Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing

Comprehensive analysis of intratumoural heterogeneity of somatic copy number alterations in diffuse glioma reveals clonality-dependent prognostic patterns

AIMS

Intratumoural heterogeneity (ITH) has been implicated in tumour growth and progression as well as therapy resistance. However, the extent of ITH of somatic copy number alterations (ITH-SCNAs) as a result of tumour evolution and its influence on clinical outcomes in diffuse glioma (DG) remain poorly understood.

METHODS

We used an integrated computational method to infer clonal and subclonal SCNAs in 760 untreated primary DGs from The Cancer Genome Atlas. ITH-SCNAs at the genome-wide, peak (region with recurrent SCNAs) and gene level were calculated. We used the Kaplan-Meier estimators and Cox proportional hazards models to examine the associations of ITH-SCNA with patient outcomes. An independent cohort of 243 patients with paired initial and recurrent tumours from the Glioma Longitudinal Analysis Consortium was used for validation.

RESULTS

DGs showed widespread ITH-SCNA, with a median of 25.5% of SCNAs identified as subclonal. We found that clonal SCNA burden had stronger prognostic power than total SCNAs in IDH-mutant astrocytoma. Coamplifications of receptor tyrosine kinases (RTKs) tended to be subclonal, and subclonal RTK amplification was significantly associated with high tumour proliferative potential and unfavourable clinical outcomes in IDH-wild-type glioblastoma. In addition, we found that the prognostic values of the peak-level SCNAs were related to their mutated clonal architecture, from which three clonality-dependent prognostic patterns of SCNAs were proposed, including clonal-dominant, subclonal-dominant and clonality-independent schemas.

CONCLUSIONS

The systematic analysis of ITH-SCNAs in large cohorts of DGs highlighted the importance of considering the clonality of SCNA in discovery of tumour prognostic markers.

Evaluation of Annexins Family as Potential Biomarker for Predicting Progression and Prognosis in Clear Renal Cell Carcinoma

Background. Annexins family (ANXAs), as a Ca2+-dependent phospholipid-binding protein superfamily, participates in a wide variety of biological activities and has been reported to be dysregulated in numerous types of human cancers. Evidence from cell lines and human tissues indicates that ANAXs are involved in kidney clear renal cell carcinoma (KIRC) tumorigenesis. However, their prognostic value and expression pattern associated with KIRC remain to be elucidated. Methods. We visited public databases, including ONCOMINE, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan–Meier plotter, cBioPortal, and GeneMANIA, to conduct comprehensive bioinformatics analysis and tried to detect basic relationships between each Annexins family member and KIRC. Results. We found that the expression level of ANXA1/2/4/5/6/7/8/13 in clear renal cell carcinoma tissue was higher than that in the kidney tissue, while the expression level of ANXA3/9/11 in the former was lower than that in the latter. The expression level of ANXA7/8/13 is related to the stage of the tumour. Survival analysis using the Kaplan–Meier plotter database showed that a high transcription level of ANXA2/5/8/10 is related to a low overall survival rate (OS) in predicting KIRC patients. In contrast, high ANXA3/4/7/9/11/13 levels are associated with a high OS in these patients. Conclusions. Our study implies that ANXA4/8/13 are potential targets of precision therapy for patients with KIRC and that ANXA2/5/8/10 are new biomarkers for the prognosis of KIRC.

Origins and Timing of Emerging Lesions in Advanced Renal Cell Carcinoma

Renal cell carcinoma (RCC) with venous tumor thrombus (VTT) arising from the primary tumor occurs in approximately 10% of cases and is thought to represent more advanced disease. The intravascular nature of VTT suggests that it may serve as a source for hematogenous metastases. RCC with VTT and distant metastasis provides unique opportunities to examine the origins and emergence timing of these distinct tumor lesions, and to identify molecular correlates with disease state. We performed multi-region exome and RNA-sequencing analysis of 16 patients with RCC with VTT, with eight patients also having sequenced metastasis, to identify genomic alterations, biological pathways, and evolutionary processes contributing to VTT and metastasis, and to ask whether metastasis arises directly from or independent of VTT. No specific genomic alterations were associated with VTT. Hallmark copy-number alterations (deletions of 14q, 8p, and 4q) were associated with metastasis and disease recurrence, and secondary driver alterations tended to accumulate in metastatic lineages. Mismatch repair mutational signatures co-occurred across most tumors, suggesting a role for intracellular DNA damage in RCC. Robust phylogenetic timing analysis indicated that metastasis typically emerged before VTT, rather than deriving from it, with the earliest metastases predicted to emerge years before diagnosis. As a result, VTT in metastatic cases frequently derived from a metastatic lineage. Relative to the primary tumor, VTT upregulated immediate-early genes and transcriptional targets of the TNFα/NF-κB pathway, whereas metastases upregulated MTOR and transcriptional targets downstream of mTORC1 activation.

IMPLICATIONS

These results suggest that VTT and metastasis formation occur independently, VTT presence alone does not necessarily imply more advanced disease with inevitably poor prognosis.

Genetic Alterations in Papillary Thyroid Carcinoma With Hashimoto's Thyroiditis： ANK3, an Indolent Maintainer of Papillary Thyroid Carcinoma

Hashimoto’s thyroiditis (TH) is a risk factor for the occurrence of papillary thyroid carcinoma (PTC), which is considered to be the most common type of thyroid cancer. In recent years, the prevalence of PTC with TH has been increasing, but little is known about the genetic alteration in PTC with TH. This study analyzed the mutation spectrum and mutation signature of somatic single nucleotide variants (SNV) for 10 non-tumor and tumor pair tissues of PTC with TH using whole-exome sequencing. The ANK3 protein expression was evaluated by immunohistochemistry in PTC with TH and PTC samples. Moreover, the functional role of ANK3 in PTC cells was determined by CCK-8 proliferation assay, colony formation assays, cell cycle analysis, cell invasion and migration and in vivo study through overexpression assay. Our results showed three distinct mutational signatures and the C>T/G>A substitution was the most common type of SNV. Gene-set enrichment analysis showed that most of the significantly mutated genes were enriched in the regulation of actin cytoskeleton signaling. Moreover, NCOR2, BPTF, ANK3, and PCSK5 were identified as the significantly mutated genes in PTC with TH, most of which have not been previously characterized. Unexpectedly, it was found that ANK3 was overexpressed in cytoplasm close to the membrane of PTC cells with TH and in almost all PTC cases, suggesting its role as a diagnostic marker of PTC. Ectopic expression of ANK3 suppressed invasion and migration, increased apoptosis of B-CPAP and TPC-1 cells. Moreover, our findings revealed that enhanced ANK3 expression inhibits growth of PTC cells both in vitro and in vivo. Ectopic expression of ANK3 significantly enhanced E-cadherin protein expression and inhibited PTC progression, at least in part, by suppression of epithelial-mesenchymal transition (EMT). Our study shows that ANK3 exerts an anti-oncogenic role in the development of PTC and might be an indolent maintainer of PTC.

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors

MOTIVATION

Multi-region sequencing of solid tumors can improve our understanding of intratumor subclonal diversity and the evolutionary history of mutational events. Due to uncertainty in clonal composition and the multitude of possible ancestral relationships between clones, elucidating the most probable relationships from bulk tumor sequencing poses statistical and computational challenges.

RESULTS

We developed a Bayesian hierarchical model called PICTograph to model uncertainty in assigning mutations to subclones, to enable posterior distributions of cancer cell fractions, and to visualize the most probable ancestral relationships between subclones. Compared to available methods, PICTograph provided more consistent and accurate estimates of cancer cell fractions and improved tree inference over a range of simulated clonal diversity. Application of PICTograph to multi-region whole exome sequencing of tumors from individuals with pancreatic cancer precursor lesions confirmed known early-occurring mutations and indicated substantial molecular diversity, including 6-12 distinct subclones and intra-sample mixing of subclones. Using ensemble-based visualizations, we highlight highly probable evolutionary relationships recovered in multiple models. PICTograph provides a useful approximation to evolutionary inference from cross-sectional multi-region sequencing, particularly for complex cases.

AVAILABILITY

https://github.com/KarchinLab/pictograph.

Panel Informativity Optimizer: An R Package to Improve Cancer Next-Generation Sequencing Panel Informativity

Mutation detection by next-generation sequencing is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (ie, the number of patients with at least one mutation in the panel), while minimizing panel length to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer next-generation sequencing panel informativity. Using patient-level mutational data from either private data sets or preloaded data set of 91 independent cohorts from 31 different cancer types, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered, such as the definition of genomic intervals at the gene or exon level and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000 kb, and accurately predicts the performance of custom or commercial panels.

Kidney Cancer Models for Pre-Clinical Drug Discovery: Challenges and Opportunities

Renal cell carcinoma (RCC) is among the most lethal urological malignancies once metastatic. The introduction of immune checkpoint inhibitors has revolutionized the therapeutic landscape of metastatic RCC, nevertheless, a significant proportion of patients will experience disease progression. Novel treatment options are therefore still needed and in vitro and in vivo model systems are crucial to ultimately improve disease control. At the same time, RCC is characterized by a number of molecular and functional peculiarities that have the potential to limit the utility of pre-clinical model systems. This includes not only the well-known genomic intratumoral heterogeneity (ITH) of RCC but also a remarkable functional ITH that can be shaped by influences of the tumor microenvironment. Importantly, RCC is among the tumor entities, in which a high number of intratumoral cytotoxic T cells is associated with a poor prognosis. In fact, many of these T cells are exhausted, which represents a major challenge for modeling tumor-immune cell interactions. Lastly, pre-clinical drug development commonly relies on using phenotypic screening of 2D or 3D RCC cell culture models, however, the problem of “reverse engineering” can prevent the identification of the precise mode of action of drug candidates thus impeding their translation to the clinic. In conclusion, a holistic approach to model the complex “ecosystem RCC” will likely require not only a combination of model systems but also an integration of concepts and methods using artificial intelligence to further improve pre-clinical drug discovery.

Expression of GOT2 Is Epigenetically Regulated by DNA Methylation and Correlates with Immune Infiltrates in Clear-Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (KIRC) is the most common and highly malignant pathological type of kidney cancer, characterized by a profound metabolism dysregulation. As part of aspartate biosynthesis, mitochondrial GOT2 (glutamic-oxaloacetic transaminase 2) is essential for regulating cellular energy production and biosynthesis, linking multiple pathways. Nevertheless, the expression profile and prognostic significance of GOT2 in KIRC remain unclear. This study comprehensively analyzed the transcriptional levels, epigenetic regulation, correlation with immune infiltration, and prognosis of GOT2 in KIRC using rigorous bioinformatics analysis. We discovered that the expression levels of both mRNA and protein of GOT2 were remarkably decreased in KIRC tissues in comparison with normal tissues and were also significantly related to the clinical features and prognosis of KIRC. Remarkably, low GOT2 expression was positively associated with poorer overall survival (OS) and disease-free survival (DFS). Further analysis revealed that GOT2 downregulation is driven by DNA methylation in the promoter-related CpG islands. Finally, we also shed light on the influence of GOT2 expression in immune cell infiltration, suggesting that GOT2 may be a potential prognostic marker and therapeutic target for KIRC patients.

Identification and Verification of m7G Modification Patterns and Characterization of Tumor Microenvironment Infiltration via Multi-Omics Analysis in Clear Cell Renal Cell Carcinoma

The epigenetic modification of tumorigenesis and progression in neoplasm has been demonstrated in recent studies. Nevertheless, the underlying association of N7-methylguanosine (m⁷G) regulation with molecular heterogeneity and tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remains unknown. We explored the expression profiles and genetic variation features of m⁷G regulators and identified their correlations with patient outcomes in pan-cancer. Three distinct m⁷G modification patterns, including MGCS1, MGCS2, and MGCS3, were further determined and systematically characterized via multi-omics data in ccRCC. Compared with the other two subtypes, patients in MGCS3 exhibited a lower clinical stage/grade and better prognosis. MGCS1 showed the lowest enrichment of metabolic activities. MGCS2 was characterized by the suppression of immunity. We then established and validated a scoring tool named m7Sig, which could predict the prognosis of ccRCC patients. This study revealed that m⁷G modification played a vital role in the formation of the tumor microenvironment in ccRCC. Evaluating the m⁷G modification landscape helps us to raise awareness and strengthen the understanding of ccRCC’s characterization and, furthermore, to guide future clinical decision making.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols.

Role of SWI/SNF chromatin remodeling genes in lung cancer development

SWI/SNF family of chromatin remodeling complexes uses the energy of ATP to change the structure of DNA, playing key roles in DNA regulation and repair. It is estimated that up to 25% of all human cancers contain alterations in SWI/SNF, although the precise molecular mechanisms for their involvement in tumor progression are largely unknown. Despite the improvements achieved in the last decades on our knowledge of lung cancer molecular biology, it remains the major cause of cancer-related deaths worldwide and it is in urgent need for new therapeutic alternatives. We and others have described recurrent alterations in different SWI/SNF genes in nearly 20% of lung cancer patients, some of them with a significant association with worse prognosis, indicating an important role of SWI/SNF in this fatal disease. These alterations might be therapeutically exploited, as it has been shown in cellular and animal models with the use of EGFR inhibitors, DNA-damaging agents and several immunotherapy approaches. Therefore, a better knowledge of the molecular mechanisms regulated by SWI/SNF alterations in lung cancer might be translated into a therapeutic improvement of this frequently lethal disease. In this review, we summarize all the evidence of SWI/SNF alterations in lung cancer, the current knowledge about the potential mechanisms involved in their tumorigenic role, as well as the results that support a potential exploitation of these alterations to improve the treatment of lung cancer patients.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

3'untranslated regions of tumor suppressor genes evolved specific features to favor cancer resistance

Cancer-related genes have evolved specific genetic and genomic features to favor tumor suppression. Previously we reported that tumor suppressor genes (TSGs) acquired high promoter CpG dinucleotide frequencies during evolution to maintain high expression in normal tissues and resist cancer-specific downregulation. In this study, we investigated whether 3'untranslated regions (3'UTRs) of TSGs have evolved specific features to carry out similar functions. We found that 3'UTRs of TSGs, especially those involved in multiple histological types and pediatric cancers, are longer than those of non-cancer genes. 3'UTRs of TSGs also exhibit higher density of binding sites for RNA-binding proteins (RBPs), particularly those having high affinities to C-rich motifs. Both longer 3'UTR length and RBP binding sites enrichment are correlated with higher gene expression in normal tissues across tissue types. Moreover, both features together with the correlated N⁶-methyladenosine modification and the extent of protein-protein interactions are positively associated with the ability of TSGs to resist cancer-specific downregulation. These results were successfully validated with independent datasets. Collectively, these findings indicate that TSGs have evolved longer 3'UTR with increased propensity to RBP binding, N⁶-methyladenosine modification and protein-protein interactions for optimizing their tumor-suppressing functions.

Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer

Cancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. In order to understand complex diseases such as cancer, diabetes and kidney diseases, high-throughput data are generated at large scale and it has helped in the research and diagnostic advancement. However, to unravel the meaningful information from such large datasets for comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available dataset which are for two different stages (I and II) for renal cell carcinoma and furthermore, the TCGA and cBioPortal database have been utilized for clinical relevance understanding. In this work, we have applied computational approach to unravel the differentially expressed genes, their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are large number of differentially expressed genes which appears to be playing critical roles in survival.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Validation of a Novel Three-Dimensional (3D Fusion) Gross Sampling Protocol for Clear Cell Renal Cell Carcinoma to Overcome Intratumoral Heterogeneity: The Meet-Uro 18 Study

We aimed to overcome intratumoral heterogeneity in clear cell renal cell carcinoma (clearRCC). One hundred cases of clearRCC were sampled. First, usual standard sampling was applied (1 block/cm of tumor); second, the whole tumor was sampled, and 0.6 mm cores were taken from each block to construct a tissue microarray; third, the residual tissue, mapped by taking pieces 0.5 × 0.5 cm, reconstructed the entire tumor mass. Precisely, six randomly derived pieces of tissues were placed in each cassette, with the number of cassettes being based on the diameter of the tumor (called multisite 3D fusion). Angiogenic and immune markers were tested. Routine 5231 tissue blocks were obtained. Multisite 3D fusion sections showed pattern A, homogeneous high vascular density (10%), pattern B, homogeneous low vascular density (8%) and pattern C, heterogeneous angiogenic signatures (82%). PD-L1 expression was seen as diffuse (7%), low (33%) and absent (60%). Tumor-infiltrating CD8 scored high in 25% (pattern hot), low in 65% (pattern weak) and zero in 10% of cases (pattern desert). Grading was upgraded in 26% of cases (G3–G4), necrosis and sarcomatoid/rhabdoid characters were observed in, respectively, 11 and 7% of cases after 3D fusion (p = 0.03). CD8 and PD-L1 immune expressions were higher in the undifferentiated G4/rhabdoid/sarcomatoid clearRCC subtypes (p = 0.03). Again, 22% of cases were set to intermediate to high risk of clinical recurrence due to new morphological findings of all aggressive G4, sarcomatoid/rhabdoid features by using 3D fusion compared to standard methods (p = 0.04). In conclusion, we propose an easy-to-apply multisite 3D fusion sampling that negates bias due to tumor heterogeneity.

Heterogeneity of the tumor immune microenvironment and its clinical relevance

During the course of tumorigenesis and subsequent metastasis, malignant cells gradually diversify and become more heterogeneous. Consequently, the tumor mass might be infiltrated by diverse immune-related components, including the cytokine/chemokine environment, cytotoxic activity, or immunosuppressive elements. This immunological heterogeneity is universally presented spatially or varies temporally along with tumor evolution or therapeutic intervention across almost all solid tumors. The heterogeneity of anti-tumor immunity shows a profound association with the progression of disease and responsiveness to treatment, particularly in the realm of immunotherapy. Therefore, an accurate understanding of tumor immunological heterogeneity is essential for the development of effective therapies. Facilitated by multi-regional and -omics sequencing, single cell sequencing, and longitudinal liquid biopsy approaches, recent studies have demonstrated the potential to investigate the complexity of immunological heterogeneity of the tumors and its clinical relevance in immunotherapy. Here, we aimed to review the mechanism underlying the heterogeneity of the immune microenvironment. We also explored how clinical assessments of tumor heterogeneity might facilitate the development of more effective personalized therapies.

Differential VHL mutation patterns in bilateral clear cell RCC distinguishes between independent primary tumors and contralateral metastatic disease

OBJECTIVE

To evaluate whether bilateral, multifocal clear cell renal cell carcinoma (ccRCC) patients can be differentiated by VHL mutation analysis into cases that represent either multiple independently arising primary tumors, or a single primary tumor which has spread ipsilaterally as well as to the contralateral kidney. The nature of kidney cancer multifocality outside of known hereditary syndromes is as yet poorly understood.

MATERIALS AND METHODS

DNA from multiple tumors per patient were evaluated for somatic VHL gene mutation and hypermethylation. A subset of tumors with shared VHL mutations were analyzed with targeted, next-generation sequencing assays.

RESULTS

This cohort contained 5 patients with multiple tumors that demonstrated a shared somatic VHL mutation consistent with metastatic spread including to the contralateral kidney. In several cases this was substantiated by additional shared somatic mutations in ccRCC-associated genes. In contrast, the remaining 14 patients with multiple tumors demonstrated unique, unshared VHL alterations in every analyzed tumor, consistent with independently arising kidney tumors. None of these latter patients showed any evidence of local spread or distant metastasis.

CONCLUSION

The spectrum of VHL alterations within evaluated bilateral, multifocal ccRCC tumors from a single patient can distinguish between multiple independent tumor growth and metastasis. This can be performed using currently available clinical genetic tests and will improve the accuracy of patient diagnosis and prognosis, as well as informing appropriate management.

Sensitivity of VHL mutant kidney cancers to HIF2 inhibitors does not require an intact p53 pathway

VHL tumor suppressor gene inactivation is a hallmark of clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer, and promotes tumor growth by stabilizing the hypoxia-inducible factor 2 (HIF2) transcription factor. HIF2 inhibitors appear to be helpful for some, but not all, ccRCC patients in clinical trials. Previous preclinical and clinical data suggested that only ccRCCs that can activate the p53 tumor suppressor in response to DNA damage would respond to HIF2 inhibitors. Here, we show that an intact p53 pathway is neither necessary nor sufficient for the sensitivity of ccRCCs to HIF2 inhibitors, suggesting that it would be premature to use p53 status to determine which ccRCC patients should be treated with a HIF2 inhibitor.

Inactivation of the VHL tumor suppressor gene is the signature initiating event in clear cell renal cell carcinoma (ccRCC), which is the most common form of kidney cancer. The VHL tumor suppressor protein marks hypoxia-inducible factor 1 (HIF1) and HIF2 for proteasomal degradation when oxygen is present. The inappropriate accumulation of HIF2 drives tumor formation by VHL tumor suppressor protein (pVHL)–defective ccRCC. Belzutifan, a first-in-class allosteric HIF2 inhibitor, has advanced to phase 3 testing for advanced ccRCC and is approved for ccRCCs arising in patients with VHL disease, which is caused by germline VHL mutations. HIF2 can suppress p53 function in some settings and preliminary data suggested that an intact p53 pathway, as measured by activation in response to DNA damage, was necessary for HIF2 dependence. Here, we correlated HIF2 dependence and p53 status across a broader collection of ccRCC cell lines. We also genetically manipulated p53 function in ccRCC lines that were or were not previously HIF2-dependent and then assessed their subsequent sensitivity to HIF2 ablation using CRISPR-Cas9 or the HIF2 inhibitor PT2399, which is closely related to belzutifan. From these studies, we conclude that p53 status does not dictate HIF2 dependence, at least in preclinical models, and thus is unlikely to be a useful biomarker for predicting which ccRCC patients will respond to HIF2 inhibitors.

Multi-site desmoplastic small round cell tumors are genetically related and immune-cold

Desmoplastic small round cell tumor (DSRCT) is a highly aggressive soft tissue sarcoma that is characterized by the EWSR1-WT1 fusion protein. Patients present with hundreds of tumor implants in their abdominal cavity at various sites. To determine the genetic relatedness among these sites, exome and RNA sequencing were performed on 22 DSRCT specimens from 14 patients, four of whom had specimens from various tissue sites. Multi-site tumors from individual DSRCT patients had a shared origin and were highly related. Other than the EWSR1-WT1 fusion, very few secondary cancer gene mutations were shared among the sites. Among these, ARID1A, was recurrently mutated, which corroborates findings by others in DSRCT patients. Knocking out ARID1A in JN-DSRCT cells using CRISPR/CAS9 resulted in significantly lower cell proliferation and increased drug sensitivity. The transcriptome data were integrated using network analysis and drug target database information to identify potential therapeutic opportunities in EWSR1-WT1-associated pathways, such as PI3K and mTOR pathways. Treatment of JN-DSRCT cells with the PI3K inhibitor alpelisib and mTOR inhibitor temsirolimus reduced cell proliferation. In addition, the low mutation burden was associated with an immune-cold state in DSRCT. Together, these data reveal multiple genomic and immune features of DSRCT and suggest therapeutic opportunities in patients.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Zafirlukast Induces VHL- and HIF-2α-Dependent Oxidative Cell Death in 786-O Clear Cell Renal Carcinoma Cells

Mutations in the Von Hippel–Lindau (VHL) gene are the driving force in many forms of clear cell renal cell carcinoma (ccRCC) and promote hypoxia-inducible factor (HIF)-dependent tumor proliferation, metastasis and angiogenesis. Despite the progress that has already been made, ccRCC generally remain resistant to conventional therapies and ccRCC patients suffer from metastasis and acquired resistance, highlighting the need for novel therapeutic options. Cysteinyl leukotriene receptor 1 (CysLTR1) antagonists, like zafirlukast, are administered in bronchial asthma to control eicosanoid signaling. Intriguingly, long-term use of zafirlukast decreases cancer risk and leukotriene receptor antagonists inhibit tumor growth, but the mechanisms still remain unexplored. Therefore, we aim to understand the mechanisms of zafirlukast-mediated cell death in ccRCC cells. We show that zafirlukast induces VHL-dependent and TNFα-independent non-apoptotic and non-necroptotic cell death in ccRCC cells. Cell death triggered by zafirlukast could be rescued with antioxidants and the PARP-1 inhibitor Olaparib, and additionally relies on HIF-2α. Finally, MG-132-mediated proteasome inhibition sensitized VHL wild-type cells to zafirlukast-induced cell death and inhibition of HIF-2α rescued zafirlukast- and MG-132-triggered cell death. Together, these results highlight the importance of VHL, HIF and proteasomal degradation in zafirlukast-induced oxidative cell death with potentially novel therapeutic implications for ccRCC.

Proof-of-Concept Pilot Study on Comprehensive Spatiotemporal Intra-Patient Heterogeneity for Colorectal Cancer With Liver Metastasis

Introduction

The mechanisms underlying high drug resistance and relapse rates after multi-modal treatment in patients with colorectal cancer (CRC) and liver metastasis (LM) remain poorly understood.

Objective

We evaluate the potential translational implications of intra-patient heterogeneity (IPH) comprising primary and matched metastatic intratumor heterogeneity (ITH) coupled with circulating tumor DNA (ctDNA) variability.

Methods

A total of 122 multi-regional tumor and perioperative liquid biopsies from 18 patients were analyzed via targeted next-generation sequencing (NGS).

Results

The proportion of patients with ITH were 53% and 56% in primary CRC and LM respectively, while 35% of patients harbored de novo mutations in LM indicating spatiotemporal tumor evolution and the necessity of multiregional analysis. Among the 56% of patients with alterations in liquid biopsies, de novo mutations in cfDNA were identified in 25% of patients, which were undetectable in both CRC and LM. All 17 patients with driver alterations harbored mutations targetable by molecularly targeted drugs, either approved or currently under evaluation.

Conclusion

Our proof-of-concept prospective study provides initial evidence on potential clinical superiority of IPH and warrants the conduction of precision oncology trials to evaluate the clinical utility of I PH-driven matched therapy.

Biomarker analysis from CheckMate 214: nivolumab plus ipilimumab versus sunitinib in renal cell carcinoma

BACKGROUND

The phase 3 CheckMate 214 trial demonstrated higher response rates and improved overall survival with nivolumab plus ipilimumab versus sunitinib in first-line therapy for advanced clear-cell renal cell carcinoma (RCC). An unmet need exists to identify patients with RCC who are most likely to benefit from treatment with nivolumab plus ipilimumab.

METHODS

In exploratory analyses, pretreatment levels of programmed death ligand 1 were assessed by immunohistochemistry. Genomic and transcriptomic biomarkers (including tumor mutational burden and gene expression signatures) were also investigated.

RESULTS

Biomarkers previously associated with benefit from immune checkpoint inhibitor-containing regimens in RCC were not predictive for survival in patients with RCC treated with nivolumab plus ipilimumab. Analysis of gene expression identified an association between an inflammatory response and progression-free survival with nivolumab plus ipilimumab.

CONCLUSIONS

The exploratory analyses reveal relationships between molecular biomarkers and provide supportive data on how the inflammation status of the tumor microenvironment may be important for identifying predictive biomarkers of response and survival with combination immunotherapy in patients with RCC. Further validation may help to provide biomarker-driven precision treatment for patients with RCC.

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Simple Summary

Most malignant tumours are highly heterogeneous at molecular and phenotypic levels. Tumour variability poses challenges for the management of patients, as it arises between patients and even evolves in space and time within a single patient. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of tumour diversity in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate tumour heterogeneity.

Abstract

Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.

The genomic landscape of metastatic clear cell renal cell carcinoma after systemic therapy

Primary clear cell renal cell carcinoma (ccRCC) has been previously characterized, but the genomic landscape of metastatic ccRCC is largely unexplored. Here, we performed whole exome sequencing (WES) in 68 samples from 44 patients with ccRCC, including 52 samples from a metastatic site. SETD2, PBRM1, APC and VHL were the most frequently mutated genes in the metastatic ccRCC cohort. RBM10 and FBXW7 were also among the 10 most frequently mutated genes in metastatic tissues. Recurrent somatic copy number variations (CNV) were observed at the previously identified regions 3p25, 9p21 and 14q25, but also at 6p21 (CDKN1A) and 13q14 (RB1). No statistically significant differences were found between samples from therapy‐naïve and pretreated patients. Clonal evolution analyses with multiple samples from 13 patients suggested that early appearance of CNVs at 3p25, 9p21 and 14q25 may be associated with rapid clinical progression. Overall, the genomic landscapes of primary and metastatic ccRCC seem to share frequent CNVs at 3p25, 9p21 and 14q25. Future work will clarify the implication of RBM10 and FBXW7 mutations and 6p21 and 13q14 CNVs in metastatic ccRCC.

Decrease of Intracellular Glutamine by STF-62247 Results in the Accumulation of Lipid Droplets in von Hippel-Lindau Deficient Cells

Kidney cancer is one of the top ten cancer diagnosed worldwide and its incidence has increased the last 20 years. Clear Cell Renal Cell Carcinoma (ccRCC) are characterized by mutations that inactivate the von Hippel-Lindau (VHL) tumor suppressor gene and evidence indicated alterations in metabolic pathways, particularly in glutamine metabolism. We previously identified a small molecule, STF-62247, which target VHL-deficient renal tumors by affecting late-stages of autophagy and lysosomal signaling. In this study, we investigated ccRCC metabolism in VHL-deficient and proficient cells exposed to the small molecule. Metabolomics profiling using 1H NMR demonstrated that STF-62247 increases levels of glucose, pyruvate, glycerol 3-phosphate while glutamate, asparagine, and glutathione significantly decreased. Diminution of glutamate and glutamine was further investigated using mass spectrometry, western blot analyses, enzymatic activities, and viability assays. We found that expression of SLC1A5 increases in VHL-deficient cells treated with STF-62247, possibly to stimulate glutamine uptake intracellularly to counteract the diminution of this amino acid. However, exogenous addition of glutamine was not able to rescue cell viability induced by the small molecule. Instead, our results showed that VHL-deficient cells utilize glutamine to produce fatty acid in response to STF-62247. Surprisingly, this occurs through oxidative phosphorylation in STF-treated cells while control cells use reductive carboxylation to sustain lipogenesis. We also demonstrated that STF-62247 stimulated expression of stearoyl-CoA desaturase (SCD1) and peripilin2 (PLIN2) to generate accumulation of lipid droplets in VHL-deficient cells. Moreover, the carnitine palmitoyltransferase 1A (CPT1A), which control the entry of fatty acid into mitochondria for β-oxidation, also increased in response to STF-62247. CPT1A overexpression in ccRCC is known to limit tumor growth. Together, our results demonstrated that STF-62247 modulates cellular metabolism of glutamine, an amino acid involved in the autophagy-lysosome process, to support lipogenesis, which could be implicated in the signaling driving to cell death.

The Role of VHL in the Development of von Hippel-Lindau Disease and Erythrocytosis

Von Hippel-Lindau disease (VHL disease or VHL syndrome) is a familial multisystem neoplastic syndrome stemming from germline disease-associated variants of the VHL tumor suppressor gene on chromosome 3. VHL is involved, through the EPO-VHL-HIF signaling axis, in oxygen sensing and adaptive response to hypoxia, as well as in numerous HIF-independent pathways. The diverse roles of VHL confirm its implication in several crucial cellular processes. VHL variations have been associated with the development of VHL disease and erythrocytosis. The association between genotypes and phenotypes still remains ambiguous for the majority of mutations. It appears that there is a distinction between erythrocytosis-causing VHL variations and VHL variations causing VHL disease with tumor development. Understanding the pathogenic effects of VHL variants might better predict the prognosis and optimize management of the patient.

TET2 suppresses VHL deficiency-driven clear cell renal cell carcinoma by inhibiting HIF signaling

Inactivating mutations of von Hippel-Lindau (VHL) are highly prevalent in clear cell renal cell carcinoma (ccRCC). Improved understanding of the vulnerabilities of VHL-deficient ccRCC could lead to improved treatment strategies. The activity of DNA dioxygenase TET2 is significantly reduced in multiple cancers by different mechanisms, but its role in ccRCC progression remains unclear. Here, we report that increased expression of TET2, but not TET1 and TET3, is negatively associated with tumor metastasis and advanced tumor stage and positively associated with good prognosis uniquely in ccRCC among all 33 types of cancer in the TCGA datasets. TET2 restrained glycolysis and pentose phosphate pathway metabolism in a VHL deficiency-dependent manner, thereby suppressing ccRCC progression. Notably, TET2 and VHL mutations tended to co-occur in ccRCC, providing genetic evidence that they cooperate to inhibit the progression of ccRCC. Mechanistically, TET2 was recruited by transcription factor HNF4α to activate FBP1 expression, which antagonized the function of HIF1/2α in metabolic reprogramming to impede ccRCC growth. Stimulating the TET2-FBP1 axis with vitamin C repressed the growth of VHL-deficient ccRCC with wild-type TET2 and increased the sensitivity to glycolysis inhibitors. Moreover, combined expression levels of the HNF4α-TET2-FBP1 axis served as a biomarker of prognosis in ccRCC patients. This study reveals a unique function of TET2 in the suppression of tumor metabolism and HIF signaling, and it also provides therapeutic targets, potential drugs, and prognostic markers for the management of ccRCC.

SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data

Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells and is especially useful for deciphering the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false-positive errors, and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show that it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.

MUC20 as a novel prognostic biomarker in ccRCC correlating with tumor immune microenvironment modulation

Tumor microenvironment (TME) broadly participates in genesis development of clear cell renal cell carcinoma (ccRCC). To recognize the immune and stromal modulation in TME, we screened the differentially expressed TME-related genes generated by the ESTIMATE algorithm in ccRCC specimens. Following the construction of protein-protein interaction (PPI) network and univariate COX regression, mucin 20 (MUC20) was judged to be a predictive factor. Further analysis, including immunohistochemistry (IHC) showed that MUC20 was positively correlated with survival and negatively correlated with the clinicopathologic characteristics (grade, clinical and TNM stages) in ccRCC patients. Gene Set Enrichment Analysis suggested that the low-expression MUC20 group was primarily enriched in immune-related activities, inflammation and epithelial-mesenchymal transition. Based on the CIBERSORT analysis for tumor-infiltrating immune cells (TICs), MUC20 was positively correlated with CD8⁺ T cells and resting mast cells and negatively correlated with activated CD4⁺ memory T cells, Treg cells, and plasma cells, implying that MUC20 may contribute to immune component in TME. Additionally, the patients with low MUC20 expression had better response to immune checkpoint blockades (ICBs) and 17 potential anticancer drugs were screened regarding calculating IC₅₀ value. Thus, MUC20 may contain a value of prognosis assessment for ccRCC patients and indicate the immune modulation status of TME, which provided a novel insight for comprehensive immunotherapy.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

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MIL model successfully predicts a sample's tumor purity from histopathology slides

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MIL model learns to spatially resolve tumor purity from sample-level labels

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Tumor purity varies spatially within a sample

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Pathologists’ region selection is vital for correct percentage tumor nuclei estimation

Given some big data and coarse-level labels, extracting fine-level information is a demanding yet rewarding challenge in data science. This study develops a machine learning model utilizing big data and exploiting coarse-level labels to reveal fine-level details within the data. Although it can be applied to different data science tasks with enormous data and coarse labels, we applied it to a computational histopathology task with gigapixel histopathology slides and sample-level labels. Specifically, the model revealed spatial resolution of tumor purity within histopathology slides using only sample-level genomic tumor purity values during training. This can also be extended to other omics features, providing precious information about cancer biology and promising personalized, precision medicine. Such studies are of great clinical importance in discovering imaging biomarkers and better understanding the tumor microenvironment.

Intratumor genetic heterogeneity and clonal evolution to decode endometrial cancer progression

Analyzing different tumor regions by next generation sequencing allows the assessment of intratumor genetic heterogeneity (ITGH), a phenomenon that has been studied widely in some tumor types but has been less well explored in endometrial carcinoma (EC). In this study, we sought to characterize the spatial and temporal heterogeneity of 9 different ECs using whole-exome sequencing, and by performing targeted sequencing validation of the 42 primary tumor regions and 30 metastatic samples analyzed. In addition, copy number alterations of serous carcinomas were assessed by comparative genomic hybridization arrays. From the somatic mutations, identified by whole-exome sequencing, 532 were validated by targeted sequencing. Based on these data, the phylogenetic tree reconstructed for each case allowed us to establish the tumors’ evolution and correlate this to tumor progression, prognosis, and the presence of recurrent disease. Moreover, we studied the genetic landscape of an ambiguous EC and the molecular profile obtained was used to guide the selection of a potential personalized therapy for this patient, which was subsequently validated by preclinical testing in patient-derived xenograft models. Overall, our study reveals the impact of analyzing different tumor regions to decipher the ITGH in ECs, which could help make the best treatment decision.

Molecular Characterization of m6A Modifications in Non-Clear Cell Renal Cell Carcinoma and Potential Relationship with Pathological Types

Background

N6-Methyladenosine (m6A) modification is a eukaryotic mRNA modification that modulates the fate of modified RNA and, therefore, the expression of proteins. m6A modifications are associated with important roles in several cancers. Most studies related to m6A modification are based on clear cell renal cell carcinoma (ccRCC) and little is known about its role in non-ccRCC.

Methods

We summarized the molecular features of different m6A modification patterns in non-ccRCC based on The Cancer Genome Atlas database and correlated them with phenotypes such as immune patterns and prognosis. We also computed the m6Ascore and assessed its prognostic value using multivariate Cox regression analysis.

Results

We found the immune-excluded phenotype to be predominant in non-ccRCC patients. We also found that in non-clear cell carcinoma, different m6A modification profiles determine different immune patterns and are associated with different prognosis. m6AgeneCluser typing strongly associated with pathological status. Based on our findings, we suggest that the m6Ascore can be used as an independent prognostic value for prognostic assessment in non-ccRCC.

Conclusion

This study confirms the important role of m6A modifications in non-ccRCC, reveals the heterogeneity of tumor immunity, and highlights the promise of non-ccRCC therapy.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

Spatial structure governs the mode of tumour evolution

Characterizing the mode-the way, manner or pattern-of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes: rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell-cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.

Renal cell carcinoma and viral infections: A dangerous relationship?

Virus-related cancers in humans are widely recognized, but in the case of renal cancer, the link with the world of viruses is not clearly established in humans, despite being known in animal biology. In the present review, we aimed to explore the literature on renal cell carcinoma (RCC) for a possible role of viruses in human RCC tumorigenesis and immune homeostasis, hypothesizing the contribution of viruses to the immunogenicity of this tumor. A scientific literature search was conducted using the PubMed, Web of Science, and Google Scholar databases with the keywords “virus” or “viruses” or “viral infection” matched with (“AND”) “renal cell carcinoma” or “kidney cancer” or “renal cancer” or “renal carcinoma” or “renal tumor” or “RCC”. The retrieved findings evidenced two main aspects testifying to the relationship between RCC and viruses: The presence of viruses within the tumor, especially in non-clear cell RCC cases, and RCC occurrence in cases with pre-existing chronic viral infections. Some retrieved translational and clinical data suggest the possible contribution of viruses, particularly Epstein-Barr virus, to the marked immunogenicity of sarcomatoid RCC. In addition, it was revealed the possible role of endogenous retrovirus reactivation in RCC oncogenesis, introducing new fascinating hypotheses about this tumor’s immunogenicity and likeliness of response to immune checkpoint inhibitors.

Mutational landscape of paired primary and synchronous metastatic lymph node in chemotherapy naive gallbladder cancer

BACKGROUND

Comprehensive genomic analysis of paired primary tumors and their metastatic lesions may provide new insights into the biology of metastatic processes and therefore guide the development of novel strategies for intervention. To date, our knowledge of the genetic divergence and phylogenetic relationships in gallbladder cancer (GBC) is limited.

METHODS

We performed whole exome sequencing for 5 patients with primary tumor, metastatic lymph node (LNM) and corresponding normal tissue. Mutations, mutation signatures and copy number variations were analyzed with state-of-art bioinformatics methods. Phylogenetic tree was also generated to infer metastatic pattern.

RESULTS

Five driver mutations were detected in these patients. Among which, TP53 was the only shared mutation between primary tumor and LNM. Although tumor mutational burden was comparable between primary tumor and LNM, higher mutation burden was observed in LNM of one patient. Copy number variations (CNVs) burden was higher in LNM than their primary tumor. Phylogenetic analysis indicated both linear and parallel progression of metastasis exist in these patients. TP53 mutation and CNVs were homogenously between primary tumor and LNM.

CONCLUSIONS

High consistence of genetic landscape were shown between primary tumor and LNM in GBC. However, heterogenicity still exist between primary tumor and LNM in particular patients in term of driver mutation, TMB and CNV burden. Phylogenetic analysis indicated both Linear and parallel progression of metastasis were exist among these patients.

Molecular mechanisms in governing genomic stability and tumor suppression by the SETD2 H3K36 methyltransferase

Epigenetic dysregulation is an important contributor to carcinogenesis. This is not surprising, as chromatin-genomic DNA organized around structural histone scaffolding-serves as the template on which occurs essential nuclear processes, such as transcription, DNA replication and DNA repair. Histone H3 lysine 36 (H3K36) methyltransferases, such as the SET-domain 2 protein (SETD2), have emerged as critical tumor suppressors. Previous work on mammalian SETD2 and its counterpart in model organisms, Set2, has highlighted the role of this protein in governing genomic stability through transcriptional elongation and splicing, as well as in DNA damage response processes and cell cycle progression. A compendium of SETD2 mutations have been documented, garnered from sequenced cancer patient genome data, and these findings underscore the cancer-driving properties of SETD2 loss-of-function. In this review, we consolidate the molecular mechanisms regulated by SETD2/Set2 and discuss evidence of its dysregulation in tumorigenesis. Insight into the genetic interactions that exist between SETD2 and various canonical intracellular signaling pathways has not only empowered pharmacological intervention by taking advantage of synthetic lethality but underscores SETD2 as a druggable target for precision cancer therapy.

RNase H1, the Gold Standard for R-Loop Detection

RNase H1 has become an essential tool to uncover the physiological and pathological roles of R-loops, three-stranded structures consisting of and RNA-DNA hybrid opposite to a single DNA strand (ssDNA). RNase H1 degrades the RNA portion of the R-loops returning the two DNA strands to double-stranded form (dsDNA). Overexpression of RNase H1 in different systems has helped to address the questions of where R-loops are located, their abundance, and mechanisms of formation, stability, and degradation. In this chapter we review multiple studies that used RNase H1 as an instrument to investigate R-loops multiple functions and their relevance in health and diseases.

Side population analysis in clear cell renal cell carcinoma

Cancer stem cells have an important role in tumour biology. While their identity in haematological malignancies is clearly defined, stem cell identity remains elusive in some solid tumours. Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer, but the identity or existence of ccRCC stem cells remains unknown. We aimed to discern their existence using the widely utilised side population approach in ccRCC cell lines. In all cells tested, a well-defined side population was identified, and cell-based assays suggested stem-like properties. However, limiting dilution assays revealed comparable tumour initiating abilities and tumour histology of side and non-side populations, and single cell RNA-sequencing revealed minimal differences between these populations. The results indicate that the side population approach is not sufficient for cancer stem cell discovery in ccRCC.

The Role of Circulating Biomarkers in the Oncological Management of Metastatic Renal Cell Carcinoma: Where Do We Stand Now?

Renal cell carcinoma (RCC) is an increasingly common malignancy that can progress to metastatic renal cell carcinoma (mRCC) in approximately one-third of RCC patients. The 5-year survival rate for mRCC is abysmally low, and, at the present time, there are sparingly few if any effective treatments. Current surgical and pharmacological treatments can have a long-lasting impact on renal function, as well. Thus, there is a compelling unmet need to discover novel biomarkers and surveillance methods to improve patient outcomes with more targeted therapies earlier in the course of the disease. Circulating biomarkers, such as circulating tumor DNA, noncoding RNA, proteins, extracellular vesicles, or cancer cells themselves potentially represent a minimally invasive tool to fill this gap and accelerate both diagnosis and treatment. Here, we discuss the clinical relevance of different circulating biomarkers in metastatic renal cell carcinoma by clarifying their potential role as novel biomarkers of response or resistance to treatments but also by guiding clinicians in novel therapeutic approaches.

Aurora A and AKT Kinase Signaling Associated with Primary Cilia

Dysregulation of kinase signaling is associated with various pathological conditions, including cancer, inflammation, and autoimmunity; consequently, the kinases involved have become major therapeutic targets. While kinase signaling pathways play crucial roles in multiple cellular processes, the precise manner in which their dysregulation contributes to disease is dependent on the context; for example, the cell/tissue type or subcellular localization of the kinase or substrate. Thus, context-selective targeting of dysregulated kinases may serve to increase the therapeutic specificity while reducing off-target adverse effects. Primary cilia are antenna-like structures that extend from the plasma membrane and function by detecting extracellular cues and transducing signals into the cell. Cilia formation and signaling are dynamically regulated through context-dependent mechanisms; as such, dysregulation of primary cilia contributes to disease in a variety of ways. Here, we review the involvement of primary cilia-associated signaling through aurora A and AKT kinases with respect to cancer, obesity, and other ciliopathies.

Characterization of Genetic Heterogeneity in Recurrent Metastases of Renal Cell Carcinoma

Metastatic renal cell carcinoma (RCC) exhibits poor prognosis. Better knowledge of distant metastases is crucial to foster personalized treatment strategies. Here, we aimed to investigate the genetic landscape of metastases, including synchronous and/or recurrent metastases to elucidate potential drug target genes and clinically relevant mutations in a real-world setting of patients. We assessed 81 metastases from 56 RCC patients, including synchronous and/or recurrent metastases of 19 patients. Samples were analysed through next-generation sequencing with a high coverage (~1000× mean coverage). We therefore established a novel sequencing panel comprising 32 genes with impact on RCC development. We observed a high frequency of mutations in known RCC driver genes (e.g., >40% carriers of VHL and PBRM1 mutations) in metastases irrespective of the metastatic site. The somatic mutational composition was significantly associated with cancer-specific survival (p(logrank) = 0.03). Moreover, we identified in 34 patients at least one drug target gene as well as clinically relevant mutations listed in the VICC Meta-Knowledgebase in 7%. In addition to significantly higher mutational burden in recurrent metastases compared to earlier ones, synchronous and/or recurrent metastases of individual patients, even after a time-period >2 yrs, shared a high proportion of somatic events. Our data demonstrate the importance of somatic profiling in metastases for precision medicine in RCC.

Pushing Past the Blockade: Advancements in T Cell-Based Cancer Immunotherapies

Successful cancer immunotherapies rely on a replete and functional immune compartment. Within the immune compartment, T cells are often the effector arm of immune-based strategies due to their potent cytotoxic capabilities. However, many tumors have evolved a variety of mechanisms to evade T cell-mediated killing. Thus, while many T cell-based immunotherapies, such as immune checkpoint inhibition (ICI) and chimeric antigen receptor (CAR) T cells, have achieved considerable success in some solid cancers and hematological malignancies, these therapies often fail in solid tumors due to tumor-imposed T cell dysfunctions. These dysfunctional mechanisms broadly include reduced T cell access into and identification of tumors, as well as an overall immunosuppressive tumor microenvironment that elicits T cell exhaustion. Therefore, novel, rational approaches are necessary to overcome the barriers to T cell function elicited by solid tumors. In this review, we will provide an overview of conventional immunotherapeutic strategies and the various barriers to T cell anti-tumor function encountered in solid tumors that lead to resistance. We will also explore a sampling of emerging strategies specifically aimed to bypass these tumor-imposed boundaries to T cell-based immunotherapies.

Novel emerging biomarkers to immunotherapy in kidney cancer

The treatment of metastatic renal cell carcinoma has significantly evolved in recent years, particularly with the advent of novel immune checkpoint inhibitors (ICI). Despite the striking benefits observed on a population level, outcomes vary and some patients do not respond to ICI-based regimens, ultimately require salvage therapies. An ever deeper understanding of the disease biology mediated by the development of multiple high-throughput molecular omics has led to significant progress in biomarkers discovery. But despite growing insights into the molecular underpinnings of the tumor microenvironment, biomarkers have not been integrated successfully into clinical practice. In this review, we discuss some of the novel emerging predictive biomarkers to ICIs in metastatic renal cell carcinoma.