Predictive genomic markers of response to VEGF targeted therapy in metastatic renal cell carcinoma

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients

BACKGROUND

Disulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC.

METHODS

The expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort.

RESULTS

We identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort.

CONCLUSION

The DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Chromosome 3p gene alterations as biomarkers for immunocombinations in metastatic renal cell carcinoma: A hypothesis-generating analysis

BACKGROUND

Identifying biomarkers for metastatic renal cell carcinoma (mRCC) is an unmet need in actual immunotherapy era. Available data regarding chromosome 3p genes (i.e., VHL, PBRM1, SETD2) mutations as potential predictors for therapy response is conflicting. We describe the impact of these mutations on clinical outcomes in mRCC patients treated with immune checkpoint inhibitor (ICI)-doublet or ICI/tyrosine kinase inhibitor (TKI) combinations.

METHODS

We performed a single-center retrospective analysis on mRCC patients treated with first line ICI/ICI or ICI/TKI. A multi-gene panel was used, allowing the amplification of 841 amplicons (54.93 kb, human reference sequence hg19/GRCh37) in the coding sequences of the following genes: ATM, BAP1, KDM5C, MET, MTOR, NF2, PBRM1, PIK3CA, PTEN, SETD2, SMARCB1, TP53, TSC1, TSC2, VHL.

RESULTS

18 patients undergoing ICI/ICI and ICI/TKI who had tumor tissue adequate for molecular analysis were included. Histology was 100% clear cell. IMDC risk was 50% intermediate, 33.4% good, 16.6% poor. First line therapy was 89% ICI/TKI, 11% ICI/ICI. 83.3% pts (n = 15) carried genomic alterations (GA). Most common GA included VHL in 44% (n = 8; 7 pathogenic - PAT and 1 variant of unknown significance - VUS), PBRM1 in 44% (n = 8; 5 PAT and 3 VUS) and SETD2 in 33% (n = 6; 4 PAT and 2 VUS). With the limit of a small sample that did not allow proper statistical analyses, SETD2-mutated patients had lower median progression free (mPFS) and overall survival (mOS) than non-SETD2 mutated patients. Higher mPFS and mOS were shown with VHL or PBRM1 GA, especially in PBRM1 +VHL mutated pts.

CONCLUSIONS

Our data shows a possible negative predictive role of SETD2 GA for ICI-based therapy in RCC. Concomitant VHL and PBRM1 GA could act as a predictor for ICI/TKI efficacy. Our hypothesis-generating analysis highlights the need of an integrated evaluation of these genes as promising biomarkers in RCC. Further larger studies are required.

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Baseline and Dynamic Changes in Hemoglobin Levels Predict Treatment Response and Prognosis in Metastatic Renal Cell Carcinoma: A Multicenter Retrospective Study

INTRODUCTION

Clinical markers of response in metastatic renal cell carcinoma (mRCC) are lacking. Low hemoglobin (Hb) is associated with poor outcomes in the IMDC risk score. This study evaluates the role of Hb as a marker of treatment outcomes in mRCC.

PATIENTS AND METHODS

This multicenter retrospective study evaluated 276 patients with mRCC treated with frontline immune checkpoint inhibitor (ICI) therapy, ICI and vascular endothelial growth factor (VEGF) inhibitor (VEGFI) combinations (ICI/VEGFI), or VEGFI monotherapy between 2014 and 2021. Hb levels at baseline, week 6 and 12 and at disease progression or death were recorded. Patients were categorized as responders (CR+PR) or nonresponders (SD+PD) using cross-sectional imaging at week 12. The association between baseline and dynamic changes in Hb and oncological outcomes was assessed.

RESULTS

Thirty-seven percent, 40% and 22% of patients received ICIs, ICI/VEGFI and VEGFI respectively. In patients receiving ICIs, there was a significant increase in Hb amongst responders from baseline to week 12 (P= .02). Amongst patients receiving ICI/VEGFI, there was an increase in Hb from baseline to week 12 which was greater in responders (P< .001). In patients receiving VEGFI monotherapy, responders had a higher Hb at baseline (P= .01), week 6 (P= .04), and week 12 (P= .003). An increase in Hb was a significant independent predictor of progression-free survival amongst patients receiving ICIs (HR 0.40, 95%CI, 0.19-0.83, P= .009).

CONCLUSION

Baseline and dynamic changes in Hb are associated with first-line treatment outcomes in patients with mRCC and represent a pragmatic early serological marker.

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.

The Molecular Characteristics of Non-Clear Cell Renal Cell Carcinoma: What's the Story Morning Glory?

Non-clear cell renal cell carcinomas are a miscellaneous group of tumors that include different histological subtypes, each one characterized by peculiarity in terms of genetic alteration, clinical behavior, prognosis, and treatment response. Because of their low incidence and poor enrollment in clinical trials, alongside their heterogeneity, additional efforts are required to better unveil the pathogenetic mechanisms and, consequently, to improve the treatment algorithm. Nowadays, tyrosine kinase inhibitors, mTOR and MET inhibitors, and even cisplatin-based chemotherapy and immunotherapy are potential weapons that are still under evaluation in this setting. Various biomarkers have been evaluated for detecting progression and monitoring renal cell carcinoma, but more studies are necessary to improve this field. In this review, we provide an overview on the molecular characteristics of this group of tumors and the recently published trials, giving an insight into what might become the future therapeutic standard in this complex world of non-clear cell kidney cancers.

Druggable targets meet oncogenic drivers: opportunities and limitations of target-based classification of tumors and the role of Molecular Tumor Boards

The therapeutic landscape of cancer is changing rapidly due to the growing number of approved drugs capable of targeting specific genetic alterations. This aspect, together with the development of noninvasive methods for the assessment of somatic mutations in the peripheral blood of patients, generated a growing interest toward a new tumor-agnostic classification system based on ‘predictive’ biomarkers. The current review article discusses this emerging alternative approach to the classification of cancer and its implications for the selection of treatments. It is suggested that different types of cancers sharing the same molecular profiles could benefit from the same targeted drugs. Although recent clinical trials have demonstrated that this approach cannot be generalized, there are also specific examples that demonstrate the clinical utility of this alternative vision. In this rapidly evolving scenario, a multidisciplinary approach managed by institutional Molecular Tumor Boards is fundamental to interpret the biological and clinical relevance of genetic alterations and the complexity of their relationship with treatment response.

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The identification of oncogenic drivers offers the opportunity to develop target-specific drugs.

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The inhibition of crucial pathways realizes the principle of druggable target to exploit cancer vulnerability.

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The approval of new anticancer agents based on target-based concept represents a paradigm shift in cancer therapy.

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However, only few drugs have been approved so far on an agnostic basis and the concept of biomarker cannot be generalized.

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Tumor Molecular Boards will have an increasing role in the identification of new therapeutic options in selected patients.

Precision Medicine, Artificial Intelligence, and Genomic Markers in Urology. Do we need to Tailor our Clinical Practice?

Precision medicine plays a key role in urological oncology practice nowadays, with the breakthrough of the poly (ADP-ribose) polymerase inhibitors (PARPi), which play a critical role in different DNA damage repair (DDR) pathways, the immune checkpoint inhibitors, the genomic expression profiles and current genome manipulation-directed targeted therapy. Information and technology (IT) are set to change the way we assess and treat patients and should be reviewed and discussed. The aim of the present article is to demonstrate a detailed revision on precision medicine, including novel therapeutic targets, genomic markers, genomic stratification of urological patients, and the top-notch technological breakthroughs that could change our clinical practice

We performed a review of the literature in four different databases (PubMed, Embase, Lilacs, and Scielo) on any information concerning prostate, bladder, kidney and urothelial cancer novel treatments with PARPi, immune checkpoint inhibitors (ICIs), targeted therapy with fibroblast growth factor receptor inhibitors (FGFRi), and theranostics with prostate-specific membrane antigen (PSMA) targeted monoclonal antibodies. Artificial intelligence, machine learning, and deep learning algorithm in urological practice were also part of the search. We included all articles written in English, published within the past 7 years, that discussed outstanding therapies and genomics in urological cancer and artificial intelligence applied to urology. Meanwhile, we excluded articles with lack of a clear methodology and written in any other language than English.

One-hundred and twenty-six articles of interest were found; of these, 65 articles that presented novel treatments of urological neoplasms, discussed precision medicine, genomic expression profiles and biomarkers in urology, and latest deep learning and machine learning algorithms as well as the use of artificial intelligence in urological practice were selected. A critical review of the literature is presented in the present article.

Urology is a constantly changing specialty with a wide range of therapeutic breakthroughs, a huge understanding of the genomic expression profiles for each urological cancer and a tendency to use cutting-edge technology to treat our patients. All of these major developments must be analyzed objectively, taking into account costs to the health systems, risks and benefits to the patients, and the legal background that comes with them. A critical analysis of these new technologies and pharmacological breakthroughs should be made before considering changing our clinical practice. Nowadays, research needs to be strengthened to help us improve results in assessing and treating our patients.