Genomic landscape and clinical actionability of renal cell carcinoma in an Indian cohort: A real-world targeted next-generation sequencing study

Abstract

BACKGROUND

Renal cell carcinoma (RCC) demonstrates extensive genomic heterogeneity. While major sequencing projects like The Cancer Genome Atlas have defined key molecular causes; real-world genomic data from Indian population is limited. This study intends to define the mutational landscape of RCC in an Indian tertiary cancer center and to evaluate the clinical actionability of identified alterations using the European Society for Medical Oncology Scale for Clinical Actionability of Molecular Targets (ESCAT).

AIM

To characterize the genomic landscape of RCC in an Indian real-world cohort using targeted next-generation sequencing (NGS) and to evaluate the clinical actionability of detected alterations according to the ESCAT.

METHODS

This retrospective real-world investigation included 48 patients with pathologically diagnosed RCC who underwent tumor genomic profiling with a focused 14-gene NGS panel encompassing chromatin-remodeling genes, DNA damage repair (DDR) pathways, and the PI3K-AKT-mTOR signaling axis. Clinicopathological features were collected and genetic changes were categorized by ESCAT tiers to assess potential clinical significance.

RESULTS

The cohort comprised 75% males with a median age of 58 years. Clear cell RCC (ccRCC) was the most common histology (81.3%) and advanced disease (American Joint Committee on Cancer stage III/IV) was found in 56.2% of patients. The most frequently altered genes were VHL (35.4%), PBRM1 (25.0%), SETD2 (14.6%), ATM (12.5%), and TP53 (12.5%). In the ccRCC subgroup (n = 39), mutation frequencies for VHL and PBRM1 were 38.5% and 25.6%, respectively. The most prevalent co-alteration pattern was VHL-PBRM1 mutations (n = 6). ESCAT analysis showed potentially actionable alterations in 18.8% of cases, mostly Tier II/III variants in the PI3K-AKT-mTOR pathway (MTOR, TSC1/TSC2, PIK3CA) and Tier III/IV alterations in DDR genes.

CONCLUSION

This study delineates the real-world genomic landscape of RCC in an Indian cohort, confirming key chromatin-remodeling alterations characteristic of ccRCC while suggesting relatively higher frequencies of ATM and TP53 mutations. Although targeted NGS provides important molecular insights, the proportion of high-tier actionable alterations remains limited, underscoring the need for broader genomic profiling and biomarker-driven studies in this population.

Key Words: Clear cell renal cell carcinoma; Next-generation sequencing; Precision oncology; Real-world data; European Society for Medical Oncology Scale for Clinical Actionability of Molecular Targets

Core Tip: Renal cell carcinoma (RCC) demonstrates significant molecular heterogeneity; however, genomic data from Indian patients remain limited. In this real-world single-center study, targeted next-generation sequencing identified recurrent alterations in VHL, PBRM1, SETD2, ATM, and TP53, with concurrent VHL-PBRM1 mutations representing the most frequent co-alteration pattern. Potentially actionable alterations, primarily involving the PI3K-AKT-mTOR pathway, were detected in a subset of patients based on the European Society for Medical Oncology Scale for Clinical Actionability of Molecular Targets. These findings provide important insights into the genomic profile and therapeutic implications of RCC in an underrepresented population.

INTRODUCTION

Renal cell carcinoma (RCC) is a heterogeneous group of primary malignant tumors with a wide spectrum of clinical, morphological and molecular features[1-4]. Clear cell RCC (ccRCC) is the most common histology worldwide and is responsible for bulk of severe disease presentations and cancer-related deaths in urologic oncology[5]. The clinical course of RCC is highly variable, ranging from indolent, incidental localized masses to rapidly progressive metastatic disease refractory to systemic therapies.

Chromosome 3p is an integral part of the basic genomic architecture of ccRCC. In the majority of sporadic ccRCC cases the initiating oncogenic event is the biallelic inactivation of the VHL tumor suppressor gene[6,7]. This results in the aberrant intracellular accumulation of hypoxia-inducible factors (HIF-1α and HIF-2α) and the subsequent upregulation of pro-angiogenic cascades. Following VHL loss, the course of tumor evolution is often influenced by subclonal loss-of-function mutations in adjacent 3p chromatin-remodelling and epigenetic regulatory genes, predominantly PBRM1, SETD2, and BAP1[1,8]. Complex structural changes and point mutations in PI3K-AKT-mTOR signalling axis and DNA damage repair (DDR) pathways often contribute further disease progression and therapy resistance.

Large-scale genomic consortia, particularly The Cancer Genome Atlas (TCGA), have provided extensive molecular blueprints of RCC[1], characterizing its mutational landscape and delineating prognostic multi-omics signals. However, the foundational data driving contemporary precision oncology guidelines are significantly biased towards Caucasian populations. Emerging literature suggests that genetic heritage and environmental factors may profoundly impact the somatic mutational landscape. This may in turn impact disease behavior and treatment sensitivity across populations[9]. RCC is increasing in the Indian subcontinent but there is a lack of real-world genomic data describing the exact mutational patterns of Indian patients and more importantly the clinical use of sequencing[2].

Moreover, routine use of next-generation sequencing (NGS) in clinical practice relies on the discovery of actionable molecular targets. Although RCC does not offer as rich a landscape for targeted precision therapies as seen in non-small cell lung cancer or melanoma, it is characterized by a relative paucity of highly actionable driver mutations. In resource-limited settings, the clinical utility of NGS is therefore best judged by carefully assessing the actionability of identified molecular alterations.

This is the first report from India describing the clinicopathological features and targeted genetic landscape of RCC patients treated at a tertiary cancer center. The study also aims to contextualize the existing biology of Indian RCC by benchmarking these real-world findings against global TCGA datasets and systematically grading the identified genomic targets using the European Society for Medical Oncology Scale for Clinical Actionability of Molecular Targets (ESCAT) and estimate the practical yield of precision oncology in this underrepresented population.

MATERIALS AND METHODS

Study design and patient selection

This investigation was a retrospective, single center, observational real-world evidence study conducted at a tertiary cancer care center in India. The methodology of this study is consistent with the STROBE standards[10] for observational research and is in accordance with the concepts of the European Society for Medical Oncology Guidance for Reporting Oncology real-world evidence framework[11].

Consecutive adult patients with histologically confirmed RCC who underwent targeted NGS profiling as part of routine clinical care between January 2021 and December 2024 were identified through queries of institutional molecular pathology registries. Eligible patients had pathologically confirmed primary or metastatic RCC of any histological subtype, availability of adequate formalin-fixed paraffin embedded (FFPE) tumor tissue with at least 30% viable neoplastic cellularity as determined by a dedicated genitourinary pathologist, and complete baseline demographic and clinical staging data. Patients were excluded if tissue samples were insufficient for comprehensive sequencing or if key co-clinical data were missing.

Clinicopathological Data Acquisition Clinical and demographic characteristics were collected from electronic health records. This included age at first diagnosis, biological sex, and clinical stage at presentation as defined by the American Joint Committee on Cancer (AJCC) 8^th edition staging manual. Histological subtypes were categorized as ccRCC, papillary RCC, chromophobe RCC, or other/unclassified variants according to the 2022 World Health Organization classification. Tumour nuclear grade was assessed using the current World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system, and for historical cases lacking WHO/ISUP scoring, Fuhrman grading was employed.

Genomic Profiling and Next-Generation Sequencing Pipeline Genomic DNA was isolated from micro-dissected FFPE tumor tissue. Targeted sequencing was performed utilizing a custom 14-gene kidney cancer NGS panel designed to identify recurrently mutated genes relevant to RCC pathogenesis and targeted therapeutics. The genes included in this panel were VHL, PBRM1, SETD2, BAP1, KDM5C, TSC1, TSC2, MTOR, PIK3CA, ATM, TP53, SMARCB1, NF2, MET, and PTEN.

Library preparation was performed following the manufacturer’s methods, and sequencing was carried out using an Illumina MiSeq platform (Illumina Inc., San Diego, CA, United States). Variants were called using a validated bioinformatic process with a minimum sequencing depth of ≥ 500 × and a variant allele frequency threshold of 5%. Bioinformatics processing, including alignment to the reference genome and variant calling, was performed using a validated bioinformatic pipeline. Analysis was limited to somatic mutations with ACMG/AMP tiering categorized as pathogenic or likely pathogenic[12].

Actionability Assessment via ESCAT All discovered pathogenic changes were annotated and ranked according to ESCAT to systematically assess the clinical value of the genomic findings[3]. The ESCAT framework classifies genomic targets into distinct tiers according to the strength of clinical evidence supporting their use as predictive biomarkers: Tier I (ready for routine clinical implementation), Tier II (investigational targets defining populations likely to benefit from specific drugs), Tier III (clinical benefit established in other tumor types), Tier IV (preclinical evidence), and Tier X (lack of evidence).

Statistical analysis

Statistical analysis was performed using SPSS software version 32.0 (IBM Corp., Armonk, NY, United States). Descriptive statistics were used to characterize patient demographics, tumor features and genetic frequencies. Continuous variables were expressed as median and range, and categorical variables as absolute numbers and percentages. Genetic alterations were categorized according to the ESCAT tiers to evaluate their potential clinical utility. Mutation frequencies were calculated for the entire RCC cohort and the ccRCC subgroup. To preserve statistical integrity, missing data for specific variables (e.g., clinical staging) were acknowledged and handled via pairwise deletion. Comparative genomic frequencies were strictly estimated using just the ccRCC subgroup denominator of the TCGA-KIRC database to avoid histologic confounding. Mutational co-occurrence was assessed via cross-tabulation matrices. No formal inferential survival analyses (e.g. Kaplan Meier estimates) were undertaken since the genomic database was retrospective and cross-sectional and there were no longitudinal survival endpoints.

Ethical approval statement

This study was performed in conformity with the ethical principles of the Declaration of Helsinki. The Institutional Ethics Committee (approval No. Res/SCM/41/2020/101) evaluated and approved the protocol for the retrospective analysis of de-identified clinical and genomic data. The retrospective character of the study involving archival tissue allowed for a formal waiver of the need for individual written informed consent.

RESULTS

Baseline patient and tumor characteristics

Forty eight patients with histologically diagnosed RCC met the strict inclusion criteria and provided high-quality NGS data. The comprehensive baseline clinicopathological characteristics of the study cohort are detailed in Table 1. The demographic profile was characterized by a distinct male predominance (75%, n = 36/48 cases with recorded sex) and median age at diagnosis was 58 years (range: 32-84 years).

Table 1 Baseline patient and tumour characteristics of the study cohort (n = 48), n (%)/median (range).

Characteristic
Age at diagnosis (years)	58 (32-84)
Biological sex
Male	36 (75)
Female	12 (25)
Histological subtype
Clear cell RCC	39 (81.3)
Papillary RCC	5 (10.4)
Other/unclassified	4 (8.3)
AJCC clinical stage
Stage I	15 (31.25)
Stage II	6 (12.5)
Stage III	16 (33.3)
Stage IV	11 (22.9)
Nuclear grade (WHO/ISUP)
Grade I-II	33 (68.75)
Grade III	7 (14.58)
Grade IV	8 (16.6)
Tumour content in block (%)	80 (30-95)

RCC: Renal cell carcinoma; WHO/ISUP: World Health Organization/International Society of Urological Pathology.

Histological examination confirmed ccRCC was the predominant subtype, accounting for 81.3% (n = 39) of the cohort. Papillary RCC accounted for 10.4% (n = 5) of cases and the remaining 8.3% (n = 4) were other or unclassifiable histological variations (Figure 1). At the time of diagnosis, a significant proportion of the cohort had locally advanced or widely metastatic disease; with 33.3% (n = 16) classified as AJCC stage III and 22.9% (n = 11) as stage IV (Figure 2). Assessment of nuclear atypia revealed that the majority of tumors were of low-to-intermediate grade, with 68.75% (n = 33) classified as WHO/ISUP grade I or II. The median tumor cellularity within the analyzed FFPE blocks was a strong 80% (range 30%-95%), ensuring high DNA input for targeted sequencing.

Figure 1 Distribution of histological subtypes in the study cohort. Bar chart showing the distribution of histological subtypes among the 48 patients with renal cell carcinoma (RCC). Clear-cell RCC was the predominant histological subtype, followed by papillary RCC and other histological variants. RCC: Renal cell carcinoma.

Figure 2 Clinical stage distribution of the study cohort. Bar chart illustrating the distribution of patients according to the American Joint Committee on Cancer clinical stage at diagnosis. Most patients presented with stage III-IV.

Somatic mutational landscape

The genomic alteration spectrum across the 48-patient cohort was predominantly driven by mutations in canonical 3p tumor suppressor genes and SWI/SNF chromatin-remodeling complexes (Table 2). As expected from known RCC biology, VHL was the most frequently mutated gene, present in 35.4% (n = 17) of the total cohort. The second most frequent alteration occurred in the epigenetic regulator PBRM1 (25.0%, n = 12), followed by the histone methyltransferase SETD2 (14.6%, n = 7), and the histone demethylase KDM5C (8.3%, n = 4). Conversely, BAP1 mutations were identified in only 4.2% of tumors (n = 2).

Table 2 Frequency of genomic alterations detected across the targeted next-generation sequencing panel, n (%).

Gene	Total cohort mutated (n = 48)	ccRCC subgroup mutated (n = 39)	TCGA benchmark[1]
VHL	17 (35.4)	15 (38.5)	Approximately 50%
PBRM1	12 (25.0)	10 (25.6)	Approximately 30%
SETD2	7 (14.6)	6 (15.4)	Approximately 12%
ATM	6 (12.5)	-	Approximately 3%
TP53	6 (12.5)	-	Approximately 3%-4%
KDM5C	4 (8.3)	-	Approximately 6%
TSC1	4 (8.3)	-	Rare
MTOR	3 (6.3)	-	Approximately 7%
TSC2	3 (6.3)	-	Rare
PIK3CA	2 (4.2)	-	Rare
BAP1	2 (4.2)	-	Approximately 10%
MET	0 (0.0)	0 (0.0)	Rare (ccRCC)/approximately 13% (pRCC)

ccRCC: Clear cell renal cell carcinoma; TCGA: The Cancer Genome Atlas; pRCC: Papillary renal cell carcinoma.

Among interesting findings in this cohort was the high prevalence of mutations in genes involved in the DNA repair and cell cycle integrity. Pathogenic mutations were found in ATM and TP53 in 12.5% of cases (n = 6).

We also traced perturbations in the canonical PI3K-AKT-mTOR signaling pathway. Genomic changes were identified in TSC1 (8.3%, n = 4), TSC2 (6.3%, n = 3), the mechanistic target of rapamycin kinase MTOR (6.3%, n = 3), and PIK3CA (4.2%, n = 2). There were no structural variants or point mutations in MET and PTEN (0.0%), consistent with the low prevalence of type 1 papillary histology in the group.

To allow a clear and unconfounded comparison with the TCGA-KIRC data set, which only contained clear cell histology, mutation frequencies were re-calculated for only the 39 ccRCC patients in our study. Within this homogenous subgroup, VHL mutations were observed in 38.5% of tumors (n = 15), PBRM1 in 25.6% (n = 10), and SETD2 in 15.4% (n = 6). The distribution of VHL mutation status according to histological subtype is shown in Figure 3.

Figure 3 Distribution of VHL mutation status according to histological subtype. Stacked bar chart showing the distribution of VHL-mutated (VHL+) and VHL wild-type (VHL-) tumors according to renal cell carcinoma histological subtype. VHL mutations were predominantly identified in clear-cell renal cell carcinoma (RCC), whereas relatively few mutations were observed in non-clear-cell RCC. RCC: Renal cell carcinoma.

An analysis of clinical stage distribution partitioned by VHL mutation status revealed diverging phenotypic trends. Among the 17 VHL-mutated tumors, clinical presentation was broadly distributed across the staging spectrum: Stage I (n = 5), stage II (n = 3), stage III (n = 5), and stage IV (n = 3). Conversely, the 28 VHL-wildtype tumors (excluding those with missing stage data) showed a clear bimodal presentation with a strong preference for the extremes of the staging index. This group had a high number of early stage incidentalomas (stage I: n = 10) vs a substantial cluster of advanced disease (stage III/IV combined: n = 15) (Figure 4).

Figure 4 Clinical stage distribution according to VHL mutation status. Grouped bar chart demonstrating the relationship between VHL mutation status and clinical stage at diagnosis. Tumors harboring VHL mutations were identified across all clinical stages, whereas VHL wild-type tumors were more frequently observed in stage I and advanced-stage disease.

Patterns of mutational co-occurrence

Pairwise interaction analysis was performed to identify the clustering of somatic events (Table 3). The most common co-alteration signature of VHL and PRBM1 mutations was seen in 6 cases. This overlap highlights the biological synergy between early dysregulation of hypoxic signaling and subsequent epigenetic remodeling. Other secondary co-mutations associated with route convergence were VHL-SETD2 (n = 4) and VHL-TSC1 (n = 3). Notably, the TP53-KDM5C co-mutation was identified in 4 patients, suggesting an aggressive subclonal trajectory independent of traditional PBRM1 pathways. Conversely, changes in PBRM1 and BAP1 exhibited near-total mutual exclusivity. The overlap of key chromatin-remodeling gene alterations is depicted in Figure 5. Pairwise relationships among the most frequently altered genes are summarized in Figure 6, which demonstrates the predominance of VHL-PBRM1 co-occurrence.

Figure 5 Co-occurrence of chromatin-remodeling gene alterations in the study cohort. Venn diagram illustrating the overlap among pathogenic alterations in VHL, PBRM1, and SETD2. The most frequent co-occurring alteration involved VHL and PBRM1, followed by VHL and SETD2.

Figure 6 Heatmap of pairwise somatic mutation co-occurrence. Heatmap illustrating the frequency of pairwise co-occurring genomic alterations among the most frequently mutated genes in the study cohort. Darker colors represent higher frequencies of co-occurrence, with the strongest association observed between VHL and PBRM1.

Table 3 Most frequent co-occurring somatic mutation pairs.

Intersecting mutation pair	Number of patients (n)
VHL + PBRM1	6
VHL + SETD2	4
TP53 + KDM5C	4
VHL + TSC1	3
PBRM1 + ATM	3

The translational relevance of the NGS data was thoroughly evaluated using the ESCAT. Overall, 9 out of 48 patients (18.8%) harbored at least one genetic mutation associated with prospective precision treatments.

The PI3K-AKT-mTOR signaling cascade dominated the actionable landscape. Pathogenic variants in MTOR (n = 3), TSC1 (n = 4), and TSC2 (n = 3) represent ESCAT Tier II/III targets in RCC. The use of mTOR inhibitors (e.g. everolimus) has limited efficacy in unselected populations but cancers with these pathway alterations have an investigative basis for remarkable response.

Moreover, patients in the subset with ATM mutations (n = 6) had an ESCAT Tier III/IV actionability profile. While not standard of care in RCC, these DDR deficiencies offer strong preclinical and basket-trial evidence for homologous recombination deficiency, imparting a potential susceptibility to PARP inhibitors or synergistic combinations including immune checkpoint blockade. The overall combinatorial mutation patterns across key RCC genes are summarized in Figure 7.

Figure 7 Combinatorial mutation patterns identified by UpSet analysis. UpSet plot demonstrating the distribution of single-gene and co-occurring genomic alterations among the five most frequently mutated genes (VHL, PBRM1, SETD2, ATM, and TP53). The upper panel shows the number of tumors within each mutational intersection, whereas the lower panel shows the total number of tumors harboring alterations in each gene.

Importantly, none of the patients in this cohort had an ESCAT Tier I change (a standard of care biomarker matched to an approved therapeutic agent uniquely designated for that genomic marker in RCC), reflecting the inherent limitations of definitive molecular matching in contemporary urological-oncology.

DISCUSSION

This real-world genomic analysis offers one of the rare contemporary descriptions of the molecular landscape of RCC in an Indian tertiary cancer center. The clinicopathological characteristics of our cohort-including a marked male predominance, median age at diagnosis in the late sixth decade, and prevalence of clear-cell histology-parallel epidemiologic patterns have been described in international datasets. Foundational genomic investigations from TCGA established the central role of chromosome 3p loss and chromatin-remodeling alterations in ccRCC biology, and our findings largely recapitulate these canonical events[1,9-11].

Clinical implications of genomic alterations

VHL: Inactivation of VHL is the driving molecular event in ccRCC by stabilising the signalling of hypoxia-inducible factor. The development of HIF-2α inhibitors has made this pathway therapeutically relevant[13].

PBRM1: PBRM1 alterations have been associated with the tumour immune microenvironment and have been investigated as potential biomarkers associated with response to immune checkpoint inhibition, although their predictive role is under investigation.

SETD2: Loss of SETD2 leads to chromatin deregulation, DNA repair defects and genome instability[14].

MTOR/TSC1/TSC2: Alterations targeting the PI3K-AKT-mTOR pathway could define a biologically relevant subgroup that might be vulnerable to mTOR-directed therapy[15,16].

ATM/DDR: Changes in ATM suggest defective DNA damage response pathways and are an emerging area for investigation of DDR targeted therapies[17].

In the present cohort, the most frequently altered genes were VHL and PBRM1. With analysis restricted to the ccRCC subgroup, to allow comparison with TCGA-KIRC benchmarks, VHL mutations were observed in 38.5% of tumors and PBRM1 mutations in 25.6%. Although these frequencies are slightly lower than the approximately 50% and 30% frequencies reported in TCGA analyses, these differences are not unexpected given the limited cohort size and the use of a targeted sequencing panel rather than whole-exome sequencing. Deep intronic mutations, structural deletions, or copy-number events that contribute considerably to VHL inactivation may not be detected in targeted panels but can be identified in comprehensive genomic datasets.

The predominance of VHL-PBRM1 co-alteration observed in our study further reinforces the prevailing model of ccRCC carcinogenesis in which early dysregulation of hypoxia signaling is followed by epigenetic reprogramming through chromatin-remodeling gene loss. Studies have previously demonstrated that PBRM1 deletion can modulate tumor differentiation and influence tumor immune microenvironment interactions. This in turn can impact response to systemic treatments. On the other hand, BAP1 alterations—associated with aggressive disease biology—were relatively uncommon in our dataset, supporting the notion that BAP1-driven evolutionary trajectories represent a unique molecular subclass of RCC.

A notable observation in our cohort was the relatively higher prevalence of ATM and TP53 mutations compared with TCGA datasets. In large international sequencing cohorts, these genes typically demonstrate mutation frequencies of approximately 3%-4% in clear-cell RCC. Several explanations may account for the increased frequency observed here. First, the patient population included in this study contained a large proportion of advanced-stage disease, with over half presenting with stage III or IV tumors. Studies of multiregional sequencing have demonstrated that TP53 mutations are frequent late subclonal events of tumor progression and metastatic dissemination rather than early truncal alterations[9]. Thus, enrichment of these variations may represent disease stage rather than genuine population-level biological differences.

An alternative hypothesis may be related to the impact of genetic heritage on tumor genomics. Emerging data indicate that somatic mutational landscapes may differ across populations due to environmental exposures, germline background, and tumor evolutionary pressures[18,19]. Studies examining cancer genomics across populations have highlighted that South Asian cohorts are underrepresented in large genomic databases. Therefore, regional sequencing studies such as ours contribute valuable data toward understanding potential ancestry-associated differences in RCC biology[20,21].

From a therapeutic point of view, changes in the PI3K-AKT-mTOR signaling pathway were the most common potentially actionable events in our cohort. Mutations affecting MTOR, TSC1, and TSC2 were collectively identified in several patients. mTOR inhibitors such as everolimus have demonstrated activity in advanced RCC[15], although response rates remain limited in unselected individuals. Importantly, emerging genomic analyses are suggesting that tumors with activating mutations within this pathway may demonstrate enhanced sensitivity to mTOR inhibition, highlighting the potential utility of molecular stratification[16,22].

The ESCAT was applied and revealed that approximately 19% of patients had potentially actionable genomic alterations. However, the bulk of these alterations were identified as Tier II or III, reflecting exploratory or cross-tumor evidence rather than established biomarkers guiding standard-of-care therapy in RCC. Notably, no ESCAT Tier I modifications were noted in this cohort. This result exemplifies a well-recognized challenge in RCC precision oncology: Unlike lung cancer or melanoma, RCC lacks a large number of clearly targetable driver mutations directly linked to approved therapies[23].

Despite this limitation, genomic profiling may nevertheless have clinical utility in some specific contexts. Dysregulations of the DDR pathway, such as ATM mutations may facilitate enrolment in biomarker-driven clinical studies evaluating PARP inhibitors or combination strategies involving immune checkpoint blockade. Moreover, broader genomic approaches-including whole-exome sequencing, transcriptomic analysis, and single-cell profiling-are increasingly revealing complex tumor microenvironment interactions and potential therapeutic vulnerabilities that extend beyond single-gene biomarkers[24,25].

Our findings therefore support a pragmatic approach of NGS in the clinical practice of RCC. Targeted genomic profiling may be most beneficial in identifying candidates for clinical trials or exploring therapeutic strategies in refractory disease settings, rather than as a universal diagnostic tool for all patients[26].

Strengths of the study

Strengths of this study are the real-world design, the uniform molecular testing with a predefined targeted NGS panel, centralised pathological review and the evaluation of genomic findings according to the ESCAT framework of clinical actionability. Importantly, the study offers one of the few contemporary descriptions of the genomic landscape of RCC in an Indian population, providing valuable data on an under-represented ethnic group to the burgeoning field of precision oncology.

Limitations

Several limitations of this study should be acknowledged. First, the limited sample size and retrospective single center design may limit the generalisability of the frequencies of genomic alterations observed. Therefore, these results should be considered hypothesis-generating and not as population-level estimates. Validation of these findings in Indian RCC patients will need larger multicenter studies including broader genomic profiling and correlation with clinical outcomes.

Second, since a targeted 14-gene sequencing panel was used in this analysis, detection was restricted to a limited set of mutations and did not capture copy-number variations, structural rearrangements, or tumor mutational burden. Third, the lack of matching germline sequencing introduces uncertainty regarding whether certain detected variants-particularly within DDR genes-represent somatic or germline alterations. Fourth, the absence of longitudinal survival outcomes hinders direct correlation between specific genomic alterations and therapeutic response.

Future studies involving multicenter cohorts, broader genomic profiling platforms, and more integration of clinical outcome data will be essential to refine precision oncology approaches for RCC in the Indian population.

CONCLUSION

In this single-center real-world Indian RCC cohort, predominantly comprising male patients (75%, n = 36/48 cases with recorded sex) with a median age at diagnosis of 58 years (range: 32-84 years), targeted NGS re-emphasizes the important role of VHL- and PBRM1-driven chromatin-remodeling alterations in clear-cell RCC pathogenesis while highlighting a potentially increased frequency of TP53 and ATM alterations in advanced disease. Genomic datasets from South Asian populations remain significantly underrepresented in international sequencing consortia, highlighting the importance of region-specific molecular studies to ensure equitable implementation of precision oncology. The absence of ESCAT Tier I biomarkers underscores the limited clinical impact of targeted sequencing in routine RCC management, even though approximately one-fifth of patients demonstrated potentially actionable genomic alterations. Continued efforts integrating comprehensive genomic profiling and prospective clinical trials will be required to realize the promise of precision oncology in RCC.