Propensity score matching as an effective strategy for biomarker cohort design and omics data analysis.

BACKGROUND: Analysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors. METHODS: We explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results. RESULTS: Unlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts. CONCLUSION: PSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.

Protein expression of the amino acid transporter SLC7A5 in tumor tissue is prognostic in early-stage colorectal cancer.

Proteins overexpressed in early-stage cancers may serve as early diagnosis and prognosis markers as well as targets for cancer therapies. In this study, we examined the expression of an essential amino acid carrier SLC7A5 (LAT1, CD98, or 4F2 light chain) in cancer tissue from two well-annotated cohorts of 575 cases of early-stage and 106 cases of late-stage colorectal cancer patients. Immunohistochemistry showed SLC7A5 overexpression in 72.0% of early-stage and 56.6% of late-stage cases. SLC7A5 expression was not influenced by patient gender, age, location, or mismatch repair status, although it appeared to be slightly less prevalent in tumors of mucinous differentiation or with lymphovascular invasion. Statistical analyses revealed a positive correlation between SLC7A5 overexpression and both overall survival and disease-free survival in early-stage but not late-stage cancers. Co-expression analyses of the TCGA and CPTAC colorectal cancer cohorts identified a network of gene transcripts positively related to SLC7A5, with its heterodimer partner SLC3A2 having the highest co-expression score. Network analysis uncovered the SLC7A network to be significantly associated with ncRNA such as tRNA processing and the mitotic cell cycle. Since SLC7A5 is also a marker of activated lymphocytes such as NK, T, and B lymphocytes, SLC7A5 overexpression in early colorectal cancers might trigger a strong anti-tumor immune response which could results in better clinical outcome. Overall, our study provides clear evidence of differential SLC7A5 expression and its prognostic value for early-stage colorectal cancer, although the understanding of its functions in colorectal tumorigenesis and cancer immunity is currently rather limited and awaits further characterization.

Technical report: The clinically useful selection of proteins protocol: An approach to identify clinically useful proteins for validation.

Clinical proteomics studies aiming to develop markers of clinical outcome or disease typically involve distinct discovery and validation stages, neither of which focus on the clinical applicability of the candidate markers studied. Our clinically useful selection of proteins (CUSP) protocol proposes a rational approach, with statistical and non-statistical components, to identify proteins for the validation phase of studies that could be most effective markers of disease or clinical outcome. Additionally, this protocol considers commercially available analysis methods for each selected protein to ensure that use of this prospective marker is easily translated into clinical practice. SIGNIFICANCE: When developing proteomic markers of clinical outcomes, there is currently no consideration at the validation stage of how to implement such markers into a clinical setting. This has been identified by several studies as a limitation to the progression of research findings from proteomics studies. When integrated into a proteomic workflow, the CUSP protocol allows for a strategically designed validation study that improves researchers' abilities to translate research findings from discovery-based proteomics into clinical practice.

Proteogenomic characterization of primary colorectal cancer and metastatic progression identifies proteome-based subtypes and signatures.

Metastatic progression of colorectal adenocarcinoma (CRC) remains poorly understood and poses significant challenges for treatment. To overcome these challenges, we performed multiomics analyses of primary CRC and liver metastases. Genomic alterations, such as structural variants or copy number alterations, were enriched in oncogenes and tumor suppressor genes and increased in metastases. Unsupervised mass spectrometry-based proteomics of 135 primary and 123 metastatic CRCs uncovered distinct proteomic subtypes, three each for primary and metastatic CRCs, respectively. Integrated analyses revealed that hypoxia, stemness, and immune signatures characterize these 6 subtypes. Hypoxic CRC harbors high epithelial-to-mesenchymal transition features and metabolic adaptation. CRC with a stemness signature shows high oncogenic pathway activation and alternative telomere lengthening (ALT) phenotype, especially in metastatic lesions. Tumor microenvironment analysis shows immune evasion via modulation of major histocompatibility complex (MHC) class I/II and antigen processing pathways. This study characterizes both primary and metastatic CRCs and provides a large proteogenomics dataset of metastatic progression.

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer.

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

Dramatic, durable response to therapy in gBRCA2-mutated pancreas neuroendocrine carcinoma: opportunity and challenge.

Poorly differentiated pancreatic neuroendocrine tumors (PDNEC), are a subtype of pancreatic cancer encompassing both small cell and large cell neuroendocrine carcinoma subtypes, and are characterized as distinct in terms of biology and prognosis compared to the more common pancreatic adenocarcinoma. Until recently, there has been a paucity of data on the genomic features of this cancer type. We describe a male patient diagnosed with PDNEC and extensive metastatic disease in the liver at diagnosis. Genomic analysis demonstrated a germline pathogenic variant in BRCA2 with somatic loss-of-heterozygosity of the BRCA2 wild-type allele. Following a favorable response to platinum-based chemotherapy (and the addition of immunotherapy), the patient received maintenance therapy with olaparib, which resulted in a further reduction on follow-up imaging (Fig. 1). After seventeen months of systemic control with olaparib, the patient developed symptomatic central nervous system metastases, which harboured a BRCA2 reversion mutation. No other sites of disease progression were observed. Herein, we report an exceptional outcome through the incorporation of a personalized management approach for a patient with a pancreatic PDNEC, guided by comprehensive genomic sequencing.

Multicenter International Study of the Consensus Immunoscore for the Prediction of Relapse and Survival in Early-Stage Colon Cancer.

Background: The prognostic value of Immunoscore was evaluated in Stage II/III colon cancer (CC) patients, but it remains unclear in Stage I/II, and in early-stage subgroups at risk. An international Society for Immunotherapy of Cancer (SITC) study evaluated the pre-defined consensus Immunoscore in tumors from 1885 AJCC/UICC-TNM Stage I/II CC patients from Canada/USA (Cohort 1) and Europe/Asia (Cohort 2). METHODS: Digital-pathology is used to quantify the densities of CD3+ and CD8+ T-lymphocyte in the center of tumor (CT) and the invasive margin (IM). The time to recurrence (TTR) was the primary endpoint. Secondary endpoints were disease-free survival (DFS), overall survival (OS), prognosis in Stage I, Stage II, Stage II-high-risk, and microsatellite-stable (MSS) patients. RESULTS: High-Immunoscore presented with the lowest risk of recurrence in both cohorts. In Stage I/II, recurrence-free rates at 5 years were 78.4% (95%-CI, 74.4−82.6), 88.1% (95%-CI, 85.7−90.4), 93.4% (95%-CI, 91.1−95.8) in low, intermediate and high Immunoscore, respectively (HR (Hi vs. Lo) = 0.27 (95%-CI, 0.18−0.41); p < 0.0001). In Cox multivariable analysis, the association of Immunoscore to outcome was independent (TTR: HR (Hi vs. Lo) = 0.29, (95%-CI, 0.17−0.50); p < 0.0001) of the patient's gender, T-stage, sidedness, and microsatellite instability-status (MSI). A significant association of Immunoscore with survival was found for Stage II, high-risk Stage II, T4N0 and MSS patients. The Immunoscore also showed significant association with TTR in Stage-I (HR (Hi vs. Lo) = 0.07 (95%-CI, 0.01−0.61); P = 0.016). The Immunoscore had the strongest (69.5%) contribution χ2 for influencing survival. Patients with a high Immunoscore had prolonged TTR in T4N0 tumors even for patients not receiving chemotherapy, and the Immunoscore remained the only significant parameter in multivariable analysis. CONCLUSION: In early CC, low Immunoscore reliably identifies patients at risk of relapse for whom a more intensive surveillance program or adjuvant treatment should be considered.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project.

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Clinical Performance of the Consensus Immunoscore in Colon Cancer in the Asian Population from the Multicenter International SITC Study.

BACKGROUND: In this study, we evaluated the prognostic value of Immunoscore in patients with stage I−III colon cancer (CC) in the Asian population. These patients were originally included in an international study led by the Society for Immunotherapy of Cancer (SITC) on 2681 patients with AJCC/UICC-TNM stages I−III CC. METHODS: CD3+ and cytotoxic CD8+ T-lymphocyte densities were quantified in the tumor and invasive margin by digital pathology. The association of Immunoscore with prognosis was evaluated for time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS). RESULTS: Immunoscore stratified Asian patients (n = 423) into different risk categories and was not impacted by age. Recurrence-free rates at 3 years were 78.5%, 85.2%, and 98.3% for a Low, Intermediate, and High Immunoscore, respectively (HR[Low-vs-High] = 7.26 (95% CI 1.75−30.19); p = 0.0064). A High Immunoscore showed a significant association with prolonged TTR, OS, and DFS (p < 0.05). In Cox multivariable analysis stratified by center, Immunoscore association with TTR was independent (HR[Low-vs-Int+High] = 2.22 (95% CI 1.10−4.55) p = 0.0269) of the patient's gender, T-stage, N-stage, sidedness, and MSI status. A significant association of a High Immunoscore with prolonged TTR was also found among MSS (HR[Low-vs-Int+High] = 4.58 (95% CI 2.27−9.23); p ≤ 0.0001), stage II (HR[Low-vs-Int+High] = 2.72 (95% CI 1.35−5.51); p = 0.0052), low-risk stage-II (HR[Low-vs-Int+High] = 2.62 (95% CI 1.21−5.68); p = 0.0146), and high-risk stage II patients (HR[Low-vs-Int+High] = 3.11 (95% CI 1.39−6.91); p = 0.0055). CONCLUSION: A High Immunoscore is significantly associated with the prolonged survival of CC patients within the Asian population.

Early-Stage Loss of GALNT6 Predicts Poor Clinical Outcome in Colorectal Cancer.

Colorectal adenocarcinomas arise from luminal lining epithelium of the colorectal tract which is covered with highly glycosylated mucins. Mucin O-glycosylation is initiated by a family of polypeptide N-acteylgalactosaminyltransferases (GALNTs). This study examined GALNT6 protein expression in 679 colorectal tumors, including 574 early-stage and 105 late-stage cancers. GALNT6 expression in cancer tissue varied widely between patients ranging from high levels to complete loss. Loss of GALNT6 occurred in 9.9% of early-stage and 15.2% of late-stage cancers and was more prevalent in grade 3 or MSI subtype tumors. Survival analyses revealed that loss of GALNT6 expression is prognostic of reduced overall survival, and univariate and multivariate analyses demonstrated that loss of GALNT6 is an independent risk variable. We also analyzed 508-case TCGA and 63-case CPTAC colorectal cancer cohorts for all members of the GALNT enzyme family, the mucin family, as well as KRAS and BRAF mutations. GLANT6 mRNA expression showed no strong correlation with other GALNTs or mucins but was significantly higher in KRAS mutated or BRAF wild-type early-stage cancers. Using large cohorts of patients and different approaches, this study shows that loss of GALNT6 enzyme in early-stage colorectal cancer predicts poor clinical outcomes.

Genomic and transcriptomic analysis of a library of small cell lung cancer patient-derived xenografts.

Access to clinically relevant small cell lung cancer (SCLC) tissue is limited because surgical resection is rare in metastatic SCLC. Patient-derived xenografts (PDX) and circulating tumor cell-derived xenografts (CDX) have emerged as valuable tools to characterize SCLC. Here, we present a resource of 46 extensively annotated PDX/CDX models derived from 33 patients with SCLC. We perform multi-omic analyses, using targeted tumor next-generation sequencing, RNA-sequencing, and immunohistochemistry to deconvolute the mutational landscapes, global expression profiles, and molecular subtypes of these SCLC models. SCLC subtypes characterized by transcriptional regulators, ASCL1, NEUROD1 and POU2F3 are confirmed in this cohort. A subset of SCLC clinical specimens, including matched PDX/CDX and clinical specimen pairs, confirm that the primary features and genomic and proteomic landscapes of the tumors of origin are preserved in the derivative PDX models. This resource provides a powerful system to study SCLC biology.

An Autoantigen Atlas From Human Lung HFL1 Cells Offers Clues to Neurological and Diverse Autoimmune Manifestations of COVID-19.

COVID-19 is accompanied by a myriad of both transient and long-lasting autoimmune responses. Dermatan sulfate (DS), a glycosaminoglycan crucial for wound healing, has unique affinity for autoantigens (autoAgs) from apoptotic cells. DS-autoAg complexes are capable of stimulating autoreactive B cells and autoantibody production. We used DS-affinity proteomics to define the autoantigen-ome of lung fibroblasts and bioinformatics analyses to study the relationship between autoantigenic proteins and COVID-induced alterations. Using DS-affinity, we identified an autoantigen-ome of 408 proteins from human HFL1 cells, at least 231 of which are known autoAgs. Comparing with available COVID data, 352 proteins of the autoantigen-ome have thus far been found to be altered at protein or RNA levels in SARS-CoV-2 infection, 210 of which are known autoAgs. The COVID-altered proteins are significantly associated with RNA metabolism, translation, vesicles and vesicle transport, cell death, supramolecular fibrils, cytoskeleton, extracellular matrix, and interleukin signaling. They offer clues to neurological problems, fibrosis, smooth muscle dysfunction, and thrombosis. In particular, 150 altered proteins are related to the nervous system, including axon, myelin sheath, neuron projection, neuronal cell body, and olfactory bulb. An association with the melanosome is also identified. The findings from our study illustrate a connection between COVID infection and autoimmunity. The vast number of COVID-altered proteins with high intrinsic propensity to become autoAgs offers an explanation for the diverse autoimmune complications in COVID patients. The variety of autoAgs related to mRNA metabolism, translation, and vesicles suggests a need for long-term monitoring of autoimmunity in COVID. The COVID autoantigen atlas we are establishing provides a detailed molecular map for further investigation of autoimmune sequelae of the pandemic, such as "long COVID" syndrome. Summary Sentence: An autoantigen-ome by dermatan sulfate affinity from human lung HFL1 cells may explain neurological and autoimmune manifestations of COVID-19.

A master autoantigen-ome links alternative splicing, female predilection, and COVID-19 to autoimmune diseases.

Chronic and debilitating autoimmune sequelae pose a grave concern for the post-COVID-19 pandemic era. Based on our discovery that the glycosaminoglycan dermatan sulfate (DS) displays peculiar affinity to apoptotic cells and autoantigens (autoAgs) and that DS-autoAg complexes cooperatively stimulate autoreactive B1 cell responses, we compiled a database of 751 candidate autoAgs from six human cell types. At least 657 of these have been found to be affected by SARS-CoV-2 infection based on currently available multi-omic COVID data, and at least 400 are confirmed targets of autoantibodies in a wide array of autoimmune diseases and cancer. The autoantigen-ome is significantly associated with various processes in viral infections, such as translation, protein processing, and vesicle transport. Interestingly, the coding genes of autoAgs predominantly contain multiple exons with many possible alternative splicing variants, short transcripts, and short UTR lengths. These observations and the finding that numerous autoAgs involved in RNA-splicing showed altered expression in viral infections suggest that viruses exploit alternative splicing to reprogram host cell machinery to ensure viral replication and survival. While each cell type gives rise to a unique pool of autoAgs, 39 common autoAgs associated with cell stress and apoptosis were identified from all six cell types, with several being known markers of systemic autoimmune diseases. In particular, the common autoAg UBA1 that catalyzes the first step in ubiquitination is encoded by an X-chromosome escape gene. Given its essential function in apoptotic cell clearance and that X-inactivation escape tends to increase with aging, UBA1 dysfunction can therefore predispose aging women to autoimmune disorders. In summary, we propose a model of how viral infections lead to extensive molecular alterations and host cell death, autoimmune responses facilitated by autoAg-DS complexes, and ultimately autoimmune diseases. Overall, this master autoantigen-ome provides a molecular guide for investigating the myriad of autoimmune sequalae to COVID-19 and clues to the rare adverse effects of the currently available mRNA and viral vector-based COVID vaccines.

Tumor stromal nicotinamide N-methyltransferase overexpression as a prognostic biomarker for poor clinical outcome in early-stage colorectal cancer.

In a quest for prognostic biomarkers in early-stage colorectal cancer, we investigated NNMT (nicotinamide N-methyltransferase) in large cohorts of patients. Immunohistochemical examination of 679 patients illustrates that NNMT protein is predominantly expressed in the cancer stroma at varying levels, and about 20% of cancer tissues overexpress NNMT when compared to levels observed in normal colorectal mucosa. Clinical correlation analyses of 572 patients with early-stage cancers reveal that NNMT protein overexpression is significantly associated with shorter overall and disease-free survival, but no such correlation is found in late-stage colorectal cancer. Analyses of TCGA and CPTAC colorectal cancer cohorts show that NNMT mRNA expression is positively correlated with protein levels, is significantly higher in CIMP-high or MSI subtypes than in CIMP-low or MSS subtypes, and is positively correlated with its paralog INMT but not with its interaction partners such as PNMT, ADK, APP, ATF6, BMF, BRD4, CDC37, or CRYZ. In early-stage cancers, NNMT expression is higher in BRAF-mutated than in BRAF wild type tumors but is not affected by KRAS or PIK3CA mutation status. As a cancer stromal protein with important roles in metabolism and cancer epigenetics, NNMT is emerging as a promising biomarker for risk stratification of early-stage cancers.

Pancreatoblastomas and mixed and pure acinar cell carcinomas share epigenetic signatures distinct from other neoplasms of the pancreas.

Pancreatic neoplasms are heterogenous and have traditionally been classified by assessing their lines of cellular differentiation using histopathologic methods, particularly morphologic and immunohistochemical evaluation. These methods frequently identify overlapping differentiation along ductal, acinar, and neuroendocrine lines, raising diagnostic challenges as well as questions regarding the relationship of these neoplasms. Neoplasms with acinar differentiation, in particular, frequently show more than one line of differentiation based on immunolabeling. Genome methylation signatures, in contrast, are better conserved within cellular lineages, and are increasingly used to support the classification of neoplasms. We characterized the epigenetic relationships between pancreatoblastomas, acinar cell carcinomas (including mixed variants), pancreatic neuroendocrine tumors, solid pseudopapillary neoplasms, and pancreatic ductal adenocarcinomas using a genome-wide array platform. Using unsupervised learning approaches, pancreatic neuroendocrine tumors, solid pseudopapillary neoplasms, ductal adenocarcinomas, and normal pancreatic tissue samples all localized to distinct clusters based on their methylation profiles, whereas all neoplasms with acinar differentiation occupied a broad overlapping region located between the predominantly acinar normal pancreatic tissue and ductal adenocarcinoma clusters. Our data provide evidence to suggest that acinar cell carcinomas and pancreatoblastomas are similar at the epigenetic level. These findings are consistent with genomic and clinical observations that mixed acinar neoplasms are closely related to pure acinar cell carcinomas rather than to neuroendocrine tumors or ductal adenocarcinomas.

Inhibition of XPO1 Sensitizes Small Cell Lung Cancer to First- and Second-Line Chemotherapy.

Small cell lung cancer (SCLC) is an aggressive malignancy characterized by early metastasis and extreme lethality. The backbone of SCLC treatment over the past several decades has been platinum-based doublet chemotherapy, with the recent addition of immunotherapy providing modest benefits in a subset of patients. However, nearly all patients treated with systemic therapy quickly develop resistant disease, and there is an absence of effective therapies for recurrent and progressive disease. Here we conducted CRISPR-Cas9 screens using a druggable genome library in multiple SCLC cell lines representing distinct molecular subtypes. This screen nominated exportin-1, encoded by XPO1, as a therapeutic target. XPO1 was highly and ubiquitously expressed in SCLC relative to other lung cancer histologies and other tumor types. XPO1 knockout enhanced chemosensitivity, and exportin-1 inhibition demonstrated synergy with both first- and second-line chemotherapy. The small molecule exportin-1 inhibitor selinexor in combination with cisplatin or irinotecan dramatically inhibited tumor growth in chemonaïve and chemorelapsed SCLC patient-derived xenografts, respectively. Together these data identify exportin-1 as a promising therapeutic target in SCLC, with the potential to markedly augment the efficacy of cytotoxic agents commonly used in treating this disease. SIGNIFICANCE: CRISPR-Cas9 screening nominates exportin-1 as a therapeutic target in SCLC, and exportin-1 inhibition enhances chemotherapy efficacy in patient-derived xenografts, providing a novel therapeutic opportunity in this disease.

An Autoantigen Profile from Jurkat T-Lymphoblasts Provides a Molecular Guide for Investigating Autoimmune Sequelae of COVID-19.

In order to understand autoimmune phenomena contributing to the pathophysiology of COVID-19 and post-COVID syndrome, we have been profiling autoantigens (autoAgs) from various cell types. Although cells share numerous autoAgs, each cell type gives rise to unique COVID-altered autoAg candidates, which may explain the wide range of symptoms experienced by patients with autoimmune sequelae of SARS-CoV-2 infection. Based on the unifying property of affinity between autoantigens (autoAgs) and the glycosaminoglycan dermatan sulfate (DS), this paper reports 140 candidate autoAgs identified from proteome extracts of human Jurkat T-cells, of which at least 105 (75%) are known targets of autoantibodies. Comparison with currently available multi-omic COVID-19 data shows that 125 (89%) of DS-affinity proteins are altered at protein and/or RNA levels in SARS-CoV-2-infected cells or patients, with at least 94 being known autoAgs in a wide spectrum of autoimmune diseases and cancer. Protein alterations by ubiquitination and phosphorylation in the viral infection are major contributors of autoAgs. The autoAg protein network is significantly associated with cellular response to stress, apoptosis, RNA metabolism, mRNA processing and translation, protein folding and processing, chromosome organization, cell cycle, and muscle contraction. The autoAgs include clusters of histones, CCT/TriC chaperonin, DNA replication licensing factors, proteasome and ribosome proteins, heat shock proteins, serine/arginine-rich splicing factors, 14-3-3 proteins, and cytoskeletal proteins. AutoAgs such as LCP1 and NACA that are altered in the T cells of COVID patients may provide insight into T-cell responses in the viral infection and merit further study. The autoantigen-ome from this study contributes to a comprehensive molecular map for investigating acute, subacute, and chronic autoimmune disorders caused by SARS-CoV-2.

Comprehensive molecular characterization of lung tumors implicates AKT and MYC signaling in adenocarcinoma to squamous cell transdifferentiation.

BACKGROUND: Lineage plasticity, the ability to transdifferentiate among distinct phenotypic identities, facilitates therapeutic resistance in cancer. In lung adenocarcinomas (LUADs), this phenomenon includes small cell and squamous cell (LUSC) histologic transformation in the context of acquired resistance to targeted inhibition of driver mutations. LUAD-to-LUSC transdifferentiation, occurring in up to 9% of EGFR-mutant patients relapsed on osimertinib, is associated with notably poor prognosis. We hypothesized that multi-parameter profiling of the components of mixed histology (LUAD/LUSC) tumors could provide insight into factors licensing lineage plasticity between these histologies. METHODS: We performed genomic, epigenomics, transcriptomics and protein analyses of microdissected LUAD and LUSC components from mixed histology tumors, pre-/post-transformation tumors and reference non-transformed LUAD and LUSC samples. We validated our findings through genetic manipulation of preclinical models in vitro and in vivo and performed patient-derived xenograft (PDX) treatments to validate potential therapeutic targets in a LUAD PDX model acquiring LUSC features after osimertinib treatment. RESULTS: Our data suggest that LUSC transdifferentiation is primarily driven by transcriptional reprogramming rather than mutational events. We observed consistent relative upregulation of PI3K/AKT, MYC and PRC2 pathway genes. Concurrent activation of PI3K/AKT and MYC induced squamous features in EGFR-mutant LUAD preclinical models. Pharmacologic inhibition of EZH1/2 in combination with osimertinib prevented relapse with squamous-features in an EGFR-mutant patient-derived xenograft model, and inhibition of EZH1/2 or PI3K/AKT signaling re-sensitized resistant squamous-like tumors to osimertinib. CONCLUSIONS: Our findings provide the first comprehensive molecular characterization of LUSC transdifferentiation, suggesting putative drivers and potential therapeutic targets to constrain or prevent lineage plasticity.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project.

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.