In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Biomarkers for Immunotherapy Efficacy in Advanced Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC), the most common primary liver malignancy and the sixth most common cancer globally, remains fatal for many patients with inappropriate responses to treatment. Recent advancements in immunotherapy have transformed the treatment landscape for advanced HCC. However, variability in patient responses to immunotherapy highlights the need for biomarkers that can predict treatment outcomes. This manuscript comprehensively reviews the evolving role of biomarkers in immunotherapy efficacy, spanning from blood-derived indicators-alpha-fetoprotein, inflammatory markers, cytokines, circulating tumor cells, and their DNA-to tissue-derived indicators-programmed cell death ligand 1 expression, tumor mutational burden, microsatellite instability, and tumor-infiltrating lymphocytes. The current body of evidence suggests that these biomarkers hold promise for improving patient selection and predicting immunotherapy outcomes. Each biomarker offers unique insights into disease biology and the immune landscape of HCC, potentially enhancing the precision of treatment strategies. However, challenges such as methodological variability, high costs, inconsistent findings, and the need for large-scale validation in well-powered two-arm trial studies persist, making them currently unsuitable for integration into standard care. Addressing these challenges through standardized techniques and implementation of further studies will be critical for the future incorporation of these biomarkers into clinical practice for advanced HCC.

Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics

Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Proteomic Investigation of Immune Checkpoints and Some of Their Inhibitors

Immune checkpoints are crucial molecules for the maintenance of antitumor immune responses. The activation or inhibition of these molecules is dependent on the interactions between receptors and ligands; such interactions can provide inhibitory or stimulatory signals to the various components of the immune system. Over the last 10 years, the inhibition of immune checkpoints, such as cytotoxic T lymphocyte antigen-4, programmed cell death-1, and programmed cell death ligand-1, has taken a leading role in immune therapy. This relatively recent therapy regime is based on the use of checkpoint inhibitors, which enhance the immune response towards various forms of cancer. For a subset of patients with specific forms of cancer, these inhibitors can induce a durable response to therapy; however, the medium response rate to such therapy remains relatively poor. Recent research activities have demonstrated that the disease response to this highly promising therapy resembles the response of many forms of cancer to chemotherapy, where an encouraging initial response is followed by acquired resistance to treatment and progress of the disease. That said, these inhibitors are now used as single agents or in combination with chemotherapies as first or second lines of treatment for about 50 types of cancer. The prevailing opinion regarding immune therapy suggests that for this approach of therapy to deliver on its promise, a number of challenges have to be circumvented. These challenges include understanding the resistance mechanisms to immune checkpoint blockade, the identification of more efficient inhibitors, extending their therapeutic benefits to a wider audience of cancer patients, better management of immune-related adverse side effects, and, more urgently the identification of biomarkers, which would help treating oncologists in the identification of patients who are likely to respond positively to the immune therapies and, last but not least, the prices of therapy which can be afforded by the highest number of patients. Numerous studies have demonstrated that understanding the interaction between these checkpoints and the immune system is essential for the development of efficient checkpoint inhibitors and improved immune therapies. In the present text, we discuss some of these checkpoints, their inhibitors, and some works in which mass spectrometry-based proteomic analyses were applied.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Regulatory T cells as crucial trigger and potential target for hyperprogressive disease subsequent to PD-1/PD-L1 blockade for cancer treatment

PD-1/PD-L1 blockade therapy has brought great success to cancer treatment. Nevertheless, limited beneficiary populations and even hyperprogressive disease (HPD) greatly constrain the application of PD-1/PD-L1 inhibitors in clinical treatment. HPD is a special pattern of disease progression with rapid tumor growth and even serious consequences of patient death, which requires urgent attention. Among the many predisposing causes of HPD, regulatory T cells (Tregs) are suspected because they are amplified in cases of HPD. Tregs express PD-1 thus PD-1/PD-L1 blockade therapy may have an impact on Tregs which leads to HPD. Tregs are a subset of CD4+ T cells expressing FoxP3 and play critical roles in suppressing immunity. Tregs migrate toward tumors in the presence of chemokines to suppress antitumor immune responses, causing cancer cells to grow and proliferate. Studies have shown that deleting Tregs could enhance the efficacy of PD-1/PD-L1 blockade therapy and reduce the occurrence of HPD. This suggests that immunotherapy combined with Treg depletion may be an effective means of avoiding HPD. In this review, we summarized the immunosuppressive-related functions of Tregs in antitumor therapy and focused on advances in therapy combining Tregs depletion with PD-1/PD-L1 blockade in clinical studies. Moreover, we provided an outlook on Treg-targeted HPD early warning for PD-1/PD-L1 blockade therapy.

Immunotherapy in melanoma: Can we predict response to treatment with circulating biomarkers?

Melanoma is the most aggressive form of skin cancer, representing approximately 4% of all cutaneous neoplasms and accounting for up to 80% of deaths. Advanced stages of melanoma involve metastatic processes and are associated with high mortality and morbidity, mainly due to the rapid dissemination and heterogeneous responses to current therapies, including immunotherapy. Immune checkpoint inhibitors (ICIs) are currently used in the treatment of metastatic melanoma (MM) and despite being linked to an increase in patient survival, a high percentage of them still do not benefit from it. Accordingly, the number of therapeutic regimens for MM patients using ICIs either alone or in combination with other therapies has increased, together with the need for reliable biomarkers that can both predict and monitor response to ICIs. In this context, circulating biomarkers, such as DNA, RNA, proteins, and cells, have emerged due to their ability to reflect disease status. Moreover, blood tests are minimally invasive and provide an attractive option to detect biomarkers, avoiding stressful medical procedures. This systematic review aims to evaluate the possibility of a non-invasive biomarker signature that can guide therapeutic decisions. The studies reported here offer valuable insight into how circulating biomarkers can have a role in personalized treatments for melanoma patients receiving ICIs therapy, emphasizing the need for rigorous clinical trials to confirm findings and establish standardized procedures.

Tumor-infiltrating macrophage associated lncRNA signature in cutaneous melanoma: implications for diagnosis, prognosis, and immunotherapy

Along with the increasing knowledge of long noncoding RNA, the interaction between the long noncoding RNA (lncRNA) and tumor immune infiltration is increasingly valued. However, there is a lack of understanding of correlation between regulation of specific lncRNAs and tumor-infiltrating macrophages within melanoma. In this research, a macrophage associated lncRNA signature was identified by multiple machine learning algorithms and the robust and effectiveness of signature also validated in other independent datasets. The signature contained six specific lncRNAs (PART1, LINC00968, LINC00954, LINC00944, LINC00518 and C20orf197) was constructed, which could diagnose melanoma and predict the prognosis of patients. Moreover, our signature achieves higher accuracy than the previous well-established markers and regarded as an independent prognostic indicator. The pathway enrichment revealed that these lncRNAs were closely correlated with many immune processes. In addition, the signature was associated with different immune microenvironment and applied to predict response of immune checkpoint inhibitor therapy (low risk of patients well respond to anti-PD-1 therapy and high risk is insensitive to anti-CTLA-4 therapy). Therefore, our finding supplies a more accuracy and effective lncRNA signature for tumor-infiltrating macrophages targeting treatment approaches and affords a new clinical application for predicting the response of immunotherapies in melanomas.

Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry

The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.

Longitudinal plasma proteomic analysis identifies biomarkers and combinational targets for anti-PD1-resistant cancer patients

The response rate of anti-PD1 therapy is limited, and the influence of anti-PD1 therapy on cancer patients is unclear. To address these challenges, we conducted a longitudinal analysis of plasma proteomic changes with anti-PD1 therapy in non-small cell lung cancer (NSCLC), alveolar soft part sarcoma (ASPS), and lymphoma patients. We included 339 plasma samples before and after anti-PD1 therapy from 193 patients with NSCLC, ASPS, or lymphoma. The plasma proteins were detected using data-independent acquisition-mass spectrometry and customable antibody microarrays. Differential proteomic characteristics in responders (R) and non-responders (NR) before and after anti-PD1 therapy were elucidated. A total of 1019 proteins were detected using our in-depth proteomics platform and distributed across 10-12 orders of abundance. By comparing the differential plasma proteome expression between R and NR groups, 50, 206, and 268 proteins were identified in NSCLC, ASPS, and lymphoma patients, respectively. Th17, IL-17, and JAK-STAT signal pathways were identified upregulated in NR group, while cellular senescence and transcriptional misregulation pathways were activated in R group. Longitudinal proteomics analysis revealed the IL-17 signaling pathway was downregulated after treatment. Consistently, many proteins were identified as potential combinatorial therapeutic targets (e.g., IL-17A and CD22). Five noninvasive biomarkers (FLT4, SFTPB, GNPTG, F5, and IL-17A) were further validated in an independent lymphoma cohort (n = 39), and another three noninvasive biomarkers (KIT, CCL3, and TNFSF1) were validated in NSCLC cohort (n = 76). Our results provide molecular insights into the anti-PD1 therapy in cancer patients and identify new therapeutic strategies for anti-PD1-resistant patients.

Circulating extracellular vesicles are monitoring biomarkers of anti-PD1 response and enhancer of tumor progression and immunosuppression in metastatic melanoma

BACKGROUND

Clinical drawback in checkpoint inhibitors immunotherapy (ICI) of metastatic melanoma (MM) is monitoring clinical benefit. Soluble forms of PD1(sPD1) and PD-L1(sPD-L1) and extracellular vesicles (EVs) expressing PD1 and PD-L1 have recently emerged as predictive biomarkers of response. As factors released in the blood, EVs and soluble forms could be relevant in monitoring treatment efficacy and adaptive resistance to ICI.

METHODS

We used pre-therapy plasma samples of 110 MM patients and longitudinal samples of 46 patients. Elisa assay and flow cytometry (FCM) were used to measure sPD-L1 and sPD1 concentrations and the percentage of PD1+ EVs and PD-L1+ EVs, released from tumor and immune cells in patients subsets. Transwell assays were conducted to investigate the impact of EVs of each patient subset on MM cells invasion and interaction between tumor cells and macrophages or dendritic cells. Viability assays were performed to assess EVs effect on MM cells and organoids sensitivity to anti-PD1. FCM was used to investigate immunosuppressive markers in EVs and immune cells.

RESULTS

The concentrations of sPD1 and sPD-L1 in pre-treatment and longitudinal samples did not correlate with anti-PD1 response, instead only tumor-derived PD1+ EVs decreased in long responders while increased during disease progression in responders. Notably, we observed reduction of T cell derived EVs expressing LAG3+ and PD1+ in long responders and their increase in responders experiencing progression. By investigating the impact of EVs on disease progression, we found that those isolated from non-responders and from patients with progression disease accelerated tumor cells invasiveness and migration towards macrophages, while EVs of long responders reduced the metastatic potential of MM cells and neo-angiogenesis. Additionally, the EVs of non-responders and of progression disease patients subset reduced the sensitivity of MM cells and organoids of responder to anti-PD1 and the recruitment of dendritic cells, while the EVs of progression disease subset skewed macrophages to express higher level of PDL-1.

CONCLUSION

Collectively, we suggest that the detection of tumor-derived PD1 + EVs may represent a useful tool for monitoring the response to anti-PD1 and a role for EVs shed by tumor and immune cells in promoting tumor progression and immune dysfunction.

Multi-omics Characterization of Response to PD-1 Inhibitors in Advanced Melanoma

Immunotherapy improves the survival of patients with advanced melanoma, 40% of whom become long-term responders. However, not all patients respond to immunotherapy. Further knowledge of the processes involved in the response and resistance to immunotherapy is still needed. In this study, clinical paraffin samples from fifty-two advanced melanoma patients treated with anti-PD-1 inhibitors were assessed via high-throughput proteomics and RNA-seq. The obtained proteomics and transcriptomics data were analyzed using multi-omics network analyses based on probabilistic graphical models to identify those biological processes involved in the response to immunotherapy. Additionally, proteins related to overall survival were studied. The activity of the node formed by the proteins involved in protein processing in the endoplasmic reticulum and antigen presentation machinery was higher in responders compared to non-responders; the activity of the immune and inflammatory response node was also higher in those with complete or partial responses. A predictor for overall survival based on two proteins (AMBP and PDSM5) was defined. In summary, the response to anti-PD-1 therapy in advanced melanoma is related to protein processing in the endoplasmic reticulum, and also to genes involved in the immune and inflammatory responses. Finally, a two-protein predictor can define survival in advanced disease. The molecular characterization of the mechanisms involved in the response and resistance to immunotherapy in melanoma leads the way to establishing therapeutic alternatives for patients who will not respond to this treatment.

Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy

The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.

Clinical roles of soluble PD-1 and PD-L1 in plasma of NSCLC patients treated with immune checkpoint inhibitors

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have significantly improved the prognosis of non-small cell lung cancer (NSCLC). However, only a limited proportion of patients can benefit from this therapy, and clinically useful predictive biomarkers remain to be elucidated.

METHODS

Blood was collected from 189 patients with NSCLC before and six weeks after the initiation of ICI treatment (anti-PD-1 or anti-PD-L1 antibody). Soluble PD-1 (sPD-1) and PD-L1 (sPD-L1) in plasma before and after treatment were analyzed to evaluate their clinical significance.

RESULTS

Cox regression analysis demonstrated that higher sPD-L1 levels before treatment significantly predicted unfavorable progression-free survival (PFS; HR 15.4, 95% CI 1.10-86.7, P = 0.009) and overall survival (OS; HR 11.4, 95% CI 1.19-52.3, P = 0.007) in NSCLC patients treated with ICI monotherapy (n = 122) but not in those treated with ICIs combined with chemotherapy (n = 67: P = 0.729 and P = 0.155, respectively). In addition, higher sPD-1 levels after treatment were significantly associated with better OS (HR 0.24, 95% CI 0.06-0.91, P = 0.037) in patients treated with anti-PD-1 monotherapy, whereas higher sPD-L1 levels after treatment were significantly associated with worse PFS (HR 6.09, 95% CI 1.42-21.0, P = 0.008) and OS (HR 42.6, 95% CI 6.83-226, P < 0.001). The levels of sPD-L1 at baseline closely correlated with those of other soluble factors, such as sCD30, IL-2Ra, sTNF-R1, and sTNF-R2, which are known to be released from the cell surface by zinc-binding proteases ADAM10/17.

CONCLUSIONS

These findings suggest the clinical significance of pretreatment sPD-L1 as well as posttreatment sPD-1 and sPD-L1 in NSCLC patients treated with ICI monotherapy.

Prognostic value of von Willebrand factor levels in patients with metastatic melanoma treated by immune checkpoint inhibitors

BACKGROUND

An increased incidence of thrombotic complications associated with an increased mortality rate has been observed under immune checkpoint inhibition (ICI). Recent investigations on the coagulation pathways have highlighted the direct role of key coagulatory proteins and platelets in cancer initiation, angiogenesis and progression. The aim of this study was to evaluate the prognostic value of von Willebrand factor (vWF) and its regulatory enzyme a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS13), D-dimers and platelets in a cohort of patients with metastatic melanoma receiving ICI.

METHODS

In a prospective cohort of 83 patients with metastatic melanoma, we measured the systemic levels of vWF-antigen (vWF:Ag), ADAMTS13 activity, D-dimers and platelets, before the beginning of the treatment (baseline), and 6, 12 and 24 weeks after. In parallel, we collected standard biological parameters used in clinical routine to monitor melanoma response (lactate deshydrogenase (LDH), S100). The impact of neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) on overall survival (OS) in patients receiving ICI was assessed. Univariable and multivariable Cox proportional models were then used to investigate any potential association of these parameters to clinical progression (progression-free survival (PFS) and OS). Baseline values and variations over therapy course were compared between primary responders and resistant patients.

RESULTS

Patients with melanoma present with dysregulated levels of vWF:Ag, ADAMTS13 activity, D-dimers, LDH, S100 and CRP at the beginning of treatment. With a median clinical follow-up of 26 months, vWF:Ag interrogated as a continuous variable was significantly associated with PFS in univariate and multivariate analysis (HR=1.04; p=0.007). Lower values of vWF:Ag at baseline were observed in the primary responders group (median: 29.4 µg/mL vs 32.9 µg/mL; p=0.048) when compared with primary resistant patients. As for OS, we found an association with D-dimers and ADAMTS13 activity in univariate analysis and vWF:Ag in univariate and multivariate analysis including v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation and Eastern Cooperative Oncology Group (ECOG) performance status. Follow-up over the course of treatment depicts different evolution profiles for vWF:Ag between the primary response and resistance groups.

CONCLUSIONS

In this prospective cohort, coagulatory parameters such as ADAMTS13 activity and D-dimers are associated with OS but baseline vWF:Ag levels appeared as the only parameter associated with response and OS to ICI. This highlights a potential role of vWF as a biomarker to monitor ICI response of patients with malignant melanoma.

Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma

Ovarian cancer is a highly lethal gynecological malignancy. Drug resistance rapidly occurs, and different therapeutic approaches are needed. So far, no biomarkers have been discovered to predict early response to therapies in the case of multi-treated ovarian cancer patients. The aim of our investigation was to identify a protein panel and the molecular pathways involved in chemotherapy response through a combination of studying proteomics and network enrichment analysis by considering a subset of samples from a clinical setting. Differential mass spectrometry studies were performed on 14 serum samples from patients with heavily pretreated platinum-resistant ovarian cancer who received the FOLFOX-4 regimen as a salvage therapy. The serum was analyzed at baseline time (T0) before FOLFOX-4 treatment, and before the second cycle of treatment (T1), with the aim of understanding if it was possible, after a first treatment cycle, to detect significant proteome changes that could be associated with patients responses to therapy. A total of 291 shared expressed proteins was identified and 12 proteins were finally selected between patients who attained partial response or no-response to chemotherapy when both response to therapy and time dependence (T0, T1) were considered in the statistical analysis. The protein panel included APOL1, GSN, GFI1, LCATL, MNA, LYVE1, ROR1, SHBG, SOD3, TEC, VPS18, and ZNF573. Using a bioinformatics network enrichment approach and metanalysis study, relationships between serum and cellular proteins were identified. An analysis of protein networks was conducted and identified at least three biological processes with functional and therapeutic significance in ovarian cancer, including lipoproteins metabolic process, structural component modulation in relation to cellular apoptosis and autophagy, and cellular oxidative stress response. Five proteins were almost independent from the network (LYVE1, ROR1, TEC, GFI1, and ZNF573). All proteins were associated with response to drug-resistant ovarian cancer resistant and were mechanistically connected to the pathways associated with cancer arrest. These results can be the basis for extending a biomarker discovery process to a clinical trial, as an early predictive tool of chemo-response to FOLFOX-4 of heavily treated ovarian cancer patients and for supporting the oncologist to continue or to interrupt the therapy.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Peripheral cytokine levels as novel predictors of survival in cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis

Background

Early identification of patients who will benefit from immune checkpoint inhibitors (ICIs) has recently become a hot issue in cancer immunotherapy. Peripheral cytokines are key regulators in the immune system that can induce the expression of immune checkpoint molecules; however, the association between peripheral cytokines and the efficiency of ICIs remains unclear.

Methods

A systematic review was conducted in several public databases from inception through 3 February 2022 to identify studies investigating the association between peripheral cytokines (i.e., IL-1β, IL-2, IL-2RA, IL-2R, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-15, IL-17, TNF-α, IFN-γ, and TGF-β) and ICI treatment. Survival data, including overall survival (OS) and/or progression-free survival (PFS), were extracted, and meta-analyses were performed.

Results

Twenty-four studies were included in this analysis. The pooled results demonstrated that the pretreatment peripheral levels of IL-6 (univariate analysis: HR = 2.53, 95% CI = 2.21–2.89, p < 0.00001; multivariate analysis: HR = 2.21, 95% CI = 1.67–2.93, p < 0.00001) and IL-8 (univariate analysis: HR = 2.17, 95% CI = 1.98–2.38, p < 0.00001; multivariate analysis: HR = 1.88, 95% CI= 1.70–2.07, p < 0.00001) were significantly associated with worse OS of cancer patients receiving ICI treatment in both univariate and multivariate analysis. However, high heterogeneity was found for IL-6, which might be attributed to region, cancer type, treatment method, sample source, and detection method.

Conclusion

The peripheral level of IL-8 may be used as a prognostic marker to identify patients with inferior response to ICIs. More high-quality prospective studies are warranted to assess the predictive value of peripheral cytokines for ICI treatment.

Increased Soluble PD-1 Predicts Response to Nivolumab plus Ipilimumab in Melanoma

BACKGROUND

Checkpoint inhibitors have revolutionized the treatment of metastatic melanoma, yielding long-term survival in a considerable proportion of the patients. Yet, 40-60% of patients do not achieve a long-term benefit from such therapy, emphasizing the urgent need to identify biomarkers that can predict response to immunotherapy and guide patients for the best possible treatment. Here, we exploited an unsupervised machine learning approach to identify potential inflammatory cytokine signatures from liquid biopsies, which could predict response to immunotherapy in melanoma.

METHODS

We studied a cohort of 77 patients diagnosed with unresectable advanced-stage melanoma undergoing treatment with first-line nivolumab plus ipilimumab or pembrolizumab. Baseline and on-treatment plasma samples were tested for levels of PD-1, PD-L1, IFNγ, IFNβ, CCL20, CXCL5, CXCL10, IL6, IL8, IL10, MCP1, and TNFα and analyzed by Uniform Manifold Approximation and Projection (UMAP) dimension reduction method and k-means clustering analysis.

RESULTS

Interestingly, using UMAP analysis, we found that treatment-induced cytokine changes measured as a ratio between baseline and on-treatment samples correlated significantly to progression-free survival (PFS). For patients treated with nivolumab plus ipilimumab we identified a group of patients with superior PFS that were characterized by significantly higher baseline-to-on-treatment increments of PD-1, PD-L1, IFNγ, IL10, CXCL10, and TNFα compared to patients with worse PFS. Particularly, a high PD-1 increment was a strong individual predictor for superior PFS (HR = 0.13; 95% CI 0.034-0.49; p = 0.0026). In contrast, decreasing levels of IFNγ and IL6 and increasing levels of CXCL5 were associated with superior PFS in the pembrolizumab group, although none of the cytokines were individually predictors for PFS.

CONCLUSIONS

In short, our study demonstrates that a high increment of PD-1 is associated with superior PFS in advanced-stage melanoma patients treated with nivolumab plus ipilimumab. In contrast, decreasing levels of IFNγ and IL6, and increasing levels of CXCL5 are associated with response to pembrolizumab. These results suggest that using serial samples to monitor changes in cytokine levels early during treatment is informative for treatment response.

Role of Biomarkers in the Integrated Management of Melanoma

Melanoma, which is an aggressive skin cancer, is currently the fifth and seventh most common cancer in men and women, respectively. The American Cancer Society reported that approximately 106,110 new cases of melanoma were diagnosed in the United States in 2021, with 7,180 people dying from the disease. This information could facilitate the early detection of possible metastatic lesions and the development of novel therapeutic techniques for melanoma. Additionally, early detection of malignant melanoma remains an objective of melanoma research. Recently, melanoma treatment has substantially improved, given the availability of targeted treatments and immunotherapy. These developments have highlighted the significance of identifying biomarkers for prognosis and predicting therapy response. Biomarkers included tissue protein expression, circulating DNA detection, and genetic alterations in cancer cells. Improved diagnostic and prognostic biomarkers are becoming increasingly relevant in melanoma treatment, with the development of newer and more targeted treatments. Here, the author discusses the aspects of biomarkers in the real-time management of patients with melanoma.

Increased Plasma Soluble PD-1 Concentration Correlates with Disease Progression in Patients with Cancer Treated with Anti-PD-1 Antibodies

Immune checkpoint inhibitors (ICIs) confer remarkable therapeutic benefits to patients with various cancers. However, many patients are non-responders or develop resistance following an initial response to ICIs. There are no reliable biomarkers to predict the therapeutic effect of ICIs. Therefore, this study investigated the clinical implications of plasma levels of soluble anti-programmed death-1 (sPD-1) in patients with cancer treated with ICIs. In total, 22 patients (13 with non-small-cell lung carcinoma, 8 with gastric cancer, and 1 with bladder cancer) were evaluated for sPD-1 concentration using enzyme-linked immunosorbent assays for diagnostic and anti-PD-1 antibody analyses. sPD-1 levels were low before the administration of anti-PD-1 antibodies. After two and four cycles of anti-PD-1 antibody therapy, sPD-1 levels significantly increased compared with pretreatment levels (p = 0.0348 vs. 0.0232). We observed an increased rate of change in plasma sPD-1 concentrations after two and four cycles of anti-PD-1 antibody therapy that significantly correlated with tumor size progression (p = 0.024). sPD-1 may be involved in resistance to anti-PD-1 antibody therapy, suggesting that changes in sPD-1 levels can identify primary ICI non-responders early in treatment. Detailed analysis of each cancer type revealed the potential of sPD-1 as a predictive biomarker of response to ICI treatment in patients with cancer.

Domont G, C. S. Nogueira F, Marko-Varga G, Sanchez A. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

Deep Proteomic Analysis on Biobanked Paraffine-Archived Melanoma with Prognostic/Predictive Biomarker Read-Out

The discovery of novel protein biomarkers in melanoma is crucial. Our introduction of formalin-fixed paraffin-embedded (FFPE) tumor protocol provides new opportunities to understand the progression of melanoma and open the possibility to screen thousands of FFPE samples deposited in tumor biobanks and available at hospital pathology departments. In our retrospective biobank pilot study, 90 FFPE samples from 77 patients were processed. Protein quantitation was performed by high-resolution mass spectrometry and validated by histopathologic analysis. The global protein expression formed six sample clusters. Proteins such as TRAF6 and ARMC10 were upregulated in clusters with enrichment for shorter survival, and proteins such as AIFI1 were upregulated in clusters with enrichment for longer survival. The cohort's heterogeneity was addressed by comparing primary and metastasis samples, as well comparing clinical stages. Within immunotherapy and targeted therapy subgroups, the upregulation of the VEGFA-VEGFR2 pathway, RNA splicing, increased activity of immune cells, extracellular matrix, and metabolic pathways were positively associated with patient outcome. To summarize, we were able to (i) link global protein expression profiles to survival, and they proved to be an independent prognostic indicator, as well as (ii) identify proteins that are potential predictors of a patient's response to immunotherapy and targeted therapy, suggesting new opportunities for precision medicine developments.

An Exploratory Analysis of Changes in Circulating Plasma Protein Profiles Following Image-Guided Ablation of Renal Tumours Provides Evidence for Effects on Multiple Biological Processes

Simple Summary

Ablation techniques use extremely hot or cold temperatures to kill small cancers. It is now known that in addition to killing the cancer cells, the ablation treatment may stimulate an immune response in patients against the cancer cells, acting like a vaccine. As a result, there is now interest in combining ablation together with drugs that target the immune system of patients with cancer to enhance the effects of both treatments. In order to develop such combined treatments and test them in clinical trials, we need to understand more about their effects so trials can be optimally designed to be most effective. We have analysed 164 circulating proteins in the blood from patients with small renal tumours undergoing ablation treatment to understand more about the effects of ablation on the patient, both at the level of the effects on the cancer cells and the response of the patients.

Abstract

Further biological understanding of the immune and inflammatory responses following ablation is critical to the rational development of combination ablation-immunotherapies. Our pilot exploratory study evaluated the circulating plasma protein profiles after image-guided ablation (IGA) of small renal masses to determine the resultant systemic effects and provide insight into impact both on the tumour and immune system. Patients undergoing cryotherapy (CRYO), radiofrequency ablation (RFA) or microwave ablation (MWA) for small renal tumours were recruited. Blood samples were obtained at four timepoints; two baselines prior to IGA and at 24 h and 1–3 months post-IGA, and a panel of 164 proteins measured. Of 55 patients recruited, 35 underwent ablation (25 CRYO, 8 RFA, 2 MWA) and biomarker measurements. The most marked changes were 24 h post-CRYO, with 29 proteins increasing and 18 decreasing significantly, principally cytokines and proteins involved in regulating inflammation, danger-associated molecular patterns (DAMPs), cell proliferation, hypoxic response, apoptosis and migration. Intra-individual variation was low but inter-individual variation was apparent, for example all patients showed increases in IL-6 (1.7 to 29-fold) but only 50% in CD27. Functional annotation analysis highlighted immune/inflammation and cell proliferation/angiogenesis-related clusters, with interaction networks around IL-6, IL-10, VEGF-A and several chemokines. Increases in IL-8, IL-6, and CCL23 correlated with cryoprobe number (p = 0.01, r_s = 0.546; p = 0.009, r_s = 0.5515; p = 0.005, r_s = 0.5873, respectively). This initial data provide further insights into ablation-induced biological changes of relevance in informing trial design of immunotherapies combined with ablation.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Plasma proteome alterations by MAPK inhibitors in BRAFV600-mutated metastatic cutaneous melanoma

Approximately half of metastatic cutaneous melanomas (CM) harbor a mutation in the BRAF protooncogene, upregulating the mitogen-activated protein kinase (MAPK)-pathway. The development of inhibitors targeting the MAPK pathway (MAPKi), i.e., BRAF- and MEK-inhibitors (BRAFi and MEKi), have substantially improved the survival in BRAFV600E/K-mutated stage IV metastatic CM. However, most patients develop resistance to treatment and no predictive biomarkers exist in practice. This study aimed at discovering plasma proteome changes during treatment MAPKi in patients with metastatic (stage IV) CM. Matched plasma samples before (pre) and during treatment (trm) from 23 patients with stage IV CM, treated with BRAF-inhibitors (BRAFi) alone or BRAF- and MEK- inhibitors combined (BRAFi and MEKi), were collected and analyzed with targeted proteomics by proximity extension assays. Additionally, plasma from 9 patients treated with BRAFi and MEKi was analyzed with in-depth high-resolution isoelectric focusing liquid-chromatography mass-spectrometry proteomics. Alterations of plasma proteins involved in granzyme and interferon gamma pathways were detected in patients treated with BRAFi, and cell adhesion-, neutrophil degranulation-, and proteolysis pathways in patients treated with BRAFi and MEKi. Several proteins were associated with progression-free survival after MAPKi treatment. We show that the majority of the altered plasma proteins were traceable to BRAFV600E-mutant metastatic CM tissue at mRNA level in 154 patients from the TCGA, further strengthening their involvement in tumoral response to treatment. This wide screen of plasma proteins unravels proteins that may serve as predictive and/or prognostic biomarkers of MAPKi treatment, opening a window of opportunity for plasma biomarker discovery in MAPKi-treatment of BRAFV600-mutant metastatic CM.

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Soluble immune checkpoints and T-cell subsets in blood as biomarkers for resistance to immunotherapy in melanoma patients

Different mechanisms lead to immune checkpoint inhibitor (ICI) resistance. Identifying clinically useful biomarkers might improve drug selection and patients’ therapy. We analyzed the soluble immune checkpoints sPD1, sPDL1, sLAG3, and sTIM3 using ELISA and their expression on circulating T cells using FACS in pre- and on-treatment blood samples of ICI treated melanoma patients. In addition, pre-treatment melanoma metastases were stained for TIM3 and LAG3 expression by IHC. Results were correlated with treatment response and progression-free survival (PFS). Resistance to anti-PD1 treatment (n = 48) was associated with high pre-treatment serum levels of sLAG3 (DCR: p = .009; PFS: p = .018; ROC cutoff >148 pg/ml) but not sPD1, sPDL1 or sTIM3. In contrast, resistance to ipilimumab plus nivolumab (n = 42) was associated with high levels of sPD1 (DCR: p = .019, PFS: p = .046; ROC cutoff >167 pg/ml) but not sPDL1, sLAG3 or sTIM3. Both treatment regimens shared a profound increase of sPD1 serum levels with treatment (p < .0001). FACS analysis revealed reduced frequencies of CD3+ CD8+ PD1 + T cells (p = .028) in anti-PD1-resistant patients, whereas increased frequencies of CD3+ CD4+ LAG3 + T cells characterized patients resistant to ipilimumab plus nivolumab (p = .033). Unlike anti-PD1 monotherapy, combination blockade significantly increased proliferating T cells (CD3+ CD8+ Ki67 + T cells; p < .0001) and eosinophils (p = .001). In melanoma metastases, an increased infiltration with TIM3+ or LAG3 + T cells in the tumor microenvironment correlated with a shorter PFS under anti-PD1 treatment (TIM3: p = .019, LAG3: p = .07). Different soluble immune checkpoints characterized checkpoint inhibitor-resistant melanoma. Measuring these serum markers may have the potential to be used in clinical routine.

Precision Medicine and Melanoma: Multi-Omics Approaches to Monitoring the Immunotherapy Response

The treatment and management of patients with metastatic melanoma have evolved considerably in the "era" of personalized medicine. Melanoma was one of the first solid tumors to benefit from immunotherapy; life expectancy for patients in advanced stage of disease has improved. However, many progresses have yet to be made considering the (still) high number of patients who do not respond to therapies or who suffer adverse events. In this scenario, precision medicine appears fundamental to direct the most appropriate treatment to the single patient and to guide towards treatment decisions. The recent multi-omics analyses (genomics, transcriptomics, proteomics, metabolomics, radiomics, etc.) and the technological evolution of data interpretation have allowed to identify and understand several processes underlying the biology of cancer; therefore, improving the tumor clinical management. Specifically, these approaches have identified new pharmacological targets and potential biomarkers used to predict the response or adverse events to treatments. In this review, we will analyze and describe the most important omics approaches, by evaluating the methodological aspects and progress in melanoma precision medicine.

Putting Proteomics Into Immunotherapy for Glioblastoma

In glioblastoma, the most aggressive brain cancer, a complex microenvironment of heterogeneity and immunosuppression, are considerable hurdles to classify the subtypes and promote treatment progression. Treatments for glioblastoma are similar to standard therapies for many other cancers and do not effectively prolong the survival of patients, due to the unique location and heterogeneous characteristics of glioblastoma. Immunotherapy has shown a promising effect for many other tumors, but its application for glioma still has some challenges. The recent breakthrough of high-throughput liquid chromatography-mass spectrometry (LC-MS/MS) systems has allowed researchers to update their strategy for identifying and quantifying thousands of proteins in a much shorter time with lesser effort. The protein maps can contribute to generating a complete map of regulatory systems to elucidate tumor mechanisms. In particular, newly developed unicellular proteomics could be used to determine the microenvironment and heterogeneity. In addition, a large scale of differentiated proteins provides more ways to precisely classify tumor subtypes and construct a larger library for biomarkers and biotargets, especially for immunotherapy. A series of advanced proteomic studies have been devoted to the different aspects of immunotherapy for glioma, including monoclonal antibodies, oncolytic viruses, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) T cells. Thus, the application of proteomics in immunotherapy may accelerate research on the treatment of glioblastoma. In this review, we evaluate the frontline applications of proteomics strategies for immunotherapy in glioblastoma research.

Integrated Analysis Reveals ENDOU as a Biomarker in Head and Neck Squamous Cell Carcinoma Progression

Background

Head and neck squamous cell carcinoma (HNSCC) is a leading cancer with high morbidity and mortality worldwide. The aim is to identify genes with clinical significance by integrated bioinformatics analysis and investigate their function in HNSCC.

Methods

We downloaded and analyzed two gene expression datasets of GSE6631 and GSE107591 to screen differentially expressed genes (DEGs) in HNSCC. Common DEGs were functionally analyzed by Gene ontology and KEGG pathway enrichment analysis. Protein-protein interaction (PPI) network was constructed with STRING database and Cytoscape. ENDOU was overexpressed in FaDu and Cal-27 cell lines, and cell proliferation and migration capability were evaluated with MTT, scratch and transwell assay. The prognostic performance of ENDOU and expression correlation with tumor infiltrates in HNSCC were validated with TCGA HNSCC datasets.

Results

Ninety-eight genes shared common differential expression in both datasets, with core functions like extracellular matrix organization significantly enriched. 15 genes showed prognostic significance, and COBL and ENDOU serve as independent survival markers in HNSCC. In-vitro ENDOU overexpression inhibited FaDu and Cal-27 cells proliferation and migration, indicating its tumor-suppressing role in HNSCC progression. GSEA analysis indicated ENDOU down-stream pathways like DNA replication, mismatch repair, cell cycle and IL-17 signaling pathway. ENDOU showed relative lower expression in HNSCC, especially HPV-positive HNSCC samples. At last, ENDOU showed negative correlation with tumor purity and tumor infiltrating macrophages, especially M2 macrophages.

Conclusion

This study identified ENDOU as a biomarker with prognostic significance in HNSCC progression.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.