Prognostic Nomogram to Predict the Overall Survival of Patients with “driver-gene-negative” Lung Adenocarcinoma (LUAD) and Its’ Biological Basis Deciphering

doi:10.21203/rs.3.rs-933879/v1

Objectives: Assessing the prognosis of patients with “driver-gene-negative” lung adenocarcinoma (LUAD patients negative for EGFR, KRAS, BRAF, HER2, MET, ALK, RET and ROS1 were identified as “driver-gene-negative”) is of significant clinical importance. We aimed to devise a prognostic nomogram for this LUAD subgroup and to define its biological basis.

Materials and methods: Prognostic nomogram were established based on a retrospective study of 294 patients with surgical resected “driver-gene-negative” LUAD at The First Affiliated Hospital of Sun Yat-sen University between September 2003 and June 2015. The concordance index (C-index) and calibration curve were used to determine its predictive accuracy and discriminatory capacity. Patients were classified into low- and high-risk subgroups according to nomogram scores. To define the biological basis, we carried out gene set enrichment analysis in an independent dataset of 49 “driver-gene-negative” LUAD patients.

Results: The nomogram built on the independent factors including CTC, age and stage achieved C-index of 0.785 [95% confidence interval (CI) 0.753-0.817] for predicting OS of “driver-gene-negative” LUAD. The calibration curves for OS probabilities showed a good agreement between the nomogram prediction and actual observation. High-risk patients had shorter OS [hazard ratio (HR) = 7.43, 95% CI 5.20-10.42, p<0.0001]. The genetic analysis suggested the biological basis for the role of the predictive model may be due to changes in cell proliferation, metabolism and neurological disease, indicating a relationship between increased proliferative potential and preferential poor prognosis.

Conclusions: The prognostic nomogram showed promising prediction efficacy and is expected to predict the prognosis of “driver-gene-negative” LUAD. The easy-to-use nomogram and the enriched pathways can help clinical decision-making, guide follow-up planning, and develop new therapeutic targets.

Cancer Biology

“driver-gene-negative” lung adenocarcinoma

nomogram

prognosis

survival

Lung cancer is the most common malignancy and leading cause of cancer death worldwide[1]. Lung adenocarcinoma (LUAD) accounts for more than 40% of lung cancer histological subtype[2]. More than half of LUAD patients present with positive EGFR/KRAS/ALK gene mutations[3], along with mutations of BRAF, HER2, MET, RET or ROS1 can be effectively treated by the corresponding targeted therapy according to NCCN(the National Comprehensive Cancer Network) guideline of non-small cell lung carcinoma(NSCLC). With such treatment targeting oncogenic driver genes, the survival of this genetically unique subset of patients defined as “driver-gene-positive” LUAD has been significantly prolonged[4]. For patients with “driver-gene-negative” LUAD, the effective therapeutic options are limited. Immune checkpoint inhibitors (ICIs) have shown great potential in the treatment of lung cancer[5, 6]. ICIs can restore the anti-tumor function of T cells mainly by blocking the inhibitory pathways of PD-1/PD-L1 and CTLA-4/CD28 to play important roles in anti-tumor immune response. However, ICIs have critical limitations including T cell dependence, non-targeted exhaustion in normal tissues, off-target toxicity, etc.[7], resulting in only about 20% of NSCLC patients can benefit from ICIs.

Various studies have shown that the proportion of "driver-gene-negative" LUAD in total LUAD varies between 10% and 55%, and in general, Caucasians and Africans have a higher proportion (22%-55%) of "driver-gene-negative" LUAD than Asians (10%-20%)[8, 9]. With the development of large panel sequencing technology, a growing number of “driver-gene-negative” LUAD cases are constantly being diagnosed, the needs of researching focusing on “driver-gene-negative” LUAD is imminent. In our previous study[10], we developed a novel Wnt-pathway-based prognosis-prediction panel to stratify “driver-gene-negative” LUAD into low-risk and high-risk groups. The prognostic marker panel could be helpful for clinical decision-making, guiding follow-up schedules and developing new therapy targets for “driver-gene-negative” LUAD.

In this study, we try to further study the prognostic value of circulating tumor cells (CTCs) and PD-L1 expression in patients with “driver-gene-negative” LUAD, and to establish an easy-to-use clinical prognostic nomogram as well as to explore the biological basis of the prognostic model by Gene Set Enrichment Analysis (GSEA), thus help clinicians develop individualized treatment programs for these patients.

Patients

Patients with “driver-gene-negative” LUAD which defined as LUAD with negative EGFR, KRAS, BRAF, HER2, MET, ALK, RET and ROS1 mutation rendered no target for current mainstream targeted therapy between September 2003 and June 2015 at The First Affiliated Hospital of Sun Yat-sen University were included in this study. This study was approved by the institutional ethics committee of the First Affiliated Hospital of Sun Yat-sen University, China (A-084), informed consent was waived. LUAD was confirmed by biopsy of the original tumor tissue, as well as immunohistochemistry. Clinical information including medical history, sex, age, smoking history, tumor size, circulating tumor cell (CTC) count, PD-L1 status, tumor TNM stage and differentiation were collected. The staging of tumors was based on AJCC (American Joint Committee on Cancer) 7th Edition Cancer Staging System. OS were measured from the date of operation to the date of death or the last date of follow up.

Genome and PD-L1 Expression Detection

All the tumor samples were collected before the patient undergoing any anti-tumor treatment and properly formalin-fixed, paraffin-embedded (FFPE). Immunohistochemical staining and immunoblotting assays in the FFPE tissues were applied to determine the patient’s “driver-gene-negative” status, which had been described in detail in our previous study[10].

The PD-L1 expression is defined by Tumor Proportion Scores (TPS). PD-L1 TPS≥1% was defined as positive expression and PD-L1 TPS≥50% was defined as high expression.

CTC Enumeration

According to a standard protocol, all blood specimens were collected into EDTA-contained vacutainer or CellSave^® tubes and were processed within 24 hour to further procedures. For experiments using lysed blood, NH4Cl was added to whole blood in 10:1 v/v ratio and mixed for 20 min at room temperature. After centrifugation at 200 g for 5 minutes, the supernatant was removed and the cell pellet was re-suspended in an equivalent volume of buffer for capture experiments. The process of CTC enumeration was detailed in our previous study[11].

Statistical Analysis

Continuous variables were expressed as median [mean ± standard deviation (SD)]. Categorical variables were displayed as frequency, n (%). Tumor size (≥5cm versus <5cm) and TNM stage (stage III/IV versus stage I/II)[10] were set as binary categorical variables. CTC count and PD-L1 were set as continuous variables (numeration and TPS%) and binary variables (<2 versus ⩾2 and <5 versus ⩾5 for CTC, positive versus negative and low versus high for PD-L1), their role in predicting prognosis were analyzed respectively.

Univariate Cox proportional hazards regression analysis and stepwise Cox proportional hazards regression model were used to identify the significant prognostic risk factors. A nomogram was built on the selected risk factors. Calibration curves were plotted and the concordance index (C-index) was calculated to assess the performance of the nomogram. Kaplan-Meier analyses were performed to assess survival differences between group, and a log-rank test for statistical comparison. Statistical analysis was performed with R software (version 3.3.3; http://www.Rproject.org). A two-sided P < 0.05 was considered statistically significant. The enrolled patients with “driver-gene-negative” LUAD were then dichotomized into low- and high-risk subgroups according to the risk score of the nomogram.

Genetic Analysis of the Clinical Nomogram

To explore the biological basis of the subgroups separated by the risk score of the nomogram, the driving biological pathways was assessed. We did a pre-ranked gene set enrichment analysis (GSEA) approach for genetic analysis as in previously published studies[12-14]. Then, we used the 4×44K whole human genome expression microarray (Agilent design ID 026652, GEO accession number GPL13497) to get profiles the expression of 27,958 genes. GeneSpring GX v12.1 software package was used to do quantile normalization and subsequent data processing, and the genes with low expression or close to the background level were excluded for analyses. Gene expression values were correlated with the OS scores of the nomogram prediction model to rank all genes by using Spearman’s rank correlation coefficient. We then put this gene rank into a ranked gene set enrichment analysis (GSEA) software version 3.8.0[15]. We tested expert-curated pathways from C2 Reactome collection available at MSigDB[16] database cp.kegg.v72.symbols GMT dataset. The weighted enrichment analysis method in GSEA was used to conduct enrichment analysis by random combination of 1000 times, and adj. p value was calculated as per to correct for multiple hypothesis testing. The Enrichment Score(ES) and Normalized Enrichment Score (NES) of GSEA software was used to quantify the correlation of the genes with pathways.

Expression data are published in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus as GSE115002 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115002) as the way of [17].

Baseline Characteristics of the Patients

A total of 294 patients with “driver-gene-negative” LUAD aged from 28 to 81 years (mean age: 58±11 years) were enrolled in this study, including 160 (54.4%) male and 134 (45.6%) female patients. Baseline characteristics of the enrolled patients was shown in Table1. Among all the patients, there were 172 (58.5%) patients had stage I LUAD, 42 (14.3%) had stage II LUAD, 57 (19.4%) stage III LUAD and 23 (7.8%) stage IV LUAD. The mean tumor size was 2.6 (±1.7) cm and most of them [254 (86.4%)] were moderately differentiated. PD-L1 TPS≥1% was observed in 144 (49%) patients and PD-L1 TPS≥50% was seen in 67 (22.8%) patients. The mean CTCs count was 4 (±6) and mean OS was 44.8±26.7 months.

Table 1 Baseline characteristics of patients with “driver-gene-negative” LUAD.
Characteristics	N = 294 (%)
Sex, n (%)
Female	134 (45.6%)
Male	160 (54.4%)
Age (years), mean ± SD	58±11
T stage, n (%)
T1	101 (34.4%)
T2	158 (53.7%)
T3	19 (6.5%)
T4	16 (5.4%)
N stage, n (%)
N0	192 (65.3%)
N1	33 (11.2%)
N2	50 (17%)
N3	19 (6.5%)
AJCC, n (%)
I	172 (58.5%)
II	42 (14.3%)
III	57 (19.4%)
IV	23 (7.8%)
Differentiation, n (%)
Low	22 (7.5%)
Median	254 (86.4%)
High	18 (6.1%)
Tumor size (cm), mean ± SD	2.6±1.7
PD-L1 TPS%, n (%)
Positive	144 (49%)
High	67 (22.8%)
CTC count, mean ± SD	4±6
OS (month), mean ± SD	44.8±26.7
Abbreviations: LUAD-lung adenocarcinoma, AJCC-American Joint Committee on Cancer, TPS-Tumor Proportion Scores of PD-L1, CTC-Circulating Tumor Cells, OS-Overall Survival, SD-Standard Deviation.

Prediction of Survival Risk

The results of univariate and multivariate analyses are demonstrated in Table 2. In Cox univariate analysis, the significant prognostic factors were age (HR = 1.04, p = 0.000), TNM stage(HR = 5.75, p = 0.000), CTCs count（numeration: HR = 1.15, p = 0.000; <2 versus ⩾2: HR = 2.90, p = 0.000; <5 versus ⩾5: HR = 3.80, p = 0.000）and PD-L1 ( TPS%: HR = 1.00, p = 0.042; low versus high, HR = 0.65, p = 0.024). Age, gender, TNM stage, CTCs count (numeration), PD-L1 (low versus high) were then included in the multivariate analysis. Note that the CTCs count was included as a continuous variable in the multivariate analysis as it can preserve more information than categorical ones³⁰. In multivariate Cox proportional hazards regression model, the independent variables related to prognostic were age (HR = 1.06, p = 0.000), TNM stage (HR = 2.83, p = 0.000), CTCs count (HR = 1.12, p = 0.000).

Then Kaplan-Meier estimates of the cumulative survival rate of CTCs, age, and TNM stage based on the Kaplan-Meier plotter database were shown in Supplement Fig. 1 (A-C). P values of the log-rank test were < 0.01 for each independent variable.

Table 2. Prognostic factors for OS on COX univariate and multivariate analyses.
Characteristics	Univariate analysis		Multivariate analysis
Characteristics	HR (95% CI)	p	HR (95% CI)	p
Sex (male VS. female) †	1.03 (0.77-1.39)	0.822	NA	NA
Age (years) †	1.04 (1.03-1.06)	0.000*	1.06 (1.04-1.08)	0.000*
Differentiation
Low	Ref
Median	1.53 (0.64-3.65)	0.338
High	1.62 (0.82-3.18)	0.163
Tumor size (≥5 versus <5)	1.32 (0.80-2.18)	0.279
TNM stage (III/IV versus I/II) †	5.75 (4.10-8.06)	0.000*	2.83 (1.82-4.39)	0.000*
CTC count
Number †	1.15 (1.12-1.18)	0.000*	1.12 (1.08-1.15)	0.000*
<2 VS. ⩾2	2.90 (2.15-3.93)	0.000*
<5 VS. ⩾5	3.80 (2.79-5.18)	0.000*
PD-L1
TPS%	1.00 (1.00-1.00)	0.042*
Positive VS. Negative	0.88 (0.66-1.18)	0.398
Low VS. High †	0.65 (0.45-0.95)	0.024*	NA	NA
Abbreviations: OS-Overall Survival, CTC-Circulating Tumor Cells, TPS-Tumor Proportion Scores of PD-L1, CI-confidence interval, HR-hazard ratio, VS.-versus. †: Variables included in multivariate Cox proportional hazard model; NA: Not Applicable.

Development, Performance and Validation of Individualized Nomogram

A nomogram to predict OS (Fig. 1A) was built based on the abovementioned independent variables including CTCs, age, and TNM stage. Each patient was assigned a score on the point scale according to these variables. The final total score locating on the total point can be calculated by adding up the score of each variable, which can be used to determine the estimated probability of survival at each time point. The total points of each case were also calculated according to the established nomogram. The calibration curves for OS probabilities showed a good agreement between the nomogram prediction and actual observation, as given in Fig. 1B, C, D. The C-index of the nomogram for predicting OS was 0.785 [95% confidence interval (CI) 0.753-0.817]. Kaplan–Meier curve showed high-risk patients had shorter OS [hazard ratio (HR) = 7.43, 95% CI 5.20-10.42, p<0.0001] (Fig. 2).

Biological Validation of the Individualized Nomogram

We explored the biological basis of the Nomogram predict models through array-based gene expression data in an independent dataset of 49 LUAD patients. In the ranked GSEA, we found 16 significantly enriched pathways in low OS score group and 2 in high OS score group (P<0.05) shown in Supplement Table and Supplement Fig.2. The top five pathway of low OS score group (Parkinsons disease, oxidative phosphorylation, Huntingtons disease, cell cycle and Alzheimers disease,) and top two pathways of high OS score group (ABC transporters, histidine metabolism) are shown in Fig.3. The pathways suggested the biological basis for the role of the predictive model may be due to changes in cell proliferation, metabolism and neurological disease.

In our study, we established an individualized nomogram to predict the prognosis of “driver-gene-negative” LUAD based on age, TNM stage and CTCs count. The nomogram showed promising prediction efficacy, with a C-index of 0.785 (95% CI, 0.753-0.817). We further explored the biological basis of the nomogram as being linked to cell cycle and transcriptional processes. The pathways suggested the biological basis for the role of the predictive model may be due to changes in cell proliferation, metabolism and neurological disease. The easy-to-use nomogram and the enriched pathways can help clinical decision-making, guide follow-up planning, and develop new therapeutic targets.

Clinical nomogram has gained a lot of attention for its more convenient and advanced prognosis ability[18]. It had already showed promising prognostic capability in different kinds of cancers including lung cancer[10, 19], gastric cancer[20, 21]], colorectal cancer[22], liver cancer[23], melanoma[24], etc. For resected NSCLC (stage I-IIIa)[19], a clinical nomogram built on sex, age, histology, sampled lymph nodes, T and N stage achieved higher C-index than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (primary cohort, 0.71 v 0.68, respectively; P .01; validation cohort, 0.67 v 0.64, respectively; P .06). Another study devised an effective clinical nomogram for assessing the survival of patients with stage I NSCLC receiving complete resection[25]. The nomogram built on age, sex, tumor size, and visceral pleural invasion had C-indices of 0.694 (95% CI 0.651–0.737) for predicting OS and 0.653 (95% CI 0.61–0.696) for DFS. Despite these promising findings, gene mutation status was unknown in these studies which may lead to variance prognosis when targeted treatment was applied. Science “driver-gene-negative” LUAD have fewer treatment options and poor prognosis, studies focused on this genetically unique subset of patients are imminent. In this study, the clinical prognosis nomogram built on pre-treatment clinical data displayed promising prediction efficacy and the suggested pathways can provides a feasible way to explore new therapeutic drugs for this genetic subgroup of patients.

CTCs count is an independent prognostic indicator for NSCLC[26], especially advanced staged NSCLC[27]. However, there are few studies on “driver-gene-negative” LUAD and CTCs. In our study, we confirmed the prognostic role of CTCs in “driver-gene-negative” LUAD patients. It can not only predict prognosis prior to treatment, but also can help to monitor the dynamic treatment response[28] and the change of survival probability during the course of the disease. In this study, we also studied the prognostic value of PD-L1 expression, unfortunately, it was not an independent risk factor of prognosis for “driver-gene-negative” LUAD. It is reported that PD-L1 expression is related to the prognosis of patients with untreated NSCLC[29, 30], and many studies have shown that the greater the PD-L1 expression, the worse the prognosis of patients with NSCLC. The discrepancy of this finding between ours and the reported may be due to the difference in patient enrollment and the potential bias caused by retrospective detection of the tissue sample.

We also do initial genomic association study to explore the biological basis of the Nomogram predict models. The result shows five pathways including Parkinsons disease, oxidative phosphorylation, Huntingtons disease, cell cycle and Alzheimers disease are the most different in the low and high OS score group. Previous studies shown that cell-cycle and oxidative phosphorylation pathway is involved in poor prognosis of NSCLC[31-33], and drugs targeting them have also achieved good results[34, 35]. Notably, three of this five pathways are neuropathy-related, which suggests that the above neurological disease pathways are closely related to lung cancer prognosis. This result is consistent with previous findings[36-39]. Currently, drugs targeting neuropathy-related pathways have also been reported to have anticancer effects. Albanol B, a drug against Alzheimer's disease, has been shown to inhibit the growth of lung cancer cells and tumors[40]. Pimavanserin, a drug for Parkinson's disease, also has some efficacy against pancreatic cancer[41]. These findings suggest that the neuropathy-related pathways enriched in this study have the potential to be therapeutic targets for lung cancer.

This study has several limitations. First, there may be potential selection bias due to the retrospective nature of this study. Second, our study focused on LUAD and did not address other histologic subtypes. Third, our findings deserve further study with expanded samples and extra external validation. Fourth, PD-L1 expressions of some cases were detected retrospectively, so the accuracy of the detection results may have potential bias. The prognostic value of PD-L1 expressions in “driver-gene-negative” LUAD deserves further study.

In conclusion, the individualized nomogram based on age, TNM stage and CTCs count showed promising prediction efficacy and is expected to predict the prognosis of “driver-gene-negative” LUAD. Furthermore, we evaluated the biological basis of the proposed biomarker as being linked to cell cycle and transcriptional processes, and found to be correlated with cell proliferation, metabolism and neurological potential. The easy-to-use nomogram and the enriched pathways can help clinical decision-making, guide follow-up planning, and develop new therapeutic targets.

CTC: circulating tumor cell

C-index: concordance index

CI: confidence interval

DFS: Disease-free survival

FFPE: formalin-fixed, paraffin-embedded

GSEA: Gene Set Enrichment Analysis

HR: hazard ratio

ICI: Immune checkpoint inhibitor

LUAD: Lung Adenocarcinoma

NSCLC: non-small cell lung carcinoma

OS: overall survival

SD: standard deviation

TPS: Tumor Proportion Scores

Ethics approval and consent to participate

This study was approved by the institutional ethics committee of the First Affiliated Hospital of Sun Yat-sen University {No.[2013] A-084}. Informed consent was waived. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Consent for publication

Not applicable.

Availability of data and material

The datasets supporting the conclusions of this article are included within the article. The gene expression data during the current study are available in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus as GSE115002 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115002).

Conflicts of interests

None of the authors has any conflict of interest to declare regarding the content of this article.

Funding

This work was supported by Grants from the National Natural Science Foundation of China (8210070554) and by Grants from Development Center for Medical Science & Technology National Health Commission of the People’s Republic of China (No. WA2020RW10).

Acknowledgements

Not applicable.

CRediT authorship contribution statement

YZ: Conceptualization, Methodology, Resources, Formal analysis, Writing-original draft, Writing-review & editing. HSY: Data curation, Methodology, Investigation, Writing-original draft, Writing-review & editing. SCS: Data curation, Investigation, Resources, Writing-review & editing. QKG: data curation, Investigation, Resources, Writing-review & editing. ZGL: Data curation, Investigation, Resources, Writing-review & editing. DDC: Data curation, Investigation, Resources, Writing-review & editing. HHL: Conceptualization, Writing-review & editing, Supervision. YYL: Conceptualization, Methodology, Writing-review & editing, Supervision. ZFK: Conceptualization, Methodology, Resources, Formal analysis, Writing-review & editing, Supervision.

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No competing interests reported.

SupplementFigure1.tif
Supplement Figure 1. Then Kaplan-Meier estimates of the cumulative survival rate of CTCs, age, and TNM stage based on the Kaplan-Meier plotter database.
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Supplement Figure 2. Separate curve of pathway enrichment results of GSEA. (A) Alzheimer’s pathway ES 0.35, NES 1.76; (B) Cell Cycle Pathway ES 0.63, NES 1.77; (C) Parkinson's Disease Pathway, ES 0.38, NES 1.94; (D) Oxidative Phosphoryl-ation Pathway, ES0.36, NES1.79; (E) Huntington's Disease Pathway, ES 0.34, NES 1.78; (F) ABC Transporter Pathway, ES 0.45, NES 1.44; (G) Histidine Metabolism Pathway, ES 0.48, NES 1.43.

Prognostic Nomogram to Predict the Overall Survival of Patients with “driver-gene-negative” Lung Adenocarcinoma (LUAD) and Its’ Biological Basis Deciphering

Status:

Version 1

Abstract

Figures

Introduction

Methods