Targeted reduction of cholesterol uptake in cholesterol-addicted lymphoma cells blocks turnover of oxidized lipids to cause ferroptosis.

Normal human cells can either synthesize cholesterol or take it up from lipoproteins to meet their metabolic requirements. In some malignant cells, de novo cholesterol synthesis genes are transcriptionally silent or mutated, meaning that cholesterol uptake from lipoproteins is required for survival. Recent data suggest that lymphoma cells dependent upon lipoprotein-mediated cholesterol uptake are also subject to ferroptosis, an oxygen- and iron-dependent cell death mechanism triggered by accumulation of oxidized lipids in cell membranes unless the lipid hydroperoxidase, glutathione peroxidase 4 (GPX4), reduces these toxic lipid species. To study mechanisms linking cholesterol uptake with ferroptosis and determine the potential role of the high-density lipoprotein (HDL) receptor as a target for cholesterol depleting therapy, we treated lymphoma cell lines known to be sensitive to the reduction of cholesterol uptake with HDL-like nanoparticles (HDL NPs). HDL NPs are a cholesterol-poor ligand that binds to the receptor for cholesterol-rich HDLs, scavenger receptor type B1 (SCARB1). Our data reveal that HDL NP treatment activates a compensatory metabolic response in treated cells toward increased de novo cholesterol synthesis, which is accompanied by nearly complete reduction in expression of GPX4. As a result, oxidized membrane lipids accumulate, leading to cell death through a mechanism consistent with ferroptosis. We obtained similar results in vivo after systemic administration of HDL NPs in mouse lymphoma xenografts and in primary samples obtained from patients with lymphoma. In summary, targeting SCARB1 with HDL NPs in cholesterol uptake-addicted lymphoma cells abolishes GPX4, resulting in cancer cell death by a mechanism consistent with ferroptosis.

Rational Targeting of Cellular Cholesterol in Diffuse Large B-Cell Lymphoma (DLBCL) Enabled by Functional Lipoprotein Nanoparticles: A Therapeutic Strategy Dependent on Cell of Origin.

Cancer cells have altered metabolism and, in some cases, an increased demand for cholesterol. It is important to identify novel, rational treatments based on biology, and cellular cholesterol metabolism as a potential target for cancer is an innovative approach. Toward this end, we focused on diffuse large B-cell lymphoma (DLBCL) as a model because there is differential cholesterol biosynthesis driven by B-cell receptor (BCR) signaling in germinal center (GC) versus activated B-cell (ABC) DLBCL. To specifically target cellular cholesterol homeostasis, we employed high-density lipoprotein-like nanoparticles (HDL NP) that can generally reduce cellular cholesterol by targeting and blocking cholesterol uptake through the high-affinity HDL receptor, scavenger receptor type B-1 (SCARB1). As we previously reported, GC DLBCL are exquisitely sensitive to HDL NP as monotherapy, while ABC DLBCL are less sensitive. Herein, we report that enhanced BCR signaling and resultant de novo cholesterol synthesis in ABC DLBCL drastically reduces the ability of HDL NPs to reduce cellular cholesterol and induce cell death. Therefore, we combined HDL NP with the BCR signaling inhibitor ibrutinib and the SYK inhibitor R406. By targeting both cellular cholesterol uptake and BCR-associated de novo cholesterol synthesis, we achieved cellular cholesterol reduction and induced apoptosis in otherwise resistant ABC DLBCL cell lines. These results in lymphoma demonstrate that reduction of cellular cholesterol is a powerful mechanism to induce apoptosis. Cells rich in cholesterol require HDL NP therapy to reduce uptake and molecularly targeted agents that inhibit upstream pathways that stimulate de novo cholesterol synthesis, thus, providing a new paradigm for rationally targeting cholesterol metabolism as therapy for cancer.

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

BACKGROUND: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples. MATERIALS AND METHODS: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone. RESULTS: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P << 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67-0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63-0.77, P ≤ 0.03). CONCLUSIONS: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.

Clinical validation of the Tempus xO assay.

We have developed a clinically validated NGS assay that includes tumor, germline and RNA sequencing. We apply this assay to clinical specimens and cell lines, and we demonstrate a clinical sensitivity of 98.4% and positive predictive value of 100% for the clinically actionable variants measured by the assay. We also demonstrate highly accurate copy number measurements and gene rearrangement identification.