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Liu, E. M.

Publications and source records attributed to Liu, E. M..

5 recordsLinked to original sources

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.

cancer biology↗

A Multimodal Atlas of Tumor Metabolism Reveals the Architecture of Gene-Metabolite Co-regulation

Tumor 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 study gene-metabolite coregulation in cancer, we assembled the Cancer Atlas of Metabolic Profiles (cAMP) of metabolomic and transcriptomic data from 988 tumor/normal specimens spanning 11 cancer types. Meta-analysis of the cAMP revealed two classes of Gene-Metabolite Interactions (GMIs) that transcended cancer types. The first corresponded to a small number of gene-metabolite pairs engaged in direct enzyme-substrate interactions, identifying putative metabolite-pool-size-controlling genes. A second class of GMIs represented a small number of hub metabolites, including quinolinate and NAD+, which correlated to many genes specifically expressed on immune cell populations. These results provide evidence that gene-metabolite coregulation in human tissue arises, in part, from both mechanistic interactions between genes/metabolites, and from metabolic remodeling in specific immune microenvironments.

cancer biology↗

Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR)-dependence due to lineage plasticity, which has become more prevalent, leads to clinically highly aggressive tumors with few therapeutic options and is mechanistically poorly defined. To identify the master transcription factors (TFs) of CRPC in a subtype-specific manner, we derived and collected 29 metastatic human prostate cancer organoids and cell lines, and generated ATAC-seq, RNA-seq and DNA sequencing data. We identified four subtypes and their master TFs using novel computational algorithms: AR-dependent; Wnt-dependent, driven by TCF; neuroendocrine, driven by ASCL1 and NEUROD1 and stem cell-like (SCL), driven by the AP-1 family. The transcriptomic signatures of these four subtypes enabled the classification of 370 patients. We find that AP-1 co-operates with the inhibitable YAP/TAZ/TEAD pathway in the SCL subtype, the second most common group of CRPC tumors after AR-dependent. Together, this molecular classification reveals new drug targets and can potentially guide therapeutic decisions.

cancer biology↗

NetBoxR: Automated Discovery of Biological Process Modules by Network Analysis in R

SummaryLarge-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have accumulated a variety of high throughput sequencing and molecular profiling data, but it is still challenging to identify potentially causal genetic mutations in cancer as well as in other diseases in an automated fashion. We developed the NetBoxR package written in the R programming language, that makes use of the NetBox algorithm to identify candidate cancer-related processes. The algorithm makes use of a networkbased approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of functionally curated gene sets. A key aspect of this approach is its ability to combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. Availability and implementationThe NetBoxR package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html Contactlium2@mskcc.org; aluna@jimmy.harvard.edu; sander.research@gmail.com Supplementary informationNone

bioinformatics↗

CNCDatabase: a database of non-coding cancer drivers

Most mutations in cancer genomes occur in the non-coding regions with unknown impact to tumor development. Although the increase in number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies and thus it is difficult to bridge the understanding of non-coding alterations, the genes they impact and the supporting evidence for their role in tumorigenesis across multiple cancer types. To address this gap, we have developed CNCDatabase, Cornell Non-Coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5 and 3 UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1,111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection in whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations and their associations with gene expression in human cancers.

cancer biology↗