NRF2 activation induces NADH-reductive stress, providing a metabolic vulnerability in lung cancer.

Multiple cancers regulate oxidative stress by activating the transcription factor NRF2 through mutation of its negative regulator, KEAP1. NRF2 has been studied extensively in KEAP1-mutant cancers; however, the role of this pathway in cancers with wild-type KEAP1 remains poorly understood. To answer this question, we induced NRF2 via pharmacological inactivation of KEAP1 in a panel of 50+ non-small cell lung cancer cell lines. Unexpectedly, marked decreases in viability were observed in >13% of the cell lines-an effect that was rescued by NRF2 ablation. Genome-wide and targeted CRISPR screens revealed that NRF2 induces NADH-reductive stress, through the upregulation of the NAD+-consuming enzyme ALDH3A1. Leveraging these findings, we show that cells treated with KEAP1 inhibitors or those with endogenous KEAP1 mutations are selectively vulnerable to Complex I inhibition, which impairs NADH oxidation capacity and potentiates reductive stress. Thus, we identify reductive stress as a metabolic vulnerability in NRF2-activated lung cancers.

SULT1A1-dependent sulfonation of alkylators is a lineage-dependent vulnerability of liver cancers.

Adult liver malignancies, including intrahepatic cholangiocarcinoma and hepatocellular carcinoma, are the second leading cause of cancer-related deaths worldwide. Most individuals are treated with either combination chemotherapy or immunotherapy, respectively, without specific biomarkers for selection. Here using high-throughput screens, proteomics and in vitro resistance models, we identify the small molecule YC-1 as selectively active against a defined subset of cell lines derived from both liver cancer types. We demonstrate that selectivity is determined by expression of the liver-resident cytosolic sulfotransferase enzyme SULT1A1, which sulfonates YC-1. Sulfonation stimulates covalent binding of YC-1 to lysine residues in protein targets, enriching for RNA-binding factors. Computational analysis defined a wider group of structurally related SULT1A1-activated small molecules with distinct target profiles, which together constitute an untapped small-molecule class. These studies provide a foundation for preclinical development of these agents and point to the broader potential of exploiting SULT1A1 activity for selective targeting strategies.

Mutant IDH Inhibits IFNγ-TET2 Signaling to Promote Immunoevasion and Tumor Maintenance in Cholangiocarcinoma.

Isocitrate dehydrogenase 1 mutations (mIDH1) are common in cholangiocarcinoma. (R)-2-hydroxyglutarate generated by the mIDH1 enzyme inhibits multiple α-ketoglutarate-dependent enzymes, altering epigenetics and metabolism. Here, by developing mIDH1-driven genetically engineered mouse models, we show that mIDH1 supports cholangiocarcinoma tumor maintenance through an immunoevasion program centered on dual (R)-2-hydroxyglutarate-mediated mechanisms: suppression of CD8+ T-cell activity and tumor cell-autonomous inactivation of TET2 DNA demethylase. Pharmacologic mIDH1 inhibition stimulates CD8+ T-cell recruitment and interferon γ (IFNγ) expression and promotes TET2-dependent induction of IFNγ response genes in tumor cells. CD8+ T-cell depletion or tumor cell-specific ablation of TET2 or IFNγ receptor 1 causes treatment resistance. Whereas immune-checkpoint activation limits mIDH1 inhibitor efficacy, CTLA4 blockade overcomes immunosuppression, providing therapeutic synergy. The findings in this mouse model of cholangiocarcinoma demonstrate that immune function and the IFNγ-TET2 axis are essential for response to mIDH1 inhibition and suggest a novel strategy for potentiating efficacy. SIGNIFICANCE: Mutant IDH1 inhibition stimulates cytotoxic T-cell function and derepression of the DNA demethylating enzyme TET2, which is required for tumor cells to respond to IFNγ. The discovery of mechanisms of treatment efficacy and the identification of synergy by combined CTLA4 blockade provide the foundation for new therapeutic strategies. See related commentary by Zhu and Kwong, p. 604. This article is highlighted in the In This Issue feature, p. 587.

Interleukin-8 blockade prevents activated endothelial cell mediated proliferation and chemoresistance of acute myeloid leukemia.

One of the greatest challenges in treating acute myeloid leukemia (AML) is chemotherapy refractory disease. Previously, we demonstrated a novel mechanism whereby AML-induced endothelial cell (EC) activation leads to subsequent leukemia cell adherence, quiescence and chemoresistance, identifying activated ECs as potential mediators of relapse. We now show mechanistically that EC activation induces the secretion of interleukin-8 (IL-8) leading to significant expansion of non-adherent AML cells and resistance to cytarabine (Ara-C). Through crystallography and computational modeling, we identified a pocket within IL-8 responsible for receptor binding, screened for small molecules that fit within this pocket, and blocked IL-8 induced proliferation and chemo-protection of AML cells with a hit compound. Results from this study show a new therapeutic strategy for targeting the sanctuary of an activated leukemia microenvironment.

A genomics-informed computational biology platform prospectively predicts treatment responses in AML and MDS patients.

Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.

High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network.

Using traditional histological methods, researchers are hampered in their ability to image whole tissues or organs in large-scale 3D. Histological sections are generally limited to <20 µm as formalin fixed paraffin section on glass slides or <500 µm for free-floating fixed sections. Therefore, extensive efforts are required for serial sectioning and large-scale image reconstruction methods to recreate 3D for samples >500 µm using traditional methods. In addition, light scatters from macromolecules within tissues, particularly lipids, prevents imaging to a depth >150 µm with most confocal microscopes. To reduce light scatter and to allow for deep tissue imaging using simple confocal microscopy, various optical clearing methods have been developed that are relevant for rodent and human tissue samples fixed by immersion. Several methods are related and use protein crosslinking with acrylamide and tissue clearing with sodium dodecyl sulfate (SDS). Other optical clearing techniques used various solvents though each modification had various advantages and disadvantages. Here, an optimized passive optical clearing method is described for studies of the human pancreas innervation and specifically for interrogation of the innervation of human islets.