MLH1 deficiency in renal cell carcinoma: a promising yet challenging biomarker for immunotherapy

Dear Editor, We read with great interest the study by Dawidek and colleagues, which identifies MLH1 deficiency as a novel predictor of exceptional response to immune checkpoint inhibitors (ICIs) in clear-cell renal cell carcinoma (ccRCC)[1]. The authors are to be commended for their multi-cohort validation, providing reliable evidence for the biological and clinical significance of this rare genomic subtype. However, several key issues require further discussion and careful interpretation. In line with TITAN 2025 Guidelines, AI was used solely for language polishing without affecting scientific content[2]. First, although MLH1 is the most common mismatch repair gene alteration in ccRCC, its incidence remains low: 2.3% (11/474) in the MSK pretreatment cohort, 0.9% (4/427) in CheckMate-214, and 4.5% (34/756) in JAVELIN Renal 101. Compared to tumors such as endometrial cancer (where the incidence of MMR deficiency can reach 25%–30%), this rarity poses a significant challenge to the cost-effectiveness of universal screening[3]. More importantly, the lack of correlation between MLH1 genomic alterations and RNA expression in the JAVELIN Renal 101 cohort reveals a fundamental diagnostic dilemma. This uncertainty complicates the establishment of a clear testing algorithm: should clinical decisions be based solely on DNA sequencing, or is functional confirmation via immunohistochemistry necessary? Without a standardized and easily implementable diagnostic pathway, the risk of false inclusions and exclusions in clinical practice is high, potentially limiting the generalizability of this finding beyond highly specialized genomic centers. Second, the study documents one non-responding patient (ID4) with a concomitant TAP1 loss-of-function mutation. The authors hypothesize that defects in key components of the antigen presentation machinery (APM) may counteract the pro-immune effects of MLH1 deficiency. Previous studies have confirmed that APM impairment is a key resistance mechanism to ICIs, and alterations in this pathway are not uncommon in ccRCC[4]. However, the prevalence and full spectrum of such co-occurring mutations in MLH1-deficient ccRCC remain undefined. If a substantial portion of these rare patients harbor concurrent genomic events that can negate the efficacy of ICIs, the positive predictive value of MLH1 testing would be significantly compromised. Future research must systematically analyze resistant cases to determine whether MLH1 deficiency is an independent predictor or merely one component of a broader, more complex predictive signature. Third, although the data are derived from large, landmark trials, the core analysis of ICI efficacy is based on limited sample sizes: only seven patients in the MSK cohort, three in CheckMate-214, and 18 in JAVELIN Renal 101. In studies of a similar nature, the research by Le et al included 86 patients with MMR deficiency, establishing the role of MMR deficiency in predicting response to PD-1 inhibitors across solid tumors[5]. In contrast, the limited cohort size in the present study inherently increases uncertainty in treatment effect estimates and makes them more susceptible to the influence of outliers. Furthermore, the authors’ observation of a late-emerging, MLH1-proficient metastasis in an initial responder (ID1) highlights the profound impact of intratumoral heterogeneity, which is difficult to fully characterize in small cohorts. Therefore, although the therapeutic signal is promising, the precision and generalizability of the exceptional response rates attributed to MLH1 deficiency must be validated in larger, aggregated datasets. In conclusion, Dawidek and colleagues have accurately identified a molecular subtype of ccRCC with marked sensitivity to ICIs. For this discovery to develop into a reliable clinical tool, future work must prioritize the development of cost-effective and standardized diagnostic strategies, elucidate the impact of coexisting resistance mechanisms, and validate the efficacy signal in expanded patient cohorts to establish its precise predictive value.