The Memorial Sloan Kettering Cancer Center (MSKCC) risk stratification model in advanced renal cell carcinoma (RCC) currently used in clinical practice stratifies patients into risk groups of favorable, intermediate, and poor.1 The stratification is based on risk scores obtained from clinical and laboratory data: low Karnofsky performance status, high lactate dehydrogenase, low serum albumin, high corrected serum calcium, and time from diagnosis to systemic treatment.1,2
The model has now been updated to incorporate patient-level genomic information, which is thought to improve the prognostic accuracy of the model. It has been described in a paper published in the New England Journal of Medicine.3
Martin Voss, MD, corresponding author on the paper, told Cancer Therapy Advisor that current risk-stratification models, including the MSKCC model, are flawed because the bulk of the patients are categorized into the intermediate-risk category. “As genomic data in kidney cancer [have] become increasingly available, we wanted to see whether integrating them into the model would improve its applicability,” Dr Voss said.
Manish Kohli, MD, of the division of medical oncology at Mayo Clinic, Rochester, Minnesota, who was not associated with the study, told Cancer Therapy Advisor: “It is extremely relevant to do this, as the previous model is based on loose clinical and physical criteria, which are not fully objective and do not reflect the tumor genome.”
“This study provides that flavor,” he added. “It integrates the old model with new genomic variables, which are critically needed in [the] future.”
Updating the MSKCC Risk Stratification
The researchers searched the PubMed database for studies that evaluated the prognostic role of somatic mutations in metastatic RCC and honed in on the mutational status of 6 genes of interest, ultimately determining that 3 of these 6 genes provided prognostic relevance — BAP1, PBRM1, and TP53.3
In this retrospective study, the researchers interrogated formalin-fixed, paraffin-embedded tumor tissue from 2 cohorts of patients determine the status of 6 genes of interest in the hopes of finding a correlation between mutational status and survival outcomes.3
Patients from the COMPARZ and RECORD-3 trials in metastatic renal cell carcinoma who had samples at baseline were included in this analysis.3 The cohort of 357 of 1110 patients from COMPARZ was used as the training set and 258 of 471 patients from RECORD-3 was used as the validation set.3
Dr Kohli noted this as a limitation of the study — the small dataset. He pointed out that, in the training set, only one-third of the patients from the COMPARZ were eligible for analysis, and only 258 patients from the RECORD-3 study were included. The results from the RECORD-3 study were used as a validation set.
This reduces the power of the study, which is further reduced when one considers the fact that the researchers ended up comparing 10% to 20% of patients harboring mutations in the 6 genes with those without the mutations, he explained. “The yield from this means that on average, only 40 to 50 patients with mutations were being compared to 300 without mutations to derive statistical comparisons. This affects the power of the study to some extent,” he added.
For the training set, mutations in BAP1, PBRM1, and TP53 were associated with significantly longer overall survival (OS) with first-line VEGF TKIs. Mutations in BAP1 or TP53 or both were correlated with worse OS compared with patients with wild-type copies of these genes, and acquired mutations in PBRM1 were associated with improved OS. A similar pattern was observed for progression-free survival (PFS).
The researchers also reported that concurrent mutations in BAP1 and TP53 were associated with worse OS outcomes compared with either mutation alone, but the presence of a concurrent mutation in PBRM1 reversed prognosis: mutation in either BAP1 or TP53 and PBRM1 improved OS. But concurrent mutations in all 3 genes were associated with significantly worse OS outcomes.
Bishal Gyawali, MD, PhD, the Brigham and Women’s Hospital, Boston, Massachusetts, told Cancer Therapy Advisor that the study was well done and the genomically annotated model adds important information to its prognostic value.
Dr Gyawali’s concern was that the model looked into mutations in only 6 genes. “There are many more genes associated with prognosis in RCC,” he said. “Did they look at other mutations, but reported only on 6?” he asked.
Dr Voss told Cancer Therapy Advisor that their panel at MSK looks at more than 400 genes, but for this study, they intentionally limited their analysis to these 6 genes of interest. The selection of the 6 specific genes was due to their relevance for the pathobiology of RCC and their frequency in the setting of metastatic disease, Dr Voss explained. “These 6 genes are sufficiently high in prevalence and are applicable to the broad patient population in metastatic RCC,” he said.