Overall tumor genomic instability: an important predictor of recurrence-free survival in patients with localized clear cell renal cell carcinoma

Role of clinicopathological variables in predicting recurrence and survival outcomes after surgery for non-metastatic renal cell carcinoma: Systematic review and meta-analysis

Renal cell carcinoma (RCC) represents 2% of all diagnosed malignancies worldwide, with disease recurrence affecting 20% to 40% of patients. Existing prognostic recurrence models based on clinicopathological features continue to be a subject of controversy. In this meta-analysis, we summarized research findings that explored the correlation between clinicopathological characteristics and post-surgery survival outcomes in non-metastatic RCC patients. Our analysis incorporates 99 publications spanning 140 568 patients. The study's main findings indicate that the following clinicopathological characteristics were associated with unfavorable survival outcomes: T stage, tumor grade, tumor size, lymph node involvement, tumor necrosis, sarcomatoid features, positive surgical margins (PSM), lymphovascular invasion (LVI), early recurrence, constitutional symptoms, poor performance status (PS), low hemoglobin level, high body-mass index (BMI), diabetes mellitus (DM) and hypertension. All of which emerged as predictors for poor recurrence-free survival (RFS) and cancer-specific survival. Clear cell (CC) subtype, urinary collecting system invasion (UCSI), capsular penetration, perinephric fat invasion, renal vein invasion (RVI) and increased C-reactive protein (CRP) were all associated with poor RFS. In contrast, age, sex, tumor laterality, nephrectomy type and approach had no impact on survival outcomes. As part of an additional analysis, we attempted to assess the association between these characteristics and late recurrences (relapses occurring more than 5 years after surgery). Nevertheless, we did not find any prediction capabilities for late disease recurrences among any of the features examined. Our findings highlight the prognostic significance of various clinicopathological characteristics potentially aiding in the identification of high-risk RCC patients and enhancing the development of more precise prediction models.

The chromosomal instability 25 gene signature is identified in clear cell renal cell carcinoma and serves as a predictor for survival and Sunitinib response

Background

Chromosomal instability (CIN) is a cancer hallmark and it is difficult to directly measure its phenotype, while a CIN25 gene signature was established to do so in several cancer types. However, it is currently unclear whether there exists this signature in clear cell renal cell carcinoma (ccRCC), and if so, which biological and clinical implications it has.

Methods

Transcriptomic profiling was performed on 10 ccRCC tumors and matched renal non-tumorous tissues (NTs) for CIN25 signature analyses. TCGA and E-MBAT1980 ccRCC cohorts were analyzed for the presence of CIN25 signature, CIN25 score-based ccRCC classification, and association with molecular alterations and overall or progression-free survival (OS or PFS). IMmotion150 and 151 cohorts of ccRCC patients treated with Sunitinib were analyzed for the CIN25 impact on Sunitinib response and survival.

Results

The transcriptomic analysis of 10 patient samples showed robustly upregulated expression of the CIN25 signature genes in ccRCC tumors, which were further confirmed in TCGA and E-MBAT1980 ccRCC cohorts. Based on their expression heterogeneity, ccRCC tumors were categorized into CIN25-C1 (low) and C2 (high) subtypes. The CIN25-C2 subtype was associated with significantly shorter patient OS and PFS, and characterized by increased telomerase activity, proliferation, stemness and EMT. The CIN25 signature reflects not only a CIN phenotype, but also levels of the whole genomic instability including mutation burden, microsatellite instability and homologous recombination deficiency (HRD). Importantly, the CIN25 score was significantly associated with Sunitinib response and survival. In IMmotion151 cohort, patients in the CIN25-C1 group exhibited 2-fold higher remission rate than those in the CIN25-C2 group (P = 0.0004) and median PFS in these two groups was 11.2 and 5.6 months, respectively (P = 7.78E-08). Similar results were obtained from the IMmotion150 cohort analysis. Higher EZH2 expression and poor angiogenesis, well characterized factors leading to Sunitinib resistance, were enriched in the CIN25-C2 tumors.

Conclusion

The CIN25 signature identified in ccRCC serves as a biomarker for CIN and other genome instability phenotypes and predicts patient outcomes and response to Sunitinib treatment. A PCR quantification is enough for the CIN25-based ccRCC classification, which holds great promises in clinical routine application.

Fusion gene recurrence in non-small cell lung cancers and its association with cigarette smoke exposure

BACKGROUND

Lung cancer remains the leading cause of cancer-related deaths in the US despite novel treatment protocols, with about 235,000 new cases and 131,000 deaths expected from this cancer in 2021 alone. Lung adenocarcinoma and squamous cell carcinoma, which are both subtypes of non-small cell lung cancer, account for most lung cancer cases, and comparing the molecular signatures in these two cancers can identify novel mechanisms that contribute to non-small cell lung cancer oncogenesis.

METHODS

We, in this study, performed a comprehensive gene fusion profiling of these cancers, which is understudied in lung cancers. Using an alignment-free fusion detection tool, 'ChimeRScope', we screened for gene fusions in lung adenocarcinoma and squamous cell carcinoma datasets from The Cancer Gene Atlas database. Fusion profiles in these two cancer subtypes were essentially different with minimal overlap.

RESULTS

Our analysis revealed a positive association of smoking to fusion frequency in lung adenocarcinoma but not in squamous cell carcinoma and identified several fusion genes that could be explored as markers associated with cigarette smoke exposure. We also identified differentially regulated pathways linked to E2F, G2M checkpoint, and MTORC1 signaling upregulated and P53 pathway downregulated in samples containing high fusions in lung adenocarcinoma. Our results indicate that downregulation of the P53 pathway leads to higher gene fusion formation in lung adenocarcinoma.

CONCLUSIONS

This manuscript provides a strong rationale for investigating the molecular mechanisms of cigarette smoke-induced gene fusion formation associated with lung cancer. Novel recurrent fusions associated with cigarette smoke were identified in our study, which could further be investigated for patient stratification, personalized therapy, and therapeutic monitoring.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

Background

Genomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.

Methods

The present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.

Results

Results of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.

Conclusions

In conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

A computational prognostic model of lncRNA signature for clear cell renal cell carcinoma with genome instability

Long non-coding RNAs (lncRNAs) play a critical role in genomic instability and prognosis of cancer patients, but the methods to identify genomic instability-related lncRNAs have yet to be established. In the present study, to assess the prognostic value of lncRNAs associated with genomic instability in clear cell renal cell carcinoma (ccRCC).A computational framework was established based on the mutation hypothesis and combined lncRNA expression and somatic mutation profiles of the ccRCC genome. Furthermore, a prognostic model was developed using the genome instability-derived lncRNA signature GILncSig based on three lncRNA genes (LINC02471, LINC01234, and LINC00460) and verified using multiple independent patient cohorts.This study established an effective computational method to study the role of lncRNAs in genomic instability, with potential applications in identifying new genomic instability-related cancer biomarkers.

A genomic instability-derived risk index predicts clinical outcome and immunotherapy response for clear cell renal cell carcinoma

This study aims to originate agenomic instability-derived risk index (GIRI) for prognostic analysis of clear cell renal cell carcinoma (ccRCC) and explore the mutation characteristics, immune characteristics, and immunotherapy response defined by GIRI. Differentially expressed genome instability-associated genes were obtained from the genomic unstable (GU) group and the genomic stable (GS) group. Rigorous screening conditions were assigned to the screening of hub genes, which were then used to generate the GIRI through multivariate Cox regression analysis. The selected samples were assigned to the high-risk group or the low-risk group based on the median GIRI. Possible reasons for the prognostic differences in risk subgroups were explored from the aspects of mutation profiles, immune profiles, immunomodulators, and biological pathway activities. The possibility of immunotherapy response was predicted by Tumor Immune Dysfunction and Exclusion analysis results. The prediction of drugs that might reverse the expression profiles of the risk subgroups was discovered through theonnectivity Map (CMap). High-risk populations manifested poor overall survival than low-risk populations and were characterized by elevated cumulative mutation counts and tumor mutation burden. Also, high-risk populations had higher immune scores, immunomodulator (PD-1, CTLA4, LAG3, and TIGIT) expression, and genomic instability-related pathway activities, and were more likely to reap benefits from immunotherapy. Besides, we predicted several drugs (PI3K inhibitor, ATPase inhibitor, and phenylalanyl tRNA synthetase inhibitor) targeting risk subgroups. The well established GIRI was an effective cancer biomarker for predicting ccRCC prognosis and provided apotential reference value for identifying immunotherapy response.

Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.

METHODS

We decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.

RESULTS

We developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.

CONCLUSION

Our study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.