The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Spliced-Leader RNA as a Dynamic Marker for Monitoring Viable Leishmania Parasites During and After Treatment.

Accurate detection of viable Leishmania parasites is critical for evaluating visceral leishmaniasis (VL) treatment response at an early timepoint. We compared the decay of kinetoplast DNA (kDNA) and spliced-leader RNA (SL-RNA) in vitro, in vivo, and in a VL patient cohort. An optimized combination of blood preservation and nucleic acid extraction improved efficiency for both targets. SL-RNA degraded more rapidly during treatment than kDNA, and correlated better with microscopic examination. SL-RNA quantitative polymerase chain reaction emerges as a superior method for dynamic monitoring of viable Leishmania parasites. It enables individualized treatment monitoring for improved prognoses and has potential as an early surrogate endpoint in clinical trials.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

Drug Exposure and Treatment Outcomes in Patients With Multidrug-Resistant Tuberculosis and Diabetes Mellitus: A Multicenter Prospective Cohort Study From China.

BACKGROUND: The management of multidrug-resistant tuberculosis (MDR-TB) remains challenging. Treatment outcome is influenced by multiple factors; the specific roles of diabetes and glycemic control remain uncertain. This study aims to assess the impact of glycemic control on drug exposure, to investigate the association between drug exposure and treatment outcomes, and to identify clinically significant thresholds predictive of treatment outcome, among patients with diabetes. METHODS: This multicenter prospective cohort study involved patients with confirmed MDR-TB and diabetes. Drug exposure level was estimated by noncompartmental analysis. The minimum inhibitory concentrations (MICs) were determined for the individual Mycobacterium tuberculosis isolates. The influence of poor glycemic control (glycated hemoglobin ≥7%) on drug exposure and the associations between drug exposure and treatment outcome were evaluated by univariate and multivariate analysis. Classification and regression tree analysis was used to identify the drug exposure/susceptibility thresholds. RESULTS: Among the 131 diabetic participants, 43 (32.8%) exhibited poor glycemic control. Poor glycemic control was independently associated with decreased exposure to moxifloxacin, linezolid, bedaquiline, and cycloserine, but not clofazimine. Additionally, a higher ratio of drug exposure to susceptibility was found to be associated with a favorable MDR-TB treatment outcome. Thresholds predictive of 6-month culture conversion and favorable outcome were bedaquiline area under the concentration-time curve (AUC)/MIC ≥245 and moxifloxacin AUC/MIC ≥67, demonstrating predictive accuracy in patients, regardless of their glycemic control status. CONCLUSIONS: Glycemic control and optimal TB drug exposure are associated with improved treatment outcomes. This dual management strategy should be further validated in randomized controlled trials of patients with MDR-TB and diabetes.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Ex vivo tissue modelling informs drug selection for rare cancers.

The identification and therapeutic targeting of actionable gene mutations across many cancer types has resulted in improved response rates in a minority of patients. The identification of actionable mutations is usually not sufficient to ensure complete nor durable responses, and in rare cancers, where no therapeutic standard of care exists, precision medicine indications are often based on pan-cancer data. The inclusion of functional data, however, can provide evidence of oncogene dependence and guide treatment selection based on tumour genetic data. We applied an ex vivo cancer explant modelling approach, that can be embedded in routine clinical care and allows for pathological review within 10 days of tissue collection. We now report that ex vivo tissue modelling provided accurate longitudinal response data in a patient with BRAFV600E -mutant papillary thyroid tumour with squamous differentiation. The ex vivo model guided treatment selection for this patient and confirmed treatment resistance when the patient's disease progressed after 8 months of treatment.

Targeted DNA sequencing of high-grade serous ovarian carcinoma reveals association of TP53 mutations with platinum resistance when combined with gene expression.

High-grade serous ovarian carcinoma (HGSC) is the most common subtype of ovarian cancer and is among the most fatal gynecological malignancies worldwide, due to late diagnosis at advanced stages and frequent therapy resistance. In 47 HGSC patients, we assessed somatic and germline genetic variability of a custom panel of 144 known or suspected HGSC-related genes by high-coverage targeted DNA sequencing to identify the genetic determinants associated with resistance to platinum-based therapy. In the germline, the most mutated genes were DNAH14 (17%), RAD51B (17%), CFTR (13%), BRCA1 (11%), and RAD51 (11%). Somatically, the most mutated gene was TP53 (98%), followed by CSMD1/2/3 (19/19/36%), and CFTR (23%). Results were compared with those from whole exome sequencing of a similar set of 35 HGSC patients. Somatic variants in TP53 were also validated using GENIE data of 1287 HGSC samples. Our approach showed increased prevalence of high impact somatic and germline mutations, especially those affecting splice sites of TP53, compared to validation datasets. Furthermore, nonsense TP53 somatic mutations were negatively associated with patient survival. Elevated TP53 transcript levels were associated with platinum resistance and presence of TP53 missense mutations, while decreased TP53 levels were found in tumors carrying mutations with predicted high impact, which was confirmed in The Cancer Genome Atlas data (n = 260). Targeted DNA sequencing of TP53 combined with transcript quantification may contribute to the concept of precision oncology of HGSC. Future studies should explore targeting the p53 pathway based on specific mutation types and co-analyze the expression and mutational profiles of other key cancer genes.

Pre-treatment plasma retinol binding protein 4 level and its change after treatments predict systemic treatment response in psoriasis patients.

BACKGROUND: Retinol binding protein 4 (RBP4) is a mediator of inflammation and related to skin lesion formation, which suggests its engagement in psoriasis pathology and progression. This study intended to explore the change in RBP4 after systemic treatments, and its ability to predict treatment response in psoriasis patients. METHODS: This prospective study enrolled 85 psoriasis patients and 20 healthy subjects. Plasma RBP4 was detected by enzyme-linked immunosorbent assay at baseline and 12th week (W12) after systemic treatments in psoriasis patients, as well as after enrollment in healthy subjects. Psoriasis Area and Severity Index (PASI) 75 and PASI 90 were evaluated at W12 in psoriasis patients. RESULTS: RBP4 at baseline was higher in psoriasis patients than in healthy subjects [median (interquartile range): 13.39 (9.71-22.92) versus 9.59 (6.57-13.72) µg/mL] (P = 0.003). In psoriasis patients, 50 (58.8%) patients achieved PASI 75 at W12, and 25 (29.4%) patients achieved PASI 90 at W12. RBP4 was decreased at W12 compared to its level at baseline (P < 0.001). Lower RBP4 at baseline predicted achieving PASI 75 at W12 (P = 0.038). Greater RBP4 change (baseline-W12) precited achieving PASI 75 (P = 0.036) and PASI 90 (P = 0.045) at W12. Receiver operating characteristic curves suggested that after adjustment for all clinical features, RBP4 at baseline and RBP4 change (baseline-W12) had an acceptable ability to predict PASI 75 and PASI 90 at W12 with all area under curve values > 0.7. CONCLUSION: Plasma RBP4 is decreased after systemic treatments, and its low baseline level and greater decline after treatments predict good treatment response in psoriasis patients.

Alterations in the innate and adaptive immune system in a real-world cohort of multiple sclerosis patients treated with ocrelizumab.

B cell depletion by the anti-CD20 antibody ocrelizumab is effective in relapsing-remitting (RR) and primary progressive (PP) multiple sclerosis (MS). We investigated immunological changes in peripheral blood of a real-world MS cohort after 6 and 12 months of ocrelizumab. All RRMS and most PPMS patients (15/20) showed treatment response. Ocrelizumab not only reduced CD20+ B cells, but also numbers of CD20+ T cells. Absolute numbers of monocytes, dendritic cells and CD8+ T cells were increased, while CD56hi natural killer cells were reduced after ocrelizumab. The residual B cell population shifted towards transitional and activated, IgA+ switched memory B cells, double negative B cells, and antibody-secreting cells. Delaying the treatment interval by 2-3 months increased mean B cell frequencies and enhanced naive B cell repopulation. Ocrelizumab reduced plasma levels of interleukin(IL)-12p70 and interferon(IFN)-α2. These findings will contribute to understanding ineffective treatment responses, dealing with life-threatening infections and further unravelling MS pathogenesis.

A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram.

BACKGROUND: Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model. METHODS: A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients' general data were recorded, and the primary endpoint is the patients' treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model. RESULTS: Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832-0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS). CONCLUSIONS: The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.

Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. RESULTS: The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson's R = 0.44, p < 0.001). CONCLUSION: The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model's robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

Quantitative MRI reveals heterogeneous impacts of treatment on diseased bone marrow in a mouse model of myelofibrosis.

PURPOSE: Analyzing bone marrow in the hematologic cancer myelofibrosis requires endpoint histology in mouse models and bone marrow biopsies in patients. These methods hinder the ability to monitor therapy over time. Preclinical studies typically begin treatment before mice develop myelofibrosis, unlike patients who begin therapy only after onset of disease. Using clinically relevant, quantitative MRI metrics allowed us to evaluate treatment in mice with established myelofibrosis. METHODS: We used chemical shift-encoded fat imaging, DWI, and magnetization transfer sequences to quantify bone marrow fat, cellularity, and macromolecular components in a mouse model of myelofibrosis. We monitored spleen volume, the established imaging marker for treatment, with anatomic MRI. After confirming bone marrow disease by MRI, we randomized mice to treatment with an approved drug (ruxolitinib or fedratinib) or an investigational agent, navitoclax, for 33 days. We measured the effects of therapy over time with bone marrow and spleen MRI. RESULTS: All treatments produced heterogeneous responses with improvements in bone marrow evident in subsets of individual mice in all treatment groups. Reductions in spleen volume commonly occurred without corresponding improvement in bone marrow. MRI revealed patterns associated with effective and ineffective responses to treatment in bone marrow and identified regional variations in efficacy within a bone. CONCLUSIONS: Quantitative MRI revealed modest, heterogeneous improvements in bone marrow disease when treating mice with established myelofibrosis. These results emphasize the value of bone marrow MRI to assess treatment in preclinical models and the potential to advance clinical trials for patients.

Anti-citrullinated protein antibody profiles predict changes in disease activity in patients with rheumatoid arthritis initiating biologics.

OBJECTIVES: To determine whether an expanded antigen-specific ACPA profile predicts changes in disease activity in patients with RA initiating biologics. METHODS: The study included participants from a prospective, non-randomized, observational RA cohort. For this sub-study, treatment groups of interest included biologic-naïve initiating anti-TNF, biologic-exposed initiating non-TNF, and biologic-naïve initiating abatacept. ACPAs to 25 citrullinated peptides were measured using banked enrolment serum. Principal component analysis (PCA) was performed and associations of resulting principal component (PC) scores (in quartiles) and anti-CCP3 antibody (≤15, 16-250 or >250 U/ml) with EULAR (good/moderate/none) treatment response at 6 months were examined using adjusted ordinal regression models. RESULTS: Participants (n = 1092) had a mean age of 57 (13) years and 79% were women. At 6 months, 68.5% achieved a moderate/good EULAR response. There were three PCs that cumulatively explained 70% of variation in ACPA values. In models including the three components and anti-CCP3 antibody category, only PC1 and PC2 were associated with treatment response. The highest quartile for PC1 (odds ratio [OR] 1.76; 95% CI: 1.22, 2.53) and for PC2 (OR 1.74; 95% CI: 1.23, 2.46) were associated with treatment response after multivariable adjustment. There was no evidence of interaction between PCs and treatment group in EULAR responses (P-value for interaction >0.1). CONCLUSION: An expanded ACPA profile appears to be more strongly associated with biologic treatment response in RA than commercially available anti-CCP3 antibody levels. However, further enhancements to PCA will be needed to effectively prioritize between different biologics available for the treatment of RA.

A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures.

OBJECTIVES: Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs. METHODS: Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis. RESULTS: A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses. CONCLUSION: This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.

Socioeconomic deprivation is associated with reduced response and lower treatment persistence with TNF inhibitors in rheumatoid arthritis.

OBJECTIVE: To investigate the association between socioeconomic deprivation and outcomes following TNF inhibitor (TNFi) treatment. METHODS: Individuals commencing their first TNFi in the British Society for Rheumatology Biologics Register for RA (BSRBR-RA) and Biologics in RA Genetics and Genomics Study Syndicate (BRAGGSS) cohort were included. Socioeconomic deprivation was proxied using the Index of Multiple Deprivation and categorized as 20% most deprived, middle 40% or 40% least deprived. DAS28-derived outcomes at 6 months (BSRBR-RA) and 3 months (BRAGGSS) were compared using regression models with the least deprived as referent. Risks of all-cause and cause-specific drug discontinuation were compared using Cox models in the BSRBR-RA. Additional analyses adjusted for lifestyle factors (e.g. smoking, BMI) as potential mediators. RESULTS: 16 085 individuals in the BSRBR-RA were included (mean age 56 years, 76% female), of whom 18%, 41% and 41% were in the most, middle and least deprived groups, respectively. Of 3459 included in BRAGGSS (mean age 57, 77% female), proportions were 22%, 36% and 41%, respectively. The most deprived group had 0.3-unit higher 6-month DAS28 (95% CI 0.22, 0.37) and were less likely to achieve low disease activity (odds ratio [OR] 0.76; 95% CI 0.68, 0.84) in unadjusted models. Results were similar for 3-month DAS28 (ß = 0.23; 95% CI 0.11, 0.36) and low disease activity (OR 0.77; 95% CI 0.63, 0.94). The most deprived were more likely to discontinue treatment (hazard ratio 1.18; 95% CI 1.12, 1.25), driven by ineffectiveness rather than adverse events. Adjusted estimates were generally attenuated. CONCLUSION: Socioeconomic deprivation is associated with reduced response to TNFi. Improvements in determinants of health other than lifestyle factors are needed to address socioeconomic inequities.

Clinical and Pathologic Response to Neoadjuvant Immunotherapy in DNA Mismatch Repair Protein-Deficient Gastroesophageal Cancers.

BACKGROUND: Mismatch repair deficient (dMMR) gastroesophageal cancers (GEC) are a distinct subgroup. Among patients with locally advanced disease, previous trial data suggest a good response to neoadjuvant immune checkpoint inhibitors (nICI). PATIENTS AND METHODS: Since 2019, our institution has routinely performed MMR testing for new GEC cases. Patients diagnosed with GEC (2019-2024) were included in the study. Quantitative data are described as the median and interquartile range (IQR); qualitative data are described as quantities and percentages. RESULTS: A total of 24 patients with dMMR GEC were identified following implementation of routine immunohistochemical testing; 14 were potentially resectable with a median follow-up of 14 months (IQR 8-27). All patients underwent pre-treatment positron emission tomography (PET; median SUV 20.9). Among the 14 potentially resectable patients, 4 underwent immediate surgery, 10 were treated with nICI, and 5 underwent surgical resection to date. All regimens included PD-1 inhibitors, with 70% receiving pembrolizumab. Re-staging PET was performed in five patients; the median post-nICI SUV was 5.1 (range 4.7-6.3). All resected specimens had gross ulceration after nICI, but 60% (N = 3) had a pathologic complete response (pCR) following nICI; one patient had a near-complete response (nCR) and one patient had a partial response (pPR). Reduction in SUV was 75% and 82% in the pCR patients, 25% in the nCR patient, and 43% in the pPR patient. CONCLUSIONS: dMMR GECs are responsive to nICI in this limited experience, mirroring early clinical trial data. Given persistent metabolic activity and visible ulceration despite pCR, studies should continue to optimize tools for estimating post-nICI pCR in these patients.

Measurement of metabolite levels and treatment-induced changes in hepatic metastases of gastro-esophageal cancer using 7-T phosphorus magnetic resonance spectroscopic imaging.

Methods for early treatment response evaluation to systemic therapy of liver metastases are lacking. Tumor tissue often exhibits an increased ratio of phosphomonoesters to phosphodiesters (PME/PDE), which can be noninvasively measured by phosphorus magnetic resonance spectroscopy (31P MRS), and may be a marker for early therapy response assessment in liver metastases. However, with commonly used 31P surface coils for liver 31P MRS, the liver is not fully covered, and metastases may be missed. The objective of this study was to demonstrate the feasibility of 31P MRS imaging (31P MRSI) with full liver coverage to assess 31P metabolite levels and chemotherapy-induced changes in liver metastases of gastro-esophageal cancer, using a 31P whole-body birdcage transmit coil in combination with a 31P body receive array at 7 T. 3D 31P MRSI data were acquired in two patients with hepatic metastases of esophageal cancer, before the start of chemotherapy and after 2 (and 9 in patient 2) weeks of chemotherapy. 3D 31P MRSI acquisitions were performed using an integrated 31P whole-body transmit coil in combination with a 16-channel body receive array at 7 T, with a field of view covering the full abdomen and a nominal voxel size of 20-mm isotropic. From the 31P MRSI data, 12 31P metabolite signals were quantified. Prior to chemotherapy initiation, both PMEs, that is, phosphocholine (PC) and phosphoethanolamine (PE), were significantly higher in all metastases compared with the levels previously determined in the liver of healthy volunteers. After 2 weeks of chemotherapy, PC and PE levels remained high or even increased further, resulting in increased PME/PDE ratios compared with healthy liver tissue, in correspondence with the clinical assessment of progressive disease after 2 months of chemotherapy. The suggested approach may present a viable tool for early therapy (non)response assessment of tumor metabolism in patients with liver metastases.

Proton pump inhibitor effect on esophageal protein signature of eosinophilic esophagitis, prediction, and evaluation of treatment response.

BACKGROUND: Recently, we have identified a dysregulated protein signature in the esophageal epithelium of eosinophilic esophagitis (EoE) patients including proteins associated with inflammation and epithelial barrier function; however, the effect of proton pump inhibitor (PPI) treatment on this signature is unknown. Herein, we used a proteomic approach to investigate: (1) whether PPI treatment alters the esophageal epithelium protein profile observed in EoE patients and (2) whether the protein signature at baseline predicts PPI response. METHODS: We evaluated the protein signature of esophageal biopsies using a cohort of adult EoE (n = 25) patients and healthy controls (C) (n = 10). In EoE patients, esophageal biopsies were taken before (pre) and after (post) an 8-week PPI treatment, determining the histologic response. Eosinophil count PostPPI was used to classify the patients: ≥15 eosinophils/hpf as non-responders (non-responder) and < 15 eosinophils/hpf as responders (R). Protein signature was determined and differentially accumulated proteins were characterized to identify altered biological processes and signaling pathways. RESULTS: Comparative analysis of differentially accumulated proteins between groups revealed common signatures between three groups of patients with inflammation (responder-PrePPI, non-responder-PrePPI, and non-responder-PostPPI) and without inflammation (controls and responder-PostPPI). PPI therapy almost reversed the EoE specific esophageal protein signature, which is enriched in pathways associated with inflammation and epithelial barrier function, in responder-PostPPI. Furthermore, we identified a set of candidate proteins to differentiate responder-PrePPI and non-responder-PrePPI EoE patients before treatment. CONCLUSION: These findings provide evidence that PPI therapy reverses the alterations in esophageal inflammatory and epithelial proteins characterizing EoE, thereby providing new insights into the mechanism of PPI clinical response. Interestingly, our results also suggest that PPI response could be predicted at baseline in EoE.

Same-day post-therapy imaging with a new generation whole-body digital SPECT/CT in assessing treatment response to [177Lu]Lu-PSMA-617 in metastatic castration-resistant prostate cancer.

PURPOSE: Lutetium-177 [177Lu]Lu-PSMA-617 radioligand therapy (RLT) represents a significant advancement for metastatic castration-resistant prostate cancer (mCRPC), demonstrating improvements in radiographic progression free survival (rPFS) and overall survival (OS) with a low rate of associated side effects. Currently, most post-therapy SPECT/CT is conducted at 24 h after infusion. This study examines the clinical utility of a next-generation multi-detector Cadmium-Zinc-Telluride (CZT) SPECT/CT system (StarGuide) in same-day post-infusion assessment and early treatment response to [177Lu]Lu-PSMA-617. METHODS: In this retrospective study, 68 men with progressive mCRPC treated with [177Lu]Lu-PSMA-617 at our center from June 2022 to June 2023 were evaluated. Digital whole-body SPECT/CT imaging was performed after [177Lu]Lu-PSMA-617infusion (mean ± SD: 1.8 ± 0.6 h, range 1.1-4.9 h). Quantitative analysis of [177Lu]Lu-PSMA-617 positive lesions was performed in patients who underwent at least 2 post-therapy SPECT/CT, using liver parenchyma uptake as reference. Metrics including [177Lu]Lu-PSMA-617 positive total tumor volume (Lu-TTV), SUVmax and SUVmean were calculated. These quantitative metrics on post-infusion SPECT/CT images after cycles 1, 2 and 3 were correlated with overall survival (OS), prostate specific antigen-progression free survival (PSA-PFS) as defined by prostate cancer working group 3 (PCWG3), and PSA decrease over 50% (PSA50) response rates. RESULTS: 56 patients (means age 76.2 ± 8.1 years, range: 60-93) who underwent at least 2 post-therapy SPECT/CT were included in the image analysis. The whole-body SPECT/CT scans (~ 12 min per scan) were well tolerated, with 221 same-day scans performed (89%). At a median of 10-months follow-up, 33 (58.9%) patients achieved PSA50 after [177Lu]Lu-PSMA-617 treatment and median PSA-PFS was 5.0 months (range: 1.0-15 months) while median OS was not reached. Quantitative analysis of SPECT/CT images showed that 37 patients (66%) had > 30% reduction in Lu-TTV, associated with significantly improved overall survival (median not reached vs. 6 months, P = 0.008) and PSA-PFS (median 6 months vs. 1 months, P < 0.001). However, changes in SUVmax or SUVmean did not correlate with PSA-PFS or OS. CONCLUSION: We successfully implemented same-day post-therapy SPECT/CT after [177Lu]Lu-PSMA-617 infusions. Quantitation of 1-2 h post-therapy SPECT/CT images is a promising method for assessing treatment response. However, the approach is currently limited by its suboptimal detection of small tumor lesions and the necessity of incorporating a third-cycle SPECT/CT to mitigate the effects of any potential treatment-related flare-up. Further investigation in a larger patient cohort and prospective validation is essential to confirm these findings and to explore the role of SPECT/CT as a potential adjunct to PSMA PET/CT in managing mCRPC.