Mutational variant allele frequency profile as a biomarker of response to immune checkpoint blockade in non-small cell lung Cancer.

INTRODUCTION: Identifying new biomarkers for predicting immune checkpoint inhibitors (ICIs) response in non-small cell lung cancer (NSCLC) is crucial. We aimed to assess the variant allele frequency (VAF)-related profile as a novel biomarker for NSCLC personalized therapy. METHODS: We utilized genomic data of 915 NSCLC patients via cBioPortal and a local cohort of 23 patients for model construction and mutational analysis. Genomic, transcriptomic data from 952 TCGA NSCLC patients, and immunofluorescence (IF) assessment with the local cohort supported mechanism analysis. RESULTS: Utilizing the random forest algorithm, a 15-gene VAF-related model was established, differentiating patients with durable clinical benefit (DCB) from no durable benefit (NDB). The model demonstrated robust performance, with ROC-AUC values of 0.905, 0.737, and 0.711 across training (n = 313), internal validation (n = 133), and external validation (n = 157) cohorts. Stratification by the model into high- and low-score groups correlated significantly with both progression-free survival (PFS) (training: P < 0.0001, internal validation: P < 0.0001, external validation: P = 0.0066) and overall survival (OS) (n = 341) (P < 0.0001). Notably, the stratification system was independent of PD-L1 (P < 0.0001) and TMB (P < 0.0001). High-score patients exhibited an increased DCB ratio and longer PFS across both PD-L1 and TMB subgroups. Additionally, the high-score group appeared influenced by tobacco exposure, with activated DNA damage response pathways. Whereas, immune/inflammation-related pathways were enriched in the low-score group. Tumor immune microenvironment analyses revealed higher proportions of exhausted/effector memory CD8 + T cells in the high-score group. CONCLUSIONS: The mutational VAF profile is a promising biomarker for ICI therapy in NSCLC, with enhanced therapeutic stratification and management as a supplement to PD-L1 or TMB.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.

Background: Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations. Methods: We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS). Results: CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively. Conclusions: CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy.

BACKGROUND: Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients. METHODS: We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA. RESULTS: We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment. CONCLUSIONS: This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.

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.

Immunogenicity assessment of elder hepatocellular carcinoma patients after inactivated whole-virion SARS-CoV-2 vaccination.

BACKGROUND: Research on immunogenicity after 3rd SARS-CoV-2 vaccine in elder hepatocellular carcinoma (HCC) was limited. This study aimed to investigate the efficacy and influencing factors of inactivated SARS-CoV-2 vaccine in elder HCC. RESEARCH DESIGN AND METHODS: We assessed total antibodies, anti-RBD IgG, and neutralizing antibodies (NAb) toward SARS-CoV-2 wild type (WT) as well as BA.4/5 in 304 uninfected HCC, 147 matched healthy control (HC), and 53 SARS-CoV-2 infected HCC, all aged over 60 years. The levels of antibodies were compared in the period 7-90, 91-180, and >180 days after 2nd or 3rd vaccination, respectively. RESULTS: HCC had lower seropositivity than HC after 2nd dose (total antibodies, 64% vs. 92%, P < 0.0001; anti-RBD IgG, 50% vs. 77%, P < 0.0001). But 3rd dose can efficaciously close the gap (total antibodies, 96% vs. 100%, P = 0.1212; anti-RBD IgG: 87% vs. 87%, P > 0.9999). Booster effect of 3rd dose can persist >180 days in HCC (2nd vs. 3rd: total antibodies, 0.60 vs. 3.20, P < 0.0001; anti-RBD IgG, 13.86 vs. 68.85, P < 0.0001; WT NAb, 11.70 vs. 22.47, P < 0.0001). Vaccinated HCC had more evident humoral responses than unvaccinated ones after infection (total antibodies: 3.85 vs. 3.20, P < 0.0001; anti-RBD IgG: 910.92 vs. 68.85, P < 0.0001; WT NAb: 96.09 vs. 22.47, P < 0.0001; BA.4/5 NAb: 86.53 vs. 5.59, P < 0.0001). CONCLUSIONS: Our findings highlight the booster effect and protective role of 3rd dose. Our results could provide a theoretical foundation for informing decisions regarding SARS-CoV-2 vaccination in elder HCC.

Antibody response to SARS-CoV-2 WT and Omicron BA.4/5 of inactivated COVID-19 vaccine in patients with lung cancer after second and booster immunization.

COVID-19 inactivated vaccine-induced humoral responses in patients with lung cancer (LCs) to SARS-CoV-2 wild-type (WT) strain and variants BA.4/5 after the primary 2-dose and booster vaccination remained unknown. We conducted a cross-sectional study in 260 LCs, 140 healthy controls (HC) and additional 40 LCs with serial samples by detecting total antibodies, IgG anti-RBD and neutralizing antibodies (NAb) toward WT and BA.4/5. SARS-CoV-2-specific antibody responses were augmented by the booster dose of inactivated vaccines in LCs, whereas they were lower than that in HCs. Enhanced humoral responses waned over time after triple injection, notably in NAb against WT and BA.4/5. The NAb against BA.4/5 was much lower than WT. Age ≥ 65 was risk factor for immunization of NAb to WT. Undergoing treatment resulted in a lower antibody response than those without and radiotherapy was a also risk factor for seroconversion of NAb to WT. Lower lymphocyte counts contributed to a lower titer of IgG anti-RBD and NAb against BA.4/5 in LCs than HCs. Specifically, total B cells, CD4+T cells and CD8+T counts were correlated with the humoral response. These results should be taken into consideration for the elderly patients under treatment.

Integrative analyses of bulk and single-cell RNA-seq identified cancer-associated fibroblasts-related signature as a prognostic factor for immunotherapy in NSCLC.

An emerging view regarding cancer-associated fibroblast (CAF) is that it plays a critical role in tumorigenesis and immunosuppression in the tumor microenvironment (TME), but the clinical significance and biological functions of CAFs in non-small cell lung cancer (NSCLC) are still poorly explored. Here, we aimed to identify the CAF-related signature for NSCLC through integrative analyses of bulk and single-cell genomics, transcriptomics, and proteomics profiling. Using CAF marker genes identified in weighted gene co-expression network analysis (WGCNA), we constructed and validated a CAF-based risk model that stratifies patients into two prognostic groups from four independent NSCLC cohorts. The high-score group exhibits a higher abundance of CAFs, decreased immune cell infiltration, increased epithelial-mesenchymal transition (EMT), activated transforming growth factor beta (TGFß) signaling, and a limited survival rate compared with the low-score group. Considering the immunosuppressive feature in the high-score group, we speculated an inferior clinical response for immunotherapy in these patients, and this association was successfully verified in two NSCLC cohorts treated with immune checkpoint blockades (ICBs). Furthermore, single-cell RNA sequence datasets were used to clarify the molecular mechanisms underlying the aggressive and immunosuppressive phenotype in the high-score group. We found that one of the genes in the risk model, filamin binding LIM protein 1 (FBLIM1), is mainly expressed in fibroblasts and upregulated in CAFs compared to fibroblasts from normal tissue. FBLIM1-positive CAF subtype was correlated with increased TGFß expression, higher mesenchymal marker level, and immunosuppressive tumor microenvironment. Finally, we demonstrated that FBLIM1 might serve as a poor prognostic marker for immunotherapy in clinical samples. In conclusion, we identified a novel CAF-based classifier with prognostic value in NSCLC patients and those treated with ICBs. Single-cell transcriptome profiling uncovered FBLIM1-positive CAFs as an aggressive subtype with a high abundance of TGFß, EMT, and an immunosuppressive phenotype in NSCLC.

Formation Optimization, Characterization and Antioxidant Activity of Auricularia auricula-judae Polysaccharide Nanoparticles Obtained via Antisolvent Precipitation

Auricularia auricula-judae polysaccharide (AAP)-based nanoparticles (NPs) prepared via an anti-solvent precipitation approach were studied. Response surface methodology (RSM) design was carried out on the basis of single factor experiments, using average size and polydispersity index (PDI) as indicators. The optimal preparation conditions were determined to include an AAP concentration of 1 mg/mL, a pH of 8, and an anti-solvent/solvent volume ratio of 6. The average particle sizes of the AAP-NPs, PDI and electrical characteristic (ζ-potential) were found to be 150.27 ± 3.21 nm, 0.135 ± 0.012 and -31.10 ± 0.52 mV, respectively. Furthermore, Fourier transform infrared spectroscopy (FTIR) was used to determine the chemical structure of the AAP-NPs. It was observed that the intensity of AAP-NPs in the wide spectral band of 3000-3750 cm-1 was significantly stronger than that of the AAP, as was the characteristic peak of carboxyl anion, and the characteristic band moved to shorter wavelengths. Subsequent thermogravimetric analysis showed that the antisolvent precipitation method improved the thermal stability of the AAP, while scanning electron microscopy (SEM) and X-ray diffraction (XRD) showed that the morphology of AAP-NPs was uniform and well-distributed, and that their single crystal structures had remained unaffected during the process. Moreover, the DPPH and ABTS scavenging activities of AAP-NPs were increased, and the IC50 values were 0.544 ± 0.241 mg/mL and 0.755 ± 0.226 mg/mL, respectively.

[MUC16: The Novel Target for Tumor Therapy].

Mucin16 (MUC16), also known as carbohydrate antigen 125 (CA125), is a glycoprotein antigen that can be recognized by the monoclonal antibody OC125 detected from epithelial ovarian carcinoma antigen by Bast et al in 1981. CA125 is not present in normal ovarian tissue but is usually elevated in the serum of epithelial ovarian carcinoma patients. CA125 is the most commonly used serologic biomarker for the diagnosis and recurrence monitoring of epithelial ovarian carcinoma. MUC16 is highly expressed in varieties of tumors. MUC16 can interact with galectin-1/3, mesothelin, sialic acid-binding immunoglobulin-type lectins-9 (Siglec-9), and other ligands. MUC16 plays an important role in tumor genesis, proliferation, migration, invasion, and tumor immunity through various signaling pathways. Besides, therapies targeting MUC16 have some significant achievements. Related preclinical studies and clinical trials are in progress. MUC16 may be a potential novel target for tumor therapy. This article will review the mechanism of MUC16 in tumor genesis and progression, and focus on the research actuality of MUC16 in tumor therapy. This article also provides references for subsequent tumor therapy studies targeting MUC16.
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Surface specifically modified NK-92 cells with CD56 antibody conjugated superparamagnetic Fe3O4 nanoparticles for magnetic targeting immunotherapy of solid tumors.

Although there has been significant progress in the development of tumor immunotherapies, many challenges still exist for the treatment of solid tumors. Natural killer (NK) cells possess broad-spectrum cytotoxicity against tumors, but their limited migration and infiltration abilities restrict their application in solid tumor therapies. Here, we combined a facile and efficient magnetic-targeting strategy with NK cell-based therapy to develop a novel immunotherapy approach for treating solid tumors. Anti-CD56 antibodies were conjugated with Fe3O4 nanoparticles, which could specifically bind with NK-92 cells endowing them with a magnetic field driven targeting ability. These NK-Fe3O4 biohybrid nanoparticles were able to facilitate directional migration to the tumor site in vivo under external magnetic field guidance and efficiently inhibit tumor growth. These functionalized NK cells represent a novel approach for solid tumor therapy and may provide a promising modality for cancer interventions in the future.