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Clinical Model to Predict Survival in Chemonaive Patients With Advanced Non–Small-Cell Lung Cancer Treated With Third-Generation Chemotherapy Regimens Based on Eastern Cooperative Oncology Group Data

From the University of Wisconsin Medical School, Madison, WI; Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA; Rush Presbyterian St Luke's Medical Center, Chicago, IL; Vanderbilt University, Nashville, TN. Trials in this study were conducted by the Eastern Cooperative Oncology Group

Address reprint requests to Tien Hoang, MD, University of Wisconsin Comprehensive Cancer Center, 600 Highland Ave, K4/562, Madison, WI 53792; e-mail: txh{at}medicine.wisc.edu

	ABSTRACT

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PURPOSE: (1) Identify clinical factors that can be used to predict survival in chemotherapy-naive patients with advanced non–small-cell lung cancer (NSCLC) treated with third-generation chemotherapy regimens, and (2) build a clinical model to predict survival in this patient population.

PATIENTS AND METHODS: Using data from two randomized, phase III Eastern Cooperative Oncology Group (ECOG) trials (E5592/E1594), we performed univariate and multivariate stepwise Cox regression analyses to identify survival prognostic factors. We used 75% of randomly sampled data to build a prediction model for survival, and the remaining 25% of data to validate the model.

RESULTS: From 1993 to 1999, 1,436 patients with stage IV or IIIB NSCLC with effusion were treated with platinum-based doublets (involving either paclitaxel, docetaxel, or gemcitabine). The response rate and median survival time were 20% and 8.2 months, respectively. One- and 2-year survivals were 33% and 11%, respectively. In multivariate analysis, six independent poor prognostic factors were identified: skin metastasis (hazard ratio [HR], 1.88), lower performance status (ECOG 1 or 2; HR, 1.46), loss of appetite (HR, 1.62), liver metastasis (HR, 1.32), four metastatic sites (HR, 1.20), and no prior surgery (HR, 1.16). A nomogram using six pretreatment prognostic factors was built to predict 1- and 2-year survival.

CONCLUSION: Six pretreatment factors can be used to predict survival in chemotherapy-naive NSCLC patients treated with standard chemotherapy. Using our prognostic nomogram, 1- and 2-year survival probability of NSCLC patients can be estimated before treatment. This prognostic model may help clinicians and patients in clinical decision making, as well as investigators in research planning.

	INTRODUCTION

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Despite international efforts spent in the investigation of novel "targeted agents" for the treatment of lung cancer, platinum-based cytotoxic doublets remain the standard first-line treatment for patients with advanced disease. Large randomized studies have demonstrated the equivalence of regimens containing a third-generation agent and cisplatin or carboplatin with median survival time and 1-year survival rate in the range of 8 to 9 months and 33% to 35%, respectively.^1,2 In reality, however, survival varies significantly between individual patients, with some patients surviving years, and others succumbing to their disease within a few months.

Several studies have attempted to identify clinical, laboratory, and molecular markers that may help clinicians and researchers distinguish subgroups of non–small-cell lung cancer (NSCLC) patients. Nevertheless, relatively few prognostic factors, such as extent of disease and performance status (PS), have been widely accepted as useful prognostic markers. Furthermore, there is no simple and reliable way to estimate the survival of individual patients undergoing chemotherapy. Therefore, we performed this retrospective analysis to identify clinical factors that influence the outcome of advanced NSCLC patients treated with standard first-line chemotherapy, and to build a model that can be used in daily practice to predict long-term survival in this patient population. Such a practical model may help oncologists and their patients make treatment decisions, and may assist investigators in designing clinical trials.

	PATIENTS AND METHODS

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Patients
We analyzed data from two randomized, phase III Eastern Cooperative Oncology Group (ECOG) trials (E5592 and E1594) in which chemotherapy-naive patients with advanced NSCLC were treated with standard platin-based chemotherapy doublets (Table 1). In E5592, 599 patients who had stage IIIB or IV and ECOG performance status (PS) of 0 to 1 were randomly assigned to receive cisplatin 75 mg/m² in combination with (1) etoposide 100 mg/m² on days 1 to 3, (2) high-dose paclitaxel 250 mg/m² plus filgrastim, or (3) low-dose paclitaxel 135 mg/m².³ Compared with the reference arm of etoposide and cisplatin, the two paclitaxel-containing arms resulted in a superior survival (median survival, 9.9 months v 7.6 months; 1-year survival, 38.9% v 31.8%; P = .048). Therefore, ECOG chose paclitaxel plus cisplatin as the standard regimen for the subsequent E1594.¹ In this four-arm trial, 1,207 patients with stage IIIB (with malignant effusion) or IV, and a PS of 0 to 2 were randomly assigned to one of four regimens: (1) cisplatin and paclitaxel, (2) cisplatin and gemcitabine, (3) cisplatin and docetaxel, or (4) carboplatin and paclitaxel. After the initial recruitment of 66 patients with PS 2, the eligibility for enrollment was amended to include only patients with PS 0 or 1. Overall, the median survival time and 1-year survival rate for all 1,155 eligible patients were 7.9 months and 33%, respectively. The study demonstrated no difference in survival between the four arms.

Statistical Methods
To identify potential prognostic factors, we analyzed 27 pretreatment clinical variables recorded in the ECOG database, including patient demographics (age, sex, race), history of prior radiation or surgery (therapeutic and/or diagnostic) for lung cancer, significant underlying conditions (chronic lung, heart diseases), disease stage (IIIB v IV), PS, systemic symptoms (loss of appetite, weight loss, fever), tumor characteristics (histology, measurability), and metastatic features (number of metastatic sites, metastatic organs), as presented in Table 2. Univariate Cox regression analysis was used first to assess the association between each variable and survival, followed by multivariate stepwise Cox regression analysis for variable selection (with entry cutoff level of 0.2 and stay cutoff 0.1). The stepwise variable selection was performed on 75% randomly sampled data from which a survival prediction model was built. This model was validated using the remaining 25% of the data. Based on the prediction model with identified prognostic factors, a nomogram was drawn for prediction of 1- and 2-year survival. The analysis was performed using the SAS program (SAS Institute, Cary, NC).

	RESULTS

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Pretreatment Prognostic Factors
Of 27 pretreatment clinical factors, 15 were associated with poor prognosis in univariate Cox analysis (P .05), including being male, lower PS (ECOG 1 or 2), loss of appetite, loss of weight, having fever, having no prior lung surgery, history of receiving radiation therapy, and metastasis-related factors (having 4 metastatic sites, having metastasis in the mediastinum, contralateral lung, bone, liver, brain, subcutaneous tissue, or other organs; Table 3). Of all the factors analyzed, subcutaneous metastasis was associated with the highest hazard ratio (HR) of 2.06. Twelve pretreatment factors were found not to be prognostically significant, including age ( 70 v < 70), stage (IIIB with pleural effusion versus IV), tumor histology (squamous v adenocarcinoma v large-cell type), and comorbid diseases.

View larger version (20K): [in this window] [in a new window]   Fig 1. Prognostic nomogram for chemotherapy-naive non–small-cell lung cancer patients treated with standard chemotherapy. PS, performance status; met, metastasis; surg, surgery.

View larger version (20K): [in this window] [in a new window]   Fig 2. Nomogram example. PS, performance status; NSCLC, non–small-cell lung cancer; met, metastasis; surg, surgery.

View larger version (14K): [in this window] [in a new window]   Fig 3. One- and 2-year predicted survival of quartile groups compared with observed survival. Dotted line is the "ideal" line if there is a perfect match between predicted and observed survival rates. Vertical bars are 95% CI of observed survival.

	DISCUSSION

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Three cooperative groups, two from the United States and one from Europe, have analyzed their data to identify important prognostic factors (Table 6). The first analysis, published in the mid 1980s by Finkelstein et al, was based on data from two ECOG randomized phase III trials (EST 2575 and EST 1581).⁸ Eight hundred ninety-three metastatic NSCLC patients were treated on seven chemotherapy arms involving older drugs (eg, cyclophosphamide, bleomycin, doxorubicin) in combination with or without cisplatin. Because few patients (4%) survived beyond two years, the main analytic end point was to identify pretreatment clinical factors to distinguish patients who survived more than 1 year ("long-term survivors") from those who did not. After logistic regression multivariate analysis of 36 on-study factors, eight were associated with favorable outcome (in order of importance): PS 0 (P < .0001), no bone metastasis (P < .0001), female sex (P = .0005), no weight loss (P = .001), no subcutaneous metastasis (P = .006), non–large-cell histology (P = .011), no prior symptom of shoulder or arm pain (P = .029), and no liver metastasis (P = .046). Further analysis performed among 1-year survivors identified three factors predicting long-term survival: being female (P = .005), no subcutaneous metastasis (P = .016), and PS 0 (P = .04).

The European Lung Cancer Working Party (ELCWP) analyzed data from 1,052 unresectable NSCLC patients from three phase III and four phase II trials conducted between 1980 and 1991.¹⁰ The patient population was heterogenous, including those with limited (stage I to III) or advanced disease, and those with or without prior chemotherapy. Some patients received chemoradiotherapy. The chemotherapy involved cisplatin with or without second-generation agents (etoposide being the most common). Of 23 pretreatment clinical and laboratory variables analyzed, eight negative factors were identified in multivariate analysis: metastatic disease (P < .0001), Karnofsky PS 70 (P = .0003), leukocytosis (P = .0003), skin metastasis (P = .008), increased calcium level (P = .007), abnormal neutrophil count (P = .02), age greater than 60 years (P = .02), and being male (P = .03). Of note, skin metastasis had the highest relative risk (2.80).

Compared with the three analyses described above, our analysis was based on more recent clinical studies involving newer, third-generation regimens. As in the case of the previous ECOG study reported by Finkelstein, the database for our analysis was built entirely from two large randomized phase III trials, providing a more homogenous patient population. All patients in our analysis had stage IV or IIIB with malignant effusion and had received no prior chemotherapy. Almost all had a PS of 0 or 1. All were treated with modern regimens consisting of cisplatin or carboplatin in combination with a third-generation agent, which are presently considered the standard of care for advanced disease. The survival outcome was similar to results from other large contemporary phase III studies. However, as with the other three analyses, this is a retrospective study with inherent limitations. For example, laboratory data were not included in our analysis because only absolute values were entered in the database, and there were concerns about differences in reference values between institutions.

Of 27 pretreatment clinical factors analyzed in univariate and multivariate analyses, six factors stood out as independent survival prognostic markers. Interestingly, subcutaneous metastasis was associated with the worse prognosis, with the highest HR at 1.88. The prognostic importance of lung cancer spreading to subcutaneous tissue or skin was also found in the previous ECOG and ELCWP analyses, as well as in another study from Great Britain.¹¹ Skin or subcutaneous metastasis from lung cancer is uncommon and is rarely included in prognostic analyses. In a retrospective study involving 200 patients with skin metastasis from solid tumors, lung (18%) was among the most common sites of tumor origin, together with breast (32%) and melanoma (16%).¹² However, with a median survival of 2.9 months, survival for patients with lung cancer was much shorter than those with breast cancer (13.8 months) or melanoma (13.5 months). The ECOG study by Finkelstein et al⁸ showed that subcutaneous metastasis is one of three important clinical features discriminating long-term survivors among those who lived beyond 1 year; the other two being PS and sex.

Other metastasis-related prognostic factors in our multivariate analysis were liver involvement and number of metastatic sites ( four), with HRs of 1.32 and 1.20, respectively. While hepatic metastases was also an important factor in the prior ECOG study, the number of metastatic lesions ( two) predicted a poor outcome in PS 0 to 1 patients in the SWOG analysis. Brain is among the most common metastatic sites in NSCLC patients. Those with brain metastasis have been generally thought to have poor outcome, and traditionally have been excluded from chemotherapy trials. In our study, brain metastasis was associated with an HR of 1.21 in univariate analysis. However, its statistical significance disappeared in multivariate analysis. Because NSCLC patients with brain metastasis are a heterogenous group, with prognosis possibly depending on the extent of intra- and extracranial disease, treatment for these patients should be individualized.

Poor performance status has been widely accepted as one of the most important negative prognostic factors in all cancer patients. The importance of this marker was validated in all four analyses, in which it was among the most significant factors in predicting survival of lung cancer patients. In the SWOG report, patients with PS 0 to 1 did significantly better than those with PS 2. In the two ECOG analyses, which included primarily patients with PS 0 to 1, those with excellent status (PS 0) lived longer than those with lower PS (1 to 2). In fact, PS 0 was the most important factor in the first ECOG analysis. In the Veterans Administration Lung Group analysis published in 1980, Stanley analyzed survival by Karnofsky score and found that a higher score (by 10% to 15% increments) correlated with increase in median survival.⁴ Thus, it seems that a better pretreatment PS is associated with a longer survival (PS 0 > PS 1 > PS 2 or higher), suggesting that participants in clinical trials should be stratified further based on their PS (PS 0 v PS 1 v PS 2).

Unlike PS, the prognostic role of weight loss has been somewhat controversial. Some studies, including the previous ECOG analysis, found significant weight loss being among the poor prognostic factors,^4,8,13 but others, including the SWOG and ELCWP analyses, did not.^9,10,14,15 In our study, we looked at both weight loss and reduced appetite as separate variables. Although the significance of both weight loss and reduced appetite was demonstrated in univariate analysis, only the latter remained an independent negative marker in multivariate analysis.

An interesting finding from our study is that a history of prior surgery for lung cancer was a positive prognostic factor. However, this variable in our database included both diagnostic and therapeutic surgery without speculation of procedures that were performed. It seems plausible that an individual who underwent resection of a primary lung lesion and then recurred several years later with multiple metastases might have a disease with a completely different biology than someone who presented "de novo" with widespread metastases who therefore never had a chance to undergo a surgical procedure. Nevertheless, without complete data for further analysis, we do not have a definitive explanation for this observation.

Of several clinical characteristics analyzed in our research, age, sex, and tumor histology were not associated with survival outcome. In the past, there had been concern of increase in mortality in elderly patients treated with chemotherapy. Although a few studies such as the ELCWP report found that advanced age was a negative factor, most, including the two ECOG analyses, have concluded that older patients have the same outcome as their younger counterparts.^16,17 Of note, age 70 years was actually considered a favorable factor in the SWOG analysis.⁹ These observations suggest that age should not be the reason to exclude older but fit patients from therapy, particularly with the availability of newer chemotherapy drugs and better supportive measures. Several studies have found that gender is an important prognostic factor, with women faring better than men.^{6,8-10,14,15,18} However, the influence of gender in survival was not seen in our patient population. The possible role of histology subtypes of NSCLC in determining clinical outcome, such as the favorable impact of squamous cell type in early stage⁶ or the negative effect of large cell histology in advanced disease,⁸ has been raised. However, in our study, we found no difference in survival between tumor histology subtypes. The 1997 American Society of Clinical Oncology guideline, updated in 2003, also suggested that NSCLC histology is not an important prognostic factor in patients with advanced, unresectable disease.^7,19

In the prior ECOG analysis, Finkelstein created a discriminant function model to predict the probability of surviving longer than 1 year.⁸ This model requires a discriminant table and different steps to calculate

where S is the calculated prognostic score. A P value greater than .18 predicts that a particular patient will survive longer than 1 year, while P < .18 gives reverse prediction. SWOG and ELCWP investigators used recursive partitioning and amalgamation method to divide patients into subgroups based on prognostic factors identified in their studies. For example, in the SWOG study, three prognostic subgroups were identified: (1) PS 0 to 1, hemoglobin 11, and age 47 years (1- and 2-year survivals of 27% and 8%, respectively), (2) PS 2 to 4 and lactate dehydrogenase higher than normal (1- and 2-year survivals of 6% and 1%, respectively), and (3) others, including PS 2 to 4 and normal lactic dehydrogenase (1- and 2-year survival of 16% and 3%).⁹ We used the Cox regression method to build a prognostic nomogram based on six negative predictive factors. Nomogram models have been used in prostate cancer to predict survival and recurrence probability.^20-22 Using our model, 1- and 2-year survival of NSCLC chemotherapy-naive patients undergoing first-line chemotherapy can be easily estimated. The use of this nomogram is limited to patients who are chemotherapy-naive, have good PS, and receive standard third-generation doublets.

In summary, we have identified six negative survival prognostic factors in advanced NSCLC patients undergoing first-line standard chemotherapy: subcutaneous metastasis, decreased PS (ECOG 1 or 2), loss of appetite, liver metastasis, four metastatic sites, and no previous lung surgery. Based on a large patient database, we have built a unique nomogram using pretreatment clinical factors to predict survival probability of chemotherapy-naive patients treated with third-generation platinum-based doublets. This prognostic model could help clinicians and patients in clinical decision making and treatment tailoring based on the estimated prognosis. In addition, the nomogram and survival markers could be applied in clinical research to stratify patients, avoid biases, as well as plan appropriate studies targeting different subgroups of patients. Using our model instead of historic control data when analyzing survival result of phase II studies, promising first-line regimens could be selected for phase III trials. Although the nomogram was already validated with internal data as described previously, we plan to further validate it using data from other studies.

	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
The following authors or their immediate family members have indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. Research funding: David H. Johnson, Bristol-Myers Squibb. For a detailed description of this category, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration form and the "Disclosures of Potential Conflicts of Interest" section of Information for Contributors found in the front of every issue.

	Acknowledgment

 
We thank the investigators and staff of the Eastern Cooperative Oncology Group for their participation in the trials listed in this study.

	NOTES

 
Supported in part by grant K12-CA 87718 (T.H.).

Presented at the 39th American Society of Clinical Oncology Annual Meeting, Chicago, IL, May 31-June 3, 2003.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... REFERENCES

 
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Submitted April 30, 2004; accepted October 6, 2004.

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