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.

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.

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.

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.

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.

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.

Proteogenomic characterization of human colon and rectal cancer.

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.

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 .

Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.

BACKGROUND AND AIMS: Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. METHODS: Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. RESULTS: Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. CONCLUSIONS: Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.

proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data.

To facilitate genome-based representation and analysis of proteomics data, we developed a new bioinformatics framework, proBAMsuite, in which a central component is the protein BAM (proBAM) file format for organizing peptide spectrum matches (PSMs)(1) within the context of the genome. proBAMsuite also includes two R packages, proBAMr and proBAMtools, for generating and analyzing proBAM files, respectively. Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data. First, the interpretation of proteomics data is significantly enhanced with the rich genomic annotation information. Second, PSMs can be easily reannotated using user-specified gene annotation schemes and assembled into both protein and gene identifications. Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration. Finally, proBAM files can be readily visualized in genome browsers and thus bring proteomics data analysis to a general audience beyond the proteomics community. Results from this study establish proBAMsuite as a useful bioinformatics framework for proteomics and proteogenomics research.

Oncogenic KRAS and BRAF Drive Metabolic Reprogramming in Colorectal Cancer.

Metabolic reprogramming, in which altered utilization of glucose and glutamine supports rapid growth, is a hallmark of most cancers. Mutations in the oncogenes KRAS and BRAF drive metabolic reprogramming through enhanced glucose uptake, but the broader impact of these mutations on pathways of carbon metabolism is unknown. Global shotgun proteomic analysis of isogenic DLD-1 and RKO colon cancer cell lines expressing mutant and wild type KRAS or BRAF, respectively, failed to identify significant differences (at least 2-fold) in metabolic protein abundance. However, a multiplexed parallel reaction monitoring (PRM) strategy targeting 73 metabolic proteins identified significant protein abundance increases of 1.25-twofold in glycolysis, the nonoxidative pentose phosphate pathway, glutamine metabolism, and the phosphoserine biosynthetic pathway in cells with KRAS G13D mutations or BRAF V600E mutations. These alterations corresponded to mutant KRAS and BRAF-dependent increases in glucose uptake and lactate production. Metabolic reprogramming and glucose conversion to lactate in RKO cells were proportional to levels of BRAF V600E protein. In DLD-1 cells, these effects were independent of the ratio of KRAS G13D to KRAS wild type protein. A study of 8 KRAS wild type and 8 KRAS mutant human colon tumors confirmed the association of increased expression of glycolytic and glutamine metabolic proteins with KRAS mutant status. Metabolic reprogramming is driven largely by modest (<2-fold) alterations in protein expression, which are not readily detected by the global profiling methods most commonly employed in proteomic studies. The results indicate the superiority of more precise, multiplexed, pathway-targeted analyses to study functional proteome systems. Data are available through MassIVE Accession MSV000079486 at ftp://MSV000079486@massive.ucsd.edu.

Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms.

Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single-sample LC-MS/MS with data-dependent acquisition to single-sample LC-MS/MS with data-independent acquisition and (2) peptide fractionation with label-free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single-sample analysis with data-independent acquisition, and 2219 proteins from single-sample analysis with data-dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single-sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.

Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma.

We hypothesized that distinct protein expression features of benign and malignant pulmonary nodules may reveal novel candidate biomarkers for the early detection of lung cancer. We performed proteome profiling by liquid chromatography-tandem mass spectrometry to characterize 34 resected benign lung nodules, 24 untreated lung adenocarcinomas (ADCs), and biopsies of bronchial epithelium. Group comparisons identified 65 proteins that differentiate nodules from ADCs and normal bronchial epithelium and 66 proteins that differentiate ADCs from nodules and normal bronchial epithelium. We developed a multiplexed parallel reaction monitoring (PRM) assay to quantify a subset of 43 of these candidate biomarkers in an independent cohort of 20 benign nodules, 21 ADCs, and 20 normal bronchial biopsies. PRM analyses confirmed significant nodule-specific abundance of 10 proteins including ALOX5, ALOX5AP, CCL19, CILP1, COL5A2, ITGB2, ITGAX, PTPRE, S100A12, and SLC2A3 and significant ADC-specific abundance of CEACAM6, CRABP2, LAD1, PLOD2, and TMEM110-MUSTN1. Immunohistochemistry analyses for seven selected proteins performed on an independent set of tissue microarrays confirmed nodule-specific expression of ALOX5, ALOX5AP, ITGAX, and SLC2A3 and cancer-specific expression of CEACAM6. These studies illustrate the value of global and targeted proteomics in a systematic process to identify and qualify candidate biomarkers for noninvasive molecular diagnosis of lung cancer.

Integrative omics analysis reveals the importance and scope of translational repression in microRNA-mediated regulation.

MicroRNAs (miRNAs) are key post-transcriptional regulators that inhibit gene expression by promoting mRNA decay and/or suppressing translation. However, the relative contributions of these two mechanisms to gene repression remain controversial. Early studies favor a translational repression-centric scenario, whereas recent large-scale studies suggest a dominant role of mRNA decay in miRNA regulation. Here we generated proteomics data for nine colorectal cancer cell lines and integrated them with matched miRNA and mRNA expression data to infer and characterize miRNA-mediated regulation. Consistent with previous reports, we found that 8mer site, site positioning within 3'UTR, local AU-rich context, and additional 3' pairing could all help boost miRNA-mediated mRNA decay. However, these sequence features were generally not correlated with increased translational repression, except for local AU-rich context. Thus the contribution of translational repression might be underestimated in recent studies in which the analyses were based primarily on the response of genes with canonical 7-8 mer sites in 3'UTRs. Indeed, we found that translational repression was involved in more than half, and played a major role in one-third of all predicted miRNA-target interactions. It was even the predominant contributor to miR-138 mediated regulation, which was further supported by the observation that differential expression of miR-138 in two genetically matched cell lines corresponded to altered protein but not mRNA abundance of most target genes. In addition, our study also provided interesting insights into colon cancer biology such as the possible contributions of miR-138 and miR-141/miR-200c in inducing specific phenotypes of SW480 and RKO cell lines, respectively.

Global stability of plasma proteomes for mass spectrometry-based analyses.

Peptide-based mass spectrometry approaches, such as multiple reaction monitoring, provide a powerful means to measure candidate protein biomarkers in plasma. A potential confounding problem is the effect of preanalytical variables, which may affect the integrity of proteins and peptides. Although some blood proteins undergo rapid physiological proteolysis ex vivo, the stability of most plasma proteins to preanalytical variables remains largely unexplored. We applied liquid chromatography-tandem mass spectrometry shotgun proteomics and multiple reaction monitoring analyses to characterize the stability of proteins at the peptide level in plasma. We systematically evaluated the effects of delay in plasma preparation at different temperatures, multiple freeze-thaw cycles and erythocyte hemolysis on peptide and protein inventories in prospectively collected human plasma. Time course studies indicated few significant changes in peptide and protein identifications, semitryptic peptides and methionine-oxidized peptides in plasma from blood collected in EDTA plasma tubes and stored for up to a week at 4 °C or room temperature prior to plasma isolation. Similarly, few significant changes were observed in similar analyses of plasma subjected to up to 25 freeze-thaw cycles. Hemolyzed samples produced no significant differences beyond the presence of hemoglobin proteins. Finally, paired comparisons of plasma and serum samples prepared from the same patients also yielded few significant differences, except for the depletion of fibrinogen in serum. Blood proteins thus are broadly stable to preanalytical variables when analyzed at the peptide level. Collection protocols to generate plasma for multiple reaction monitoring-based analyses may have different requirements than for other analyses directed at intact proteins.

In-depth proteomic analysis of nonsmall cell lung cancer to discover molecular targets and candidate biomarkers.

Advances in proteomic analysis of human samples are driving critical aspects of biomarker discovery and the identification of molecular pathways involved in disease etiology. Toward that end, in this report we are the first to use a standardized shotgun proteomic analysis method for in-depth tissue protein profiling of the two major subtypes of nonsmall cell lung cancer and normal lung tissues. We identified 3621 proteins from the analysis of pooled human samples of squamous cell carcinoma, adenocarcinoma, and control specimens. In addition to proteins previously shown to be implicated in lung cancer, we have identified new pathways and multiple new differentially expressed proteins of potential interest as therapeutic targets or diagnostic biomarkers, including some that were not identified by transcriptome profiling. Up-regulation of these proteins was confirmed by multiple reaction monitoring mass spectrometry. A subset of these proteins was found to be detectable and differentially present in the peripheral blood of cases and matched controls. Label-free shotgun proteomic analysis allows definition of lung tumor proteomes, identification of biomarker candidates, and potential targets for therapy.

Phase Ib trial of IRX-2 plus durvalumab in patients with recurrent and/or metastatic head and neck squamous cell carcinoma.

OBJECTIVES: IRX-2 is a multi-cytokine immune-activating agent with anti-tumor activity in non-metastatic head and neck squamous cell carcinoma (HNSCC). Here, we evaluated combined IRX-2 and durvalumab in patients with recurrent and/or metastatic HNSCC. MATERIALS AND METHODS: This was a phase Ib trial consisting of dose escalation and expansion. Primary endpoints were safety and biomarkers to assess the immune response in the tumor microenvironment including significant increases in PD-L1 expression and CD8 + tumor infiltrating lymphocytes (TIL) comparing pre- and on-treatment tumor biopsies. Secondary endpoints were objective response rates (ORR) and survival outcomes. RESULTS: Sixteen patients were evaluable for response, and nine patients were evaluable for biomarkers. Thirteen patients (68 %) had exposure to prior anti-PD-1 therapy. No dose-limiting or grade ≥ 3 treatment-related adverse events were observed. On-treatment biopsies showed significantly increased PD-L1 (p = 0.005), CD3+ (p = 0.020), CD4+ (p = 0.022), and CD8 + T cells (p = 0.017) compared to pre-treatment. Median overall survival and progression-free survival (PFS) were 6.18 months (95 % CI, 2.66-8.61) and 2.53 months (95 % CI, 1.81-4.04), respectively. One patient had an objective response (ORR, 5.3 %) with an ongoing PFS of > 25 months. Disease control rate was 42 %. The responder harbored an ARID1A variant of unknown significance (VUS) that was predicted to bind her HLA-I alleles with a higher affinity than the reference peptide. CONCLUSIONS: IRX-2 and durvalumab were safe and elicited the evidence of immune activation in the tumor microenvironment determined by increased PD-L1 expression and CD8+ TILs. CLINICAL TRIAL REGISTRATION NUMBER: NCT03381183.

Comparison of protein immunoprecipitation-multiple reaction monitoring with ELISA for assay of biomarker candidates in plasma.

Quantitative analysis of protein biomarkers in plasma is typically done by ELISA, but this method is limited by the availability of high-quality antibodies. An alternative approach is protein immunoprecipitation combined with multiple reaction monitoring mass spectrometry (IP-MRM). We compared IP-MRM to ELISA for the analysis of six colon cancer biomarker candidates (metalloproteinase inhibitor 1 (TIMP1), cartilage oligomeric matrix protein (COMP), thrombospondin-2 (THBS2), endoglin (ENG), mesothelin (MSLN) and matrix metalloproteinase-9 (MMP9)) in plasma from colon cancer patients and noncancer controls. Proteins were analyzed by multiplex immunoprecipitation from plasma with the ELISA capture antibodies, further purified by SDS-PAGE, digested and analyzed by stable isotope dilution MRM. IP-MRM provided linear responses (r = 0.978-0.995) between 10 and 640 ng/mL for the target proteins spiked into a "mock plasma" matrix consisting of 60 mg/mL bovine serum albumin. Measurement variation (coefficient of variation at the limit of detection) for IP-MRM assays ranged from 2.3 to 19%, which was similar to variation for ELISAs of the same samples. IP-MRM and ELISA measurements for all target proteins except ENG were highly correlated (r = 0.67-0.97). IP-MRM with high-quality capture antibodies thus provides an effective alternative method to ELISA for protein quantitation in biological fluids.

Proteomic analysis of oropharyngeal carcinomas reveals novel HPV-associated biological pathways.

Oropharyngeal carcinoma (OPC) can be classified into two equally prevalent subtypes depending on the presence of human papillomavirus (HPV). Patients with HPV-positive (HPV+) OPC represent a unique cohort with a distinct tumor biology and clinical behavior compared to HPV-negative (HPV-) OPC. Genetic studies have demonstrated chromosomal and gene expression changes associated with distinct subclasses of OPC; however, the proteomic consequences of HPV infection are not known. We analyzed sets of ten HPV+ and ten HPV- OPCs and ten normal adult oral epithelia using a standardized global proteomic analysis platform. This analysis yielded a total of 2,653 confidently identified proteins from which we chose 31 proteins on the basis of expression differences between HPV+, HPV- and normal epithelium for targeted protein quantitation. Analysis of differentially expressed proteins by HPV status revealed enrichment of proteins involved in epithelial cell development, keratinization and extracellular matrix organization in HPV- OPC, whereas enrichment of proteins in DNA initiation and replication and cell cycle control was found for HPV+ OPC. Enrichment analysis for transcription factor targets identified transcription factors E2F1 and E2F4 to be highly expressed in HPV+ OPC. We also found high expression of argininosuccinate synthase 1 in HPV+ OPC, suggesting that HPV+ OPC is more dependent on conditionally essential amino acid, arginine, and this was confirmed on a OPC-specific tissue microarray. These identified proteomic changes reveal novel driving molecular pathways for HPV+ and HPV- OPCs that may be pertinent in therapeutic strategies and outcomes of OPC.

A bioinformatics workflow for variant peptide detection in shotgun proteomics.

Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics.