Sensitizing tumors to checkpoint inhibitors with redox state modifiers

Abstract

The present invention relates to methods of sensitizing a subject having cancer or pre-cancer to treatment with immune checkpoint inhibitors, as well as agents for use in sensitizing a subject to such treatment.

Description

[Technical Field]

[0001] The present invention relates to methods of sensitizing a subject having cancer or pre-cancer to treatment with immune checkpoint inhibitors, as well as agents for use in sensitizing a subject to such treatment. [Background technology]

[0002] Cancer immunotherapy involves attacking cancer cells with a patient's immune system. Regulation and activation of T lymphocytes depend on signaling by the T cell receptor and co-signaling receptors, which send positive or negative signals for activation. T cell-mediated immune responses are controlled by a balance of costimulatory and inhibitory signals, known as immune checkpoints.

[0003] Immunotherapy with immune checkpoint inhibitors is transforming cancer treatment. However, some patients show little or no improvement with immune checkpoint inhibitor therapy. Therefore, there remains a need for methods to sensitize patients to such treatment. The present invention aims, at least in part, to address this need. Summary of the Invention

[0004] The present invention is based on the surprising discovery by the present inventors that cancer cells harboring heteroplasmic deleterious mutations in mitochondrial DNA (mtDNA) can have an altered tumor microenvironment. Specifically, the present inventors have shown that cancer cells harboring deleterious mtDNA mutations present at a high mutation load are associated with various immune cell populations present in the tumor microenvironment. As discussed in the Examples section of this application, the present inventors have found that the tumor microenvironment containing such cancer cells is enriched in natural killer (NK) cells, monocytes, CD4+ T cells, and interferon-stimulated gene (ISG)-expressing immune cells, but has reduced levels of macrophages and tumor-associated neutrophils, compared to the microenvironment of tumors harboring cancer cells with no or low deleterious mtDNA mutation load.

[0005] The present inventors believe that the presence of deleterious mtDNA mutation load alters cancer cell metabolism in a way that modifies the tumor microenvironment to favor infiltration by certain populations of immune cells. As discussed in the Examples section of this disclosure, the present inventors confirmed that cancer cells with heteroplasmic mutations in the MT-ND5 gene exhibited increased levels of reduced nicotinamide adenine dinucleotide (NADH), which disrupted the NAD+:NADH ratio and altered the cellular redox balance. This may result in the reversal of malate dehydrogenase 2 (MDH2) flux and the accumulation of cytosol-derived malate via malate dehydrogenase 1 (MDH1). Increased MDH1 activity may drive glycolysis, resulting in excessive glucose consumption and lactate release. Indeed, the inventors have shown that these mutations promote the utilization of pyruvate as a terminal electron acceptor, increasing glycolytic flux driven by the excessively reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. Surprisingly, the inventors found that despite these changes, oxygen consumption and ATP synthesis remained unaffected at a mutation load of 60% (also referred to herein as "variant allele frequency" or "VAF"), although the inventors believe these parameters would be affected by higher mutation loads. While not wishing to be bound by this hypothesis, the inventors believe that these metabolic changes promote the recruitment and / or survival of certain immune cell types (such as those mentioned above) to tumors that are less sensitive to altered redox states (e.g., altered glucose-to-lactate ratios). The inventors hypothesize that this lower sensitivity may be due to the cells' preferential utilization of lactate as a carbon fuel source or a lower dependency on glucose.

[0006] We then undertook studies to determine whether these findings could be related to clinical outcomes. Using mouse models, we surprisingly showed that tumors with a VAF of >40% responded well to PD1 inhibitors, whereas tumors with little or no VAF responded less well.

[0007] The inventors found that this difference in therapeutic response is relevant in terms of treatment with immune checkpoint inhibitors (e.g., PD-1 inhibitors, PD-L1 inhibitors, or CTLA4 inhibitors). These findings were also supported by a retrospective study of a small clinical cohort of human patients with VAF >50% due to mutations in a variety of different mtDNA genes (e.g., MT-COI, MT-ND4, MT-CYB, MT-TY, and / or mtDNA control regions). The inventors believe that this difference in response to treatment with immune checkpoint inhibitors (e.g., PD-1 inhibitors, PD-L1 inhibitors, and / or CTLA4 inhibitors) is due to metabolic changes (and resulting changes to the immune microenvironment of the cancer or precancerous tumor) that sensitize cancer or precancerous cells in the tumor to this treatment. Based on these data, the inventors conclude that cancers or precancers can be sensitized to treatment with immune checkpoint inhibitors (such as, for example, PD-1 inhibitors, PD-L1 inhibitors, and / or CTLA4 inhibitors) by mimicking this metabolic change (i.e., by altering the redox state in the cancer or precancer, e.g., the lactate to glucose ratio in the cancer or precancer).

[0008] The present inventors further demonstrated that providing cancer cells (e.g., melanoma cancer cells) with agents that alter the redox state, e.g., the lactate-to-glucose ratio, enhanced their response to immune checkpoint inhibitor treatment (e.g., anti-PD1 therapy). Specifically, the present inventors modified wild-type Hcmel12 cells to constitutively express cytoLBnox. These cells recapitulate key cell-extrinsic factors, the mutant Mt-Nd5-associated metabolic phenotype, with pronounced glucose uptake and lactate release. When implanted in mice, Hcmel12 cytoLBnox tumors exhibited comparable time to endpoint and tumor weight at endpoint to wild-type or Mt-Nd5 mutant tumors. However, when challenged with anti-PD1 therapy, Hcmel12 cytoLBnox tumors exhibited comparable time to endpoint and tumor weight at endpoint to Hcmel12 mt-Nd5 m.12,436 80% Tumor responses were reproduced, confirming that specific changes in redox metabolism are sufficient to sensitize tumors to immune checkpoint blockades (e.g., PD-1 inhibitors, PD-L1 inhibitors, and / or CTLA4 inhibitors).

[0009] Furthermore, we found that therapeutic response to immune checkpoint inhibitors can be further (synergistically) improved in tumors with high mtDNA mutation burden or expressing cytoLbNOX by combination treatment with compounds that reduce the levels of tumor-resident neutrophils (e.g., anti-Ly6G antibodies).

[0010] The inventors have also shown that agents that alter the redox state, e.g., lactate to glucose ratio, in cancers or precancers (e.g., cytoLbNOX or mitoLbNOX), can increase sensitivity to immune checkpoint inhibitors in cancers with baseline sensitivity to immune checkpoint inhibitors, as shown in the immunogenic 4434 mouse model.

[0011] Thus, the present invention provides agents that alter the redox state (e.g., alter the lactate to glucose ratio) in a cancer or precancer for use in sensitizing a subject having cancer or precancer to an immune checkpoint inhibitor.

[0012] Also provided is an immune checkpoint inhibitor for use in treating a subject having cancer or precancer, wherein the subject has been exposed to an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in the cancer or precancer.

[0013] The present invention also provides a method of sensitizing a subject having cancer or precancer to an immune checkpoint inhibitor, comprising exposing the subject to an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in the cancer or precancer.

[0014] Also provided are methods of treating cancer or precancer in a subject, comprising administering to the subject an immune checkpoint inhibitor, wherein the subject is exposed to an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in the cancer or precancer.

[0015] The present invention provides a method of treating cancer or precancer in a subject, comprising: (i) exposing the subject to an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in the cancer or precancer; and (ii) administering to the subject an immune checkpoint inhibitor. Also provided is a method comprising:

[0016] Suitably, the immune checkpoint inhibitor may be selected from the group consisting of a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a CTLA4 inhibitor, a TIGIT inhibitor, a LAG-3 inhibitor, a TIM-3 inhibitor, a BTLA inhibitor, and a KIR inhibitor.

[0017] Suitably, the immune checkpoint inhibitor may be selected from the group consisting of a PD-1 inhibitor, a PD-L1 inhibitor, and a CTLA4 inhibitor.

[0018] Preferably, the agent is capable of increasing the lactate to glucose ratio.

[0019] Preferably, the agent alters (eg, increases) the lactate to glucose ratio in the interstitial fluid of the cancer or pre-cancer.

[0020] Suitably, the increase in lactate to glucose ratio may be to greater than 3:1.

[0021] Preferably, the cancer or pre-cancerous sample may have a deleterious mitochondrial DNA (mtDNA) mutation load of less than 50%.

[0022] Suitably, the deleterious mitochondrial DNA (mtDNA) mutation load may be less than 40%, less than 30%, or less than 20%.

[0023] Preferably, the agent is a) a compound that drives glycolytic flux through MDH1, which may optionally be selected from the group consisting of isocitrate, aconitate, citrate, oxaloacetate, NADH, and NAD+ precursors; b) a compound that modulates NAD(H) redox processing via the malate-aspartate shuttle, optionally selected from the group consisting of isocitrate, aconitate, citrate, oxaloacetate, malate, fumarate, and argininosuccinate; c) lactic acid; d) glucose and / or lactate metabolic enzymes; e) an inhibitor of an enzyme that reduces glycolytic flux in cancer or precancerous cells, where appropriate the enzyme may be pyruvate dehydrogenase or pyruvate carboxylase, and where appropriate the inhibitor may be a small molecule; f) activators of enzymes that increase glycolytic flux in cancer or precancerous cells; g) an activator of an enzyme that increases lactate excretion in cancer or precancerous cells, where optionally the enzyme is MDH1 or GAPDH, and where optionally the activator is a small molecule; h) inhibitors of enzymes that reduce lactate excretion in cancerous or precancerous cells; i) small molecule inhibitors of enzymes in the malate-aspartate shuttle, optionally wherein the enzymes are selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter; j) small molecule activators of enzymes in the malate-aspartate shuttle, optionally wherein the enzymes are selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter; k) inhibitors of complex I, complex II, complex III, or complex IV; l) compounds that increase the deleterious mtDNA mutation load in cancers or precancers and, where appropriate, may cause deleterious mtDNA mutations; and / or m) a compound that reduces neutrophils in a subject and / or reduces neutrophils in cancer or pre-cancer, where the neutrophils may optionally be tumor infiltrating neutrophils (TANs); may be selected from the group consisting of:

[0024] Preferably, the agent may be selected from the group consisting of NADH oxidase and NADPH oxidase.

[0025] Preferably, the agent is the enzyme NADH oxidase (e.g., from Lactobacillus brevis) or a nucleic acid encoding said enzyme. Preferably, the NADH oxidase may be cytosolic or mitochondrial.

[0026] Suitably, the agent may be for use in combination with a tumor-associated neutropenia compound (such as an anti-Ly6G antibody).

[0027] Suitably, the cancer or pre-cancer may be selected from the group consisting of childhood cancer, blood cancer, and bone marrow cancer.

[0028] Suitably, the childhood cancer may be selected from the group consisting of leukemia, brain tumor, spinal cancer, neuroblastoma, Wilms' tumor, lymphoma (such as Hodgkin's lymphoma and non-Hodgkin's lymphoma), rhabdomyosarcoma, retinoblastoma, and bone cancer (such as osteosarcoma and Ewing's sarcoma).

[0029] Suitably, the PD-1 inhibitor may be nivolumab.

[0030] Preferably, the compound that increases the deleterious mtDNA mutation load in a cancer or precancer may be selected from the group consisting of a mitochondrial base editing enzyme (e.g., DdCBE, etc.) and a mitochondrial heteroplasmy-manipulating enzyme (e.g., mtZFN or mitoTALEN, etc.).

[0031] Preferably, the deleterious mtDNA mutation is: (i) tRNA mutations with a MitoTIP RAW score of at least 12.6 or at least 16.25; (ii) rRNA mutations; (iii) truncation mutations in mtDNA genes; (iv) a missense mutation in an mtDNA gene, having an Apogee score of greater than 0.5, optionally selected from a frameshift mutation, an insertion mutation, or a deletion mutation; and / or (v) a mutation in the mtDNA D-loop region selected from the group consisting of the heavy chain promoter (545-567), MT-HV2 (hypervariable segment 2) m.57-372, and MT-HV1 (hypervariable segment 1) m.16024-16390 may be selected from the group consisting of:

[0032] Preferably, the deleterious mtDNA mutation can be in a gene selected from the group consisting of MT-ND5, MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND4L, MT-ND6, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ATP6, MT-ATP8, MT-TL1, MT-TA, MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI, MT-TK, MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT, MT-TV, MT-TW, MT-TY, MT-RNR1, and MT-RNR2.

[0033] Preferably, the MT-ND5 deleterious mtDNA mutation may be a truncating mutation present in a region selected from m.12418-12425:A indel or m.12385-12390:C indel.

[0034] Suitably, the deleterious mtDNA mutation may be a truncation mutation, a missense mutation, an insertion mutation, or a frameshift mutation.

[0035] As will be clear to one of skill in the art, any embodiment relating to sensitizing a subject to (including methods or medicaments for use in) an immune checkpoint inhibitor (such as a PD-1 inhibitor, a PD-L1 inhibitor, and / or a CTLA4 inhibitor) equally applies to the methods of treatment (or medicaments for use in treatment) described herein, unless the context specifically requires otherwise.

[0036] Throughout the description and claims of this specification, the words "comprise" and "contain" and variations thereof mean "including but not limited to," and they are not intended to (and do not) exclude other moieties, additives, ingredients, integers, or steps.

[0037] Throughout the description and claims of this specification, the singular encompasses the plural unless the context requires otherwise. In particular, where the indefinite article is used, the specification should be understood as contemplating the plural in addition to the singular, unless the context requires otherwise.

[0038] It is to be understood that any feature, integer, property, compound, chemical moiety, or group described in connection with a particular aspect, embodiment, or example of the invention is also applicable to any other aspect, embodiment, or example described herein, unless incompatible.

[0039] Various aspects of the invention are described in greater detail below. [Brief explanation of the drawings]

[0040] Embodiments of the present invention are further described below with reference to the accompanying drawings, in which:

[0041] [Figure 1]Figure 1 shows the abundance of mtDNA mutations in various cancers and provides information on MT-ND5. A - Percentage of tumors fully covered by various types of somatic mtDNA variants per cancer type. From left to right, boxes represent truncating, non-truncating (2+ types), rRNA, tRNA, missense, silent, and wild-type. Right: Number of fully covered samples per cancer type. NSC stands for non-small cell carcinoma. Data from Gorelick et al., 2021. B - Circular mtDNA genome annotated with 73 homopolymeric repeat loci ≥5 bp in length. The height of the dots from the circular mtDNA genome indicates the number of affected samples, and the width of the dot indicates the length of the repeat region (5–8 bp). The six highlighted solid-color homopolymeric loci were found to be statistically enriched hotspots for frameshift indels in tumors. Data from Gorelick et al., 2021. C - Space-filling model of respiratory complex I with key reactions / functions annotated. D - Space-filling model of respiratory complex I with internally localized and embedded MT-ND5 and its interaction with solvent-exposed NDUFB8 protein on both the top and bottom surfaces of complex I highlighted.

[0042] [Figure 2]Figure 2 shows how recurrent mutations in tumor Mt-Nd5 were modeled. A - Schematic of the mouse mitochondrial genome showing the sites where DdCBEs are targeted. B - Schematic of the TALE DNA-binding domain for the DdCBE pair targeted to induce premature stop codons at m. 11,944 and m. 12,436. C - Schematic of the TALE-DdCBE library screening method used. Briefly, candidate pairs are cloned into vectors co-expressing a fluorescent marker protein, which allows for sorting of transfected B78 mouse melanoma cells by fluorescence-activated cell sorting (FACS). Cells are then assessed for mutagenic efficiency by sequencing.

[0043] [Figure 3] Figure 3 shows how recurrent mutations in tumor Mt-Nd5 were generated. A - Heteroplasmy of cells transfected with the indicated constructs, identified by pyrosequencing. N transfections indicate cells harvested after one or four consecutive transfections. B - mtDNA copy number of the cells in Figure 4A, as measured by droplet digital PCR (ddPCR). C - Western blot analysis of marker proteins for respiratory chain complexes: Complex I (Ndufb8), Complex II (Sdhb), Complex III (Uqcrc2), Complex IV (Mt-Co1), and Complex V (Atp5a). Ponceau is shown as a loading control.

[0044] [Figure 4]Figure 4 shows how recurrent mutations in tumor Mt-Nd5 were generated. A - Blue Native (BN) PAGE and blotting for respiratory chain complexes using the same antibodies as in Figure 4C. In-gel activity of complex I and complex II after BN PAGE is also shown, along with Coomassie loading controls. B - Basal oxygen consumption rate (OCR) of cells as assessed by Seahorse. C - Analysis of cellular energy charge state using metabolite abundance data for AMP, ADP, and ATP obtained from mass spectrometry metabolomics measurements. D - NAD+:NADH ratio as calculated using metabolite abundance data obtained from mass spectrometry metabolomics measurements.

[0045] [Figure 5] Figure 5 shows the effects of the mt-Nd5 mutations on cellular energetics and metabolism. Metabolite abundances obtained from mass spectrometry metabolomics measurements of high VAF mutant cells are plotted against each other, revealing consistent metabolic changes resulting from the two different truncating mutations in Mt-Nd5.

[0046] [Figure 6] Figure 6 shows that glutamine tracing reveals increased abundance of MDH1-derived malate in the cytosol. A - Heatmap showing significant increases in the abundance of specific metabolites related to the tricarboxylic acid (TCA) cycle, urea cycle, and fumarate adducts. B - Schematic of the labeling fate of l-13C glutamine. C - Abundance of malate m+1. D - Abundance of argininosuccinate m+1. E - Abundance of α-ketoglutarate (α-KG) m+1. F - Abundance of aconitate m+1. G - Abundance of aspartate m+1.

[0047] [Figure 7]Figure 7 shows that glucose tracing suggests increased malate abundance due to reverse MDH2 flux. A - Schematic of U-13C glucose labeling fate. B - Malate m+3:Citrate m+3 ratio obtained from mass spectrometry analysis. C - Citrate m+3:Aconitate m+3 ratio obtained from mass spectrometry analysis. D - Citrate m+3:Pyruvate m+3 ratio obtained from mass spectrometry analysis.

[0048] [Figure 8] Figure 8 shows that MDH1 can mediate an increase in glycolytic intermediates in mutant cells. A - Heat map representation of the abundance of glycolytic intermediates as detected by mass spectrometry. B - Heat map representation of the abundance of glycolytic intermediates upon siRNA-mediated depletion of MDH1 as detected by mass spectrometry. C - Western blot analysis demonstrating knockdown of MDH1 compared to a scrambled siRNA control.

[0049] [Figure 9] Figure 9 shows the abundance of specific metabolites in cells treated with siRNA. A - Schematic of the labeling fate of 4-2H1 glucose. B - Abundance of malate+1 in cells treated with scrambled siRNA obtained from mass spectrometry analysis. C - Abundance of lactate+1 in cells treated with scrambled siRNA obtained from mass spectrometry analysis. D - Abundance of NADH+1 in cells treated with scrambled siRNA obtained from mass spectrometry analysis. E - Abundance of malate+1 in cells treated with siRNA against MDH1 obtained from mass spectrometry analysis. F - Abundance of lactate+1 in cells treated with siRNA against MDH1 obtained from mass spectrometry analysis.

[0050] [Figure 10] Figure 10 shows the effects on cancer metabolism in relation to the Krebs cycle (A and B).

[0051] [Figure 11] FIG. 11 shows the experimental setup used to analyze the in vivo situation.

[0052] [Figure 12] Figure 12 shows the in vivo effects. A - Survival curves of mice bearing subcutaneous tumors. Humane endpoint was tumor size of 15 mm. n=12 mice per genotype. B - Resected tumor weight. n=10-12 tumors per genotype. C - Difference between average heteroplasmy between injected cancer cells and resulting bulk tumor heteroplasmy measurements as determined by pyrosequencing. n=11-12 per genotype. D - mtDNA copy number analysis of bulk tumors as determined by ddPCR. n=10-12 per genotype. E - Metabolite abundance in tumors with the indicated genotypes. n=7-10 per genotype.

[0053] [Figure 13] Figure 13 shows tumor transcriptional profiling. A - Volcano plot representing differential gene expression between bulk transcriptomics from mtDNA wild-type and 60% VAF m.11,944G>A tumors. B - PCA plot of samples compared in A. Each point represents a single tumor. C - Volcano plot representing differential gene expression between bulk transcriptomics from mtDNA wild-type and 40% VAF m.11,944G>A tumors. D - PCA plot of samples compared in C. Each point represents a single tumor. E - Volcano plot representing differential gene expression between bulk transcriptomics from 40% VAF m.11,944G>A and 60% VAF m.11,944G>A tumors. F - PCA plot of samples compared in E. Each point represents a single tumor.

[0054] [Figure 14]Figure 14 shows differentially expressed genes; bulk tumor GSEA-wild type vs. VAF>50%. A - Summary of differentially expressed genes between mtDNA wild type tumors and 60% VAF m.11,944G>A tumors. B - Provides the same information as Figure 13A. C - Significant hits from gene set enrichment analysis (GSEA) of differentially expressed genes.

[0055] [Figure 15] Figure 15 shows differentially expressed genes; bulk tumor GSEA-VAF<50% vs. VAF>50%. A - Summary of differentially expressed genes between 40% VAF and 60% VAF m.11,944G>A tumors. B - Provides the same information as Figure 13C. C - Significant hits from Gene Set Enrichment Analysis (GSEA) of differentially expressed genes.

[0056] [Figure 16] Figure 16 shows the remodeled immune microenvironment in mtDNA mutant tumors. A - Percentage of natural killer (NK) cells detected in tumors after isolation and flow cytometry. n=4-8. B - Percentage of tumor-associated macrophages (TAM) detected in tumors after isolation and flow cytometry. n=9-14. C - Percentage of immature monocytes detected in tumors after isolation and flow cytometry. n=10-14. D - Flow cytometry gating strategy for defining TAM and monocyte populations. E - Flow cytometry gating strategy for defining natural killer cells.

[0057] [Figure 17]Figure 17 shows that scRNAseq profiling of tumors defines altered immune populations. A - UMAP representation of Seurat clustered single-cell RNA sequencing (scRNAseq) data of >100,000 cells taken from isolated mtDNA wild-type and 60% VAF m.12,436G>A whole tumors. n=3 for each genotype. B - Cell type assignment of scRNAseq data based on CellRanger. C - Relative proportion of intermediate monocytes in cluster 3. D - Relative proportion of NK cells in cluster 11. E - Relative proportion of macrophages in cluster 6. F - Relative proportion of classical monocytes in cluster 13. G - Relative proportion of interferon-stimulated gene-expressing immune cells (ISG Expr. Imm. cells) in cluster 7. H - Relative proportion of CD4+ NK-like T cells in cluster 24.

[0058] [Figure 18] Figure 18 shows scRNAseq profiling of the tumors illustrated in Figure 17. A - GSEA results across all defined clusters for hallmark gene set interferon gamma response. B - GSEA results across all defined clusters for hallmark gene set interferon alpha response.

[0059] [Figure 19]Figure 19 shows that VAF>50% mtDNA mutant melanoma responds to PD1 immune checkpoint blockade. A - Schematic of the experimental timeline. Cells are implanted at DO. When tumors become palpable, typically on day 7, mice are administered intraperitoneal (IP) anti-PD1 monoclonal antibody every 3 days until the experiment is terminated on day 21. Mice are sacrificed and tumors are excised. B - Weights of tumors excised from mice bearing tumors of the indicated genotypes treated with anti-PD1 antibodies as defined in A. n=4-5. C - Representative images of excised tumors in B. D - Weights of tumors excised from mice bearing tumors of the indicated genotypes treated with anti-CTLA4 antibodies as defined in A. n=4-7. E - Representative images of excised tumors in D.

[0060] [Figure 20] Figure 20 shows that this results in treatment sensitivity in humans. A - Stratification of metastatic melanoma patient cohorts by mtDNA status. B - Patient response rates to nivolumab by tumor mtDNA mutation status. P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test (C, F) and one-tailed Student's t-test (I) or chi-squared test (H) were applied. Error bars indicate SD. Centrality measure is the mean.

[0061] [Figure 21]Figure 21 shows that mitochondrial base editing generates isogenic cell lines with two independent truncating mutations in mt-Nd5. A. Schematic of the TALE-DdCBE design employed. TALEs were incorporated into a scaffold containing a mitochondrial targeting cassette, a split-half DdCBE, and a uracil glycosylase inhibitor (UGI). B. Schematic of mouse mtDNA. Target sites within mt-Nd5 are indicated. C. TALE-DdCBE pair used to induce G>A mutations at mt.12,436 and mt.11,944. D. Workflow used to generate mt-Nd5 mutant isogenic cell lines. Heteroplasmy measurements of cells generated during ED (n=6 separate wells sampled). F. Immunoblot of indicator respiratory chain subunits. Representative results are shown. G. Abundance and in-gel activity of assembled complex I. Representative results are shown. H mtDNA copy number (sampled from n = 9 separate wells). I Basal oxygen consumption rate (OCR) (n = 9-12 measurements (12 wells per measurement) were performed). J Energy (adenylate) charge status (sampled from n = 17 separate wells). K Proliferation rate of cell lines in permissive growth medium (n = 12 separate wells measured in triplicate). L NAD+:NADH ratio (n = 11-12 separate wells measured). All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test (E, H-I, K) or Fisher's LSD test (J, L). Error bars indicate SD. Centrality measures are means.

[0062] [Figure 22]Figure 22 shows that mutant cells undergo a metabolic shift toward glycolysis due to cellular redox imbalance. A. Heatmap of unlabeled steady-state abundance of selected mitochondrial metabolites, arginine, argininosuccinate (AS), and the terminal fumarate adducts succinylcysteine ​​(succ.Cys) and succinic GSH (succ.GSH). B. Labeling fate of 13C derived from 1-13C glutamine. C. Abundance of malate m+1 derived from 1-13C glutamine following the indicated treatments (n = 6-11 separate wells sampled). D. Heatmap of unlabeled steady-state metabolite abundance for selected intracellular glycolytic intermediates and extracellular lactate (ex. lactate). E. Labeling fate of U-13C glucose. F. Abundance of lactate m+3 derived from U-13C glucose following the indicated treatments (n = 6-9 separate wells sampled). G. Fate of 2H labeling derived from 4-2H1 glucose; mitoLbNOX is not shown for clarity. H. Abundance of malate m+1 derived from 4-2H1 glucose following the indicated treatments (n = 5–16 separate wells were sampled). I. IC50 curves for 2-DG (n = 4 separate wells were measured per drug concentration). This was repeated three times, and representative results are shown. P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test (A, D) or Fisher's LSD test (C, F, H). Error bars indicate SD. Centrality is measured by the mean.

[0063] [Figure 23]Figure 23 shows that tumor mtDNA mutations reshape the immune microenvironment. A Survival rate of C57 / BL6 mice subcutaneously injected with the indicated cells (n = 5–12 animals per condition). B Tumor weight at endpoint (n = 5–12 tumors per genotype). C Gene set enrichment analysis (GSEA) of bulk tumor RNA sequencing (RNAseq) data (n = 5–6 tumors per genotype). Only gene sets with adjusted P values ​​< 0.1 are shown. D GSEA of RNAseq data from a cohort of metastatic melanoma patients from the Hartwig Medical Foundation (HMF). Cancers are stratified by mtDNA status into wild-type and mtDNA mutant types with a variant allele frequency (VAF) > 50%. E UMAP of seruat-clustered whole-tumor scRNAseq data from the indicated samples. F UMAP showing cell type IDs. DC, dendritic cell; pDC, plasmacytoid dendritic cell. G. GSEA of malignant cells identified in scRNAseq analysis. UMAPs are color-coded by GSEA scores for: H, interferon-alpha response; I, interferon-gamma response; J, inflammatory response; K, IL2-Stat5 signaling. L. Ratio of tumor-resident neutrophils to total malignant and non-malignant cells (n = 17 tumors). M. UMAPs color-coded by GSEA for the OXPHOS gene set. One-way ANOVA with Sidak's multiple comparison test (B), Wilcoxon signed-rank test (G-K), and two-tailed Student's t-test (L-O) were applied. Error bars indicate SD (B) or SEM (L-O). Centrality measures are means. Box plots indicate interquartile ranges (J-M). NES: normalized expression score. In Figure 23C, the upper bar in each pair is m. 11,944, and the lower bar is m. 12,436.

[0064] [Figure 24]Figure 24 shows that mtDNA mutation-associated microenvironment remodeling sensitizes tumors to checkpoint blockade. A. Schematic of the experimental design and dosing regimen for B78-D14 tumors using anti-PD1 monoclonal antibody (mAb). B. Representative images of tumors harvested on day 21. C. Tumor weights on day 21 (n = 10-19 tumors per genotype). D. Schematic of the experimental design and dosing regimen for Hcmel12 tumors using anti-PD1 mAb. E. Representative images of tumors harvested on day 13. F. Tumor weights on day 13 (n = 7 tumors per genotype). G. Stratification of metastatic melanoma patient cohorts by mtDNA status. H. Patient response rates to nivolumab by tumor mtDNA mutation status. One-way ANOVA with Sidak's multiple comparison test (C), one-tailed Student's t-test (F), or chi-squared test (H) was applied. Error bars indicate SD. The measure of centrality is the mean value.

[0065] [Figure 25] Figure 25 shows the results of mitochondrial base editing on two independent targets in mt-Nd5. A. Immunoblot of DdCBE pair expression after sorting. αHA and αFLAG indicate expression of left TALE (TALE-L) and right TALE (TALE-R), respectively. Representative results are shown. B. Off-target C>T activity of DdCBE in mtDNA. The figure shows mutations detected at >2% heteroplasmy, a measure of detected mutations compared to wild-type. These mutations likely do not affect our primary observation, as both models behave similarly between experiments.

[0066] [Figure 26]Figure 26 shows that proteomic analysis of isogenic mt-Nd5 mutant cell lines reveals significant changes primarily in complex I genes. A Volcano plot showing the detected differences in protein abundance in mt.12436 60% cells and B mt.11944 60% cells vs. wild type. Differences with p<0.05 and log2 fold change >0.5 are displayed in red (n=3 separately collected cell pellets measured per cell line). C Heatmap of protein abundance of nuclear and mitochondrial subunits of complex I, D Complex II, E Complex III, F Complex IV, and G Complex V. Wilcoxon signed-rank test (A, B) and one-way ANOVA with Sidak multiple comparison test (C-G) were applied.

[0067] [Figure 27] Figure 27 shows that mt.-Nd5 truncation alters intracellular redox state without significantly affecting mitochondrial mRNA expression or membrane potential. A. Mitochondrial gene expression (samples were taken from n = 12 separate cell pellets per genotype). B. Measurements of the electrical component of the proton-motive force, ΔΨ, the chemical component of the proton-motive force, ΔpH, and the total proton-motive force, ΔP (samples were taken from n = 4 separate wells per genotype). C. GSH:GSSG ratio (samples were taken from n = 6–12 separate wells per cell type). A higher GSH:GSSG ratio represents a more reducing intracellular environment. D. Mitochondrial NADH oxidation state (samples were taken from n = 4 separate wells per genotype). All p values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means. In Figure 27A, the upper bar of each of the three groups is m.11,944, the middle bar is m.12,436, and the lower bar is wild type.

[0068] [Figure 28]Figure 28 shows results from U-13C glutamine labeling showing that the percentage increase in malate abundance is derived from cytosolic reductive carboxylation of glutamine. A. Labeling fate of 13C derived from U-13C glutamine by oxidative decarboxylation vs. reductive carboxylation of glutamine. B. Abundance of malate m+3 derived from U-13C glutamine (n=9 separate wells sampled per genotype). C. Ratio of malate m+3:malate m+2 derived from U-13C glutamine (n=9 separate wells sampled per genotype). D. Ratio of ASm+3:ASm+2 derived from U-13C glutamine (n=9 separate wells sampled per genotype). All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means.

[0069] [Figure 29]Figure 29 shows that increased abundance of malate in the cytosol occurs at the level of MDH1 but is not directly attributable to global redox changes. A. Labeling fate of 13C derived from 1-13C glutamine, which exclusively labels metabolites resulting from reductive carboxylation of glutamine. B. Abundance of aconitate m+1 derived from 1-13C glutamine (n=9 separate wells sampled per genotype). C. Abundance of aspartate m+1 derived from 1-13C glutamine (n=9 separate wells sampled per genotype). D. Abundance of AS m+1 derived from 1-13C glutamine (n=9 separate wells sampled per genotype). E. Immunoblot of siRNA-mediated depletion of Mdh1. Representative images are shown. F. Immunoblot of cytoLbNOX expression after 36 hours of sorting, detected using αFLAG. Representative images are shown. G. Abundance of ASm+1 derived from l-13C glutamine following the indicated treatments (n = 6–12 separate wells were sampled per genotype per condition). All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means.

[0070] [Figure 30]Figure 30 shows that increased malate abundance in mutant cells is partially due to MDH2 reversal. A. Labeling fate of 13C derived from U-13C glucose. B. Abundance of pyruvate m+3 derived from U-13C glucose (n = 7-8 separate wells sampled per genotype). C. Citrate m+2:pyruvate m+3 ratio derived from U-13C glucose (n = 6-7 separate wells sampled per genotype). D. Malate m+3:citrate m+3 ratio derived from U-13C glucose (n = 7-8 separate wells sampled per genotype). E. Immunoblot of mitoLbNOX expression 36 hours post-transfection, detected using αFLAG. Representative images are shown. All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means.

[0071] [Figure 31] Figure 31 shows the results of 4-2H1 glucose tracing demonstrating that electron shuttling between MDH1 and GAPDH drives aerobic glycolysis. A. Lactate m+1 abundance from 4-2H1 glucose with the indicated treatments (n = 7-9 separate wells sampled per genotype per condition). B. NADH m+1 abundance from 4-2H1 glucose with the indicated treatments (n = 6-8 separate wells sampled per genotype per condition). All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means.

[0072] [Figure 32]FIG. 32 shows that mutant cells exhibit heteroplasmic dose-dependent sensitivity to respiratory chain inhibitors. A. Metformin IC50 curves: IC50 = 26.31 ± 1.49 mM for wild type, IC50 = 16.60 ± 2.43 mM for mt.1243660%, IC50 = 5.89 ± 0.71 mM for mt.1243680%, and IC50 = 22.93 ± 0.70 mM for mt.1194480%; B. Rotenone IC50 curves: IC50 = 0.236 ± 0.026 μM for wild type, IC50 = 0.235 ± 0.035 μM for mt.1243660%, IC50 = 0.493 ± 0.108 μM for mt.1243680%, and IC50 = 0.205 ± 0.033 μM for mt.1194460%; and C. Oligomycin IC curves: IC = 13.81 ± 3.80 µM for wild type, IC = 13.52 ± 3.32 µM for mt.12436 (60%), IC = 7.75 ± 0.56 µM for mt.12436 (80%), and IC = 13.54 ± 3.32 µM for mt.11944 (80%) (n = 4 separate wells per drug concentration per genotype). This was repeated three times, and representative results are shown.

[0073] [Figure 33] Figure 33 shows that there are no significant macroscopic differences between allografted B78-D14 tumors. A. Representative H&E subsections of wild-type tumors, B. m.12,436 40% tumors, and C. m.12,436 60% tumors. D. Heteroplasmy changes detected in bulk tumor samples (n = 5-12 tumors per genotype). E. Bulk tumor mtDNA copy number (n = 4-13 tumors per genotype). F. Heatmap of steady-state abundance of metabolic terminal fumarate adducts, succinylcysteine, and succinic GSH (n = 12 tumors per genotype), demonstrating that the metabolic changes observed in vitro are maintained in vivo. All P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test. Error bars indicate SD. Centrality measures are means.

[0074] [Figure 34] Bulk tumor transcriptional signatures show dose-dependent heteroplasmic changes in immune-related transcriptional phenotypes. GSEA of bulk tumor RNA-seq data (n = 5–6 tumors per genotype) showing A 40% mutant vs. wild type and B 60% mutant vs. 40% mutant. Only gene sets with adjusted p-values ​​<0.1 are shown unless otherwise noted. Wilcoxon signed-rank test was applied. For each pair, the upper bar represents m. 11,944 and the lower bar represents m. 12,436.

[0075] [Figure 35] Figure 35 shows that scRNAseq analysis identified malignant cells as aneuploid cells with low or no Ptprc (CD45) expression and a high epithelial score. A. UMAP showing aneuploidy determined by Ptprc expression, B. Epithelial score, and C. Copycat prediction. These criteria were adopted because B78 cells lack a distinct transcriptional signature. In each pair, the upper bar is m. 11,944 and the lower bar is m. 12,436.

[0076] [Figure 36] Figure 36 shows that mutant cells did not have significant changes in transcriptional signature in vitro. A Significantly co-regulated transcripts from mixed 60% mutant cells vs. wild-type cells (n=12 cell pellets were sampled per genotype). Volcano plot showing differences in gene expression in A mt.12436 60% cells and B mt.11944 60% cells vs. wild-type. Differences of p<0.05 and log2 fold change>1 are displayed in red (n=12 separate wells were sampled). Wilcoxon signed-rank test was applied.

[0077] [Figure 37]Figure 37 shows the results of scRNAseq analysis revealing distinct alterations in the tumor immune microenvironment of mtDNA-mutated tumors. Tumor-resident versus total malignant and non-malignant cells: A. Immature monocytes; and B. Percentage of CD4+ T cells (n = 3-7 tumors per genotype). C. UMAP color-coded by GSEA NES score for the allograft rejection gene set. Tumor-resident versus total malignant and non-malignant cells: D. CD4+ T cells; and E. Percentage of natural killer (NK) cells (n = 3-7 tumors per genotype). F. Relative PD-L1 expression within each cell type (n = 3-7 tumors per genotype). One-way ANOVA with Wilcoxon signed-rank test (A) and two-tailed Student's t-test (A-B, D-E) were applied. Error bars indicate SEM. Centrality measures are means. Box plots indicate interquartile range (A-B, D-E). NES: normalized expression score. DC is dendritic cell.

[0078] [Figure 38] Figure 38 shows that remodeling the tumor microenvironment in mutant cells sensitizes tumors to checkpoint blockade. Tumor weights harvested on day 21 (n = 5-15 tumors per genotype). One-way ANOVA with Sidak's multiple comparison test was applied. Error bars indicate SD. Centrality measure is the mean. In panels a, b, d, and e, the order of the bars in the graphs is, from left to right: control (ctrl), ND560%, and ND580%.

[0079] [Figure 39]Figure 39 shows that HcMel12 mutant cells recapitulate the cellular and metabolic phenotypes observed in B78-D14 cells. A. Heteroplasmic changes following transfection of melanoma cell lines (n = 3 separate cell pellets per genotype). B. Immunoblots of indicator respiratory chain subunits. Representative results are shown. C. mtDNA copy number (n = 12 separate wells per genotype). D. Basal oxygen consumption rate (OCR) (n = 6 measurements per genotype (12 wells per measurement)). E. Growth rate of cell lines in permissive growth medium (n = 3 separate wells per genotype). F. Energy (adenylate) charge status (n = 9 separate wells per genotype). G. NAD+:NADH ratio (n = 9 separate wells per genotype). H. GSH:GSSG ratio (n = 8-9 separate wells per genotype). I. Heatmaps (n = 9 separate wells per genotype) of unlabeled steady-state abundance for selected mitochondrial metabolites, arginine, argininosuccinate (AS), and the terminal fumarate adducts succinylcysteine ​​(succ.Cys) and succinylated GSH (succ.GSH). J. Heatmaps (n = 9 separate wells per genotype) of unlabeled steady-state metabolite abundance for selected intracellular glycolytic intermediates and extracellular lactate (ex. lactate). P values ​​were determined using (C-D) Sidak's multiple comparison test, (E) Fisher's LSD test, or (F-J) one-way ANOVA with one-tailed Student's t-test. Error bars indicate SD. Centrality measures are means.

[0080] [Figure 40]Figure 40 shows that untreated Hcmel12 line tumors recapitulate B78-D14 line tumors. A Survival rate of C57 / BL6 mice subcutaneously injected with the indicated cells (n = 9-10 animals per genotype). B Tumor weight at endpoint (n = 9-10 tumors per genotype). C Heteroplasmy changes detected in bulk tumor samples (n = 9 tumors per genotype). D Bulk tumor mtDNA copy number (n = 9 tumors per genotype). E Heatmap of steady-state abundance of metabolic terminal fumarate adducts, succinylcysteine, and succinic GSH (n = 9 tumors per genotype), demonstrating that the metabolic changes observed in B78 mutant tumors are maintained in vivo. P values ​​were determined using one-way ANOVA with Sidak's multiple comparison test (B, D) or one-tailed Student's t-test (E). Error bars indicate SD. The measure of centrality is the mean value.

[0081] [Figure 41]Figure 41 shows that constitutive expression of cytoLbNOX phenocopies the metabolic changes observed in mt-Nd5 mutant cells. A. Immunoblot of cytoLbNOX expression in clonal populations detected using αFLAG. Representative images are shown. B. Immunoblot of indicator respiratory chain subunits. Representative results are shown. C. mtDNA copy number (n = 9 separate wells per genotype). D. Basal oxygen consumption rate (OCR) (n = 9-15 measurements per genotype (6 wells per measurement)). Similar to the decrease in basal OCR measured in m.12,436 80% cells, a significant decrease in HcMel12 cytoLbNOX is observed. E. NAD+:NADH ratio (n = 11-12 separate wells per genotype). F. Heatmap of metabolite abundances for glucose m+3, lactate m+3, pyruvate m+3, and the terminal fumarate adducts succinylcysteine ​​(succ.Cys) and succinic GSH (succ.GSH) in U-13C glucose labeling of B78 cells. B78 wild-type cells were transiently transfected with cytoLbNOX, and metabolites were extracted 3 days after sorting. A significant increase in lactate abundance was observed in cytoLbNOX-expressing cells, mimicking that observed in m.12,436 80% cells (n = 9–13 separate wells per genotype). All p values ​​were determined using a one-paired Student's t-test. Error bars indicate SD. Centrality is measured by the mean.

[0082] [Figure 42]Hcmel12 mutant and cytoLbNOX tumors showed decreased neutrophils and increased CD4+ T cell infiltration compared to wild-type tumors. A. Gating strategy for Zombie+ live cells. B. Gating strategy for neutrophils in tumor, lymph node, and spleen. C. Gating strategy for CD4+ T cells, CD8+ T cells, NK T cells, and macrophages in tumor, lymph node, and spleen. D. Abundance of specific immune cells in tumor, E. tumor-draining lymph node, and F. spleen of untreated mice (n = 4–8 samples per genotype). Tissues were harvested on day 13. Natural killer T cells: CD4- CD8- NK1.1+. Macrophages: Cd11b+ Ly6C- F4 / 80+. Neutrophils: CD11b+ Ly6C+ Ly6G+. All P values ​​were determined using one-way ANOVA with Fisher's LSD test. The measure of centrality is the mean.

[0083] [Figure 43] Anti-PD1 response depends on the abundance of tumor-resident neutrophils in a syngeneic model of Hcmel12 melanoma. A. Schematic of the experimental design and dosing regimen for Hcmel12 tumors using anti-PD1 monoclonal antibody (mAb) and either G-CSF or anti-Ly6G. B. Tumor weight in untreated mice compared with mice treated with G-CSF or anti-Ly6G (n = 7–8 tumors per genotype). C. Log2 fold change in tumor neutrophils in untreated and treated mice compared with untreated controls for G-CSF and D. anti-Ly6G (n = 4–8 tumors per genotype). Tumor weight in mice treated with anti-PD1 or anti-PD1 and E. G-CSF or F. anti-Ly6G (n = 7–8 tumors per genotype). Neutrophils: CD11b+ Ly6C+ Ly6G+. All P values ​​were determined using one-way ANOVA with Fisher's LSD test. Error bars indicate SD. Centrality measures are means.

[0084] [Figure 44]Hcmel12 mutant tumors and cytoLbNOX tumors show different sensitivities to immune checkpoint inhibitors (also referred to herein as immune checkpoint blockade, or ICB). A Schematic of the experimental design and dosing regimen for Hcmel12 tumors using anti-PD1, anti-PDL1, or anti-CTLA4 mAbs. B Representative images of tumors harvested on day 13 for each dosing regimen. C Tumor weights on day 13 for each dosing regimen (n = 10-12 tumors per genotype). Survival rates of C57BL / 6 mice subcutaneously injected with the indicated cells on continuous anti-PD1 therapy (n = 10-15 animals per genotype). For cytoLbNOX, only tumors that reached the 15 mm endpoint are shown. E Tumor weights at endpoint in mice on continuous anti-PD1 therapy (n = 3-15 tumors per genotype). Change in tumor volume recorded from the day of injection for F wild-type and m.1243680% tumors (n=15 tumors per genotype) and G cytoLbNOX tumors (n=10 tumors per genotype) in persistent anti-PD1. One-way ANOVA with Sidak's multiple comparison test (C, E) or log-rank (Mantel-Cox) test (D) was applied. Tumor volume was calculated as 0.5*L*W2 based on caliper measurements. Error bars indicate SD. Centrality measure is the mean.

[0085] [Figure 45] Immunogenic 4434 tumors maintain differential anti-PD1 sensitivity. A. Schematic of the experimental design and dosing regimen for 4434 tumors with anti-PD1 mAb. B. Representative images of treated tumors at day 20 and untreated tumors at endpoint. C. Tumor weights at day 21 (n=13 tumors per genotype). All P values ​​were determined using a one-tailed Student's t-test. Error bars indicate SD. The measure of centrality is the mean.

[0086] [Figure 46]Wild-type tumors implanted on the opposite flank to mitochondrial mutant or cytoLbNOX tumors are sensitized to anti-PD1. A. Schematic of tumor injection site and dosing regimen for Hcmel12 tumors with anti-PD1 mAb. B. Representative images of sacrificed mice and C. harvested tumors on day 13 for each condition. D. Tumor weights on day 13 (n = 6-10 tumors per genotype). E. Wild-type tumor weights on day 13 (n = 8-11 wild-type tumors) with adjacent tumor genotypes. F. Heatmap of circulating immune populations in blood collected on day 11. NK cells: CD4- CD8- NK1.1+. Neutrophils: CD11b+ Ly6C+ Ly6G+. Monocytes: CD11b+ Ly6C+ F4 / 80-. Conventional dendritic cells (cDCs): CD11c+ MHCII+. All P values ​​were determined using one-way ANOVA with Fisher's LSD test. Error bars indicate SD. Centrality measures are means.

[0087] [Figure 47] Flow cytometry of treated contralateral tumors reveals an increase in CD4+ T cells. A CD4+ T cells, B NK T cells, C CD8+ T cells, D tumor-associated macrophages (TAMs), E neutrophils, and F monocytes (n = 6–12 tumors per condition). All P values ​​were determined using one-way ANOVA with Fisher's LSD test. Error bars indicate SD. Centrality measures are means.

[0088] [Figure 48] Tumor weight and growth rate of wild-type and complex IV mutant tumors.

[0089] [Figure 49]The effect of anti-PD1 treatment on wild-type tumors and complex IV mutant tumors. The patents, scientific literature, and technical literature referred to in this specification establish the knowledge that was available to those skilled in the art at the time of filing. The entire disclosures of issued patents, published and pending patent applications, and other publications cited in this specification are hereby incorporated by reference into this specification to the same extent as if each was specifically and individually indicated to be incorporated by reference. In the event of any discrepancy, the present disclosure shall prevail.

[0090] [Figure 50] A) Tumor weights at endpoint in C57 / BL6 mice injected subcutaneously with the indicated tumor cell genotypes (n = 9-18 animals per genotype). Centrality measures are means. Error bars indicate SD. B) Survival rates in C57 / BL6 mice injected subcutaneously with the indicated cells (n = 9-18 animals per genotype). A log-rank (Mantel-Cox) test was applied. *** indicates P = < 0.001. C) Heatmap of unlabeled steady-state abundance of selected metabolites from endpoint tumors of the indicated genotypes grown subcutaneously in C57 / BL6 animals. succ.cys indicates succinyl cysteine. n = 5-42 tumors per genotype. All P values ​​were determined using one-way ANOVA with Fisher's LSD test. * indicates P = < 0.05. D) Immunoblot analysis of endpoint total tumor protein extracts from the indicated genotypes grown subcutaneously in C57 / BL6 animals. E) Quantification of pSTAT1 levels across the indicated tumor genotypes. n=3 tumors per genotype. All P values ​​were determined using one-way ANOVA with Fisher's LSD test. Centrality measures are means. Error bars indicate SD. * denotes P=<0.05, ** denotes P=<0.01, and *** denotes P=<0.001.

[0091] [Figure 51]There are no common bulk tumor metabolite changes across conditions. Heatmap of metabolite abundance changes for each tumor line compared to wild-type tumors (n = 6-38 tumors per genotype). One-tailed Student's t-test (B78 and 4434) or one-way ANOVA with Sidak's multiple comparison test (Hcmel12) was applied. Error bars indicate SD. Centrality measure is the mean.

[0092] Various aspects of the invention are described in greater detail below. DETAILED DESCRIPTION OF THE INVENTION

[0093] DETAILED DESCRIPTION The present disclosure is based on the inventors' identification of a subpopulation of cancer or pre-cancer patients who respond better to treatment with immune checkpoint inhibitors (e.g., PD-1 inhibitors, PD-L1 inhibitors, PD-L2 inhibitors, CTLA4 inhibitors, TIGIT inhibitors, LAG-3 inhibitors, TIM-3 inhibitors, BTLA inhibitors, and / or KIR inhibitors, etc.). Based on the data provided in the Examples below, the inventors conclude that these patients have an altered cancer or pre-cancer lactate-to-glucose ratio and therefore an altered cancer or pre-cancer redox status (indicative of a Warburg-like metabolic shift). This altered redox status results in changes to the overall tumor microenvironment, resulting in the presence of different ratios of immune cells within the cancer or pre-cancer. Specifically, the inventors have shown that cancers or precancers with an altered redox state, e.g., an altered lactate-to-glucose ratio, have increased numbers of natural killer (NK) cells, monocytes, CD4+ NK-like T cells, and interferon-stimulated gene (ISG)-expressing immune cells, and decreased numbers of macrophages.

[0094] Thus, in one aspect, the invention provides an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in a cancer or precancer for use in sensitizing a subject having cancer or precancer to an immune checkpoint inhibitor. In one example, the agent alters the redox state (e.g., alters the lactate to glucose ratio) in the interstitial fluid of the cancer or precancer.

[0095] In a related aspect, the invention provides a method of sensitizing a subject having cancer or precancer to an immune checkpoint inhibitor, comprising exposing the subject to an agent that alters the redox state (e.g., alters the lactate-to-glucose ratio) in the cancer or precancer. In one example, the agent alters the redox state (e.g., alters the lactate-to-glucose ratio) in the interstitial fluid of the cancer or precancer.

[0096] In a further aspect, the invention provides an immune checkpoint for use in treating a subject having cancer or precancer, wherein the subject has been exposed to an agent that alters the redox state (e.g., alters the lactate-to-glucose ratio) in the cancer or precancer. In one example, the agent alters the redox state (e.g., alters the lactate-to-glucose ratio) in the interstitial fluid of the cancer or precancer.

[0097] The present invention further provides a method of treating cancer or precancer in a subject, comprising administering an immune checkpoint inhibitor to the subject, wherein the subject has been exposed to an agent that alters the redox state (e.g., alters the lactate-to-glucose ratio) in the cancer or precancer. In one example, the agent alters the redox state (e.g., alters the lactate-to-glucose ratio) in the interstitial fluid of the cancer or precancer.

[0098] In a further aspect, the present invention provides a method of treating cancer or precancer in a subject, comprising: (i) exposing the subject to an agent that alters the redox state (e.g., alters the lactate to glucose ratio) in the cancer or precancer; and (ii) administering to the subject an immune checkpoint inhibitor. In one example, the agent alters the redox state (e.g., alters the lactate to glucose ratio) in the interstitial fluid of the cancer or precancer.

[0099] As used herein, the term "sensitizing," in the context of treatment with an immune checkpoint inhibitor, means increasing the sensitivity or decreasing the resistance of a subject's cancer or precancer to immune checkpoint inhibitor treatment. Sensitization can be the sensitization of a cancer or precancer that was not sensitive to immune checkpoint inhibitor treatment before the subject was exposed to the agent, or it can be increasing the sensitivity of a cancer or precancer that was (at least partially) sensitive to immune checkpoint inhibitor treatment before the subject was exposed to the agent. A sensitized subject (or the subject's cancer or precancer) is more likely to respond well to or benefit from such treatment. In other words, immune checkpoint inhibitor treatment is likely or expected to have a therapeutic effect on the subject's cancer or precancer and / or improve the therapeutic effect on the subject's cancer or precancer. Such a therapeutic effect can include clinical improvement of the cancer or precancer in a subject with the disease or condition. Clinical improvement can be demonstrated by improvement in pathology and / or symptoms associated with the cancer or precancer. Preferably, the therapeutic effect can be demonstrated by preventing the onset of cancer or precancer in a subject, delaying or halting the progression of cancer or precancer in a subject, or reversing cancer or precancer. Preferably, the cancer or precancer can be partially or completely reversed. Clinical improvement of pathology can be demonstrated by one or more of the following: a decrease in the level of a cancer or precancer biomarker in a subject, a decrease in the number of cancer or precancerous cells in a subject, an increase in the time until cancer regrowth after cessation of treatment, prevention or delay of the progression of precancer to cancer, prevention of cancer regrowth after cessation of treatment, a decrease in tumor invasiveness, a decrease or complete elimination of metastasis, an increase in cancer cell differentiation, or an increase in survival rate. Other suitable signs of clinical improvement of pathology will be known to those skilled in the art. It will be understood that signs of clinical improvement of pathology will vary depending on the type of cancer.Clinical improvement in cancer-related symptoms can be, but is not limited to, partial or complete relief of pain and / or swelling, increased appetite, reduced weight loss, and / or reduced fatigue.

[0100] Suitably, a sensitized subject may have about a 1.25-fold, 1.50-fold, 1.75-fold, 2-fold, 2.25-fold, 2.5-fold, 2.75-fold, 3-fold, or more increased likelihood of a PD-1 inhibitor and / or PD-L1 inhibitor treatment having a therapeutic effect when compared to a non-sensitized subject.

[0101] As used herein, the term "cancer" refers to a large family of diseases involving abnormal cell proliferation that can invade or metastasize to other parts of the body due to the presence of "cancer cells." Cancer cells can form a subset of neoplasms or tumors. A neoplasm or tumor is a group of uncontrolled growing cells, often forming a mass or lump, but may also be distributed diffusely. A tumor or neoplasm can contain a mixture of cancerous (and / or precancerous) cells and healthy (i.e., noncancerous) cells. The term "tumor," as used herein, encompasses cancerous and / or precancerous cells, healthy cells (e.g., stromal cells), and the tumor microenvironment, which includes immune cells and interstitial fluid. Immune cells in the tumor microenvironment are sometimes referred to as the "immune microenvironment" of the tumor.

[0102] The term "interstitial fluid" refers to the fluid that occupies the space between tumor cells (healthy cells, cancer cells, and / or precancerous cells). Interstitial fluid may contain metabolites, ions, signaling molecules, proteins, extracellular vesicles, and / or other components secreted by tumor cells and immune cells present therein. As will be understood by those skilled in the art, changes in tumor cells can cause changes in interstitial fluid. By way of example only, changes in the metabolic state of tumor cells can result in alterations in metabolites in the interstitial fluid. As shown by the present inventors, such changes in the metabolic state of tumor cells can alter the tumor microenvironment, for example, by altering the immune cell population within the tumor.

[0103] "Cancer cells" may be defined by one or more of the following characteristics: reduced differentiation, self-sufficiency in growth signaling, insensitivity to anti-growth signals, evasion of apoptosis, enabling unlimited replicative capacity, inducing and sustaining angiogenesis, and / or activating tissue metastasis and invasion.

[0104] The cancer may be a solid cancer or a liquid cancer. Preferably, the cancer may be selected from the group consisting of childhood cancer, blood cancer, and bone marrow cancer. Preferably, the childhood cancer may be selected from the group consisting of leukemia, brain tumor, spinal cancer, neuroblastoma, Wilms' tumor, lymphoma (e.g., Hodgkin's lymphoma and non-Hodgkin's lymphoma), rhabdomyosarcoma, retinoblastoma, and bone cancer (e.g., osteosarcoma and Ewing's sarcoma). Preferably, the cancer may be skin cancer. Preferably, the skin cancer may be selected from the group consisting of melanoma, basal cell carcinoma, squamous cell carcinoma, Kaposi's sarcoma, and keratoacanthoma. More preferably, the skin cancer may be melanoma. This application provides examples related to melanoma. However, those skilled in the art will understand that aspects of the present invention may also be applied to other cancers. Nevertheless, aspects of the present invention may work particularly well in the context of melanoma.

[0105] As used herein, a "precancer" or "precancerous condition" refers to an abnormality that has the potential to develop into cancer (such as those cancers mentioned above), where the likelihood of developing into cancer is greater than if the abnormality were not present, i.e., normal. Examples of precancer include, but are not limited to, adenoma, hyperplasia, metaplasia, dysplasia, benign neoplasia (benign tumor), pregestational intraepithelial neoplasia, and polyps. In one example, a precancer is a precancerous tumor. Such a tumor may contain precancerous cells and normal cells.

[0106] As will be clear to one of skill in the art, a "cancer" and / or a "precancer" may also be referred to as a "tumor."

[0107] Preferably, in the context of the present disclosure, the cancer or precancer may have a deleterious mitochondrial DNA (mtDNA) mutation load. Typically, in the context of the present disclosure, subjects likely to benefit from the sensitization described herein will have a cancer or precancer with a low deleterious mitochondrial DNA (mtDNA) mutation load. In this context, sensitization may mimic the metabolic changes seen in subjects with a high deleterious mitochondrial DNA (mtDNA) mutation load (see Examples below). Nevertheless, the sensitization described herein may also be beneficial for subjects with a cancer or precancer with a high deleterious mitochondrial DNA (mtDNA) mutation load (e.g., to further enhance the therapeutic effect of PD-1 inhibitor and / or PD-L1 inhibitor treatment).

[0108] Preferably, the cancer or precancer may have a low deleterious mitochondrial DNA (mtDNA) mutation load. In the context of the present disclosure, a low deleterious mitochondrial DNA (mtDNA) mutation load may be a mutation load of less than 50% when identified in cancer cells or precancerous cells alone or substantially only. For example, a low deleterious mitochondrial DNA (mtDNA) mutation load may be a mutation load of less than 40% or less than 30% when identified in cancer cells or precancerous cells alone or substantially only. More preferably, a low deleterious mitochondrial DNA (mtDNA) mutation load may be a mutation load of less than 20% when identified in cancer cells or precancerous cells alone or substantially only. Preferably, in the context of the present disclosure, a low deleterious mitochondrial DNA (mtDNA) mutation load may be a mutation load of less than 30%, less than 20%, or less than 10% when identified in a sample derived from a subject. It will be understood by those skilled in the art that a sample will typically contain a mixture of cancer cells (and / or precancerous cells) and healthy cells found within a tumor.

[0109] Preferably, the cancer or precancer may have a high deleterious mitochondrial DNA (mtDNA) mutational load. In the context of the present disclosure, a high deleterious mitochondrial DNA (mtDNA) mutational load, when identified in cancer cells or precancerous cells alone or substantially only, may be at least 50% or at least 60% or more. For example, a high deleterious mitochondrial DNA (mtDNA) mutational load, when identified in cancer cells or precancerous cells alone or substantially only, may be at least 70%, at least 80% or more. More preferably, a high deleterious mitochondrial DNA (mtDNA) mutational load, when identified in cancer cells or precancerous cells alone or substantially only, may be at least 60%. Preferably, in the context of the present disclosure, a high deleterious mitochondrial DNA (mtDNA) mutational load, when identified in a sample from a subject, may be at least 30%, at least 40%, at least 50% or more. It will be understood by those skilled in the art that the sample will typically contain a mixture of cancerous (and / or pre-cancerous) and healthy cells found within a tumor.

[0110] Preferably, the cancer or precancer may have a high nuclear mutation burden. Such cancers may be referred to as TMB-H (tumor mutation burden-high) cancers. Preferably, the TBM-H cancer may be a solid cancer. Preferably, the solid cancer may be selected from the group consisting of skin cancer (e.g., melanoma), lung cancer, liver cancer, kidney cancer, and head and neck cancer. Such cancers have generally been found to have better sensitivity to immune checkpoint inhibitors, and the inventors believe that treating these cancers with agents that alter the redox state (e.g., alter the lactate-to-glucose ratio) may further increase sensitivity to checkpoint inhibitors. Indeed, as shown in Figure 45, cancers with altered redox state (e.g., altered lactate-to-glucose ratio) due to mtDNA mutations have been found to completely regress upon treatment with checkpoint inhibitors (e.g., anti-PD1 antibodies).

[0111] Suitably, the cancer or precancer may have a high nuclear mutation burden and a high mtDNA mutation load.

[0112] In the context of the present disclosure, the term "subject" includes humans and mammals (e.g., mice, rats, pigs, cats, dogs, and horses). In preferred embodiments, the subject is a mammal, particularly a primate, especially a human. In preferred embodiments, the subject is a livestock animal, such as a cow, sheep, goat, cow, pig, etc.; a poultry animal, such as a chicken, duck, goose, turkey, etc.; and a domestic animal, particularly a pet animal, such as a dog or cat. In certain embodiments (e.g., particularly in the context of research), the subject mammal may be, for example, a rodent (e.g., a mouse, rat, hamster), a rabbit, a primate, or a pig, such as an inbred pig. As used herein, the terms "patient" and "subject" may be used interchangeably.

[0113] Immune checkpoint inhibitors are drugs that inhibit proteins or peptides (e.g., immune checkpoint proteins) that block the immune system from attacking, for example, cancer cells. In some examples, immune checkpoint proteins that block the immune system prevent the generation and / or activation of T cells. Immune checkpoint inhibitors can be antibodies or antigen-binding fragments thereof, proteins, peptides, small molecules, or combinations thereof. Typically, the inhibitors directly interact with the target immune checkpoint protein (or its ligand, if appropriate), thereby disrupting its function / biological activity. For example, the inhibitors can directly bind to the target immune checkpoint protein (or its ligand, if appropriate). In one example, direct binding to the target immune checkpoint protein (or its ligand, if appropriate) inhibits, prevents, or reduces the formation of a protein complex necessary for the function / biological activity of the immune checkpoint protein.

[0114] PD-1 inhibitors, PD-L1, and PD-L2 inhibitors are a group of checkpoint inhibitors that block or reduce the activity of PD-1, PD-L1, and PD-L2 immune checkpoint proteins. A review describing immune checkpoint pathways and the blockade of such pathways by immune checkpoint inhibitor compounds is provided by Pardoll in Nature Reviews Cancer (April, 2012). Immune checkpoint inhibitor compounds exhibit antitumor activity by blocking one or more of the endogenous immune checkpoint pathways that downregulate antitumor immune responses. Inhibition or blockade of immune checkpoint pathways involves inhibiting the interaction of checkpoint receptors and ligands with immune checkpoint inhibitor compounds to reduce or eliminate the signal and the resulting reduction in antitumor responses.

[0115] Immune checkpoint inhibitor compounds can inhibit the signaling interaction between immune checkpoint receptors and their corresponding ligands. Immune checkpoint inhibitor compounds can act by blocking the activation of immune checkpoint pathways through the inhibition (antagonism) of immune checkpoint receptors (some examples of receptors include CTLA-4, PD-1, and NKG2A) or by the inhibition of ligands of immune checkpoint receptors (some examples of ligands include PD-L1 and PD-L2). In such examples, the effect of immune checkpoint inhibitor compounds is to reduce or eliminate downregulation of certain aspects of the immune system's anti-tumor response in the tumor microenvironment.

[0116] The immune checkpoint receptor programmed death 1 (PD-1) is expressed by activated T cells upon prolonged exposure to antigen. Binding of PD-1 to its known binding ligands, PD-L1 and PD-L2, occurs primarily within the tumor microenvironment, resulting in downregulation of antitumor-specific T cell responses. Both PD-L1 and PD-L2 are known to be expressed on tumor cells. Expression of PD-L1 and PD-L2 in tumors has been shown to correlate with decreased survival outcomes.

[0117] Many PD-1 inhibitors and / or PD-L1 inhibitors are known in the art. In some examples, the PD-1 inhibitor and / or PD-L1 inhibitor is a small organic molecule (molecular weight less than 1000 daltons), a peptide, a polypeptide, a protein, an antibody, an antibody fragment, or an antibody derivative. In some embodiments, the inhibitor compound is an antibody. In some embodiments, the antibody is a monoclonal antibody, particularly a human or humanized monoclonal antibody.

[0118] In some embodiments, the PD-1 inhibitor is an anti-PD-1 antibody or a derivative or antigen-binding fragment thereof. In some embodiments, the anti-PD-1 antibody selectively binds to the PD-1 protein or a fragment thereof. In some embodiments, the anti-PD-1 antibody is nivolumab, pembrolizumab, or pidilizumab.

[0119] In some examples, the PD-L1 inhibitor is an anti-PDL-1 antibody, or a derivative or antigen-binding fragment thereof. In some examples, the anti-PD-L1 antibody, or a derivative or antigen-binding fragment thereof, selectively binds to the PD-L1 protein or a fragment thereof. Examples of anti-PD-L1 antibodies and derivatives and fragments thereof are described in, for example, WO01 / 14556, WO2007 / 005874, WO2009 / 089149, WO2011 / 066389, WO2012 / 145493; US8,217,149, US8,779,108; US2012 / 0039906, US2013 / 0034559, US2014 / 0044738, and US2014 / 0356353. In some embodiments, the anti-PD-L1 antibody is MEDI4736 (durvalumab), MDPL3280A, 2.7A4, AMP-814, MDX-1105, atezolizumab (MPDL3280A), or BMS-936559.

[0120] In some examples, the anti-PD-L1 antibody is MEDI4736, also known as durvalumab. MEDI4736 is an anti-PD-L1 antibody that is selective for the PD-L1 polypeptide and blocks PD-L1 binding to the PD-1 and CD80 receptors. MEDI4736 can alleviate PD-L1-mediated suppression of human T cell activation in vitro and further inhibit tumor growth in xenograft models through a T cell-dependent mechanism. MEDI4736 is further described, for example, in US 8,779,108. The fragment crystallizable (Fc) domain of MEDI4736 contains a triple mutation in the constant domain of the IgG1 heavy chain that reduces binding to the complement components C1q and Fey receptor, which are responsible for mediating antibody-dependent cell-mediated cytotoxicity (ADCC).

[0121] CTLA4 inhibitors are inhibitors that block or reduce the activity of CTLA4. Immune checkpoint receptor cytotoxic T-lymphocyte associated antigen 4 (CTLA4 or CTLA-4) is expressed on T cells and is involved in a signal transduction pathway that reduces the level of T cell activation. CTLA4 is thought to be able to downregulate T cell activation by competitively binding and capturing CD80 and CD86. Furthermore, CTLA4 is thought to be able to downregulate T cell activation by competitively binding and capturing CD80 and CD86. Reg It has been shown to be involved in enhancing the immunosuppressive activity of cells.

[0122] CTLA4 inhibitor can prevent or reduce binding to CD80 and / or CD86.In some embodiments, CTLA-4 inhibitor comprises antibody binding compound, for example, antibody or its antigen-binding fragment.US Patent No. 5,855,887;US Patent No. 5,811,097;US Patent No. 6,682,736;US Patent No. 7,452,535 discloses antibodies specific to human CTLA-4, for example, antibodies specific to the extracellular domain of CTLA-4 and can block its binding to CD80 or CD86; the method for producing such antibodies and the method for using such antibodies as anti-cancer drugs.In some examples, anti-CTLA-4 antibody is tremelimumab, ipilimumab or pembrolizumab.

[0123] TIGIT (T cell immunoreceptor containing Ig and ITIM domains) belongs to the immunoglobulin superfamily and is also known as Wucam, Vstm3, or Vsig9. TIGIT contains an extracellular immunoglobulin domain, a type I transmembrane domain, and two immunoreceptor tyrosine-based inhibitory motifs (ITIMs). TIGIT is primarily distributed in regulatory T cells (Tregs), activated T cells, and natural killer cells (NKs). It is a co-inhibitory receptor protein and can bind to the positive protein CD226 (Dnam-1) on T cells and APCs. Its expressed ligands CD155 (Pvr or Necl-5) and CD112 (Pvrl-2 or Nectin2) constitute a costimulatory network. Notably, TIGIT binds to CD155 and CD112 in competition with CD226, and TIGIT binds to its ligands with higher affinity than CD226. The connection between TIGIT and CD155 or CD112 is mediated by its cytoplasmic ITIM or ITT-like motif, which recruits the phosphatase SHIP-1 to the tail of TIGIT, triggering inhibitory signaling. Furthermore, the ITIM domain is also responsible for the inhibitory ability of mouse TIGIT.

[0124] Preferably, the TIGIT inhibitor (e.g., an anti-TIGIT antibody) can inhibit, reduce, or neutralize one or more activities of TIGIT, for example, resulting in blocking or reducing immune checkpoints in T cells or NK cells, or activating immune responses by modulating antigen-presenting cells. Examples of anti-TIGIT antibodies include vibostolimab, etigilimab, tiragolumab, and donvanalimab.

[0125] The term "LAG-3," "LAG3," or "Lymphocyte Activation Gene-3" refers to lymphocyte activation gene 3. The primary ligand of LAG-3 is MHC class II, which it binds with higher affinity than CD4. The protein has been reported to negatively regulate T cell proliferation, activation, and homeostasis in a manner similar to CTLA-4 and PD-1, and to play a role in Treg suppressive function. LAG-3 is known to be involved in the maturation and activation of dendritic cells. LAG-3 inhibitors can reduce or block the binding of LAG-3 to MHC class II molecules, thereby reducing or blocking its activity. Preferably, the LAG-3 inhibitor can be an anti-LAG-3 antibody, such as favezelimab or leratolimab.

[0126] TIM-3 is an immune checkpoint receptor that suppresses anti-tumor responses by negatively regulating the activity of CD8 T cells and antigen-presenting cells. TIM-3 inhibitors can reduce or block the activity of TIM-3. Preferably, the TIM-3 inhibitor can be an anti-TIM-3 antibody, such as covolimab.

[0127] B and T lymphocyte attenuator (BTLA) is an important co-signaling molecule. It belongs to the CD28 superfamily and is similar in structure and function to programmed cell death-1 (PD-1) and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4). BTLA can be detected in most lymphocytes and causes immunosuppression by inhibiting the activation and proliferation of B and T cells. BTLA has been found to be expressed in tumor-infiltrating lymphocytes (TILs) and is often associated with impaired anti-tumor immune responses. BTLA inhibitors can reduce or block the activity of BTLA. Such reduction or blockage can increase the activation and proliferation of B and T cells. Preferably, the BTLA inhibitor is an anti-BTLA antibody, such as tifsemalimab.

[0128] Killer immunoglobulin-like receptors (KIR) are a family of cell surface proteins found on natural killer (NK) cells. They inhibit the killing function of these cells by interacting with MHC class I molecules. KIR inhibitors can reduce or block the activity of KIR. Such reduction or blockage can increase the killing ability of NK cells. Preferably, the KIR inhibitor can be an anti-KIR antibody, such as lirilumab.

[0129] Suitably, the immune checkpoint inhibitor may be selected from the group consisting of a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a CTLA4 inhibitor, a TIGIT inhibitor, a LAG-3 inhibitor, a TIM-3 inhibitor, a BTLA inhibitor, and a KIR inhibitor.

[0130] Preferably, the immune checkpoint inhibitor can be an antibody, for example, an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-PD-L2 antibody, an anti-CTLA4 antibody, an anti-TIGIT antibody, an anti-LAG-3 antibody, an anti-TIM-3 antibody, an anti-BTLA antibody, and / or an anti-KIR antibody.

[0131] Monoclonal antibodies, antibody fragments, and antibody derivatives for blocking immune checkpoint pathways can be prepared by any of several methods known to those skilled in the art, including, but not limited to, somatic cell hybridization techniques and hybridoma methods. Hybridoma production is described in "Antibodies, A Laboratory Manual," Harlow and Lane, 1988, Cold Spring Harbor Publications, New York. Human monoclonal antibodies can be identified and isolated by screening phage display libraries of human immunoglobulin genes using methods described, for example, in U.S. Patent Nos. 5,223,409, 5,403,484, 5,571,698, 6,582,915, and 6,593,081. Monoclonal antibodies can be prepared using the general method described in U.S. Patent No. 6,331,415 (Cabilly).

[0132] As an example, human monoclonal antibodies can be prepared using XenoMouse™ (Abgenix, Fremont, Calif.) or hybridomas of XenoMouse-derived B cells, a mouse host harboring functional human immunoglobulin genes, as described in U.S. Patent No. 6,162,963 (Kucherlapati).

[0133] Methods for the preparation and use of immune checkpoint antibodies are well known in the art, and by way of example only, some are described in the following exemplary publications: The preparation and therapeutic use of anti-CTLA-4 antibodies are described in U.S. Patent Nos. 7,229,628 (Allison), 7,311,910 (Linsley), and 8,017,144 (Korman). The preparation and therapeutic use of anti-PD-1 antibodies are described in U.S. Patent No. 8,008,449 (Korman) and U.S. Patent Application Publication No. 2011 / 0271358 (Freeman). The preparation and therapeutic use of anti-PD-L1 antibodies are described in U.S. Patent No. 7,943,743 (Korman). The preparation and therapeutic use of anti-TIM-3 antibodies are described in U.S. Patent Nos. 8,101,176 (Kuchroo) and 8,552,156 (Tagayanagi). The preparation and therapeutic use of anti-LAG-3 antibodies are described in US Patent Application Publication No. 2011 / 0150892 (Thudium) and International Publication No. WO2014 / 008218 (Lonberg). The preparation and therapeutic use of anti-KIR antibodies are described in US Patent No. 8,119,775 (Moretta). The preparation of antibodies that block the inhibitory pathway regulated by BTLA (anti-BTLA antibodies) is described in US Patent No. 8,563,694 (Mataraza). In certain examples, PD1 and / or PD-L1 inhibitors may be as described in US Patent No. 8,354,509 B2 and US Patent No. 8,900,587 B2, which are incorporated herein by reference. For example, an immune checkpoint therapy is pembrolizumab (also known as KEYTRUDA).

[0134] Immune checkpoint inhibitors can be administered in an amount and for a period of time (e.g., for a particular treatment regimen over time) to provide amelioration of pathology and / or symptoms associated with cancer or precancer as described herein above.

[0135] Immune checkpoint inhibitors can be formulated, dosed, and administered in a manner consistent with good medical practice. Factors to consider in this context include the specific subject being treated, the clinical condition of the individual patient, the cause of the disorder, the site to which the drug is delivered, the method of administration, the administration schedule, and other factors known to medical professionals. The "therapeutically effective amount" of the administered immune checkpoint inhibitor is determined by such considerations and is the minimum amount necessary to prevent, ameliorate, or treat or stabilize benign, precancerous, or early-stage cancer; or, for example, when used as a neoadjuvant, to treat or prevent the development or recurrence of tumors, dormant tumors, or micrometastases. Immune checkpoint inhibitors may, but do not necessarily, be formulated with one or more agents currently used in the prevention or treatment of cancer, as appropriate.

[0136] Suitable routes of administration of immune checkpoint inhibitors include, but are not limited to, oral, parenteral, subcutaneous, rectal, transmucosal, intestinal administration, intramuscular, intramedullary, intrathecal, direct intracerebroventricular, intravenous, intravitreal, intraperitoneal, intranasal, or intraocular injection. Alternatively, immune checkpoint inhibitors can be administered locally rather than systemically, for example, by directly injecting the immune checkpoint inhibitor into a solid tumor or by topical application (e.g., for skin cancer).

[0137] Immune checkpoint inhibitors can be formulated according to known methods for preparing pharmaceutically useful compositions, whereby the inhibitor is combined in a mixture with a pharmaceutically suitable excipient or carrier. Sterile phosphate-buffered saline is an example of a pharmaceutically suitable excipient. Other suitable excipients are well known to those skilled in the art. See, for example, Ansel et al., PHARMACEUTICAL DOSAGE FORMS AND DRUG DELIVERY SYSTEMS, 5th Edition (Lea & Febiger 1990), and Gennaro (ed.), REMINGTON'S PHARMACEUTICAL SCIENCES, 18th Edition (Mack Publishing Company 1990), and revised editions thereof.

[0138] Generally, the dosage of an immune checkpoint inhibitor administered to a human will vary depending on factors such as the patient's age, weight, height, sex, overall medical condition, and past medical history. It may be desirable to provide a subject with a dosage ranging from approximately 1 mg / kg to 24 mg / kg as a single intravenous infusion, although lower or higher dosages may be administered as appropriate. For example, a dosage of 1 to 20 mg / kg for a 70 kg patient would be 70 to 1,400 mg for a 1.7 m patient, or 41 to 824 mg / m². Dosages may be repeated as needed, for example, once weekly for 4 to 10 weeks, once weekly for 8 weeks, or once weekly for 4 weeks. It may also be administered less frequently, for example, every other week for several months, or monthly or quarterly for several months, as needed.

[0139] Suitably, immune checkpoint inhibitors may be used in the uses or methods described herein as a sole treatment for cancer or pre-cancer, or in combination with a second treatment for cancer or pre-cancer, such as, for example, surgery, radiation therapy, chemotherapy, immunotherapy, hormonal therapy, vaccine therapy, or any combination thereof.

[0140] Preferably, immune checkpoint inhibitors may be used as first-line, second-line, third-line, or later-line treatments for cancer or pre-cancer.

[0141] In some embodiments, the present invention relates to agents that alter the redox state (e.g., alter the lactate-to-glucose ratio) in a cancer or precancer, and that are used in sensitizing a subject (with their cancer or precancer) to an immune checkpoint inhibitor (e.g., a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a CTLA4 inhibitor, a TIGIT inhibitor, a LAG-3 inhibitor, a TIM-3 inhibitor, a BTLA inhibitor, and / or a KIR inhibitor, etc.). For example, the agent alters the lactate-to-glucose ratio in the interstitial fluid of the cancer or precancer.

[0142] In order for an agent to alter the redox state (e.g., alter the lactate-to-glucose ratio), cancer or precancerous cells must be exposed to the agent. In this context, the term "exposing" refers to the active process of contacting cancer or precancerous cells with an agent to alter the redox state (e.g., lactate-to-glucose ratio) and / or providing cancer or precancerous cells with an agent that alters the redox state (e.g., lactate-to-glucose ratio). Exposure can be performed in vitro, in vivo, or ex vivo. For in vitro or ex vivo exposure, the cells can be introduced (e.g., reintroduced) into a subject with cancer or precancerous cancer.

[0143] The term "redox state" or "metabolic state," as used herein, refers to the cytosolic and / or mitochondrial ratio of NAD+:NADH in a cancer or precancerous microenvironment, such as the tumor as a whole and / or cancer or precancerous interstitial fluid (also referred to herein as tumor interstitial fluid). The inventors have found that both lowering the NAD+:NADH ratio (mtDNA mutation) and / or increasing the NAD+:NADH ratio from homeostatic levels in cancer and / or precancerous cells exert immunomodulatory effects on tumors, making them more responsive to immune checkpoint inhibitors. Homeostatic levels in this context may refer to wild-type levels (e.g., non-cancer cells and / or cancer cells without mtDNA mutations). The NAD+:NADH ratio is tightly regulated within cells, as the directionality and activity of numerous reactions, such as glycolysis, gluconeogenesis, fatty acid synthesis, DNA repair (PARP is NAD+-dependent), and histone acetylation, depend on it.

[0144] In some embodiments, the agent can be provided by transducing or transfecting a cancer or precancerous cell with a nucleic acid encoding an agent that alters the redox state (e.g., alters the lactate-to-glucose ratio). Preferably, the encoded agent can be an enzyme. Preferably, the enzyme can be an enzyme that increases glucose uptake and / or lactate release. For example, the enzyme can be selected from the group consisting of NADH oxidase and NADPH oxidase. As an example, NADH oxidase can be derived from Lactobacillus brevis. Such an enzyme can be referred to herein as "LbNOX." The enzyme can be suitably expressed in the cytosol of cancer or precancerous cells. LbNOX expressed in the cytosol can be referred to herein as cytoLbNOX. Alternatively, or additionally, the enzyme can be suitably expressed in the mitochondria of cancer or precancerous cells. LbNOX expressed in the mitochondria can be referred to herein as mitoLbNOX.

[0145] B78-D14 m.12,436 showed no changes in common metabolites 80% , Hcmel12 m.12,436 80% By comparing bulk tumor metabolite changes in Hcmel12 cytoLbNOX tumors, the inventors surprisingly find that alterations in redox state, regardless of direction (e.g., alterations in the cellular redox state of cancers and / or precancers), rather than changes in total metabolite abundance, are sufficient to alter the immune microenvironment of tumors and sensitize cancers or precancers to treatment with immune checkpoint inhibitors. In this context, "regardless of direction" can refer to a directional change in the NAD:NADH ratio.

[0146] Preferably, the agent (e.g., an NADH oxidase, e.g., cytoLbNOX and / or mitoLbNOX) may be used in combination with a tumor-associated neutropenia compound. A tumor-associated neutropenia compound is a compound that reduces the number of tumor-resident neutrophils in a tumor. Herein, tumor-resident neutrophils may also be referred to as tumor-associated neutrophils. This reduction can be achieved, for example, by blocking the infiltration of tumor-resident neutrophils into the tumor, by reducing the number of neutrophils in a subject (e.g., by killing neutrophils and / or blocking neutrophil production / maturation), or both. Killing neutrophils can be achieved by antibody-dependent cell-mediated cytotoxicity (ADCC). Compounds that can reduce tumor-resident neutrophils include, for example, anti-Ly6G antibodies, anti-GR1 antibodies, and / or other antibodies specific for certain neutrophil antigens (e.g., antibodies specific for human neutrophil antigens (HNA) selected from the group consisting of HNA-1a, HNA-1b, and HNA-1c). These antibodies can be used to identify and deplete neutrophils that express these antigens.

[0147] The term "altered" as used herein means a change that can be an increase or decrease relative to a baseline value.

[0148] Preferably, the agents described herein alter the NAD+:NADH ratio in the cancer or precancer. As noted elsewhere herein, this alteration can be an increase or a decrease in the NAD+:NADH ratio.

[0149] Preferably, the agents described herein increase the lactate to glucose ratio in a cancer or precancer. Preferably, the agents increase the lactate to glucose ratio in the tumor to greater than 2.5:1, 3:1, 3.5:1, 4:1, or more.

[0150] Preferably, the agents described herein increase the lactate to glucose ratio in the interstitial fluid of a cancer or pre-cancer. Preferably, the agents increase the lactate to glucose ratio in the interstitial fluid of a tumor to greater than 2.5:1, 3:1, 3.5:1, 4:1, or more.

[0151] The terms "increased" or "increase," as used herein, generally refer to the difference between a relevant level (metabolite, mutation load, etc.) and a suitable corresponding reference value, meaning at least about 10% greater than the reference value, e.g., at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90% greater than the reference value.

[0152] The terms "decrease" or "decreased," as used herein, generally refer to the difference between a relevant level (metabolite, mutation load, etc.) and a suitable corresponding reference value, meaning a decrease of at least about 5%, at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, etc., from the reference value.

[0153] As used herein, in the context of an agent that alters the lactate-to-glucose ratio, a "baseline value" can refer to the corresponding parameter (e.g., NAD+:NADH ratio, or lactate-to-glucose ratio, etc.) of the cancer or precancer before the cancer or precancer is exposed to the agent. Many agents that alter the redox state of cancer or precancer (e.g., alter the lactate-to-glucose ratio) are known in the art. Furthermore, methods for determining lactate and glucose levels are known in the art and can be routinely used (see, for example, Cengiz et al. 2009 doi: 10.1089 / dia.2009.0002; and Spahar-Deleze et al. 2021 doi: 10.3390 / chemosensors9080195). Assays for measuring the NAD+:NADH ratio are also widely known in the art.

[0154] The agent can be used as a pretreatment. In this context, the agent can be considered a neoadjuvant. The agent can be provided before or simultaneously with an immune checkpoint inhibitor (e.g., a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a CTLA4 inhibitor, a TIGIT inhibitor, a LAG-3 inhibitor, a TIM-3 inhibitor, a BTLA inhibitor, and / or a KIR inhibitor, etc.).

[0155] The agent can be formulated as needed. For example, the agent can be infusion. As used herein, "infusion" refers to a solution, emulsion, or suspension. In one example, the agent can be injected into a cancer or precancer. Typically, the agent is a cell-permeable compound or a precursor thereof.

[0156] The agent may be in the form of a pharmaceutical composition. The pharmaceutical composition may further comprise a pharmaceutically acceptable diluent, carrier, or excipient. Such compositions may further comprise pharmaceutically acceptable concentrations of salts, buffers, preservatives, compatible carriers, supplementary immune enhancing agents such as adjuvants and cytokines, and, if appropriate, other therapeutic agents, as is customary.

[0157] The composition may also contain an antioxidant and / or preservative.Antioxidants include thiol derivatives (e.g., thioglycerol, cysteine, acetylcysteine, cystine, dithioerythreitol, dithiothreitol, glutathione, etc.), tocopherol, butylated hydroxyanisole, butylated hydroxytoluene, sulfites (e.g., sodium sulfate, sodium bisulfite, sodium acetone bisulfite, sodium metabisulfite, sodium sulfite, sodium formaldehyde sulfoxylate, sodium thiosulfate), and nordihydroguaiaretic acid.Suitable preservatives may be, for example, phenol, chlorobutanol, benzyl alcohol, methylparaben, propylparaben, benzalkonium chloride, and cetylpyridinium chloride.

[0158] The phrase "pharmaceutically acceptable" is used herein to mean compounds, materials, compositions, and / or dosage forms that are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings or animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit / risk ratio.

[0159] The pharmaceutical compositions described above may be suitable for use in the treatment of various forms of cancer or precancer, and in particular those cancers described herein.

[0160] The agent may be for administration to a subject by any suitable route that can provide a therapeutically effective amount of the agent.

[0161] The agent can be any suitable agent, for example, a small molecule, a metabolite, an antibody, a nucleic acid, an enzyme, or the like.

[0162] Preferably, the enzyme is NADH oxidase, e.g., from Lactobacillus brevis. Such an enzyme may be referred to herein as "LbNOX." The enzyme may be preferably expressed in the cytosol of cancer or precancerous cells.

[0163] Preferably, the nucleic acid can encode an NADH oxidase (i.e., LbNOX), for example, from Lactobacillus brevis. The nucleic acid can be incorporated into a separate nucleic acid sequence, such as a vector.

[0164] In one example, the vector is a plasmid, a viral vector, or a cosmid, and optionally the vector may be selected from the group consisting of a lentivirus, a retrovirus, an adeno-associated virus, an adenovirus, a vaccinia virus, a canarypox virus, a herpes virus, a minicircle vector, and a synthetic DNA or RNA.

[0165] As used herein, the term "vector" refers to a nucleic acid sequence capable of transporting another nucleic acid sequence to which it is operably linked. A vector may be capable of autonomous replication or may be capable of integrating into host DNA. A vector may contain a restriction enzyme site for insertion of recombinant DNA, as well as one or more selectable markers or suicide genes. A vector may be a nucleic acid sequence in the form of a plasmid, bacteriophage, or cosmid. Preferably, the vector is suitable for expression in a cell (i.e., the vector is an "expression vector"). Preferably, the vector is a CD8 + T cells or CD4 +The vector is suitable for expression in human T cells, such as T cells, or stem cells, iPS cells, or NK cells. In certain embodiments, the vector is a viral vector, such as a retroviral vector, a lentiviral vector, or an adeno-associated vector. Optionally, the vector may be selected from the group consisting of adenovirus, vaccinia virus, canarypox virus, herpes virus, minicircle vector, and synthetic DNA or synthetic RNA.

[0166] Preferably, the (expression) vector is capable of propagation in the host cell and is stably inherited by future generations.

[0167] A vector may contain a regulatory sequence. As used herein, "regulatory sequence" refers to a DNA or RNA element that can control gene expression. Examples of expression control sequences include promoters, enhancers, silencers, TATA boxes, internal ribosome entry sites (IRES), transcription factor binding sites, transcription terminators, polyadenylation sites, etc. Optionally, a vector may contain one or more regulatory sequences operably linked to the nucleic acid sequence to be expressed. Regulatory sequences include sequences that direct constitutive expression, as well as tissue-specific regulatory sequences and / or inducible sequences.

[0168] Optionally, the vector may contain a nucleic acid sequence of interest operably linked to a promoter. As used herein, "promoter" refers to a nucleotide sequence in DNA to which RNA polymerase binds to initiate transcription. The promoter may be inducibly or constitutively expressed. Alternatively, the promoter may be under the control of a repressor or stimulatory protein. The promoter may not be naturally found in the host cell (e.g., it may be an exogenous promoter). Those skilled in the art are well aware of suitable promoters for use in expressing target proteins, and the promoter selected will depend on the host cell.

[0169] "Operably linked" means that the control elements described below, alone or in combination, together with the coding sequence, are in a functional relationship with each other, e.g., in a linkage that directs the expression of the coding sequence.

[0170] The vector may contain a transcription terminator. As used herein, "transcription terminator" refers to a DNA element that terminates the function of RNA polymerase, which is responsible for transcribing DNA into RNA. A preferred transcription terminator is characterized by a series of T residues preceded by a GC-rich dyad symmetry region.

[0171] The vector may contain a translational control element. As used herein, "translational control element" refers to a DNA or RNA element that controls the translation of mRNA. A preferred translational control element is a ribosome binding site. Preferably, the translational control element is derived from the same homologous system as the promoter, e.g., the promoter and its associated ribozyme binding site. Preferred ribosome binding sites are known and will depend on the selected host cell.

[0172] A vector may contain a restriction enzyme recognition site. As used herein, "restriction enzyme recognition site" refers to a motif on DNA that is recognized by a restriction enzyme.

[0173] The vector may contain a selectable marker. As used herein, a "selectable marker" refers to a protein that, when expressed in a host cell, confers a phenotype on the cell that allows for the selection of cells expressing the selectable marker gene. Generally, this may be a protein that confers a new beneficial property to the host cell (e.g., antibiotic resistance), or a protein that is expressed on the cell surface and is thereby accessible to antibody binding. Suitable selectable markers are well known in the art.

[0174] Optionally, the vector may also contain a suicide gene. As used herein, "suicide gene" encodes a protein that induces the death of modified cells when treated with a specific drug. For example, suicide can be induced in cells modified with herpes simplex virus thymidine kinase gene by treating with a specific nucleoside analogue such as ganciclovir, in cells modified with human CD20 by treating with anti-CD20 monoclonal antibody, and in cells modified with inducible caspase 9 (iCasp9) by treating with AP1903 (reviewed by BS Jones, LS Lamb, F Goldman, A Di Stasi; Improving the safety of cell therapy products by suicide gene transfer. Front Pharmacol. (2014) 5:254). Suitable suicide genes are well known in the art.

[0175] Preferably, the vector contains the genetic elements necessary for expression of the binding proteins described herein by a host cell, including a promoter, a coding region for the protein of interest, and a transcription terminator for transcription and translation in the host cell.

[0176] Those skilled in the art will be familiar with the molecular techniques available for preparing (expression) vectors and the methods by which (expression) vectors can be introduced or transfected into suitable host cells (thereby generating modified cells, as further described below). The (expression) vector systems described herein can be introduced into cells by conventional techniques, such as transformation, transfection, or transduction. "Transformation," "transfection," and "transduction" generally refer to techniques for introducing foreign (exogenous) nucleic acid sequences into host cells, and thus include methods such as electroporation, microinjection, gene gun delivery, transduction with retroviruses, lentiviruses, or adeno-associated vectors, lipofection, superfection, and the like. The specific method used typically depends on both the type of vector and the type of cell. Suitable methods for introducing nucleic acid sequences and vectors into host cells, such as human cells, are well known in the art; see, for example, Sambrook et al. (1989) Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY; Ausubel et al. (1987) Current Protocols in Molecular Biology, John Wiley and Sons, Inc., NY; Cohen et al. (1972) Proc. Natl. Acad. Sci. USA 69, 2110; Luchansky et al. (1988) Mol. Microbiol. 2, 637-646.

[0177] Suitable examples of agents that alter the redox state (e.g., lactate to glucose ratio) in cancer or precancer are provided below. As will be apparent to one of skill in the art, these agents can be used to alter the redox state (e.g., lactate to glucose ratio) in the interstitial fluid of a cancer or precancer.

[0178] Preferably, the agent can be a compound that drives glycolytic flux through MDH1. For example, the agent can be isocitrate, aconitate, citrate, oxaloacetate, or an NADH or NAD+ precursor.

[0179] Preferably, the agent is a compound that regulates the oxidation-reduction process of NAD(H) via the malate-aspartate shuttle, for example, the compound may be selected from the group consisting of isocitrate, aconitate, citrate, oxaloacetate, malate, fumarate, and argininosuccinate.

[0180] Preferably, the agent may be lactic acid. In one example, lactic acid may be administered to the cancer or pre-cancer as a lactate infusion.

[0181] Preferably, the agent may be a glucose metabolic enzyme and / or a lactate metabolic enzyme. Optionally, the glucose metabolic enzyme may be selected from the group consisting of hexokinase, phosphorglucoisomerase, phosphofructokinase, aldolase, isomerase, triosephosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase, enolase, and pyruvate kinase. Optionally, the lactate metabolic enzyme may be lactate dehydrogenase (LDH) (e.g., lactate dehydrogenase A and / or lactate dehydrogenase B).

[0182] Suitably, the agent may be an inhibitor of an enzyme that reduces glycolytic flux in cancer or pre-cancerous cells. Optionally, the enzyme may be pyruvate dehydrogenase or pyruvate carboxylase.

[0183] Preferably, the agent is an activator of an enzyme that increases lactate efflux in cancer or pre-cancerous cells. Optionally, the enzyme may be MDH1 or GAPDH.

[0184] Suitably, the agent may be a small molecule inhibitor of an enzyme in the malate-aspartate shuttle. Optionally, the enzyme may be selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter.

[0185] Suitably, the agent may be a small molecule activator of an enzyme in the malate-aspartate shuttle. Optionally, the enzyme may be selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter.

[0186] Preferably, the agent may be an inhibitor of Complex I, Complex II, Complex III, or Complex IV. Preferably, the inhibitor of Complex I inhibitor may be rotenone. Preferably, the inhibitor of Complex II inhibitor may be thenoyltrifluoroacetone. Preferably, the inhibitor of Complex III inhibitor may be selected from the group consisting of antimycin A, myxothiazol, and stigmatellin. Preferably, the inhibitor of Complex IV inhibitor may be cyanide.

[0187] Suitably, an agent that alters the redox state may alter the pyruvate to lactate ratio, and therefore an altered redox state may be indicated by an alteration in the pyruvate to lactate ratio.

[0188] The inventors have demonstrated a link between altered immune cell populations within the tumor microenvironment, altered metabolic status in cancers or precancers, and high deleterious mtDNA mutation burden. Based on this, the inventors believe that increasing the deleterious mtDNA mutation burden in cancers or precancers will sensitize the cancers or precancers to treatment with immune checkpoint inhibitors.

[0189] Therefore, agents that change the redox state in cancer or precancer, such as agents that change (e.g., increase) the lactate-to-glucose ratio, can be compounds that increase the harmful mtDNA mutation load in cancer or precancer. Compounds that increase the harmful mtDNA mutation load can increase it by either mutating individual mtDNA molecules or by removing non-mutated mtDNA molecules. Methods for identifying harmful mitochondrial DNA (mtDNA) mutation load in cancer or precancer samples from subjects are known in the art.

[0190] Optionally, the compound induces a deleterious mtDNA mutation (ie, introduces a mutation into the cancerous or precancerous mtDNA).

[0191] Preferably, the agent can be a compound that increases the deleterious mtDNA mutation load in the cancer or precancer, where the compound is selected from the group consisting of a mitochondrial base editing enzyme (e.g., DdCBE, etc.) and a mitochondrial heteroplasmy manipulating enzyme (e.g., mtZFN, mitoTALEN, or other nuclease, etc.). It will be understood that an increase in this context is compared to the mutation load before the cancer or precancer was exposed to the agent described herein.

[0192] The term "deleterious mtDNA mutation," as used herein, refers to a mutation that adversely affects the structure and / or function of an encoded mtDNA element, as opposed to a neutral mutation (e.g., a silent point mutation), which has neither a positive nor a negative mutation to the corresponding encoded element.

[0193] Methods for identifying deleterious mtDNA mutations will be known in the art. By way of example only, the deleterious mtDNA mutation may be selected from the group consisting of: (i) tRNA mutations with a MitoTIP RAW score of at least 12.6 or at least 16.25; (ii) rRNA mutations; (iii) truncation mutations in mtDNA genes; (iv) a missense mutation in an mtDNA gene, having an apogee score greater than 0.5 and selected from a frameshift mutation, an insertion mutation, or a deletion mutation, as appropriate; and / or (v) A mutation in the mtDNA D-loop region selected from the group consisting of: heavy chain promoter (m.545-567), hypervariable segment 2 (MT-HV2; m.57-372), and hypervariable segment 1 (MT-HV1; m.16024-16390).

[0194] The tRNA mutation can be in a gene selected from the group consisting of MT-TL1, MT-TA, MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI, MT-TK, MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT, MT-TV, MT-TW, and MT-TY.

[0195] The rRNA mutation may be in a gene selected from the group MT-RNR1 and MT-RNR2.

[0196] The truncating or missense mutation can be in a tRNA, rRNA, or protein-coding gene.

[0197] The protein-coding gene may be selected from the group consisting of MT-ND5, MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND4L, MT-ND6, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ATP6, and MT-ATP8. These genes encode proteins that are subunits of mitochondrial respiratory chain complexes, specifically, NADH:ubiquinone oxidoreductase (complex I), ubiquinol:cytochrome c oxidoreductase (complex III), cytochrome c oxidase (complex IV), or ATP synthase (complex V). Thus, the mutation may be present in an mtDNA gene encoding a subunit of a mitochondrial respiratory chain complex selected from the group consisting of complex I, complex III, complex IV, and complex V.

[0198] Preferably, the deleterious mtDNA mutation is a truncation mutation, a missense mutation, an insertion mutation, or a frameshift mutation.

[0199] Preferably, the deleterious mutation is present in the MT-ND5 gene. Preferably, the deleterious mutation is a truncating mutation present in a region selected from m.12418-12425:A indel or m.12385-12390:C indel.

[0200] Preferably, the deleterious mutation can be a missense mutation in the MT-CO1, MT-ND5, MT-ND4, MT-CYB, or MT-TY gene.

[0201] Suitably, the missense mutation may be selected from the group consisting of m.6318C>T, m.12730G>A, m.11736T>C, m.15140G>A, m.5843A>G, and m.6214G>A.

[0202] Suitably, the insertion mutation may be selected from the group consisting of m.16183:CC indel and m.16192:T indel.

[0203] The terms "mtDNA mutation load," "mtDNA heteroplasmy," "mutant allele frequency," or "VAF" refer to mtDNA mutations that occur in the same cell or group of cells and coexist with wild-type alleles. In the context of this disclosure, the terms "determine" or "determining" refer to measuring the level of mtDNA molecules containing a deleterious mutation in a cell or group of cells and comparing that level with the level of mtDNA molecules that do not contain such a deleterious mutation (or the total number of mtDNA molecules present in the cell or group of cells). mtDNA molecules that do not contain a deleterious mutation may contain other mutations, provided that these mutations are not deleterious within the meaning of this disclosure.

[0204] The mtDNA mutation load can typically be expressed as a percentage. For example, a 30% mutation load means that 30% of the mtDNA molecules in a cell or a group of cells (e.g., a sample) carry a deleterious mtDNA mutation. The deleterious mutation may be the same or different in all mutated mtDNA molecules. More preferably, for purposes of measuring the mutation load, the deleterious mutation may be the same in all mutated mtDNA molecules. However, it will be understood that the mtDNA molecules carrying the deleterious mutations used to determine the mutation load may carry further (additional) deleterious mutations.

[0205] Methods for determining mtDNA mutation load are well known in the art, and include, for example, mtDNA sequencing, such as single-cell mtDNA sequencing. Methods for determining mtDNA mutation load are described, for example, in Sobenin et al., 2014 (doi: 10.1155 / 2014 / 292017).

[0206] In the context of some of the methods disclosed herein, a deleterious mtDNA mutation burden is identified in a cancer or precancerous sample from a subject.

[0207] The term "sample" refers to any group of cells, including cancerous and / or precancerous cells, obtained from a subject. The sample may typically contain a mixture of healthy cells (i.e., non-cancerous and non-precancerous cells) and cancerous (and / or precancerous) cells. The sample may contain components of the tumor, such as cells (cancer, precancerous, and healthy cells), as well as interstitial fluid.

[0208] Preferably, the sample will contain at least 5%, at least 10%, at least 15%, at least 20%, or more cancerous and / or precancerous cells. For example, the sample may contain at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, or more cancerous and / or precancerous cells.

[0209] The presence of healthy cells that may be substantially free of a deleterious mtDNA mutational load may lower the identified (total) deleterious mtDNA mutational load in a sample compared to when a deleterious mtDNA mutational load is identified only or substantially only in cancer or precancerous cells.

[0210] In this context, the term "substantially only" means that at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or more of the cells in the sample are cancer cells.

[0211] Thus, by way of example only, if the deleterious mtDNA mutation load in a cancer or precancerous sample obtained from a subject is identified as being about 30%, the deleterious mtDNA mutation load of the cancer or precancerous cells present in the sample may specifically be greater than 30%, greater than 40%, greater than 50%, greater than 60%, greater than 70%, greater than 80%, or greater than 90%.

[0212] Suitably, for solid cancers, the sample may be a biopsy, smear, or interstitial fluid sample.

[0213] Suitably, for liquid cancers, the sample may be a blood sample (eg, a whole blood sample, a plasma sample, or a serum sample) or a urine sample.

[0214] As will be apparent to those skilled in the art, the amount of mtDNA molecules with deleterious mutations is used to identify the level of mutational load in a cell or group of cells. Herein, the proportion of mtDNA molecules with deleterious mutations is referred to as the "deleterious mtDNA mutation load." Those skilled in the art will understand that a low proportion of mtDNA molecules with deleterious mutations corresponds to a low deleterious mtDNA mutation load, which may be asymptomatic (i.e., have little or no effect on the overall mitochondrial function of the cell). Conversely, a high proportion of mtDNA molecules with deleterious mutations corresponds to a high deleterious mtDNA mutation load, which, in the context of the present disclosure, may be symptomatic, i.e., have a deleterious effect on the overall mitochondrial function of the cell. The deleterious effect on the overall mitochondrial function of the cell may be identified by an altered redox state (e.g., mitochondrial and / or cytosolic metabolic state), which may result from altered mitochondrial redox homeostasis, decreased oxidative phosphorylation, increased oxidative stress, or any combination thereof. These changes may further cause alterations in the cancer or precancerous microenvironment, such as the bulk tumor and / or the cancer or precancerous interstitial fluid (also referred to herein as tumor interstitial fluid). The altered tumor microenvironment may be more or less favorable for particular immune cell populations, as described in more detail below.

[0215] The altered redox state may be indicated by an increase in one or more cellular metabolites selected from the group consisting of fumarate, lactate, malate, acetyl-CoA, aspartate, glucose, glucose 6-phosphate, glutamine, glucose 3-phosphate, glycolytic intermediates, fumarate adducts (e.g., GSH succinate, and / or succinylcysteine), and argininosuccinate. In particular, the altered redox state may be indicated by an increase in the fumarate adducts GSH succinate and / or succinylcysteine ​​(also referred to herein as succ.cys and succ.gsh, respectively).

[0216] Additionally or alternatively, the alteration of the redox state may include a decrease in one or more cellular metabolites selected from the group consisting of alpha-ketoglutarate, pyruvate, phosphoenolpyruvate, and succinate.

[0217] The deleterious mtDNA mutation load can alter the NAD+:NADH ratio in mitochondria and / or cytosol. Preferably, the deleterious mtDNA mutation load can increase the NAD+:NADH ratio in mitochondria and / or cytosol. As shown in the examples, the disturbance in the NAD+:NADH ratio can result in partial reversal of MDH2 in mitochondria (which can be determined from the ratio of pyruvate carboxylase-derived (m+3) malate, citrate, and aconitate to pyruvate).

[0218] By way of example only, the alteration of the redox state may include changes in metabolites of the TCA cycle and / or the urea cycle. Preferably, these metabolites may be associated with the malate-aspartate shuttle (MAS) and fumarate in the mitochondria and / or cytosol. As will be explained in detail in the Examples, the present inventors have demonstrated that 1- 13Using a C glutamine trace, we found that changes in the NAD+:NADH ratio were associated with increased abundance of malate m+1 and argininosuccinate m+1, but not α-KG m+1, aconitate m+1, or aspartate m+1, indicating the involvement of increased MDH1 flux. Thus, alterations in mitochondrial metabolic state may involve increased MDH1 flux.

[0219] By way of example only, an alteration in the redox state can include an imbalance between lactate and glucose within a tumor (e.g., within the interstitial fluid of a tumor). As described elsewhere herein, an alteration in the lactate-to-glucose ratio in a cancer or precancer can sensitize the cancer or precancer to a PD-1 inhibitor and / or a PD-L1 inhibitor. In this context, an alteration in the lactate-to-glucose ratio can be an increase in the lactate-to-glucose ratio.

[0220] By way of example only, alterations in redox status can include an imbalance between pyruvate and lactate within a tumor (e.g., within the interstitial fluid of a tumor). Thus, deleterious mtDNA mutation load can alter the redox status (e.g., alter the pyruvate to lactate ratio within a tumor (e.g., within the interstitial fluid of a tumor)).

[0221] The inventors believe that altered redox status (e.g., altered lactate-to-glucose ratio) in cancers or precancers is the reason for the significantly different proportions of immune cells in the tumor microenvironment in such cancers or precancers. In light of the data in the Examples section herein, the inventors believe that altered lactate-to-glucose ratio in cancers or precancers is associated with increased levels of immune cells selected from the group consisting of NK cells; monocytes; CD4+ T cells; and ISG-expressing immune cells, and / or decreased levels of macrophages (e.g., tumor-associated macrophages) and / or neutrophils.

[0222] Suitably, in the context of the present disclosure, the agent may be an agent that increases the level of immune cells selected from the group consisting of NK cells; monocytes; CD4+ T cells; and ISG-expressing immune cells, and / or decreases the level of macrophages (e.g., tumor-associated macrophages) and / or neutrophils.

[0223] Preferably, the agent may reduce the level of neutrophils, e.g., tumor-infiltrating neutrophils. It will be appreciated that such an agent may reduce the level of neutrophils (e.g., tumor-infiltrating neutrophils) by altering the redox state in the cancer or precancer.

[0224] Accordingly, in a further aspect, the present invention provides an immune checkpoint inhibitor for use in treating a subject having cancer or precancer, wherein the subject has been exposed to an agent that reduces neutrophils (e.g., tumor-infiltrating neutrophils).

[0225] The present invention also provides a method of sensitizing a subject having cancer or pre-cancer to an immune checkpoint inhibitor, comprising exposing the subject to an agent that reduces neutrophils (e.g., tumor-infiltrating neutrophils).

[0226] The present invention also provides a method of treating cancer or precancer in a subject, comprising administering an immune checkpoint inhibitor to the subject, wherein the subject has been exposed to an agent that reduces neutrophils (e.g., tumor-infiltrating neutrophils).

[0227] The present invention provides a method of treating cancer or precancer in a subject, comprising: (i) exposing a subject to an agent that reduces neutrophils (e.g., tumor-infiltrating neutrophils); (ii) administering to the subject an immune checkpoint inhibitor; Also provided is a method comprising:

[0228] The term "NK cells" or "natural killer cells," as used herein, refers to a subset of peripheral blood lymphocytes defined by expression of CD56 or CD16 and the lack of a T cell receptor (CD3).

[0229] The term "monocyte" as used herein refers to a subset of immune cells that are generated in bone marrow, migrate to tissues in the body via blood, and become macrophages there. Preferably, monocytes are immature monocytes, intermediate monocytes, or classical monocytes. Immature monocytes are Lys6C and F480 positive. Intermediate monocytes are CD14+ and CD16+. Classical monocytes are CD14+ and CD16-.

[0230] The term "CD4 NK-like T cells," as used herein, refers to a subset of immune cells that are cytotoxic T cells that co-express NK receptors, e.g., CD56, CD16, and / or CD57.

[0231] The term "CD4+ T cells" refers to T helper cells.

[0232] The term "ISG-expressing immune cells" refers to a subset of cells that express interferon-stimulated genes.

[0233] The term "macrophage" refers to a subgroup of phagocytes generated by differentiation of monocytes. The term "tumor-associated macrophages" (TAM) generally refers to macrophages present in the microenvironment of cancer, e.g., tumors.

[0234] The term "neutrophil" refers to a type of granulocyte white blood cell that is a first responder of inflammatory cells. In some cancers and / or precancers, neutrophils may be present within tumors. Such neutrophils may be called tumor-infiltrating neutrophils (TANs). The presence of TANs may be associated with a poor prognosis.

[0235] Preferably, NK cell levels may be increased by at least 100%, at least 150%, at least 200%, etc. Preferably, tumor-associated macrophage levels may be decreased by at least 25%, at least 50%, at least 75%, etc. Preferably, immature monocyte levels may be increased by at least 100%, at least 150%, at least 200%, etc.

[0236] Suitably, CD4+ T cell levels may be increased by at least 20%, at least 50%, at least 100%, at least 200%, etc.

[0237] Suitably, neutrophil levels may be reduced by at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or more.

[0238] Unless otherwise defined herein, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains.For example, Singleton and Sainsbury, Dictionary of Microbiology and Molecular Biology, 2nd Ed., John Wiley and Sons, NY (1994); and Hale and Marham, The Harper Collins Dictionary of Biology, Harper Perennial, NY (1991), provide those skilled in the art with a general dictionary of many of the terms used in this invention.Although any methods and materials similar or equivalent to those described herein can be used in the practice of this invention, preferred methods and materials are described herein.Therefore, the terms defined immediately below will be more fully explained by referring to the entire specification.In addition, as used herein, the singular terms "a", "an", and "the" also encompass plural references unless the context clearly indicates otherwise. Unless otherwise specified, nucleic acids are written left to right in 5' to 3' orientation; amino acid sequences are written left to right in amino-terminus to carboxy-terminus orientation, respectively. It is to be understood that the present invention is not limited to the particular methodology, protocols, and reagents described, as these may vary depending on the context used by those skilled in the art.

[0239] Aspects of the present invention are demonstrated by the following non-limiting examples. [Example]

[0240] Example 1 result We performed the experiments detailed below, and the data generated are shown in the corresponding figures and described in the corresponding figure legends.

[0241] The present inventors previously identified mtDNA mutations as being abundant in cancer (see Figure 1 herein, derived from data from Gorelick et al., 2021). Interestingly, the present inventors observed high levels of recurrent truncating mutations at specific mtDNA locations not previously observed. Most of these were in complex I genes (MT-ND), with ND5 being the most commonly affected. Complex I is part of the respiratory chain and oxidizes NADH to NAD+ and transfers these electrons to ubiquinone (Q) in a two-electron reduction to produce ubiquinol (QH2), the energy of which is involved in pumping protons across the inner mitochondrial membrane.

[0242] To study the role of truncating mutations in ND5, we now engineered DdCBEs (mitochondrial base editors) into the region in ND5 where premature stop codons are likely introduced (see Figure 2). After screening a candidate library in B78 melanoma cells, we identified reagents that efficiently mutated m.12,436G>A and m.11,944G>A in Mt-Nd5, both of which convert tryptophan codons to in-frame stop codons (TGA>TAA).

[0243] Using these reagents, we were able to generate cell lines with either approximately 40% or approximately 60% heteroplasmy (or mutant allele frequency, VAF) of both truncating mutations (see Figure 3). These mutations did not affect mtDNA copy number; however, a mutation-dose-dependent decrease in Ndufb8 protein expression was observed, as would be expected if lower levels of Mt-Nd5 were generated.

[0244] Using native gels, we determined that the levels of intact complex I in 60% VAF cells were reduced compared to parental cells (see Figure 4). However, oxygen consumption rate (OCR) was not affected, and adenylate charge was not adversely affected, suggesting that these cells were not in an energy crisis. Importantly, these cells exhibited altered NAD:NADH ratios, which is expected since complex I is the primary cellular site of NADH oxidation. Comparison of 60% VAF cells using metabolic profiling revealed comparable changes in metabolic profiling between the two genetically distinct models of Mt-Nd5 truncation (see Figure 5).

[0245] The changes in cytosolic components of the malate-aspartate shuttle (MAS) and TCA cycle metabolites potentially related to fumarate processing led us to evaluate these components in the cytosol, as well as components of MAS in mitochondria (see Figure 6). To study cytosol-derived MAS, we used 1- 13 Using a C glutamine trace, this revealed that changes in the NAD+:NADH ratio were associated with increased abundance of malate m+1 and argininosuccinate m+1, but not with the abundance of α-KG m+1, aconitate m+1, or aspartate m+1, indicating the involvement of a) increased reductive carboxylation of glutamine or b) increased production of argininosuccinate (subsequently, fumarate and malate) from aspartate, rather than increased MDH1 flux, which contributed to the increased steady-state abundance of malate.

[0246] The changes in cytosolic components of the malate-aspartate shuttle (MAS) and TCA cycle metabolites potentially related to fumarate processing led us to evaluate components of MAS derived from mitochondria in addition to evaluating these components in the cytosol. 13 Using C glucose, the results showed that the disturbance in the NAD+:NADH ratio resulted in a partial reversal of MDH2 in the mitochondria, as determined by the ratio of pyruvate carboxylase-derived (m+3) malate, citrate to aconitate and pyruvate (see Figure 7).

[0247] The ND5 mutation in these cells is also associated with increased abundance of glycolytic intermediates (see Figure 8, especially Figure 8A). MDH1 has previously been described to promote NADH shuttling between GAPDH and MDH1 (by an unknown but likely physical interaction). We speculated that the increase in cellular NADH could possibly be counteracted by enhanced glucose oxidation and MDH1 regenerating NAD+ through its interaction with GAPDH. When Mdh1 was knocked down using siRNA (see Figure 8B), a significant change in the abundance of glycolytic intermediates was observed, further indicating the involvement of MDH1 activity driven by an NAD+:NADH imbalance in supporting the increased abundance of glycolytic intermediates seen in Figure 8A.

[0248] 4- 2Using H1 glucose tracing, we demonstrated that electrons are preferentially shuttled from glucose to malate in Mt-Nd5 mutant cells, and this is not due to elevated levels of cellular NADH m+1 (see Figure 9). Lactate m+1 is unaffected. This preferential shuttling is abolished upon knockdown of MDH1. Knockdown of MDH1 is also associated with a decrease in glycolytic flux (see Figure 8) and a proportional increase in lactate m+1 labeling in ND5 mutant cells, suggesting that GAPDH may also shuttling electrons to LDH under certain circumstances.

[0249] Figure 10A provides an overview of the metabolic pathways involved. A summary of how these change in the presence of the Mt-Nd5 truncation is shown in Figure 10B. Briefly, high NADH results in the backflow of MDH2 and the accumulation of malate generated in the cytosol via MDH1. This results in elevated fumarate, which in turn leads to increased argininosuccinate synthesis and the production of the fumarate adduct GSH / succinylcysteine ​​(not shown in the schematic for clarity). Increased MDH1 activity drives glycolysis, resulting in excessive glucose consumption and lactate release. While oxygen consumption and ATP synthesis remain unaffected at 60% VAF, they are likely affected at higher VAFs.

[0250] We then characterized the mouse melanoma cells in vivo by implanting them into immunocompetent mice and allowing tumor formation (see experimental design in Figure 11). No significant differences in time to endpoint or tumor weight were observed between wild-type (WT) and mutants or between different VAFs. The difference in VAF between implanted cells and the resulting tumors (estimated from bulk DNA extraction) was not VAF-dependent and showed no clear selection (the downward trend was likely due to stromal contamination), and no observable differences were observed in tumor mtDNA copy number. However, clear metabolic changes were observed between both low- and high-VAF tumors and controls (see Figure 12). Of particular note were succ.Cys and succ.GSH.

[0251] Although the tumors do not appear to be different by the means used so far, when examined by bulk RNA-seq, there are clear and striking differences in the transcriptional profiles between control and high VAF tumors as well as control and low VAF tumors. There are more modest, but still some significant, changes between low and high VAF tumors (see Figure 13).

[0252] Comparison of control and high VAF transcriptional data using gene set enrichment analysis revealed many differentially regulated processes, primarily related to cell-cell interactions, receptor signaling, and the immune system (see Figure 14). Of particular note is natural killer cell-mediated cytotoxicity.

[0253] When comparing low and high VAF tumors, the list of significantly altered GSEA outputs is significantly shorter, now focusing more broadly on the immune system (see Figure 15). Again, of particular note is natural killer cell-mediated cytotoxicity.

[0254] We then profiled tumors using flow cytometry (see Figure 16). Significantly altered populations were demonstrated, with NK cell, TAM, and immature monocyte tumor resident populations appearing to be differentially regulated by tumor mtDNA VAF. Single-cell RNA sequencing further supported these data, showing multiple macrophage, monocyte, and NK cell resident populations differentially regulated by the presence of high VAF mtDNA mutations (see Figure 17).

[0255] These changes in resident immune cells are associated with a pan-tumor interferon-stimulated gene response (see Figure 18), which is thought to be due to natural killer cells, with CD4+ NK-like T cells being the predominant source of interferon-gamma. Interestingly, the only cell populations that do not show an interferon-gamma response are clusters 24 and 25, which are CD4+ NK-like T cells and myeloid dendritic cells, respectively. Dendritic cells are also the major source of interferon-alpha, and interestingly, cluster 25 is one of only two populations that do not show a significantly enhanced interferon-alpha response.

[0256] We then attempted to replicate this in our mice using treatments (e.g., checkpoint blockade agents such as anti-PD1 or anti-CTLA-4 treatments) to analyze the effect of this on how the mice responded. Surprisingly, high VAF mtDNA mutant melanoma tumors were shown to be differentially sensitive to anti-PD1 monoclonal antibody treatment in this highly aggressive model of mouse melanoma (Figure 19). However, they were not sensitive to anti-CTLA4 monoclonal antibody treatment.

[0257] We evaluated whether this correlates with clinical outcomes. By conducting a small clinical cohort study (Riaz et al., 2017), we identified mtDNA mutant tumors with pathogenic mtDNA mutations >50% VAF and calculated that these were 2.5 times more likely to respond to nivolumab (anti-PD1) immunotherapy than mtDNA wild-type tumors or low VAF tumors, with 17% of tumors <50% VAF responding compared with 40% of tumors >50% VAF (see Figure 20). Of the entire metastatic melanoma cohort treated with nivolumab (n = 70), only 15 patients responded to treatment. Of these 15, 12 had a partial response and 3 had a complete response. Surprisingly, two of the three complete responders were classified herein as >50% VAF.

[0258] Thus, the inventors have identified a novel method for identifying cancer subjects who are likely to benefit from anti-PD1 therapy.

[0259] material and method 1. Cell Line Maintenance B78 melanoma cells were cultured in standard Dulbecco's Modified Eagle's Medium (DMEM) (Gibco) containing 4.5 g / L glucose and 110 mg / L sodium pyruvate, along with 20% fetal bovine serum (FBS) (Gibco), 1% penicillin-streptomycin (Gibco), 1X GLUTAMAX (Gibco), and 100 μg / mL uridine (Sigma). Cells were incubated at 37°C and 5% CO and split when approximately 80% confluent.

[0260] 2. Animal Model All animal experiments were performed in accordance with the UK Animals (Scientific Procedures) Act 1986 under PPL P72BA642F. C57BL / 6 mice were used for all studies and were housed in a temperature-controlled (21°C) room with a 12-hour light / dark cycle, with a maximum of five mice per cage. Male mice with an average age of 6 weeks were used.

[0261] 0.25×10 6 The cells were resuspended in 50 μL of 1:1 RPMI (Gibco) and Matrigel® Matrix (Corning). Cells were injected into the flank, and mice were sacrificed at a tumor endpoint of 15 mm. Mice receiving checkpoint blockade therapy were administered 200 mg of Ultra-LEAF™ purified anti-mouse CD279 (PD-1) (Biolegend) via intraperitoneal injection. Mice were dosed 7 days after tumor cell injection and continued to be dosed twice weekly until 21 days after injection, when the mice were sacrificed and tumors were harvested.

[0262] 3. Constructs and Plasmids We designed a transcription activation-like effector (TALE) domain and cloned it into either the pcmCherry or pTracer backbone.

[0263] 4. Primers and siRNA PyroMark assay primers for measuring mt.11944 heteroplasmy Forward: CTTCATTATTAGCCTCTTAC (SEQ ID NO: 1) Reverse: GTCTGAGTGTATATATCATG (SEQ ID NO: 2) Sequencing: CTATTGAATTTATGGTGACT (SEQ ID NO: 3)

[0264] PyroMark assay primers for measuring mt.11944 heteroplasmy Forward: ATATTCTCCAACAACAACG (SEQ ID NO: 4) Reverse: GTTATTATTAGTCGTGAGG (SEQ ID NO: 5) Sequencing: CTATTGCTGATGGTAGT (SEQ ID NO: 6)

[0265] ddPCR EvaGreen primers ND5 forward: TGCCTAGTAATCGGAAGCCTCGC (SEQ ID NO: 7) ND5 reverse: TCAGGCGTTGGTGTTGCAGG (SEQ ID NO: 8) VDAC1 forward: CTCCCACATACGCCGATCTT (SEQ ID NO: 9) VDAC1 reverse: GCCGTAGCCCTTGGTGAAG (SEQ ID NO: 10)

[0266] siRNA for metabolic experiments ON-TARGETplus Mouse MDH1 siRNA-SMART Pool (L-051206-01-0005) ON-TARGETplus non-targeting control pool (D-001810-10-05)

[0267] 5. Antibodies Primary and secondary antibodies for immunoblotting and BN-PAGE All OXPHOS Rodent WB antibody cocktail (ab110413) was used at 1:800 MDH1 polyclonal antibody (15904-1-AP) was used at 1:1000. IRDye® 800CW goat anti-rabbit IgG (Licor) was used at 1:10,000 IRDye® 680RD donkey anti-mouse IgG (Licor) was used at 1:10,000

[0268] Antibodies for flow cytometry All antibodies were purchased from Biolegend and are anti-mouse antibodies.

[0269] [Table 1]

[0270] [Table 2]

[0271] 6. Cell Transfection and FACS B78 cells were seeded in 10 cm dishes to achieve approximately 50% confluency on the day of transfection. 20 μg of DNA was mixed with 40 μL of P3000™ reagent and combined with 30 μL of Lipofectamine™ 3000 in a final volume of 1000 μL of OptiMEM. The transfection reagent was purchased from Invitrogen. A negative control was placed alongside the dish, and the mixture was incubated at room temperature for 15–20 minutes before being added to the dish. Cells were incubated at 37°C and 5% CO2 for 24 hours.

[0272] Cells were prepared for fluorescence-activated cell sorting (FACS) in 1 ml of DMEM and 1 μg / mL of 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI). Live cells were sorted for co-expression of mCherry and GFP and allowed to recover for 10 days before heteroplasmy measurement.

[0273] 7. DNA Extraction The cell culture medium was aspirated, and the cells were washed once with PBS. The cells were detached using 1X trypsin (Gibco), resuspended in cell culture medium, and centrifuged at 300 g for 5 minutes. The pellet was resuspended in 200 μL of PBS for DNA extraction using a DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer's instructions. The DNA concentration was then measured using a NanoDrop.

[0274] Tumor tissue (up to 20 mg) was processed using the DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer's instructions.

[0275] 8. Pyromark PCR Forty nanograms of genomic DNA extracted from cells as described in Section 7 was mixed with 12.5 μL of 5X PyroMark PCR Master Mix, 0.05 μL of 100 μM forward and reverse primers, 2.5 μL of CoralLoad concentrate, and water to a final volume of 25 μL. All reagents were purchased from Qiagen. PCR was performed at 60°C for annealing according to the manufacturer's instructions.

[0276] Pyromark assays were designed using PyroMark Assay Design 2.0 software and were performed in a PyroMark Q48 Autoprep according to the manufacturer's instructions using 10 μL of each PCR product.

[0277] 9. Digital Droplet PCR (ddPCR) 1 ng / μL sample DNA was mixed with 10 μL of ddPCR Supermix for EvaGreen (2X) (BioRad), 110 nM forward and reverse primers, and water to a final volume of 20 μL per well. Samples were prepared in triplicate in a 96-well plate. The plate was sealed at 180°C for 10 seconds using a PX1™ PCR plate sealer (Bio-Rad) and briefly centrifuged to remove air bubbles. An Automated Droplet Generator (Bio-Rad) was used to generate droplets in a new 96-well plate. The plate was resealed and placed in a C1000 Touch Thermal Cycler (Bio-Rad) for PCR. PCR was performed according to the Bio-Rad ddPCR protocol for EvaGreen. Upon completion, DNA was quantified using a QX200™ Droplet Reader (Bio-Rad).

[0278] 10. Immunoblotting Cultured cells were detached and centrifuged at 1000 g. The pellets were washed once with PBS and kept on ice. An appropriate amount of lysis buffer (10 mL of radioimmunoprecipitation assay (RIPA) buffer (Invitrogen), 100 μL of 1% Triton X-100 (Invitrogen), and 100 μL of Halt™ protease and phosphatase inhibitor single-use cocktail (100X) (Invitrogen)) was added to each pellet and left on ice for 10 minutes. The lysate solution was centrifuged at 14,000 g for 5 minutes at 4°C. Protein quantification was performed in a 96-well plate using the Pierce BCA Protein Assay Kit (Invitrogen) according to the manufacturer's instructions.

[0279] Protein samples were prepared to a final concentration of 100 μg in 50 μL. The appropriate amount of supernatant, based on the BCA assay, was mixed with a total volume of 1:4 NuPAGE™ LDS Sample Buffer (4X) (Invitrogen) and a total volume of 1:10 NuPAGE™ Sample Reducing Agent (10X) (Invitrogen). Samples were incubated at 37°C for 20 minutes and then loaded into Bolt™ 4-12% Bis-Tris Plus gels (Invitrogen) containing 1x MOPS buffer. PageRuler™ prestained protein ladder (Invitrogen) was used. Gels were run at 180V until the dye front reached the end of the gel.

[0280] After electrophoresis, proteins were transferred onto a nitrocellulose membrane by wet transfer. The gel was placed in a "transfer sandwich" consisting of a sponge, filter paper, gel, nitrocellulose membrane, filter paper, and sponge in that order. Transfer was performed at 100V for 1 hour using an aqueous solution containing 25 mM Tris, 192 mM glycine (pH 8.3), and 20% methanol as the buffer. The membrane was then washed with 1xTBST and then blocked with 5% nonfat milk in 1xTBST for 1 hour at room temperature on a roller. The solution was then replaced with primary antibody prepared in 5% nonfat milk in 1xTBST. The membrane was left on a roller overnight at 4°C. The next day, the membrane was washed three times with 1xTBST for 5 minutes on a roller at room temperature, after which secondary antibody in 1xTBST was added. The membrane was covered and incubated on a roller at room temperature for 1 hour. The membrane was then washed three times for 5 minutes with 1×TBST before being imaged on a Licor Odyssey Fc Imaging System.

[0281] 11. Blue Native PAGE 11.1 Mitochondrial isolation Approximately 100 x 10 for mitochondrial isolation 6Cells were packed together to obtain 100 cells. Cells were trypsinized and pelleted in a 15 ml Falcon tube. The cell pellet was washed twice with ice-cold PBS, centrifuged at 600 g between washes, and then resuspended in half the volume of Hypotonic Buffer IB 0.1 (3.5 mM Tris-HCl, pH 7.8, 2.5 mM NaCl, 0.5 mM MgCl2). The cell suspension was homogenized using a Dounce homogenizer with 80-100 strokes. One-tenth the original volume of packed cells in Hypertonic Buffer IB 10 (0.35 M Tris-HCl, pH 7.8, 0.25 M NaCl, 50 mM MgCl2) was immediately added to the suspension, and the homogenate was transferred to a clean 15 ml Falcon tube. Isotonic buffer IB 1 (35 mM Tris-HCl, pH 7.8, 25 mM NaCl, 5 mM MgCl) was used to wash the homogenizer to collect excess cells and added to the homogenate. To remove nuclear contamination, the sample was centrifuged at 12,000 g for 3 minutes at 4°C. The supernatant was transferred to a clean tube, and this step was repeated to ensure minimal contamination. The supernatant was then centrifuged at 17,000 g for 2 minutes at 4°C to pellet the mitochondria. The mitochondrial pellet was then washed with homogenization media (0.32 M sucrose, 10 mM Tris-HCl, pH 7.4, 1 mM EDTA) and centrifuged again. The mitochondrial fraction was then immediately used for BN-PAGE.

[0282] 11.2 BN-PAGE gels and imaging All reagents were purchased through Invitrogen.

[0283] The mitochondrial pellet was solubilized in cold 1X NativePage™ sample buffer containing 1% digitonin. Samples were incubated on ice for 15 minutes and then centrifuged at 20,000 g for 30 minutes at 4°C. Protein concentration was determined using the Pierce™ BCA Assay Kit according to the manufacturer's instructions. Samples were prepared up to 100 μg in 50 μl using 1X NativePage™ sample buffer containing 1% digitonin. Immediately before loading, NativePAGE 5% G-250 sample additive was added to each sample to a final concentration of 0.5%.

[0284] NativePage™ Novex 3-12% Bis-Tris gels were used for this experiment. The cassette was removed, and the wells were washed with dark blue cathode buffer (1X NativePage™ running buffer, 1X NativePage™ cathode additive in water). The gel was securely secured in an XCell SureLock Mini-Cell. The outer chamber was filled with approximately 600 mL of anode buffer (950 mL of water, 1X NativePage™ running buffer), and the inner chamber was filled with approximately 200 mL of dark blue cathode buffer. Samples were then loaded into the wells along with NativeMark™ unstained protein standards. The gel was run at 150 V, and when the dye front had descended approximately one-third of the way, the dark blue cathode buffer was switched to light blue cathode buffer (1X NativePage™ running buffer, 0.1X NativePage™ cathode additive in water). The dye front was then allowed to run until it reached the bottom of the cell.

[0285] Proteins were transferred onto a PVDF membrane using the wet transfer method highlighted in section 11. The transfer buffer used in this experiment was 1X NuPage transfer buffer, and transfer was performed at 60V for 1 hour.

[0286] The membrane was then blocked and blotted according to the method in Section 10.

[0287] 12. Seahorse Assay The day before the assay, cells were seeded at 20,000 cells / well into Seahorse XF96 cell culture microplates (Agilent). Cells were seeded along each column, leaving the outer rows and columns empty. Each well of the Seahorse XF96 sensor cartridge microplate was also filled with 200 μL of MQ water and incubated overnight at 37°C with 50 mL of Seahorse XF calibrant solution.

[0288] The next day, the water in the Seahorse XF96 sensor cartridge microplate was discarded and replaced with 200 μL of preheated calibrant. The cartridge was incubated at 37°C for 45–60 minutes. Oligomycin, FCCP, rotenone, and antimycin A were added to separate ports of the Seahorse cartridge to a final concentration of 1 mM. The cartridge was then placed in the Seahorse XF96 analyzer for calibration.

[0289] In parallel, the cell culture plate was washed once with PBS. 150 μL of Seahorse medium (25 mM glucose, 1 mM sodium pyruvate, 2 mM L-glutamine, and 1% FBS in Seahorse XF medium) was added to each well and incubated at 37°C for 30 minutes. After successful calibration, the plate was then placed in the Seahorse XF96 analyzer. Oxygen consumption rate and extracellular acidification rate were measured using the Mito Stress template from the manufacturer's website.

[0290] 13. Metabolomics 13.1 Experimental Media For steady-state metabolomics experiments, cell culture medium containing 2 mM L-glutamine (Gibco) instead of 1X GLUTAMAX was used according to section 1. Plasmax was purchased from Ximbio and supplemented with 2.5% dialyzed FBS for these experiments.

[0291] U- 13 C glucose and 4- 2 H-glucose was prepared using glucose-free DMEM (Gibco) supplemented with 20% FBS, 1 mM sodium pyruvate (Gibco), 100 μg / mL uridine, and 25 mM U- 13 C glucose or 4- 2 H glucose.

[0292] U- 13 C glutamine and 1- 13 C glutamine medium supplemented with 20% FBS, 100 μg / mL uridine, and 4 mM U- 13 C glutamine or 1- 13 C. The medium was prepared using standard DMEM supplemented with either glutamine or DMSO.

[0293] 13.2 Extraction of intracellular and medium metabolites On the day of extraction, cells were seeded in triplicate into 12-well plates to reach approximately 70-80% confluency. The following day, the medium was aspirated and replaced with experimental medium, and the cells were incubated for 24 hours for the next day's extraction.

[0294] All procedures were performed on ice to prevent significant changes in metabolites. For medium analysis, 20 μL of medium from each well was added to 980 μL of ice-cold extraction medium (50% LC / MS-grade methanol, 30% LC / MS-grade acetonitrile, and 20% LC / MS-grade water). The medium was then quickly removed from each plate and washed twice with ice-cold PBS. The plate was gently tapped with tissue paper, and the remaining PBS was aspirated. 200 μL of ice-cold extraction medium was then added to each well. The plate was stored at 4°C, and the extraction medium was transferred to ice-cold microcentrifuge tubes. Samples were centrifuged at 14,000 g for 10 minutes at 4°C and then transferred to screw cap vials. Samples were stored at -80°C until analysis by mass spectrometry using an in-house facility.

[0295] Traces from all experiments were analyzed using Tracefinder 4.0.

[0296] 13.3 Sample Normalization Plates used for extraction were air dried at room temperature and stored at 4°C for up to 2 weeks until protein assays were performed.

[0297] The Lowry assay was used to measure the protein concentration of each well. Briefly, 200 μL of solution A (0.5% sodium deoxycholate and 1 M sodium hydroxide solution) was added to each well, as well as to an additional plate containing a BSA standard curve, and the plates were vigorously shaken at room temperature for 40 minutes. 2 mL of solution B (0.629 mM copper disodium ethylenediaminetetraacetate, 189 mM sodium carbonate, and 200 mM sodium hydroxide) was added, and the plates were shaken for 10 minutes. 200 μL of Folin & Ciocalteu phenol reagent (Sigma) was then added to each well and incubated on a shaker for 40 minutes at room temperature. 200 μL from each well was then transferred to a 96-well plate, and the absorbance at 750 nm was read using a SpectraMax ABS Plus (Molecular Devices). Protein concentrations were calculated from the standard curve and used to normalize the traces.

[0298] 13.4 siRNA knockdown For each siRNA experiment, 12,000 cells were seeded per well of a 12-well plate. Each condition was seeded in triplicate. The next day, 5 μL of 5 μM siRNA was added to 95 μL of Opti-MEM for each well. In a separate tube, 5 μL of DharmaFECT 1 transfection reagent (Horizon Discovery) was added to 95 μL of Opti-MEM. The tube was left at room temperature for 5 minutes to equilibrate and then mixed. The sample was left at room temperature for 15-20 minutes. 800 μL of standard medium was then added to the suspension and added to the cells. After 48 hours, experimental medium was added, and extraction was performed as described in Section 14.2.

[0299] 14. Cell and Tissue Bulk Transcriptomics 1×10 6Cells were pelleted in a 1.5 ml microcentrifuge tube and stored at −80° C. Tumor tissue (approximately 20 mg) was preserved in RNAlater™ stabilization solution (Invitrogen) and kept at −80° C. Samples were then shipped on dry ice to Azenta for sample processing, sequencing, and analysis.

[0300] 15. Flow Cytometry Harvested tumors (approximately 30 mg) were minced and resuspended in digestion buffer (500 U / mL collagenase I, 100 U / mL collagenase IV, and 0.2 mg / mL DNase I in RPMI). Samples were incubated at 37°C for 40 minutes on a shaking rotor. Samples were then passed through a 40 μm filter and centrifuged at 800 g for 3 minutes to pellet the cells. Cells were resuspended in 200 μL of FACS buffer and divided into two wells of a round-bottom 96-well plate. The plate was centrifuged at the same speed, and the supernatant was removed. The cell pellet was resuspended in 100 μL of 1:1000 Zombie Aqua (BioLegend) in PBS. The plate was kept at 4°C for 20 minutes. The plates were centrifuged again and the cell pellets were resuspended in 100 μL of each flow panel prepared in FACS buffer as outlined in section 6. The plates were kept at 4°C for at least 60 minutes. The plates were centrifuged again and the cell pellets were resuspended in 100 μL of 4% Pierce™ 16% formaldehyde (Invitrogen) and incubated at room temperature for 10 minutes. The plates were centrifuged again and the samples were resuspended in 100 μL of FACS buffer. The plates were wrapped in parafilm and aluminum foil and kept at 4°C for up to 2 weeks.

[0301] Fixed samples were resuspended in FACS buffer and transferred to FACS tubes when needed to run. 10 x 10 per sample on the Fortessa 6 Events were recorded and analyzed using FlowJo.

[0302] 16. Tumor Single-Cell RNA Sequencing Tumor tissue was digested according to Section 15. Cells were then resuspended in 1 ml of FACS buffer (2% FBS and 0.5 mM EDTA in PBS) containing 1 μg / mL DAPI. Live cells were sorted and submitted to an in-house facility for barcoding using the 10x Genomics Chromium platform and 3' library preparation kit. Sequencing was performed at the Glasgow Polyomics facility. Single-cell sequencing reads were aligned to the mouse GRCm39 reference genome using CellRanger (version 7.0.1), and the matrix of raw expression counts was analyzed using the Seurate package (version 4.0.6). Malignant and non-malignant cells were further separated based on genome-wide copy number landscape changes estimated by the CopyCat package (version 1.1.0). The cell types of each cluster identified in the Seurate workflow were annotated using SingleR (version 1.10.0) by gene expression correlation comparison with known cell types in the mouse reference dataset. Differential gene expression analyses were performed on log-normalized gene expression using the MAST algorithm within Seurate's FindMarkers function.

[0303] 17. Clinical trial data analysis by Riaz et al. Aligned reads for ChrM from patient tumor and normal samples were obtained and processed according to a previously described pipeline that is now publicly available (see, e.g., Gorelick et al., Nature Metabolism 2021). Using mutant cells and calculated heteroplasmy, patients were assigned to mtDNA wild-type, <50% VAF, or >50% VAF groups.

[0304] The reader's attention is drawn to all articles and documents related to this application that have been filed contemporaneously or previously hereto and that are open to public inspection herewith, the contents of all such articles and documents being incorporated herein by reference.

[0305] All features disclosed in this specification (including all accompanying claims, abstracts, and drawings), and / or all steps of any methods or processes so disclosed, may be combined in any combination, except combinations in which at least some of such features and / or steps are mutually exclusive.

[0306] Each feature disclosed in this specification (including any accompanying claims, abstract, and drawings), unless otherwise stated, may be replaced by alternative features serving the same, equivalent, or similar purpose. Thus, unless otherwise stated, each feature disclosed is only an example of a generic series of equivalent or similar features.

[0307] The invention is not limited to the details of the foregoing embodiments, and extends to any novel or any novel combination of features disclosed in this specification (including all accompanying claims, abstracts, and drawings), or any novel or any novel combination of method or process steps so disclosed.

[0308] array PyroMark assay primers for measuring mt.11944 heteroplasmy Forward: CTTCATTATTAGCCTCTTAC (SEQ ID NO: 1) Reverse: GTCTGAGTGTATATATCATG (SEQ ID NO: 2) Sequencing: CTATTGAATTTATGGTGACT (SEQ ID NO: 3)

[0309] PyroMark assay primers for measuring mt.11944 heteroplasmy Forward: ATATTCTCCAACAACAACG (SEQ ID NO: 4) Reverse: GTTATTATTAGTCGTGAGG (SEQ ID NO: 5) Sequencing: CTATTGCTGATGGTAGT (SEQ ID NO: 6)

[0310] ddPCR EvaGreen primers ND5 forward: TGCCTAGTAATCGGAAGCCTCGC (SEQ ID NO: 7) ND5 reverse: TCAGGCGTTGGTGTTGCAGG (SEQ ID NO: 8) VDAC1 forward: CTCCCACATACGCCGATCTT (SEQ ID NO: 9) VDAC1 reverse: GCCGTAGCCCTTGGTGAAG (SEQ ID NO: 10)

[0311] References Gorelick et al., 2021, Nature Metabolism Apr;3(4):558-570. Riaz et al., 2017 doi: 10.1016 / j.cell.2017.09.028. Epub 2017 Oct 12.

[0312] Example 2 result We induced a premature stop codon at the tryptophan (TGA) codon in mouse mt-Nd5, which resembles a hotspot mutation found in the human MT-ND5 gene in tumors. 1 (Figure 21A-C). TALE-DdCBE G1397 / G1333 candidates, which carry a nuclear export signal and target the m.12,436G>A and m.11,944G>A sites, were synthesized and cultured in murine B78-D14 melanin-deficient melanoma cells (B.16 derivative, Cdkn2a null) to identify efficient pairs. 9Expression of functional pairs (Figure 25A) resulted in isogenic cell populations with approximately 40% or 60% mutation heteroplasmy for the m.12,436G>A or m.11,944G>A truncating mutations (m.12,436G>A, respectively) after either a single transfection or four sequential transfections with limited off-target mutations. 40% , m.12,436 60% , m.11,944 40% , and m. 11,944 60% The resulting stable isogenic cell lines showed a heteroplasmy-dependent reduction in the expression of complex I subunit Ndufb8 (Figure 21E) without affecting other respiratory chain components (Figure 21F). This was confirmed by tandem mass tagging (TMT)-based mass spectrometry proteomics (Figure 26), as well as m.12,436 60% and m.11,944 60% Corroborated by blue native PAGE analysis of cell lines (Figure 12G), we confirmed that the abundance of individual complex I subunits, in addition to the percentage of fully assembled complex I, was reduced without significantly affecting other components of the OXPHOS system. In-gel activity assays of complex I and complex II activity further supported this finding (Figure 21G). mtDNA copy number was not affected by mutation rate or heteroplasmy level (Figure 21H), and mt-Nd5 transcript levels were significantly reduced by m.12,436 compared to controls. 60% and m.11,944 60%There was no change in mutant cells, consistent with the lack of nonsense-mediated reduction in mammalian mitochondria (Figure 27A). Interestingly, heteroplasmic cells did not exhibit significant reductions in oxygen consumption (Figure 21I), adenylate energy charge state (Figure 21J), or cell proliferation (Figure 21K). However, there was a corresponding trend toward a ∼10 mV increase in the chemical component ΔpH of the mitochondrial proton motive force, ΔP, coupled with a corresponding trend toward a ∼10 mV increase in the chemical component ΔpH, resulting in no change in the total proton motive force ΔP. Ψ A decrease of approximately 10 mV in the NAD+:NADH ratio was detected (Figure 27B). The NAD+:NADH ratio was significantly affected in mutant cells (Figure 21L), which was also reflected in the reduced:oxidized glutathione (GSH:GSSG) ratio (Figure 27C). The effect on the cellular redox balance was assessed using NAD(P)H fluorescence at m. 60% and m.11,944 60% We further characterized this in mt-Nd5 cells (Figure 27D). Taken together, these data demonstrate that the truncating mutations in mt-Nd5 exert heteroplasmy-dependent effects on the abundance of complex I. Thus, partial absence of complex I disrupts cellular redox balance without significantly affecting cellular energy homeostasis, oxygen consumption, or proliferation.

[0313] m.12,436 60% and m.11,944 60% Label-free metabolic measurements of the cells revealed consistent differences in metabolite abundance in these cells compared to controls (Figure 5), where the steady-state abundance of malate, lactate, fumarate, argininosuccinate (AS), and the metabolic terminal fumarate adducts succinylcysteine ​​and succinic GSH were significantly increased (Figure 22A). The heteroplasmy-dependent increase in lactate and malate abundance relative to constant succinate in mutant cells suggested that electron flow into mitochondria via the malate-aspartate shuttle (MAS) may be affected by changes in the cellular redox state. To investigate this, we first analyzed U-13 Using C glutamine isotope tracing, we measured the contribution of glutamine-derived carbon to tricarboxylic acid (TCA) cycle metabolites (Figure 28A). This showed an increase in the abundance of malate derived from cytosolic oxaloacetate (OAA), derived from citrate via ATP citrate lyase, as determined by the abundance of malate m+3 and the ratio of malate m+3:m+2, which showed a significant heteroplasmy-dependent increase compared to controls (Figure 28B, C). In this case, a similar pattern of m+3:m+2 labeling was observed for the urea cycle metabolite AS (Figure 28D). We then used 1-C glutamine isotope tracing, which exclusively labels metabolites resulting from reductive carboxylation (RC) of glutamine. 13 We followed the metabolic fate of carbon derived from glutamine (Fig. 22B, Fig. 29A). This revealed that an increase in the abundance of malate m+1 occurred at the level of MDH1 (Fig. 22C), but not the downstream or upstream metabolites aconitate and aspartate (Fig. 29B,C), in which case the m+1 labeling pattern of AS again matched that of malate (Fig. 29D). The increase in the abundance of malate m+1 and AS+1 was sensitive to siRNA-mediated depletion of Mdh1, but not to expression of cytosolically targeted LbNOX (cytoLbNOX), a water-forming NADH oxidase. 10 (Figure 22C, Figure 29E-G), suggesting that the increased abundance of malate occurs, at least in part, in the cytosol via MDH1 but is not directly due to a significant alteration of the cytosolic NAD+:NADH redox balance.

[0314] The rise in intracellular and extracellular lactate, along with the increased abundance of several glycolytic intermediates (Figure 22D), suggested the utilization of pyruvate as an electron acceptor to rebalance NAD+:NADH via lactate dehydrogenase (LDH). 13 By using a 1C glucose trace (Figure 22E), we found that m.12,436 60% and m.11,94460% We observed an increase in the abundance of lactate m+3 in cells (Figure 22F, Figure 30A). The increase in lactate m+3 did not alter pyruvate m+3 levels (Figure 30B) or the entry of glucose-derived carbon into the TCA cycle via pyruvate dehydrogenase (PDH), which is determined by the ratio of citrate m+2:pyruvate m+3 (Figure 30C). However, the fate of carbon entering the TCA cycle via pyruvate carboxylase (PC) was substantially altered, and the ratio of malate m+3:citrate m+3 showed a reversal of MDH2 (Figure 30D). The link between MAS and glycolysis has been a topic of recent interest, and mitochondrial dysfunction can be linked to GAPDH and MDH1 / LDH. 11,12 There have been several reports linking this to NADH shuttling between 4- 2 By using H glucose isotope tracing (Figure 22G), we found that m. 60% and m.11,944 60% We observed an increase in the abundance of malate m+1 in cells, with a similar trend in lactate m+1 abundance, which was sensitive to mitoLbNOX treatment and siRNA-mediated depletion of Mdh1 (Fig. S22H, Fig. S31A, B), supporting the idea that the resulting NAD+:NADH imbalance caused by the partial absence of complex I supports enhanced glycolytic flux by linking the cytosolic portion of MAS to glycolysis. This increase in glycolytic flux is thereby due to the increased abundance of m. 60% Cell (IC 50 =0.81mM±0.064mM) and m.11,944 60% Cell (IC 50 = 1.04 mM ± 0.040 mM) in wild-type cells (IC 50 2-DG (Fig. 22I), which was more sensitive to the competitive phosphoglucoisomerase inhibitor 2-deoxyglucose (2-DG) compared to the phosphoglucosomerase inhibitor 2-DG (m.s. = 1.62 mM ± 0.063 mM). 80% Model (IC 50 =0.46mM±0.080mM). 60% , m.12,436 80%, and m. 11,944 60% The cells also showed enhanced sensitivity to the low affinity complex I inhibitor metformin compared to the wild type (Figure 32A). Interestingly, the 60% mutant was not differentially sensitive to the potent complex I inhibitor rotenone, but m. 80% showed resistance compared to the wild type (Figure 32B). Neither mutant showed differential sensitivity to the complex V inhibitor oligomycin (Figure 32C). Collectively, these data indicate that truncating mutations in mt-Nd5 in complex I induce a Warburg-like metabolic state through redox imbalance, but not an energy crisis. This affected both the cytosolic and mitochondrial components of MAS, increased glycolytic flux, enhanced sensitivity to inhibition of this adaptive metabolic strategy, and resulted in elevated levels of the characteristic terminal fumarate adducts succinate GSH and succinylcysteine.

[0315] Having established the specific changes in redox metabolism driven by the truncating mutation of complex I, we next sought to identify the impact of these metabolic alterations on tumor biology. Allografts of m.11,944G>A cells, m.12,436G>A cells, and wild-type controls were performed subcutaneously in immunocompetent C57 / B16 mice, establishing 100% tumor engraftment. All tumors grew at similar rates to reach a humane endpoint (Figure 23A) and had similar weights and macroscopic histological characteristics (Figure 23B, Figures 33A-C). Bulk measurements of tumor heteroplasmy revealed a similar, slight decrease of approximately 10% heteroplasmy between the transplanted cells and the resulting tumors, likely reflecting stromal and immune cell infiltration (Figure 33D), with no consistent changes in mtDNA copy number detected at the bulk level (Figure 33E). 60%Measurement of metabolites from mutant and control tumors revealed increased abundance of the terminal fumarate adducts succinyl-GSH and succinylcysteine, characteristic of metabolic pathway reorganization observed in vitro (Figure 33F). Consistently, these markers of altered tumor metabolic profiles were associated with distinct transcriptional signatures between control and mutant tumors (Figure 23C), where several signatures of altered immune infiltration and signaling were significantly increased in mutant tumors compared to controls, particularly allograft rejection, interferon gamma (Ifng), and interferon alpha (Ifna) responses. Higher heteroplasmy correlated with increased signal in the same gene sets (Figure 34), suggesting a dose-dependent antitumor immune response of heteroplasmy. To compare and evaluate these findings with human data, we employed the Hartwig Medical Foundation (HMF) metastatic melanoma cohort, which was stratified by pathogenic mtDNA mutation status into wild-type and >50% variant allele frequency (VAF) groups (see Methods section). This resulted in a set of 355 tumor samples (272 wild-type and 83 >50% VAF), of which 233 had transcriptional profiles. GSEA analysis revealed concordant transcriptional phenotypes between patient tumors with high heteroplasmy pathogenic mtDNA mutations and those identified in our model system (Figure 23D), confirming our observations. To further analyze these effects, we compared seven control tumors, three m. 12,436 60% Tumors, 3 m. 11,944 60% tumors, and 3 m.12,436 80%Tumor-specific, whole-tumor single-cell RNA sequencing (scRNAseq) was employed, resulting in 163,343 single-cell transcriptomes. Cells were clustered using Seurat and cellRanger, and preliminary cell identities were determined by scType (see Methods section) (Figure 35E, F). Malignant cells were assigned based on the following criteria: i) low or zero Ptprc (CD45) expression; ii) high epithelial score. 13 ;iii) aneuploidy determined by copy cut analysis 14 Consistent with the transcriptional profile of the bulk tumor, GSEA in malignant cells revealed an increase in Ifna and Ifng signatures associated with a decrease in glycolysis signature in high-heteroplasmy tumors (Figure 23G), which was not observed in vitro prior to transplantation (Figure 36). The downstream regulation of primary metabolism and subsequent immune signaling in malignant cells is also reflected in altered nutrient sensing by mTORC1, transcriptional regulation of metabolic genes by myc, and TNFα signaling (Figure 23G). GSEA in non-malignant cell clusters revealed similar tumor-wide changes in transcriptional phenotype, in this case, increases in Ifna, Ifng, inflammatory responses, and IL2-Stat5 signaling were again observed (Figure 23H-K). These indications of a broad anti-tumor immune response were accompanied by a decrease in neutrophil residency (Figure 3L) and altered monocyte maturation (Figure 37A, B), where a switch in neutrophil metabolic state was indicated by increased OXPHOS gene expression (Figure 23M). An additional set of genes typical of enhanced antitumor responses, such as allograft rejection, was also increased, along with a biphasic trend in the proportions of tumor-resident natural killer cells and CD4+ T cells (Figures 37C-E). Collectively, these data demonstrate that the mt-Nd5 mutation is sufficient to remodel the tumor microenvironment (TME) and promote antitumor immune responses in a heteroplasmy-dependent manner.

[0316] Treatment of malignant melanoma can include immune checkpoint blockade (ICB) with monoclonal antibodies (mAbs) directed against T and B cells expressing the immune checkpoint receptor PD1, which block PD-L1 / 2 binding to limit tumor-induced immune tolerance. However, the efficacy of anti-PD1 therapy, and ICB response in melanoma patients more broadly, is bimodal, with a significant proportion of patients failing to respond to treatment and concurrently experiencing a poor morbidity profile. Limited efficacy of ICB has previously been associated with immunosuppressive tumor-associated neutrophils. 15 Therefore, we reasoned that mt-Nd5 mutant tumors may exhibit differential sensitivity to ICB, even in aggressive models of melanoma with poor immunogenicity, such as B78-D14. Furthermore, depletion of neutrophil populations in mt-Nd5 mutant tumors also exhibited maximal PD-L1 expression (Figure 37F). To verify this, we further investigated the effect of m.12,436 on ICB in immunocompetent animals. 40% , m.12,436 60% , m.12,436 80% , m.11,944 40% , m.11,944 60% Subcutaneous allografts of mtDNA-mutant tumors were performed. Tumors were allowed to grow untreated for 7 days after implantation, and animals were administered anti-PD1 mAb intraperitoneally every 3 days according to the regimen until the end of the experiment (Figure 24A). A heteroplasmy-defined decrease in tumor weight at the endpoint was observed across mtDNA-mutant tumors, where higher mutation heteroplasmy indicated a greater response to treatment (Figures 24B, C, Figure 38), consistent with the increased sensitivity of mtDNA-mutant tumors to immunotherapy. To validate these data, we sought to generate an additional independent model of aggressive, less immunogenic murine melanoma (Figure 39A). This involved the development of a mouse model of Hcmel12 (Hgf, Cdk4) engineered to harbor a >80% m.12,436G>A mutation. R24C ) 16Cells were obtained that displayed a cellular and metabolic phenotype consistent with B78-D14 (Figures 39B-J). 80% and wild-type Hcmel12 cells were transplanted into mice using the same experimental workflow as before (Figure 24D). 80 Hcmel12 and wild-type tumors showed comparable time to endpoint and tumor weight at endpoint (Figure 40A, B). Bulk heteroplasmy, copy number, and changes in tumor metabolism were also similar to those in B78-D14 tumors (Figure 40C-D). Furthermore, upon administration of anti-PD1 treatment, a mtDNA mutation-dependent response comparable to that seen in B78-D14 was observed in Hcmel12 (Figure 44E, F). To decompose the enhanced ICB response into metabolic versus non-metabolic effects of mtDNA mutations, we engineered wild-type Hcmel12 cells to constitutively express cytoLbNOX, which recapitulates key elements of the cell-extrinsic mutant Mt-Nd5-associated metabolic phenotype, specifically glucose uptake and lactate release (Figure 41). When implanted in mice, Hcmel12 cytoLbNOX tumors exhibited comparable time to endpoint and endpoint tumor weight to wild-type or Mt-Nd5 mutant tumors (Figure 40A, B). When anti-PD1 treatment was attempted, Hcmel cytoLbNOX tumors outperformed Hcmel mt-Nd5 m.12,436 80% The tumors responded identically to ICB, suggesting that specific alterations in redox metabolism associated with mtDNA mutations are sufficient to sensitize tumors to ICB (Figure 24E, F). To compare these findings from mice with real-world clinical data, we reanalyzed a large cohort of treatment-naive metastatic melanoma patients who received a previously reported and well-characterized dosing regimen of the anti-PD1 mAb nivolumab. 17By identifying mtDNA-mutated cancers and classifying this patient cohort based solely on the mtDNA mutation status of their cancer (Figure 24G), the 70 patients in this cohort were divided into three groups: mtDNA wild-type (33), <50% VAF (23), and >50% VAF (14). The response rate for the mtDNA mutation-naive cohort was 22% for partial or complete response to nivolumab; however, the response rate for >50% mtDNA-mutated VAF cancers was 2.6-fold higher than for wild-type or <50% VAF cancers (Figure 24H), which replicated our laboratory findings in patients.

[0317] These data confirm that somatic mtDNA mutations, commonly observed in human tumors, can exert direct effects on the metabolic phenotype of cancer cells, as opposed to clinically presented germline mtDNA mutations. 6 Tumor mtDNA mutations can exert these effects at a relatively low heteroplasmic burden and without adversely affecting oxygen consumption or energy homeostasis. The observed direct link between redox perturbations and enhanced glycolytic flux subtly shifts our view of mtDNA mutations to adaptive gain-of-function rather than exclusive loss-of-function events. The discovery that mtDNA mutations can support aerobic glycolysis supports the classical Warburg metabolism. 18 and mtDNA mutation status.

[0318] Beyond the intrinsic effects on cancer cells, our data reveal that a functional consequence of somatic mtDNA mutations in tumor biology is TME remodeling, mediating therapeutic sensitivity to ICB. Similar to those described here, truncating mtDNA mutations affect 10% of all cancers, regardless of histology, whereas non-truncating pathogenic mtDNA mutations are present in an additional 40-50% of all cancers. Broader implications for the antitumor immune response in these cancers can also be anticipated.

[0319] Beyond effectively exploiting the vulnerability of mtDNA-mutant tumors, our data suggest that the ICB response-controlling effects we observe are primarily metabolic in nature. Therefore, recapitulating such metabolic conditions in mtDNA wild-type or "immune cold" tumor types may also be beneficial.

[0320] Furthermore, we showed that pSTAT1 levels were indeed significantly elevated in mitoLbNOX-expressing tumors compared to wild-type tumors. Combined with the rest of the data shown in Figure 50, this suggests that mitoLbNOX may be as effective as cytoLbNOX in response to immunotherapy treatment, if not more effective (since mitoLbNOX tumors grow significantly slower than wild-type tumors in immunocompetent animals in the untreated setting, where cytoLbNOX tumor growth is comparable to wild-type tumors in the untreated immunocompetent setting).

[0321] method Cell line maintenance, transfection, and FACS B78 melanoma cells (RRID:CVCL_8341) and Hcmel12 cells 16 were maintained in DMEM containing GLUTAMAX™, 0.11 g / L sodium pyruvate, 4.5 g / L D-glucose (Life Technologies), and supplemented with 1% penicillin / streptomycin (P / S) (Life Technologies) and 10% FBS (Life Technologies). Cells were cultured in an incubator at 37°C and 5% CO2. Cells were transfected using Lipofectamine 3000 (Life Technologies) at a ratio of 5 μg DNA:7.5 μl Lipofectamine 3000. Cells were transfected using Lipofectamine 3000 (Life Technologies). 19and then cultured in the same basal DMEM medium supplemented with 20% FBS and 100 μg / mL uridine (Sigma).

[0322] Use of animal models Animal experiments were conducted in accordance with the UK Animals (Scientific Procedures) Act 1986 (P72BA642F) and by adhering to the ARRIVE guidelines, approved by the University of Glasgow's local animal welfare and ethical review committee. Mice were housed in conventional cages in the animal care room under a 12-hour light-dark cycle, with controlled temperature (19-23°C) and humidity (55±10%). Only male C57BL / 6 mice approximately 8 weeks old were used in the experiments. 2.5 × 10 mice were cultured in a 1:1 RPMI (Life Technologies) and Matrigel (Merck) solution. 5 1 x 10 B78 cells or 1 x 10 4 HcMel12 cells were subcutaneously injected into the mice. At the endpoint of tumor measurement of 15 mm, the mice were sacrificed.

[0323] For immunotherapy experiments, mice received 200 μg of anti-PD1 intraperitoneally twice weekly, with the first dose administered on day 7 post-injection. All mice were sacrificed on days 21 and 13 post-injection for B78 and HcMel12 cells, respectively.

[0324] Construction of DdCBE plasmid TALEs targeting mt.12,436 and mt.11,944 were designed with the help of Beverly Mok and David Liu (Broad Institute, USA). TALEs were synthesized (ThermoFisher GeneArt) as shown in Figure 1A. In this case, the left TALE was pcDNA3.1(-)_mCherry. 19 The TALE on the right is pTracer CMV / Bsd 19The vectors were cloned into , which allowed for the co-expression of mCherry and GFP, respectively.

[0325] Pyrosequencing assay DNA was extracted from cell pellets using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's instructions. PCR was then performed for 50 cycles using PyroMark PCR Mix (Qiagen) at an annealing temperature of 50°C and an extension time of 30 seconds. PCR products were run on a PyroMark Q48 Autoprep (Qiagen) according to the manufacturer's instructions.

[0326] PCR primers for mt.12,436 Forward: 5'-ATATTCTCCAACAACAACG-3' Reverse: 5'-biotin-GTTATTATTAGTCGTGAGG-3'

[0327] PCR primers for mt.11,944 Forward: 5'-CTTCATTATTAGCCTCTTAC-3' Reverse: 5'-biotin-GTCTGAGTGTATATATCATG-3'

[0328] Sequencing primer for mt.12,436 5'-TTGGCCTCCACCCAT-3'

[0329] Sequencing primers for mt.11,944 5'-TAATTACAACCTGGCACT-3'

[0330] Protein extraction and measurement The cell pellet was dissolved in RIPA buffer (Life Technologies) supplemented with cOmplete Mini tablets and cOmplete Mini protease inhibitor tablets (Roche). The sample was incubated on ice for 20 minutes and then centrifuged at 14,000 g for 20 minutes. The isolated supernatant, containing total cellular protein, was then quantified using the DC protein assay (Bio-Rad Laboratories) according to the manufacturer's instructions.

[0331] Immunoblotting To detect proteins by Western blotting, 60 μg of protein was separated on an SDS-PAGE 4-12% Bis-Tris Bolt gel (Life Technologies). Proteins were transferred to a nitrocellulose membrane using a Mini Trans-Bolt Cell (Bio-Rad Laboratories). To measure loading, the membrane was then stained with Ponceau S staining solution (Life Technologies) followed by overnight incubation with primary antibodies prepared in 5% milk in 1X TBST. Images were captured using an Odyssey DLx imaging system (Licor).

[0332] antibody: Total OXPHOS rodent WB antibody cocktail (1:800, ab110413, Abcam) Monoclonal anti-FLAG® M2 antibody (1:1000, F1804, Sigma) Recombinant anti-vinculin antibody (1:10,000, ab129002, Abcam)

[0333] Isolation of mitochondria Cells were grown in Falcon cell culture 5-layer flasks (Scientific Laboratory Supplies) and allowed to grow to approximately 100% confluency. Cells were then harvested and 20 Mitochondria were extracted as described in.

[0334] Blue Native PAGE Isolated mitochondria were solubilized in 1X NativePage sample buffer supplemented with 1% digitonin (Life Technologies). Samples were incubated on ice for 10 minutes and then centrifuged at 20,000 g for 30 minutes at 4°C. The supernatant was isolated, and total extracted protein was quantified using the DC protein assay (Bio-Rad Laboratories). Samples were prepared and run on NativePage 4-12% Bis-Tris gels according to the manufacturer's instructions (Life Technologies). For immunoblotting, samples were transferred to PVDF membranes using a Mini Trans-Bolt Cell (Bio-Rad Laboratories). Subsequently, probing and imaging were performed as described above for immunoblotting. Loading was visualized using Coomassie blue on duplicate gels.

[0335] Regarding the activity of complexes I and II, 20 In-gel assays were performed as described in.

[0336] Digital Droplet PCR mt-Nd5 primer Forward: 5'-TGCCTAGTAATCGGAAGCCTCGC-3' Reverse: 5'-TCAGGCGTTGGTGTTGCAGG-3'

[0337] VDAC1 primer Forward: 5'-CTCCCACATACGCCGATCTT-3' Reverse: 5'-GCCGTAGCCCTTGGTGAAG-3'

[0338] Samples were prepared in triplicate in a 96-well plate using 1 ng of DNA, 100 nM of each primer, 10 μL of QX200 ddPCR EvaGreen Supermix, and 20 μL of water, and droplet generation, PCR, and measurements were performed in a QX200 Droplet Digital PCR System (Bio-Rad Laboratories) according to the manufacturer's instructions, with the primer annealing temperature set to 60°C.

[0339] Seahorse assay The Seahorse XF Cell Mito Stress Test (Agilent) was performed according to the manufacturer's instructions. Briefly, 1 day before the assay, cells were seeded at 2 × 10 cells / well into a Seahorse 96-well plate. 4 Cells were seeded at 1000 cells / well. The sensor cartridge was also hydrated overnight at 37°C in water. The water was replaced with Seahorse XF calibrator, and the sensor cartridge was re-incubated for 45 minutes. Oligomycin, FCCP, rotenone, and antimycin A were then added to each Seahorse port to a final well concentration of 1 μM, followed by sensor calibration in a Seahorse XF96 analyzer (Agilent). Meanwhile, the cell culture medium was replaced with 150 μL of Seahorse XF medium supplemented with 1% FBS, 25 mM glucose, 1 mM sodium pyruvate, and 2 mM glutamine, and incubated at 37°C for 30 minutes. The cell plate was then inserted into the calibrated analyzer for analysis.

[0340] For read normalization, protein extraction and measurement was performed as described above.

[0341] In vitro metabolomics Cells were seeded two days before metabolite extraction, reaching 70-80% confluency on the day of extraction. Plates were incubated overnight at 37°C and 5% CO2. The following day, cells were replenished with excess fresh medium to prevent starvation at the time of extraction. For steady-state experiments, medium was prepared as described above, using 2 mM L-glutamine instead of GLUTAMAX™. U- 13 C glucose and 4- 2 For H glucose isotope tracing experiments, medium was prepared as follows: glucose-free DMEM (Life Technologies) was supplemented with 0.11 g / L sodium pyruvate, 2 mM L-glutamine, 20% FBS, 100 μg / mL uridine, and 25 mM glucose isotopes (Cambridge Isotopes). 13 C glutamine and 1- 13 For isotope tracing experiments with C glutamine, DMEM containing 4.5 g / L D-glucose and 0.11 g / L sodium pyruvate was supplemented with 20% FBS, 100 μg / mL uridine, and 4 mM glutamine isotope (Cambridge Isotopes).

[0342] On the day of extraction, 20 μL of medium was added to 980 μL of extraction buffer from each well. The cells were then washed twice with ice-cold PBS. Extraction buffer (50:30:20, v / v / v, methanol / acetonitrile / water) was then added to each well (2 × 10 6 The samples were centrifuged at 16,000 g for 10 minutes at 4° C., and the supernatants were transferred to liquid chromatography-mass spectrometry (LC-MS) glass vials and stored at −80° C. until measured in the mass spectrometer.

[0343] Mass spectrometry and subsequent targeted metabolomic analysis were performed using 21 Compound peak areas were normalized using the total measured protein per well, quantified by a modified Lzowry assay. 21 .

[0344] In vitro determination of fumaric acid Samples were prepared as described above. Fumaric acid analysis was performed using a Q Exactive Orbitrap mass spectrometer (Thermo Scientific) connected to an Ultimate 3000 HPLC system (Themo Fisher Scientific). Metabolite separation was performed using a HILIC-Z column (InfinityLab Poroshell 120, 150 × 2.1 mm, 2.7 μm, Agilent) with a mobile phase consisting of a mixture of A (40 mM ammonium formate pH = 3) and B (90% ACN / 10% 40 mM ammonium formate). The flow rate was set at 200 μL / min, and the injection volume was 5 μL. The gradient started with 10% A for 2 min, followed by a linear increase to 90% A over 15 min; then, 90% A was maintained for 2 min, followed by a linear decrease to 10% A over 2 min, with a final re-equilibration step at 10% A over 5 min. The total run time was 25 min. The Q Exactive mass spectrometer was operated in negative mode over the range of 100–150 m / z with a resolution of 70,000 at 200 m / z (automatic gain control (AGC) 1 × 10 6 target and a maximum injection time (IT) of 250 ms).

[0345] siRNA knockdown for metabolomics 1.2×12 4Cells were seeded into 12-well cell culture plates and incubated overnight at 37°C and 5% CO2. The following day, cells were transfected with 5 μL of 5 μM siRNA using 5 μL of DharmaFECT 1 transfection reagent (Horizon Discovery). Cells were transfected with either ON-TARGET plus MDH1 siRNA (L-051206-01-0005, Horizon Discovery) or ON-TARGET plus non-targeting control siRNA (D-001810-10-05, Horizon Discovery). The following day, cells were replenished with excess medium, and metabolites were extracted 48 hours after transfection as outlined above.

[0346] LbNOX processing for metabolomics pUC57-LbNOX (addgene #75285) and pUC57-mitoLbNOX (addgene #74448) were donated. Both enzyme sequences were amplified using Phusion PCR (Life Technologies) according to the manufacturer's instructions. These products were transfected into pcDNA3.1(-)_mCherry via the NheI and BamHI restriction sites. 19 and used in subsequent experiments. Forward for LbNOX: 5'-GGTGGTGCTAGCCGCATGAAGGTCACCG-3' Forward for mitoLbNOX: 5'-GGTGGTGCTAGCCGCATGCTCGCTACAAG-3' Reverse: 5'-GGTGGTGGATCCTTACTTGTCATCGTCATC-3'

[0347] Transfect and sort cells as described above, and plate 3 x 10 cells per well in a 12-well plate. 4 mCherry+ cells were seeded. Cells were allowed to recover overnight at 37°C and 5% CO2, after which excess medium was added to each well. Metabolites were extracted the following day and analyzed as outlined above.

[0348] Bulk tumor metabolomics Tumor fragments (20-40 mg) were snap-frozen on dry ice at the time of collection. Metabolites were extracted using a Precellys Evolution homogenizer (Bertin) with 25 μL of extraction buffer per mg of tissue. Samples were then centrifuged at 16,000 g for 10 minutes at 4°C, and the supernatant was transferred to LC-MS glass vials and stored at -80°C until analysis.

[0349] The sample was subjected to LC-MS, and then 21 Targeted metabolomics analysis was performed as described in. Compound peak areas were normalized using tissue mass.

[0350] Calculation of cellular sensitivity to 2-DG Cells were seeded in 96-well plates at 500 cells / well in 200 μL of cell culture medium. Plates were incubated overnight at 37°C and 5% CO2. The next day, medium was replaced with 0-100 mM 2-DG in quadruplicate. Plates were imaged every 4 hours for 5 days on an IncuCyte Zoom (Essen Bioscience). Final confluency measurements were calculated using the system algorithm and expressed as IC 50 were identified by GraphPad Prism.

[0351] Bulk tumor RNAseq Tumor fragments (20-40 mg) were stored in RNAlater (Sigma) and stored at −80° C. Samples were sent to GeneWiz Technologies for RNA extraction and sequencing.

[0352] HcMel12 transduction cytoLbNOX was cloned into the lentiviral plasmid pLex303 via the NheI and BamHI restriction sites, 22Transduction of HcMel12 was performed as described in. Transduced cells were selected by supplementation with 8 μg / mL blasticidin, and single clones were selected from the surviving population. Expression of cytoLbNOX was confirmed using immunoblotting.

[0353] pLEX303 was a gift from David Bryant (Addgene plasmid #162032; http: / / n2t.net / addgene:162032; RRID:Addgene_162032).

[0354] Hartwig dataset analysis The Hartwig Medical Foundation (HMF) dataset contains WGS data from tumor-metastasis and healthy matched samples from 355 melanoma patients (cutaneous primary tumor site), of which 233 had additional RNA sequencing data for tumor samples. mtDNA somatic mutations were analyzed as previously described. 1The variants were called and annotated. Briefly, variants called by both Mutect2 and samtools mpileup were retained and merged using vcf2maf, which was then embedded with the Variant Effect Predictor (VEP) variant annotator. Variants within repeat regions (chrM:302-315, chrM:513-525, and chrM:3105-3109) were excluded. Next, variants were excluded if their variant allele fraction (VAF) was less than 1% in tumor samples and less than 0.24% in healthy samples, as previously described (Yuan et al., 2020). Finally, somatic variants were retained if they were supported by at least one read in both the forward and reverse directions. Samples with >50% VAF mtDNA complex I truncating mutations (frameshift indels, translation start site, and nonsense mutations) and missense mutations were classified as mutant, and the rest were classified as wild-type. Gene expression data were obtained from the output generated by the isofox pipeline provided by HMF. The adjusted transcript per million ("adjTPM") gene counts per sample were integrated into a matrix. Gene expression and mutation data were used to perform differential expression analysis with DESeq2 in R using the DESeqDataSetFromMatrix function. Gene set enrichment analysis (GSEA) was performed on the mSigDB Hallmark gene set collection (v.7.5.1) using fGSEA in R with a minimum set size of 15 genes, a maximum set size of 500 genes, and 20,000 permutations. Normalized enrichment scores (NES) were ranked for significantly up- and down-regulated gene sets.

[0355] statistical methods No statistical tests were used to determine sample size. Mice were randomly assigned to different experimental groups. Samples were blinded to the machine operators (metabolomics, proteomics, RNAseq). Researchers were blinded to the experimental groups for the in vivo anti-PD1 experiments. Specific statistical tests were used to determine significance, and group sizes (n) and P values ​​are provided in the figure legends. P values ​​of <0.05, <0.01, and <0.001 are indicated in the figures as *, **, and ***, respectively. All statistical analyses were performed using Prism (GraphPad) and Rstudio.

[0356] Data and Code Availability Statement All non-commercial plasmids used have been deposited at addgene (Gammage Lab). All metabolomics data, mtDNA sequencing, whole- and single-cell RNAseq, and proteomic data included in this study are available in the Supplementary Information or from designated public repositories.

[0357] mtDNA sequencing Cellular DNA was amplified using PrimeStar GXL DNA polymerase (Takara Bio) according to the manufacturer's instructions to generate two approximately 8-kbp overlapping mtDNA products.

[0358] Primer Forward 1: 5'-ACTGATATTACTATCCCTAGGAGG-3' Reverse 1: 5'-TTTGAGTAGAACCCTGTTAGG-3' Forward 2: 5'-GGCCTGATAATAGTGACGC-3' Reverse 2: 5'-GGTTGGGTTTAGTTTTTGTTTGG-3'

[0359] The resulting amplicons were sequenced using the Illumina Nextera kit (150 cycles, paired end). To determine the proportion of non-targeted C mutations in mtDNA, we first identified all C / G nucleotides with adequate sequencing coverage (>1000X) in both the reference and experimental samples. Then, for each of the four experimental samples, we identified positions in the experimental samples where sequencing reads corresponded to G>A / C>T mutations. We further filtered the resulting list of mutations to retain only those with heteroplasmy greater than 2% and excluded mutations also present in the control sample. Finally, the proportion of non-targeted C / G positions was calculated as the proportion of all possible mutated C / G positions.

[0360] Sample preparation for MS analysis Cells were lysed in a buffer containing 4% SDS in 100 mM Tris-HCl and 55 mM iodoacetamide at pH 7.5, with minor modifications. 23 Samples were prepared as previously described. First, alkylated proteins were digested with endoprotease Lys-C (1:33 enzyme:lysate) for 1 hour, followed by overnight digestion with trypsin (1:33 enzyme:lysate). Digested peptides from each experimental condition and pooled samples were differentially labeled using TMT16-plex reagent (Thermo Scientific) according to the manufacturer's instructions. Equal amounts of fully labeled samples were mixed and desalted using 100 mg Sep Pak C18 reversed-phase solid-phase extraction cartridges (Waters). TMT-labeled peptides were fractionated using high-pH reversed-phase chromatography on a C18 column (150 × 2.1 mm id-Kinetex EVO (5 μm, 100 Å)) on an HPLC system (LC 1260 Infinity II, Agilent). A two-step gradient was applied, increasing from 1% to 28% B (80% acetonitrile) over 42 min, then from 28% to 46% B over 13 min, to obtain a total of 21 fractions for MS analysis.

[0361] UHPLC-MS / MS analysis Peptides were separated by nanoscale C18 reversed-phase liquid chromatography using an EASY-nLC II 1200 (Thermo Scientific) coupled to an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific). Elution was performed using a binary gradient of buffer A (water) and buffer B (80% acetonitrile), both containing 0.1% formic acid. Samples were loaded with 6 μl of buffer A onto a 50 cm fused silica emitter (New Objective) packed in-house with ReproSil-Pur C18-AQ, 1.9 μm resin (Dr Maisch GmbH). The packed emitter was maintained at 50 °C by a column oven (Sonation) integrated into a nanoelectrospray ion source (Thermo Scientific). Peptides were eluted at a flow rate of 300 nl / min using different gradients optimized for three fraction sets: 1–7, 8–15, and 16–21. 23Each fraction was acquired over a 185-minute period. Eluted peptides were electrosprayed into a mass spectrometer using a nanoelectrospray ion source (Thermo Scientific). An Active Background Ion Reduction Device (ESI Source Solutions) was used to reduce the signal level of airborne contaminants. Data acquisition was performed using Xcalibur software (Thermo Scientific). Full scans over the mass range of 350–1400 m / z were acquired at a resolution of 60,000 at 200 m / z with a target value of 500,000 ions for a maximum injection time of 50 ms. Higher-energy collisional dissociation fragmentation was performed on the most intense ions during a 3-second cycle time with a maximum injection time of 120 ms or a target value of 100,000 ions. Peptide fragments were analyzed in the Orbitrap at a resolution of 50,000.

[0362] Proteomics data analysis MS Raw data were analyzed using MaxQuant software 24 Processed by v.1.6.1.4 and Andromeda search engine 25 Search by SwissProt 26A query was performed against Mus musculus (25,198 entries). The initial and main searches were performed with precursor mass tolerances of 20 ppm and 4.5 ppm, respectively, and an MS / MS tolerance of 20 ppm. The minimum peptide length was set to 6 amino acids, requiring specificity for tryptic cleavage, which allowed for up to two missing cleavage sites. MaxQuant was configured to quantify in "Reporter ion MS2," with TMT16plex set as the isobaric label. Interference between TMT channels was corrected by MaxQuant using the correction factor provided by the manufacturer. A "Reporter ion tolerance" of 0.003 Da was used with the "Filter by PIF" option enabled. Iodoacetamide modifications on cysteine ​​residues (carbamidomethylation) were specified as variables, as were methionine oxidation and N-terminal acetylation modifications. The false discovery rate (FDR) for peptides, proteins, and sites was set to 1%. The MaxQuant output ProteinGroup.txt file was imported into the Perseus software. 27 Protein quantification analysis was performed using Perseus's LIMMA plugin (version 1.6.13.0). The dataset was filtered to remove potential contamination, reverse peptides matching the decoy database, and proteins identified only by site. Only proteins with at least one unique peptide and quantified in all replicates of at least one experimental group were used for analysis. Missing values ​​were added for each column individually. Protein TMT-corrected intensities were first normalized by the median of all intensities measured in each replicate, and then analyzed using the LIMMA plugin in Perseus. 28 Proteins significantly regulated between the two groups were selected using a permutation-based Student's t-test with an FDR set to 1%.

[0363] Mitochondrial membrane potential and pH gradient The membrane potential and pH gradient are 29~30Briefly, cultured cells were detached by tapping, then centrifuged, and 1 × 10 cells were placed on FluroBrite supplemented with 2 mM glutamine in a temperature-controlled chamber. 7 The cells were resuspended at a density of 1000 cells / mL. Changes in the oxidation state of mitochondrial cytochromes were then measured by multi-wavelength spectroscopy. Baseline oxidation states were measured by back-calculation using anoxic conditions to fully reduce the cytochromes and a combination of 4 μM FCCP and 1 μM rotenone to fully oxidize the cytochromes. The membrane potential was then calculated from the redox equilibrium of the b-heme in the bc1 complex, and a turnover model was used. 30 The pH gradient was measured from the turnover rate and redox span of the bc1 complex using .

[0364] Mitochondrial NADH oxidation state Changes in NAD(P)H fluorescence were measured simultaneously with mitochondrial membrane potential using 365 nm excitation. The resulting emission spectra were then analyzed by multi-wavelength spectroscopy. 29 Assuming that the cytosolic NADH and NADPH pools were unchanged by these interventions and in the short term, the baseline oxidation state of the mitochondrial NADH pool was back-calculated using anoxic conditions to completely reduce and 4 μM FCCP to completely oxidize the mitochondrial NADH pool, respectively.

[0365] H&E staining Hematoxylin and eosin (H&E) staining and slide scanning were performed. 31 was carried out as described in.

[0366] scRNAseq methodology 1- Preprocessing, batch effect correction, and clustering of single-cell RNA transcriptomics data CellRanger (v.7.0.1) was used to align the reads in the FASTQ files with the mouse reference genome (GRCm39).32 CellRanger 33 The Seurat (v.4.2.0) package in R (v.4.2.1) was used to process the preprocessed gene count matrix generated by [ 1 ]. As an initial quality control step, cells with fewer than 200 genes and genes expressed in fewer than three cells were filtered out. Cells with a mitochondrial count of >5%, a UMI count of >37,000, and a gene count of <500 were then filtered out. The filtered gene count matrix (31,647 genes and 127,356 cells) was normalized using the NormalizeData function using the log(normalization) method and a scale factor of 10,000. The FindVariableFeatures function was used to identify 2,000 highly variable genes for principal component analysis. The first 50 principal components were selected for downstream analysis. Batch effects were corrected using the RunHarmony function in the harmony package (v.0.1.0) with default parameters. 34 The RunUMAP function with reduction from "harmony" was used to generate UMAPs for cluster analysis. The FindClusters function was used with the resolution parameter set to 1.6.

[0367] 2-Epithelial score The average gene expression from cytokeratin, Epcan, and Sfn was used to calculate the epithelial score.

[0368] 3- Single-cell copy number estimation Copy number status of each cell was estimated using CopyKat (v.1.1.0). 14 Parameters were set as ingene.chr=5, win.size=25, KS.cut=0.1, genome="mm10", and cells were annotated as T cells or NK cells in UMAP as diploid reference cells.

[0369] 4- Identification of differentially expressed marker genes The top differentially expressed genes in each cluster were identified using the FindAllMarkers function in the Seurat R package. The parameter for differential expression was set to a fold change of at least 1.25 (logfc.threshold=1.25) and adjusted to a p-value <0.05 (min.pct=0.1) with gene expression detected in at least 10% of cells in each cluster. The top 20 differentially expressed genes in each cluster, ranked by average fold change, were defined as marker genes.

[0370] 5. Pathway enrichment analysis of single-cell transcriptomics data For cells in each identified cluster in UMAP, a Wilcox rank sum test was performed using the wilcoxauc function in the presto R package (version 1.0.0), and fold changes and p values ​​of all genes between cells in the high heteroplasmy group relative to both the mutant and control groups were obtained. 35 Genes are expressed as sign(log2FC)*(-log 10 The genes were ranked in descending order according to p-value (p-value). This ranked gene list, and the mouse hallmark pathway (mh.all.v2002.1.Mm.symbols.gmt) from the MSigDB database, were combined into a single database with eps=0, minSize=5, maxSize=500. 36 were used as input for gene set enrichment analysis using the fgsea function in the fgsea R package (v.1.22.0) with the parameters

[0371] References 1. Gorelick, AN et al. Respiratory complex and tissue lineage drive recurrent mutations in tumor mtDNA. Nat. Metab. (2021) doi:10.1038 / s42255-021-00378-8. 2. Hopkins, J. F. et al. Mitochondrial mutations drive prostate cancer aggression. Nat. Commun. (2017) doi:10.1038 / s41467-017-00377-y. 3. Schopf, B. et al. OXPHOS remodeling in high-grade prostate cancer involves mtDNA mutations and increased succinate oxidation. Nat. Commun. 11, (2020). 4. Mok, B. Y. et al. A bacterial cytidine deaminase toxin enables CRISPR-free mitochondrial base editing. Nature 583, 631-637 (2020). 5. Kim, M., Mahmood, M., Reznik, E. & Gammage, P. A. Mitochondrial DNA is a major source of driver mutations in cancer. Trends in Cancer 8, 1046-1059 (2022). 6. Gorman, G. S. et al. Mitochondrial diseases. Nat. Rev. Dis. Prim. (2016) doi:10.1038 / nrdp.2016.80. 7. Yuan, Y. et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat. Genet. 52, 342-352 (2020). 8. Guerrero-Castillo, S. et al. The Assembly Pathway of Mitochondrial Respiratory Chain Complex I. Cell Metab. (2017) doi:10.1016 / j.cmet.2016.09.002. 9. Graf, L. H., Kaplan, P. & Silagi, S. Efficient DNA-mediated transfer of selectable genes and unselected sequences into differentiated and undifferentiated mouse melanoma clones. Somat. Cell Mol. Genet. (1984) doi:10.1007 / BF01534903. 10. Titov, D. V. et al. Complementation of mitochondrial electron transport chain by manipulation of the NAD+ / NADH ratio. Science (80-. ). (2016) doi:10.1126 / science.aad4017. 11. Gaude, E. et al. NADH Shuttling Couples Cytosolic Reductive Carboxylation of Glutamine with Glycolysis in Cells with Mitochondrial Dysfunction. Mol. Cell 69, 581-593.e7 (2018). 12. Wang, Y. et al. Saturation of the mitochondrial NADH shuttles drives aerobic glycolysis in proliferating cells. Mol. Cell 82, 3270-3283.e9 (2022). 13. Dong, J. et al. Single-cell RNA-seq analysis unveils a prevalent epithelial / mesenchymal hybrid state during mouse organogenesis. Genome Biol. (2018) doi:10.1186 / s13059-018-1416-2. 14. Gao, R. et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat. Biotechnol. (2021) doi:10.1038 / s41587-020-00795-2. 15. Coffelt, S. B., Wellenstein, M. D. & De Visser, K. E. Neutrophils in cancer: Neutral no more. Nat. Rev. Cancer 16, 431-446 (2016). 16. Bald, T. et al. Ultraviolet-radiation-induced inflammation promotes angiotropism and metastasis in melanoma. Nature 507, 109-113 (2014). 17. Riaz, N. et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell (2017) doi:10.1016 / j.cell.2017.09.028. 18. DeBerardinis, R. J. & Chandel, N. S. We need to talk about the Warburg effect. Nat. Metab. 2, 127-129 (2020). 19. Gammage, P. A., Van Haute, L. & Minczuk, M. Engineered mtZFNs for manipulation of human mitochondrial DNA heteroplasmy. in Methods in Molecular Biology vol. 1351 145-162 (Humana Press Inc., 2016). 20. Fernandez-Vizarra, E. & Zeviani, M. Blue-Native Electrophoresis to Study the OXPHOS Complexes. in Methods in Molecular Biology (2021). doi:10.1007 / 978-1-0716-0834-0_20. 21. Villar, V. H. et al. Hepatic glutamine synthetase controls N 5-methylglutamine in homeostasis and cancer. Nat. Chem. Biol. (2022) doi:10.1038 / s41589-022-01154-9. 22. Nacke, M. et al. An ARF GTPase module promoting invasion and metastasis through regulating phosphoinositide metabolism. Nat. Commun. (2021) doi:10.1038 / s41467-021-21847-4. 23. Cao, X. et al. The mammalian cytosolic thioredoxin reductase pathway acts via a membrane protein to reduce ER-localised proteins. J. Cell Sci. 133, (2020). 24. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, (2008). 25. Cox, J. et al. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794-1805 (2011). 26. Apweiler, R. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res. 38, (2010). 27. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods (2016) doi:10.1038 / nmeth.3901. 28. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). 29. Rocha, M. & Springett, R. Measuring the functionality of the mitochondrial pumping complexes with multi-wavelength spectroscopy. Biochim. Biophys. Acta - Bioenerg. 1860, 89-101 (2019). 30. Kim, N., Ripple, M. O. & Springett, R. Measurement of the mitochondrial membrane potential and pH gradient from the redox poise of the hemes of the bc 1 complex. Biophys. J. 102, 1194-1203 (2012). 31. Papalazarou, V., Drew, J., Juin, A., Spence, H. J. & Nixon, C. Collagen-VI expression is negatively mechanosensitive in pancreatic cancer cells and supports the metastatic niche. J. Cell Sci. 135, (2022). 32. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 2017 81 8, 1-12 (2017). 33. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587.e29 (2021). 34. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 2019 1612 16, 1289-1296 (2019). 35. Korsunsky, I., Nathan, A., Millard, N. & Raychaudhuri, S. Presto scales Wilcoxon and auROC analyzes to millions of observations. bioRxiv 653253 (2019). 36. Korotkevich, G. & Sukhov, V. Fast gene set enrichment analysis. bioRxiv 1-29 (2016).

[0372] Example 2 The influence of tumor-infiltrating neutrophils on the response to immune checkpoint inhibitors Tumor-associated neutrophils may function to suppress melanoma patients' responses to ICB. Indeed, scRNA sequencing of B78-D14 mutant tumors revealed a significant decrease in tumor neutrophil percentage (data not shown). This was further confirmed using flow cytometry (Figure 42A-C), where we observed a 3% decrease in neutrophil infiltration in Hcmel12 m.12 and cytoLbNOX tumors compared to wild-type tumors (Figure 42D). This was reflected in tumor-draining lymph nodes (Figure 42E). Interestingly, an increase in CD4+ T cells was observed in tumors and associated lymph nodes (Figure 42D, E), whereas only an increase in CD8+ T cells, NK cells, and macrophages was observed in cytoLbNOX tumors (Figure 3D, 4E). Conversely, in the spleen, the proportions of NK cells and neutrophils were increased in mice allografted with mtDNA mutant and cytoLbNOX tumors compared with wild-type (FIG. 42F).

[0373] To determine whether neutrophil depletion was necessary for the observed sensitivity to ICB, we chose to manipulate the proportion of tumor-associated neutrophils using G-CSF and anti-Ly6G treatment. 83%, and cytoLbNOX cells were allografted into C57BL / 6 mice and treated with G-CSF or anti-Ly6G in the presence or absence of anti-PD1 (Figure 43A).

[0374] As expected, we observed an increase in tumor-associated neutrophils across genotypes using G-CSF, whereas anti-Ly6G significantly reduced the percentage of neutrophils (Fig. 43B, C). This did not affect tumor weight in untreated tumors when measured at the same endpoint (Fig. 43D). Surprisingly, G-CSF treatment significantly reduced the m.12,436 response to anti-PD1. 83% and cytoLbNOX tumors abolished their response (Figure 43E). Conversely, depletion of tumor-associated neutrophils sensitized wild-type tumors to ICB treatment (Figure 43F). Taken together, we demonstrate that tumor-associated neutrophils modulate and negatively regulate the response to anti-PD1 therapy.

[0375] These results may justify the use of agents that alter the lactate-to-glucose ratio (such as cytoLbNOX or another NADH oxidase) in combination with tumor-resident neutrophil depletion agents (such as anti-Ly6G antibodies).

[0376] material and method Use of animal models Animal experiments were conducted in accordance with the UK Animals (Scientific Procedures) Act 1986 (P72BA642F) and by adhering to the ARRIVE guidelines, which were approved by the local animal welfare and ethical review committee at the University of Glasgow. Mice were housed in conventional cages in the animal care room under a 12-hour light-dark cycle, with controlled temperature (19-23°C) and humidity (55±10%). Only male C57BL / 6 mice approximately 8 weeks old were used in the experiments. Both mice were kept in 2.5 × 10 PBS-prepared cultures. 5 1 x 10 B78 cells or 1 x 10 4 HcMel12 cells were subcutaneously injected into the mice. At the endpoint of tumor measurement of 15 mm, the mice were sacrificed.

[0377] For immunotherapy experiments, mice received 200 μg of anti-PD1 intraperitoneally twice weekly, with the first dose administered on day 7 post-injection. All mice were sacrificed on days 21 and 13 post-injection for B78 and HcMel12 cells, respectively.

[0378] For neutrophil depletion experiments, mice were intraperitoneally administered 5 μg of mouse recombinant G-CSF (Stemcell) or 100 μg of anti-mouse Ly6G-clone 1A8 (2B Scientific) every 2 days after transplantation.

[0379] Example 3 Response of surrogate models of melanoma to immune checkpoint inhibitors Surprisingly, cytoLbNOX tumors were sensitive to anti-PD1 therapy, whereas catalytically impaired mutant tumors demonstrated no role for redox dysfunction alone in immunotherapy. CytoLbNOX tumor weight was observed to be <50% of that of mtDNA mutant tumors (Figure 44A-C), which was reflected in anti-PDL1 treatment (Figure 44A-C). Interestingly, anti-CTLA4 therapy, which controls tumor growth through spatially and temporally distinct mechanisms, resulted in no difference in tumor weight reduction between mtDNA mutant and cytoLbNOX tumors (Figure 44A-C). Anti-PD1 therapy to an extended humane endpoint in Hcmel12 wild-type, m.12436 80% Further treatment of cytoLbNOX and cytoLbNOX tumors resulted in limited survival benefit in mice bearing mtDNA mutant tumors, whereas the majority of cytoLbNOX tumors showed complete regression (Figure 44D). Tumors that reached the 15 mm endpoint did not differ in tumor weight or volume (Figure 44E-G).

[0380] Both immunogenic 4434 wild-type and m.12,436 tumors responded to anti-PD1 therapy, but the mtDNA mutant tumors completely regressed by day 20, while wild-type tumor weights remained measurable, maintaining our observation of differential responses similar to those observed in the non-immunogenic B78-D14 and Hcmel12 models (Figure 45). Overall, the data suggest that truncation of complex I induces differential heteroplasmic dose-dependent responses to immunotherapy, regardless of melanoma cell lineage, and that the mechanism may be mediated by altered redox balance, as observed through the use of cytoLbNOX-expressing Hcmel12 cells.

[0381] These results indicate that treatment with agents that alter the lactate-to-glucose ratio in cancers or precancers can increase the sensitivity of cancers or precancers that are already sensitive (at least to some extent) to immune checkpoint inhibitors.

[0382] Example 4 Sensitivity of contralateral WT tumors to checkpoint inhibitors We tested whether the reconstitution of the immune environment extends beyond the tumor niche in a mouse model of melanoma. Mice were subcutaneously injected in the opposite flank with Hcmel12 cells of either the same or different genotype, treated with anti-PD1 according to the same regimen as previously described (Fig. 46A). 83% Both cytoLbNOX and cytoLbNOX tumors responded to immunotherapy when injected into each flank of the same mouse, whereas wild-type tumors did not (Figure 46B-D). Surprisingly, however, wild-type tumors were significantly more potent than m.12,436 tumors in the same mouse. 83% When injected into the contralateral side of the tumor or cytoLbNOX tumor, they were sensitized to anti-PD1 treatment (Figure 46B-D). 83%Comparison of the weight of wild-type tumors when injected contralaterally with cytoLbNOX tumors reveals a ~50% reduction in tumor weight compared to wild-type tumors implanted contralaterally (Figure 46E). Interestingly, the reduction in wild-type tumor weight was observed in mice injected with m.12,436. 83% No difference was observed when either cytoLbNOX or cytoLbNOX were injected contralaterally, suggesting that both genotypes have similar effects on the systemic immune system (Figure 46E).

[0383] Analysis of immune cell populations in circulating blood collected prior to endpoint did not reveal any gross changes in cellular proportions (Figure 46F). However, further flow cytometry analysis of tumor immune populations revealed that m.12,436 genotypes were significantly increased when injected into the contralateral side of the tumor. 83% We found a significant increase in CD4+ T cells in both cytoLbNOX and wild-type tumors (Fig. 3, 47A). Conversely, NK T cells and CD8+ T cells showed no significant changes in tumor-associated populations across samples (Fig. 46B, C). Due to the size of cytoLbNOX tumors when injected into both the right and left flanks, they could not be used to estimate lymphoid cell populations.

[0384] Characterization of bone marrow cells revealed a decrease in TAMs and neutrophils, while the monocyte population in responding tumors was increased compared to non-responding wild-type tumors, although the changes were less pronounced than those observed in CD4+ T cells (Figure 4D-F).

[0385] Taken together, these data suggest the emergence of a long-range effect after treatment with anti-PD1, which is consistent with m.12,436 83% and a systemic change in the immune environment as indicated by similar changes in the proportion of immune cells in wild-type tumors when injected contralaterally with cytoLbNOX, but not when injected contralaterally with wild-type.

[0386] Example 5 mtDNA mutations in complex 4 Mt-Co1 is a mitochondrially encoded subunit of Complex IV. We engineered a DddA-derived cytosine base editor (DdCBE) to introduce a G>A point mutation into this protein at position m.6214 of the mouse mitochondrial genome.

[0387] When implanted into Bl6 mice, these tumors grew at a rate comparable to wild-type tumors and reached comparable endpoint weights in similar times (Figures 48A-B).

[0388] When challenged with anti-PD1, Mt-Co1 mutant tumors showed a significant decrease in size at endpoint compared with wild-type tumors. This heteroplasmy was significantly lower than that required for a robust immune response to the Mt-Nd5 truncation, likely due to the more severe effect on the respiratory chain caused by loss of complex IV (Figure 4C).

[0389] These results demonstrate that mutations in complexes other than complex I result in tumor sensitization to checkpoint inhibitors, further supporting the important role that intracellular metabolic changes play in sensitizing tumors to checkpoint inhibitors.

[0390] Example 6 Increased response to anti-CTLA4 and anti-PD-L1 therapy As seen in Figure 44B, 12,436 83% Tumors, as well as tumors expressing cytoLbNOX, were significantly smaller upon treatment with various types of immune checkpoint inhibitors (e.g., PD1 inhibitors, PD-L1 inhibitors, and CTLA4 inhibitors).

Claims

1. An agent that alters the redox state in a cancer or pre-cancer for use in sensitizing a subject having cancer or pre-cancer to an immune checkpoint inhibitor.

2. 1. An immune checkpoint inhibitor for use in treating a subject having cancer or precancer, wherein the subject has been exposed to an agent that alters the redox state in the cancer or precancer.

3. 1. A method of sensitizing a subject having cancer or precancer to an immune checkpoint inhibitor, the method comprising exposing the subject to an agent that alters the redox state in the cancer or precancer.

4. 1. A method of treating cancer or precancer in a subject, the method comprising administering an immune checkpoint inhibitor to the subject, wherein the subject has been exposed to an agent that alters the redox status in the cancer or precancer.

5. 1. A method of treating cancer or precancer in a subject, comprising: (i) exposing the subject to an agent that alters the redox state in the cancer or precancer; and (ii) administering to the subject an immune checkpoint inhibitor. A method comprising:

6. 6. The agent for use according to claim 1, the inhibitor for use according to claim 2, or the method according to any one of claims 3 to 5, wherein the agent that alters the redox state alters the lactate to glucose ratio in the cancer or precancer.

7. 7. The agent for use, inhibitor for use, or method of claim 6, wherein the agent increases the lactate to glucose ratio, and the increase in lactate to glucose ratio may be an increase to greater than 3:

1.

8. 10. The agent for use, the inhibitor for use, or the method according to any preceding claim, wherein the cancer or precancerous sample has a deleterious mitochondrial DNA (mtDNA) mutation load of less than 50%.

9. 9. The agent for use, the inhibitor for use, or the method of claim 8, wherein the deleterious mitochondrial DNA (mtDNA) mutation load is less than 40%, less than 30%, or less than 20%.

10. The drug is a) a compound that drives glycolytic flux through MDH1, which may be selected from the group consisting of isocitrate, aconitate, citrate, oxaloacetate, NADH, and NAD+ precursors; b) a compound that modulates NAD(H) redox processing by the malate-aspartate shuttle, which may be selected from the group consisting of isocitrate, aconitate, citrate, oxaloacetate, malate, fumarate, and argininosuccinate; c) lactic acid, pyruvate; d) glucose and / or lactate metabolic enzymes; e) an inhibitor of an enzyme that reduces glycolytic flux in cancer or precancerous cells, wherein the enzyme may be pyruvate dehydrogenase or pyruvate carboxylase, and the inhibitor may be a small molecule; f) activators of enzymes that increase glycolytic flux in cancer or precancerous cells; g) an activator of an enzyme that increases lactate efflux in cancer or precancerous cells, wherein the enzyme may be MDH1 or GAPDH, and the activator may be a small molecule; h) inhibitors of enzymes that reduce lactate excretion in cancerous or precancerous cells; i) a small molecule inhibitor of an enzyme in the malate-aspartate shuttle, wherein the enzyme may be selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter; j) a small molecule activator of an enzyme in the malate-aspartate shuttle, wherein the enzyme may be selected from the group consisting of GOT1, GOT2, MDH1, MDH2, glutamate-aspartate transporter, and α-ketoglutarate-malate transporter; k) inhibitors of Complex I, Complex II, Complex III or Complex IV; l) a compound that increases the deleterious mtDNA mutation load in said cancer or precancer, and may cause deleterious mtDNA mutations; and / or m) a compound that reduces neutrophils in said subject and / or reduces neutrophils in said cancer or precancer, wherein said neutrophils may be tumor-infiltrating neutrophils (TANs).

10. The agent for use, the inhibitor for use, or the method according to any preceding claim, selected from the group consisting of:

11. 10. The agent for use, the inhibitor for use, or the method according to any preceding claim, wherein the agent is the enzyme NADH oxidase or a nucleic acid encoding said enzyme, and the enzyme may be derived from Lactobacillus brevis, and further, the enzyme may be selected from the group consisting of cytoLbNOX and mitoLbNOX.

12. 10. The agent for use, the inhibitor for use, or the method according to any preceding claim, wherein the cancer or precancer is selected from the group consisting of childhood cancer, blood cancer, and bone marrow cancer.

13. 13. The agent for use, inhibitor for use, or method of claim 12, wherein the childhood cancer is selected from the group consisting of leukemia, brain tumor, spinal cancer, neuroblastoma, Wilms' tumor, lymphoma (such as Hodgkin's lymphoma and non-Hodgkin's lymphoma), rhabdomyosarcoma, retinoblastoma, and bone cancer (such as osteosarcoma and Ewing's sarcoma).

14. The medicament for use, the inhibitor for use, or the method according to any preceding claim, wherein the immune checkpoint inhibitor is selected from the group consisting of a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a CTLA4 inhibitor, a TIGIT inhibitor, a LAG-3 inhibitor, a TIM-3 inhibitor, a BTLA inhibitor, and a KIR inhibitor, and the immune checkpoint inhibitor may be selected from the group consisting of a PD-1 inhibitor, a PD-L1 inhibitor, and a CTLA4 inhibitor, and further the PD-1 inhibitor may be nivolumab.

15. 11. The agent for use, inhibitor for use, or method of claim 10, wherein the compound that increases the deleterious mtDNA mutation load in the cancer or precancer is selected from the group consisting of a mitochondrial base editing enzyme (such as, for example, DdCBE) and a mitochondrial heteroplasmy manipulating enzyme (such as, for example, mtZFN or mitoTALEN).

16. the deleterious mtDNA mutation is (i) a tRNA mutation with a MitoTIP RAW score of at least 12.6, or at least 16.25; (ii) rRNA mutations; (iii) truncation mutations in mtDNA genes; (iv) a missense mutation in an mtDNA gene, the missense mutation having an apogy score greater than 0.5 and optionally selected from a frameshift mutation, an insertion mutation, or a deletion mutation; and / or (v) a mutation in the mtDNA D-loop region selected from the group consisting of the heavy chain promoter (545-567), MT-HV2 (hypervariable segment 2) m. 57-372, and MT-HV1 (hypervariable segment 1) m. 16024-16390. The agent for use, the inhibitor for use, or the method according to any one of claims 8 to 15, selected from the group consisting of:

17. The deleterious mtDNA mutations are MT-ND5, MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND4L, MT-ND6, MT-CO1, MT-CO2, MT -CO3, MT-CYB, MT-ATP6, MT-ATP8, MT-TL1, MT-TA, MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI MT-TK, MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT, MT-TV, MT-TW, MT-TY, MT-RNR1 and MT-RNR2.

18. The agent for use, the inhibitor for use, or the method according to any one of claims 8 to 17, wherein the MT-ND5 deleterious mtDNA mutation is a truncating mutation present in a region selected from m. 12418-12425:A indel or m. 12385-12390:C indel.

19. The agent for use, the inhibitor for use, or the method according to any one of claims 7 to 18, wherein the deleterious mtDNA mutation is a truncation mutation, a missense mutation, an insertion mutation, or a frameshift mutation.

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