Predict Therapy Response Research Articles

Quantitative amplification ratio for established and emerging biomarkers in gastroesophageal (GE) cancer.

470 Background: Gene copy number (CN) amplifications (amps) and/or protein overexpression in ERBB2 , EGFR , FGFR2 , and MET represent established and emerging clinical targets in GE. Various methodologies are used to report gene amp or overexpression with some data suggesting FISH ratio may predict therapy response. Quantitative methods for NGS CN and relationship to ISH/IHC remains largely unresolved. We studied the landscape of targetable CN amps in GE to understand correlations of quantitative CN amp ratio and outcomes. Methods: GE tissue (N = 20,468) and liquid (N = 1,760) samples underwent NGS-based comprehensive genomic profiling (CGP). A genome-wide CN model for each sample was generated, segmented, and combined with variant allele frequencies of heterozygous SNPs to estimate the sample purity, ploidy, and CN for each segment. Genes were considered amplified at CN ≥ ploidy + 3 for ERBB2 and + 4 for other genes. Sub-analysis using amp ratio (ratio of the gene CN to the sample ploidy) ≥ 3 was performed. ctDNA tumor fraction (TF) was quantified using a composite algorithm (PMID: 38990098). The Flatiron Health-Foundation Medicine clinico genomic database, a nationwide (US-based) de-identified EHR-derived database linked to CGP data, was queried to assess external HER2 FISH or IHC and describe real-world (rw) progression-free survival (rwPFS) and overall survival (rwOS) of GE patients (pts) with ERBB2 amp on trastuzumab-based regimens. Results: ≥1 gene amps were detected in 15,341 (75%) GE tissue cases, and in 67% of cases using an amp ratio ≥3 threshold. The rates of amps in current clinical targets using each threshold were ERBB2 : 17%/12%, KRAS : 13%/12%, EGFR: 8.7%/7.2%, MET: 5.2%/4.0% , FGFR2: 3.1%/2.7% of tissue samples. The median amp ratio and percent of amps with ratio ≥ 3 were ERBB2 : 7.1/73%, KRAS : 14/92%, EGFR : 13/82%, MET : 6.3/76%, and FGFR2 : 29/88%. Amp ratio ≥ 3 was associated with focal amplicons vs amp ratio < 3 (median amplicon size 0.95 vs. 3.8 Mbp, p < 0.001). Of 1,382 pts with HER2 NGS and IHC results, concordance with IHC3+ was 68% and increasing ERBB2 amp ratio was associated with IHC 3+ status. Of 604 pts with HER2 NGS and FISH, concordance was 80% (118 positive, 366 negative). Higher ERBB2 amp ratio was also associated with longer rwOS and rwPFS as a continuous variable on first-line trastuzumab-based regimens. ≥1 gene amps were detected in 597 (34%) liquid samples (median TF 18%) and 32% with the amp ratio ≥3 threshold, increasing to 79% and 73% with TF ≥ 20% (n=355). Conclusions: Gene amps are common established or emerging GE targets and ERBB2 amp ratio is associated with outcomes for pts on trastuzumab-based regimens. Amp detection in liquid correlates with higher TF and amp ratio. As the number of therapies targeting gene amps and overexpression grows, quantification of the degree of amplification and harmonization across methodologies may be an important tool to select pts for optimal outcomes.

Evaluating lactate metabolism for prognostic assessment and therapy response prediction in gastric cancer with emphasis on the oncogenic role of SLC5A12.

Gastric cancer remains a common malignancy with poor prognosis. While lactate metabolism is recognized as a significant factor in tumor progression, its potential as a predictive tool for treatment response remains unexplored. This study introduces a novel Lactate-Related Gene Signature (LRGS) designed to predict both survival outcomes and therapy responses in gastric cancer patients. We comprehensively analyzed 335 lactate-related genes from 11 metabolic pathways using MSigDB, identifying 278 differentially expressed genes between gastric cancer and normal tissues. Employing the LASSO Cox regression model, we developed an innovative LRGS formula based on the expression of 16 key lactate-related genes. The impact of Solute Carrier Family 5 Member 12 (SLC5A12), a gene of particular interest, on gastric cancer cell functions was evaluated using in vitro assays and an in vivo zebrafish model. Our newly established LRGS demonstrated robust capability in stratifying gastric cancer patients by survival risk. Notably, the LRGS-low subtype showed significantly improved overall and disease-free survival rates compared to the LRGS-high subtype. A key finding was LRGS's ability to predict patient responses to both adjuvant chemotherapy and immunotherapy. Random forest analysis identified SLC5A12 as the most significant gene differentiating gastric cancer from normal tissues. Functional experiments confirmed SLC5A12's role in promoting gastric cancer cell proliferation, invasion, and migration both in vitro and in vivo. The LRGS is a dependable and efficient prognostic tool for assessing the survival outcomes in individuals with gastric cancer, as well as a predictor of patient response to adjuvant chemotherapy and immunotherapy.

Kinomic profiling to predict sunitinib response of patients with metastasized clear cell Renal Cell Carcinoma.

Treatment with Sunitinib, a potent multitargeted receptor tyrosine kinase inhibitor (TKI) has increased the progression-free survival (PFS) and overall-survival (OS) of patients with metastasized renal cell carcinoma (mRCC). With modest OS improvement and variable response and toxicity predictive and/or prognostic biomarkers are needed to personalize patient management: Prediction of individual TKI therapy response and resistance will increase successful treatment outcome while reducing unnecessary drug use and expense. The aim of this study was to investigate whether kinase activity analysis can predict sunitinib response and/or toxicity using tissue samples obtained from primary clear cell RCC (ccRCC) from a cohort of clinically annotated patients with mRCC receiving sunitinib as first-line treatment. EuroTARGET partners collected ccRCC and matched normal kidney tissue samples immediately after surgery, snap-frozen and stored at -80°C until use. Phosphotyrosine-activity profiling was performed using PamChip® peptide microarrays (144 peptides derived from known phosphorylation sites in Protein Tyrosine Kinase substrates) of lysed tissue samples (5 µg protein input) of 163 mRCC patients. Evolve software Was used to analyze kinome profiles and Bionavigator was used for unsupervised and supervised clustering. The kinexus kinase predictor (www.phosphonet.ca) was used to analyze the peptide lists within the clusters. Kinome data was available from 94 patients who received sunitinib as 1st-line treatment and had complete follow-up of their clinical data (PFS, OS and toxicity) for at least 6 months. Matched normal tissue was available from 14 mRCC patients. Supervised clustering of basal kinome activity could correctly classify mRCC patients with PFS >9 months versus PFS<9 months with an accuracy of 61 %. Unsupervised hierarchical clustering revealed 3 major clusters related to immune signaling, VEGF pathway, and immune signaling/cell adhesion. Basal kinase activity levels of patients with short PFS were substantially higher compared to patients who experienced extended PFS. Based on kinase levels ccRCC tumors can be subdivided into 3 clusters which may reflect the aggressiveness of these tumors. The accuracy of response prediction of 61 % based on basal kinase levels is too low to justify implementation. STK assays may help to predict sunitinib toxicity and guide clinical management. Additionally, it is possible that mRCC patients with an immune kinase signature are better checkpoint inhibitor candidates, but this needs to be studied.

DOP045 Novel AI-Driven Detection, Localisation and Quantification of Neutrophils for Prediction of Early Response to Therapy in a Phase 2 Ulcerative Colitis Clinical Trial

Abstract Background Achieving histological remission is a key treatment goal in Ulcerative Colitis (UC) as it is associated with better disease management and predicts response to therapy. Neutrophils are the key drivers of disease activity in UC and are, therefore, integral to many histological scoring systems. However, current scoring methods remain complex and subject to inter-rater variability. Thus, our study aimed to develop a novel AI-driven model to standardise detection, localisation, and quantification of neutrophils, supporting evaluation of histological activity and enabling prediction of early therapy response in a cohort of UC patients from the Phase 2 clinical trial of Mirikizumab (NCT02589665). Methods An AI-driven system was developed by integrating two deep learning models to [1] segment whole slide images (WSI) into epithelium, lamina propria, other tissue, and background and [2] detect neutrophils. Outputs from these models were combined to compute neutrophil densities in the lamina propria, epithelium, and overall tissue. Optimal cell density cut-offs were determined by the Youden Index to assess disease activity (Geboes score &amp;gt;2B.0) and to predict early response to therapy at weeks 12 (W12) and 52 (W52), defined as histological improvement (Geboes score &amp;lt;3.1) and histological remission (Geboes score ≤2B.0). Validation performance was assessed using 5-fold cross-validation. Results 266 WSIs from active UC patients were considered. The model for segmentation and detection reached an average Dice similarity coefficient of 67.6% and F1 score of 72.0%, respectively. Table 1 shows the AI diagnostic performance in assessing disease activity and predicting response to therapy at W12 and W52. The system identified an overall optimal neutrophil density cut-off (cells/mm²) predictive of disease activity of 112.0 (53.8 and 153 for epithelium and lamina propria, respectively), with an accuracy of 80.8% (77.4% and 81.6%, respectively). Moreover, the overall optimal cut-off of neutrophils to predict histological remission at W52 was &amp;lt;71.8 (&amp;lt;65.9 and &amp;lt;113.9 for epithelium and lamina propria, respectively), with an accuracy of 85.0% (71.3% and 81.3%, respectively). Conclusion Our AI-based system shows strong potential as an automated tool for detecting, localising, and quantifying neutrophils to assess histological activity and predict response to therapy at weeks 12 and 52 in a clinical trial UC cohort. Although further refinement and extensive validation are needed, this framework could be applied in clinical trials and routine clinical practice, offering objective guidance for personalised management in UC patients.

P0565 Spatial Metabolic Profiles in Ulcerative colitis patients determine response to therapy

Abstract Background Ulcerative Colitis (UC) is an heterogenous debilitating disease without a cure. The step-up therapy approach aims at controlling and alleviating intestinal inflammation by achieving and maintaining clinical and endoscopic remission. However, the rate of primary non-response or loss of response to therapies is still high. Predictive markers that accurately guide decisions about the best therapeutic choice are an unmet need. This study aims to determine the intestinal metabolic profiles of UC patients and explore differences in response groups of given treatments, thus helping a better patient stratification. Methods Mucosal biopsies collected from active UC patients (N=26) at week 0 and 14 after treatment (vedolizumab, tofacitinib and filgotinib) were sectioned at 10 µm thickness. Patients were classified as non-responders (NR), responders (R) and super responders (SR) based on both endoscopic and partial Mayo scores. Sections were processed by matrix-assisted laser desorption/ionization (MALDI) coupled with Orbitrap Exploris™ 120 Mass Spectrometer. Raw spectral data was processed with MSConverter and MSIpixel for metabolite quantification and annotation. Comprehensive statistical analyses including quantitative metabolite distributions, Moran’s I spatial correlations and pathway enrichment analysis were performed via an in-house developed computational pipeline. Results Spatial metabolomic analysis revealed different metabolite patterns between R and NR across all drugs. Notably, all non-responders exhibited a dysregulated lipid metabolism. In the Vedolizumab group, a fragmented pattern distribution of metabolites was found in NR characterized by an increase of canavalmine, N1-acetylspermidine and heptanenitrile at week 0, whereas SR showed a significant tissue reorganization environment, with increased number of metabolites such as LysoPA, LysoPE and 5alpha-Cholesta-7,24dien-3beta-ol, involved in anti-inflammatory pathways, as well as glutathione and purine metabolism. Additionally, SR showed histamine- and IgE-related pathways supporting cellular regeneration. Conclusion Overall, these data depicted the alteration of mucosal lipid metabolism as a hallmark of non-response to therapies, and evidenced that the effectiveness of a specific treatment in UC is impacted by the initial mucosal metabolic state conditioned by diet and medications. The validation of these results in a larger sample size will aid the prediction of therapy response. References ImmUniverse project: this project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 853995. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The content provided in this abstract reflects only the author's view and neither the IMI JU nor the European Commission are responsible for any use that may be made of the information it contains.

Neutrophil-to-lymphocyte ratio at diagnosis predicts colonoscopic activity in pediatric inflammatory bowel diseases (pIBD).

Neutrophil-to-lymphocyte ratio (NLR) is a novel biomarker studied in several autoimmune diseases including inflammatory bowel diseases (IBD) in adults, but poorly characterized in pediatric IBD (pIBD). We aimed to primarily investigate the relationship between NLR and pIBD endoscopic disease severity. We also examined whether NLR predicted hospitalization, surgery, and therapy response by 52 weeks. We used the Canadian children IBD Network (CIDsCaNN) prospective inception cohort including patients<18 years old with baseline data from 2013-2022. We excluded patients with concurrent diseases affecting NLR. Both Mayo endoscopic score (MES) and simple endoscopic scale for Crohn Diseases (SES-CD) were dichotomized as low (quiescent-mild), and high activity (moderate-severe). For therapy responses, we examined year-1 steroid- and biologic-free remission. We used logistic regression for binary outcomes. 580 UC and 1081 CD patients were included. High NLR was associated with high activity MES and SES-CD in both univariate and multivariable analyses (OR=1.45, 95%CI= 1.07-1.97, p-value=0.016; and OR=1.42, 95%CI= 1.04-1.94, p-value=0.026, respectively). We also calculated the best NLR cutoff point to predict MES (1.90, sensitivity=68%, specificity=67%, AUC=0.67, AUC 95%CI= 0.59-0.74) and SES-CD (2.50, sensitivity=63%, specificity=69%, AUC=0.66, AUC 95%CI= 0.59-0.75) high activity. NLR did not predict therapy response in either UC or CD. pIBD patients with high baseline NLR are more probable to have worse endoscopic disease at diagnosis. This highlights NLR potential as a reliable non-invasive biomarker of disease activity. The predictive power of NLR is based mostly on neutrophils and the balance between neutrophils and lymphocytes.

Exploring utilities of [64Cu]Cu-DOTA-trastuzumab immunoPET in breast cancer: a systematic review and meta-analysis.

[64Cu]Cu-DOTA-trastuzumab represents a novel immunopositron emission tomography (immunoPET) agent with emerging diagnostic applications in human epidermal growth factor receptor-2 (HER2)-expressing breast cancer (BC). This systematic review and meta-analysis evaluates the current diagnostic utilities of [64Cu]Cu-DOTA-trastuzumab PET/computed tomography (CT) and explores tumor uptake metrics in HER2-positive BC lesions. A systematic literature search of PubMed, Scopus, and Ovid databases was conducted using relevant keywords to identify eligible studies. Of the 123 articles reviewed, six met the inclusion criteria. Qualitative data analysis was applied to all included studies. Several promising utilities were identified, including [64Cu]Cu-DOTA-trastuzumab's capacity to detect HER2-positive primary BC lesions, lymph nodes, and distant metastases. Additionally, [64Cu]Cu-DOTA-trastuzumab PET/CT demonstrated potential in predicting therapy response in HER2-positive lesions. The overall lesion detectability was 91% [95% confidence interval (CI), 81-98%] for HER2-positive BC. HER2-positive BC lesions exhibited significantly higher maximum standardized uptake values compared to HER2-negative lesions, with a weighted mean difference of 2.14 (95% CI, 0.18-4.09; P = 0.03). These findings underscore the need for further large-scale and prospective investigations of this promising radiotracer in the near future.

Beyond the Norm: The emerging interplay of complement system and extracellular matrix in the tumor microenvironment.

Ground-breaking awareness has been reached about the intricate and dynamic connection between developing tumors and the host immune system. Being a powerful arm of innate immunity and a functional bridge with adaptive immunity, the complement system (C) has also emerged as a pivotal player in the tumor microenvironment (TME). Its "double-edged sword" role in cancer can find an explanation in the controversial relationship between C capability to mediate tumor cell cytolysis or, conversely, to sustain chronic inflammation and tumor progression by enhancing cell invasion, angiogenesis, and metastasis to distant organs. However, comprehensive knowledge about the actual role of C in cancer progression is impaired by several limitations of the currently available studies. In the current review, we aim to bring a fresh eye to the controversial role of C in cancer by analyzing the interplay between C and extracellular matrix (ECM) components as potential orchestrators of the TME. The interaction of C components with specific ECM components can determine C activation or inhibition and promote specific non-canonical functions, which can, in the tumor context, favor or limit progression based on the cancer setting. An in-depth and tumor-specific characterization of TME composition in terms of C components and ECM proteins could be essential to determine their potential interactions and become a key element for improving drug development, prognosis, and therapy response prediction in solid tumors.

Predictors of response to bDMARDs and tsDMARDs in psoriatic arthritis: a pilot study on the role of musculoskeletal ultrasound

ObjectivesThis pilot study aimed to identify early predictors of drug retention in patients with clinically active peripheral psoriatic arthritis who initiated or switched to therapy with biologic and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs).MethodsClinical and ultrasound assessments were conducted at baseline (t0) and subsequently at 1 (t1), 3 (t3), and 6 (t6) months. Ultrasound evaluations targeted joints/entheses according to PsASon-Score13 and the most clinically involved joint/enthesis/tendon or the two most clinically involved joints/entheses/tendons (MIJET and 2MIJET). After 6 months of follow-up, patients were divided into two groups based on drug retention, determined by the clinician's assessment of treatment efficacy (cResponder vs. non-cResponder). Main endpoints were ultrasound changes in MIJET, 2MIJET, and GUIS (Global US Inflammation Subscore) derived from PsASon-13.ResultsTwenty-nine patients were enrolled, 22 cResponders and 7 non-cResponders at t6. In the comparison between cResponders and non-cResponders, GUIS variation significantly differed in Δt6-t0, while MIJET and 2MIJET variations were significant as early as Δt3-t0 and confirmed in Δt6-t0. The ultrasound response of MIJET and 2MIJET was faster in cResponders treated with JAKi vs. those treated with TNFi and IL-17/12-23i, significant in Δt1-t0.ConclusionsUltrasound imaging of clinically involved joint sites may be a valuable early predictor of therapy response for predicting drug retention at 6 months in patients with psoriatic arthritis.

Imaging of tumor-associated macrophage dynamics during immunotherapy using a CD163-specific nanobody-based immunotracer

Immunotherapies have emerged as an effective treatment option for immune-related diseases, such as cancer and inflammatory diseases. However, variations in patient responsiveness limit the broad applicability and success of these immunotherapies. Noninvasive whole-body imaging of the immune status of individual patients during immunotherapy could enable the prediction and monitoring of the patient's response, resulting in more personalized treatments. In this study, we developed a nanobody-based immunotracer targeting CD163, a receptor specifically expressed on macrophages. This anti-CD163 immunotracer bound to human and mouse CD163 with high affinity and specificity without competing for ligand binding. Furthermore, the tracer showed no unwanted immune cell activation and was nonimmunogenic. Upon radiolabeling of the anti-CD163 immunotracer, specific imaging of CD163+ macrophages using micro-single-photon emission computerized tomography/computed tomography or micro-positron emission tomography/CT was performed. The anti-CD163 immunotracer was able to stratify immunotherapy responders from nonresponders (NR) by visualizing differences in the intratumoral CD163+ TAM distribution in Lewis lung carcinoma-ovalbumin tumor-bearing mice receiving an anti-programmed cell death protein-1 (PD-1)/CSF1R combination treatment. Immunotherapy-responding mice showed a more homogeneous distribution of the PET signal in the middle of the tumor, while CD163+ TAMs were located at the tumor periphery in NR. As such, visualization of CD163+ TAM distribution in the tumor microenvironment could allow a prediction or follow-up of therapy response. Altogether, this study describes an immunotracer, specific for CD163+ macrophages, that allows same-day imaging and follow-up of these immune cells in the tumor microenvironment, providing a good basis for the prediction and follow-up of immunotherapy responses in cancer patients.

Downregulation of the splicing regulator NSRP1 confers resistance to CDK4/6 inhibitors via activation of interferon signaling in breast cancer.

The combination of CDK4/6 inhibitors (CDK4/6i) and endocrine therapy is the first-line therapy for ER+/Her2- breast cancer, however, the development of drug resistance limited the efficacy of the agents. Although activation of the IFN signaling pathway has been identified as a critical driver of intrinsic and acquired CDK4/6i resistance, it remains unknown how the IFN signaling pathway was activated in resistant cells. Here, we report that NSRP1, a regulator of alternative mRNA splicing is downregulated in CDK4/6i resistant breast cancer cells and contributes to CDK4/6i resistance by mediating alternative splicing of NSD2 mRNA and activation of the IFN signaling pathway. Knockdown of NSRP1 reduces CDK4/6i (palbociclib) sensitivity of MCF7 cells while overexpression of NSRP1 sensitizes MCF7-PalR cells towards palbociclib treatment. Mechanistically, RNA sequencing suggests that NSRP1 knockdown strongly activates the IFN signaling pathway in MCF7 cells and elevates the expression of most of the "IFN-related palbociclib-resistance Signature" (IRPS) genes. NSRP1 also regulates numerous alternative splicing (AS) events in breast cancer cells, several of which are key regulators of the IFN signaling pathway. Among them, the inclusion of NSD2 exon 2 is elevated upon NSRP1 knockdown. Our data further show that the inclusion of NSD2 exon 2 is increased in breast cancer and associated with the poor prognosis of patients. In addition, including NSD2 exon 2 elevates NSD2 protein expression to activate the IFN signaling pathway. This study unveils the critical role of NSRP1 in regulating the IFN signaling pathway and the CDK4/6i resistance, which could be a promising biomarker for predicting therapy response.

HER2(-Low) Expression on Circulating Tumor Cells and Corresponding Metastatic Tissue in Metastatic Breast Cancer

Introduction: Significant progress has been made in the targeted therapy of metastatic breast cancer (mBC) in recent years. In this context, new biomarkers enable personalized therapy management and individualized therapy monitoring. Therefore, the systemic treatment is based increasingly on the biological characteristics of the tumor disease. Given the challenges of obtaining fresh tumor tissue through biopsies, the significance of liquid biopsies for assessing circulating tumor cells (CTCs) or circulating tumor DNA is of growing importance for the detection of prognostic and predictive biomarkers. Multiple studies have shown that the number of CTCs decreases under therapy, especially under anti-HER2-targeted therapy, and that the expression of the HER2 status on CTCs could play a role in predicting therapy response and therapeutic monitoring. The aim of this study was to analyze the HER2 status of CTCs in mBC patients before and after 3 months of systemic therapy to evaluate changes in the number of HER2-positive CTCs. The study focuses on HER2-low, which plays an increasingly important role in clinical practice due to new developments of HER2 targeting antibody-drug conjugates. In this context, temporal and spatial heterogeneity of the disease represent a major diagnostic challenge. Methods: A total of 324 patients with complete immunohistochemistry of biopsied metastases were divided into five groups: HER2 negative (−)/hormone receptor (HR) negative (−), HER2 −/HR positive (+), HER2 +/HR±, HER2-low/HR+, and HER2-low/HR−. Before and after 3 months of a new therapeutic line for mBC, CTCs were enumerated and analyzed for HER2 expression using the CellSearch® system. Overall survival of all subgroups was calculated. Results: The analyses revealed a discrepancy between the HER2 status of CTCs and corresponding tumor tissues in 98 patients (30.2%). The number of CTCs in general and the number of HER2+ CTCs decreased during systemic treatment, mainly in HER2+ tumors, but also in the other subgroups. Conclusions: Discrepancy in the HER2 status of the metastases and of CTCs was observed in approximately one-third of patients. Measuring HER2 on CTCs could potentially offer a means to longitudinally monitor HER2 status during therapy and simultaneously address challenges such as tumor heterogeneity. Therefore, the predictive value of HER2 on CTCs should be further investigated in clinical trials.

Short-term treatment response assessment in non-surgical treatment of advanced non-small cell lung cancer based on radiomics of dual-energy CT

PurposeTo build and evaluate a pre-treatment dual-energy CT(DECT)-based clinical-radiomics nomogram for individualized prediction of short-term treatment response to non-surgical treatment in advanced non-small cell lung cancer (NSCLC). MethodsPre-treatment DECT images were retrospectively collected from 98 pathologically confirmed NSCLC with clinical stage III or IV. Short-term treatment response was determined with follow-up CT of 4–6 courses of treatment. Quantitative radiomics metrics of the lesion were extracted from dual-energy mixed images at venous phase. Least absolute shrinkage and selection operator and correlation analysis were used to select the most relevant radiomics features. Radiomics model, clinical model and clinical-radiomics model were established by multivariate logistic regression. The model with the best prediction performance was visualized as a nomogram, and the consistency between the probability of the actual occurrence of the outcome and the probability predicted by the model was measured by calibration curves. ResultsClinical stage, difference in electron density in arteriovenous phase, difference in slope of energy spectrum in arteriovenous phase, and slope of energy spectrum in venous phase of the tumor were significant clinical predictors of therapy response (P < 0.05). The clinical-radiomics model showed a higher predictive capability (AUC: 0.87 and 0.85 in training and validation sets, respectively) than the radiomics models and the clinical model. The clinical-radiomics nomogram integrating the DECT radiomics signature with clinical stage and spectrum parameters showed good calibration and discrimination. ConclusionThe clinical-radiomics nomogram based on pre-treatment DECT showed good performance in predicting clinical response to non-surgical therapy in NSCLC.

Predictive Value of Neutrophil Extracellular Traps in Neoadjuvant Chemotherapy for Muscle-Invasive Bladder Cancer.

Cisplatin-based chemotherapy is the recommended therapy for muscle-invasive bladder cancer (MIBC). However, the efficacy of MIBC for chemotherapy is only about 40%. Therefore, predictors of therapy response are urgently needed. Neutrophils form neutrophil extracellular traps (NETs), a network structure, and growing evidence indicated that it could be a prognostic and predictive marker in cancer. In MIBC, the predictive role of NETs in chemotherapy resistance is unclear. We used the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analyses to develop a NETs-associated signature score (NETs-score) for therapeutic response prediction in the discovery cohort (GSE169455). Then the NETs score-based risk stratification was verified in two validation cohorts (Taber et al.'s cohort, our institutional cohort). In the training cohort, high NETs-score was associated with poor chemotherapy response (AUC = 0.781) and reduced recurrence-free survival (RFS) (hazard ratio [HR] = 2.07, 95% confidence interval [CI]: [1.26-3.40], p = 0.003) in MIBC patients. The NETs-score was also demonstrated to be a predictive factor for the efficacy of neoadjuvant chemotherapy in the validation cohort (AUC = 0.731). The accuracy of the NETs-score was superior to other chemotherapy response predictors such as Ba/Sq expression subtype (AUC = 0.711), BRCA2 mutation (AUC = 0.692) and ERCC2 mutation (AUC = 0.548). Furthermore, in our center cohort, the expression level of H3Cit showed a significant difference between the response and no-response group (p = 0.01). Through immunohistochemical validation, NETs was an independent predictor of MIBC neoadjuvant chemotherapy efficacy as determined by the multivariate logistic regression analysis (OR = 5.94, 95% CI: 1.20-45.50, p = 0.045). Patients with high levels of NETs predicted poor response to neoadjuvant chemotherapy. This study was the first to reveal the correlation between the level of NETs in MIBC and the efficacy of chemotherapy, which may provide a theoretical basis regarding NETs inhibitors.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

Prognostic and Immunotherapeutic Role of TACC3 in Pancancer and Its Impact on Proliferation and Docetaxel Resistance in Lung Adenocarcinoma

Introduction: Transforming acidic coiled-coil containing protein 3 (TACC3) exerts a vital role in cancer progression by modulating cell division and facilitating tumor growth. Given the lack of comprehensive research on the pancancer implications of TACC3, our study aimed to analyze the functional role of TACC3 in pancancer and validate it through experimental investigations in lung adenocarcinoma. Methods: We first employed various bioinformatics techniques to investigate the expression and prognostic significance of TACC3 in pancancer. Subsequently, we analyzed the correlation between TACC3 and immune infiltration, immune checkpoints, drug sensitivity, as well as the prediction of immune therapy response. Finally, we validated the association between TACC3 and the proliferation of lung adenocarcinoma, as well as its resistance to docetaxel, through in vitro experiments. Results: Here, TACC3 exhibited high expression in human cancers and was associated with poor prognosis in various cancer types. It was also involved in immune infiltration and demonstrated a strong predictive ability for immune therapy response. Through drug sensitivity prediction, we further identified a potential association between TACC3 and docetaxel resistance, which was subsequently validated in lung adenocarcinoma. Conclusion: Our investigation of TACC3 revealed its potential as a promising target both for immunosuppression and docetaxel resistance in pancancer, especially in lung adenocarcinoma.

Prediction of electroconvulsive therapy response and remission in late-life depression: a review.

Electroconvulsive therapy is an effective and well-tolerated antidepressant treatment for the elderly population. The place of electroconvulsive therapy in the treatment sequence for depression in the elderly is currently not well established. This review aims to identify the factors that contribute to a positive response and remission in elderly patients with depression undergoing electroconvulsive therapy treatment. We searched five bibliographic databases (Medline ALL Ovid, Embase.com, APA PsycInfo Ovid, Cochrane Library Wiley and Web of Science Core Collection) for articles published between 1995 and June 2023. Of the 2149 articles screened, 19 were included in the review. No significant associations were found between remission and/or response and salivary cortisol, baseline hippocampal and white matter hyperintensities, total amyloid load or global cortical atrophy. The reviewed articles did not show a significant difference in remission between unilateral and bilateral electroconvulsive therapy treatment. Other interesting findings are that moderately elevated levels of CRP and S100B levels, lower retardation scores, poorer performance on the word reading task at baseline and longer post-ictal reorientation time may be associated with higher remission and/or response rates. Medial temporal atrophy can be associated with lower Montgomery-Åsberg Depression Rating Scale (MADRS) decrease after electroconvulsive therapy. Finally, elderly patients had higher rates of electroconvulsive therapy response; retardation and psychotic features may mediate this association. Incorporation of this data into clinical practice may facilitate a personalised approach to electroconvulsive therapy. However, research on this topic is scarce and there are few studies that focus specifically on older people.

Systematic Identification of Gene-Drug Interactions Using an Advanced CRISPR Screening Platform to Predict Therapy Response across Cancer Types

Systematic Identification of Gene-Drug Interactions Using an Advanced CRISPR Screening Platform to Predict Therapy Response across Cancer Types

Drug induced sleep endoscopy and simultaneous polysomnography to predict the effectiveness of mandibular advancement device in obstructive sleep apnea treatment.

To evaluate whether mandibular advancement device therapy is recommended in patients affected by obstructive sleep apnea. In order to predict oral appliances therapy response, drug induced sleep endoscopy with cardio-respiratory polygraphy and mandibular advancement device simulator was carried out. Patients in which upper airway obstruction was resolved on all levels and AHI was normalized (< 5/h), were referred for oral appliance therapy. At 5 months follow up, a cardio-respiratory polygraphy with MAD was performed. 36 patients who have evidence of resolution of UA collapse and AHI below 5 events per hour, were referred for MAD therapy. At follow up, the mean AHI decreased from 29.1 ± 13.1 to 3.3/h ± 1.9 (p < 0.001). All the patients were responders. Combining the evaluation of drug induced sleep endoscopy and cardio-respiratory polygraphy data simultaneously during mandibular protrusion, has the potential to be a useful tool for prediction of MAD therapy response.

From Cancer to Immune Organoids: Innovative Preclinical Models to Dissect the Crosstalk between Cancer Cells and the Tumor Microenvironment

Genomic-oriented oncology has improved tumor classification, treatment options, and patient outcomes. However, genetic heterogeneity, tumor cell plasticity, and the ability of cancer cells to hijack the tumor microenvironment (TME) represent a major roadblock for cancer eradication. Recent biotechnological advances in organotypic cell cultures have revolutionized biomedical research, opening new avenues to explore the use of cancer organoids in functional precision oncology, especially when genomics alone is not a determinant. Here, we outline the potential and the limitations of tumor organoids in preclinical and translational studies with a particular focus on lung cancer pathogenesis, highlighting their relevance in predicting therapy response, evaluating treatment toxicity, and designing novel anticancer strategies. Furthermore, we describe innovative organotypic coculture systems to dissect the crosstalk with the TME and to test the efficacy of different immunotherapy approaches, including adoptive cell therapy. Finally, we discuss the potential clinical relevance of microfluidic mini-organ technology, capable of reproducing tumor vasculature and the dynamics of tumor initiation and progression, as well as immunomodulatory interactions among tumor organoids, cancer-associated fibroblasts (CAFs) and immune cells, paving the way for next-generation immune precision oncology.