Selected Publications

2026

1. 

   A health-system-scale, episode-resolved multimodal atlas of cancer cachexia

   Venise Jan Castillon^*, Amy X. Xie^*, Sonia Boscenco, Ethan Tse, Neil Ruthen, Jamie Wang, Ziad Bakouny, Michele Waters, Chenlian Fu, Christopher Fong, Mustafa Saleh, Tarek Baroud, Wungki Park, Urvi A. Shah, A. Ari Hakimi, Puneeth Iyengar, Justin Jee, Eileen White, Alexander Gusev, Tobias Janowitz, Marcus D. Goncalves, and Ed Reznik^†

   Nature Medicine, Oct 2026

   Publisher: Nature Publishing Group

   Abs DOI Code

   Cancer cachexia is a wasting syndrome with outsized impact on morbidity and mortality, yet its drivers and systemic consequences remain poorly defined. In this study, we repurposed longitudinal clinicogenomic data from 52,236 patients with cancer profiled by MSK-IMPACT (2010–2024) at Memorial Sloan Kettering Cancer Center to define episode-resolved trajectories of consensus-defined cachectic weight loss and linked these events to outcomes, serology and tumor genotypes. Cachectic episodes clustered near periods of disease progression and were associated with inferior outcomes in nearly all cancer types. Across diverse malignancies, cachectic weight loss episodes exhibited a consistent serologic signature characterized by lower albumin and hemoglobin and higher levels of liver enzymes. Numerous somatic tumor genotypes, including TP53 mutation across several diseases, were also associated with more frequent onset of cachectic episodes. Motivated by these observations, we developed a multivariate model to predict impending risk of cachectic weight loss in patients with lung, colorectal and prostate cancer, validated in an independent, external cohort from Dana-Farber Cancer Institute (n = 5,785). Routine clinicogenomic data are, therefore, a scalable resource for discovery in cancer cachexia.

2025

1. 

   Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer

   Mark Zucker^*, Maria A. Perry^*, Samuel I. Gould, Arielle Elkrief, Anton Safonov, Rohit Thummalapalli, Miika Mehine, Debyani Chakravarty, A. Rose Brannon, Marc Ladanyi, Pedram Razavi, Mark T. A. Donoghue, Yonina R. Murciano-Goroff, Kristiana Grigoriadis, Nicholas McGranahan, Mariam Jamal-Hanjani, Charles Swanton, Yuan Chen, Ronglai Shen, Sarat Chandarlapaty, David B. Solit, Nikolaus Schultz, Michael F. Berger, Jason Chang, Adam J. Schoenfeld, Francisco J. Sánchez-Rivera, Ed Reznik^†, and Chaitanya Bandlamudi^†

   Cell, Feb 2025

   Publisher: Elsevier

   Abs DOI Code

   The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

2. 

   Sample Site Impacts RNA Biomarkers for Renal Cell Carcinoma

   Lennert Eismann^*, Amy X. Xie^*, Cerise Tang, Andrea Knezevic, Irina Ostrovnaya, Fengshen Kuo, A. Ari Hakimi, Ed Reznik^†, and Ritesh R. Kotecha^†

   European Urology, Jan 2025

   Abs DOI

   Immunotherapy (ICIs) remains a mainstay for treatment of advanced clear-cell renal cell carcinoma (ccRCC). Biomarker analyses have demonstrated that gene expression profiles are associated with regimen-specific outcomes. These transcriptomic analyses used mixed sample cohorts (primary and metastatic tumor specimens) and it is unknown whether the clinical relevance of transcriptomic signatures is impacted by tissue site. We evaluated data for 1132 patients with metastatic ccRCC treated with ICI in prior studies (IMmotion151 and CheckMate-009, -010, and -025). We identified significant and reproducible differences in gene expression by tissue site. We tested the association between previously described molecular tissue clusters (MTCs) by tissue site (MTC1-primary and MTC1-metastasis) and progression-free survival (PFS) and objective response to systemic therapy. In IMmotion151, MTC2-metastasis was significantly associated with better PFS on sunitinib (hazard ratio [HR] 3.39, 95% confidence interval [CI] 1.32–8.69; p = 0.01) in comparison to MTC2-primary (HR 0.95, 95% CI 0.65–1.38; p = 0.80; pinteraction = 0.02). Evaluation of known RNA signatures in the CheckMate trials revealed that JAVELIN-metastasis was associated with better PFS on ICI (HR 0.77, 95% CI 0.62–0.97; p = 0.03) in comparison to JAVELIN-primary (HR 1.04, 95% CI 0.91–1.19; p = 0.56; pinteraction = 0.02). These results indicate that tissue site may be a relevant confounder in biomarker analyses.

3. 

   UnitedMet harnesses RNA–metabolite covariation to impute metabolite levels in clinical samples

   Amy X. Xie, Wesley Tansey^†, and Ed Reznik^†

   Nature Cancer, May 2025

   Publisher: Nature Publishing Group

   Abs DOI Code

   Comprehensively studying metabolism requires metabolite measurements. Such measurements, however, are often unavailable in large cohorts of tissue samples. To address this basic barrier, we propose a Bayesian framework (‘UnitedMet’) that leverages RNA–metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. UnitedMet is equally capable of imputing whole pool sizes and outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying an 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 anti-angiogenic therapy and that kidney cancer metastases demonstrate elevated oxidative phosphorylation. UnitedMet provides a scalable tool for assessing metabolic phenotypes when direct measurements are infeasible, facilitating unexplored avenues for metabolite-focused hypothesis generation.

4. 

   Functionally dominant hotspot mutations of mitochondrial ribosomal RNA genes in cancer

   Sonia Boscenco^*, Jacqueline Tait-Mulder^*, Minsoo Kim^*, Cerise Tang, Tricia Park, Flora McNulty, Sergio Lilla, Sara Zanivan, Alejandro Huerta-Uribe, Benan Nalbant, Mark Zucker, David Sumpton, Geoffray Monteuuis, Christopher B. Jackson, Wei Wei, Patrick F. Chinnery, Ronan Chaligne, Caleb A. Lareau, Ed Reznik^†, and Payam A. Gammage^†

   Nature Genetics, Nov 2025

   Abs DOI Code

   The vast majority of recurrent somatic mutations arising in tumors affect protein-coding genes in the nuclear genome. Here, through population-scale analysis of 14,106 whole tumor genomes, we report the discovery of highly recurrent mutations affecting both the small (12S, MT-RNR1) and large (16S, MT-RNR2) mitochondrial RNA subunits of the mitochondrial ribosome encoded within mitochondrial DNA (mtDNA). Compared to non-hotspot positions, mitochondrial rRNA hotspots preferentially affected positions under purifying selection in the germline and demonstrated structural clustering within the mitoribosome at mRNA and tRNA interacting positions. Using precision mtDNA base editing, we engineered models of an exemplar MT-RNR1 hotspot mutation, m.1227G>A. Multimodal profiling revealed a heteroplasmy-dependent decrease in mitochondrial function and loss of respiratory chain subunits from a heteroplasmic dosage of  10%. Mutation of conserved positions in ribosomal RNA that disrupt mitochondrial translation therefore represent a class of functionally dominant, pathogenic mtDNA mutations that are under positive selection in cancer genomes.

2024

1. 

   Obesity-dependent selection of driver mutations in cancer

   Cerise Tang, Venise Jan Castillon, Michele Waters, Chris Fong, Tricia Park, Sonia Boscenco, Susie Kim, Kelly Pekala, Jian Carrot-Zhang, A. Ari Hakimi, Nikolaus Schultz, Irina Ostrovnaya, Alexander Gusev, Justin Jee^†, and Ed Reznik^†

   Nature Genetics, Nov 2024

   Publisher: Nature Publishing Group

   Abs DOI

   Obesity is a risk factor for cancer, but whether obesity is linked to specific genomic subtypes of cancer is unknown. We examined the relationship between obesity and tumor genotype in two clinicogenomic corpora. Obesity was associated with specific driver mutations in lung adenocarcinoma, endometrial carcinoma and cancers of unknown primaries, independent of clinical covariates, demographic factors and genetic ancestry. Obesity is therefore a driver of etiological heterogeneity in some cancers.

2. 

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

   Minsoo Kim, Alexander N. Gorelick, Ignacio Vàzquez-García, Marc J. Williams, Sohrab Salehi, Hongyu Shi, Adam C. Weiner, Nick Ceglia, Tyler Funnell, Tricia Park, Sonia Boscenco, Ciara H. O’Flanagan, Hui Jiang, Diljot Grewal, Cerise Tang, Nicole Rusk, Payam A. Gammage, Andrew McPherson, Sam Aparicio, Sohrab P. Shah^†, and Ed Reznik^†

   Nature Genetics, May 2024

   Publisher: Nature Publishing Group

   Abs DOI

   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.

3. 

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

   Mahnoor Mahmood, Eric Minwei Liu, Amy L. Shergold, Elisabetta Tolla, Jacqueline Tait-Mulder, Alejandro Huerta-Uribe, Engy Shokry, Alex L. Young, Sergio Lilla, Minsoo Kim, Tricia Park, Sonia Boscenco, Javier L. Manchon, Crístina Rodríguez-Antona, Rowan C. Walters, Roger J. Springett, James N. Blaza, Louise Mitchell, Karen Blyth, Sara Zanivan, David Sumpton, Edward W. Roberts, Ed Reznik^†, and Payam A. Gammage^†

   Nature Cancer, Apr 2024

   Publisher: Nature Publishing Group

   Abs DOI

   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 \textgreater50% 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.

2023

1. 

   Immunometabolic coevolution defines unique microenvironmental niches in ccRCC

   Cerise Tang, Amy X. Xie, Eric Minwei Liu, Fengshen Kuo, Minsoo Kim, Renzo G. DiNatale, Mahdi Golkaram, Ying-Bei Chen, Sounak Gupta, Robert J. Motzer, Paul Russo, Jonathan Coleman, Maria I. Carlo, Martin H. Voss, Ritesh R. Kotecha, Chung-Han Lee, Wesley Tansey, Nikolaus Schultz, A. Ari Hakimi^†, and Ed Reznik^†

   Cell Metabolism, Aug 2023

   Publisher: Elsevier

   Abs DOI Code

   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.

2. 

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

   Elisa Benedetti^*, Eric Minwei Liu^*, Cerise Tang^*, Fengshen Kuo, Mustafa Buyukozkan, Tricia Park, Jinsung Park, Fabian Correa, A. Ari Hakimi, Andrew M. Intlekofer, Jan Krumsiek^†, and Ed Reznik^†

   Nature Metabolism, Jun 2023

   Publisher: Nature Publishing Group

   Abs DOI Code

   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.

2022

1. 

   MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

   Benjamin A. Freeman^*, Sophie Jaro^*, Tricia Park, Sam Keene, Wesley Tansey^†, and Ed Reznik^†

   Genome Biol, Sep 2022

   Abs DOI Code

   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.

2. 

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

   Ian Ganly^*†, Eric Minwei Liu^*, Fengshen Kuo, Vladimir Makarov, Yiyu Dong, Jinsung Park, Yongxing Gong, Alexander N. Gorelick, Jeffrey A Knauf, Elisa Benedetti, Jacqueline Tait-Mulder, Luc G.T. Morris, James A. Fagin, Andrew M Intlekofer, Jan Krumsiek, Payam A. Gammage, Ronald Ghossein, Bin Xu, Timothy A. Chan^†, and Ed Reznik^†

   Science Advances, Jun 2022

   Publisher: American Association for the Advancement of Science

   Abs DOI

   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.

2021

1. 

   Respiratory complex and tissue lineage drive recurrent mutations in tumour mtDNA

   Alexander N. Gorelick, Minsoo Kim, Walid K. Chatila, Konnor La, A. Ari Hakimi, Michael F. Berger, Barry S. Taylor, Payam A. Gammage^†, and Ed Reznik^†

   Nature Metabolism, Apr 2021

   Publisher: Nature Publishing Group

   Abs DOI Code

   Mitochondrial DNA (mtDNA) encodes protein subunits and translational machinery required for oxidative phosphorylation (OXPHOS). Using repurposed whole-exome sequencing data, in the present study we demonstrate that pathogenic mtDNA mutations arise in tumours at a rate comparable to those in the most common cancer driver genes. We identify OXPHOS complexes as critical determinants shaping somatic mtDNA mutation patterns across tumour lineages. Loss-of-function mutations accumulate at an elevated rate specifically in complex I and often arise at specific homopolymeric hotspots. In contrast, complex V is depleted of all non-synonymous mutations, suggesting that impairment of ATP synthesis and mitochondrial membrane potential dissipation are under negative selection. Common truncating mutations and rarer missense alleles are both associated with a pan-lineage transcriptional programme, even in cancer types where mtDNA mutations are comparatively rare. Pathogenic mutations of mtDNA are associated with substantial increases in overall survival of colorectal cancer patients, demonstrating a clear functional relationship between genotype and phenotype. The mitochondrial genome is therefore frequently and functionally disrupted across many cancers, with major implications for patient stratification, prognosis and therapeutic development.

2020

1. 

   Phase and context shape the function of composite oncogenic mutations

   Alexander N. Gorelick, Francisco J. Sánchez-Rivera, Yanyan Cai, Craig M. Bielski, Evan Biederstedt, Philip Jonsson, Allison L. Richards, Neil Vasan, Alexander V. Penson, Noah D. Friedman, Yu-Jui Ho, Timour Baslan, Chaitanya Bandlamudi, Maurizio Scaltriti, Nikolaus Schultz, Scott W. Lowe, Ed Reznik^†, and Barry S. Taylor^†

   Nature, Jun 2020

   Publisher: Nature Publishing Group

   Abs DOI Code

   Cancers develop as a result of driver mutations1,2 that lead to clonal outgrowth and the evolution of disease3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes5 and therapeutic vulnerabilities6. However, the serial genetic evolution of mutant cancer genes7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex—often neomorphic—functions of biological and therapeutic importance.

2019

1. 

   Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma

   Jianing Xu^*, Ed Reznik^*†, Ho-Joon Lee, Gunes Gundem, Philip Jonsson, Judy Sarungbam, Anna Bialik, Francisco Sanchez-Vega, Chad J Creighton, Jake Hoekstra, Li Zhang, Peter Sajjakulnukit, Daniel Kremer, Zachary Tolstyka, Jozefina Casuscelli, Steve Stirdivant, Jie Tang, Nikolaus Schultz, Paul Jeng, Yiyu Dong, Wenjing Su, Emily H Cheng, Paul Russo, Jonathan A Coleman, Elli Papaemmanuil, Ying-Bei Chen, Victor E Reuter, Chris Sander, Scott R Kennedy, James J Hsieh, Costas A Lyssiotis^†, Satish K Tickoo^†, and A Ari Hakimi^†

   eLife, Mar 2019

   Publisher: eLife Sciences Publications, Ltd

   Abs DOI

   While genomic sequencing routinely identifies oncogenic alterations for the majority of cancers, many tumors harbor no discernable driver lesion. Here, we describe the exceptional molecular phenotype of a genomically quiet kidney tumor, clear cell papillary renal cell carcinoma (CCPAP). In spite of a largely wild-type nuclear genome, CCPAP tumors exhibit severe depletion of mitochondrial DNA (mtDNA) and RNA and high levels of oxidative stress, reflecting a shift away from respiratory metabolism. Moreover, CCPAP tumors exhibit a distinct metabolic phenotype uniquely characterized by accumulation of the sugar alcohol sorbitol. Immunohistochemical staining of primary CCPAP tumor specimens recapitulates both the depletion of mtDNA-encoded proteins and a lipid-depleted metabolic phenotype, suggesting that the cytoplasmic clarity in CCPAP is primarily related to the presence of glycogen. These results argue for non-genetic profiling as a tool for the study of cancers of unknown driver.

2018

1. 

   A Landscape of Metabolic Variation across Tumor Types

   Ed Reznik^*†, Augustin Luna^*, Bülent Arman Aksoy, Eric Minwei Liu, Konnor La, Irina Ostrovnaya, Chad J. Creighton, A. Ari Hakimi, and Chris Sander^†

   Cell Systems, Mar 2018

   Publisher: Elsevier

   DOI

Postdoctoral Work

2017

1. 

   Mitochondrial respiratory gene expression is suppressed in many cancers

   Ed Reznik^*†, Qingguo Wang^*, Konnor La, Nikolaus Schultz^†, and Chris Sander^†

   eLife, Jan 2017

   Publisher: eLife Sciences Publications, Ltd

   Abs DOI

   The fundamental metabolic decision of a cell, the balance between respiration and fermentation, rests in part on expression of the mitochondrial genome (mtDNA) and coordination with expression of the nuclear genome (nuDNA). Previously we described mtDNA copy number depletion across many solid tumor types (Reznik et al., 2016). Here, we use orthogonal RNA-sequencing data to quantify mtDNA expression (mtRNA), and report analogously lower expression of mtRNA in tumors (relative to normal tissue) across a majority of cancer types. Several cancers exhibit a trio of mutually consistent evidence suggesting a drop in respiratory activity: depletion of mtDNA copy number, decreases in mtRNA levels, and decreases in expression of nuDNA-encoded respiratory proteins. Intriguingly, a minority of cancer types exhibit a drop in mtDNA expression but an increase in nuDNA expression of respiratory proteins, with unknown implications for respiratory activity. Our results indicate suppression of respiratory gene expression across many cancer types.

2. 

   Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

   Ed Reznik, Dimitris Christodoulou, Joshua E. Goldford, Emma Briars, Uwe Sauer, Daniel Segrè, and Elad Noor

   Cell Reports, Sep 2017

   Publisher: Elsevier

   DOI

2016

1. 

   Mitochondrial DNA copy number variation across human cancers

   Ed Reznik^†, Martin L Miller, Yasin Şenbabaoğlu, Nadeem Riaz, Judy Sarungbam, Satish K Tickoo, Hikmat A Al-Ahmadie, William Lee, Venkatraman E Seshan, A Ari Hakimi, and Chris Sander

   eLife, Feb 2016

   Publisher: eLife Sciences Publications, Ltd

   Abs DOI

   Mutations, deletions, and changes in copy number of mitochondrial DNA (mtDNA), are observed throughout cancers. Here, we survey mtDNA copy number variation across 22 tumor types profiled by The Cancer Genome Atlas project. We observe a tendency for some cancers, especially of the bladder, breast, and kidney, to be depleted of mtDNA, relative to matched normal tissue. Analysis of genetic context reveals an association between incidence of several somatic alterations, including IDH1 mutations in gliomas, and mtDNA content. In some but not all cancer types, mtDNA content is correlated with the expression of respiratory genes, and anti-correlated to the expression of immune response and cell-cycle genes. In tandem with immunohistochemical evidence, we find that some tumors may compensate for mtDNA depletion to sustain levels of respiratory proteins. Our results highlight the extent of mtDNA copy number variation in tumors and point to related therapeutic opportunities.

2015

1. 

   Extensive Decoupling of Metabolic Genes in Cancer

   Ed Reznik^† and Chris Sander

   PLOS Computational Biology, May 2015

   Publisher: Public Library of Science

   Abs DOI

   Tumorigenesis requires the re-organization of metabolism to support malignant proliferation. We examine how the altered metabolism of cancer cells is reflected in the rewiring of co-expression patterns among metabolic genes. Focusing on breast and clear-cell kidney tumors, we report the existence of key metabolic genes which act as hubs of differential co-expression, showing significantly different co-regulation patterns between normal and tumor states. We compare our findings to those from classical differential expression analysis, and counterintuitively observe that the extent of a gene’s differential co-expression only weakly correlates with its differential expression, suggesting that the two measures probe different features of metabolism. Focusing on this discrepancy, we use changes in co-expression patterns to highlight the apparent loss of regulation by the transcription factor HNF4A in clear cell renal cell carcinoma, despite no differential expression of HNF4A. Finally, we aggregate the results of differential co-expression analysis into a Pan-Cancer analysis across seven distinct cancer types to identify pairs of metabolic genes which may be recurrently dysregulated. Among our results is a cluster of four genes, all components of the mitochondrial electron transport chain, which show significant loss of co-expression in tumor tissue, pointing to potential mitochondrial dysfunction in these tumor types.

PhD Work

2013

1. 

   Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

   Ed Reznik, Pankaj Mehta, and Daniel Segrè^†

   PLOS Computational Biology, Aug 2013

   Publisher: Public Library of Science

   Abs DOI

   Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.

2. 

   The dynamics of hybrid metabolic-genetic oscillators

   Ed Reznik, Tasso J. Kaper, and Daniel Segrè

   Chaos: An Interdisciplinary Journal of Nonlinear Science, Mar 2013

   Abs DOI

   The synthetic construction of intracellular circuits is frequently hindered by a poor knowledge of appropriate kinetics and precise rate parameters. Here, we use generalized modeling (GM) to study the dynamical behavior of topological models of a family of hybrid metabolic-genetic circuits known as “metabolators.” Under mild assumptions on the kinetics, we use GM to analytically prove that all explicit kinetic models which are topologically analogous to one such circuit, the “core metabolator,” cannot undergo Hopf bifurcations. Then, we examine more detailed models of the metabolator. Inspired by the experimental observation of a Hopf bifurcation in a synthetically constructed circuit related to the core metabolator, we apply GM to identify the critical components of the synthetically constructed metabolator which must be reintroduced in order to recover the Hopf bifurcation. Next, we study the dynamics of a re-wired version of the core metabolator, dubbed the “reverse” metabolator, and show that it exhibits a substantially richer set of dynamical behaviors, including both local and global oscillations. Prompted by the observation of relaxation oscillations in the reverse metabolator, we study the role that a separation of genetic and metabolic time scales may play in its dynamics, and find that widely separated time scales promote stability in the circuit. Our results illustrate a generic pipeline for vetting the potential success of a circuit design, simply by studying the dynamics of the corresponding generalized model.

3. 

   The stubborn roots of metabolic cycles

   Ed Reznik^†, Alex Watson, and Osman Chaudhary

   Journal of The Royal Society Interface, Jun 2013

   Publisher: Royal Society

   Abs DOI

   Efforts to catalogue the structure of metabolic networks have generated highly detailed, genome-scale atlases of biochemical reactions in the cell. Unfortunately, these atlases fall short of capturing the kinetic details of metabolic reactions, instead offering only topological information from which to make predictions. As a result, studies frequently consider the extent to which the topological structure of a metabolic network determines its dynamic behaviour, irrespective of kinetic details. Here, we study a class of metabolic networks known as non-autocatalytic metabolic cycles, and analytically prove an open conjecture regarding the stability of their steady states. Importantly, our results are invariant to the choice of kinetic parameters, rate laws, equilibrium fluxes and metabolite concentrations. Unexpectedly, our proof exposes an elementary but apparently open problem of locating the roots of a sum of two polynomials S = P + Q, when the roots of the summand polynomials P and Q are known. We derive two new results named the Stubborn Roots Theorems, which provide sufficient conditions under which the roots of S remain qualitatively identical to the roots of P. Our study illustrates how complementary feedback, from classical fields such as dynamical systems to biology and vice versa, can expose fundamental and potentially overlooked questions.

2012

1. 

   Temporal Expression-based Analysis of Metabolism

   Sara B. Collins^*, Ed Reznik^*, and Daniel Segrè^†

   PLOS Computational Biology, Nov 2012

   Publisher: Public Library of Science

   Abs DOI

   Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

2010

1. 

   On the Stability of Metabolic Cycles

   Ed Reznik and Daniel Segrè

   Journal of Theoretical Biology, Oct 2010

   Abs DOI

   We investigate the stability properties of two different classes of metabolic cycles using a combination of analytical and computational methods. Using principles from structural kinetic modeling (SKM), we show that the stability of metabolic networks with certain structural regularities can be studied using exclusively analytical techniques. We then apply these technique to a class of single input, single output metabolic cycles, and find that stability is guaranteed under a wide range of conditions. Next, we extend our analysis to a small autocatalytic cycle, and determine parameter regimes within which the cycle is very likely to be stable. We demonstrate that analytical methods can be used to understand the relationship between kinetic parameters and stability, and that results from these analytical methods can be confirmed with computational experiments. In addition, our results suggest that elevated metabolite concentrations and certain crucial saturation parameters can strongly affect the stability of the entire metabolic cycle. We discuss our results in light of the possibility that evolutionary forces may select for metabolic network topologies with a high intrinsic probability of being stable. Furthermore, our conclusions support the hypothesis that certain types of metabolic cycles may have played a role in the development of primitive metabolism despite the absence of regulatory mechanisms.