Literature DB >> 27153551

Cell cycle progression score is a marker for five-year lung cancer-specific mortality risk in patients with resected stage I lung adenocarcinoma.

Takashi Eguchi1,2, Kyuichi Kadota3,4, Jamie Chaft5, Brent Evans6, John Kidd6, Kay See Tan7, Joe Dycoco1, Kathryn Kolquist6, Thaylon Davis6, Stephanie A Hamilton6, Kraig Yager6, Joshua T Jones6, William D Travis3, David R Jones1, Anne-Renee Hartman6, Prasad S Adusumilli1,8.   

Abstract

PURPOSE: The goals of our study were (a) to validate a molecular expression signature (cell cycle progression [CCP] score and molecular prognostic score [mPS; combination of CCP and pathological stage {IA or IB}]) that identifies stage I lung adenocarcinoma (ADC) patients with a higher risk of cancer-specific death following curative-intent surgical resection, and (b) to determine whether mPS stratifies prognosis within stage I lung ADC histological subtypes.
METHODS: Formalin-fixed, paraffin-embedded stage I lung ADC tumor samples from 1200 patients were analyzed for 31 proliferation genes by quantitative RT-PCR. Prognostic discrimination of CCP score and mPS was assessed by Cox proportional hazards regression, using 5-year lung cancer-specific mortality as the primary outcome.
RESULTS: In multivariable analysis, CCP score was a prognostic marker for 5-year lung cancer-specific mortality (HR=1.6 per interquartile range; 95% CI, 1.14-2.24; P=0.006). In a multivariable model that included mPS instead of CCP, mPS was a significant prognostic marker for 5-year lung cancer-specific mortality (HR=1.77; 95% CI, 1.18-2.66; P=0.006). Five-year lung cancer-specific survival differed between low-risk and high-risk mPS groups (96% vs 81%; P<0.001). In patients with intermediate-grade lung ADC of acinar and papillary subtypes, high mPS was associated with worse 5-year lung cancer-specific survival (P<0.001 and 0.015, respectively), compared with low mPS.
CONCLUSION: This study validates CCP score and mPS as independent prognostic markers for lung cancer-specific mortality and provides quantitative risk assessment, independent of known high-risk features, for stage I lung ADC patients treated with surgery alone.

Entities:  

Keywords:  CCP score; adjuvant therapy; chemotherapy; molecular prognostic score; overall survival

Mesh:

Substances:

Year:  2016        PMID: 27153551      PMCID: PMC5085225          DOI: 10.18632/oncotarget.9129

Source DB:  PubMed          Journal:  Oncotarget        ISSN: 1949-2553


INTRODUCTION

The estimated 5-year overall survival (OS) for patients with stage IA and IB lung adenocarcinoma (ADC) is 81%–87% and 72%, respectively [1], despite curative-intent surgical resection. Whether adjuvant therapy would improve OS among patients with stage I lung ADC is undetermined. Current National Comprehensive Cancer Network (NCCN) guidelines recommend adjuvant therapy for patients with stage IB lung ADC on the basis of tumor size; this follows results from the CALGB 9633 trial [2]. Recent findings from our group and others indicate that size alone may not be adequate when determining tumor aggressiveness [3-8]. To expand prognostic information beyond tumor size alone, the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) proposed a new lung ADC classification in 2011 [9]. which has been validated in independent cohorts [3, 10, 11] and is now adopted by the 2015 WHO classification [12]. A major limitation of this classification is that the majority (40%–70%) of cases of stage I lung ADC are intermediate-grade acinar predominant (ACI) or papillary predominant (PAP) subtypes [11, 13, 14]. An objective, quantitative, reproducible molecular expression signature that is applicable to both stage IA and IB lung ADC tumors and that can be used to further stratify intermediate-grade ADCs, to better identify high-risk patients, would be beneficial. A 46-gene panel was developed to assess cell cycle gene expression, and generate a cell cycle progression (CCP) score for pathological stage I and II lung ADC tumors [15-18]. CCP score was previously shown to be a prognostic factor for lung ADC mortality in combined cohorts of patients with stage I or II lung ADC [16-18]. Using a cohort of 650 patients with stage I or II disease, a molecular prognostic score (mPS; inclusive of CCP score and stage) was developed to provide quantitative prognostic information [17]. In our study, we assessed the ability of CCP score and mPS to predict lung cancer–specific mortality in a cohort of 1200 patients with stage I ADC treated with surgery alone. The use of this well-annotated, previously reported cohort allowed us to assess the prognostic ability of all known clinical, surgical, pathological, histological, and molecular features indicative of aggressiveness—which included lymphatic and vascular invasion [4], type of surgical procedure [7, 19], histological subtypes (defined in the IASLC/ATS/ERS classification) [3, 7, 19], and EGFR and KRAS mutation status [20]. This expansive analysis, inclusive of all known high-risk factors, demonstrated that CCP score and mPS are independent prognostic markers for patients with stage I lung ADC.

RESULTS

Evaluable analysis set

Among the 1200 patients with samples, 11 (0.9%) had follow-up <30 days after surgery, 34 (2.8%) received adjuvant therapy, and 52 (4.3%) had tumor samples for which a CCP score could not be computed. Among the evaluable analysis set (Figure 1; N = 1103), patients had a median age at diagnosis of 69 years (Table 1). The majority of patients were women (61.0%), former smokers (68.1%), and had stage IA disease (72.4%). Since the number of bilobectomies and pneumonectomies was small (n < 10), these two surgical procedures were combined with lobectomies when the surgical procedure variable was defined.
Figure 1

Disposition schematic

The evaluable analysis set included all patients who have a follow-up duration ≥30 days, did not receive adjuvant therapy, have a cell cycle progression (CCP) score, and have follow-up information. FFPE, formalin-fixed, paraffin-embedded; H&E, hematoxylin and eosin.

Table 1

Patient clinical characteristics

CharacteristicEvaluable analysis set (N = 1103)
Age at diagnosis (years)
 Mean (SD)68.3 (10.0)
 Median69
 Min, Max23, 96
Sex
 Male430 (39.0)
 Female673 (61.0)
Smoking status
 Never192 (17.4)
 Former751 (68.1)
 Current160 (14.5)
Surgical procedure
 Pneumonectomy/bilobectomy/lobectomy824 (74.7)
 Segmentectomy96 (8.7)
 Wedge resection183 (16.6)
Tumor size (centimeters)
 Mean (SD)2.1 (1.0)
 Median2.0
 Min, Max0.3, 5.0
Pathological stage
 IA799 (72.4)
 IB304 (27.6)
Pleural invasion
 PLX/PL0931 (84.4)
 PL1154 (14.0)
 PL218 (1.6)
Lymphatic invasion
 Absent750 (68.0)
 Present353 (32.0)
Vascular invasion
 Absent812 (73.6)
 Present291 (26.4)
Morphological grade
 Low156 (14.1)
 Intermediate690 (62.6)
 High257 (23.3)

Note. Data are no. (%), unless otherwise noted. Max = maximum, Min = minimum, SD = standard deviation.

Disposition schematic

The evaluable analysis set included all patients who have a follow-up duration ≥30 days, did not receive adjuvant therapy, have a cell cycle progression (CCP) score, and have follow-up information. FFPE, formalin-fixed, paraffin-embedded; H&E, hematoxylin and eosin. Note. Data are no. (%), unless otherwise noted. Max = maximum, Min = minimum, SD = standard deviation.

CCP score and 5-year lung cancer–specific mortality

On univariable analysis, CCP score (P < 0.001) and all clinical variables—except sex and smoking status—were significant prognostic factors for 5-year lung cancer–specific mortality (Table 2). On multivariable analysis, CCP score was an independent significant prognostic marker (P = 0.006) for 5-year lung cancer–specific mortality (hazard ratio [HR] = 1.6; 95% CI = 1.14–2.24; Table 3). Other significant variables in the multivariable analysis were age at diagnosis (P = 0.016), surgical procedure (P < 0.001), tumor size (P < 0.001), lymphatic invasion (P = 0.02), and morphological grade (P = 0.012).
Table 2

Univariable Cox proportional hazards regression analyses of cell cycle progression (CCP) score, molecular prognostic score (mPS), and clinical characteristics with 5-year lung cancer mortality in the evaluable analysis set

VariableHazard ratio (95% CI)P value
CCP score2.46 (1.85, 3.29)<0.001
mPS2.67 (2.08, 3.42)<0.001
Age at diagnosis1.03 (1.01, 1.06)0.003
Sex0.065
 Male1
 Female0.68 (0.45, 1.03)
Smoking status0.109
 Never1
 Former1.94 (1.02, 4.17)
 Current2.06 (0.92, 4.91)
Surgical procedure<0.001
 Pneumonectomy/bilobectomy/lobectomy1
 Segmentectomy2.11 (1.07, 3.81)
 Wedge resection2.79 (1.74, 4.38)
Tumor size1.56 (1.29, 1.87)<0.001
Pathological stage<0.001
 IA1
 IB3.65 (2.43, 5.53)
Pleural invasion<0.001
 PLX/PL01
 PL13.29 (2.06, 5.14)
 PL26.12 (2.36, 13.05)
Lymphatic invasion<0.001
 Absent1
 Present3.32 (2.20, 5.06)
Vascular invasion<0.001
 Absent1
 Present3.16 (2.09, 4.76)
Morphological grade<0.001
 Low1
 Intermediate5.74 (1.78, 35.11)
 High13.41 (4.13, 82.37)

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively.

CI = confidence interval.

Table 3

Multivariable Cox proportional hazards regression analysis of cell cycle progression (CCP) score, molecular prognostic score (mPS), and clinical characteristics with 5-year lung cancer mortality in the evaluable analysis set

VariableAnalysis with CCP scoreAnalysis with mPS
Hazard ratio (95% CI)P valueHazard ratio (95% CI)P value
CCP score1.60 (1.14, 2.24)0.006
mPS1.77 (1.18, 2.66)0.006
Age at diagnosis1.03 (1.01, 1.05)0.0161.03 (1.01, 1.05)0.016
Sex0.3430.342
 Male11
 Female0.82 (0.54, 1.25)0.82 (0.54, 1.24)
Smoking status0.6760.675
 Never11
 Former1.35 (0.69, 2.95)1.35 (0.69, 2.95)
 Current1.41 (0.60, 3.51)1.41 (0.60, 3.51)
Surgical procedure<0.001<0.001
 Pneumonectomy/Bilobectomy/Lobectomy11
 Segmentectomy3.31 (1.62, 6.30)3.31 (1.62, 6.30)
 Wedge resection4.65 (2.71, 7.91)4.65 (2.71, 7.90)
Tumor size1.64 (1.24, 2.14)<0.0011.64 (1.24, 2.15)<0.001
Pathological stage0.7200.158
 IA11
 IB0.87 (0.39, 1.90)0.53 (0.22, 1.28)
Pleural invasion0.0560.057
 PLX/PL011
 PL11.94 (0.91, 4.12)1.94 (0.91, 4.12)
 PL23.39 (1.15, 8.79)3.38 (1.15, 8.76)
Lymphatic invasion0.0200.019
 Absent11
 Present1.73 (1.09, 2.77)1.74 (1.10, 2.78)
Vascular invasion0.0890.091
 Absent11
 Present1.52 (0.94, 2.44)1.51 (0.94, 2.44)
Morphological grade0.0120.011
 Low11
 Intermediate3.08 (0.92, 19.13)3.08 (0.92, 19.13)
 High5.09 (1.46, 32.18)5.10 (1.46, 32.22)

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively.

CI = confidence interval.

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively. CI = confidence interval. Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively. CI = confidence interval.

mPS and 5-year lung cancer–specific mortality

On univariable analysis, mPS (P < 0.001) was a significant prognostic factor for 5-year lung cancer–specific mortality (Table 2). On multivariable analysis, mPS was an independent significant prognostic marker (P = 0.006) for 5-year lung cancer–specific mortality (HR = 1.77; 95% CI = 1.18–2.66; Table 3). Other significant variables included age at diagnosis (P = 0.016), surgical procedure (P < 0.001), tumor size (P < 0.001), lymphatic invasion (P = 0.019), and histological grade (P = 0.011). When a predefined threshold of 27 was used to define the high/low mPS variable [17, 18], the evaluable analysis set had 614 low-mPS patients and 489 high-mPS patients. The difference in 5-year lung cancer–specific survival was statistically significant (P < 0.001): 96% for low-mPS patients vs 81% for high-mPS patients (Figure 2).
Figure 2

The Kaplan-Meier survival estimates for patients with low molecular prognostic score (mPS; N = 614) and high mPS (N = 489) show that the 5-year lung cancer–specific survival rate is 96% for patients with low mPS and 81% for patients with high mPS (P < 0.001)

mPS and histological subtypes

Figure 3 shows the 5-year lung cancer–specific survival curves for the high- and low-mPS groups by predominant histological subtype. For all cases, patients with high mPS had worse lung cancer–specific survival than those with low mPS; however, only ACI (P < 0.001), PAP (P = 0.015), and invasive mucinous adenocarcinoma (IMA) (P < 0.001) tumors had statistically significant differences between high and low mPS.
Figure 3

The Kaplan-Meier survival estimates for the high and low molecular prognostic score (mPS) groups, by morphological subtype, are shown

The 5-year lung cancer–specific survival for low mPS vs high mPS for each subtype: lepidic predominant, 99% vs 93% (P = 0.152); acinar predominant, 95% vs 83% (P < 0.001); papillary predominant, 95% vs 85% (P = 0.015); micropapillary predominant, 89% vs 69% (P = 0.16); solid predominant, 92% vs 79% (P = 0.101); and invasive mucinous adenocarcinoma, 100% vs 42% (P < 0.001).

The Kaplan-Meier survival estimates for the high and low molecular prognostic score (mPS) groups, by morphological subtype, are shown

The 5-year lung cancer–specific survival for low mPS vs high mPS for each subtype: lepidic predominant, 99% vs 93% (P = 0.152); acinar predominant, 95% vs 83% (P < 0.001); papillary predominant, 95% vs 85% (P = 0.015); micropapillary predominant, 89% vs 69% (P = 0.16); solid predominant, 92% vs 79% (P = 0.101); and invasive mucinous adenocarcinoma, 100% vs 42% (P < 0.001). On the basis of our and others' previous publications that demonstrated worse prognosis in patients with micropapillary (MIP) subtype tumors who had undergone limited resection compared with those who had undergone lobectomy [19, 21], we investigated the utility of mPS in this cohort of patients (Figure 4). Within each setting, patients with high mPS had significantly lower survival estimates (P < 0.001). This was particularly true for patients with presence of the MIP subtype (≥5%)—5-year lung cancer–specific survival estimates were 95% for the low-mPS group vs 75% for the high-mPS group (P < 0.001). This difference was particularly pronounced among patients who had undergone limited resection (MIP ≥ 5%); the 5-year lung cancer–specific survival estimate was 88% for the low-mPS group vs 57% for the high-mPS group (P < 0.001).
Figure 4

The Kaplan-Meier survival estimates for the high and low molecular prognostic score (mPS) groups, by the absence (<5%) or presence (≥5%) of micropapillary (MIP) pattern and surgical procedure, are shown

The 5-year lung cancer–specific survival for low mPS vs high mPS: all cases (MIP < 5%), 97% vs 86% (P < 0.001); all cases (MIP ≥ 5%), 95% vs 75% (P < 0.001); lobectomy (MIP < 5%), 98% vs 89% (P < 0.001); lobectomy (MIP ≥ 5%), 96% vs 82% (P < 0.001); limited resection (MIP < 5%), 93% vs 75% (P = 0.007); and limited resection (MIP ≥ 5%), 88% vs 57% (P < 0.001).

The Kaplan-Meier survival estimates for the high and low molecular prognostic score (mPS) groups, by the absence (<5%) or presence (≥5%) of micropapillary (MIP) pattern and surgical procedure, are shown

The 5-year lung cancer–specific survival for low mPS vs high mPS: all cases (MIP < 5%), 97% vs 86% (P < 0.001); all cases (MIP ≥ 5%), 95% vs 75% (P < 0.001); lobectomy (MIP < 5%), 98% vs 89% (P < 0.001); lobectomy (MIP ≥ 5%), 96% vs 82% (P < 0.001); limited resection (MIP < 5%), 93% vs 75% (P = 0.007); and limited resection (MIP ≥ 5%), 88% vs 57% (P < 0.001). The relationship between high mPS/low mPS and cohorts of patients with increasing percentage of solid (SOL) pattern in their tumors is shown in Figure 5. As the percentage of SOL pattern in the tumor increases, the proportion of patients with high mPS increases (P < 0.001).
Figure 5

The relationship between high/low molecular prognostic score (mPS) and categories of increasing solid (SOL) pattern shows that, as the SOL pattern of the tumor increases, the proportion of high mPS increases (P < 0.001)

Relationship between mPS and mutation status

Figure 6 shows the relationship between high mPS/low mPS and mutation status. Wild-type tumors had approximately equal proportions of high mPS and low mPS (48% vs 52%); however, high mPS was less common in tumors with EGFR (35%) or KRAS (30%) mutations (P = 0.002).
Figure 6

The relationship between high/low molecular prognostic score (mPS) and mutation status shows that wild-type tumors had an approximately equal proportion of high versus low mPS (48% vs 52%), whereas high mPS was less common in tumors with EGFR (35%) or KRAS (30%) mutations (P = 0.002)

Exploratory analyses with OS

Exploratory analyses were performed to evaluate CCP score and mPS as prognostic markers for death from any cause in patients with stage I lung ADC, after adjustment for clinical variables, including pathological stage. Univariable analyses showed that CCP score, mPS, and all evaluated clinical variables—except smoking status—were significant prognostic factors for 5-year OS (Table 4). On multivariable analysis, CCP score was an independent significant prognostic marker for 5-year OS (HR = 1.33; 95% CI = 1.06–1.67; P = 0.014). Other significant variables included age at diagnosis (P < 0.001), sex (P = 0.019), surgical procedure (P < 0.001), tumor size (P < 0.001), and pleural invasion (P = 0.017) (Table 5). In a separate multivariable analysis, mPS was an independent significant prognostic marker for 5-year OS (HR = 1.41; 95% CI = 1.07–1.85; P = 0.016).
Table 4

Univariable Cox proportional hazards regression analyses of cell cycle progression (CCP) score, molecular prognostic score (mPS), and clinical characteristics with overall survival in the evaluable analysis set

VariableHazard ratio (95% CI)P value
CCP score1.73 (1.41, 2.11)<0.001
mPS1.88 (1.58, 2.23)<0.001
Age at diagnosis1.05 (1.03, 1.07)<0.001
Sex<0.001
 Male1
 Female0.61 (0.46, 0.81)
Smoking status0.190
 Never1
 Former1.44 (0.96, 2.26)
 Current1.48 (0.87, 2.53)
Surgical procedure<0.001
 Pneumonectomy/bilobectomy/lobectomy1
 Segmentectomy1.62 (1.00, 2.49)
 Wedge resection2.08 (1.48, 2.86)
Tumor size1.37 (1.19, 1.56)<0.001
Pathological stage<0.001
 IA1
 IB2.44 (1.83, 3.23)
Pleural invasion<0.001
 PLX/PL01
 PL12.71 (1.95, 3.70)
 PL23.06 (1.30, 6.07)
Lymphatic invasion<0.001
 Absent1
 Present2.15 (1.62, 2.84)
Vascular invasion<0.001
 Absent1
 Present2.31 (1.74, 3.06)
Morphological grade<0.001
 Low1
 Intermediate1.95 (1.16, 3.55)
 High3.01 (1.74, 5.59)

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively.

CI = confidence interval.

Table 5

Multivariable Cox proportional hazards regression analysis of cell cycle progression (CCP) score, molecular prognostic score (mPS), and clinical characteristics with overall survival in the evaluable analysis set

VariableAnalysis with CCP scoreAnalysis with mPS
Hazard ratio (95% CI)P valueHazard ratio (95% CI)P value
CCP score1.33 (1.06, 1.67)0.014
mPS1.41 (1.07, 1.85)0.016
Age at diagnosis1.04 (1.03, 1.06)<0.0011.04 (1.03, 1.06)<0.001
Sex0.0190.019
 Male11
 Female0.71 (0.53, 0.95)0.71 (0.53, 0.95)
Smoking status0.4680.464
 Never11
 Former1.18 (0.78, 1.87)1.18 (0.78, 1.87)
 Current1.42 (0.81, 2.50)1.42 (0.81, 2.50)
Surgical procedure<0.001<0.001
 Pneumonectomy/Bilobectomy/Lobectomy11
 Segmentectomy2.03 (1.24, 3.20)2.03 (1.24, 3.19)
 Wedge resection2.45 (1.69, 3.52)2.45 (1.69, 3.51)
Tumor size1.42 (1.17, 1.72)<0.0011.42 (1.17, 1.72)<0.001
Pathological stage0.3310.073
 IA11
 IB0.76 (0.43, 1.32)0.56 (0.30, 1.05)
Pleural invasion0.0170.017
 PLX/PL011
 PL12.13 (1.23, 3.70)2.13 (1.23, 3.70)
 PL22.35 (0.92, 5.30)2.35 (0.92, 5.29)
Lymphatic invasion0.0910.089
 Absent11
 Present1.32 (0.96, 1.82)1.32 (0.96, 1.83)
Vascular invasion0.0740.073
 Absent11
 Present1.36 (0.97, 1.90)1.36 (0.97, 1.90)
Morphological grade0.2160.209
 Low11
 Intermediate1.32 (0.77, 2.45)1.33 (0.77, 2.46)
 High1.65 (0.91, 3.20)1.66 (0.91, 3.22)

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively.

CI = confidence interval.

Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively. CI = confidence interval. Note. Hazard ratios for CCP score and mPS are per the interquartile range of each score variable, respectively. CI = confidence interval.

DISCUSSION

Our study validates both CCP score and mPS as independent prognostic markers for lung cancer–specific mortality and OS in a large, uniform cohort of patients with stage I lung ADC treated with surgery alone. Since CCP score is a quantitative prognostic measure and derivative of multiple cell cycle genes, it may provide a more reliable and objective measure for lung cancer–specific mortality than the currently reported subjective variables, such as lymphatic and vascular invasion and histological grade. Our selection of 5-year lung cancer–specific mortality as the primary endpoint is relevant for the design of prospective studies investigating the potential benefit of adjuvant therapy for these patients. Our study is distinct from previously published studies detailing the utility of CCP score and mPS, for the following reasons: (1) this is the first study to analyze a prognostic molecular signature in a large, uniform cohort of patients with stage I lung ADC; (2) the multivariable analysis performed includes all known high-risk clinical, surgical, and pathological factors in stage I lung ADC, as well as the recently described IASLC/ATS/ERS and WHO classification; (3) our study identifies a high-risk group of patients even among patients with stage IA lung ADC (P < 0.001); and (4) exploratory analyses included OS in addition to 5-year lung cancer–specific mortality. The fact that mPS was able to stratify prognostically different groups, even among intermediate-risk lung ADC patients, underscores the utility of this molecular signature. Among invasive ADC, for the high-grade subtypes (MIP, SOL, and IMA), 5-year cancer-specific mortality in patients with low mPS is far lower than in patients with high mPS (11%, 8%, 0% vs 31%, 21%, and 58%). In patients with the SOL predominant subtype, who we reported have early, extrathoracic, and multisite recurrences and poor postrecurrence survival [7], 17% of patients (25/147) had low mPS, compared with 83% with high mPS. There was also strong evidence of an increasing proportion of high mPS with increasing percentage of SOL component, in all patient cohorts. These findings are plausible, as our previous study showed that mitotic counts of the SOL subtype were 2-fold greater than those of other histological subtypes, which therefore suggests that the presence and increasing percentage of SOL pattern would be strongly associated with high CCP [22]. The strong prognostic effect of CCP score, which is a quantitative and clinical laboratory independent measure of risk, supports the use of this RNA-based expression assay as an adjunct to conventional pathological features. A major limitation of the IASLC/ATS/ERS classification is that the majority (40%–70%) of cases of stage I lung ADC are intermediate-grade ACI and PAP subtypes [3, 10, 13, 14]. In an attempt to identify a high-risk group among patients with ACI and PAP subtype tumors, we and others have reported that high mitotic counts [22], presence of the cribriform pattern [4], lack of thyroid transcription factor–1 expression [23], immunoinhibitory tumor microenvironment [8], and nuclear estrogen receptor–α expression [24] are indicative of poor prognosis. Although these pathological high-risk factors can be helpful in identifying a cohort of high-risk patients with stage I lung ADC, their qualitative and subjective aspects, interobserver variability [25, 26], and difficulty of standardization pose a practical problem when attempting to apply them universally. The biological factors underlying the described pathological information are unknown. Importantly, mPS is able to significantly distinguish the prognosis of intermediate-grade subtypes (ACI and PAP), which account for the majority of tumors (63% in our cohort) in stage I patients. Furthermore, in our study, there was no correlation between high mPS and EGFR or KRAS mutations, thereby suggesting that CCP score and mPS can provide risk information regardless of driver mutation status. Tumor RNA signatures have shown high accuracy as prognostic markers in breast cancer [27-29]. An examination of prognostic breast RNA profiles revealed a common profile of cell cycle–regulated mRNAs [15, 27]. CCP signature has been previously shown to be a superior prognostic tool in the treatment of prostate cancer [15]. The expression levels of cell cycle genes in our study indicate that these gene profiles measure tumor growth irrespective of underlying histological grading, morphological grade, or genetic aberrations; this underscores the utility of identifying a high-risk cohort that may benefit from chemotherapy that targets cell proliferation, as well as a low-risk cohort that can forgo adjuvant therapies. Many of the CCP signature genes evaluated in our study (BIRC5, BUB1B, CDKN3, CENPF, PRC1, RRM2, and TOP2A) [30-34] have been linked to chemotherapy sensitivity. Current NCCN guidelines provide a category 2A recommendation for the use of adjuvant therapy for patients with stage IB (T2N0R0) disease with high-risk features, which includes tumor size >4 cm [35]. In our study, when a previously identified threshold was applied [17], 27% of patients (212/799) had stage IA disease and high mPS, which is a relatively higher distribution than the originally expected rate of 15%; however, the low mPS/high mPS threshold successfully stratified 5-year cancer-specific mortality, with a nearly 4-fold difference between the two (low vs high, 4% vs 15%). In comparison, in stage IB patients with tumors >4 cm, 5-year cancer-specific survival was 100% in patients with low mPS (12% of the cohort; 6/50) compared with 68% in patients with high mPS (88%; 44/50). Conversely, in stage IB patients with tumors ≤4 cm, those with a low mPS (8%; 21/254) had 5-year cancer-specific survival of 95%, compared with 79% for those with high mPS (92%; 233/254). This suggests that many patients with stage IB disease with tumors ≤4 cm, as well as a considerable number of patients with stage IA disease, may benefit from investigation of chemotherapy, in terms of improved 5-year cancer-specific survival [36, 37]. In conclusion, our data validate an RNA expression–based prognostic signature in a large cohort of patients with stage I lung ADC. CCP score and mPS are significantly and independently associated with risk of 5-year lung cancer–specific mortality. The current NCCN criteria for evaluating the appropriateness of adjuvant chemotherapy for stage IB patients, with the possible exception of tumor size, are largely qualitative, and their measurement is subjective—thus making standardization across locations difficult, expensive, and prone to interobserver variability. This CLIA-certified quantitative PCR platform assay provides a reproducible and quantitative measure of tumor aggressiveness that can provide important prognostic information to add to conventional clinicopathological factors. The use of this measure may help advance the multidisciplinary management of stage I lung ADC by prompting investigation of chemotherapy benefit following surgical resection.

MATERIALS AND METHODS

Patients

A set of 1200 patients consecutively treated at Memorial Sloan Kettering Cancer Center were included in the study if they had histological stage I lung ADC as defined by the seventh edition of the AJCC/UICC TNM criteria and had undergone complete resection. Exclusion criteria were as follows: follow-up duration <30 days after lung resection, synchronous or previous cancers diagnosed within 2 years of the lung cancer resection, multiple nodules or primary lung tumors, and receipt of neoadjuvant or combination adjuvant chemotherapy and/or radiation. However, patients who received chemotherapy and/or radiation following recurrence were included. Our retrospective study was approved by the Institutional Review Board at Memorial Sloan Kettering Cancer Center (WA0269-08).

Sample processing

From tumor blocks with areas of ≥50% tumor, 2 10-μm formalin-fixed, paraffin-embedded (FFPE) unstained slides were processed and analyzed by quantitative PCR, using a published protocol, to determine expression levels of 31 cell cycle genes and 15 housekeeping genes [15]. Passing criteria to calculate CCP score included amplification of ≥13 housekeeping genes and 22 cell cycle genes with measurable raw CT values and a standard deviation of <0.5 between CCP scores from 3 replicate measurements for each sample.

CCP score and mPS

CCP score is an unweighted average of 31 cell cycle genes normalized by the average of 15 housekeeping genes, as previously described [15]. The formula for mPS is 20 × (0.33 × CCP score + 0.52 × stage) + 15, where CCP score is rounded to the nearest tenth and stage is treated as a numerical variable (stage IA = 1; stage IB = 2). As previously published, Cox proportional hazards regression was employed with data from 3 patient cohorts to help derive the mPS formula [17]. In previous publications, a threshold for categorizing low-risk and high-risk patients was predefined as the 85th percentile of mPS; this threshold was chosen based on literature showing that approximately 15% of stage IA patients died from lung cancer within 5 years [38-42]. The threshold mPS of 27 was established to distinguish low (mPS ≤ 27) and high (mPS > 27), reflecting low- and high-risk of survival [17].

Histological evaluation

Hematoxylin and eosin–stained tumor slides (average, 4; range, 2–10) were reviewed by two pathologists, both of whom were unaware of patient clinical outcomes. The percentage of each histological pattern was recorded in 5% increments, and, according to the IASLC/ATS/ERS and 2015 WHO classifications, tumors were classified by the predominant subtype: adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), lepidic predominant (LEP), ACI, PAP, MIP, SOL, IMA, and colloid adenocarcinoma (COL) [9, 12].

Analysis of mutations

EGFR exon 19 deletion, exon 21 L858R mutation, and KRAS exon 2 mutation were detected as previously described [20].

Statistical analysis

The following clinical variables were collected and used in analysis: age at diagnosis, sex (male, female), smoking status (never, former, and current), surgical procedure (pneumonectomy, bilobectomy, lobectomy, segmentectomy, and wedge resection), tumor size (centimeters, rounded to the nearest millimeter), pathological stage (IA and IB), pleural invasion (PLX/PL0, PL1, and PL2), lymphatic invasion (absent and present), vascular invasion (absent and present), and morphological grade (low = AIS, MIA, and LEP; intermediate = ACI and PAP; high = MIP, SOL, IMA, and COL). The primary endpoint was 5-year lung cancer–specific mortality. The primary endpoint was met if the patient died within 5 years of surgery and the cause of death was lung cancer or was unknown following lung cancer recurrence. If a patient did not experience either, then the patient was censored at the date of death from other causes, if the patient died within 5 years of surgery. If the patient did not die within 5 years of surgery, then the patient was censored at the date of the last follow-up or the date 5 years after surgery, whichever came first. The exploratory endpoint was 5-year OS. The exploratory endpoint was met if the patient died of any cause within 5 years of surgery. If the patient did not die within 5 years of surgery, then the patient was censored at the date of the last follow-up or the date 5 years after surgery, whichever came first. Linear association of CCP score with 5-year lung cancer–specific mortality, adjusted for clinical variables, was evaluated using Cox proportional hazards regression. The P value was based on a χ2 test statistic that was the difference of likelihood ratio statistics from models that excluded and included CCP score. Linear association of mPS with 5-year lung cancer–specific mortality was analyzed similarly. All reported P values were 2-sided. HRs with 95% CIs were reported for each interquartile range of the score distribution. The exploratory endpoint of 5-year OS was analyzed similarly. Using the log-rank test, we evaluated whether 5-year lung cancer–specific survival was significantly more favorable for patients in the low-mPS group than in the high-mPS group. The Kaplan-Meier method was used to compute survival function estimates. The relationships between high and low mPS with SOL pattern and mutation status (wild type, EGFR, and KRAS) were examined using Fisher's exact test. All analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC) and R (version 3.1.1 or later; R Core Team, Vienna, Austria).
  41 in total

1.  Associations between mutations and histologic patterns of mucin in lung adenocarcinoma: invasive mucinous pattern and extracellular mucin are associated with KRAS mutation.

Authors:  Kyuichi Kadota; Yi-Chen Yeh; Sandra P D'Angelo; Andre L Moreira; Deborah Kuk; Camelia S Sima; Gregory J Riely; Maria E Arcila; Mark G Kris; Valerie W Rusch; Prasad S Adusumilli; William D Travis
Journal:  Am J Surg Pathol       Date:  2014-08       Impact factor: 6.394

2.  Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.

Authors:  Jack Cuzick; Gregory P Swanson; Gabrielle Fisher; Arthur R Brothman; Daniel M Berney; Julia E Reid; David Mesher; V O Speights; Elzbieta Stankiewicz; Christopher S Foster; Henrik Møller; Peter Scardino; Jorja D Warren; Jimmy Park; Adib Younus; Darl D Flake; Susanne Wagner; Alexander Gutin; Jerry S Lanchbury; Steven Stone
Journal:  Lancet Oncol       Date:  2011-03       Impact factor: 41.316

3.  The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival.

Authors:  Arne Warth; Thomas Muley; Michael Meister; Albrecht Stenzinger; Michael Thomas; Peter Schirmacher; Philipp A Schnabel; Jan Budczies; Hans Hoffmann; Wilko Weichert
Journal:  J Clin Oncol       Date:  2012-03-05       Impact factor: 44.544

4.  A grading system combining architectural features and mitotic count predicts recurrence in stage I lung adenocarcinoma.

Authors:  Kyuichi Kadota; Kei Suzuki; Stefan S Kachala; Emily C Zabor; Camelia S Sima; Andre L Moreira; Akihiko Yoshizawa; Gregory J Riely; Valerie W Rusch; Prasad S Adusumilli; William D Travis
Journal:  Mod Pathol       Date:  2012-04-13       Impact factor: 7.842

5.  Adjuvant Chemotherapy After Lobectomy for T1-2N0 Non-Small Cell Lung Cancer: Are the Guidelines Supported?

Authors:  Paul J Speicher; Lin Gu; Xiaofei Wang; Matthew G Hartwig; Thomas A D'Amico; Mark F Berry
Journal:  J Natl Compr Canc Netw       Date:  2015-06       Impact factor: 11.908

6.  Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma.

Authors:  Ignacio I Wistuba; Carmen Behrens; Francesca Lombardi; Susanne Wagner; Junya Fujimoto; M Gabriela Raso; Lorenzo Spaggiari; Domenico Galetta; Robyn Riley; Elisha Hughes; Julia Reid; Zaina Sangale; Steven G Swisher; Neda Kalhor; Cesar A Moran; Alexander Gutin; Jerry S Lanchbury; Massimo Barberis; Edward S Kim
Journal:  Clin Cancer Res       Date:  2013-09-18       Impact factor: 12.531

7.  Dependence of paclitaxel sensitivity on a functional spindle assembly checkpoint.

Authors:  Tamotsu Sudo; Masayuki Nitta; Hideyuki Saya; Naoto T Ueno
Journal:  Cancer Res       Date:  2004-04-01       Impact factor: 12.701

8.  Risk factor analysis of locoregional recurrence after sublobar resection in patients with clinical stage IA non-small cell lung cancer.

Authors:  Terumoto Koike; Teruaki Koike; Katsuo Yoshiya; Masanori Tsuchida; Shin-ichi Toyabe
Journal:  J Thorac Cardiovasc Surg       Date:  2013-08       Impact factor: 5.209

9.  The cribriform pattern identifies a subset of acinar predominant tumors with poor prognosis in patients with stage I lung adenocarcinoma: a conceptual proposal to classify cribriform predominant tumors as a distinct histologic subtype.

Authors:  Kyuichi Kadota; Yi-Chen Yeh; Camelia S Sima; Valerie W Rusch; Andre L Moreira; Prasad S Adusumilli; William D Travis
Journal:  Mod Pathol       Date:  2013-11-01       Impact factor: 7.842

10.  Validation of a molecular and pathological model for five-year mortality risk in patients with early stage lung adenocarcinoma.

Authors:  Raphael Bueno; Elisha Hughes; Susanne Wagner; Alexander S Gutin; Jerry S Lanchbury; Yifan Zheng; Michael A Archer; Corinne Gustafson; Joshua T Jones; Kristen Rushton; Jennifer Saam; Edward Kim; Massimo Barberis; Ignacio Wistuba; Richard J Wenstrup; William A Wallace; Anne-Renee Hartman; David J Harrison
Journal:  J Thorac Oncol       Date:  2015-01       Impact factor: 15.609

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1.  What is better/reliable, mitosis counting or Ki67/MIB1 staining?

Authors:  Mark Kriegsmann; Arne Warth
Journal:  Transl Lung Cancer Res       Date:  2016-10

2.  BRMS1 Expression in Surgically Resected Lung Adenocarcinoma Predicts Future Metastases and Is Associated with a Poor Prognosis.

Authors:  Peter R Bucciarelli; Kay See Tan; Neel P Chudgar; Whitney Brandt; Joseph Montecalvo; Takashi Eguchi; Yuan Liu; Rania Aly; William D Travis; Prasad S Adusumilli; David R Jones
Journal:  J Thorac Oncol       Date:  2017-10-31       Impact factor: 15.609

Review 3.  Prognostic and predictive biomarkers post curative intent therapy.

Authors:  Rebecca Feldman; Edward S Kim
Journal:  Ann Transl Med       Date:  2017-09

4.  A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee.

Authors:  Andre L Moreira; Paolo S S Ocampo; Yuhe Xia; Hua Zhong; Prudence A Russell; Yuko Minami; Wendy A Cooper; Akihiko Yoshida; Lukas Bubendorf; Mauro Papotti; Giuseppe Pelosi; Fernando Lopez-Rios; Keiko Kunitoki; Dana Ferrari-Light; Lynette M Sholl; Mary Beth Beasley; Alain Borczuk; Johan Botling; Elisabeth Brambilla; Gang Chen; Teh-Ying Chou; Jin-Haeng Chung; Sanja Dacic; Deepali Jain; Fred R Hirsch; David Hwang; Sylvie Lantuejoul; Dongmei Lin; John W Longshore; Noriko Motoi; Masayuki Noguchi; Claudia Poleri; Natasha Rekhtman; Ming-Sound Tsao; Erik Thunnissen; William D Travis; Yasushi Yatabe; Anja C Roden; Jillian B Daigneault; Ignacio I Wistuba; Keith M Kerr; Harvey Pass; Andrew G Nicholson; Mari Mino-Kenudson
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5.  AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients.

Authors:  Marc A Schneider; Petros Christopoulos; Thomas Muley; Arne Warth; Ursula Klingmueller; Michael Thomas; Felix J F Herth; Hendrik Dienemann; Nikola S Mueller; Fabian Theis; Michael Meister
Journal:  Int J Oncol       Date:  2017-01-02       Impact factor: 5.650

6.  Histologic subtyping in pathologic stage I-IIA lung adenocarcinoma provides risk-based stratification for surveillance.

Authors:  Yusuke Takahashi; Takashi Eguchi; Koji Kameda; Shaohua Lu; Raj G Vaghjiani; Kay See Tan; William D Travis; David R Jones; Prasad S Adusumilli
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7.  SP70-Targeted Imaging for the Early Detection of Lung Adenocarcinoma.

Authors:  Jian Xu; Shichang Zhang; Wei Zhang; Erfu Xie; Min Gu; Yue Wang; Lu Yang; Bingfeng Zhang; Jiexin Zhang; Chunrong Gu; Ting Xu; Daqian Li; Fang Wang; Peijun Huang; Shiyang Pan
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8.  A Recurrence-Specific Gene-Based Prognosis Prediction Model for Lung Adenocarcinoma through Machine Learning Algorithm.

Authors:  Shaohua Xu; Jie Zhou; Kai Liu; Zhoumiao Chen; Zhengfu He
Journal:  Biomed Res Int       Date:  2020-11-07       Impact factor: 3.411

9.  Prognostic factors and patients' profile in treated stage I and II lung adenocarcinoma: a Hospital's Cancer Registry-based analysis.

Authors:  Fernando Conrado Abrão; Stela Verzinhasse Peres; Igor Renato Louro Bruno de Abreu; Riad Naim Younes
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10.  MTBP promotes migration and invasion by regulation of ZEB2-mediated epithelial-mesenchymal transition in lung cancer cells.

Authors:  Bo Pan; Haibo Han; Lina Wu; Ying Xiong; Jianzhi Zhang; Bin Dong; Yue Yang; Jinfeng Chen
Journal:  Onco Targets Ther       Date:  2018-10-10       Impact factor: 4.147

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