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Predicting Outcomes in Metastatic Melanoma

Departments of Surgery, Oncology, and Dermatology, Johns Hopkins Medical Institutions, Baltimore, MD; Melanoma Staging Committee, American Joint Committee on Cancer, Chicago, IL

Seng-jaw Soong

Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, University of Alabama at Birmingham, AL; Biostatistics Committee, American Joint Committee on Cancer, Chicago, IL

Our 1983 study, published in the first volume of the Journal of Clinical Oncology (JCO), was the fourth in a series of multifactorial analyses of melanoma patients treated at the University of Alabama at Birmingham^1-4 (Birmingham, AL) and was the first paper published on melanoma in JCO. These were the first studies using the Cox multifactorial analysis for analyzing prognostic factors in melanoma, including this analysis of distant metastases (designated as stage III in the 1980s and stage IV today). These publications were not simply the result of adopting a new statistical methodology and providing statistical services to clinical investigators. A major component of our success can be ascribed to the synergistic research collaboration that resulted from blending together our different training and perspectives. Thus, we spent considerable time teaching each other about melanoma and about iterative statistical approaches to address a series of hypotheses that were mutually agreed on.

Although the number of patients with distant metastases was relatively small in this 1983 publication, they were a representative population from a single institution. Remarkably, the independent prognostic factors we identified for stage IV melanoma almost 25 years ago remain valid today. We attribute this to the quality of the data, the use of sophisticated statistical methodology, and the meaningful collaborations between clinical oncologists and biostatisticians. In addition, these survival results probably reflect the true natural history of distant metastatic melanoma without the influence of systemic therapy on survival rates, given that there were few systemic treatment options during the 1970s when these patient results were accrued. Thus, the number and site of distant metastases and the remission duration still are the primary independent factors for stage IV melanoma; these results have been validated by subsequent published studies.

The introduction of the Cox proportional hazards regression model represents the most important methodologic development for multivariate analysis of survival data during the last three decades.⁵ In melanoma research, a large number of clinical factors (eg, age, sex, lesion site, performance status) and pathologic factors of the primary tumor (eg, tumor thickness, tumor ulceration, mitotic rate, level of invasion, growth pattern) and the metastatic tumor (eg, number, site or sites, size). Although these factors related to disease recurrence and patient survival were all studied extensively before the 1980s, the results were variable depending on such factors as sample size, case mix, and statistical methods. With an application of the Cox regression model, almost all major multivariate prognostic factor studies have identified a remarkably consistent set of independent prognostic factors for melanoma patients treated worldwide.⁶ In addition, several useful predictive models based on the Cox model for predicting individual patient survival and disease recurrence in melanoma were also developed from several large melanoma databases.^6-10 These advances, in turn, have facilitated a fundamental revamping in the staging of melanoma and the criteria for interpreting results of prospective clinical trials in melanoma using the dominant prognostic factors identified by Cox regression analyses.

Since the year 2000, we have collaborated with melanoma clinical investigators worldwide to create a unique melanoma staging and prognosis database under the auspices of the American Joint Committee on Cancer and the International Union Against Cancer. The first version of this Melanoma Staging Database incorporated the clinical and pathologic results of more than 17,600 prospectively observed melanoma patients treated on three continents. The results using the Cox multifactorial methodology led to a major revision of the melanoma staging criteria and stage grouping. These results were first published in JCO in 2001.^11,12

Since 2007, an updated American Joint Committee on Cancer Melanoma Staging Database has been created that contains pathology and treatment outcome data on more than 50,000 prospectively observed melanoma patients treated in the United States, Australia, and Europe. Results of the data analysis are still preliminary. The prognostic factors described above are still the dominant clinical and pathologic features of melanoma for stage I, II, and III melanoma, with the exception that tumor mitotic rate and patient age are highly predictive and independent predictors of outcome. The stage IV data analysis is still pending. A mathematical predictive model has been developed and validated that will provide an electronic predictive tool that enables the integration of multiple and continuous variables to predict in an individual patient the risk of regional and distant metastases, and the actuarial melanoma-specific survival rates at 5 and 10 years.

Although the Cox model has demonstrated its flexibility and usefulness in modeling survival data in melanoma, it also has inherent limitations because of its proportional hazards assumption and its inability to generate a hazard function. We are now using a new parametric statistical modeling that can integrate all independent predictive factors and thereby calculate an individual patient's predicted outcome at the onset of his or her disease stage and at any time point thereafter.^13,14 We have demonstrated that the parametric model can serve as a powerful alternative or complementary model to the Cox model in predicting melanoma survival rates with estimated hazard function (Ding et al, submitted for publication). Parametric models offer several advantages over the widely used Cox model because the proportional hazards assumption is not required and the hazard function can be estimated. The individualized hazard function estimate based on a patient's clinical and pathologic characteristics provides a clinically useful risk (of dying) profile over time to aid clinicians in making decisions regarding patient follow-up, as an evaluation tool for treating melanoma, and as a methodologic tool in clinical trials. Thus, the individualized hazard functions are important clinically to be able to closely monitor the patient's risk of dying from the melanoma over a long period of time based on this patient's presenting clinical and pathologic characteristics. On the basis of the pattern of hazard function over time, a patient-specific clinical treatment plan could be implemented to calibrate the intensity of follow-up with the declining risk of dying over time.

A critical component of managing oncology patients is understanding the natural history of their cancer and identifying those features of the tumor and the patient that predict the metastatic process and survival outcome. Nowhere is this more important than in understanding the remarkable heterogeneity of metastatic melanoma. For the clinical oncologist treating a melanoma patient, this information is vital to determine: (1) the stage of disease, (2) the intensity of the metastatic evaluation, (3) the predicted incidence of metastatic disease, (4) the treatment plan that calibrates the combination and sequence of modalities with the biologic aggressiveness of the disease, and (5) predicted survival rates after treatment when counseling the patient during follow-up evaluations. Our criteria for staging melanoma also depend on knowing the most relevant predictive or prognostic factors of survival outcome. For the clinical investigator, knowing the most independent and dominant prognostic factors is essential in both the design of the clinical trial and the interpretation of the results. Only with this knowledge can one reliably distinguish between the impact of the treatment under study versus the survival outcome based on the natural history of the metastatic process that varies among patients. Otherwise, treatment differences, or lack thereof, may be influenced more by the mix of the patient's prognostic factors than by the treatment effect being studied.

Finally, the future of predicting outcomes and staging melanoma patients will be enhanced in the near future with the more widespread availability of electronic tools that can predict an individual patient's risk of metastases and his or her survival. This will overcome the limitation of current staging tools that cannot accommodate multiple and continuous variables and cannot predict survival rates after periods of disease-free survival.¹⁴ Working with the American Joint Committee on Cancer, we plan to have an electronic staging and metastatic predictive tool available in 2008 that will enable the clinician and clinical investigator to input individual data about the melanoma patient, and will be able to reliably predict the risk of regional and distant metastases, as well as the actuarial melanoma-specific survival rates, both at the time of diagnosis (or disease progression) and also at any time point in patient follow-up through the years.

A key element of our melanoma clinical research is the close collaboration as clinical investigators from different disciplines that has spanned three decades. Working together, with the use of sophisticated statistical methods such as the Cox model and appropriate parametric models, has enabled us to understand better a complex disease process and helped to improve the care of our patients.

Authors' Disclosures of Potential Conflicts of Interest

The author(s) indicated no potential conflicts of interest.

Author Contributions

Conception and design: Charles M. Balch, Seng-jaw Soong

Provision of study materials or patients: Charles M. Balch

Collection and assembly of data: Seng-jaw Soong

Data analysis and interpretation: Charles M. Balch, Seng-jaw Soong

Manuscript writing: Charles M. Balch, Seng-jaw Soong

Final approval of manuscript: Charles M. Balch, Seng-jaw Soong

ACKNOWLEDGMENTS

Supported in part by a grant from the National Cancer Institute (Grant No. P30 CA13148), National Institutes of Health.

REFERENCES

1. Balch CM, Murad TM, Soong SJ, et al: A multifactorial analysis of melanoma: Prognostic histopathological features comparing Clark's and Breslow's staging methods. Ann Surg 188:732-742, 1978[Medline]

2. Balch CM, Soong SJ, Murad TM, et al: A multifactorial analysis of melanoma: II. Prognostic factors in patients with stage I (localized) melanoma. Surgery 86:343-351, 1979[Medline]

3. Balch CM, Soong SJ, Murad TM, et al: A multifactorial analysis of melanoma: III. Prognostic factors in melanoma patients with lymph node metastases (stage II). Ann Surg 193:377-388, 1981[Medline]

4. Balch CM, Soong SJ, Murad TM, et al: A multifactorial analysis of melanoma: IV. Prognostic factors in 200 melanoma patients with distant metastases (stage III). J Clin Oncol 1:126-134, 1983[Abstract]

5. Cox DR: Regression models and life-tables (with discussion). J R Stat Soc B 34:187-220, 1972

6. Balch CM, Milton GW, Shaw HM, et al: Cutaneous Melanoma: Clinical Management of Treatment Results Worldwide. Philadelphia, PA, JB Lippincott Co, 1985

7. Balch CM, Soong SJ, Milton GW, et al: A comparison of prognostic factors and surgical results in 1,786 patients with localized (stage I) melanoma treated in Alabama, USA, and New South Wales, Australia. Ann Surg 196:677-684, 1982[Medline]

8. Soong SJ, Shaw HM, Balch CM, et al: Predicting survival and recurrence in localized melanoma: A multivariate approach. World J Surg 16:191-195, 1992[CrossRef][Medline]

9. Soong SJ, Zhang Y, Desmond RA: Models for predicting outcome of melanoma, in Balch CM, Houghton AN, Sober AJ, et al (eds): Cutaneous Melanoma. St Louis, MO, Quality Medical Publishing Inc, 2003, pp 77-90

10. Balch CM, Soong SJ, Ross MI, et al: Long-term results of a multi-institutional randomized trial comparing prognostic factors and surgical results for intermediate thickness melanomas (1.0 to 4.0 mm): Intergroup Melanoma Surgical Trial. Ann Surg Oncol 7:87-97, 2000[Abstract]

11. Balch CM, Buzaid AC, Soong SJ, et al: Final version of the American Joint Committee on Cancer staging sys tem for cutaneous melanoma. J Clin Oncol 19:3635-3648, 2001[Abstract/Free Full Text]

12. Balch CM, Soong SJ, Gershenwald JE, et al: Prognostic factors analysis of 17,600 melanoma patients: Validation of the American Joint Committee on Cancer melanoma staging system. J Clin Oncol 19:3622-3634, 2001[Abstract/Free Full Text]

13. Cox C, Chu H, Schneider MF, Munoz A: Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution. Stat Med 2007 26:4352-4354, 2007

14. Wong SL, Kattan MW, McMasters KM, et al: A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system. Ann Surg Oncol 12:282-288, 2005[Abstract/Free Full Text]

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