A Sensitive NanoString-Based Assay to Score STK11 (LKB1) Pathway Disruption in Lung Adenocarcinoma

ORIGINAL ARTICLE

A Sensitive NanoString-Based Assay to Score STK11 (LKB1) Pathway Disruption in Lung Adenocarcinoma Lu Chen, PhD,a,b Brienne E. Engel, PhD,a Eric A. Welsh, PhD,b Sean J. Yoder,c Stephen G. Brantley, MD,d Dung-Tsa Chen, PhD,b Amer A. Beg, PhD,e Chunxia Cao, PhD,f Frederic J. Kaye, MD,f Eric B. Haura, MD,g Matthew B. Schabath, PhD,h W. Douglas Cress, PhDa,* a

Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida c Molecular Genomics Core, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida d Pathology Services M2Gen, Tampa, Florida e Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida f Department of Medicine, University of Florida, Gainesville, Florida g Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida h Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida b

Received 5 October 2015; revised 22 January 2016; accepted 6 February 2016 Available online - 22 February 2016

ABSTRACT Introduction: Serine/threonine kinase 11 gene (STK11), better known as liver kinase b1, is a tumor suppressor that is commonly mutated in lung adenocarcinoma (LUAD). Previous work has shown that mutational inactivation of the STK11 pathway may serve as a predictive biomarker for cancer treatments, including phenformin and cyclooxygenase-2 inhibition. Although immunohistochemical (IHC) staining and diagnostic sequencing are used to measure STK11 pathway disruption, there are serious limitations to these methods, thus emphasizing the importance of validating a clinically useful assay. Methods: An initial STK11 mutation mRNA signature was generated using cell line data and refined using three large, independent patient databases. The signature was validated as a classifier using The Cancer Genome Atlas (TCGA) LUAD cohort as well as a 442-patient LUAD cohort developed at Moffitt. Finally, the signature was adapted to a NanoStringbased format and validated using RNA samples isolated from formalin-fixed, paraffin-embedded tissue blocks corresponding to a cohort of 150 patients with LUAD. For comparison, STK11 IHC staining was also performed.

Conclusion: The described NanoString-based STK11 assay is a sensitive biomarker to study emerging therapeutic modalities in clinical trials.
2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: STK11; LKB1; NanoString; biomarker; COX-2 inhibition

Introduction Serine/threonine kinase 11 gene (STK11) is a serine/ threonine kinase, also known as liver kinase b1 (LKB1), that was discovered to be the gene responsible for familial Peutz-Jeghers syndrome.1 Although somatic STK11

*Corresponding author. Drs. Chen and Engel contributed equally to this work.

Results: The STK11 signature was found to correlate with null mutations identified by exon sequencing in multiple cohorts using both microarray and NanoString formats. Although there was a statistically significant correlation between reduced STK11 protein expression by IHC staining and mutation status, the NanoString-based assay showed superior overall performance, with a –0.1588 improvement in area under the curve in receiver-operator characteristic curve analysis (p < 0.012).

Disclosure: The authors declare no conflict of interest. Address for correspondence: W. Douglas Cress, PhD, H. Lee Moffitt Cancer and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612. E-mail: Douglas.Cress@moffitt.org ª 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/). ISSN: 1556-0864 http://dx.doi.org/10.1016/j.jtho.2016.02.009

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mutations are uncommon in many cancers, including lung squamous cell carcinoma,2 the incidence of STK11 mutations in lung adenocarcinoma (LUAD) is exceeded only by that of Kirsten rat sarcoma viral oncogene homolog gene (KRAS) and tumor protein p53 gene (TP53) mutations.3–10 The STK11 protein is catalytically active as a heterotrimeric complex with the STE20-related adaptor protein a and mouse protein 25 (MO25). Many mutations in STK11 have been found to interfere with its ability to bind these partners,11 rendering the expressed protein inactive. STK11 has multiple targets that likely mediate its tumor suppressor activity. Foremost, STK11 is responsible for phosphorylating the adenosine monophosphate (AMP)-activated kinase (AMPK) at amino acid residue T172 under conditions of energy stress.12 Our published work links STK11 to the regulation of cyclooxygenase 2 (COX-2) and many other genes through indirect regulation of the CREB-regulated transcription coactivator 1 transcription factor.13–15 Additionally, we find that STK11 mutations can be linked to immunosuppression through potential influence on the nuclear factor k light-chain enhancer of activated B cells signaling pathway.16 Although there are currently no drugs in routine clinical use that specifically target STK11 there is a growing number of approaches that may differentially benefit patients with disrupted STK11.17–25 Therapies that reactivate AMPK, such as metformin, phenformin,20 and 5-aminoimidazole-4-carboxamide ribonucleotide,22 and an AMP mimetic (2-deoxyglucose)21 have been shown to render STK11-mutant tumors more susceptible to chemotherapies, possibly by promoting a transition from adenocarcinoma to squamous cell carcinoma.17 Recent work suggests that STK11 mutation enhances the sensitivity to energetic stress that is induced by erlotinib treatment.25 Additionally, drugs that target the downstream proteins that are up-regulated by loss of STK11, such as mammalian target of rapamycin (mTOR) inhibitors (e.g., rapamycin24 and everolimus23), cyclooxygenase 2 (COX-2) inhibitors (e.g., NS-398 and niflumic acid13), reactive oxygen species–inducing agents,17 and lysyl oxidase inhibitors (e.g., BAPN b-aminopropionitrile26), have shown efficacy in STK11-mutant cells as compared with their wild-type (WT) counterparts. In summary, despite extensive preclinical data, the lack of progress in developing clinical treatments for patients with STK11-null cancers is partly due to the lack of a validated method to reliably score STK11 status using available formalin-fixed, paraffin-embedded (FFPE) archival samples. Immunohistochemical (IHC) staining27 and diagnostic sequencing are both potential means to measure STK11 pathway disruption. However, many of the variants identified in STK11 lead to single amino acid changes that

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may not alter STK11 function.16,28 Furthermore, STK11 function can be lost by epigenetic inactivation29 or by homozygous and intragenic deletions,7,30 which are difficult to detect by genomic sequencing. In fact, recent work suggests that as many as one-half of instances of STK11 functional loss are undetected by sequencing.31 Given the potential of assessing STK11 status as a biomarker in LUAD, a robust STK11 mutation signature was adapted into a format based on NanoString Elements (NanoString Technologies Inc., Seattle, WA) that is amenable to use with FFPE samples. This assay was then validated using RNA isolated from tissue blocks from a cohort 150 LUAD surgical specimens. For comparison, STK11 IHC staining was performed. The results demonstrate that the NanoString-based assay yields overall performance superior to that of STK11 IHC staining, suggesting that NanoString may be a useful tool in a multianalyte approach to identify patients with LUAD with disruption of the STK11 pathway.

Materials and Methods Expression Vectors, Cell Culture, and Molecular Assays Detailed methods are provided in the Supplementary Materials. Briefly, the pcDNA3-FLAG-STK11 vector (plasmid 8590) was purchased from Addgene (Cambridge, MA). pNTAPb and the pNTAPb-Mef2a affinitytagged negative control (kit 240103) were purchased from Agilent Technologies (Santa Clara, CA) as a component of the InterPlay N-terminal Mammalian TAP System. Cells were cultured in Roswell Park Memorial Institute medium supplemented with 10% fetal bovine serum and transfected with a vector (pNTAPb), Mef2a control, or one of three STK11 variants (D194Y, P281fs*6, or F354L) with Lipofectamine-2000 (Thermo Fisher Scientific, Waltham, MA) in serum-free media. Proteins were visualized using horseradish peroxidase– conjugated secondary antibodies and enhanced chemiluminescence (GE Healthcare Life Sciences, Pittsburgh, PA). NanoString assays were performed with 150-ng aliquots of RNA using the NanoString nCounter Analysis system (NanoString Technologies). Tissue microarray (TMA) slides were cut into 4-mM sections and stained with anti-STK11 (Santa Cruz Biotechnology, Santa Cruz, CA) at a 1:100 dilution.

Clinical and Molecular Data Sets Five public data sets were used: (1) the Molecular Classification of Lung Adenocarcinoma (MCLA) microarray study32, (2) the Cancer Genome Atlas (TCGA) LUAD RNAseq study,28 and (3 through 5) three microarray studies from the Gene Expression Omnibus33 (GSE30219,34 GSE37745,35 and GSE1481436). The

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Figure 1. Serine/threonine kinase 11 (STK11) protein expression in cell lines and tumor samples. (A) Cell lines and (B) patient tumors with previously determined serine/threonine kinase 11 gene (STK11) mutational status16 were subjected to Western blotting for STK11. Samples specifically highlighted in the text are boxed. Repeated cell lines were obtained from different laboratory sources. The three STK11 isoforms are indicated. (C) Empty pNTAPb vector (Vector), a vector expressing a negative control affinity-tagged Mef2a protein (Mef2a control), wild-type (WT) STK11, and each of the three indicated STK11 variants were transfected into H1299 cells, immunoprecipitated using streptavidin beads, and blotted for binding to mouse protein 25 (MO25) and STE20-related adaptor protein a (STRAD). (D) A549 cells were transfected with the indicated combination of plasmids, extracted and blotted for adenosine monophosphate–activated kinase (AMPK) phosphorylated at T172, total AMPK, and b-actin. The first lane (–) represents empty vectors (pcDNA3 and pNTAPb), the second lane (þ) represents cells transfected with pcDNA3 WT STK11 and empty pNTAPb, and the lanes labeled D194Y received pNTAPb-D194Y alone (–) or pNTAPb-D194Yplus WT in 1:1 (þ), 1:5 (þþ), and 1:10 (þþþ) ratios. Lanes labeled F354L received pNTAPb-F354L alone (–) or pNTAPb-F354L plus WT in 1:1 (þ), 1:5 (þþ), and 1:10 (þþþ) ratios.

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Figure 2. Validation of the serine/threonine kinase 11 (STK11) mutation signature in two large patient-derived data sets. (A, B) PCA was performed on the indicated patient-derived data sets using the refined STK11 signature. Summary signature scores for first the first and second principal components for each individual patient are plotted. Patients with wild type (WT)

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Table 1. Measures of Test Performance between the STK11 Signature and Detected DNA Variation in TCGA and the MLOS Cohorts Detected DNA Variation

Signature Prediction

c

Class

Metric

p Value

95% CI

Mutant Mutant WT WT

STK11þ WT STK11þ WT

71 4 109 298

TPs FNs FPs TNs

Sensitivity Specificity Precision NPV False-positive rate False discovery rate

0.947 0.732 0.394 0.987 0.268 0.606

(0.869–0.985) (0.686–0.77) (0.322–0.469) (0.966–0.996) (0.22–0.313) (0.53–0.677)

Mutant Mutant WT WT

STK11þ WT STK11þ WT

66 2 97 277

TPs FNs FPs TNs

Sensitivity Specificity Precision NPV False-positive rate False discovery rate

0.971 0.741 0.405 0.993 0.259 0.595

(0.897–0.996) (0.693–0.784) (0.328–0.484) (0.974–0.999) (0.215–0.306) (0.515–0.671)

TCGA

MLOS

STK11, serine threonine kinase 11 gene; TCGA, The Cancer Genome Atlas; MLOS, Moffitt lung adenocarcinoma overall survival cohort; TPs, true positives; FNs, false negatives; WT, wild type, FPs, false positives; NPV, negative predictive value; TNs, true negatives.

MCLA32 and GSE1481436 sets were combined to form the MCLAþ cohort. Data from a cohort of 442 patients with LUAD (herein referred to as the Moffitt LUAD overall survival [MLOS] cohort) who consented to the Moffitt Cancer Center’s Total Cancer Care Protocol (see Supplementary Table 1) was also used. Surgical tissue blocks from 150 patients (see Supplementary Table 2) who were treated at Moffitt were used to construct a TMA as described in the Supplementary Methods and as the source for RNA for NanoString analysis. Herein, these 150 patients are referred to as the Moffitt LUAD, Complete cohort because it was possible to acquire medical charts and tissue blocks for these patients. Most members of the Moffitt LUAD, Complete are also in the MLOS cohort. All patient-related work was approved by the University of South Florida Institutional Review Board.

Statistical Analysis Statistical analyses were performed using R Project for Statistical Computing, version 3.2.2,37 and SAS 9.4 software (SAS Institute, Cary, NC). The chi-square test exact method with Monte Carlo estimation was used to test for differences in the distributions of mutational status by study population characteristics. Principal component analysis (PCA) was used to summarize STK11 signature scores in a single number. Briefly, PCA is used to find orthogonal vectors that capture the largest sources of variance within a data set.38,39 The scores from the first principal component of a gene signature are commonly used to score samples within a

data set. The gene loadings, p[1], corresponding to the first principal component, reflect the relative weights of each gene with respect to their contribution to the sample scores. The second principal component in our analyses did not correlate with STK11 mutation status; however, it was used as a means to plot the first principal component in two dimensions. Measures of test performance and agreement of the STK11 gene signature to predict STK11 mutation status, including true positives, true negatives, false positives, false negatives, sensitivity, specificity, negative predictive value, false-positive rate, and false discovery rate, were calculated. For the TCGA samples, the first principal component of the STK11 signature (t[1]) was used to assign WT and STK11 phenotypes (WT: t[1]  0; STK11: t[1] > 0) to the samples. The MLOS cohort sample phenotypes were assigned in a similar fashion (WT: t[1]  2.1; STK11: t[1] > 2.1). Splice site mutations were treated as mutant for two-state analysis purposes. Receiver operating characteristic curves were used to compare overall assay sensitivity and specificity on the basis of area under the curve (AUC) calculations.

Results Expression of Stable, but Catalytically Inactive STK11 Variants May Obscure STK11 Pathway Mutations Previous work has identified three alternatively spliced isoforms of STK11: (1) the full-length 50-kDa

STK11 are plotted with green symbols, splice-site variants are plotted in blue, exon mutations are plotted with yellow symbols and The Cancer Genome Atlas (TCGA) samples of unknown mutation status are plotted in red. (C, D) ROC curves for the TCGA and Moffitt lung adenocarcinoma overall survival (MLOS) cohorts.

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Figure 3. A NanoString-based serine/threonine kinase 11 gene (STK11) assay applied to clinical samples. (A) Principal component analysis using 122 NanoString codesets was performed on 140 RNA samples isolated from formalin-fixed, plasmaembedded (FFPE) blocks, and the loading coefficients from the first (p[1]) and second (p[2]) principal components from human tumors were plotted. The signal variation covered by each principal component is indicated as a percentage of total variation. Blue symbols represent probesets with a negative loading coefficient in cell lines and red symbols represent those with positive values in cell lines. (B) Summary signature scores for first (PC 1) and second (PC 2) principal components for each individual patient are plotted. See “Materials and Methods” for details of the analysis. Patients with wild-type (WT) mutations are plotted with green symbols, splice site variants are plotted in blue, and exon mutations are plotted with yellow

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isoform; (2) a 48-kDa isoform with an alternative Cterminus that is expressed in the testis40; and (3) an oncogenic, but kinase-inactive 42-kDa isoform that is mainly expressed in normal heart and skeletal muscle41 and is reported to be expressed (along with other shortened STK11 isoforms) at high levels in tumors with mutations in STK11 codons 1 and 2.42 Such isoforms could potentially obscure standard assessment of STK11 pathway status by IHC staining. Thus, we sought to examine the expression of STK11 isoforms in established cells lines and tumors. Forty-two cell lines and 56 tumor samples with known STK11 status were examined by STK11 Western blotting (Fig. 1). Figure 1A reveals that as expected, the full-length 50-kDa STK11 band was present in STK11 WT cell lines, with H2170 cells being the exception. The 48-kDa isoform was observed in only two cell lines (H292 and HCC4006, both STK11 WT) and was coexpressed with the 50-kDa band. In H2170, H520, H1395, Calu-6, and H1581 cells intense faster migrating bands were observed in the 42-kDa range and were generally coexpressed with the 50-kDa isoform. Fulllength STK11 protein expression was absent in all STK11 mutant cell lines, as expected. In patient tumor samples, STK11 Western blotting revealed a wide range of protein isoform expression for both STK11 WT and variant tumors (Fig. 1B). Expression of both the 42- and 48-kDa isoforms was much more common in tumors than in cell lines, and in many tumors the shorter isoforms were expressed at higher levels than the full-length protein was. We did not observe expression of the 42-kDa isoform to be correlated with STK11 mutations as previously reported.42 Considering only the 50-kDa full-length protein, many STK11 WT tumors expressed less STK11 protein than did STK11 variant tumors. Figure 1B highlights three examples of common or recurring STK11 variants (D194Y, F354L and P281fs*6), which were further explored. The STK11 variants D194Y and F354L expressed high protein levels in four patients, whereas a patient with the P281fs*6 variant (and other similar frameshift variants) expressed very little STK11 protein. The F354L variant, which has previously been found in Asian populations with a frequency of approximately 10%,43 is likely a polymorphism. It was explored further, however, because it has also been reported to affect cell polarity through an AMPK-dependent mechanism.44 First, whether these three variants could bind the other members of the catalytically active STK11 trimeric complex, MO25 and STE20-related adaptor protein a

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(STRAD), was determined. A negative control vector, WT STK11, or each of the three variants was then transfected into H1299 cells and the extracts were subjected to coimmunoprecipation with STK11 antibody/streptavidin beads. The exogenous WT STK11, as well as the D194Y and F354L variants, were stably expressed and efficiently coimmunoprecipitated MO25 and STRAD (Fig. 1C) relative to the control vector. However, the P281fs*6 mutant was unstable (as expected) and did not coimmunoprecipate either MO25 or STRAD. Next, the two stable variants were tested for dominant negative activity by transfecting the variant alone and the variant plus WT STK11 (in 1:1, 1:5, and 1:10 ratios) into A549 cells lacking endogenous STK11. Catalytic activity was measured by examining downstream phosphorylation of AMPK by using a phospho-specific antibody. The D194Y mutant was able to suppress activation of AMPK by the WT STK11 at all ratios examined (Fig. 1D). Conversely, the F354L variant phosphorylated AMPK in the presence or absence of WT protein and is therefore likely a common polymorphism.

Development and Validation of a 122-Gene STK11 Mutation Signature Cell line data and three published Affymetrix-based gene expression databases32,34,35 representing nearly 1000 patients with non–small cell lung cancer were used to develop and refine a 122-gene STK11 mutation signature. Development of this signature is described in the Supplementary Materials and the included genes along with their relative contributions (loading coefficients) to the PCA-based signature are listed in Supplementary Table 7. The ability of this STK11 signature to classify LUAD tumors from two large data sets with available expression and mutation and copy number data was tested using the TCGA LUAD RNAseq data set,28 including 488 patient samples and a second recently described16 microarray-based expression data set from 442 patients with LUAD (herein referred to as the MLOS cohort, described in Supplementary Table 1). Figures 2A and 2B show the results of PCA analysis of the two large patient data sets, in which the first principal component (the STK11 score, x axis) and the second principal component (unrelated to STK11 mutation, y axis) for each patient (color coded as either STK11 WT or one of three types of mutations) are plotted. Various measures of agreement with sequenced STK11 mutation status were then calculated (Table 1) and demonstrated that the

symbols. (C–F) Waterfall plots of the NanoString-based STK11 signature score. The number of WT and mutant (MUT) samples are indicated for each gene. p Values indicating correlation by the Wilcoxon rank sum test demonstrated significant correlation with STK11 and epidermal growth factor receptor gene (EGFR) (mutually exclusive) mutation status. (C) STK11, (D) Kirsten rat sarcoma viral oncogene homolog gene (KRAS), (E) tumor protein p53 gene (TP53), and (F) EGFR.

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Figure 4. Direct comparison between serine/threonine kinase 11 (STK11) immunohistochemical (IHC) staining and NanoString. STK11 IHC staining was performed on a tissue microarray representing cell lines, normal tissues, and lung adenocarcinoma tumors (Moffitt LUAD, Complete cohort). Scale bar represents 200 mM. (A) STK11 IHC staining in representative cores: (i) core 231, H1299 cells with WT STK11 and expression of the 52-kDa isoform (see Figure 1A); (ii) core 207, A549 cells with mutant STK11 and lacking expression of any STK11 isoform (see Figure 1A); (iii) core 141, normal alveolar tissue, þ4 staining; (iv) core 78, normal bronchial tissue, þ4 staining; (v) core 1, carcinoma with WT STK11, þ4 staining; (vi) core 139, carcinoma with mutant STK11, þ4 staining; (vii) core 29, carcinoma with WT STK11, 0 staining, negative; and (viii) core 66, carcinoma with mutant STK11, 0 staining, negative. (B) Receiver operating characteristic (ROC) curves comparing STK11 NanoString with IHC staining (area under the curve [AUC] was 0.8377 for NanoString versus 0.6789 for IHC staining). Supplementary Table 6 identifies cores by STK11 mutation status.

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A NanoString Assay for STK11 Pathway Disruption

Table 2. STK11 IHC Staining Results and Test Performance Comparison with the STK11 NanoString Assay No. Patients with Indicated IHC Staining Scores IHC Pathology Score Normal cores Carcinomas STK11 mutant STK11 WT

þ1

þ2

0

0

7 15

6 6

0

þ3

þ4

0

3

51

2 6

5 18

7 68

Difference in AUC and 95% CI

p Value

Receiver Operator Characteristics Modeling Model

AUC

95% CI

NanoString 0.8377 (0.76–0.9154) 0.1588 (0.035–0.2825) 0.0119 IHC 0.6789 (0.566–0.7918) pathology score STK11, serine threonine kinase 11; IHC, immunohistochemical; STK11, serine threonine kinase 11 gene; WT, wild type; AUC, area under the curve, CI, confidence interval.

sensitivity of the signature relative to determined mutations is good (0.95–0.97) but specificity (0.73–0.74) is poor when sequencing is used as the standard. These are the expected results because there may be many tumors with WT STK11 DNA that down-regulate the STK11 protein or pathway by other mechanisms.27,31

Adaptation of the STK11 Signature to Clinical Specimens Using NanoString Given the potential of assessing STK11 status as a clinical biomarker, the STK11 mutation signature was adapted into a NanoString Elements–based format (see Supplementary Tables 3–5) that is amenable to use with FFPE samples. NanoString assays utilize fluorescently barcoded probes that hybridize to targeted RNA molecules fixed to a slide. Advantages of the technology include the following: (1) it does not contain any enzymatic steps that can be inhibited by contaminants; (2) the assay works well on the partially degraded RNA that is generally retrieved from fixed tissues; and (3) because RNA molecules are detected and counted individually, the data analysis is simple. This NanoString panel was used to examine RNA samples isolated from paraffin blocks from a cohort of 150 patients with LUAD (Supplementary Table 2). Of the 150 blocks, 10 did not yield adequate NanoString counts when 150 ng of RNA was used. Figure 3 highlights the results of PCA. Figure 3A demonstrates that the loading coefficients of most genes in the signature have the same sign as in cell lines despite the great difference in source material (fresh frozen tissue versus fixed tissue) and in spite of the difference in methodology (microarray versus

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NanoString). Figure 3B demonstrates that the first principal component significantly segregates STK11 WT and mutant tumors. Figures 3C through 3F show waterfall plots of the first principal component score of each patient, highlighting patients with mutations in STK11, KRAS, TP53, or epidermal growth factor receptor gene (EGFR) in red. Clearly, the signature correlates with STK11 mutations (Wilcoxon rank sum test, p < 0.0001), whereas mutations in KRAS and TP53 do not show a statistically significant tendency (p ¼ 0.9965 and p ¼ 0.2548, respectively) in spite of the fact that STK11 and KRAS mutations are known to favor co-occurrence (p ¼ 0.003).45 In contrast, EGFR mutants have a significantly lower STK11 score (Wilcoxon rank sum test, p < 0.0132), which would be expected because STK11 and EGFR mutations are mutually exclusive in LUADs.45

Direct Comparison between STK11 IHC Staining and NanoString Previous work has demonstrated STK11 IHC staining as a means to assess STK11 mutation status.27 Having established in Figure 1B that direct measurement of STK11 protein in patient tumors might have complications, STK11 IHC staining and the STK11 NanoString assay were directly compared. To do this, a tissue microarray representing cell lines of known STK11 status (Fig. 1A), 54 normal lung tissue cores, and 140 LUAD tumors (135 of which were also assayed by STK11 NanoString) was stained with STK11 antibody, as previously described.27 Comparison of the IHC staining of H1299 cells (Figure 4Ai), which expresses the WT 50kDa STK11 protein (Fig. 1A), and A549 cells (Fig. 4Aii), which express no STK11 (Fig. 1A), demonstrates the specificity of the STK11 IHC staining. Figures 4Aiii and 4Aiv demonstrate that STK11 staining in normal lung epithelial tissue (alveolar or bronchial) was generally light, cytoplasmic, and diffuse and was set to a maximum value of þ4. IHC staining scores of tumor tissue ranged from þ4 to 0 (see Supplementary Fig. 2 for examples of each) and the IHC staining scores for the various cores are listed in Table 2. Figures 4Av and Avi highlight two carcinomas with an IHC staining score of þ4 that are STK11 WT and mutant, respectively. Figures 4Avii and Aviii highlight two STK11 IHC staining–negative carcinomas that are STK11 WT and mutant, respectively. These examples demonstrate the potential limitations of STK11 IHC staining. Figure 4B compares STK11 IHC staining with STK11 NanoString assay using receiver operator characteristics modeling. For the NanoString assay, the AUC (or C-statistic) was 0.8377, whereas IHC staining achieved an AUC of only 0.6789. Thus, the NanoString-based assay shows superior overall performance, with a 0.1588 improvement in AUC relative to IHC staining (p < 0.0119, see Table 2).

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Discussion This work describes a NanoString-based assay that can assess deregulation of the STK11 pathway in patient samples derived from FFPE tissues. The genes comprising the assay were validated in multiple data sets representing six different studies examining a total of 1894 patient samples, and the assay itself was further validated using RNA isolated from FFPE tissues. Because anti-STK11 antibodies can cross-react with nonspecific species and occasional null mutations do not affect STK11 steady-state protein levels, this assay showed a higher sensitivity and specificity than did STK11 IHC staining using exome sequencing as the standard.16 In data not shown, no correlation between STK11 mutation status and cell cycle progression, whether measured by Ki-67 staining or by assessment of a previously defined proliferative signature, was observed.46 Nor was any statistically significant prognostic value associated with STK11 mutations or the STK11 signature whether considered alone or in the context of concurrent KRAS or TP53 mutations. Finally, analysis did not reveal any association between STK11 disruption and metastasis among patients with KRAS mutations, as has been previously shown.27 We propose that this new NanoString assay can serve as the standard procedure to be subsequently tested along with a combination of exon sequencing and IHC staining as predictive biomarkers for newly emerging immunotherapies16 or other anticancer treatments exploiting AMPK activation, such as metformin/phenformin or COX-2 inhibition studies.13 This NanoStringbased STK11 assay will likely emerge as the best of the three methods for several reasons. First, the assay is based on a very comprehensive examination of existing data on STK11 incorporating both extensive cell line data as well as thousands of patient samples. The strong recapitulation of the cell line data into patient samples supports the robust nature of the assay and preservation of at least a subset of the critical biological pathways disrupted downstream of STK11 mutations. The fact that the signature has been validated in multiple human tissue data sets with multiple technical methodologies (including microarray, RNA Seq and NanoString) ensures that the signature it is likely to be broadly applicable going forward. In fact, during the development of this STK11 signature, a 16-gene STK11 signature (classifier) was published.31 Ten of the 16 genes in the published signature are also in our signature, suggesting significant convergence of the best classifiers using different approaches. In our analysis, nine of these 10 genes had individual p values less than 0.05, suggesting that they will translate very well. We also note that the NanoString-based assays are emerging as a reliable and

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highly general and flexible method to assess gene expression in very limited fixed tissues.47 A NanoStringbased version of the prediction analysis of microarray 50 panel was recently U.S. Food and Drug Administration approved for prognosis in breast cancer.48 We find the NanoString methodology to be highly reproducible, and given its general simplicity, we anticipate that it will be readily adapted clinically as an alternative to quantitative polymerase chain reaction and sequencing methods. The straightforward multiplex nature of the NanoString assay will allow us to further optimize the codesets included in the assay as additional data are acquired.

Acknowledgments This work was supported by National Institutes of Health/National Cancer Institute grant CA90489 (Dr. Chen), a Specialized Programs of Research Excellence P50 grant CA119997 (Dr. Chen), and a University of South Florida Presidential Fellowship (Dr. Engel) and by the Tissue Core Facility at the H. Lee Moffitt Cancer Center and Research Institute, a National Cancer Institute–designated comprehensive cancer center (P30CA076292). We would also like to thank A. Kasprzak for microscopy, N. Clark for IHC staining, F. Kinose for cell line assistance, and the Moffitt Cancer Registry (director, Karen A. Coyne) for contribution in data curation.

Supplementary Data Note: To access the supplementary material accompanying this article, visit the online version of the Journal of Thoracic Oncology at www.jto.org and at http://dx.doi. org/10.1016/j.jtho.2016.02.009.

References 1. Hemminki A, Markie D, Tomlinson I, et al. A serine/ threonine kinase gene defective in Peutz-Jeghers syndrome. Nature. 1998;391:184–187. 2. Hammerman PS, Hayes DN, Wilkerson MD, et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489:519–525. 3. Sanchez-Cespedes M, Parrella P, Esteller M, et al. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res. 2002;62:3659–3662. 4. Shah U, Sharpless NE, Hayes DN. LKB1 and lung cancer: more than the usual suspects. Cancer Res. 2008;68:3562– 3565. 5. Sanchez-Cespedes M. The role of LKB1 in lung cancer. Fam Cancer. 2011;10:447–453. 6. Gao Y, Ge G, Ji H. LKB1 in lung cancerigenesis: a serine/ threonine kinase as tumor suppressor. Protein Cell. 2011;2:99–107. 7. Matsumoto S, Iwakawa R, Takahashi K, et al. Prevalence and specificity of LKB1 genetic alterations in lung cancers. Oncogene. 2007;26:5911–5918.

---

2016

8. Ji H, Ramsey MR, Hayes DN, et al. LKB1 modulates lung cancer differentiation and metastasis. Nature. 2007;448:807–810. 9. Beroukhim R, Mermel CH, Porter D, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899–905. 10. Ding L, Getz G, Wheeler DA, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455:1069–1075. 11. Boudeau J, Scott JW, Resta N, et al. Analysis of the LKB1STRAD-MO25 complex. J Cell Sci. 2004;117:6365–6375. 12. Woods A, Johnstone SR, Dickerson K, et al. LKB1 is the upstream kinase in the AMP-activated protein kinase cascade. Curr Biol. 2003;13:2004–2008. 13. Cao C, Gao R, Zhang M, et al. Role of LKB1-CRTC1 on glycosylated COX-2 and response to COX-2 inhibition in lung cancer. J Natl Cancer Inst. 2015;107:358. 14. Gu Y, Lin S, Li JL, et al. Altered LKB1/CREB-regulated transcription co-activator (CRTC) signaling axis promotes esophageal cancer cell migration and invasion. Oncogene. 2012;31:469–479. 15. Komiya T, Coxon A, Park Y, et al. Enhanced activity of the CREB co-activator Crtc1 in LKB1 null lung cancer. Oncogene. 2010;29:1672–1680. 16. Schabath MB, Welsh EA, Fulp WJ, et al. Differential association of STK11 and TP53 with KRAS mutationassociated gene expression, proliferation and immune surveillance in lung adenocarcinoma [e-pub ahead of print]. Oncogene. 2015. http://dx.doi.org/10.1038/ onc.2015.375, accessed September 1, 2015. 17. Li F, Han X, Li F, et al. LKB1 Inactivation elicits a redox imbalance to modulate non-small cell lung cancer plasticity and therapeutic response. Cancer Cell. 2015;27:698–711. 18. Chen Z, Cheng K, Walton Z, et al. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature. 2012;483:613–617. 19. Carretero J, Shimamura T, Rikova K, et al. Integrative genomic and proteomic analyses identify targets for Lkb1-deficient metastatic lung tumors. Cancer Cell. 2010;17:547–559. 20. Shackelford DB, Abt E, Gerken L, et al. LKB1 inactivation dictates therapeutic response of non-small cell lung cancer to the metabolism drug phenformin. Cancer Cell. 2013;23:143–158. 21. Inge LJ, Coon KD, Smith MA, et al. Expression of LKB1 tumor suppressor in non-small cell lung cancer determines sensitivity to 2-deoxyglucose. J Thorac Cardiovasc Surg. 2009;137:580–586. 22. Carretero J, Medina PP, Blanco R, et al. Dysfunctional AMPK activity, signalling through mTOR and survival in response to energetic stress in LKB1-deficient lung cancer. Oncogene. 2007;26:1616–1625. 23. Klumpen HJ, Queiroz KC, Spek CA, et al. mTOR inhibitor treatment of pancreatic cancer in a patient With PeutzJeghers syndrome. J Clin Oncol. 2011;29:e150–e153. 24. Mahoney CL, Choudhury B, Davies H, et al. LKB1/KRAS mutant lung cancers constitute a genetic subset of NSCLC with increased sensitivity to MAPK and mTOR signalling inhibition. Br J Cancer. 2009;100:370–375. 25. Whang YM, Park SI, Trenary IA, et al. LKB1 deficiency enhances sensitivity to energetic stress induced by

A NanoString Assay for STK11 Pathway Disruption

26.

27.

28. 29.

30.

31.

32.

33. 34.

35.

36.

37.

38.

39. 40.

41.

42.

11

erlotinib treatment in non-small-cell lung cancer (NSCLC) cells [e-pub ahead of print]. Oncogene. 2015. http://dx.doi.org/10.1038/onc.2015.140, accessed. Gao Y, Xiao Q, Ma H, et al. LKB1 inhibits lung cancer progression through lysyl oxidase and extracellular matrix remodeling. Proc Natl Acad Sci U S A. 2010;107:18892–18897. Calles A, Sholl LM, Rodig SJ, et al. Immunohistochemical loss of LKB1 is a biomarker for more aggressive biology in KRAS mutant lung adenocarcinoma. Clin Cancer Res. 2015. TCGA. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–550. Esteller M, Avizienyte E, Corn PG, et al. Epigenetic inactivation of LKB1 in primary tumors associated with the Peutz-Jeghers syndrome. Oncogene. 2000;19:164– 168. Gill RK, Yang SH, Meerzaman D, et al. Frequent homozygous deletion of the LKB1/STK11 gene in non-small cell lung cancer. Oncogene. 2011;30:3784–3791. Kaufman JM, Amann JM, Park K, 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. Shedden K, Taylor JM, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14:822–827. NCBI. Gene Expression Omnibus. http://www.ncbi.nlm. nih.gov/geo/. Accessed April 17, 2015. Rousseaux S, Debernardi A, Jacquiau B, et al. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Sci Transl Med. 2013;5:186ra166. Botling J, Edlund K, Lohr M, et al. Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin Cancer Res. 2013;19:194–204. Zhu CQ, Ding K, Strumpf D, et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol. 2010;28:4417–4424. R Foundation. R project for statistical computing, version 3.2.2. http://www.r-project.org. Accessed September 1, 2015. Jolliffe I. Principal component analysis and factor analysis. In: Principal Component Analysis. 2nd ed. New York, NY: Springer-Verlag; 2002:150–166. Ringner M. What is principal component analysis? Nat Biotechnol. 2008;26:303–304. Towler MC, Fogarty S, Hawley SA, et al. A novel short splice variant of the tumour suppressor LKB1 is required for spermiogenesis. Biochem J. 2008;416:1–14. Dahmani R, Just PA, Delay A, et al. A novel LKB1 isoform enhances AMPK metabolic activity and displays oncogenic properties. Oncogene. 2015;34:2337–2346. Pecuchet N, Laurent-Puig P, Mansuet-Lupo A, et al. Different prognostic impact of STK11 mutations in nonsquamous non-small-cell lung cancer [e-pub ahead of print]. Oncotarget. 2015. http://dx.doi.org/10.18632/ oncotarget.6379, accessed September 1, 2015.

12 Chen et al 43. Gao B, Sun Y, Zhang J, et al. Spectrum of LKB1, EGFR, and KRAS mutations in chinese lung adenocarcinomas. J Thorac Oncol. 2010;5:1130–1135. 44. Forcet C, Etienne-Manneville S, Gaude H, et al. Functional analysis of Peutz-Jeghers mutations reveals that the LKB1 C-terminal region exerts a crucial role in regulating both the AMPK pathway and the cell polarity. Hum Mol Genet. 2005;14:1283–1292. 45. Cancer Genome Atlas Research N. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–550. 46. Chen DT, Hsu YL, Fulp WJ, et al. Prognostic and predictive value of a malignancy-risk gene signature in

Journal of Thoracic Oncology

Vol.

-

No.

-

early-stage non-small cell lung cancer. J Natl Cancer Inst. 2011;103:1859–1870. 47. Veldman-Jones MH, Brant R, Rooney C, et al. Evaluating robustness and sensitivity of the NanoString Technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res. 2015;75:2587–2593. 48. Gnant M, Filipits M, Greil R, et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 risk of recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann Oncol. 2014;25:339–345.