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[Cancer Research 63, 8555-8557, December 1, 2003]
© 2003 American Association for Cancer Research

Letters to the Editor

Correspondence re: S. Maula et al., Intratumoral Lymphatics Are Essential for the Metastatic Spread and Prognosis in Squamous Cell Carcinoma of the Head and Neck. Cancer Res., 63: 1920–1926, 2003.

Timothy P. Padera, Yves Boucher and Rakesh K. Jain

Edwin L. Steele Laboratory, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA

Letter

We read with interest the article by Maula et al. (1) , showing that CD44 expression of the primary tumor is an independent prognostic parameter for decreased disease-free survival in patients with HNSCC.1 However, the authors also suggest, as the title states, that intratumor lymphatic vessels identified by LYVE-1 staining are essential for the metastatic spread of HNSCC. The data presented do not support this statement and, in fact, contradict it. From the total population of 97 patients studied, 38 had nodal disease. Of the 71 HNSCC tumor samples stained, only 9 had intratumor LYVE-1-positive structures. Thus, a significant portion of patients had lymph node metastasis without intratumor LYVE-1 staining, proving in fact that intratumor lymphatic vessels are not essential for metastatic spread to lymph nodes. Data from spontaneous pancreatic islet cell tumors in mice (2) and human lung tumors in patients (3) also show lymph node metastases in the absence of intratumor LYVE-1 staining. Furthermore, multivariate analysis of case-matched melanoma patients revealed that only peritumor lymphatic area was independently predictive for the time to lymph node metastasis, even though intratumor LYVE-1 staining was found in some tumors (4) .

Although the results of Maula et al. (1) suggest that patients with intratumor lymphatics have a worse prognosis (Fig. 4B in Ref. 1 ), the role of lymphatic endothelial markers in predicting outcomes in human cancer is far from clear (Table 1)Citation . This may be attributable to the lack of specificity of the lymphatic markers (5 , 6) , the role of the immune response mediated by tumor-associated lymphatic vessels (1 , 7) , the impaired function of intratumor lymphatic vessels (3 , 8) , and differences in location (tumor margin versus intratumor) of stained lymphatics (1 , 3 , 9) . While we await answers to these critical questions, titles like "Intratumoral Lymphatics are Essential for the Metastatic Spread... " can only be misleading.


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  		Table 1 Association between putative lymphatic markers and clinical end points (updated from Ref. 9 )

 

FOOTNOTES

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 The abbreviations used are: HNSCC, squamous cell carcinoma of the head and neck; LYVE-1, lymphatic vessel endothelial hyaluronan receptor 1. Back

Received 5/ 2/03. Revised 7/18/03. Accepted 8/ 6/03.

REFERENCES

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Response

Lawrence M. Wein

Paul E. Holden, Professor of Management Science

Graduate School of Business, Stanford University, Stanford, CA

David H. Kirn, M.D. President

Jennerex Pharmaceuticals and Visiting Professor of Human Gene Ther., Oxford University Medical School, Mill Valley, CA

Reply

The letter by Wodarz claims that the main difference between our model and his is not the explicit treatment of space but rather the difference in the underlying viral dynamics. According to Wodarz, our model unrealistically assumes that the number of virus particles released by an infected cell is independent of the infected cell’s lifetime, whereas his model more plausibly assumes that the number of virus particles released is linear in the infected cell’s lifetime (i.e., there is a constant rate of accumulation of virus in the infected cell). He then goes on to discuss the unrealistic ramifications of our assumption (e.g., an infinitely short life span of the infected cells is most beneficial for therapy, the basic reproductive ratio of the virus does not depend on the life span of infected cells) and the possible clinical implications of his assumption (an optimal intermediate death rate of virus-infected cells).

In Refs. 1 and 2 , Wodarz treats the infected cell lifetime as a controllable variable to be optimized, under the assumption that there is a constant rate of virus accumulation in infected cells. In contrast, in Refs. 3 4 5 , we do not attempt to optimize any variables and hence have no need to impose any specific relationship between the number of virus particles released by an infected cell and the infected cell’s lifetime. Wodarz’s optimization orientation causes him to project onto our model an optimization framework that is not present in our article and, consequently, gives him the mistaken impression that we assume independence of the release size and the infected cell lifetime.

Our work (3 4 5) follows the traditional approach to modeling the viral dynamic process (see Ref. 6 ) for an oft-cited example in HIV, although there are countless others), which assumes that the mean infected cell life span is 1/{Delta} and that each infected cell produces N virus particles during its life span [this standard notation is used in (3 4 5 6) ]; in this traditional approach, the parameter {Delta} is not viewed as a parameter to be separately optimized. Wodarz also assumes that the mean infected cell lifetime is 1/{Delta} (in our notation) but then assumes that infected cells produce virus at rate k, which implies that an infected cell produces k/{Delta} virus particles in its lifetime. For fixed values of k and {Delta} (i.e., when no attempt is made to optimize {Delta}), these viral dynamic processes are identical by defining k/{Delta} = N, and in Ref. 3 , we even state that "N{Delta} is the release rate of free virus particles/unit time/infected cell." Indeed, when one ignores the spatial complexities of our approach and the various second-order aspects of our model and Wodarz’s model, the two models are very similar: the model on page 154 of Ref. 2 is very similar to the one in section 3.2 of Ref. 4 in the absence of an immune response, and the model on page 3501 of Ref. 1 is very similar to equations 11–13 in Ref. 5 in the presence of an immune response. Hence, it is not surprising that many of the basic results of Refs. 1 and 2 agree with our findings (3 4 5) in the uniform injection case. We disagree with Wodarz about the lack of importance of the spatial aspects of our model: the spatial model allowed us to compare core injection and ring injection to uniform injection, which revealed the importance of administering these agents in a spatially aggressive manner.

However, Wodarz raises an interesting issue about the relationship between the burst size and the infected cell lifetime. We agree with Wodarz that this relationship is likely to be complex and merits additional experimental investigation. It is the complexity and current uncertainty of this relationship that prevented us from exploring specific relationships such as the linear relationship considered by Wodarz. In fact, it is now clear that virus production over time in an infected cell is not linear but rather starts with an eclipse phase (no virus present) followed by a sigmoidal curve (example in Ref. 7 ). In addition, total virus production can be separated from infected cell lifetime. For example, adenoviruses have been described that kill faster but have an equivalent or even higher total viral burst than the wild-type adenovirus control (e.g., E1B-19kD and E1A-conserved region 2 mutants; Refs. 7 , 8 ). Finally, virus release can be separated from total virus production (i.e., much of the virus produced is not released as free virus). For example, we have demonstrated that E1B-19kD gene deletion mutants kill tumor cells faster than wild-type Ad2 but actually have a significantly increased titer of virus released into the extracellular milieu (by 100-1000-fold).1 These adenoviruses motivated the example in Fig. 5 of Ref. 3 , which considers different values of {Delta} without changing N and perhaps led Wodarz to misinterpret our viral dynamic assumptions. Other virus species can also kill cells significantly faster but still maintain high viral bursts. Therefore, Wodarz’s linearity assumption is not valid in many circumstances.

In our view, each replication-competent agent possesses an unknown vector of parameter values related not only to its burst size and infected cell lifetime but also to its infectivity, viral clearance, and the rate at which the immune system is stimulated; the analyses by us and by Wodarz provide insights into how these various factors impact treatment efficacy. The key challenge remains to estimate some of these parameters in vitro and in vivo, with the hope that simple in vitro experiments can identify promising treatments. Our Cancer Research article makes a first stab at estimating some of these parameters from in vivo human data, and we (page 1321 of Ref. 3 ) and Wodarz (pages 157–158 of Ref. 2 ) both briefly discuss this important issue but clearly much more work along these lines needs to be done.

FOOTNOTES

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 T. Lin, G. Hallden, Y. Wang, G. Brooks, and D. Kirn. E1B-19kD deletion results in enhanced potency and selectivity of oncolytic adenovirus, manuscript submitted. Back

Received 7/ 9/03. Revised 8/19/03. Accepted 9/12/03.

REFERENCES

  1. Wodarz D. Viruses as antitumor weapons: defining conditions for tumor remission. Cancer Res., 61: 3501-3507, 2001.[Abstract/Free Full Text]
  2. Wodarz D. Gene therapy for killing p53-negative cancer cells: use of replication versus nonreplicating agents. Hum. Gene Ther., 14: 153-159, 2003.[Medline]
  3. Wein L. M., Wu J. T., Kirn D. H. Validation and analysis of a mathematical model of a replication-competent oncolytic virus for cancer treatment: implications for virus design and delivery. Cancer Res., 63: 1317-1324, 2003.[Abstract/Free Full Text]
  4. Wu J. T., Byrne H. M., Kirn D. H., Wein L. M. Modeling and analysis of a virus that replicates selectively in tumor cells. Bull. Math. Biol., 63: 731-768, 2001.[Medline]
  5. Wu, J. T., Kirn, D. H., and Wein, L. M. Analysis of a three-way race between tumor growth, a replication-competent virus and an immune response. Conditionally accepted by Bull. Math. Biol., in press, 2003.
  6. Perelson A., Neumann A. U., Markowitz M., Leonard J. M., Ho D. D. HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science (Wash. DC), 271: 1582-1586, 1996.[Abstract]
  7. Heise C., Hermiston T., Johnson L., Brooks G., Sampson-Johannes A., Williams A., Hawkins L., Kirn D. An adenovirus E1A mutant that demonstrates potent and selective antitumoral efficacy. Nat. Med., 6: 1134-1139, 2000.[Medline]
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