the prognostic power of two different signatures originally
validated in localized RCC, the ClearCode34 (a 34-
gene signature model) and an 8-gene signature model, in
a cohort of 54 mRCC patients treated with TTx in 5 institutions
as part of The Cancer Genome Atlas (TCGA).26
Only the joint model with ClearCode34 reached statistical
significance in improving the IMDC prognostic model
accuracy, using C-Index analysis and F-test. The results
require further validation in a larger dataset before they
can be integrated in guiding clinical decision. Like many
of these genomic signature reports, there are few limitations
related to the generalizability of data (as the results
may be irreproducible with a cohort of different composition
or size), heterogeneity of the cancer and sampling
artifact. Moreover, from a financial perspective, the incremental
increase in c-index from genomic information
will need to be evaluated in light of the cost of these tests.
Further clarification of the role of tumor molecular
characteristics appeared in the report of Beuselinck et
al.27,28 In this first integrative genomic study of m-ccRCC,
Beuselinck et al. identified four molecular subtypes of
mccRCC (ccrcc1 to 4) based on unsupervised transcriptome
analysis. The ccrc1 and 4 are associated with poorer
PFS and OS compared to ccrc2 and 3. Furthermore, the
molecular subtypes were correlated to IMDC groups as
well as sunitinib response. The good risk IMDC group is
enriched by ccrc2 molecular subtype and presents a high
expression of genes involved in angiogenesis such as
HIF2A, VEGFR1, -2 and -3. Furthermore, the expression
of immune-related genes was similar across IMDC subgroups.
These findings support the proposed hypothesis
that the good-risk IMDC group benefits more from sunitinib
compared to intermediate- and poor- risk IMDC
group in CheckMate214 because of dependency to the
VEGF pathway observed.
The complex interplay between the immune system
and cancer development, progression and treatment response
is an expanding area of research. With the emergence
of ICP, immune-based biomarkers, such as PD-L1
expression, are being studied extensively. In the exploratory
analysis of CHECKMATE-214, OS of the intermediate
and poor-risk patients according to PD-L1
expression status was described using the Kaplan-Meier
estimate.1 Although not powered to draw statistically significant
conclusions, these results are hypothesis generating.
In the intermediate- and poor-risk subgroups treated
with sunitinib, the survival rate with PD-L1 expression of
1% or more was lower than for those with PD-L1 expression
of less than 1%. Also, intermediate- and poor-risk patients
treated with ipilimumab plus nivolumab had an
improved survival compared to the patients treated with
sunitinib regardless of the PD-L1 expression status. This
suggests that PD-L1 expression of 1% or more can on the
one hand be a negative-prognostic factor and on the
other a positive-predictive factor to ICP. In support of
this, the negative-prognostic value of PD-1/PD-L1 expression
was also demonstrated in some other reports.18,29
However, the validation of PD-L1 expression as a biomarker
has several limitations related to PD-L1 dynamic expression
through the course of the cancer, assay
variability, and lack of standardization of sample collection
and cell analysis.30 Currently, PD-L1 testing does
46 Kidney Cancer Journal
not have sufficient negative predictive value to exclude
patients from CPI as there are PDL1 negative patients that
still benefit from CPI.
Conclusion
Innovative clinical trials of targeted- and immune-based
therapies have reshaped the mRCC treatment landscape.
Emerging information on these new management approaches
has focused greater attention on the importance
of updating traditional prognostic models to more accurately
determine patient risk stratification and outcomes.
To date, the IMDC prognostic model is the most consistently
validated system to assess risk groups and, thus, the
preferred prognostic model used in clinical practice and
trial design. The integration of specific molecular and genomic
alterations in the prognostication scheme is an essential
part of future directions in this area.
Funding:
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit
sectors.
Disclosure:
Marie-France Savard
Honoraria from: Amgen
Daniel Y.C. Heng
Honararia/Consultary/Research Funding from:
AstraZeneca, BMS, Ipsen, Merck, Novartis, Pfizer
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