine overall significant difference in NF-κB activity among indicated groups and Tukey honest significant difference method was used to adjust the P-value for pairwise comparison. (f) Boxplots and (g) pairwise comparison plots indicating NF-κB signature activity in indicated co-occurring and exclusive mutations in KRAS and TP53. (h) Boxplots indicating TCR gene expression PC1 activity. T-test was used to determine significant differences in TCR expression between indicated mut and wt groups.

The primary goal of this study was to define molecular heterogeneity in KRAS-mutant tumors resulting from co-occurring STK11 and TP53 tumor suppressor mutations. Toward that goal, a key finding reported here is that STK11 mutations can positively impact the activity of a novel KRAS mutation-associated gene expression signature. Thus, mutations in STK11 may enhance KRAS-associated signaling responses, both independently and concurrently with KRAS mutations. While the association of the KRAS signature with underlying tumor cell biology remains to be defined, our results suggest that STK11 mutations may potentiate KRAS-induced signaling and gene expression responses that drive tumorigenesis. Indeed, mouse studies demonstrate acceleration of KRAS-induced tumorigenesis and increased metastasis in the presence of concurrent STK11 null mutations.19

A key finding reported here is that tumors with TP53 and STK11 mutations are associated with distinct proliferative versus immune surveillance responses. Specifically, our results indicate that TP53 mutations are strongly associated with enhanced tumor cell proliferation, a finding consistent with prior studies of this key tumor suppressor. In contrast, STK11 mutations were not associated with differences in proliferation but strongly associated with suppression of the immune surveillance response. The relative lack of co-occurrence of STK11 and TP53 mutations is also noteworthy (Figure 1a), and indicates that distinct tumor-promoting mechanisms resulting from these mutations dominate in different tumors. Immune suppression appears to be a specific feature of STK11 mutations, which may enhance tumor progression in addition to activation of SRC-like kinases as reported previously.46 Since the response to immunotherapy is typically associated with an immune-active tumor microenvironment,36, 37, 38 our results further suggest that STK11-mutant tumors may be less responsive to immunotherapy.

In conclusion, these findings not only provide novel insights into how KRAS-mutant tumor biology is shaped by co-occurring mutations, but may also provide insights for therapeutic targeting of lung cancers with distinct tumor suppressor mutations. Specifically, these studies illustrate the potentially significant effect that mutations in tumor suppressor genes could have on therapeutic strategies, especially immunotherapy. These findings also necessitate additional studies to understand specifically how STK11/liver kinase β1 impacts KRAS mutation-associated gene expression as well as the tumor immune surveillance response. Interestingly, recent studies showed reduced phosphoinositide 3-kinase pathway activity, including activity of NF-κB activating kinase PDK1, in STK11-mutant human lung adenocarcinoma.45 Therefore, an interesting possibility is that STK11 mutations directly impact activity of NF-κB and potentially other pathways involved in immune surveillance. Future studies should therefore be directed not only toward understanding mechanisms through which STK11 mutations promote tumorigenesis through enhancement of KRAS induced responses but also by mediating suppression of immune surveillance. Finally, we believe that the extensive data set described here will prove to be a valuable resource for cancer researchers, especially for interrogating gene expression networks that are prevalent in tumors (Data are available for download at GEO, http://www.ncbi.nlm.nih.gov/geo/, accession number GSE72094).

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Gene Expression Omnibus

References

  1. 1

    Siegel R, Naishadham D, Jemal A . Cancer statistics, 2013. CA Cancer J Clin 2013; 63: 11–30.

    Article  Google Scholar 

  2. 2

    Pao W, Girard N . New driver mutations in non-small-cell lung cancer. Lancet Oncol 2011; 12: 175–180.

    CAS  Article  PubMed  Google Scholar 

  3. 3

    Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008; 455: 1069–1075.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4

    Weir BA, Woo MS, Getz G, Perner S, Ding L, Beroukhim R et al. Characterizing the cancer genome in lung adenocarcinoma. Nature 2007; 450: 893–898.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5

    Cox AD, Der CJ . Ras history: the saga continues. Small GTPases 2010; 1: 2–27.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6

    Mitin N, Rossman KL, Der CJ . Signaling interplay in Ras superfamily function. Curr Biol 2005; 15: R563–R574.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7

    Roberts PJ, Der CJ . Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene 2007; 26: 3291–3310.

    CAS  Article  Google Scholar 

  8. 8

    Bang D, Wilson W, Ryan M, Yeh JJ, Baldwin AS . GSK-3alpha promotes oncogenic KRAS function in pancreatic cancer via TAK1-TAB stabilization and regulation of noncanonical NF-kappaB. Cancer Discov 2013; 3: 690–703.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9

    Baker NM, Yee Chow H, Chernoff J, Der CJ . Molecular pathways: targeting RAC-p21-activated serine-threonine kinase signaling in RAS-driven cancers. Clin Cancer Res 2014; 20: 4740–4746.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10

    Kim HS, Mendiratta S, Kim J, Pecot CV, Larsen JE, Zubovych I et al. Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer. Cell 2013; 155: 552–566.

    CAS  Article  PubMed  Google Scholar 

  11. 11

    Sos ML, Michel K, Zander T, Weiss J, Frommolt P, Peifer M et al. Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J Clin Invest 2009; 119: 1727–1740.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12

    Gibbons DL, Byers LA, Kurie JM . Smoking, p53 mutation, and lung cancer. Mol Cancer Res 2014; 12: 3–13.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13

    Shackelford DB, Shaw RJ . The LKB1-AMPK pathway: metabolism and growth control in tumour suppression. Nat Rev Cancer 2009; 9: 563–575.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14

    Sanchez-Cespedes M, Parrella P, Esteller M, Nomoto S, Trink B, Engles JM et al. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res 2002; 62: 3659–3662.

    CAS  Google Scholar 

  15. 15

    Shah U, Sharpless NE, Hayes DN . LKB1 and lung cancer: more than the usual suspects. Cancer Res 2008; 68: 3562–3565.

    CAS  Article  PubMed  Google Scholar 

  16. 16

    Sanchez-Cespedes M . The role of LKB1 in lung cancer. Fam Cancer 2011; 10: 447–453.

    CAS  Article  PubMed  Google Scholar 

  17. 17

    Gao Y, Ge G, Ji H . LKB1 in lung cancerigenesis: a serine/threonine kinase as tumor suppressor. Protein Cell 2011; 2: 99–107.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Matsumoto S, Iwakawa R, Takahashi K, Kohno T, Nakanishi Y, Matsuno Y et al. Prevalence and specificity of LKB1 genetic alterations in lung cancers. Oncogene 2007; 26: 5911–5918.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Ji H, Ramsey MR, Hayes DN, Fan C, McNamara K, Kozlowski P et al. LKB1 modulates lung cancer differentiation and metastasis. Nature 2007; 448: 807–810.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20

    O'Neill GM, Seo S, Serebriiskii IG, Lessin SR, Golemis EA . A new central scaffold for metastasis: parsing HEF1/Cas-L/NEDD9. Cancer Res 2007; 67: 8975–8979.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21

    Gao Y, Xiao Q, Ma H, Li L, Liu J, Feng Y et al. LKB1 inhibits lung cancer progression through lysyl oxidase and extracellular matrix remodeling. Proc Natl Acad Sci USA 2010; 107: 18892–18897.

    CAS  Article  PubMed  Google Scholar 

  22. 22

    Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006; 439: 353–357.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23

    Singh A, Greninger P, Rhodes D, Koopman L, Violette S, Bardeesy N et al. A gene expression signature associated with ‘K-Ras addiction’ reveals regulators of EMT and tumor cell survival. Cancer Cell 2009; 15: 489–500.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24

    Loboda A, Nebozhyn M, Klinghoffer R, Frazier J, Chastain M, Arthur W et al. A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors. BMC Med Genomics 2010; 3: 26.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25

    Chen DT, Hsu YL, Fulp WJ, Coppola D, Haura EB, Yeatman TJ et al. Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. J Natl Cancer Inst 2011; 103: 1859–1870.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26

    Chen DT, Nasir A, Culhane A, Venkataramu C, Fulp W, Rubio R et al. Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue. Breast Cancer Res Treat 2010; 119: 335–346.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27

    Dunn GP, Koebel CM, Schreiber RD . Interferons, immunity and cancer immunoediting. Nat Rev Immunol 2006; 6: 836–848.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Schreiber RD, Old LJ, Smyth MJ . Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science 2011; 331: 1565–1570.

    CAS  Article  Google Scholar 

  29. 29

    Shankaran V, Ikeda H, Bruce AT, White JM, Swanson PE, Old LJ et al. IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature 2001; 410: 1107–1111.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30

    Zhang L, Conejo-Garcia JR, Katsaros D, Gimotty PA, Massobrio M, Regnani G et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med 2003; 348: 203–213.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31

    Fridman WH, Galon J, Pages F, Tartour E, Sautes-Fridman C, Kroemer G . Prognostic and predictive impact of intra- and peritumoral immune infiltrates. Cancer Res 2011; 71: 5601–5605.

    CAS  Article  PubMed  Google Scholar 

  32. 32

    Pages F, Berger A, Camus M, Sanchez-Cabo F, Costes A, Molidor R et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 2005; 353: 2654–2666.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Yu H, Pardoll D, Jove R . STATs in cancer inflammation and immunity: a leading role for STAT3. Nat Rev Cancer 2009; 9: 798–809.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34

    Yu H, Kortylewski M, Pardoll D . Crosstalk between cancer and immune cells: role of STAT3 in the tumour microenvironment. Nat Rev Immunol 2007; 7: 41–51.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35

    Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ . Natural innate and adaptive immunity to cancer. Annu Rev Immunol 2011; 29: 235–271.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36

    Ji RR, Chasalow SD, Wang L, Hamid O, Schmidt H, Cogswell J et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol Immunother 2012; 61: 1019–1031.

    CAS  Article  PubMed  Google Scholar 

  37. 37

    Ulloa-Montoya F, Louahed J, Dizier B, Gruselle O, Spiessens B, Lehmann FF et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J Clin Oncol 2013; 31: 2388–2395.

    CAS  Article  PubMed  Google Scholar 

  38. 38

    Gajewski TF, Schreiber H, Fu YX . Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 2013; 14: 1014–1022.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39

    Fuertes MB, Woo SR, Burnett B, Fu YX, Gajewski TF . Type I interferon response and innate immune sensing of cancer. Trends Immunol 2013; 34: 67–73.

    CAS  Article  PubMed  Google Scholar 

  40. 40

    Gajewski TF, Schumacher T . Cancer immunotherapy. Curr Opin Immunol 2013; 25: 259–260.

    CAS  Article  PubMed  Google Scholar 

  41. 41

    Gajewski TF, Woo SR, Zha Y, Spaapen R, Zheng Y, Corrales L et al. Cancer immunotherapy strategies based on overcoming barriers within the tumor microenvironment. Curr Opin Immunol 2013; 25: 268–276.

    CAS  Article  PubMed  Google Scholar 

  42. 42

    Hopewell EL, Zhao W, Fulp WJ, Bronk CC, Lopez AS, Massengill M et al. Lung tumor NF-kappaB signaling promotes T cell-mediated immune surveillance. J Clin Invest 2013; 123: 2509–2522.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43

    Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res 2011; 39 (Database issue): D945–D950.

    CAS  Article  Google Scholar 

  44. 44

    Meng D, Yuan M, Li X, Chen L, Yang J, Zhao X et al. Prognostic value of K-RAS mutations in patients with non-small cell lung cancer: a systematic review with meta-analysis. Lung Cancer 2013; 81: 1–10.

    Article  PubMed  Google Scholar 

  45. 45

    Kaufman JM, Amann JM, Park K, Arasada RR, Li H, Shyr Y et al. LKB1 Loss induces characteristic patterns of gene expression in human tumors associated with NRF2 activation and attenuation of PI3K-AKT. J Thorac Oncol 2014; 9: 794–804.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Carretero J, Shimamura T, Rikova K, Jackson AL, Wilkerson MD, Borgman CL et al. Integrative genomic and proteomic analyses identify targets for Lkb1-deficient metastatic lung tumors. Cancer Cell 2010; 17: 547–559.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by a National Institutes of Health SPORE Grant (P50 CA119997). We would also like to thank Mr Andrew ‘Ross’ Myers, Anastasia Belock, Mercedes Rodriguez, Edward T Chwieseni, Marek Wloch, Hiba Gohar, and Moffitt’s Tissue Core facility, Moffitt’s Cancer Registry (Director: Karen A Coyne), Research Information Technology (IT) group, and the Data Management and Integration Technology (DMIT) group. Total Cancer Care® is enabled, in part, by the generous support of the DeBartolo Family, and we thank the many patients who provided data and tissue to the Total Cancer Care Consortium. Our study also received valuable assistance from the following Core Facilities at the Moffitt Cancer Center: Biostatistics and Cancer Informatics, Tissue, and Molecular Genomics. This work has been supported in part by a Cancer Center Support Grant (CCSG grant P30-CA76292) at the H. Lee Moffitt Cancer Center and Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center.

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Correspondence to M B Schabath or A A Beg.

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Schabath, M., Welsh, E., Fulp, W. 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). https://doi.org/10.1038/onc.2015.375

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