The post Kidney Cancer Research Highlights from ASCO 2022 Annual Meeting first appeared on GUcancers.
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Updated
Yasser Ged,1 and Nirmish Singla2,*
1) Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
2) Department of Urology, The James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
ABSTRACT
The 2022 American Society of Clinical Oncology (ASCO) annual meeting was held June 3-7, 2022, in Chicago, Illinois. This hybrid meeting gathered international cancer experts across multidisciplinary specialties and was held both virtually and in-person. Here, we highlight key kidney cancer research updates presented at the meeting. Slides from the meeting’s presentations are available on the ASCO meeting library website.
INTRODUCTION
Adjuvant Therapy Updates Locally advanced kidney cancer has traditionally been managed surgically alone1. However, approximately 30% of patients develop recurrent metastatic disease after surgical resection despite curative intent, and the optimal approaches to integrate surgery with systemic therapies in a neoadjuvant or adjuvant approach to reduce the risk of recurrence has been an area of active research.2 The U.S. Food and Drug Administration (FDA) has approved two adjuvant therapies in renal cell carcinoma (RCC) thus far, including sunitinib in 2017 and most recently pembrolizumab in 2021.3,4 The use of adjuvant sunitinib has been limited despite FDA approval because of its increased toxicity and lack of overall survival benefit.5 Pembrolizumab is the first approved adjuvant immunotherapy for clear cell RCC patients with intermediate-high or high risk of recurrence after nephrectomy based on the phase 3 doubleblind, multicenter, randomized KEYNOTE-564 study (NCT03142334).4
Updated analysis from KEYNOTE-564 was presented at the meeting evaluating the time to first subsequent drug treatment or any-cause death (TFST) and time from randomization to progression on next line of therapy or any-cause death (PFS2) after treatment with pembrolizumab or placebo in the study.6 Overall 67 patients (13.5%) in the pembrolizumab group and 99 patients (19.9%) in the placebo group received ≥1 line of subsequent anticancer drug therapy. A total of 108 PFS2 events were observed, 40 (8.1%; 12 death events and 28 progression events) in the pembrolizumab group and 68 (13.7%; 14 death events and 54 progression events) in the placebo group. PFS2 was also delayed with pembrolizumab compared with placebo (HR, 0.57; 95% CI, 0.39-0.85; medians not reached). The authors concluded that treatment with adjuvant pembrolizumab reduced risk for TFST and PFS2 compared with placebo. LITESPARK-022 (NCT05239728) is the next iteration of the KEYNOTE-564 study which is a phase 3 study designed to compare the efficacy and safety of belzutifan plus pembrolizumab with that of placebo plus pembrolizumab as adjuvant treatment for clear cell RCC after nephrectomy, and this study is currently actively enrolling.
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Multiple adjuvant and neoadjuvant vascular endothelial growth factor tyrosine kinase inhibitors (VEGF-TKIs) studies in RCC were reported previously.5 To better understand the role of mammalian target of rapamycin (mTOR) inhibitors in the adjuvant setting, the Southwest Oncology Group (SWOG) launched the phase 3 study of everolimus in treating patients with kidney cancer who have undergone surgery (EVEREST) study (NCT01120249), which was reported at ASCO 2022.7 Individuals with clear or non-clear cell RCC immediately post-nephrectomy whose tumors show intermediate high-risk to high risk features were included in the study. Between 4/2011 and 9/2016, 1545 patients were randomized to e ither 1 2 m onths o f a djuvant e verolimus (n = 7 75) o r placebo (n = 7 70) including 83% w ith clear cell RCC and 17% with non-clear cell RCC. With a median follow-up of 76 months, the recurrence free survival was improved with everolimus compared to placebo (HR 0.85, 95% CI, 0.72 – 1.00; P (one sided) = 0.0246), narrowly missing the pre-specified, one-sided significance level of 0.022 which accounted for interim analyses, and the effect of everolimus was especially pronounced in patients with very high risk disease. Adverse events were consistent with safety profiles of everolimus, although there was a high discontinuation rate of everolimus in this population (47%).
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First Line Metastatic Kidney Cancer Treatment Updates The first line treatment landscape of metastatic RCC has rapidly evolved in recent years.8 New updates on some of the registration first line metastatic RCC studies were presented during the meeting. The CheckMate 9ER trial is a phase 3 trial which compared nivolumab plus cabozantinib versus sunitinib in patients with untreated advanced clear cell RCC and demonstrated superior overall survival (OS), progression free survival (PFS) and objective responses of the nivolumab plus cabozantinib combination9. Updated analysis from the depth of response was presented at ASCO 2022.10 Patients’ responses were classified as complete response (CR) or partial response (PR) subdivided by a tumor reduction of ≥80%–<100% (PR1), ≥60%–<80% (PR2), or ≥30%–<60% (PR3). Overall, greater proportions of patients receiving nivolumab plus cabozantinib had deeper responses versus sunitinib (CR, PR1, PR2), and deeper responses with nivolumab plus cabozantinib were associated with improved 12-months PFS rate versus sunitinib for CR (94.9% vs 82.4%), PR1 (81.3% vs 37.5%), and PR2 (72.1% vs 53.2%).
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Updates on health-related quality of life (HRQoL) from the CheckMate-214 phase 3 clinical trial, which compared nivolumab plus ipilimumab versus sunitinib in patients with untreated advanced clear cell RCC, were also presented during the meeting.11,12 As previously reported, nivolumab plus ipilimumab was associated with improved HRQoL compared to sunitinib. At ASCO 2022, the investigators reported on a post-hoc analysis on the prognostic ability of HRQoL to inform the risk of disease progression or death. The results of the analysis showed that higher (better) baseline scores were associated with significantly reduced risk of death (HR [95% CI] for FKSI- 19 Total Score and DRS score was 0.83 [0.80-0.87] and 0.80 [0.76-0.84], respectively). Furthermore, patients with improved/stable HRQoL had a 52% reduction in risk of death compared to patients who had worsened (HR 0.48 [95% CI: 0.39-0.59]).
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Post-hoc exploratory analyses of PFS2 were conducted in the KEYNOTE 426 (phase 3 study comparing pembrolizuamb plus axitinib versus sunitinib in patients with untreated advanced clear cell RCC)13,14 and the CLEAR (phase 3 study comparing pembrolizumab plus lenvatinib versus sunitinib in patients with untreated advanced clear cell RCC)15,16 studies. Both analyses demonstrated prolongation of PFS2 in patients who received pembrolizumab plus axitinib in KEYNOTE 426 study and pembrolizumab plus lenvatinib in the CLEAR study. Novel Kidney Cancer Therapies Highlights Several exciting data were presented on novel therapies in RCC. Batiraxcept is a GAS6-AXL inhibitor, a pathway which is overexpressed in clear cell RCC.17 Interim results of a phase 1b study of batiraxcept plus cabozantinib 60 mg daily were presented at the meeting.18 A total of 26 patients were enrolled in the phase 1b study so far, and the recommended phase 2 dose of batiraxcept was identified as 15 mg/kg every 2 weeks. Encouraging early anti-tumor efficacy results of the combination were observed with an objective response rate of 67% and 6 months PFS of 79%. Hypoxia-inducible factor 2α (HIF-2α) is a key oncogenic driver in RCC.19 Belzutifan is a HIF-2α inhibitor which was recently approved by the FDA for patients with VHL syndrome and currently under investigation in sporadic RCC.20,21 LITESPARK-001 is a phase 1 study which was designed to evaluate belzutifan in heavily pretreated RCC and showed durable antitumor activity and an acceptable safety profile.21 An update of the clear cell RCC cohort in the study with more than 3 years of total followup was presented at the meeting.22 With extended followup of 41 months, the objective response rate was 25% with 80% disease control rate and median PFS of 14.5 months (95% CI, 7.3-22.1). Belzutifan monotherapy continued to show a high rate of disease control and durable responses in this heavily pre-treated population.
The CALYPSO study results were presented at the meeting as well.23 This is a randomized phase II study of durvalumab alone or with savolitinib or tremelimumab in previously treated advanced clear cell RCC. Savolitinib is a potent MET inhibitor with established dosing and activity in papillary RCC; however, its role in clear cell RCC is unclear.24 Between 2017 and 2021, 139 patients were randomized across the treatment arms. Savolitinib alone and in combination with duravlumab was associated with modest confirmed response rates (5% and 13%, respectively) compared to confirmed response rates of 10% for durvalumab and 28% for durvalumab plus tremelimumab. All regimens studied in the trial appeared to be safe and tolerable.
SUMMARY
In summary, ASCO 2022 was enriched with novel results and concepts continually expanding the field of kidney cancer research. Indeed, the data presented are both hypothesis-generating and practice-informing. Herein, we highlighted a snapshot of some of the oral presentations from the meeting in the kidney cancer space; however, there are considerably more exciting abstract and poster presentations that are available for review on the meeting’s website. In addition to the scientific content, ASCO 2022 also provided ample opportunities for networking and collaborations among the academic kidney cancer community, with the first in-person option since the beginning of the COVID-19 pandemic.
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REFERENCES
1. Campbell SC, Clark PE, Chang SS, Karam JA, Souter L, Uzzo RG. Renal Mass and Localized Renal Cancer: Evaluation, Management, and Follow-Up: AUA Guideline: Part I. J Urol. 2021;206(2):199-208.
2. Apolo AB, Msaouel P, Niglio S, et al. Evolving Role of Adjuvant Systemic Therapy for Kidney and Urothelial Cancers. Am Soc Clin Oncol Educ Book. 2022;42:1-16.
3. Mejean A, Ravaud A, Thezenas S, et al. Sunitinib Alone or after Nephrectomy in Metastatic Renal-Cell Carcinoma. N Engl J Med. 2018;379(5):417-427.
4. Choueiri TK, Tomczak P, Park SH, et al. Adjuvant Pembrolizumab after Nephrectomy in Renal-Cell Carcinoma. N Engl J Med. 2021;385(8):683-694.
5. Gleeson JP, Motzer RJ, Lee CH. The current role for adjuvant and neoadjuvant therapy in renal cell cancer. Curr Opin Urol. 2019;29(6):636-642.
6. Choueiri TK, Tomczak P, Park SH, et al. Adjuvant pembrolizumab for postnephrectomy renal cell carcinoma (RCC): Expanded efficacy analyses from KEYNOTE-564. 2022;40(16_suppl):4512-4512.
7. Ryan CW, Tangen C, Heath EI, et al. EVEREST: Everolimus for renal cancer ensuing surgical therapy—A phase III study (SWOG S0931, NCT01120249). 2022;40(17_suppl):LBA4500-LBA4500.
8. McKay RR, Bosse D, Choueiri TK. Evolving Systemic Treatment Landscape for Patients With Advanced Renal Cell Carcinoma. J Clin Oncol. 2018:JCO2018790253.
9. Choueiri TK, Powles T, Burotto M, et al. Nivolumab plus Cabozantinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2021;384(9):829-841.
10. Suárez C, Choueiri TK, Burotto M, et al. Association between depth of response (DepOR) and clinical outcomes: Exploratory analysis in patients with previously untreated advanced renal cell carcinoma (aRCC) in CheckMate 9ER. 2022;40(16_suppl):4501-4501.
11. Motzer RJ, Tannir NM, McDermott DF, et al. Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med. 2018;378(14):1277-1290.
12. Cella D, Hamilton M, Blum SI, et al. The relationship between health-related quality of life (HRQoL) and clinical outcomes in patients with advanced renal cell carcinoma (aRCC) in CheckMate (CM) 214. 2022;40(16_suppl):4502-4502.
13. Rini BI, Plimack ER, Stus V, et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2019;380(12):1116-1127.
14. Powles T, Plimack ER, Stus V, et al. Pembrolizumab (pembro) plus axitinib (axi) versus sunitinib as first-line therapy for advanced clear cell renal cell carcinoma (ccRCC): Analysis of progression after first subsequent therapy in KEYNOTE-426. 2022;40(16_suppl):4513-4513.
15. Motzer R, Alekseev B, Rha SY, et al. Lenvatinib plus Pembrolizumab or Everolimus for Advanced Renal Cell Carcinoma. N Engl J Med. 2021;384(14):1289-1300.
16. Voss MH, Powles T, McGregor BA, et al. Impact of subsequent therapies in patients (pts) with advanced renal cell carcinoma (aRCC) receiving lenvatinib plus pembrolizumab (LEN + PEMBRO) or sunitinib (SUN) in the CLEAR study. 2022;40(16_suppl):4514-4514.
17. Rankin EB, Fuh KC, Castellini L, et al. Direct regulation of GAS6/AXL signaling by HIF promotes renal metastasis through SRC and MET. Proc Natl Acad Sci U S A. 2014;111(37):13373-13378.
18. Shah NJ, Beckermann K, Vogelzang NJ, et al. A phase 1b/2 study of batiraxcept (AVB-S6-500) in combination with cabozantinib in patients with advanced or metastatic clear cell renal cell (ccRCC) carcinoma who have received front-line treatment (NCT04300140). 2022;40(16_suppl):4511-4511.
19. Choueiri TK, Kaelin WG, Jr. Targeting the HIF2-VEGF axis in renal cell carcinoma. Nat Med. 2020;26(10):1519-1530.20. Jonasch E, Donskov F, Iliopoulos O, et al. Belzutifan for Renal Cell Carcinoma in von Hippel- Lindau Disease. N Engl J Med. 2021;385(22):2036-2046.
21. Choueiri TK, Bauer TM, Papadopoulos KP, et al. Inhibition of hypoxia-inducible factor-2alpha in renal cell carcinoma with belzutifan: a phase 1 trial and biomarker analysis. Nat Med. 2021;27(5):802-805.
22. Jonasch E, Bauer TM, Papadopoulos KP, et al. Phase 1 LITESPARK-001 (MK-6482-001) study of belzutifan in advanced solid tumors: Update of the clear cell renal cell carcinoma (ccRCC) cohort with more than 3 years of total follow-up. 2022;40(16_suppl):4509-4509.
23. Powles T, Mendez-Vidal MJ, Rodriguez-Vida A, et al. CALYPSO: A three-arm randomized phase II study of durvalumab alone or with savolitinib or tremelimumab in previously treated advanced clear cell renal cancer. 2022;40(17_suppl):LBA4503-LBA4503.
24. Choueiri TK, Heng DYC, Lee JL, et al. Efficacy of Savolitinib vs Sunitinib in Patients With METDriven Papillary Renal Cell Carcinoma: The SAVOIR Phase 3 Randomized Clinical Trial. JAMA Oncol. 2020;6(8):1247-1255.
Correspondence to:
Nirmish Singla, MD, MSCS. Departments of Urology and Oncology, The James Buchanan Brady Urological Institute, The Johns Hopkins School of Medicine. Baltimore MD 21287.
EMAIL: nsingla2@jhmi.edu
The post Kidney Cancer Research Highlights from ASCO 2022 Annual Meeting first appeared on GUcancers.
]]>The post Comparison of Papillary Renal Cell Carcinoma Type 1 and Type 2: A Secondary Data Analysis first appeared on GUcancers.
]]>1. Clemson University, Hampton, GA 30228 USA.
2. School of Nursing, Clemson University, Clemson, SC 29634.
3. Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634.
4. Department of Public Health Sciences, Clemson University, Clemson, SC 29634
ABSTRACT
OBJECTIVE: The overall aim of this study was to determine if there are significant differences between type 1 and type 2 papillary renal cell carcinoma (PRCC) that can be utilized by healthcare providers. MATERIALS AND METHODS: This study performed a secondary data analysis using The Cancer Genome Atlas Kidney Renal Papillary Cell Carcinoma data to determine if there are clinically significant differences in survival, demographics (age, ethnicity, gender, and race), increased risk factors (body mass index [BMI] smoking history, neoplasm history, and malignancy history) and preferential genetic pathways between type 1 and type 2 PRCC tumors. RESULTS: Descriptive statistics were performed on a total of 156 cases to determine demographics, increased risk factors and genetic pathways. The hazard ratio, with type 1 as the reference group, was 2.459 (with 95% CI 0.9723, 6.217). Of the risk factor variables investigated, we found that smoking appeared to be associated with an increased risk of type 2 (OR 3.241 95% CI 1.066, 9.853). In the pathways analysis, we observed one significant difference between MAPK and PI3K, with the latter being significantly associated with type 2 (OR 4.968 95% CI 1.759, 14.031 Table 6). CONCLUSION: This study provides the framework for future more comprehensive research on the demographic, increased risk factor and genetic pathway differences between PRCC type 1 and type 2 tumors. Future investigations should include a more complete dataset with additional potential risk factors
INTRODUCTION
Renal cell carcinoma (RCC) is the 14th most common cancer worldwide and was the cause of 175,098 deaths in 20181. RCC consists of numerous subtypes including clear cell renal carcinoma, papillary renal cell carcinoma and most recently clear cell papillary renal cell carcinoma. Currently, papillary renal cell carcinoma (PRCC) is the second most common type of RCC, after clear cell renal cell carcinoma, comprising approximately 15-20% of all RCC cases2,3. PRCC is considered a heterogeneous disease consisting of two subtypes; type 1 and type 2. These subtypes are primarily distinguished by their histology and vary in prognosis, treatment and patient outcomes. Type 1 is histologically characterized by a single layer of cells with sparse basophilic cytoplasm and small oval shaped nuclei that are present in either the renal tubules or renal papillae. This type can be associated with both hereditary and sporadic PRCC.4,5 Conversely, type 2 tumors are histologically characterized by large pseudostratified cells with eosinophilic cytoplasm with large spherically shaped nuclei that are present in the renal papillae. These tumors can be associated with hereditary PRCC but are more often associated with the sporadic form of PRCC.6 Furthermore, research 6 has shown that patients with PRCC type 2 tumors are correlated with a higher rate of metastasis and have a lower overall survival rate compared with patients with type 1 tumors.7 The overall aim of this study was to determine if there are significant differences between type 1 and type 2 PRCC that can be utilized by healthcare p r o v i d e r s . Specifically, this study sought to determine if there are clinically s i g n i f i c a n t d i f f e r e n c e s in survival, demographics (age, ethnicity, gender, and race), increased risk factors (body mass index [BMI] smoking history, neoplasm history, and malignancy history) and preferential genetic pathways between type 1 and type 2 PRCC tumors.
Table 1 | Descriptive Statistics for Demographic Factors
T h e epidemiology and risk factors for PRCC are largely based on the broader RCC. However, there are certain conditions that may increase an individual’s risk of developing PRCC. For instance, individuals with Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC) have a greater chance of developing PRCC type 2. There is some evidence that suggests individuals with renal insufficiencies have a greater risk of developing PRCC.8,9 Ethnicity is also linked to increased risk of developing RCC with African Americans having the highest incidence of RCCs. Sankin et al. (2011) found that African Americans had a four times greater incidence of PRCC as compared to non-African Americans.10,11 Research has demonstrated that malignant tumors utilize a wide variety of genetic alterations to modify the normal cell cycle in order to be able to divide and grow without restrictions. These modifications are accomplished by altering cell signaling pathways to promote cell growth, angiogenesis and obstruct apoptosis.12 Considering the heterogeneous nature of PRCC, there are numerous genetic alterations that occur within both type 1 and type 2 PRCC. Approximately 20% of hereditary type 1 tumors have been associated with variations in the protooncogene mesenchymal epithelial transition (MET). However, sporadic type 1 tumors have numerous genes associations as well as chromosomal abnormalities. Type 2 tumors have also been correlated with a large number of genetic and chromosomal alterations.4,13 Similarly, research has shown that renal cancers in general utilize several signaling pathways. The alteration of MET has been shown to activate the MAPK and PI3K pathways as well as other proteins involved with tumor growth.14 Gaps in research still exist for determining if there are pathway preferences between type 1 and type 2 PRCC tumors.
Table 1 | Descriptive Statistics for Demographic Factors
Most research on PRCC has either been umbrellaed under RCC or focused on developing a basic understanding of the disease with minimal attention to the differences between type 1 and type 2 PRCC tumors. Recently, Wong et al. (2019) investigated survival rates associated with type 1 and type 2 PRCC. The researchers found that type 2 PRCC was associated with a higher all-cause mortality rate as well as with worse reoccurrence rates as compared to type 1.7 As part of our research, we analyzed the allcause mortality for discrepancies in survival rates between type 1 and 2 PRCC. Next, we selected a demographic (baseline) model to identify a set of demographic variables that are likely to be associated with the different types of PRCC. Lastly, we investigated environmental and gene pathway associations with prevalence of the two types of PRCC.
METHODS
Sample
This study was a secondary data analysis using data from The Cancer Genome Atlas Kidney Renal Papillary Cell Carcinoma (TCGAKIRP). A review of the literature was conducted to determine the appropriate inclusion criteria which included: 1) PRCC tumors, 2) distinguishes between type 1 and type 2, 3) demographics data, gender, race, age and ethnicity, 4) clinical data, prognosis, treatment, preexisting conditions, 5) increased risk factors, smoking history, BMI, prior neoplasms and prior malignancies, and 6) genetic analysis of the tumors. A further review of the literature revealed that TCGA-KIRP is the most current and appropriate dataset to use for this secondary data analysis. The cBioPortal for cancer genomics (cBioPortal) was used in conjunction to analyze the TCGA-KIRP data.
TCGA-Kidney Renal Papillary Cell Carcinoma (KIRP) data was collected from 41 institutions from 1996 to 2013. The database adheres to a strict inclusion policy; TCGA tumors are untreated samples that were snap frozen. Each tumor sample has to have a matched normal sample from the same patient which generally comes in the form of the patient’s blood. The tumors and subsequent molecular data are cross referenced by Biospecimen Core Resource (BCR) to ensure validity. Furthermore, the BCR analyzes each sample for pathological quality control. This maintains that TCGA has a highquality tumor samples as well as consistent molecular data.15 Additionally, each sample was reviewed by a panel of six experienced pathologist to in order to be classified into type 1, type 2 or unclassified PRCC. Moreover, any samples that were preclassified were reassessed by the same panel to ensure proper classification.15 The cBioPortal is a resource that incorporates data from TCGA as well as actively curates data sets from the literature into a researchfriendly source. The cBioPortal separates PRCC genetic variations into categories such as copy number variations and mutations. Furthermore, the cBioPortal predetermines and denotes driver genes through specific algorithms.16 The cBioPortal allows the user to analyze specific genes, as opposed to TCGA, which only allows users to view the dataset as a whole and does not denote potential driver genes.16 Even though the cBioPortal contains the same data as TCGA, the cBioPortal was used to aid in the analysis of TCGA data.
Data extraction
Both databases showed the same cases which totaled 292. The first step in evaluating the dataset was determining the demographic and clinical data. TCGA contained a manifest of demographic, clinical, and environmental data. This manifest was downloaded and converted into an Excel file. Once retrieved, the dataset was reviewed and irrelevant data was removed; such data included serum levels, blood cell counts, IDH level, tumor laterality, lymph node data, tumor dimensions, treatment data, tissue collection data, sample weights, calcium levels, and vial numbers. Data categories that were redundant were also eliminated.
Next, the cBioPortal resource was used to determine pertinent genetic information related to PRCC. The first step was to download the copy number alteration (CNA) data from this resource. A total of 10,837 genes exhibited a copy number variation. Genes that were not considered to be driver genes according to the GISTIC algorithm were eliminated from the dataset. This elimination left a total of 426 driver genes with CNA. The driver genes were then put into the BCG query to determine how many cases included one or more of the driver CNA genes. A total of 193 of the cases (66%) contained one of the driver CNA genes. In order to increase the sample population, mutated driver genes (as determined by Mutsig) were added to the query bringing the total of genes to 517 and 255 (87%) cases. Thirty-six cases did not have an association with one of the 517 driver genes and were eliminated. The driver genes were divided into categories based on their cytoband for future reference.
The remaining 255 cases were reviewed to determine whether or not they were designated type 1 or type 2 PRCC. Out of the 255 cases, 115 cases had no designation in the type category. The pathology report of each of the 115 cases was reviewed to see if a pathologist had designated the tumor as either type 1 or type 2. Seven more cases were determined to be a mix of type 1 and type 2 histology and were also removed. Additionally, eight more cases were either mislabeled as PRCC or determined to favor a different cancer type per the reviewing pathologist. These eight cases did not include a TCGA addendum that disputed the cancer typing and therefore were removed from this dataset. (See Figure 1). At the conclusion of this analysis, 88 cases were designated as type 2, 69 cases were type 1, and 83 cases were undesignated. The 83 undesignated cases were subsequently removed from the dataset in order to preserve the validity and continuity of the data.
ANALYSIS
Descriptive Statistics and Survival Analysis
Descriptive statistics were utilized to determine demographics, increased risk factors and genetic pathways. The survival analysis was conducted for the TCGA-KIRP analytic file using R version 3.6.2. , the survival(v3.2-13) and the survminer (v0.4.9) packages.21-23 A cox-proportional hazard model was fitted on the overall survival times of 156 patients (1 had a survival time of 0 indicating that they were diagnosed post-mortem or there was an error in entry) to determine if there were evidence that survival rates differ between type 1 and 2 PRCC.
Logistic RegressionFor the next three phases of our statistical analysis, SASTM software, Version 9.4 of the SAS system for Windows was utilized. The demographic model selection included age at diagnosis, race, ethnicity and sex, as candidate descriptors relating to PRCC tumor type. The demographic model selection utilized forward selection with a relaxed p value (<0.1) to determine the appropriate variables to be included in the model. The selected demographic model included Age at Diagnosis (OR 1.045 95% CI 1.014, 1.078, Table 5) as well as 3 Category Race (White, Black or African American and Other) was used as the baseline model for the increased risk factor variables. Each increased risk factor variable; BMI, smoking status, prior neoplasms and prior malignancies, were added univariately to the demographic model controlling for age at diagnosis and race to identify associations.
Figure 2 | Kaplan Meier curves for Type 1 and 2 PRCC survival
RESULTS
Descriptive StatisticsFor the 69 patients designated as type 1 tumors, 50 were male and 19 were female with a median age of 60 (range 28 to 82). In terms of race, 46 were white, 18 were black or African American, and 5 were unspecified. Ethnicity was reported as 62 non- Hispanic or Latino, 2 were Hispanic or Latino and 5 were unspecified.
For the 88 patients designated as type 2 tumors, 61 were male and 27 were female with a median age of 65 (range 28 to 88). In terms of race, 66 were white, 15 were black or African American, and 7 were unspecified. Ethnicity was reported as 75 were non-Hispanic or Latino, 5 were Hispanic or Latino and 8 were unspecified (Table 1). Due to the sparsity in the demographic factor levels, the following variable levels were collapsed; Asian and American Indian.
Table 2 | Descriptive Statistics for Increased Risk Factors
Smoking categories were defined as life-long non-smoker (1), current smoker (2), reformed smoker >15years (3), reformed smoker <15 years (4) and reformed smoker unknown length (5). Table 2 describes the smoking status of type 1 and type 2 PRCC tumors. Smoking categories 4 and 5 were collapsed together due to data sparsity in the increased risk factor variables. The existence of prior neoplasm was defined in the database as ‘yes’ or ‘no’. Two patients with type 1 PRCC had known prior neoplasm were as 9 patients with Type 2 reported prior neoplasm. Similarly, prior malignancies were also defined as ‘yes’ or ‘no’. Sixteen patients with type 1 reported prior malignancies and 14 patients with type 2 reported prior malignancies (Figure 2). The most common pathway in type 1 was the MAPK pathway and in type 2 was the PI3K pathway Table 3).
Table 4 | Demographics Model
Overall SurvivalThe hazard ratio, with type 1 as the reference group, was 2.459 (with 95% CI 0.9723, 6.217). This result did not provide sufficient evidence that the two types differ significantly in all-cause survival (α=.05). However, given the relatively small sample size and high rate of censoring, it is not surprising that our results do not provide as striking a contrast between the two as supported by Wong et al. (2019). (Censoring rates were 91.3% for Type 1 and 79.5% for type 2, respectively, which consequently prevents us from being able to report median survival without making parametric assumptions). Survival rates are illustrated via the Kaplan Meier curve included in Figure 2.
Table 4 | Increased Risk Factor Model
Logistic RegressionOdd ratios (OR) and confidence intervals (CI) are reported in Tables 5 and 6 for each variable in the increased risk factor and pathway analyses. Of the risk factor variables investigated, we found that smoking appeared to be associated with an increased risk of type 2. Specifically, being a reformed smoker of unknown length or less than 15 years, was positively associated with type 2 PRCC compared to lifelong non-smokers (OR 3.241 95% CI 1.066, 9.853 Table 5). None of the other increased risk factors had significant association with tumor type. In the pathways analysis, we observed one significant difference between MAPK and PI3K, with the latter being significantly associated with type 2 (OR 4.968 95% CI 1.759, 14.031 Table 6). All pairwise comparisons were made between pathways and the MAPK/PI3K comparison was the only one found to be significant. In all analyses, type 1 was used as the reference level for each model and the OR corresponds to odds of type 2 Vs 1.
Table 2 | Descriptive Statistics for Increased Risk Factors
DISCUSSION
It is important to note that current findings from the International Society of Urological Pathology (ISUP) suggests that the PRCC type 1 subtype is the most uniform morphologically, immunohistochemically, and in terms of molecular features. ISUP also suggests that PRCC type 2 is not a distinct neoplasm but rather a combination of multiple distinct neoplasms. As such, type 2 PRCC is a distinctly different disease as compared to type 1 and contains multiple clinically and molecularly heterogeneous subtypes.24 Additionally, the use of type 1 and type 2 terminology is evolving as PRCC becomes better understood. To the best of our knowledge, our study is the first to collectively examine the demographic, increased risk and pathway associations between type 1 and type 2 PRCC tumors. Furthermore, while our findings with respect to the survival analysis were not significant, it does provide marginal evidence to confirm the findings of Wong et al. (2019) in that survival rates for type 2 are shorter than those diagnosed with type 1. 7 While our analysis was limited by small sample size, certain variables were linked to increased probability of type 2 PRCC tumors. The age at diagnosis variable was considered significant with an older adult having increased risk of type 2. Our result is consistent with Wong et al. (2019) who reported a higher age at time of nephrectomy for patients with type 2 tumors as compared with type 1 tumors.7
Smoking was the only increased risk factor that was significant in determining the probability of having the type 2 tumor type versus type 1. Individuals who were reformed smokers of less than 15 years (as well as reformed smokers of unknown length) had a greater risk of developing a type 2 tumors as compared to lifelong non-smokers. Furthermore, type 2 PRCC tumors tend to be sporadic as compared to type 1, meaning that increased risk factors may have a greater impact on the development of type 2 tumors.6 However, further research needs to be conducted on the effects of smoking on the growth of specific tumor subtypes.
Although smoking was the only significant increased risk factor variable, further research should be conducted on a larger sample size with less missingness to better compare increased risk factors variables between tumor types. Specific focus should be put on prior neoplasms since they have been associated with a number of renal cell cancer syndromes that are considered to increase the risk of PRCC. For example, the most common renal cell cancer syndrome, von Hippel-Lindau syndrome, is characterized by benign tumor growths and has a 40% chance of developing renal cancer, including type 2 PRCC. Additionally, hereditary leiomyomatosis and renal cell cancer (HLRCC), is characterized by harmatomas with an increased risk of developing type 2 PRCC. 8,17 Considering the number of renal cell cancer syndromes that are both associated with an increased PRCC risk and are characterized by neoplasms; further research should be conducted to determine if prior neoplasms is a determining factor in PRCC subtype.
The findings in this study have potential implications for future treatment options. The higher rate of MAPK pathway in type 1 supports ongoing studies of the role of the MET gene in clinical trials. The MET gene codes for c-Met, a tyrosine kinase protein that is involved with the MAPK pathway. When c-Met binds to its ligand, HGF, a downstream cascade is started that leads to the activation of the MAPK pathway which promotes cell migration and tumor proliferation. 18 Seeing as 20% of type 1 tumors contain a MET mutation, it is not surprising that MAPK is the preferred pathway of type 1 tumors. Furthermore, the PI3K pathway was found to be significant in the probability of having a type 2 tumor as well as being the preferred pathway of type 2. The findings in this study support the ongoing efforts in determine drug treatment therapies that target the PI3K pathway. PI3K is comprised of lipid kinases that once activated, begin a downstream cascade that leads to cell growth and survival. PI3K pathway has a strong association with the inactivation of PTEN, which has been correlated poor patient outcomes.19,20
CONCLUSION
Despite the imperfect database this study found that there is a trend in the data that is clinically significant Furthermore, this study provides the framework for future more comprehensive research on the demographic, increased risk factor and genetic pathway differences between PRCC type 1 and type 2 tumors. Future investigations should include a more complete dataset with additional potential risk factors. Given the differences in survival rates, such investigations will provide clinicians a better understanding of tumor types allowing for quicker more accurate diagnosis and evidence-based treatment plans.
CONFLICT OF INTEREST
All authors listed on this study have no conflicts of interest that may be relevant to the contents of this manuscript.
FUNDING
None
ACKNOWLEDGMENTS
None
REFERENCES
Correspondence to: Melissa Paquin, PhD. 235 Galway Lane, Hampton, GA 30228 Email: mpaquin@clemson.edu
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A list of global guidelines for kidney cancer are provided in this section. These guidelines provide evidence-based recommendations to serve as a guide and support best quality standards of care.
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