Prediction Of Therapeutic Response Research Articles

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BackgroundCancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.MethodsTwo cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.ResultsFCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.ConclusionsThe CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BackgroundCancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.MethodsTwo cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.ResultsFCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.ConclusionsThe CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

Established a therapeutic response prediction model in metastatic gastric cancer by liquid biopsy

AbstractThis study by Okuno et al. successfully identified eight exo‐miRNAs through exosome‐based discovery and established an exo‐miRNA‐based liquid biopsy assay for predicting therapeutic response in metastatic gastric cancer (mGC). Exosomes, a subpopulation of extracellular vesicles originating from endosomes, play a crucial role in this context. Liquid biopsy, analyzing blood for circulating tumor cells, extracellular vesicles, or cell‐free nucleic acid, has revolutionized cancer diagnosis and monitoring. It significantly contributes to early detection, staging, and relapse detection in various cancers. Numerous studies have highlighted the clinical significance of miRNA and lncRNA within extracellular vesicles. The authors developed a response‐prediction model for chemo‐responsiveness in mGC patients. This study's model predicts responses robustly, demonstrating its potential efficacy in clinical practice. It offers a non‐invasive and accessible method for therapeutic response prediction, crucial for precision medicine in mGC. Successful translation of these findings into clinical applications promises substantial benefits for patient care.

Clinical impact and potential utility of non-enhanced computed tomography performed immediately after transarterial chemoembolization for hepatocellular carcinoma.

Intratumoral lipiodol deposition following transarterial chemoembolization (TACE) is associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, there is insufficient evidence regarding the actual clinical significance of the imaging tests conducted to evaluate the lipiodol uptake after TACE. This study evaluates the clinical impact and potential utility of performing immediate post-TACE non-enhanced computed tomography (NECT) on the treatment of HCC. This retrospective study at a tertiary referral center included patients undergoing their first session of conventional TACE for initial treatment of HCC from November 2021 to December 2022 with available immediate post-TACE NECT. Patients were categorized based on lipiodol uptake into Cohorts A (incomplete uptake with additional treatment before the first follow-up 1 month after TACE), B incomplete uptake without additional treatment before first follow-up), and C (complete uptake). Survival curves for the time to progression (TTP) were estimated using the Kaplan-Meier method and were compared by using the log-rank test. Out of 189 patients, 58 (29.6%) showed incomplete lipiodol uptake; 2 in Cohort A and 56 in Cohort B. Cohort C included 131 patients (69.3%). Cohort B had the highest rate of residual viable tumor (48.2%) 1 month after TACE, compared to the other cohorts (0% in Cohort A and 32.1% in Cohort C). The median TTP of Cohort B was 7.9 months [95% confidence interval (CI): 4.6-15.7 months], significantly shorter than the 15.4 months (95% CI: 10.9-20.9 months) for Cohort C (P=0.03). During follow-up, no progression occurred in Cohort A. Assessment of lipiodol uptake by performing immediate post-TACE NECT can stratify HCC patients and facilitate early prediction of therapeutic response. Identifying suboptimal lipiodol uptake immediately after TACE can aid future treatment adjustments and potentially improving oncologic outcomes.

Serial Changes of Circulating Tumor Cells in Patients with Hepatocellular Carcinoma Treated with Atezolizumab Plus Bevacizumab.

Immune checkpoint inhibitors have promising outcomes in patients with hepatocellular carcinoma (HCC); however, there is no reliable biomarker for predicting disease progression. Circulating tumor cells (CTCs) derived from peripheral blood have attracted attention in monitoring therapeutic efficacy. In this study, CTCs were serially collected from HCC patients undergoing atezolizumab plus bevacizumab (Atezo+Bev), and changes in molecular expression and CTC numbers were analyzed to identify effective biomarkers. Changes in CTC numbers during Atezo+Bev reflected the tumor volume. Targeted RNA sequencing with next-generation sequencing (NGS) revealed that patients with elevated transforming growth factor (TGF)-β signaling molecules had a poorer response, whereas those with elevated apoptosis signaling molecules had a favorable response. In addition, compared with changes in CTC counts, changes in TGF-β signaling molecule expression in CTCs accurately and promptly predicted treatment response. Overall, NGS analysis of CTC-derived RNA showed that changes in TGF-β signaling molecules predict treatment response earlier than changes in CTC counts. These findings suggest that changes in the expression of TGF-β molecules in CTCs could serve as novel biomarkers for the early prediction of therapeutic response in patients with unresectable HCC undergoing Atezo+Bev.

Insights into the role of mitophagy in lung cancer: current evidence and perspectives.

Lung cancer, recognized globally as a leading cause of malignancy-associated morbidity and mortality, is marked by its high prevalence and lethality, garnering extensive attention within the medical community. Mitophagy is a critical cellular process that plays a crucial role in regulating metabolism and ensuring quality control within cells. Its relevance to lung cancer has garnered significant attention among researchers and scientists. Mitophagy's involvement in lung cancer encompasses its initiation, progression, metastatic dissemination and treatment. The regulatory landscape of mitophagy is complex, involving numerous signaling proteins and pathways that may exhibit aberrant alterations or mutations within the tumor environment. In the field of treatment, the regulation of mitophagy is considered key to determining cancer chemotherapy, radiation therapy, other treatment options, and drug resistance. Contemporary investigations are directed towards harnessing mitophagy modulators, both inhibitors and activators, in therapeutic strategies, with an emphasis on achieving specificity to minimize collateral damage to healthy cellular populations. Furthermore, molecular constituents and pathways affiliated with mitophagy, serving as potential biomarkers, offer promising avenues for enhancing diagnostic accuracy, prognostic assessment, and prediction of therapeutic responses in lung cancer. Future endeavors will also involve investigating the impact of mitophagy on the composition and function of immune cells within the tumor microenvironment, aiming to enhance our understanding of how mitophagy modulates the immune response to lung cancer. This review aims to comprehensively overview recent advancements about the role of mitophagy in the tumor genesis, progenesis and metastasis, and the impact of mitophagy on the treatment of lung cancer. We also discussed the future research direction of mitophagy in the field of lung cancer.

A molecular subtyping associated with the cGAS-STING pathway provides novel perspectives on the treatment of ulcerative colitis

Ulcerative colitis (UC) is characterized by an abnormal immune response, and the pathogenesis lacks clear understanding. The cGAS-STING pathway is an innate immune signaling pathway that plays a significant role in various pathophysiological processes. However, the role of the cGAS-STING pathway in UC remains largely unclear. In this study, we obtained transcriptome sequencing data from multiple publicly available databases. cGAS-STING related genes were obtained through literature search, and differentially expressed genes (DEGs) were analyzed using R package limma. Hub genes were identified through protein–protein interaction (PPI) network analysis and module construction. The ConsensuClusterPlus package was utilized to identify molecular subtypes based on hub genes. The therapeutic response, immune microenvironment, and biological pathways of subtypes were further investigated. A total of 18 DEGs were found in UC patients. We further identified IFI16, MB21D1 (CGAS), TMEM173 (STING) and TBK1 as the hub genes. These genes are highly expressed in UC. IFI16 exhibited the highest diagnostic value and predictive value for response to anti-TNF therapy. The expression level of IFI16 was higher in non-responders to anti-TNF therapy. Furthermore, a cluster analysis based on genes related to the cGAS-STING pathway revealed that patients with higher gene expression exhibited elevated immune burden and inflammation levels. This study is a pioneering analysis of cGAS-STING pathway-related genes in UC. These findings provide new insights for the diagnosis of UC and the prediction of therapeutic response.

Prediction of therapeutic response and cancer outcomes in solid tumours via in silico clinical trials.

e13603 Background: Oncology therapeutic development has high failure rates with substantial costs. Advances in technology, including molecular characterisations of cancer and computational power, provide the opportunity to better model therapeutic response for existing and emerging therapies. Methods: Concr adapted Bayesian statistical principles used by astrophysicists to effectively integrate multi-dimensional data (genomic & transcriptomic) for accurate modelling of cancer biology. Our proprietary computational framework was applied to predict clinical response and survival through construction of digital twins and in silico simulations of clinical trials. Predicted log odds ratios (LOR) were generated for drug response and compared to published data from 8 suitable historical clinical trials. Three distinct models underpin this framework: Drug Efficacy, Treatment Response & Overall Survival (OS). Three publicly available datasets were used for training: Cancer Therapeutic Response Portal ( in vitro dose-response data), Cancer Cell Line Encyclopaedia and TCGA. Results: For all 8 clinical studies, the digital twin model accurately simulated both trial arms, compared drug efficacy across arms and predicted which treatment was most active. Unblinded evaluation: Our model accurately predicted gemcitabine would have greater clinical benefit than 5-FU (LOR -0.10, P <0.0001) in metastatic pancreatic cancer. In advanced breast cancer, the model predicted docetaxel had a higher response rate than doxorubicin, and carboplatin response rates were similar to docetaxel in BRCA wild-type patients. In platinum-sensitive recurrent ovarian cancer, the model predicted cisplatin-based combination had a higher response rate than paclitaxel. Blinded evaluation: Using data for paclitaxel, the model correctly predicted that nab-paclitaxel+gemcitabine response rates were higher than gemcitabine in metastatic pancreatic cancer (predicted LOR -0.090, p = <0.001). In all 3 early breast cancer studies, predictions mirrored clinical trial results, adjuvant ECF > CMF, adjuvant TC > AC and adjuvant tamoxifen was superior to capecitabine. The underlying pan-tumour Overall Survival model used a Random Survival Forest approach, trained on 23 cancers with predictive accuracy of 0.78 AUC ROC and C-index 0.71. To enable cohort enrichment for drug response, the model segmented patient cohorts into treatment responder and non-responder. Using this approach, the log odds increased for drug response rate for 16 of the 19 different cancers and 14 of the different 17 drugs. Conclusions: Our digital twin model represents transformative technology for clinical trial simulation with strong potential for accelerating and de-risking clinical drug development, and utility for synthetic control arms.

Clinical application of individualized tumor-informed circulating tumor DNA for therapeutic response and relapse prediction in patients with neuroblastoma.

10053 Background: Patients with high-risk neuroblastoma (NB) generally present with widely metastatic disease and often relapse despite intensive therapy. At present, circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) has been studied in various tumors and proven to be valuable in guiding treatment and predicting recurrence, but its application in NB has rarely been reported. This study aimed to assess the effectiveness of tumor-informed ctDNA monitoring for evaluating the response to induction therapy in NB patients, as well as its role in predicting the risk of relapse. Methods: We conducted a prospective cohort study of NB patients. Primary tumor samples were collected at baseline and blood samples were collected sequentially from baseline until disease progression or the last follow-up. Tumor tissue samples were subjected to whole exome sequencing (WES), and clonal mutations were selected for the individualized MRD panel. Sequencing was conducted at OrigiMed, a College of American Pathologists accredited and Clinical Laboratory Improvement Amendments certified laboratory. Results: Our cohort included 43 individuals diagnosed with NB, with a median age of 3.5 years (0.42-40 years). WES revealed that 75% (2054/2753) of the mutated genes were unique to each patient, and 98.9% (704/712) of the selected tumor-informed single nucleotide variants were variants of unknown significance, suggesting that individualized tumor-informed MRD (n = 23) is superior to fixed tumor-agnostic panel (n = 20) MRD in NB. A successful individualized MRD panel was constructed for 53.5% (23/43) of patients, with a remarkable 92.3% success rate for those older than 5 years (p < 0.01). The initial positivity rate for ctDNA detection was 68%, with the initial positivity rate being associated with the stage (p < 0.01). During treatment and follow-up, 0/6 of the patients whose ctDNA levels remained negative experienced a relapse, whereas 2/5 patients who had a negative conversion or an increase in positivity experienced a relapse. In addition, ctDNA changes matched radiological evaluations in 94% of patients. Notably, ctDNA was detected prior to radiological relapse, with a lead time of 6 months in one patient. Patients can be divided into four groups according to the mutation landscape: ALK mutation, MYCN amplification, chromosome amplification, and other groups. Among them, the rate of ctDNA clearance was the highest in the chromosome amplification group, indicating that patients in this group were the most sensitive to chemotherapy. Conclusions: Individualized tumor-informed ctDNA monitoring shows promise for evaluating therapeutic response and forecasting relapse in NB patients. This approach proves more effective than fixed tumor-agnostic panel-based MRD due to high genetic mutation uniqueness. Clinical trial information: NCT05076071 .

Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma

Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome. We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

Abstract A009: Integrative drug screening and multi-omic characterization of patient-derived bladder cancer organoids reveals novel molecular correlates of chemotherapy response

Abstract Introduction: Predicting response to therapy for each patient’s tumor is critical to improving long-term outcomes for muscle-invasive bladder cancer (MIBC). Our objective is to establish ex vivo bladder cancer patient-derived organoid (PDO) models that are representative of patients’ tumors and determine potential efficacy of standard-of-care and curated experimental therapies. Methods: Tumor material was prospectively collected from consented patients with bladder cancer to generate short-term PDO models, which were screened against a panel of clinically relevant drugs in ex vivo 3D culture. Drugs were tested at the maximum plasma concentration (Cmax) in human trials, so as to provide physiologic relevance. When material was sufficient, cells were additionally tested using full dose response. The number of drugs screened was dependent upon amount of tissue available, with up to 34 drugs screened at Cmax and 9 drugs screened in dose response format. Multiomic profiling was utilized to validate the PDO models, establish the molecular characteristics of each tumor, assess gene expression (GEX) patterns between paired primary tissue and PDOs, and identify potential biomarkers of drug response using Pearson correlation coefficients and Kruskal-Wallis tests with Dunn’s post-hoc pairwise comparison testing. Results: 106 tumors were collected from 97 patients, with 65 samples yielding sufficient material for complete multiomic molecular characterization and PDO screening with 6 to 32 drugs/combinations. RNA sequencing and GSEA enriched pathways across organoids were compared to other published datasets, noting high levels of correlation between our organoid models and in particular the Cancer Cell Line Encyclopedia urethral lines. Moreover, analysis of subtype and mutation/copy number data further indicate that our organoid dataset is representative of the full spectrum of disease. RNA sequencing and subtyping of cultured organoids compared to patient tissue indicate that organoid models are representative of patient tissue and do not undergo subtype shifts in our short-term 3D cultures. Our drug screening results highlight the large spectrum of response to chemotherapies that in fact, clinically benefit only a small proportion of patients in the neoadjuvant setting. Utilizing an integrative approach, novel correlations between ex vivo drug responses and genomic alterations, GEX, and protein expression were identified, including a multiomic signature of gemcitabine response. Conclusions: Rapid organoid development, characterization, and drug screening allows for the prediction of therapeutic response in ~10 days following sample collection. Use of this technique on tissue provided during disease work-up may further guide selection of effective therapeutic agents in patients with bladder cancer, beyond the typical unranked lists derived from genomic analyses. This work would help maximize the overall benefits of standard of care therapies and could be used to identify alternative therapeutics for patients who progress on standard therapies. Citation Format: Nathan M. Merrill, Samuel D. Kaffenberger, Liwei Bao, Nathalie Vandecan, Laura E. Goo, Athena Apfel, Xu Cheng, Zhaoping Qin, Chia-Jen Liu, Armand Bankhead, Yin Wang, Varun Kathawate, Lila Tudrick, Habib Serhan, Zackariah Farah, Chad Ellimoottil, Khaled S. Hafez, Lindsey A. Herrel, Jeffrey S. Montgomery, Todd M. Morgan, Simpa S. Salami, Alon Z. Weizer, Peter J. Ulintz, Mark L. Day, Matthew B. Soellner, Phillip L. Palmbos, Sofia D. Merajver, Aaron M. Udager. Integrative drug screening and multi-omic characterization of patient-derived bladder cancer organoids reveals novel molecular correlates of chemotherapy response [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A009.

JAK2 as Predictor of Therapeutic Response in Patients with Chronic Myeloid Leukemia Treated with Imatinib.

Chronic myeloid leukemia (CML) or chronic granulocytic leukemia is a myeloproliferative neoplasm indicated by the presence of the Philadelphia (Ph+) chromosome. First-line tyrosine kinase inhibitor, imatinib, is the gold standard for treatment. However, there has been known unresponsiveness to treatment, especially due to the involvement of other genes, such as the Janus kinase 2 (JAK2) gene. This study aimed to evaluate the relationships between JAK2 levels and complete hematological response (CHR), as well as early molecular response (EMR) after 3 months of imatinib treatment in patients with chronic phase CML. Patients with Ph+ CML in the chronic phase (n = 40; mean age, 40 ± 11 years) were recruited to complete assessments consisting of clinical examination and blood test, including evaluation of complete blood counts and the JAK2 levels, at baseline and following 3 months of therapy with imatinib (at an oral dose of 400 mg per day). Subjects were divided into two groups according to the presence of CHR and EMR. JAK2 gene levels, phosphorylated, and total JAK2 proteins at baseline were significantly lower in the group with the presence of CHR and EMR. In addition, baseline JAK2 levels, including JAK2 gene expression, phosphorylated, and total JAK2 proteins, were negatively correlated with the presence of CHR and EMR. Based on these findings, JAK2 levels may be a potential indicator for evaluating treatment response on imatinib due to its role in the pathophysiology of CML.

Abstract LB434: Rapid and reproducible expansion of rare tumor cells: The R3CE platform for personalized medicine

Abstract We have developed a novel single-cell derived 3D organoid culture platform, Rapid, Reproducible, Rare Cell 3D Expansion (R3CE), capable of generating 3D organoids from solid tumors and normal tissues obtained via surgical tissues and needle biopsies within one week. This method demonstrates over 90% success in banking for at least two passages with over 106 cells across various cancer types, including breast cancer, colorectal cancer, hepatoma cancer, and ovarian cancer. The organoids maintain high fidelity to original tissues, as evidenced by H&E staining, WES, and protein marker analysis across generations. Notably, the R3CE platform can also cultivate circulating tumor cells (CTCs) from peripheral blood. The drug sensitivity profiles of RCE-cultured CTCs closely mirrored clinical outcomes in breast cancer patients. For a patient with stage IV HER2-positive breast cancer, our real-time assay results led to an adjustment in treatment from lapatinib/5-FU to lapatinib/Trastuzumab emtansine (TDM-1), which resulted in substantial clinical improvement. To our knowledge, the R3CE platform is the first 3D organoid system that does not require aggregation or 3D scaffold such as matrigel system. These advancements position the R3CE platform as a promising tool for personalized medicine, offering a rapid and accurate method for drug screening and therapeutic response prediction to guide clinical decision-making in cancer treatment. Citation Format: Wen-Hung Kuo, Yen-Jang Huang, Jia-Yang Chen, Yi-Chun Wu, Chia-Chun Chen, Mark D. Pegram, Ying-Chih Chang. Rapid and reproducible expansion of rare tumor cells: The R3CE platform for personalized medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB434.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

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.

Pan-cancer analysis reveals PDK family as potential indicators related to prognosis and immune infiltration

Pyruvate dehydrogenase kinases (PDKs) play a key role in glucose metabolism by exerting negative regulation over pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibition of PDKs holds the potential to enhance PDC activity, prompting cells to adopt a more aerobic metabolic profile. Consequently, PDKs emerge as promising targets for condition rooted in metabolic dysregulation, including malignance and diabetes. However, a comprehensive exploration of the distinct contribution of various PDK family members, particularly PDK3, across diverse tumor types remain incomplete. This study undertakes a systematic investigation of PDK family expression patterns, forging association with clinical parameters, using data from the TCGA and GTEx datasets. Survival analysis of PDKs is executed through both Kaplan–Meier analysis and COX regression analysis. Furthermore, the extent of immune infiltration is assessed by leveraging the CIBERSORT algorithm. Our study uncovers pronounced genetic heterogeneity among PDK family members, coupled with discernible clinical characteristic. Significantly, the study establishes the potential utility of PDK family genes as prognostic indicators and as predictors of therapeutic response. Additionally, our study sheds light on the immune infiltration profile of PDK family. The results showed the intimate involvement of these genes in immune-related metrics, including immune scoring, immune subtypes, tumor-infiltrating lymphocytes, and immune checkpoints expression. In sum, the findings of this study offer insightful strategies to guide the therapeutic direction, aiming at leveraging the impact of PDK family genes in cancer treatment.

Development of a haematological indices-based nomogram for prognostic prediction and immunotherapy response assessment in primary pulmonary lymphoepithelioma-like carcinoma patients.

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC. This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram's predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts. The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 vs. 18.0 months, P<0.001) and lower overall response rates (22.2% vs. 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively. The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.

Prediction of functional therapeutic response to thrombectomy in acute stroke using neural networks

Prediction of functional therapeutic response to thrombectomy in acute stroke using neural networks

Noncoding RNAs and programmed cell death in hepatocellular carcinoma: Significant role of epigenetic modifications in prognosis, chemoresistance, and tumor recurrence rate.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high death rate in the world. The molecular mechanisms related to the pathogenesis of HCC have not been precisely defined so far. Hence, this review aimed to address the potential cross-talk between noncoding RNAs (ncRNAs) and programmed cell death in HCC. All related papers in the English language up to June 2023 were collected and screened. The searched keywords in scientific databases, including Scopus, PubMed, and Google Scholar, were HCC, ncRNAs, Epigenetic, Programmed cell death, Autophagy, Apoptosis, Ferroptosis, Chemoresistance, Tumor recurrence, Prognosis, and Prediction. According to the reports, ncRNAs, comprising long ncRNAs, microRNAs, circular RNAs, and small nucleolar RNAs can affect cell proliferation, migration, invasion, and metastasis, as well as cell death-related processes, such as autophagy, ferroptosis, necroptosis, and apoptosis in HCC by regulating cancer-associated genes and signaling pathways, for example, phosphoinositide 3-kinase/Akt, extracellular signal-regulated kinase/MAPK, and Wnt/β-catenin signaling pathways. It seems that ncRNAs, as epigenetic regulators, can be utilized as biomarkers in diagnosis, prognosis, survival and recurrence rates prediction, chemoresistance, and evaluation of therapeutic response in HCC patients. However, more scientific evidence is suggested to be accomplished to confirm these results.