Characterization of endoplasmic reticulum stress unveils ZNF703 as a promising target for colorectal cancer immunotherapy.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignant tumors globally, with high morbidity and mortality. Endoplasmic reticulum is a major organelle responsible for protein synthesis, processing, and transport. Endoplasmic reticulum stress (ERS) refers to the abnormal accumulation of unfolded and misfolded proteins in the endoplasmic reticulum, which are involved in tumorigenesis and cancer immunity. Nevertheless, the clinical significance of ERS remains largely unexplored in CRC. METHODS: In present study, we performed an unsupervised clustering to identify two types of ERS-related subtypes [ERS clusters, and ERS-related genes (ERSGs) clusters] in multiple large-scale CRC cohorts. Through the utilization of machine learning techniques, we have successfully developed an uncomplicated yet robust gene scoring system (ERSGs signature). Furthermore, a series of analyses, including GO, KEGG, Tumor Immune Dysfunction and Exclusion (TIDE), the Consensus Molecular Subtypes (CMS), were used to explore the underlying biological differences and clinical significance between these groups. And immunohistochemical and bioinformatics analyses were performed to explore ZNF703, a gene of ERSGs scoring system. RESULTS: We observed significant differences in prognosis and tumor immune status between the ERS clusters as well as ERSGs clusters. And the ERSGs scoring system was an independent risk factor for overall survival; and exhibited distinct tumor immune status in multicenter CRC cohorts. Besides, analyses of TNM stages, CMS groups demonstrated that patients in advanced stage and CMS4 had higher ERSGs scores. In addition, the ERSGs scores inversely correlated with positive ICB response predictors (such as, CD8A, CD274 (PD-L1), and TIS), and directly correlated with negative ICB response predictors (such as, TIDE, T cell Exclusion, COX-IS). Notably, immunohistochemical staining and bioinformatics analyses revealed that ZNF70 correlated with CD3 + and CD8 + T cells infiltration. CONCLUSION: Based on large-scale and multicenter transcriptomic data, our study comprehensively revealed the essential role of ERS in CRC; and constructed a novel ERSGs scoring system to predict the prognosis of patients and the efficacy of ICB treatment. Furthermore, we identified ZNF703 as a potentially promising target for ICB therapy in CRC.

Inhibition of HSP90 in Driver Oncogene-Defined Lung Adenocarcinoma Cell Lines: Key Proteins Underpinning Therapeutic Efficacy.

The use of 90 kDa heat shock protein (HSP90) inhibition as a therapy in lung adenocarcinoma remains limited due to moderate drug efficacy, the emergence of drug resistance, and early tumor recurrence. The main objective of this research is to maximize treatment efficacy in lung adenocarcinoma by identifying key proteins underlying HSP90 inhibition according to molecular background, and to search for potential biomarkers of response to this therapeutic strategy. Inhibition of the HSP90 chaperone was evaluated in different lung adenocarcinoma cell lines representing the most relevant molecular alterations (EGFR mutations, KRAS mutations, or EML4-ALK translocation) and wild-type genes found in each tumor subtype. The proteomic technique iTRAQ was used to identify proteomic profiles and determine which biological pathways are involved in the response to HSP90 inhibition in lung adenocarcinoma. We corroborated the greater efficacy of HSP90 inhibition in EGFR mutated or EML4-ALK translocated cell lines. We identified proteins specifically and significantly deregulated after HSP90 inhibition for each molecular alteration. Two proteins, ADI1 and RRP1, showed independently deregulated molecular patterns. Functional annotation of the altered proteins suggested that apoptosis was the only pathway affected by HSP90 inhibition across all molecular subgroups. The expression of ADI1 and RRP1 could be used to monitor the correct inhibition of HSP90 in lung adenocarcinoma. In addition, proteins such as ASS1, ITCH, or UBE2L3 involved in pathways related to the inhibition of a particular molecular background could be used as potential response biomarkers, thereby improving the efficacy of this therapeutic approach to combat lung adenocarcinoma.

Hyperpolarized 13C-Pyruvate to Assess Response to Anti-PD1 Immune Checkpoint Inhibition in YUMMER 1.7 Melanoma Xenografts.

There is currently no consensus to determine which advanced melanoma patients will benefit from immunotherapy, highlighting the critical need to identify early-response biomarkers to immune checkpoint inhibitors. The aim of this work was to evaluate in vivo metabolic spectroscopy using hyperpolarized (HP) 13C-pyruvate and 13C-glucose to assess early response to anti-PD1 therapy in the YUMMER1.7 syngeneic melanoma model. The xenografts showed a significant tumor growth delay when treated with two cycles of an anti-PD1 antibody compared to an isotype control antibody. 13C-MRS was performed in vivo after the injection of hyperpolarized 13C-pyruvate, at baseline and after one cycle of immunotherapy, to evaluate early dynamic changes in 13C-pyruvate-13C-lactate exchange. Furthermore, ex vivo 13C-MRS metabolic tracing experiments were performed after U-13C-glucose injection following one cycle of immunotherapy. A significant decrease in the ratio of HP 13C-lactate to 13C-pyruvate was observed in vivo in comparison with the isotype control group, while there was a lack of change in the levels of 13C lactate and 13C alanine issued from 13C glucose infusion, following ex vivo assessment on resected tumors. Thus, these results suggest that hyperpolarized 13C-pyruvate could be used to assess early response to immune checkpoint inhibitors in melanoma patients.

Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials.

This article studies generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly-robust augmented inverse probability weighted complete-case (AIPW) approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.

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Designing a Study of Correlates of Risk for Ebola Vaccination.

The recombinant vesicular stomatitis virus (rVSV) Ebola vaccine was shown to be very efficacious in a novel ring vaccination trial in Guinea. However, no correlates of vaccine protection have been established for Ebola vaccines. Several Ebola vaccine candidates are available, but conducting randomized trials of additional candidates in outbreaks is difficult. Establishing correlates of vaccine protection is essential. Here we explore power and sample-size calculations to evaluate potential correlates of risk during an Ebola vaccination campaign in an outbreak. The method requires that a blood draw be made at a predetermined time after vaccination. The statistical analysis estimates the relative risk of the Ebola endpoint occurring from after the blood draw through to the end of follow-up, contrasting vaccine recipients with different values of the immune response marker. The analysis can be done assuming a trichotomous or continuous marker. Under certain assumptions, at an overall vaccine efficacy of 75%, 50 Ebola endpoints in the vaccinees provided good power. At an overall vaccine efficacy of 90%, 20 Ebola endpoints gave good power. Power was highest when more vaccinees were in the high- and low-responder groups versus the middle group and when vaccine efficacy differed the most between the high- and low-responder groups.

Functional imaging of RAS pathway targeting in malignant peripheral nerve sheath tumor cells and xenografts.

BACKGROUND: Malignant peripheral nerve sheath tumor (MPNST) is an aggressive form of soft-tissue sarcoma (STS) in children. Despite intensive therapy, relatively few children with metastatic and unresectable disease survive beyond three years. RAS pathway activation is common in MPNST, suggesting MEK pathway inhibition as a targeted therapy, but the impact on clinical outcome has been small to date. PROCEDURE: We conducted preclinical pharmacokinetic (PK) and pharmacodynamic studies of two MEK inhibitors, trametinib and selumetinib, in two MPNST models and analyzed tumors for intratumor drug levels. We then investigated 3'-deoxy-3'-[18 F]fluorothymidine (18 F-FLT) PET imaging followed by 18 F-FDG PET/CT imaging of MPNST xenografts coupled to short-term or longer-term treatment with selumetinib focusing on PET-based imaging as a biomarker of MEK inhibition. RESULTS: Trametinib decreased pERK expression in MPNST xenografts but did not prolong survival or decrease Ki67 expression. In contrast, selumetinib prolonged survival of animals bearing MPNST xenografts, and this correlated with decreased pERK and Ki67 staining. PK studies revealed a significantly higher fraction of unbound selumetinib within a responsive MPNST xenograft model. Thymidine uptake, assessed by 18 F-FLT PET/CT, positively correlated with Ki67 expression in different xenograft models and in response to selumetinib. CONCLUSION: The ability of MEK inhibitors to control MPNST growth cannot simply be predicted by serum drug levels or drug-induced changes in pERK expression. Tumor cell proliferation assessed by 18 F-FLT PET imaging might be useful as an early response marker to targeted therapies, including MEK inhibition, where a primary effect is cell-cycle arrest.

Chemotherapy and Inflammatory Cytokine Signalling in Cancer Cells and the Tumour Microenvironment.

Cancer is the result of a cell's acquisition of a variety of biological capabilities or 'hallmarks' as outlined by Hanahan and Weinberg. These include sustained proliferative signalling, the ability to evade growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and the ability to invade other tissue and metastasize. More recently, the ability to escape immune destruction has been recognized as another important hallmark of tumours. It is suggested that genome instability and inflammation accelerates the acquisition of a variety of the above hallmarks. Inflammation, is a product of the body's response to tissue damage or pathogen invasion. It is required for tissue repair and host defense, but prolonged inflammation can often be the cause for disease. In a cancer patient, it is often unclear whether inflammation plays a protective or deleterious role in disease progression. Chemotherapy drugs can suppress tumour growth but also induce pathways in tumour cells that have been shown experimentally to support tumour progression or, in other cases, encourage an anti-tumour immune response. Thus, with the goal of better understanding the context under which each of these possible outcomes occurs, recent progress exploring chemotherapy-induced inflammatory cytokine production and the effects of cytokines on drug efficacy in the tumour microenvironment will be reviewed. The implications of chemotherapy on host and tumour cytokine pathways and their effect on the treatment of cancer patients will also be discussed.

Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis.

BACKGROUND: There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL). METHODS: We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL. RESULTS: We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL. CONCLUSIONS: Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

Plasma neurofilament light chain levels in patients with MS switching from injectable therapies to fingolimod.

BACKGROUND: Neurofilament light chain (NFL) is a cerebrospinal fluid (CSF) marker of neuroaxonal damage in multiple sclerosis (MS). OBJECTIVE: To determine the correlation of NFL in CSF and serum/plasma, and in plasma after switching from injectable MS therapies to fingolimod. METHODS: A first cohort consisted of MS patients ( n = 39) and neurological disease controls ( n = 27) where CSF and plasma/serum had been collected for diagnostic purposes. A second cohort ( n = 243) consisted of patients from a post-marketing study of fingolimod. NFL was determined with Single Molecule Array (Simoa™) technology (detection threshold 1.95 pg/mL). RESULTS: Mean NFL pg/mL (standard deviation ( SD)) was 341 (267) and 1475 (2358) in CSF and 8.2 (3.58) and 17.0 (16.94) in serum from controls and MS, respectively. CSF/serum and plasma/serum levels were highly correlated ( n = 66, rho = 0.672, p < 0.0001 and n = 16, rho = 0.684, p = 0.009, respectively). In patients starting fingolimod ( n = 243), mean NFL pg/mL ( SD) in plasma was reduced between baseline (20.4 (10.7)) and at 12 months (13.5 (7.3), p < 3 × 10-6), and levels remained stable at 24 months (13.2 (6.2)). CONCLUSION: NFL in serum and CSF are highly correlated and plasma NFL levels decrease after switching to highly effective MS therapy. Blood NFL measurement can be considered as a biomarker for MS therapy response.

Power/sample size calculations for assessing correlates of risk in clinical efficacy trials.

In a randomized controlled clinical trial that assesses treatment efficacy, a common objective is to assess the association of a measured biomarker response endpoint with the primary study endpoint in the active treatment group, using a case-cohort, case-control, or two-phase sampling design. Methods for power and sample size calculations for such biomarker association analyses typically do not account for the level of treatment efficacy, precluding interpretation of the biomarker association results in terms of biomarker effect modification of treatment efficacy, with detriment that the power calculations may tacitly and inadvertently assume that the treatment harms some study participants. We develop power and sample size methods accounting for this issue, and the methods also account for inter-individual variability of the biomarker that is not biologically relevant (e.g., due to technical measurement error). We focus on a binary study endpoint and on a biomarker subject to measurement error that is normally distributed or categorical with two or three levels. We illustrate the methods with preventive HIV vaccine efficacy trials and include an R package implementing the methods. Copyright © 2016 John Wiley & Sons, Ltd.

Variations in the metabolome in response to disease activity of rheumatoid arthritis.

BACKGROUND: Anti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis. The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response. METHODS: Blood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography-QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF. RESULTS: After 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p < 0.05) and 31 significant ions in negative mode (p < 0.05). Once intersected with PLS results, only 35 ions remained. Carbohydrate derivates emerged as strong candidate determinants of therapeutic response. CONCLUSIONS: This is the first study describing metabolic profiling in response to anti-TNF treatments using plasma samples. The study highlighted two different metabolic profiles splitting good responders from non-responders.

Evaluation of diffusion-weighted imaging (DWI) and MR spectroscopy (MRS) as early response biomarkers in cervical cancer patients.

PURPOSE: To prospectively assess whether choline levels and Apparent Diffusion Coefficient (ADC) values within cervical cancers before, during, and after non-surgical therapy are predictive of tumour response. PATIENTS AND METHODS: Patients undergoing MR examination for staging of cervical cancer, candidate for non-surgical therapy, were prospectively enrolled. According to the status at the end of therapies, patients were divided into responders and non-responders. The final outcome after a 5-year follow-up was classified as No Evidence of Disease (NED) or Progression of Disease (PD). Baseline values of mean ADC and Cho/H2O were compared between responders and non-responders, as well as between patients with NED and PD. The percent variation of ADC and Cho/H2O values over time was compared. P values <0.05 were considered significant. RESULTS: 16 patients were included. There was no significant difference at baseline between responders (n = 12) and non-responders (n = 4), nor between NED (n = 11) PD patients (n = 5), in ADC values and Cho/H2O ratio. There was no significant difference in percent variation of ADC values and of Cho/H2O, comparing responders and non-responders. There was a significant increase in absolute values of ADC from the initial to mid-therapy MRI (p = 0.0001), while Cho/H2O was stable (p value: 0.61). In the four non-responders, the ADC increase was not significant (p value: 0.25), while it was significant in the 11 responders (p value: 0.001). Values of spectroscopy were stable in both responders and non-responders. CONCLUSIONS: High increases of ADC values from baseline to mid-therapy MR reflect response to therapies. There were no significant variations in choline/water ratios over time.

[Preclinical models to establish innovative therapy strategies : Ex­vivo assessment of head and neck tumor chemo- and immune responses]. / Präklinische Modelle zur Etablierung innovativer Therapiestrategien : Ex­vivo­Testung der Chemo­ und Immunresponse von Kopf­Hals­Tumoren.

The pharmacological treatment of head and neck squamous cell carcinoma (HNSCC) is currently experiencing an expansion of the spectrum of targeting therapies. It can be expected that use of immune modulators, e.g., checkpoint-inhibitors, and their combination with chemotherapy will lead to a plethora of therapeutic options in the near future, from which the best one for the individual patient can be selected. HNSCCs are heterogeneous in their biology, and responses to chemotherapy are nonuniform and often only observable in subgroups. It would be valuable to know the chance of success of a particular treatment in advance. Evidence-based selection of the best individual treatment is difficult, since predictive biomarkers which are assessable prior to the treatment decision and reliably indicate the suitability of particular therapeutics are lacking. Pretherapeutic predictive ex-vivo chemoresponse testing of HNSCC biopsy specimens could enable identification of responders and allow a more suitable therapy regimen to be chosen for potential non-responders, without exposing them to likely ineffective therapy attempts. However, early ex-vivo assays failed regarding reliable prediction of therapeutic success, even with tolerable doses of pharmaceuticals and, in particular, their combinations. Predictive testing was hence deemed improper for the clinic. Improved methodology has now led to a reappraisal of predictive testing and its additional use in analysis of antitumor immune responses ex vivo. Here we describe recent advances and new results from ex-vivo chemoresponse testing of HNSCC and highlight their ability to facilitate establishment of innovative therapy strategies.

A comprehensive multi-omics analysis identifies a robust scoring system for cancer-associated fibroblasts and intervention targets in colorectal cancer.

BACKGROUND: Cancer-associated fibroblasts (CAF) play a critical role in promoting tumor growth, metastasis, and immune evasion. While numerous studies have investigated CAF, there remains a paucity of research on their clinical application in colorectal cancer (CRC). METHODS: In this study, we collected differentially expressed genes between CAF and normal fibroblasts (NF) from previous CRC studies, and utilized machine learning analysis to differentiate two distinct subtypes of CAF in CRC. To enable practical application, a CAF-related genes (CAFGs) scoring system was developed based on multivariate Cox regression. We then conducted functional enrichment analysis, Kaplan-Meier plot, consensus molecular subtypes (CMS) classification, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to investigate the relationship between the CAFGs scoring system and various biological mechanisms, prognostic value, tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy. Moreover, single-cell transcriptomics and proteomics analyses have been employed to validate the significance of scoring system-related molecules in the identity and function of CAF. RESULTS: We unveiled significant distinctions in tumor immune status and prognosis not only between the CAF clusters, but also across high and low CAFGs groups. Specifically, patients in CAF cluster 2 or with high CAFGs scores exhibited higher CAF markers and were enriched for CAF-related biological pathways such as epithelial-mesenchymal transition (EMT) and angiogenesis. In addition, CAFGs score was identified as a risk index and correlated with poor overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and recurrence-free survival (RFS). High CAFGs scores were observed in patients with advanced stages, CMS4, as well as lymphatic invasion. Furthermore, elevated CAFG scores in patients signified a suppressive tumor microenvironment characterized by the upregulation of programmed death-ligand 1 (PD-L1), T-cell dysfunction, exclusion, and TIDE score. And high CAFGs scores can differentiate patients with lower response rates and poor prognosis under ICB therapy. Notably, single-cell transcriptomics and proteomics analyses identified several molecules related to CAF identity and function, such as FSTL1, IGFBP7, and FBN1. CONCLUSION: We constructed a robust CAFGs score system with clinical significance using multiple CRC cohorts. In addition, we identified several molecules related to CAF identity and function that could be potential intervention targets for CRC patients.

Treatment Response Biomarkers: Working Toward Personalized Radiotherapy for Lung Cancer.

Owing to major advances in the field of radiation oncology, patients with lung cancer can now receive technically individualized radiotherapy treatments. Nevertheless, in the era of precision oncology, radiotherapy-based treatment selection needs to be improved as many patients do not benefit or are not offered optimum therapies. Cost-effective robust biomarkers can address this knowledge gap and lead to individuals being offered more bespoke treatments leading to improved outcome. This narrative review discusses some of the current achievements and challenges in the realization of personalized radiotherapy delivery in patients with lung cancer.

High CTLA-4 transcriptomic expression correlates with high expression of other checkpoints and with immunotherapy outcome.

Background: CTLA-4 impedes the immune system's antitumor response. There are two Food and Drug Administration-approved anti-CTLA-4 agents - ipilimumab and tremelimumab - both used together with anti-PD-1/PD-L1 agents. Objective: To assess the prognostic implications and immunologic correlates of high CTLA-4 in tumors of patients on immunotherapy and those on non-immunotherapy treatments. Design/methods: We evaluated RNA expression levels in a clinical-grade laboratory and clinical correlates of CTLA-4 and other immune checkpoints in 514 tumors, including 489 patients with advanced/metastatic cancers and full outcome annotation. A reference population (735 tumors; 35 histologies) was used to normalize and rank transcript abundance (0-100 percentile) to internal housekeeping gene profiles. Results: The most common tumor types were colorectal (140/514, 27%), pancreatic (55/514, 11%), breast (49/514, 10%), and ovarian cancers (43/514, 8%). Overall, 87 of 514 tumors (16.9%) had high CTLA-4 transcript expression (⩾75th percentile rank). Cancers with the largest proportion of high CTLA-4 transcripts were cervical cancer (80% of patients), small intestine cancer (33.3%), and melanoma (33.3%). High CTLA-4 RNA independently/significantly correlated with high PD-1, PD- L2, and LAG3 RNA levels (and with high PD-L1 in univariate analysis). High CTLA-4 RNA expression was not correlated with survival from the time of metastatic disease [N = 272 patients who never received immune checkpoint inhibitors (ICIs)]. However, in 217 patients treated with ICIs (mostly anti-PD-1/anti-PD- L1), progression-free survival (PFS) and overall survival (OS) were significantly longer among patients with high versus non-high CTLA-4 expression [hazard ratio, 95% confidence interval: 0.6 (0.4-0.9) p = 0.008; and 0.5 (0.3-0.8) p = 0.002, respectively]; results were unchanged when 18 patients who received anti-CTLA-4 were omitted. Patients whose tumors had high CTLA-4 and high PD-L1 did best; those with high PD-L1 but non-high CTLA-4 and/or other expression patterns had poorer outcomes for PFS (p = 0.004) and OS (p = 0.009) after immunotherapy. Conclusion: High CTLA-4, especially when combined with high PD-L1 transcript expression, was a significant positive predictive biomarker for better outcomes (PFS and OS) in patients on immunotherapy.

High CTLA-4 expression and immunotherapy outcome High CTLA-4 expression was not a prognostic factor for survival in patients not receiving ICIs but was a significant positive predictive biomarker for better outcome (PFS and OS) in patients on immunotherapy, perhaps because it correlated with expression of other checkpoints such as PD-1 and PD-L2.

Biomarkers in pulmonary infections: a clinical approach.

Severe acute respiratory infections, such as community-acquired pneumonia, hospital-acquired pneumonia, and ventilator-associated pneumonia, constitute frequent and lethal pulmonary infections in the intensive care unit (ICU). Despite optimal management with early appropriate empiric antimicrobial therapy and adequate supportive care, mortality remains high, in part attributable to the aging, growing number of comorbidities, and rising rates of multidrug resistance pathogens. Biomarkers have the potential to offer additional information that may further improve the management and outcome of pulmonary infections. Available pathogen-specific biomarkers, for example, Streptococcus pneumoniae urinary antigen test and galactomannan, can be helpful in the microbiologic diagnosis of pulmonary infection in ICU patients, improving the timing and appropriateness of empiric antimicrobial therapy since these tests have a short turnaround time in comparison to classic microbiology. On the other hand, host-response biomarkers, for example, C-reactive protein and procalcitonin, used in conjunction with the clinical data, may be useful in the diagnosis and prediction of pulmonary infections, monitoring the response to treatment, and guiding duration of antimicrobial therapy. The assessment of serial measurements overtime, kinetics of biomarkers, is more informative than a single value. The appropriate utilization of accurate pathogen-specific and host-response biomarkers may benefit clinical decision-making at the bedside and optimize antimicrobial stewardship.

Effectiveness of PD-1 inhibitor-based first-line therapy in Chinese patients with metastatic gastric cancer: a retrospective real-world study.

Objective: Programmed cell death protein-1 (PD-1) inhibitor-based therapy has demonstrated promising results in metastatic gastric cancer (MGC). However, the previous researches are mostly clinical trials and have reached various conclusions. Our objective is to investigate the efficacy of PD-1 inhibitor-based treatment as first-line therapy for MGC, utilizing real-world data from China, and further analyze predictive biomarkers for efficacy. Methods: This retrospective study comprised 105 patients diagnosed with MGC who underwent various PD-1 inhibitor-based treatments as first-line therapy at West China Hospital of Sichuan University from January 2018 to December 2022. Patient characteristics, treatment regimens, and tumor responses were extracted. We also conducted univariate and multivariate analyses to assess the relationship between clinical features and treatment outcomes. Additionally, we evaluated the predictive efficacy of several commonly used biomarkers for PD-1 inhibitor treatments. Results: Overall, after 28.0 months of follow-up among the 105 patients included in our study, the objective response rate (ORR) was 30.5%, and the disease control rate (DCR) was 89.5% post-treatment, with two individuals (1.9%) achieving complete response (CR). The median progression-free survival (mPFS) was 9.0 months, and the median overall survival (mOS) was 22.0 months. According to both univariate and multivariate analyses, favorable OS was associated with patients having Eastern Cooperative Oncology Group performance status (ECOG PS) of 0-1. Additionally, normal baseline levels of carcinoembryonic antigen (CEA), as well as the combination of PD-1 inhibitors with chemotherapy and trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive MGC, independently predicted longer PFS and OS. However, microsatellite instability/mismatch repair (MSI/MMR) status and Epstein-Barr virus (EBV) infection status were not significantly correlated with PFS or OS extension. Conclusion: As the first-line treatment, PD-1 inhibitors, either as monotherapy or in combination therapy, are promising to prolong survival for patients with metastatic gastric cancer. Additionally, baseline level of CEA is a potential predictive biomarker for identifying patients mostly responsive to PD-1 inhibitors.

Combining host immune response biomarkers and clinical scores for early prediction of sepsis in infection patients.

BACKGROUND: The performance of host immune responses biomarkers and clinical scores was compared to identify infection patient populations at risk of progression to sepsis, ICU admission and mortality. METHODS: Immune response biomarkers were measured and NEWS, SIRS, and MEWS. Logistic and Cox regression models were employed to evaluate the strength of association. RESULTS: IL-10 and NEWS had the strongest association with sepsis development, whereas IL-6 and CRP had the strongest association with ICU admission and in-hospital mortality. IL-6 [HR (95%CI) = 2.68 (1.61-4.46)] was associated with 28-day mortality. Patient subgroups with high IL-10 (≥ 5.03 pg/ml) and high NEWS (> 5 points) values had significantly higher rates of sepsis development (88.3% vs 61.1%; p < 0.001), in-hospital mortality (35.0% vs. 16.7%; p < 0.001), 28-day mortality (25.0% vs. 5.6%; p < 0.001), and ICU admission (66.7% vs. 38.9%; p < 0.001). CONCLUSIONS: Patients exhibiting low severity signs of infection but high IL-10 levels showed an elevated probability of developing sepsis. Combining IL-10 with the NEWS score provides a reliable tool for predicting the progression from infection to sepsis at an early stage. Utilizing IL-6 in the emergency room can help identify patients with low NEWS or SIRS scores.

Criteria for assessing evidence for biomarker-targeted therapies in rare cancers-an extrapolation framework.

Background: Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making. Objectives: To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker. Design: A series of questions articulating essential criteria for extrapolation. Methods: The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia's Medical Services Advisory Committee were incorporated. Results: We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies. Conclusion: This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers.