A cross-sectional study of the association of dental health factors with progression and all-cause mortality in men diagnosed with HPV-associated oropharyngeal cancer.

BACKGROUND: Human Papillomavirus-associated oropharyngeal cancer (HPV-OPC) incidence is increasing among men in the United States. Poor dental health has previously been associated with risk of head and neck cancers, oral HPV infection, and persistence but it is not understood whether dental health is associated with outcomes. We sought to determine the association of dental health with progression free survival and overall mortality among men with an HPV-OPC. METHODS: A cross sectional study of men diagnosed with HPV-OPC between 2014-2020 at Moffitt Cancer Center in Tampa, FL was conducted. Dental records were abstracted for assessment of dental fitness prior to cancer treatment. Five dental factors including number of teeth lost, pocket depth, gingival score, loss of attachment, and bone loss were individually examined. Risk factor and outcome data were collected from a patient risk questionnaire and medical record. Using item response theory, an overall dental fitness score from five dental factors was developed in which missing data were multiply imputed. Cox proportional hazards model was used to assess whether dental factors were associated with progression-free survival or overall mortality. RESULTS: Among 206 HPV-OPC cases, median follow-up was 3.4 years (IQR: 2.4-4.4) during which 40 cases involved progression or mortality and 25 deaths occurred. Overall dentition was significantly associated with progression free survival (p = 0.04) and with overall survival (p = 0.03) though findings were not significant after adjustment for age at diagnosis, stage, and smoking history (p = 0.146 and p = 0.120, respectively). A pocket depth of 7 mm or more was associated with overall survival (HR: 5.21; 95% CI: 1.43-19.11) and this remained significant after adjustment for confounding (aHR: 4.14; 95% CI: 1.72-16.26). CONCLUSIONS: Among men diagnosed with an HPV-associated OPC in the US, worse dental health was associated with reduced progression free survival and overall survival, but not after adjustment for confounders. Further studies are needed to examine whether dental health is associated with other prognostic factors and subsequent treatment-related outcomes.

Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV- HNSCC patients into these four molecular subtypes and characterized the tumor microenvironment using deconvolution method. We determined that atypical and mesenchymal subtypes have greater immune enrichment and exhibit a T-cell exhaustion phenotype, compared to classical and basal subtypes. Further analyses revealed different B cell maturation and antibody isotypes enrichment patterns, and distinct immune microenvironment crosstalk in the atypical and mesenchymal subtypes. Taken together, our study suggests that treatments that enhances B cell activity may benefit patients with HNSCC of the atypical subtypes. The rationale can be utilized in the design of future precision immunotherapy trials based on the molecular subtypes of HPV- HNSCC.

An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at https://github.com/nadeemlab/DeepLIIF.

Virtual alignment of pathology image series for multi-gigapixel whole slide images.

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at \url{https://github.com/nadeemlab/DeepLIIF}.

EGFR Inhibition by Cetuximab Modulates Hypoxia and IFN Response Genes in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) has one of the most hypoxic and immunosuppressive tumor microenvironments (TME) among solid tumors. However, there is no proven therapeutic strategy to remodel the TME to be less hypoxic and proinflammatory. In this study, we classified tumors according to a Hypoxia-Immune signature, characterized the immune cells in each subgroup, and analyzed the signaling pathways to identify a potential therapeutic target that can remodel the TME. We confirmed that hypoxic tumors had significantly higher numbers of immunosuppressive cells, as evidenced by a lower ratio of CD8+ T cells to FOXP3+ regulatory T cells, compared with nonhypoxic tumors. Patients with hypoxic tumors had worse outcomes after treatment with pembrolizumab or nivolumab, anti-programmed cell death-1 inhibitors. Our expression analysis also indicated that hypoxic tumors predominantly increased the expression of the EGFR and TGFß pathway genes. Cetuximab, an anti-EGFR inhibitor, decreased the expression of hypoxia signature genes, suggesting that it may alleviate the effects of hypoxia and remodel the TME to become more proinflammatory. Our study provides a rationale for treatment strategies combining EGFR-targeted agents and immunotherapy in the management of hypoxic HNSCC. Significance: While the hypoxic and immunosuppressive TME of HNSCC has been well described, comprehensive evaluation of the immune cell components and signaling pathways contributing to immunotherapy resistance has been poorly characterized. We further identified additional molecular determinants and potential therapeutic targets of the hypoxic TME to fully leverage currently available targeted therapies that can be administered with immunotherapy.

Combined nivolumab and ipilimumab with or without stereotactic body radiation therapy for advanced Merkel cell carcinoma: a randomised, open label, phase 2 trial.

BACKGROUND: Merkel cell carcinoma is among the most aggressive and lethal of primary skin cancers, with a high rate of distant metastasis. Anti-programmed death receptor 1 (anti-PD-1) and programmed death ligand 1 (PD-L1) monotherapy is currently standard of care for unresectable, recurrent, or metastatic Merkel cell carcinoma. We assessed treatment with combined nivolumab plus ipilimumab, with or without stereotactic body radiotherapy (SBRT) in patients with advanced Merkel cell carcinoma as a first-line therapy or following previous treatment with anti-PD-1 and PD-L1 monotherapy. METHODS: In this randomised, open label, phase 2 trial, we randomly assigned adults from two cancer sites in the USA (one in Florida and one in Ohio) to group A (combined nivolumab and ipilimumab) or group B (combined nivolumab and ipilimumab plus SBRT) in a 1:1 ratio. Eligible patients were aged at least 18 years with histologically proven advanced stage (unresectable, recurrent, or stage IV) Merkel cell carcinoma, a minimum of two tumour lesions measureable by CT, MRI or clinical exam, and tumour tissue available for exploratory biomarker analysis. Patients were stratified by previous immune-checkpoint inhibitor (ICI) status to receive nivolumab 240 mg intravenously every 2 weeks plus ipilimumab 1 mg/kg intravenously every 6 weeks (group A) or the same schedule of combined nivolumab and ipilimumab with the addition of SBRT to at least one tumour site (24 Gy in three fractions at week 2; group B). Patients had to have at least two measurable sites of disease so one non-irradiated site could be followed for response. The primary endpoint was objective response rate (ORR) in all randomly assigned patients who received at least one dose of combined nivolumab and ipilimumab. ORR was defined as the proportion of patients with a complete response or partial response per immune-related Response Evaluation Criteria in Solid Tumours. Response was assessed every 12 weeks. Safety was assessed in all patients. This trial is registered with ClinicalTrials.gov, NCT03071406. FINDINGS: 50 patients (25 in both group A and group B) were enrolled between March 14, 2017, and Dec 21, 2021, including 24 ICI-naive patients (13 [52%] of 25 group A patients and 11 [44%] of 25 group B patients]) and 26 patients with previous ICI (12 [48%] of 25 group A patients and 14 [56%] of 25 group B patients]). One patient in group B did not receive SBRT due to concerns about excess toxicity. Median follow-up was 14·6 months (IQR 9·1-26·5). Two patients in group B were excluded from the analysis of the primary endpoint because the target lesions were irradiated and so the patients were deemed non-evaluable. Of the ICI-naive patients, 22 (100%) of 22 (95% CI 82-100) had an objective response, including nine (41% [95% CI 21-63]) with complete response. Of the patients who had previously had ICI exposure, eight (31%) of 26 patients (95% CI 15-52) had an objective response and four (15% [5-36]) had a complete response. No significant differences in ORR were observed between groups A (18 [72%] of 25 patients) and B (12 [52%] of 23 patients; p=0·26). Grade 3 or 4 treatment-related adverse events were observed in 10 (40%) of 25 patients in group A and 8 (32%) of 25 patients in group B. INTERPRETATION: First-line combined nivolumab and ipilimumab in patients with advanced Merkel cell carcinoma showed a high ORR with durable responses and an expected safety profile. Combined nivolumab and ipilimumab also showed clinical benefit in patients with previous anti-PD-1 and PD-L1 treatment. Addition of SBRT did not improve efficacy of combined nivolumab and ipilimumab. The combination of nivolumab and ipilimumab represents a new first-line and salvage therapeutic option for advanced Merkel cell carcinoma. FUNDING: Bristol Myers Squibb Rare Population Malignancy Program.

Deep Learning of Histopathology Images at the Single Cell Level.

The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to "weakly-label" the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Quantitative analysis of CDX2 protein expression improves its clinical utility as a prognostic biomarker in stage II and III colon cancer.

AIM: Better stratification of patients with stage II and stage III colon cancer for risk of recurrence is urgently needed. The present study aimed to validate the prognostic value of CDX2 protein expression in colon cancer tissue by routine immunohistochemistry and to evaluate its performance in a head-to-head comparison with tandem mass spectrometry-based proteomics. PATIENT AND METHODS: CDX2 protein expression was evaluated in 386 stage II and III primary colon cancers by immunohistochemical staining of tissue microarrays and by liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis using formalin-fixed paraffin-embedded tissue sections of a matched subset of 23 recurrent and 23 non-recurrent colon cancers. Association between CDX2 expression and disease-specific survival (DSS) was investigated. RESULTS: Low levels of CDX2 protein expression in stage II and III colon cancer as determined by immunohistochemistry was associated with poor DSS (hazard ratio [HR] = 1.97 (95% confidence interval [CI]: 1.26-3.06); p = 0.002). Based on analysis of a selected sample subset, CDX2 prognostic value was more pronounced when detected by LC-MS/MS (HR = 7.56 (95% CI: 2.49-22.95); p < 0.001) compared to detection by immunohistochemistry (HR = 1.60 (95% CI: 0.61-4.22); p = 0.34). CONCLUSION: This study validated CDX2 protein expression as a prognostic biomarker in stage II and III colon cancer, conform previous publications. CDX2 prognostic value appeared to be underestimated when detected by routine immunohistochemistry, probably due to the semiquantitative and subjective nature of this methodology. Quantitative analysis of CDX2 substantially improved its clinical utility as a prognostic biomarker. Therefore, development of routinely applicable quantitative assays for CDX2 expression is needed to facilitate its clinical implementation.

Effects of checkpoint kinase 1 inhibition by prexasertib on the tumor immune microenvironment of head and neck squamous cell carcinoma.

Prognosis for patients with recurrent and/or metastatic head and neck squamous cell carcinoma (HNSCC) remains poor. Development of more effective and less toxic targeted therapies is necessary for HNSCC patients. Checkpoint kinase 1 (CHK1) plays a vital role in cell cycle regulation and is a promising therapeutic target in HNSCC. Prexasertib, a CHK1 inhibitor, induces DNA damage and cell death, however, its effect on the tumor immune microenvironment (TIME) is largely unknown. Therefore, we evaluated a short-term and long-term effects of prexasertib in HNSCC and its TIME. Prexasertib caused increased DNA damage and cell death in vitro and significant tumor regression and improved survival in vivo. The gene expression and multiplex immunohistochemistry (mIHC) analyses of the in vivo tumors demonstrated increased expression of genes that are related to T-cell activation and increased immune cell trafficking, and decreased expression of genes that related to immunosuppression. However, increased expression of genes related to immunosuppression emerged over time suggesting evasion of immune surveillances. These findings in gene expression analyses were confirmed using mIHC which showed differential modulation of TIME in the tumor margins and as well as cores over time. These results suggest that evasion of immune surveillance, at least in part, may contribute to the acquired resistance to prexasertib in HNSCC.

Histoecology: Applying Ecological Principles and Approaches to Describe and Predict Tumor Ecosystem Dynamics Across Space and Time.

Cancer cells exist within a complex spatially structured ecosystem composed of resources and different cell types. As the selective pressures imposed by this environment determine the fate of cancer cells, an improved understanding of how this ecosystem evolves will better elucidate how tumors grow and respond to therapy. State of the art imaging methods can now provide highly resolved descriptions of the microenvironment, yielding the data required for a thorough study of its role in tumor growth and treatment resistance. The field of landscape ecology has been studying such species-environment relationship for decades, and offers many tools and perspectives that cancer researchers could greatly benefit from. Here, we discuss one such tool, species distribution modeling (SDM), that has the potential to, among other things, identify critical environmental factors that drive tumor evolution and predict response to therapy. SDMs only scratch the surface of how ecological theory and methods can be applied to cancer, and we believe further integration will take cancer research in exciting new and productive directions. Significance: Here we describe how species distribution modeling can be used to quantitatively describe the complex relationship between tumor cells and their microenvironment. Such a description facilitates a deeper understanding of cancers eco-evolutionary dynamics, which in turn sheds light on the factors that drive tumor growth and response to treatment.

Long noncoding RNA, LINC00460, as a prognostic biomarker in head and neck squamous cell carcinoma (HNSCC).

Head and neck squamous cell carcinoma (HNSCC) is an aggressive epithelial malignancy characterized by frequent mutations and metastasis. Long noncoding RNAs (lncRNAs) have been implicated in tumorigenesis and serve as novel prognostic biomarkers in different cancers. To enhance our understanding of lncRNAs that may have biological significance in HNSCC and may serve as prognostic biomarkers, we globally profiled lncRNAs in HNSCC by analyzing the RNA-seq data from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. Of 3576 lncRNAs, we identified 926 (higher-688, lower-238) lncRNAs with a 2-fold abundance difference among the forty HNSCC and paired adjacent normal tissue. We investigated differential abundance of lncRNAs based on TP53 mutation and p16 status. We found 133 lncRNAs to have differential abundance by 2-fold among the mutant vs wild-type TP53 samples, whereas among p16-negative vs positive samples, we identified 710 lncRNAs with the same criteria. Meanwhile, analysis of the 15 most abundant lncRNAs in the tumor samples identified five lncRNAs whose higher abundance was associated with poor overall patient survival. Among these five, higher abundance of LINC00460 associated with poor patient survival in an independent cohort of 82 HNSCC patients. To further evaluate the potential function of LINC00460, we performed lncRNA-mRNAs co-expression analysis and found that higher abundance of LINC00460 associated with cancer-related biological pathways including EMT and other inflammatory response pathways. In summary, we report LINC00460 is more abundant in tumors compared to adjacent normal tissue and that it may serve as a potential prognostic biomarker in HNSCC.

Accelerated instability testing reveals quantitative mass spectrometry overcomes specimen storage limitations associated with PD-L1 immunohistochemistry.

Immunohistochemistry (IHC) using formalin-fixed, paraffin embedded (FFPE) tissue is limited by epitope masking, posttranslational modification and immunoreactivity loss that occurs in stored tissue by poorly characterized mechanisms. Conformational epitopes recognized by many programmed-death-ligand-1 (PD-L1) IHC assays are particularly susceptible to degradation and provide an ideal model for understanding signal loss in stored FFPE tissue. Here we assessed 1206 tissue sections to evaluate environmental factors impacting immunoreactivity loss. PD-L1 IHC using four antibodies (22C3, 28-8, E1L3N, and SP142), raised against intracellular and extracellular epitopes, was assessed in stored FFPE tissue alongside quantitative mass spectrometry (MS). Global proteome analyses were used to assess proteome-wide oxidation across an inventory of 3041 protein groups (24,737 distinct peptides). PD-L1 quantitation correlated well with IHC expression on unaged sections (R2 = 0.744; P < 0.001), with MS demonstrating no loss of PD-L1 protein, even in sections with significant signal loss by IHC impacting diagnostic category. Clones 22C3 and 28-8 were most susceptible to signal loss, with E1L3N demonstrating the most robust signal (56%, 58%, and 33% reduction respectively; p < 0.05). Increased humidity and temperature resulted in significant acceleration of immunoreactivity loss, which was mitigated by storage with desiccant. MS demonstrated only modest oxidation of 274 methionine-containing peptides and aligned with IHC results suggesting peptide oxidation is not a major factor. These data imply immunoreactivity loss driven by humidity and temperature results in structural distortion of epitopes rendering them unsuitable for antibody binding following epitope retrieval. Limitations of IHC biomarker analysis from stored tissue sections may be mitigated by cost-effective use of desiccant when appropriate. In some scenarios, complementary MS is a preferred approach for retrospective analyses of archival FFPE tissue collections.

Effects of Tobacco Smoking on the Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Patients with head and neck squamous cell carcinoma (HNSCC) who actively smoke during treatment have worse survival compared with never-smokers and former-smokers. We hypothesize the poor prognosis in tobacco smokers with HNSCC is, at least in part, due to ongoing suppression of immune response. We characterized the tumor immune microenvironment (TIM) of HNSCC in a retrospective cohort of 177 current, former, and never smokers. EXPERIMENTAL DESIGN: Tumor specimens were subjected to analysis of CD3, CD8, FOXP3, PD-1, PD-L1, and pancytokeratin by multiplex immunofluorescence, whole-exome sequencing, and RNA sequencing. Immune markers were measured in tumor core, tumor margin, and stroma. RESULTS: Our data indicate that current smokers have significantly lower numbers of CD8+ cytotoxic T cells and PD-L1+ cells in the TIM compared with never- and former-smokers. While tumor mutation burden and mutant allele tumor heterogeneity score do not associate with smoking status, gene-set enrichment analyses reveal significant suppression of IFNα and IFNγ response pathways in current smokers. Gene expression of canonical IFN response chemokines, CXCL9, CXCL10, and CXCL11, are lower in current smokers than in former smokers, suggesting a mechanism for the decreased immune cell migration to tumor sites. CONCLUSIONS: These results suggest active tobacco use in HNSCC has an immunosuppressive effect through inhibition of tumor infiltration of cytotoxic T cells, likely as a result of suppression of IFN response pathways. Our study highlights the importance of understanding the interaction between smoking and TIM in light of emerging immune modulators for cancer management.

Proteogenomic landscape of squamous cell lung cancer.

How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.

lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA.

BACKGROUND: Long non-coding RNA (lncRNA) expression data have been increasingly used in finding diagnostic and prognostic biomarkers in cancer studies. Existing differential analysis tools for RNA sequencing do not effectively accommodate low abundant genes, as commonly observed in lncRNAs. RESULTS: We investigated the statistical distribution of normalized counts for low expression genes in lncRNAs and mRNAs, and proposed a new tool lncDIFF based on the underlying distribution pattern to detect differentially expressed (DE) lncRNAs. lncDIFF adopts the generalized linear model with zero-inflated Exponential quasi-likelihood to estimate group effect on normalized counts, and employs the likelihood ratio test to detect differential expressed genes. The proposed method and tool are applicable to data processed with standard RNA-Seq preprocessing and normalization pipelines. Simulation results showed that lncDIFF was able to detect DE genes with more power and lower false discovery rate regardless of the data pattern, compared to DESeq2, edgeR, limma, zinbwave, DEsingle, and ShrinkBayes. In the analysis of a head and neck squamous cell carcinomas data, lncDIFF also appeared to have higher sensitivity in identifying novel lncRNA genes with relatively large fold change and prognostic value. CONCLUSIONS: lncDIFF is a powerful differential analysis tool for low abundance non-coding RNA expression data. This method is compatible with various existing RNA-Seq quantification and normalization tools. lncDIFF is implemented in an R package available at https://github.com/qianli10000/lncDIFF .

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Human Papillomavirus Genotype Detection in Oral Gargle Samples Among Men With Newly Diagnosed Oropharyngeal Squamous Cell Carcinoma.

Importance: The most common cause of oropharyngeal squamous cell carcinoma is human papillomavirus (HPV) infection, and currently the standard of care to determine the HPV infection status in this type of carcinoma is to use p16 immunohistochemistry as a surrogate marker of high-risk HPV infection. Although p16 immunohistochemistry is limited by the inability to determine the specific HPV genotypes, oral gargle samples may be a readily available source of HPV DNA for genotyping. Objective: To determine the specific HPV genotypes present in both oral gargle samples and tumor specimens. Design, Setting, and Participants: This prospective, biomarker cohort study conducted at a single specialized cancer hospital in Florida screened approximately 800 potentially eligible participants from May 2014 through October 2017. To be eligible for participation, patients had to meet all of the following criteria: 18 years of age or older, male sex, newly diagnosed as having stage I to IV cancer of the oropharynx, a squamous cell carcinoma diagnosis, treatment naive or at least 4 weeks after chemoradiation or surgical treatment of other diseases, fully understand the study procedures and risks involved, and voluntarily agree to participate by signing an informed consent statement. Main Outcomes and Measures: Detection rate of HPV infection and HPV genotypes in oral gargle samples and tumor specimens. Results: A cohort of 204 male participants with newly diagnosed oropharyngeal squamous cell carcinoma was assessed in this prospective collection of comprehensive clinical data and oral gargle samples. Most study participants (190 [93.1%]) were white and ever smokers (114, 55.9%), with a median age of 61 years (range, 35-87 years). The HPV infection status could be assessed in 203 of 204 participants (99.5%) using oral gargle samples: 35 samples (17.2%) were negative for HPV infection, whereas 168 samples (82.8%) were positive for HPV infection. The detection rate of HPV genotypes was 93.0% in tumor specimens (160 specimens) and 82.8% (168 samples) in oral gargle samples. The oral gargle samples frequently had low-risk HPV genotypes that were not detected in tumors, but these low-risk genotypes were always a coinfection with high-risk genotypes. Conclusions and Relevance: Oral gargle samples can be used to detect the majority of clinically relevant HPV genotypes found in oropharyngeal squamous cell carcinoma, but the interpretation of HPV detected in these samples should be assessed with caution for general cancer risk assessment given that sensitive assays can concomitantly detect low-risk genotypes.