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Evaluation of perirenal fat as a predictor of cT(1a) renal cortical neoplasm histopathology and surgical outcomes, "Beyond the Abstract," by Zhamshid Okhunov, MD and Jaime Landman, MD

BERKELEY, CA (UroToday.com) - Due to increased use of cross-sectional imaging over the last two decades, the annual incidence of renal cell carcinoma has been rising, with the greatest increase observed in number of small (cT1a) renal masses (SRM).[1]

The current treatment guidelines for solid renal masses support their expedient removal upon detection.[2] Subsequently, wide adoption of minimally invasive surgical modalities led to an increase in the rate of extirpative surgery performed for SRM and has paralleled the rising incidence of renal cell carcinoma.[3]

Further evaluation of perirenal fat on the molecular level has far reaching potential in understanding tumor behavior.

It has been reported that nearly 20% of solid renal tumors less than 4cm are benign, and over 80% of these small tumors that are identified to be renal cell carcinomas (RCC) are low grade.[4] Additionally, active surveillance data clearly demonstrated that the vast majority of SRM grow slowly and have very low metastatic potential.[5,6] The relatively indolent nature of the majority of SRM supports the argument for the use of minimally invasive treatment modalities. The ability to match renal mass biology with an appropriate treatment strategy should be the ultimate goal of contemporary urologic oncology. The differential management of different RCC histopathologic subtypes is critical as these represent very different tumors with recognized differences in survival outcomes.[7,8] While tumor size remains an independent prognostic feature in patients with RCC treated surgically, there are limitations to tumor size alone to predict pathology and hence biology.

Currently the histopathologic diagnosis of SRM is based exclusively on preoperative biopsies, and this technique remains the most accurate predictive tool of tumor histology. However, percutaneous biopsy is an invasive method to predict the diagnosis, and to date it has been very sparingly applied by the majority of urologists. This prompted us to look for non-invasive and easily obtained preoperative metrics to predict SRM histopathology.

In this retrospective study we evaluated whether the amount of perirenal fat (PF), as measured on preoperative imaging, is a predictor for histopathologic and surgical outcomes in the management of small SRM. We clinically observed that patients with a larger amount of PF tended to have a worse prognosis. We then hypothesized that a greater volume of PF may alter or engender local pathologic pathways, which influence renal oncogenesis. In support to our hypothesis, previously several case-control and prospective observational studies have shown obesity as a risk factor for RCC. Studies have demonstrated the critical influence of adipose tissue-derived factors in cancer cells, including gastrointestinal and prostate tumor cells.[9,10,11] Collectively, these studies indicate that factors produced by adipose tissue, particularly adipocytes, may stimulate the progression of cancer cells.

Our results demonstrated that increased PF is significantly predictive of clear cell RCC (p<0.01). Separating clear cell carcinoma from other subtypes of kidney cancer in this evaluation was a critical step. Clear cell carcinoma has a well-defined oncogenic pathway that is very different from other RCC subtypes. Similarly, clear cell RCC has very different survival outcomes from other RCC subtypes. As such, the ability to recognize clear cell RCC from benign disease and other less aggressive RCC subtypes (eg. papillary type I, chromophobe, etc.) is an important distinction which is not universally recognized by urologists. Indeed, even in the initial editorial reviews of the manuscript, reviewers did not clearly understand the critical value of the ability of PF distance to distinguish clear cell RCC from other forms of RCC.

In the review of our patients, we found that a patient is 1.1 times more likely to have clear cell RCC with each mm increase in PF (95% CI: 1.02 to 1.11). As previously stated, identifying clear cell RCC is of great value in patient management. It was not surprising to us that PF distance did not correlate with other RCC subtypes such as papillary type I and chromophobe. Again, each RCC subtype has a specific and defined molecular mechanism and behaves as a different disease. We speculate that the possible autocrine factors related to PF may only influence some oncogenic pathways. Indeed, the relationship between PF and clear cell RCC histopathology may be particularly useful as clear cell RCC has been shown to be associated with poor survival rates compared to most other RCC subtypes.[12,13] Non-invasive metrics such as PF may ultimately be superior to the relatively invasive practice of pre-operative biopsy for predicting which RCN will require intervention, and may decrease potentially unnecessary procedures for benign RCN. PF measurement is simple, fast, and measured on already available preoperative imaging, which makes it easily applicable during routine busy office hours. PF is measured using the digital measuring tool function on standard imaging software, at the level of the renal hilum as the perpendicular distance between the posterior surface of the kidney and the external margin of the psoas muscle. Additionally, as CT and MRI are part of standard treatment, PF does not add any cost to the management algorithm.

After the initial observation that PF has value in predicting clear cell RCC, the next steps become very clear. Currently, at the University of California Irvine, we are working with our urology basic science team led by Dr. Xioalin Zi to evaluate fat specimens from patients undergoing renal surgery for benign and malignant disease. Collected fat is being tested for effects on RCC cell lines and we hope to identify factors which may influence the development or behavior of RCC. Further evaluation of PF on the molecular level has far reaching potential in understanding tumor behavior. Future evaluation of mechanisms underlying our findings may allow for the correlation with clinical outcomes, enabling not only a prognostic tool but also furthering progress toward individualized treatment strategies.

References:

  1. Cho E, Adami HO and Lindblad P: Epidemiology of renal cell cancer. Hematol Oncol Clin North Am. 25: 651-65, 2011.
  2. AUA clinical guidelines: guideline for management of the clinical stage 1 renal mass. J Urol.: 1-76, 2009.
  3. Hollenbeck BK, Taub DA, Miller DC, Dunn RL and Wei JT: National utilization trends of partial nephrectomy for renal cell carcinoma: a case of underutilization? Urology. 67: 254-9, 2006.
  4. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL and Zincke H: Solid renal tumors: an analysis of pathological features related to tumor size. J Urol. 170: 2217-20, 2003.
  5. Rosales JC, Haramis G, Moreno J, Badani K, Benson MC, McKiernan J, Casazza C and Landman J: Active surveillance for renal cortical neoplasms. J Urol. 183: 1698-702, 2010.
  6. Haramis G, Mues AC, Rosales JC, Okhunov Z, Lanzac AP, Badani K, Gupta M, Benson MC, McKiernan J and Landman J: Natural history of renal cortical neoplasms during active surveillance with follow-up longer than 5 years. Urology. 77: 787-91, 2011.
  7. Teloken PE, Thompson RH, Tickoo SK, Cronin A, Savage C, Reuter VE and Russo P: Prognostic impact of histological subtype on surgically treated localized renal cell carcinoma. J Urol. 182: 2132-6, 2009.
  8. Leibovich BC, Lohse CM, Crispen PL, Boorjian SA, Thompson RH, Blute ML and Cheville JC: Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. J Urol. 183: 1309-15, 2010.
  9. Tokuda Y, Satoh Y, Fujiyama C, Toda S, Sugihara H and Masaki Z: Prostate cancer cell growth is modulated by adipocyte-cancer cell interaction. BJU Int. 91: 716-20, 2003.
  10. Ribeiro RJ, Monteiro CP, Cunha VF, Azevedo AS, Oliveira MJ, Monteiro R, Fraga AM, Principe P, Lobato C, Lobo F et al.: Tumor cell-educated periprostatic adipose tissue acquires an aggressive cancer-promoting secretory profile. Cell Physiol Biochem. 29: 233-40, 2012.
  11. Erarslan E, Turkay C, Koktener A, Koca C, Uz B and Bavbek N: Association of visceral fat accumulation and adiponectin levels with colorectal neoplasia. Dig Dis Sci. 54: 862-8, 2009.
  12. Amin MB, Tamboli P, Javidan J, Stricker H, de-Peralta Venturina M, Deshpande A and Menon M: Prognostic impact of histologic subtyping of adult renal epithelial neoplasms: an experience of 405 cases. Am J Surg Pathol. 26: 281-91, 2002.
  13. Ficarra V, Martignoni G, Galfano A, Novara G, Gobbo S, Brunelli M, Pea M, Zattoni F and Artibani W: Prognostic role of the histologic subtypes of renal cell carcinoma after slide revision. Eur Urol. 50: 786-93; discussion 793-4, 2006.

 

 

 

Written by:
Zhamshid Okhunov, MD and Jaime Landman, MD as part of Beyond the Abstract on UroToday.com. This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations etc... of their research by referencing the published abstract.

Department of Urology
University of California-Irvine
Orange, CA

 

 

Evaluation of perirenal fat as a predictor of cT(1a) renal cortical neoplasm histopathology and surgical outcomes - Abstract

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