Background

In this study, we investigated whether PKR protein expression is correlated with mRNA levels and also evaluated molecular biomarkers that are associated with PKR, such as phosphorylated PKR (p-PKR) and phosphorylated eIF2α (p-eIF2α).

Methodology and Findings

We determined the levels of PKR protein expression and mRNA in 36 fresh primary lung tumor tissues by using Western blot analysis and real-time reverse-transcriptase PCR (RT-PCR), respectively. We used tissue microarrays for immunohistochemical evaluation of the expression of p-PKR and p-eIF2α proteins. We demonstrated that PKR mRNA levels are significantly correlated with PKR protein levels (Spearman's rho = 0.55, p<0.001), suggesting that PKR protein levels in tumor samples are regulated by PKR mRNA. We also observed that the patients with high p-PKR or p-eIF2α expression had a significantly longer median survival than those with little or no p-PKR or p-eIF2α expression (p = 0.03 and p = 0.032, respectively). We further evaluated the prognostic effect of combined expression of p-PKR plus PKR and p-eIF2α plus PKR and found that both combinations were strong independent prognostic markers for overall patient survival on stage I and all stage patients.

Conclusions

Our findings suggest that PKR protein expression may controlled by transcription level. Combined expression levels of PKR and p-PKR or p-eIF2α can be new markers for predicting the prognosis of patients with NSCLC.

Background

In this study, we investigated whether PKR protein expression is correlated with mRNA levels and also evaluated molecular biomarkers that are associated with PKR, such as phosphorylated PKR (p-PKR) and phosphorylated eIF2α (p-eIF2α).

Methodology and findings

We determined the levels of PKR protein expression and mRNA in 36 fresh primary lung tumor tissues by using Western blot analysis and real-time reverse-transcriptase PCR (RT-PCR), respectively. We used tissue microarrays for immunohistochemical evaluation of the expression of p-PKR and p-eIF2α proteins. We demonstrated that PKR mRNA levels are significantly correlated with PKR protein levels (Spearman's rho = 0.55, p<0.001), suggesting that PKR protein levels in tumor samples are regulated by PKR mRNA. We also observed that the patients with high p-PKR or p-eIF2α expression had a significantly longer median survival than those with little or no p-PKR or p-eIF2α expression (p = 0.03 and p = 0.032, respectively). We further evaluated the prognostic effect of combined expression of p-PKR plus PKR and p-eIF2α plus PKR and found that both combinations were strong independent prognostic markers for overall patient survival on stage I and all stage patients.

Conclusions

Our findings suggest that PKR protein expression may controlled by transcription level. Combined expression levels of PKR and p-PKR or p-eIF2α can be new markers for predicting the prognosis of patients with NSCLC.

Tumor extracellular matrix has been associated with drug resistance and immune suppression. Here, proteomic and RNA profiling reveal increased collagen levels in lung tumors resistant to PD-1/PD-L1 blockade. Additionally, elevated collagen correlates with decreased total CD8⁺ T cells and increased exhausted CD8⁺ T cell subpopulations in murine and human lung tumors. Collagen-induced T cell exhaustion occurs through the receptor LAIR1, which is upregulated following CD18 interaction with collagen, and induces T cell exhaustion through SHP-1. Reduction in tumor collagen deposition through LOXL2 suppression increases T cell infiltration, diminishes exhausted T cells, and abrogates resistance to anti-PD-L1. Abrogating LAIR1 immunosuppression through LAIR2 overexpression or SHP-1 inhibition sensitizes resistant lung tumors to anti-PD-1. Clinically, increased collagen, LAIR1, and TIM-3 expression in melanoma patients treated with PD-1 blockade predict poorer survival and response. Our study identifies collagen and LAIR1 as potential markers for immunotherapy resistance and validates multiple promising therapeutic combinations.

Heightened secretion of protumorigenic effector proteins is a feature of malignant cells. Yet, the molecular underpinnings and therapeutic implications of this feature remain unclear. Here, we identify a chromosome 1q region that is frequently amplified in diverse cancer types and encodes multiple regulators of secretory vesicle biogenesis and trafficking, including the Golgi-dedicated enzyme phosphatidylinositol (PI)-4-kinase IIIβ (PI4KIIIβ). Molecular, biochemical, and cell biological studies show that PI4KIIIβ-derived PI-4-phosphate (PI4P) synthesis enhances secretion and accelerates lung adenocarcinoma progression by activating Golgi phosphoprotein 3 (GOLPH3)-dependent vesicular release from the Golgi. PI4KIIIβ-dependent secreted factors maintain 1q-amplified cancer cell survival and influence prometastatic processes in the tumor microenvironment. Disruption of this functional circuitry in 1q-amplified cancer cells with selective PI4KIIIβ antagonists induces apoptosis and suppresses tumor growth and metastasis. These results support a model in which chromosome 1q amplifications create a dependency on PI4KIIIβ-dependent secretion for cancer cell survival and tumor progression.

Little is known of the geospatial architecture of individual cell populations in lung adenocarcinoma (LUAD) evolution. Here, we perform single-cell RNA sequencing of 186,916 cells from five early-stage LUADs and 14 multiregion normal lung tissues of defined spatial proximities from the tumors. We show that cellular lineages, states, and transcriptomic features geospatially evolve across normal regions to LUADs. LUADs also exhibit pronounced intratumor cell heterogeneity within single sites and transcriptional lineage-plasticity programs. T regulatory cell phenotypes are increased in normal tissues with proximity to LUAD, in contrast to diminished signatures and fractions of cytotoxic CD8⁺ T cells, antigen-presenting macrophages, and inflammatory dendritic cells. We further find that the LUAD ligand-receptor interactome harbors increased expression of epithelial CD24, which mediates protumor phenotypes. These data provide a spatial atlas of LUAD evolution, and a resource for identification of targets for its treatment. SIGNIFICANCE: The geospatial ecosystem of the peripheral lung and early-stage LUAD is not known. Our multiregion single-cell sequencing analyses unravel cell populations, states, and phenotypes in the spatial and ecologic evolution of LUAD from the lung that comprise high-potential targets for early interception.This article is highlighted in the In This Issue feature, p. 2355.

For patients with advanced non-small-cell lung cancer (NSCLC), dual immune checkpoint blockade (ICB) with CTLA4 inhibitors and PD-1 or PD-L1 inhibitors (hereafter, PD-(L)1 inhibitors) is associated with higher rates of anti-tumour activity and immune-related toxicities, when compared with treatment with PD-(L)1 inhibitors alone. However, there are currently no validated biomarkers to identify which patients will benefit from dual ICB1,2. Here we show that patients with NSCLC who have mutations in the STK11 and/or KEAP1 tumour suppressor genes derived clinical benefit from dual ICB with the PD-L1 inhibitor durvalumab and the CTLA4 inhibitor tremelimumab, but not from durvalumab alone, when added to chemotherapy in the randomized phase III POSEIDON trial3. Unbiased genetic screens identified loss of both of these tumour suppressor genes as independent drivers of resistance to PD-(L)1 inhibition, and showed that loss of Keap1 was the strongest genomic predictor of dual ICB efficacy-a finding that was confirmed in several mouse models of Kras-driven NSCLC. In both mouse models and patients, KEAP1 and STK11 alterations were associated with an adverse tumour microenvironment, which was characterized by a preponderance of suppressive myeloid cells and the depletion of CD8+ cytotoxic T cells, but relative sparing of CD4+ effector subsets. Dual ICB potently engaged CD4+ effector cells and reprogrammed the tumour myeloid cell compartment towards inducible nitric oxide synthase (iNOS)-expressing tumoricidal phenotypes that-together with CD4+ and CD8+ T cells-contributed to anti-tumour efficacy. These data support the use of chemo-immunotherapy with dual ICB to mitigate resistance to PD-(L)1 inhibition in patients with NSCLC who have STK11 and/or KEAP1 alterations.

Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options. SIGNIFICANCE: Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494.

(Cell Reports 23, 313–326; April 3, 2018) In the originally published version of this article, the author list contained two errors. Specifically, David J. Kwiatkowski was misspelled as David J. Kwaitkowski, and William Y. Kim was inadvertently written as William T. Kim. Both names have been corrected online. The authors regret this error.

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]