UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.

Comprehensively studying metabolism requires the measurement of metabolite levels. However, in contrast to the broad availability of gene expression data, metabolites are rarely measured in large molecularly-defined cohorts of tissue samples. To address this basic barrier to metabolic discovery, we propose a Bayesian framework ("UnitedMet") which leverages the empirical strength of RNA-metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. We demonstrate that UnitedMet is equally capable of imputing whole pool sizes as well as the outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying a novel association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to combination therapy, and that kidney cancer metastases themselves demonstrate elevated oxidative phosphorylation relative to primary tumors. UnitedMet therefore enables the assessment of metabolic phenotypes in contexts where metabolite measurements were not taken or are otherwise infeasible, opening new avenues for the generation and evaluation of metabolite-centered hypotheses. UnitedMet is open source and publicly available (https://github.com/reznik-lab/UnitedMet).

Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes.

The extent of cell-to-cell variation in tumor mitochondrial DNA (mtDNA) copy number and genotype, and the phenotypic and evolutionary consequences of such variation, are poorly characterized. Here we use amplification-free single-cell whole-genome sequencing (Direct Library Prep (DLP+)) to simultaneously assay mtDNA copy number and nuclear DNA (nuDNA) in 72,275 single cells derived from immortalized cell lines, patient-derived xenografts and primary human tumors. Cells typically contained thousands of mtDNA copies, but variation in mtDNA copy number was extensive and strongly associated with cell size. Pervasive whole-genome doubling events in nuDNA associated with stoichiometrically balanced adaptations in mtDNA copy number, implying that mtDNA-to-nuDNA ratio, rather than mtDNA copy number itself, mediated downstream phenotypes. Finally, multimodal analysis of DLP+ and single-cell RNA sequencing identified both somatic loss-of-function and germline noncoding variants in mtDNA linked to heteroplasmy-dependent changes in mtDNA copy number and mitochondrial transcription, revealing phenotypic adaptations to disrupted nuclear/mitochondrial balance.

Mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade response in melanoma.

The mitochondrial genome (mtDNA) encodes essential machinery for oxidative phosphorylation and metabolic homeostasis. Tumor mtDNA is among the most somatically mutated regions of the cancer genome, but whether these mutations impact tumor biology is debated. We engineered truncating mutations of the mtDNA-encoded complex I gene, Mt-Nd5, into several murine models of melanoma. These mutations promoted a Warburg-like metabolic shift that reshaped tumor microenvironments in both mice and humans, consistently eliciting an anti-tumor immune response characterized by loss of resident neutrophils. Tumors bearing mtDNA mutations were sensitized to checkpoint blockade in a neutrophil-dependent manner, with induction of redox imbalance being sufficient to induce this effect in mtDNA wild-type tumors. Patient lesions bearing >50% mtDNA mutation heteroplasmy demonstrated a response rate to checkpoint blockade that was improved by ~2.5-fold over mtDNA wild-type cancer. These data nominate mtDNA mutations as functional regulators of cancer metabolism and tumor biology, with potential for therapeutic exploitation and treatment stratification.

Obesity shapes selection for driver mutations in cancer.

Obesity is a leading risk factor for cancer, but whether obesity is linked to specific genomic subtypes of cancer is unknown. Here, we examined the relationship between obesity and tumor genotype in two large clinicogenomic corpora. Obesity was associated with specific driver mutations in lung adenocarcinoma, endometrial carcinoma, and cancers of unknown primary, independent of clinical covariates and genetic ancestry. Obesity is therefore a putative driver of etiologic heterogeneity across cancers.

Genomic ancestry in kidney cancer: Correlations with clinical and molecular features.

INTRODUCTION: Genetic ancestry (GA) refers to population hereditary patterns that contribute to phenotypic differences seen among race/ethnicity groups, and differences among GA groups may highlight unique biological determinants that add to our understanding of health care disparities. METHODS: A retrospective review of patients with renal cell carcinoma (RCC) was performed and correlated GA with clinicopathologic, somatic, and germline molecular data. All patients underwent next-generation sequencing of normal and tumor DNA using Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets, and contribution of African (AFR), East Asian (EAS), European (EUR), Native American, and South Asian (SAS) ancestry was inferred through supervised ADMIXTURE. Molecular data was compared across GA groups by Fisher exact test and Kruskal-Wallis test. RESULTS: In 953 patients with RCC, the GA distribution was: EUR (78%), AFR (4.9%), EAS (2.5%), SAS (2%), Native American (0.2%), and Admixed (12.2%). GA distribution varied by tumor histology and international metastatic RCC database consortium disease risk status (intermediate-poor: EUR 58%, AFR 88%, EAS 74%, and SAS 73%). Pathogenic/likely pathogenic germline variants in cancer-predisposition genes varied (16% EUR, 23% AFR, 8% EAS, and 0% SAS), and most occurred in CHEK2 in EUR (3.1%) and FH in AFR (15.4%). In patients with clear cell RCC, somatic alteration incidence varied with significant enrichment in BAP1 alterations (EUR 17%, AFR 50%, SAS 29%; p = .01). Comparing AFR and EUR groups within The Cancer Genome Atlas, significant differences were identified in angiogenesis and inflammatory pathways. CONCLUSION: Differences in clinical and molecular data by GA highlight population-specific variations in patients with RCC. Exploration of both genetic and nongenetic variables remains critical to optimize efforts to overcome health-related disparities.

A Phase 2 Trial of Talazoparib and Avelumab in Genomically Defined Metastatic Kidney Cancer.

BACKGROUND: Although different kidney cancers represent a heterogeneous group of malignancies, multiple subtypes including Von Hippel-Lindau (VHL)-altered clear cell renal cell carcinoma (ccRCC), fumarate hydratase (FH)- and succinate dehydrogenase (SDH)-deficient renal cell carcinoma (RCC), and renal medullary carcinoma (RMC) are affected by genomic instability. Synthetic lethality with poly ADP-ribose polymerase inhibitors (PARPis) has been suggested in preclinical models of these subtypes, and paired PARPis with immune checkpoint blockade (ICB) may achieve additive and/or synergistic effects in patients with previously treated advanced kidney cancers. OBJECTIVE: To evaluate combined PARPi + ICB in treatment-refractory metastatic kidney cancer. DESIGN, SETTING, AND PARTICIPANTS: We conducted a single-center, investigator-initiated phase 2 trial in two genomically selected advanced kidney cancer cohorts: (1) VHL-altered RCC with at least one prior ICB agent and one vascular endothelial growth factor (VEGF) inhibitor, and (2) FH- or SDH-deficient RCC with at least one prior ICB agent or VEGF inhibitor and RMC with at least one prior line of chemotherapy. INTERVENTION: Patients received talazoparib 1 mg daily plus avelumab 800 mg intravenously every 14 d in 28-d cycles. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was objective response rate (ORR) by Immune Response Evaluation Criteria in Solid Tumors at 4 mo, and the secondary endpoints included progression-free survival (PFS), overall survival, and safety. RESULTS AND LIMITATIONS: Cohort 1 consisted of ten patients with VHL-altered ccRCC. All patients had previously received ICB. The ORR was 0/9 patients; one patient was not evaluable due to missed doses. In this cohort, seven patients achieved stable disease (SD) as the best response. The median PFS was 3.5 mo (95% confidence interval [CI] 1.0, 3.9 mo). Cohort 2 consisted of eight patients; four had FH-deficient RCC, one had SDH-deficient RCC, and three had RMC. In this cohort, six patients had previously received ICB. The ORR was 0/8 patients; two patients achieved SD as the best response and the median PFS was 1.2 mo (95% CI 0.4, 2.9 mo). The most common treatment-related adverse events of all grades were fatigue (61%), anemia (28%), nausea (22%), and headache (22%). There were seven grade 3-4 and no grade 5 events. CONCLUSIONS: The first clinical study of combination PARPi and ICB therapy in advanced kidney cancer did not show clinical benefit in multiple genomically defined metastatic RCC cohorts or RMC. PATIENT SUMMARY: We conducted a study to look at the effect of two medications, talazoparib and avelumab, in patients with metastatic kidney cancer who had disease progression on standard treatment. Talazoparib blocks the normal activity of molecules called poly ADP-ribose polymerase, which then prevents tumor cells from repairing themselves and growing, while avelumab helps the immune system recognize and kill cancer cells. We found that the combination of these agents was safe but not effective in specific types of kidney cancer.

Immunometabolic coevolution defines unique microenvironmental niches in ccRCC.

Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. To study IMH, we profiled tumor/normal regions from clear cell renal cell carcinoma (ccRCC) patients. A common pattern of IMH transcended all patients, characterized by correlated fluctuations in the abundance of metabolites and processes associated with ferroptosis. Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we inferred metabolomic profiles from the RNA sequencing data of ccRCC patients enrolled in 7 clinical trials, and we ultimately identifyied metabolite biomarkers associated with response to anti-angiogenic agents. Local metabolic phenotypes, therefore, emerge in tandem with the immune microenvironment, influence ongoing tumor evolution, and are associated with therapeutic sensitivity.

A multimodal atlas of tumour metabolism reveals the architecture of gene-metabolite covariation.

Tumour metabolism is controlled by coordinated changes in metabolite abundance and gene expression, but simultaneous quantification of metabolites and transcripts in primary tissue is rare. To overcome this limitation and to study gene-metabolite covariation in cancer, we assemble the Cancer Atlas of Metabolic Profiles of metabolomic and transcriptomic data from 988 tumour and control specimens spanning 11 cancer types in published and newly generated datasets. Meta-analysis of the Cancer Atlas of Metabolic Profiles reveals two classes of gene-metabolite covariation that transcend cancer types. The first corresponds to gene-metabolite pairs engaged in direct enzyme-substrate interactions, identifying putative genes controlling metabolite pool sizes. A second class of gene-metabolite covariation represents a small number of hub metabolites, including quinolinate and nicotinamide adenine dinucleotide, which correlate to many genes specifically expressed in immune cell populations. These results provide evidence that gene-metabolite covariation in cellularly heterogeneous tissue arises, in part, from both mechanistic interactions between genes and metabolites, and from remodelling of the bulk metabolome in specific immune microenvironments.

Tumour mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade.

The mitochondrial genome encodes essential machinery for respiration and metabolic homeostasis but is paradoxically among the most common targets of somatic mutation in the cancer genome, with truncating mutations in respiratory complex I genes being most over-represented1. While mitochondrial DNA (mtDNA) mutations have been associated with both improved and worsened prognoses in several tumour lineages1-3, whether these mutations are drivers or exert any functional effect on tumour biology remains controversial. Here we discovered that complex I-encoding mtDNA mutations are sufficient to remodel the tumour immune landscape and therapeutic resistance to immune checkpoint blockade. Using mtDNA base editing technology4 we engineered recurrent truncating mutations in the mtDNA-encoded complex I gene, Mt-Nd5, into murine models of melanoma. Mechanistically, these mutations promoted utilisation of pyruvate as a terminal electron acceptor and increased glycolytic flux without major effects on oxygen consumption, driven by an over-reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. In turn, without modifying tumour growth, this altered cancer cell-intrinsic metabolism reshaped the tumour microenvironment in both mice and humans, promoting an anti-tumour immune response characterised by loss of resident neutrophils. This subsequently sensitised tumours bearing high mtDNA mutant heteroplasmy to immune checkpoint blockade, with phenocopy of key metabolic changes being sufficient to mediate this effect. Strikingly, patient lesions bearing >50% mtDNA mutation heteroplasmy also demonstrated a >2.5-fold improved response rate to checkpoint inhibitor blockade. Taken together these data nominate mtDNA mutations as functional regulators of cancer metabolism and tumour biology, with potential for therapeutic exploitation and treatment stratification.

Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature.

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and 11C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, 99Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.

Spatiotemporal evolution of the clear cell renal cell carcinoma microenvironment links intra-tumoral heterogeneity to immune escape.

BACKGROUND: Intratumoral heterogeneity (ITH) is a hallmark of clear cell renal cell carcinoma (ccRCC) that reflects the trajectory of evolution and influences clinical prognosis. Here, we seek to elucidate how ITH and tumor evolution during immune checkpoint inhibitor (ICI) treatment can lead to therapy resistance. METHODS: Here, we completed a single-arm pilot study to examine the safety and feasibility of neoadjuvant nivolumab in patients with localized RCC. Primary endpoints were safety and feasibility of neoadjuvant nivolumab. Then, we spatiotemporally profiled the genomic and immunophenotypic characteristics of 29 ccRCC patients, including pre- and post-therapy samples from 17 ICI-treated patients. Deep multi-regional whole-exome and transcriptome sequencing were performed on 29 patients at different time points before and after ICI therapy. T cell repertoire was also monitored from tissue and peripheral blood collected from a subset of patients to study T cell clonal expansion during ICI therapy. RESULTS: Angiogenesis, lymphocytic infiltration, and myeloid infiltration varied significantly across regions of the same patient, potentially confounding their utility as biomarkers of ICI response. Elevated ITH associated with a constellation of both genomic features (HLA LOH, CDKN2A/B loss) and microenvironmental features, including elevated myeloid expression, reduced peripheral T cell receptor (TCR) diversity, and putative neoantigen depletion. Hypothesizing that ITH may itself play a role in shaping ICI response, we derived a transcriptomic signature associated with neoantigen depletion that strongly associated with response to ICI and targeted therapy treatment in several independent clinical trial cohorts. CONCLUSIONS: These results argue that genetic and immune heterogeneity jointly co-evolve and influence response to ICI in ccRCC. Our findings have implications for future biomarker development for ICI response across ccRCC and other solid tumors and highlight important features of tumor evolution under ICI treatment. TRIAL REGISTRATION: The study was registered on ClinicalTrial.gov (NCT02595918) on November 4, 2015.

Urethral Melanoma - Clinical, Pathological and Molecular Characteristics.

BACKGROUND: Mucosal melanoma involving the urethra is a rare disease with distinct clinical and molecular characteristics and poor outcomes. Our current knowledge is limited by the small number of reports regarding this disease. OBJECTIVE: To describe the clinical, pathological, and molecular characteristics of urethral melanoma. METHODS: We summarized the clinicopathologic data for 31 patients treated for urethral melanoma from 1986-2017 at our institution. Genomic data from our institutional sequencing platform MSK-IMPACT (n = 5) and gene-specific PCR data on BRAF, KIT, and/or NRAS (n = 8) were compared to genomic data of cutaneous melanomas (n = 143), vulvar/vaginal melanomas (n = 24), and primary non-melanoma urethral tumors (n = 5) from our institutional database. RESULTS: Twenty-three patients were diagnosed with localized disease, 7 had regional/nodal involvement and one had metastases. Initial treatment included surgery in 25 patients; seven had multimodal treatment. Median follow-up was 46 months (IQR 33-123). Estimated 5-year cancer-specific survival was 45%. No significant change in survival was observed based on a year of treatment.Primary urethral melanomas showed a higher frequency of TP53 mutations compared to cutaneous (80.0% vs. 18.2%, p = 0.006) and vulvar/vaginal melanomas (80.0 vs. 25.0%, p = 0.04). BRAF mutations were absent in urethral primaries (0% vs. 46% in cutaneous melanoma, p = 0.02). Tumor mutation burden was higher in cutaneous than urethral melanomas (p = 0.04). Urethral melanomas had a higher number of somatic alterations compared to non-melanoma urethral tumors (median 11 vs. 5, p = 0.03). CONCLUSIONS: Our findings support a unique mutational landscape of urethral melanoma compared to cutaneous melanoma. Survival remains poor and is unchanged over the time studied.

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization.

Out of the thousands of metabolites in a given specimen, most metabolomics experiments measure only hundreds, with poor overlap across experimental platforms. Here, we describe Metabolite Imputation via Rank-Transformation and Harmonization (MIRTH), a method to impute unmeasured metabolite abundances by jointly modeling metabolite covariation across datasets which have heterogeneous coverage of metabolite features. MIRTH successfully recovers masked metabolite abundances both within single datasets and across multiple, independently-profiled datasets. MIRTH demonstrates that latent information about otherwise unmeasured metabolites is embedded within existing metabolomics data, and can be used to generate novel hypotheses and simplify existing metabolomic workflows.

Mitochondrial DNA is a major source of driver mutations in cancer.

Mitochondrial DNA (mtDNA) mutations are among the most common genetic events in all tumors and directly impact metabolic homeostasis. Despite the central role mitochondria play in energy metabolism and cellular physiology, the role of mutations in the mitochondrial genomes of tumors has been contentious. Until recently, genomic and functional studies of mtDNA variants were impeded by a lack of adequate tumor mtDNA sequencing data and available methods for mitochondrial genome engineering. These barriers and a conceptual fog surrounding the functional impact of mtDNA mutations in tumors have begun to lift, revealing a path to understanding the role of this essential metabolic genome in cancer initiation and progression. Here we discuss the history, recent developments, and challenges that remain for mitochondrial oncogenetics as the impact of a major new class of cancer-associated mutations is unveiled.

Molecular Characterization of the Tumor Microenvironment in Renal Medullary Carcinoma.

Renal medullary carcinoma (RMC) is a highly aggressive disease associated with sickle hemoglobinopathies and universal loss of the tumor suppressor gene SMARCB1. RMC has a relatively low rate of incidence compared with other renal cell carcinomas (RCCs) that has hitherto made molecular profiling difficult. To probe this rare disease in detail we performed an in-depth characterization of the RMC tumor microenvironment using a combination of genomic, metabolic and single-cell RNA-sequencing experiments on tissue from a representative untreated RMC patient, complemented by retrospective analyses of archival tissue and existing published data. Our study of the tumor identifies a heterogenous population of malignant cell states originating from the thick ascending limb of the Loop of Henle within the renal medulla. Transformed RMC cells displayed the hallmarks of increased resistance to cell death by ferroptosis and proteotoxic stress driven by MYC-induced proliferative signals. Specifically, genomic characterization of RMC tumors provides substantiating evidence for the recently proposed dependence of SMARCB1-difficient cancers on proteostasis modulated by an intact CDKN2A-p53 pathway. We also provide evidence that increased cystine-mTORC-GPX4 signaling plays a role in protecting transformed RMC cells against ferroptosis. We further propose that RMC has an immune landscape comparable to that of untreated RCCs, including heterogenous expression of the immune ligand CD70 within a sub-population of tumor cells. The latter could provide an immune-modulatory role that serves as a viable candidate for therapeutic targeting.

A Targetable Myeloid Inflammatory State Governs Disease Recurrence in Clear-Cell Renal Cell Carcinoma.

It is poorly understood how the tumor immune microenvironment influences disease recurrence in localized clear-cell renal cell carcinoma (ccRCC). Here we performed whole-transcriptomic profiling of 236 tumors from patients assigned to the placebo-only arm of a randomized, adjuvant clinical trial for high-risk localized ccRCC. Unbiased pathway analysis identified myeloid-derived IL6 as a key mediator. Furthermore, a novel myeloid gene signature strongly correlated with disease recurrence and overall survival on uni- and multivariate analyses and is linked to TP53 inactivation across multiple data sets. Strikingly, effector T-cell gene signatures, infiltration patterns, and exhaustion markers were not associated with disease recurrence. Targeting immunosuppressive myeloid inflammation with an adenosine A2A receptor antagonist in a novel, immunocompetent, Tp53-inactivated mouse model significantly reduced metastatic development. Our findings suggest that myeloid inflammation promotes disease recurrence in ccRCC and is targetable as well as provide a potential biomarker-based framework for the design of future immuno-oncology trials in ccRCC. SIGNIFICANCE: Improved understanding of factors that influence metastatic development in localized ccRCC is greatly needed to aid accurate prediction of disease recurrence, clinical decision-making, and future adjuvant clinical trial design. Our analysis implicates intratumoral myeloid inflammation as a key driver of metastasis in patients and a novel immunocompetent mouse model. This article is highlighted in the In This Issue feature, p. 2221.

Mitonuclear genotype remodels the metabolic and microenvironmental landscape of Hürthle cell carcinoma.

Hürthle cell carcinomas (HCCs) display two exceptional genotypes: near-homoplasmic mutation of mitochondrial DNA (mtDNA) and genome-wide loss of heterozygosity (gLOH). To understand the phenotypic consequences of these genetic alterations, we analyzed genomic, metabolomic, and immunophenotypic data of HCC and other thyroid cancers. Both mtDNA mutations and profound depletion of citrate pools are common in HCC and other thyroid malignancies, suggesting that thyroid cancers are broadly equipped to survive tricarboxylic acid cycle impairment, whereas metabolites in the reduced form of NADH-dependent lysine degradation pathway were elevated exclusively in HCC. The presence of gLOH was not associated with metabolic phenotypes but rather with reduced immune infiltration, indicating that gLOH confers a selective advantage partially through immunosuppression. Unsupervised multimodal clustering revealed four clusters of HCC with distinct clinical, metabolomic, and microenvironmental phenotypes but overlapping genotypes. These findings chart the metabolic and microenvironmental landscape of HCC and shed light on the interaction between genotype, metabolism, and the microenvironment in cancer.