Understanding the Kinetics of Cancer: Implications from Prevention to Prognostication

OncologyONCOLOGY Vol 25 No 1
Volume 25
Issue 1

The choices that patients and clinicians make when dealing with cancer are dictated by time, whether they are arranging for screening mammography and colonoscopy, compiling treatment plans, or determining follow-up intervals and the age of freedom from follow-up.

The choices that patients and clinicians make when dealing with cancer are dictated by time, whether they are arranging for screening mammography and colonoscopy, compiling treatment plans, or determining follow-up intervals and the age of freedom from follow-up. While a patient who has received a diagnosis of metastatic cancer may have a rather limited amount of remaining time and may require prognostication in the order of weeks, patient with early cancers may require reassurance that waiting a few weeks for their surgery is not going to cause them undo harm. While to some it may appear that the clinician is justifying a scheduling delay, the reality is that “the tumor has been there for years, after all.”

Patrone et al present an interesting analysis of retrospective and prospective studies examining local recurrence rates after primary surgical management of breast, lung, and rectal cancers. By using an extrapolation of Collins’ law, the authors correlate the time to local recurrence with the age of the index lesion, suggesting that breast cancers typically have an age of 5 to 6 years at diagnosis, and that lung and rectal cancers have an age of 3 to 4 years. Collins’ law was initially applied to pediatric Wilms tumors as a method for determining the period of risk for disease recurrence on the basis of the child’s age at diagnosis plus 9 months, the assumed maximum age of the tumor.[1] This method has been validated in other pediatric cancers, including medulloblastoma, ependymoma, and neuroblastoma, as a reliable way to estimate the age after which a child may be considered to be free from risk of further cancer recurrence.[2-4]

Using Collins’ law requires the assumption that tumor growth rates are constant over time, which allows equivalence between the rate of growth of the index lesion and that of the recurrence. Such a view may be considered overly simplistic, since Gompertzian growth dynamics, in which growth rates are faster with smaller tumors and slower with larger tumors, has largely supplanted the former concept of exponential tumor growth with a linear rate constant.[5,6] This inverse relationship between tumor growth rates and tumor size is not accounted for by Collins’ law, and the applicability of this theory may be limited to certain tumors at an early stage of growth dynamics, as has been manifest in its utility in pediatric tumors with potential embryonic origin. Furthermore, the application of Collins’ law does not account for tumor dormancy, a phenomenon of cellular quiescence that may explain drug resistance and some late recurrences.[7,8] Despite this, the generalizations made by the authors provide us with a crude but valuable estimate of tumor age based on tumor primary, and more importantly, they give us insight into the concept of tumor kinetics.

Clinical trials with extended follow-up have already shown evidence that for breast cancer, recurrence risk over time can be predicted by factors such as estrogen receptor status and nodal positivity.[9] Focused exploration in different tumor types might further elucidate the influence of specific biological parameters or molecular signatures that may dictate aggressiveness, and perhaps time to recurrence. Ideally, follow-up recommendations for cancer could then be objectively tailored to these predictors of rapid tumor kinetics. In addition, if tumor kinetics were found to be dependent on age, gender, ethnicity, or genotype, cancer screening recommendations could be adjusted to better fit those populations at risk-an approach that would be both cost-effective and practical.

The idea of judging tumor biology by the time to local recurrence is an important one. Time has already become one of the most important tests for metastatic burden. The modern surgical oncologist, when faced with decisions of whether to resect colorectal metastases to the liver, often must judge the level of disease that is beneath the surface and gain an understanding of what is below the threshold of detection by imaging studies. The correct decision in these complex clinical scenarios typically becomes clear with time, which not only provides an opportunity to assess response to neoadjuvant therapies, but also allows the disease process to unfold; an unnecessary operation is thus avoided. However, the clinician and patient may be better served, rather than by waiting, by understanding the tumor growth rate and other parameters dictated by their primary disease, then solving the equation for the unknown variable that can elucidate the degree of the metastatic burden; such an approach will help them gain a better sense of when the disease will actually progress.

Mathematical modeling of cancer growth dynamics is a field that warrants further exploration, especially in diseases such as breast cancer, which may have clinical courses much greater than 10 years.[8] With the emergence of computational biology, investigators have modeled tumors at a microscopic level, including parameters such as cell adhesion, proliferation, angiogenesis, apoptosis, necrosis, hypoxia, and nutrient gradients, and the influence of these parameters on growth and invasion.[10] Such interactions are certainly complex, but they only underscore the vast network of systems and circuits that influence cancer progression. At the macroscopic level at which clinicians and patients operate, simplicity counts in a world of increasing complexity. As algorithm-based decision-making enters clinical practice with nomograms, online risk calculators, and genetic profiling for predictors of recurrence, one can only hope that we can answer our patients’ questions with greater definition, and treat them with greater accuracy, all in a time-efficient manner. In the future, when a patient asks, “How long have I had my cancer?” hopefully we can answer with certainty, “Much longer than the average. That’s good. It looks like you have a very favorable tumor, which has been completely excised, and you don’t need any other therapies. I recommend you follow up with me every 3 years for now. There is a slight risk of recurrence up to about 15 years out, but from my perspective, you’re effectively cured.”

Financial Disclosure: The authors have no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.



1. Collins VP, Loeffler RK, Tivey H. Observations on growth rates of human tumors. Am J Roentgenol Radium Ther Nucl Med. 1956;76:988-1000.

2. Brown WD, Tavare CJ, Sobel EL, Gilles FH. The applicability of Collins' law to childhood brain tumors and its usefulness as a predictor of survival. Neurosurg.1995;36:1093-6.

3. Sure U, Berghorn WK, Bertalanffy H. Collins' law: prediction of recurrence or cure in childhood medulloblastoma? Clinical Neurol Neurosurg. 1997;99:113-6.

4. Paulino AC. Collins' law revisited: can we reliably predict the time to recurrence in common pediatric tumors? Pediatr Hematol Oncol. 2006;23:81-6.

5. Norton L. A Gompertzian model of human breast cancer growth. Cancer Res. 1988;48:7067-71.

6. Schmidt C. The Gompertzian view: Norton honored for role in establishing cancer treatment approach. J Natl Cancer Inst. 2004;96:1492-3.

7. Aguirre-Ghiso JA. Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer. 2007;7:834-46.

8. Retsky M. New concepts in breast cancer emerge from analyzing clinical data using numerical algorithms. Int J Environ Res Public Health. 2009;6:329-48.

9. Saphner T, Tormey DC, Gray R. Annual hazard rates of recurrence for breast cancer after primary therapy. J Clin Oncol.1996;14:2738-46.

10. Bearer EL, Lowengrub JS, Frieboes HB, et al. Multiparameter computational modeling of tumor invasion. Cancer Res. 2009;69:4493-501.

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