Prediction Of Response Research Articles

Abstract 6400: Tislelizumab combined with XELOX as a neoadjuvant therapy for locally advanced gastric and gastroesophageal junction adenocarcinomas: A prospective, phase II trial

Abstract Background: Combing anti-PD-(L)1 antibody with chemotherapy in neoadjuvant therapy of gastric/gastroesophageal junction (G/GEJ) adenocarcinomas is a promising strategy, but clinical data are remain insufficient. Whether immunotherapy biomarkers in previous studies (TMB, MSI, PD-L1, etc.) have equivalent predictive value for G/GEJ remains to be verified. In this phase II clinical trial of tislelizumab in combination with chemotherapy, potential therapy-related biomarkers in tumor samples, and changes in the tumor microenvironment during treatment were analyzed, and a predictive model for efficacy was developed. Methods: This was a prospective, single-center, single-arm, phase II trial. Patients with G/GEJ adenocarcinomas (stage cT3-4aN+M0) were treated with PD-1 inhibitor tislelizumab in combination with oxaliplatin plus capecitabine (XELOX) as neoadjuvant treatment for 2 or 3 cycles. The primary endpoint was the major pathological response (MPR). The secondary endpoints included pathological complete response (pCR) rate, R0 resection rate, and safety. Tumor samples underwent RNA-sequencing (Amoy Diagnostics Co Ltd, Xiamen, China), to identify potential biomarkers, and a predictive model was developed to explore factors influencing tumor response and survival. Results: 28 patients were enrolled; mean age was 59.5 years (SD = 8.2), 19 (67.9%) patients were male; 14 (50.0%) patients with primary tumor location in the stomach, and 14 (50.0%) patients with primary tumor location in the GEJ. All patients were EB virus negative. Except one patient with PMS2 loss, 27 patients were pMMR. Nine (32.1%) patients achieved MPR after treatment, with four (14.3%) of them achieved pCR. The R0 resection rate was 96.4%, with 14 (50.0%) patients achieved objective response. No serious adverse events were reported. The most common grade 2 TRAEs during neoadjuvant treatment were anemia (5 of 28, 17.86%) and nausea (3 of 28, 10.71%). Analysis of differentially expressed genes revealed enrichment of the antigen processing and presentation pathway in the MPR group, while the TGF-β signaling pathway was enriched in the non-MPR group. Notably, the differential immune microenvironment analysis also uncovered significant differences in the TGF-β signature between the MPR and non-MPR groups. Additionally, a therapeutic response prediction model (HT score) was developed with an AUC of 0.98 (0.932-1) in the training set, and its performance was validated in publicly available datasets. Conclusion: Tislelizumab combined with XELOX holds promise as a neoadjuvant therapy for resectable G/GEJ adenocarcinomas, especially for EBV- and pMMR population, offering a well-tolerated safety profile. Moreover, the HT score has the potential to predict treatment efficacy in this regimen. Trial registration numbers: NCT05507658 and NCT05508399. Citation Format: Haikun Zhou, Tao Han, Lei Tuo, Qingchuan Yang, Ruiqi Gao, Pengfei Yu, Jiangpeng Wei, Changbin Zhu, Jin Wang, Peng Cheng, Xiang Kang, Sihui Pi, Hui Liu, Xiaoxi Pan, Xiaohua Li, Gang Ji. Tislelizumab combined with XELOX as a neoadjuvant therapy for locally advanced gastric and gastroesophageal junction adenocarcinomas: A prospective, phase II trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6400.

Abstract 2516: Influence of pre-treatment nutrition and inflammation on immunotherapy response in non-small cell lung cancer

Abstract Purpose: Immune checkpoint inhibitor therapy (ICI) is effective in non-small-cell lung cancer (NSCLC) but only a minority respond, likely due in part to the gut microbiome. Given the coupling of host nutritional status and gut microbiota composition, we investigated the pre-treatment associations of nutritional status and biomarkers with ICI treatment response and survivals. Methods: We recruited stage III-IV NSCLC patients. Pre-ICI treatment clinical information and blood samples were obtained. Clinician assessed ICI response and survival data were recorded. The nutritional/inflammatory indicators were compared based on treatment response and survivals. ICI Clinical Benefit was defined as 12-month progression free survival. Pearson Chi-square or Fisher’s Exact test was used to compare categorical metrics; non-parametric analysis compared continuous metrics by treatment response. Overall survivals were assessed using multivariable Cox proportional hazards models to estimate associations. Results: Of the 76 patients recruited, all were ECOG performance status 0 (14.5%) or 1 (85.5%). 36 (47%) had Clinical Benefit with median survival 21.3 mo. vs. 6.6 mo. among the No Clinical Benefit patients (p<0.01). Non-responders had significantly more often weight loss, lower serum albumin, elevated red cell distribution index (RDW) and worse scores for Frailty Index, Geriatric Nutritional Risk Index and Mini-Nutritional Assessment (MNA). Non-responders had significantly more inflammation with a higher Systemic Immune Response Index (SIRI) and RDW. Overall survival was negatively correlated with: 1. weight loss; 2. low serum albumin; 3. low serum sodium; 4. high RDW; 5. elevated total neutrophils, total white blood cells, total monocytes, neutrophil to lymphocyte ratio, and monocyte to lymphocyte ratio; 6. adverse MNA and Geriatric Nutritional Risk Index; 7. elevated SIRI and Systemic Immune Inflammation Index (SII). ICI response prediction accuracy using a solely MNA-based model was significantly high in terms of AUROC of 0.85 [95% CI= (0.76, 0.93); p<0.05]. In multivariable analysis, the predictor of immunotherapy response and survival was the 6-measure MNA which strongly correlates with weight loss, low serum albumin and depressed Frailty Index. Conclusions: An adverse nutritional status with accompanying chronic inflammation is strong predictor of non-response to ICI. Since lung cancer patients with depressed pre-treatment nutrition have a low likelihood of an ICI response, they should be promptly referred for dietician consultation for intensive nutritional therapy which may improve their clinical outlook. Considering the negative association of malnutrition/inflammation with response to ICI treatment in NSCLC patients who still qualify as ECOG performance status 0-1 patients, prospective clinical trials should also stratify their results by nutritional status. Citation Format: Lary A. Robinson, Youngchul Kim, Acadia Buro, Stephanie R. Hogue, Alexis Bailey, Gina M. DeNicola, Christine Pierce, Mark Umbarger, Doratha A. Byrd. Influence of pre-treatment nutrition and inflammation on immunotherapy response in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2516.

Abstract 6210: The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction

Abstract Background: Determining a patient’s response to a specific therapy is a vital step in developing personalized cancer treatment. Personalized treatment relies on two key technological advancements: algorithms tailored to each patient’s molecular profiles, and comprehensive datasets detailing the effects of patient diversity on drug response. Despite recent advancements in machine learning-based models, each new algorithm requires curation of numerous datasets, which poses a significant burden to researchers who need to evaluate and compare various algorithms. Currently, datasets include extensive genomics, transcriptomics, proteomics and metabolomics measurements along with drug response data (i.e., cell viability) for each cell line, collected through the Cancer Cell Line Encyclopedia (CCLE) and other initiatives. Additional omics data is available for tumor organoids and patient samples generated by consortia such as Human Cancer Model Initiative (HCMI) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). However, to date there has been little public effort to annotate and harmonize these data across repositories. Methods: To address this need, we developed the Python package coderdata that collates the most up-to-date cancer omics and drug response data. We curated four public cancer datasets, establishing reproducible pipelines that standardize data retrieval, formatting, and integration so the datasets can be built on a cluster if needed. The datasets undergo bi-monthly synchronization with FigShare, ensuring the data is up to date and available. Scientists can use coderdata to retrieve and format data on the command line interface as well as within Python scripts. Results: Coderdata is a straightforward Python package that will enable the development and benchmarking of diverse machine learning applications in cancer research. At its core, it represents reproducible data harmonization that enables heterogeneous datasets to be analyzed in bulk. This Python package downloads and standardizes data from multiple consortia-related resources including HCMI, CPTAC, CCLE, and BeatAML, together representing 4931 multi-omics samples from across 250+ cancer types. These datasets include cancer data metrics such as copy number, mutation, transcriptomics, proteomics, miRNA, methylation, and drug response data. Conclusions: The applications of machine learning in cancer biology are hampered by the distributed nature of existing datasets. As such, the collection and standardization of data by coderdata substantially reduces the time investment required for data curation in cancer research. Direct access to benchmark multi-omics and drug response datasets enables scientists to focus on algorithm development for tumor drug response prediction, potentially accelerating the discovery of therapeutic strategies. Citation Format: Jeremy Jacobson, Sydney Schwartz, M. Ryan Weil, Neeraj Kumar, Sara Gosline. The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6210.

Multi-Objective Optimization and Prediction of Responses Using GRA and ANN in Micro-EDM of Ti6Al4V Using Different Tool Materials

An experimental investigation was conducted to evaluate the machinability of a titanium alloy (Ti6Al4V) using copper (Cu), tungsten carbide (WC), and graphite (C) tools. Voltage (V), capacitance (pF), pulse-on time ([Formula: see text][Formula: see text]), and pulse-off time ([Formula: see text][Formula: see text]) were considered as the input machining parameters, whereas the material removal rate (MRR) and tool wear rate (TWR) were considered as the output machining parameters. A Taguchi L[Formula: see text] orthogonal array and gray relational analysis (GRA) were utilized to design and optimize the machining parameters for both responses. Artificial neural network (ANN) analysis was performed to predict the experimental outcomes. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to assess the surface morphology and determine the elemental composition of the machined surface. The results indicated that the optimum machining conditions for the copper tool were 150 V, 1000 pF, 15 [Formula: see text]s ([Formula: see text][Formula: see text]), and 15 [Formula: see text]s ([Formula: see text][Formula: see text]). However, the optimal machining conditions for the WC were 200 V, 100 pF, 25 [Formula: see text]s ([Formula: see text][Formula: see text]), and 10 [Formula: see text]s ([Formula: see text][Formula: see text]), and the optimal conditions for the C were 200 V, 1000 pF, 20 [Formula: see text]s ([Formula: see text][Formula: see text]), and 25 [Formula: see text]s ([Formula: see text][Formula: see text]), respectively. The highest MRR achieved using the WC tool was 9.4510 mg/s, whereas the TWR of the Cu, WC, and C tools were 1.1039 mg/s, 1.0307 mg/s, and 1.2796 mg/s, respectively. The results showed that machining with the graphite tool had a higher TWR than machining with the Cu and WC tools.

Noninvasively Deciphering the Immunosuppressive Tumor Microenvironment Using Galectin-1 PET to Inform Immunotherapy Responses.

Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

Prediction of complete response after neoadjuvant chemotherapy for invasive breast cancers: The utility of shear wave elastography and superb microvascular imaging in pretreatment breast ultrasound

PurposeTo investigate whether multiparametric parameters of pretreatment breast ultrasound (US) and clinicopathologic factors are associated with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer. MethodsBetween November 2018 and September 2022, 88 patients who underwent NAC and subsequent surgery were included in this study (median age, 55 years; interquartile range [IQR], 45, 59.3). Multiparametric breast US including grayscale, shear wave elastography (SWE) and superb microvascular imaging (SMI) of pathologically proven invasive breast cancers were retrospectively reviewed. Clinicopathological and multiparametric parameters of breast US, including size, SWEmax, SWEratio and vascular index on SMI (SMIVI) were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine factors predicting pCR after NAC. AUROC curve analysis was performed to determine the predictors’ optimal cut-off values and diagnostic performance. ResultsThe pCR group (n = 24) showed a significantly smaller tumor size, lower SWEmax, higher Ki-67 index, higher hormone receptor negativity and negative axillary lymph node metastasis compared to the non-pCR group (n = 64). Multivariate regression analysis showed that SWEmax (adjusted odds ratio[aOR] = 0.956, 95 % confidence interval [CI] = 0.919–0.994, P = 0.025) and Ki-67 index (aOR = 1.083, 95 % CI = 1.012–1.159, P = 0.021) were independently associated with pathologically complete response. The optimal cut-off values for predicting pCR were 27.5 % for Ki-67 with an AUC of 0.743 and 134.8 kPa for SWEmax with an AUC of 0.779. A combination model including clinical factors and SWEmax showed the best diagnostic performance with an AUC of 0.876. ConclusionA higher Ki-67 index and lower SWEmax measured on pretreatment breast US were independently associated with pCR in invasive breast cancer after NAC.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35T MRI-guided radiotherapy study.

The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. Patients from three international sites were included and divided into training and validation sets. 0.35T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35T MRIgRT treatments of LARC patients.

Neutrophil-to-lymphocyte ratio for the prediction of soft tissue sarcomas response to pre-operative radiation therapy

Purpose/ObjectiveThis study aims to assess the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in soft tissue sarcomas (STS) treated with pre-operative hypofractionated radiotherapy (HFRT). Materials/MethodsThis retrospective analysis included patients treated with pre-operative HFRT of 30 Gy in 5 fractions between 2016 and 2023. Clinical, demographic, and complete blood count (CBC) data were collected. NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count. Only patients with CBCs conducted within 6 months after radiotherapy were included. Cox proportional-hazard regression models were used to assess the impact of NLR and different variables on outcomes. Kaplan Meier were used to illustrate survival curves. A p-value < 0.05 was considered significant, and 95 % confidence intervals (CI) were employed. ResultsA total of 40 patients received HFRT and had CBCs within 6 months after radiotherapy. There were 17 (42.5 %) females and 23 (57.5 %) males with a mean age of 66 years. The mean largest tumor size dimension was 7.1 cm, and the mean NLR post-RT was 5.3. The most frequent histological subtypes were myxofibrosarcoma (17.5 %), pleomorphic spindle cell sarcoma (10 %), leiomyosarcoma (7.5 %), and myxoid liposarcoma (5 %). The median follow-up period was 15.4 months. From all patients, 14 patients had disease progression, 12 metastatic disease and 3 died of disease. Multivariable Cox proportional-hazards regression analysis displayed that a higher post-RT NLR was associated with worse disease-free survival (DFS) (HR: 1.303 [1.098–1.548], p = 0.003), and distant metastasis-free survival (DMFS) (HR: 1.38 [1.115–1.710], p = 0.003). Moreover, post-NLR ≥ 4 as a single variable was associated with worse DFS, DMFS, but not worse local recurrence or overall survival. ConclusionThis study is the first to evaluate NLR as a prognostic biomarker in STS patients treated with pre-operative radiotherapy. A higher NLR after pre-operative radiotherapy was associated with increased disease progression.

Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia.

This study aimed to identify key proteomic analytes correlated with response to splenectomy in primary immune thrombocytopenia (ITP). Thirty-four patients were retrospectively collected in the training cohort and 26 were prospectively enrolled as validation cohort. Bone marrow biopsy samples of all participants were collected prior to the splenectomy. A total of 12 modules of proteins were identified by weighted gene co-expression network analysis (WGCNA) method in the developed cohort. The tan module positively correlated with megakaryocyte counts before splenectomy (r = 0.38, p = 0.027), and time to peak platelet level after splenectomy (r = 0.47, p = 0.005). The blue module significantly correlated with response to splenectomy (r = 0.37, p = 0.0031). KEGG pathways analysis found that the PI3K-Akt signalling pathway was predominantly enriched in the tan module, while ribosomal and spliceosome pathways were enriched in the blue module. Machine learning algorithm identified the optimal combination of biomarkers from the blue module in the training cohort, and importantly, cofilin-1 (CFL1) was independently confirmed in the validation cohort. The C-index of CFL1 was >0.7 in both cohorts. Our results highlight the use of bone marrow proteomics analysis for deriving key analytes that predict the response to splenectomy, warranting further exploration of plasma proteomics in this patient population.

Molecular Profiling of Axial Spondyloarthritis Patients Reveals an Association between Innate and Adaptive Cell Populations and Therapeutic Response to Tumor Necrosis Factor Inhibitors.

This study aims at identifying molecular biomarkers differentiating responders and non-responders to treatment with Tumor Necrosis Factor inhibitors (TNFi) among patients with axial spondyloarthritis (axSpA). Whole blood mRNA and plasma proteins were measured in a cohort of biologic-naïve axSpA patients (n = 35), pre and post (14 weeks) TNFi treatment with adalimumab. Differential expression analysis was used to identify the most enriched pathways and in predictive models to distinguish responses to TNFi. A treatment-associated signature suggests a reduction in inflammatory activity. We found transcripts and proteins robustly differentially expressed between baseline and week 14 in responders. C-reactive protein (CRP) and Haptoglobin (HP) proteins showed strong and early decrease in the plasma of axSpA patients, while a cluster of apolipoproteins (APOD, APOA2, APOA1) showed increased expression at week 14. Responders to TNFi treatment present higher levels of markers of innate immunity at baseline, and lower levels of adaptive immunity markers, particularly B-cells. A logistic regression model incorporating ASDAS-CRP, gender, and AFF3, the top differentially expressed gene at baseline, enabled an accurate prediction of response to adalimumab in our cohort (AUC = 0.97). In conclusion, innate and adaptive immune cell type composition at baseline may be a major contributor to response to adalimumab in axSpA patients. A model including clinical and gene expression variables should also be considered.

Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis.

This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

Non-Invasive Biomarkers for Diagnosis, Risk Prediction, and Therapy Guidance of Glomerular Kidney Diseases: A Comprehensive Review.

Effective management of glomerular kidney disease, one of the main categories of chronic kidney disease (CKD), requires accurate diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for the assessment of specific aspects of glomerular diseases have been reported in the literature. Though, the vast majority of these have not been implemented in clinical practice or are not available on a global scale due to limited access, missing medical infrastructure, or economical as well as political reasons. The aim of this review is to compile all currently available information on the diagnostic, prognostic, and predictive biomarkers currently available for the management of glomerular diseases, and provide guidance on the application of these biomarkers. As a result of the compiled evidence for the different biomarkers available, we present a decision tree for a non-invasive, biomarker-guided diagnostic path. The data currently available demonstrate that for the large majority of patients with glomerular diseases, valid biomarkers are available. However, despite the obvious disadvantages of kidney biopsy, being invasive and not applicable for monitoring, especially in the context of rare CKD etiologies, kidney biopsy still cannot be replaced by non-invasive strategies.

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index.

Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction. In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment. Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature. Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Blood platelet RNA profiles do not enable for nivolumab response prediction at baseline in patients with non-small cell lung cancer.

Anti-PD-(L)1 immunotherapy has emerged as a promising treatment approach for non-small cell lung cancer (NSCLC), though the response rates remain low. Pre-treatment response prediction may improve patient allocation for immunotherapy. Blood platelets act as active immune-like cells, thereby constraining T-cell activity, propagating cancer metastasis, and adjusting their spliced mRNA content. We investigated whether platelet RNA profiles before start of nivolumab anti-PD1 immunotherapy may predict treatment responses. We performed RNA-sequencing of platelet RNA samples isolated from stage III-IV NSCLC patients before treatment with nivolumab. Treatment response was scored by the RECIST-criteria. Data were analyzed using a predefined thromboSeq analysis including a particle-swarm-enhanced support vector machine (PSO/SVM) classification algorithm. We collected and processed a 286-samples cohort, separated into a training/evaluation and validation series and subjected those to training of the PSO/SVM-classification algorithm. We observed only low classification accuracy in the 107-samples validation series (area under the curve (AUC) training series: 0.73 (95% -CI: 0.63-0.84, n = 88 samples), AUC evaluation series: 0.64 (95% -CI: 0.51-0.76, n = 91 samples), AUC validation series: 0.58 (95% -CI: 0.45-0.70, n = 107 samples)), employing a five-RNAs biomarker panel. We concluded that platelet RNA may have minimally discriminative capacity for anti-PD1 nivolumab response prediction, with which the current methodology is insufficient for diagnostic application.

Application of Data-Driven computing to patient-specific prediction of the viscoelastic response of human brain under transcranial ultrasound stimulation.

We present a class of model-free Data-Driven solvers that effectively enable the utilization of in situ and in vivo imaging data directly in full-scale calculations of the mechanical response of the human brain to sonic and ultrasonic stimulation, entirely bypassing the need for analytical modeling or regression of the data. The well-posedness of the approach and its convergence with respect to data are proven analytically. We demonstrate the approach, including its ability to make detailed spatially resolved patient-specific predictions of wave patterns, using public-domain MRI images, MRE data and commercially available solid-mechanics software.

Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer.

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

Scoliosis Research Society-22r and Ceiling Effects: Limited Capabilities for Precision-Medicine with Adolescent Idiopathic Scoliosis.

Retrospective registry analysis. To examine predictions of individual Scoliosis Research Society-22r (SRS-22r) questions one year after surgery for adolescent idiopathic scoliosis (AIS). A precision-medicine approach to AIS surgery will inform patients of the likelihood of achieving particular results from surgery, specifically individual responses to the SRS-22r questionnaire. A multi-center AIS registry was queried for surgical AIS patients treated between 2002-2020. Preoperative data collected included standard demographic data, deformity descriptive data, and SRS-22r scores. Postoperative 1yr SRS-22r scores were modeled using ordinal logistic regression. . The highest probability was the most likely response. Model performance was examined by c-statistics, where c>.8 was considered excellent. Ceiling effects were measured by the proportion of patients reporting "5" to each question. 3251 patients contributed data to the study; mean age 14.4 (±2.2) yrs, female 2631 (81%), major thoracic coronal curve 53°, mean lumbar 41°. C-statistic values ranged from .6 (poor) to .8 (excellent) evidence of varied predictive capabilities. Q17 ("days off work/school", c = .84, ceiling achieved 75%) and Q15 ("financial difficulties", c = .86, ceiling achieved 82%) had the greatest predictive capabilities while Q11 ("pain medication", c=.73, ceiling achieved 67%), Q10 ("appearance", c=.72, ceiling achieved 35%), and Q19 ("attractive", c=.69, ceiling achieved 37%) performed poorly. Prediction of individual SRS-22r item responses perhaps most germane to AIS treatment was poor. Prediction of less relevant outcomes, where ceiling effects are present, was greater as the models chose "5" for all responses. These ceiling effects may limit discrimination and hamper efforts at personalized outcome predictions. 3.

Serum tumor markers for response prediction and monitoring of advanced lung cancer: A review focusing on immunotherapy and targeted therapies.

The value of serum tumor markers (STMs) in the current therapeutic landscape of lung cancer is unclear. This scoping review gathered evidence of the predictive, prognostic, and monitoring value of STMs for patients with advanced lung cancer receiving immunotherapy (IT) or targeted therapy (TT). Literature searches were conducted (cut-off: May 2022) using PubMed and Cochrane CENTRAL databases. Medical professionals advised on the search strategies. Study heterogeneity limited the evidence and inferences from the 36 publications reviewed. While increased baseline levels of serum cytokeratin 19 fragment antigen (CYFRA21-1) and carcinoembryonic antigen (CEA) may predict IT response, results for TT were less clear. For monitoring IT-treated patients, STM panels (including CYFRA21-1, CEA, and neuron-specific enolase) may surpass the power of single analyses to predict non-response. CYFRA21-1 measurement could aid in monitoring TT-treated patients, but the value of CEA in this context requires further investigation. Overall, baseline and dynamic changes in individual or combined STM levels have potential utility to predict treatment outcome and for monitoring of patients with advanced lung cancer. In advanced lung cancer, STMs provide additional relevant clinical information by predicting treatment outcome, but further standardization and validation is warranted.

Vibration response prediction and vibration safety assessment method for building structures around viaducts under the action of high-speed railway trains

Concerning the cumulative damage of building structures caused by the long-term effects from high-speed railway train loads and various environmental impacts, and to ensure the service safety of building structures around viaducts, this paper establishes a vibration safety assessment method for the entire life cycle of building structures with less workload and good applicability, while ensuring the accuracy and effectiveness of the prediction of vibration response of building structures. Firstly, the vibration source function is formed based on the mechanical model of the mobile vibration source of high-speed railway trains, and the vibration wave of high-speed railway trains is numerically simulated. Then the time and frequency characteristics of the simulated high-speed railway train vibration waves are verified through on-site monitoring tests. In addition, the peak particle vibration velocity attenuation formula for the foundation site of the viaduct is derived by the dimension analysis method, and it is used to correct the attenuation characteristics of the simulated high-speed railway train vibration waves. Finally, based on the response spectrum method to realize the prediction and assessment of the overall vibration response of the building structures, and using the cumulative damage coefficient to reflect the cumulative degree of the service damage of the building structure, a vibration safety assessment method for the whole lifecycle of the building structure is proposed. The method overcomes the shortcoming that vibration velocity does not reflect the impact of service damage on the vibration safety of building structures, and avoids neglecting localized vibration damage, and is therefore useful for the rapid, overall vibration safety assessment of any long-term service building structure in a viaduct site.