Faculty Bibliography

Approximately 30% of human lung cancers acquire mutations in either Keap1 or Nfe2l2, resulting in the stabilization of Nrf2, the Nfe2l2 gene product, which controls oxidative homeostasis. Here, we show that heme triggers the degradation of Bach1, a pro-metastatic transcription factor, by promoting its interaction with the ubiquitin ligase Fbxo22. Nrf2 accumulation in lung cancers causes the stabilization of Bach1 by inducing Ho1, the enzyme catabolizing heme. In mouse models of lung cancers, loss of Keap1 or Fbxo22 induces metastasis in a Bach1-dependent manner. Pharmacological inhibition of Ho1 suppresses metastasis in a Fbxo22-dependent manner. Human metastatic lung cancer display high levels of Ho1 and Bach1. Bach1 transcriptional signature is associated with poor survival and metastasis in lung cancer patients. We propose that Nrf2 activates a metastatic program by inhibiting the heme- and Fbxo22-mediated degradation of Bach1, and that Ho1 inhibitors represent an effective therapeutic strategy to prevent lung cancer metastasis.

The BCL-2 family plays important roles in acute myeloid leukemia (AML). Venetoclax, a selective BCL-2 inhibitor, has received FDA approval for the treatment of AML. However, drug resistance ensues after prolonged treatment, highlighting the need for a greater understanding of the underlying mechanisms. Using a genome-wide CRISPR/Cas9 screen in human AML, we identified genes whose inactivation sensitizes AML blasts to Venetoclax. Genes involved in mitochondrial organization and function were significantly depleted throughout our screen, including the mitochondrial chaperonin CLPB. We demonstrated that CLPB is upregulated in human AML, it is further induced upon acquisition of Venetoclax resistance and its ablation sensitizes AML to Venetoclax. Mechanistically, CLPB maintains the mitochondrial cristae structure via its interaction with the cristae-shaping protein OPA1, whereas its loss promotes apoptosis by inducing cristae remodeling and mitochondrial stress responses. Overall, our data suggest that targeting mitochondrial architecture may provide a promising approach to circumvent Venetoclax resistance.

The bone marrow microenvironment has a key role in regulating haematopoiesis, but its molecular complexity and response to stress are incompletely understood. Here we map the transcriptional landscape of mouse bone marrow vascular, perivascular and osteoblast cell populations at single-cell resolution, both at homeostasis and under conditions of stress-induced haematopoiesis. This analysis revealed previously unappreciated levels of cellular heterogeneity within the bone marrow niche and resolved cellular sources of pro-haematopoietic growth factors, chemokines and membrane-bound ligands. Our studies demonstrate a considerable transcriptional remodelling of niche elements under stress conditions, including an adipocytic skewing of perivascular cells. Among the stress-induced changes, we observed that vascular Notch delta-like ligands (encoded by Dll1 and Dll4) were downregulated. In the absence of vascular Dll4, haematopoietic stem cells prematurely induced a myeloid transcriptional program. These findings refine our understanding of the cellular architecture of the bone marrow niche, reveal a dynamic and heterogeneous molecular landscape that is highly sensitive to stress and illustrate the utility of single-cell transcriptomic data in evaluating the regulation of haematopoiesis by discrete niche populations.

Background: Immune response is key in defense against cutaneous squamous cell carcinoma (cSCC). We aim to better target T cell killing to treat cancer. Method(s): CD8+ tumor infiltrating lymphocytes (TILs) obtained from fresh SCC tumor specimens from immunocompetent (n=5) versus organ transplant recipients (OTRs; n=6) were subject to single-cell RNA profiling and T-cell receptor (TCR) sequencing. Data were analyzed using scSeqR. Result(s): CD8+ TILs were clustered based on gene expression as follows: effector T cells (GZMA, GZMB, IFN-gamma); naive T cells (CCR7, LEF1, TCF7, IL7R); exhausted T cells (BTLA, CTLA4, PDCD1, TIM3, LAG3); and regulatory T cells (FOXP3, STAT3, TNFSRF). CD8+ TIL clonotypes show potential for response to known and uncharacterized antigens. CD8+ TILs from OTRs exhibited more homogeneous gene expression compared with immunocompetent patients. Regulatory and exhausted T cells were reduced in OTRs. TILs from both OTRs and immunocompetent patients showed clonality. However, more TCR clonotypes were observed in immunocompetent patients (mean = 1140) than OTRs (mean = 544). We analyzed common beta TCR from SCC via multiple bioinformatics-based approaches. Clonotypes common to more than one SCC from immune competent patients included breast cancer-associated keratin 19 and proliferation-associated striatin. OTR clonotypes included tumor-suppressor associated MOB kinase 1A and breast cancer-associated ADAM9. Conclusion(s): CD8+ TILs from cSCC showed clonotype restriction, implying T cell mediated anti-tumor response. We will refine our understanding of the SCC T cell landscape and identify and screen neoantigens with the goal of developing rational T cell-based cancer therapy.

Here, we report the draft genome of Streptococcus halitosis sp. nov. strain VT-4, a novel bacterium isolated from the dorsal part of the tongue of a patient with halitosis. The genome comprised 1,880,608 bp with a GC content of 41.0%. There were 1,782 predicted protein-coding genes, including those associated with virulence and antibiotic resistance.

PURPOSE/OBJECTIVE:Isocitrate dehydrogenase (IDH) mutant gliomas are a distinct glioma molecular subtype for which no effective molecularly-directed therapy exists. Low-grade gliomas, which are 80-90% IDH mutant, have high RNA levels of the cell surface Notch ligand DLL3. We sought to determine DLL3 expression by immunohistochemistry in glioma molecular subtypes and the potential efficacy of an anti-DLL3 antibody drug conjugate (ADC), rovalpituzumab tesirine (Rova-T), in IDH mutant glioma. EXPERIMENTAL DESIGN/METHODS:We evaluated DLL3 expression by RNA using TCGA data and by immunohistochemistry in a discovery set of 63 gliomas and 20 non-tumor brain tissues and a validation set of 62 known IDH wildtype and mutant gliomas using a monoclonal anti-DLL3 antibody. Genotype was determined using a DNA methylation array classifier or by sequencing. The effect of Rova-T on patient-derived endogenous IDH mutant glioma tumorspheres was determined by cell viability assay. RESULTS:Compared to IDH wildtype glioblastoma, IDH mutant gliomas have significantly higher DLL3 RNA (P<1x10-15) and protein by immunohistochemistry (P=0.0014 and P<4.3x10-6 in the discovery and validation set, respectively). DLL3 immunostaining was intense and homogeneous in IDH mutant gliomas, retained in all recurrent tumors, and detected in only 1 of 20 non-tumor brains. Patient-derived IDH mutant glioma tumorspheres overexpressed DLL3 and were potently sensitive to Rova-T in an antigen-dependent manner. CONCLUSIONS:DLL3 is selectively and homogeneously expressed in IDH mutant gliomas and can be targeted with Rova-T in patient-derived IDH mutant glioma tumorspheres. Our findings are potentially immediately translatable and have implications for therapeutic strategies that exploit cell surface tumor-associated antigens.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

Background: There are no predictive biomarkers of ipilimumab (IPI) toxicity. Of metastatic melanoma (MM) patients (pts) receiving IPI (3 mg/kg), 35% require systemic therapies to treat immune-related adverse events (irAEs) and 20% must terminate treatment [1]. Here we tested the hypothesis that a pre-existing autoantibody (autoAb) profile is predictive of IPI irAEs.
Method(s): We measured autoAb levels in pre- and post-treatment sera from MM pts who received IPI (3 mg/kg) monotherapy on a proteome microarray containing ~ 20,000 unique full-length human proteins (HuProt array, CDI Laboratories). Clinical data were prospectively collected with protocol-driven follow-up. IrAEs were categorized by CTCAE guidelines as none (grade 0), mild (grade 12), or severe (grade 34). AutoAb levels were standardized using median quantile normalization and considered positive hits if > 2-SD above the peak array signal and differed by >= 2-fold with p < 0.05 between toxicity groups (Non-parametric Analysis/Wilcox test).
Result(s): Seventy-eight sera from 37 MM pts were analyzed. Antibodies against CTLA-4 were significantly elevated post IPI treatment (p < 0.0001), validating the assay. The pre-treatment levels of 190 IgG autoAbs were significantly different in pts who experienced irAEs (n = 28) compared to those with no irAEs (n = 9). Comparison of severe irAE (n = 9) and no irAE (n = 9) groups revealed 129 IgG auto- Abs that significantly differed in pre-treatment sera. Localization and pathway analysis (UniProt, KEGG, Reactome) showed 81/190 (43%) of the autoAbs targeted nuclear and mitochondrial antigens and were enriched in metabolic pathways (p = 0.015). AutoAbs associated with irAEs did not correlate with treatment response.
Conclusion(s): AutoAbs to antigens enriched in metabolic pathways prior to treatment may predict IPI-induced toxicity in MM. The subcellular localization of targeted antigens could explain the autoimmune toxicities associated with IPI. Studies in larger cohorts and in pts receiving other checkpoint inhibitors and/or combination therapies are essential to determine the validity of the data. If validated, our results would support the discovery of the first toxicity predictor in cancer immunotherapy