Drug Sensitivity Prediction Research Articles

PTBP1 as a Promising Predictor of Poor Prognosis by Regulating Cell Proliferation, Immunosuppression, and Drug Sensitivity in SARC.

Background Sarcomas (SARC) have been found as rare and heterogeneous malignancies with poor prognosis. PTBP1, belonging to the hnRNPs family, plays an essential role in some biological functions (e.g., pre-mRNA splicing, cell growth, and nervous system development). However, the role of PTBP1 in SARC remains unclear. In this study, the aim was to investigate the potential role of PTBP1 with a focus on SARC. Methods The expression, prognostic value, possible biological pathways of PTBP1, and its relationship with immune infiltration and drug sensitivity were comprehensively analyzed based on multiple databases. PTBP1 was further validated in osteosarcoma as the most prominent bone SARC. The expression of PTBP1 was investigated through IHC. The prognostic value of PTBP1 was verified in TARGET-OS databases. CRISPR/Cas9-mediated PTBP1 knockout HOS human osteosarcoma cell lines were used to assess the effect of PTBP1 on cell proliferation, migration, metastasis and cell cycle by CCK-8, Transwell migration, invasion, and FACS experiment. Result PTBP1 was highly expressed and significantly correlated with poor prognosis in several cancers, especially in SARC, which was validated in the clinical cohort and osteosarcoma cell lines. The genetic alteration of PTBP1 was found most frequently in SARC. Besides, PTBP1 played a role in oncogenesis and immunity through cell cycle, TGFB, autophagy, and WNT pathways at a pan-cancer level. Knockout of PTBP1 was observed to negatively affect proliferation, migration, metastasis, and cell cycle of osteosarcoma in vitro. Furthermore, PTBP1 was significantly correlated with tumor immune infiltration, DNA methylation, TMB, and MSI in a wide variety of cancers. Moreover, the potential of the expression level of PTBP1 in predicting drug sensitivity was assessed. Conclusions PTBP1 is highly expressed and correlated with prognosis and plays a vital pathogenic role in oncogenesis and immune infiltration of various cancers, especially for SARC, which suggests that it may be a promising prognostic marker and therapeutic target in the future.

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data.

Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.

Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature.

Background Adrenocortical carcinoma (ACC) is a rare and poor prognosis malignancy. Necroptosis is a special type of cell apoptosis, which is regulated in caspase-independent pathways and mainly induced through the activation of receptor-interacting protein kinase 1, receptor-interacting protein kinase 3, and mixed lineage kinase domain-like pseudokinase. A precise predictive tool based on necroptosis is needed to improve the level of diagnosis and treatment. Method Four ACC cohorts were enrolled in this study. The Cancer Genome Atlas ACC (TCGA-ACC) cohort was used as the training cohort; three datasets (GSE19750, GSE33371, and GSE49278) from Gene Expression Omnibus (GEO) platform were combined as the GEO testing cohort after removing of batch effect. Forty-nine necroptosis-associated genes were obtained from a prior study and further filtered by least absolute shrinkage and selection operator Cox regression analysis; corresponding coefficients were used to calculate the necroptosis-associated gene score (NAGs). Patients in the TCGA-ACC cohort were equally divided into two groups with the mean value of NAGs. We investigated the associations between NAGs groups and clinicopathological feature distribution and overall survival (OS) in ACC, the molecular mechanisms, and the value of NAGs in therapy prediction. A nomogram risk model was established to quantify risk stratification for ACC patients. Finally, the results were confirmed in the GEO-combined cohort. Result Patients in the TCGA-ACC cohort were divided into high and low NAGs groups. The high NAGs group had more fatal cases and advanced stage patients than the low NAGs group (P < 0.001, hazard ratio (HR) = 13.97, 95% confidence interval (95% CI): 4.168–46.844; survival rate: low NAGs, 7.69% vs. high NAGs, 61.53%). NAGs were validated to be negatively correlated with OS (R = −0.48, P < 0.001) and act as an independent factor in ACC with high discriminative efficacy (P < 0.001, HR = 11.76, 95% CI: 2.86–48.42). In addition, a high predictive efficacy nomogram risk model was established combining NAGs with tumor stage. Higher mutation rates were observed in the high NAGs group, and the mutation of TP53 may lead to a high T cell infiltration level among the NAGs groups. Patients belonged to the high NAGs are more sensitive to the chemotherapy of cisplatin, gemcitabine, paclitaxel, and etoposide (all P < 0.05). Ultimately, the same statistical algorithms were conducted in the GEO-combined cohort, and the crucial role of NAGs prediction value was further validated. Conclusion We constructed a necroptosis-associated gene signature, revealed the prognostic value between ACC and it, systematically explored the molecular alterations among patients with different NAGs, and manifested the value of drug sensitivity prediction in ACC.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

Consensus molecular subtype differences linking colon adenocarcinoma and obesity revealed by a cohort transcriptomic analysis.

Colorectal cancer (CRC) is the third-leading cause of cancer-related deaths in the United States and worldwide. Obesity-a worldwide public health concern-is a known risk factor for cancer including CRC. However, the mechanisms underlying the link between CRC and obesity have yet to be fully elucidated in part because of the molecular heterogeneity of CRC. We hypothesized that obesity modulates CRC in a consensus molecular subtype (CMS)-dependent manner. RNA-seq data and associated tumor and patient characteristics including body weight and height data for 232 patients were obtained from The Cancer Genomic Atlas-Colon Adenocarcinoma (TCGA-COAD) database. Tumor samples were classified into the four CMSs with the CMScaller R package; body mass index (BMI) was calculated and categorized as normal, overweight, and obese. We observed a significant difference in CMS categorization between BMI categories. Differentially expressed genes (DEGs) between obese and overweight samples and normal samples differed across the CMSs, and associated prognostic analyses indicated that the DEGs had differing associations on survival. Using Gene Set Enrichment Analysis, we found differences in Hallmark gene set enrichment between obese and overweight samples and normal samples across the CMSs. We constructed Protein-Protein Interaction networks and observed differences in obesity-regulated hub genes for each CMS. Finally, we analyzed and found differences in predicted drug sensitivity between obese and overweight samples and normal samples across the CMSs. Our findings support that obesity impacts the CRC tumor transcriptome in a CMS-specific manner. The possible associations reported here are preliminary and will require validation using in vitro and animal models to examine the CMS-dependence of the genes and pathways. Once validated the obesity-linked genes and pathways may represent new therapeutic targets to treat colon cancer in a CMS-dependent manner.

Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer.

Background: Necroptosis has been an alternatively identified mechanism of programmed cancer cell death, which plays a significant role in cancer. However, research about necroptosis-related long noncoding RNAs (lncRNAs) in cancer are still few. Moreover, the potentially prognostic value of necroptosis-related lncRNAs and their correlation with the immune microenvironment remains unclear. The present study aimed to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship to immune microenvironment in triple-negative breast cancer (TNBC). Methods: The RNA expression matrix of patients with TNBC was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Finally, 107 patients of GSE58812, 159 patients of TCGA, and 143 patients of GSE96058 were included. Necroptosis-related lncRNAs were screened by Cox regression and Pearson correlation analysis with necroptosis-related genes. By LASSO regression analysis, nine necroptosis-related lncRNAs were employed, and a cell necroptosis index (CNI) was established; then, we evaluated its prognostic value, clinical significance, pathways, immune infiltration, and chemotherapeutics efficacy. Results: Based on the CNI value, the TNBC patients were divided into high- and low-CNI groups, and the patients with high CNI had worse prognosis, more lymph node metastasis, and larger tumor (p < 0.05). The receiver operating characteristic (ROC) analysis showed that the signature performed well. The result of the infiltration proportion of different immune cell infiltration further explained that TNBC patients with high CNI had low immunogenicity, leading to poor therapeutic outcomes. Moreover, we found significant differences of the IC50 values of various chemotherapeutic drugs in the two CNI groups, which might provide a reference to make a personalized chemotherapy for them. Conclusion: The novel prognostic marker CNI could not only precisely predict the survival probability of patients with TNBC but also demonstrate a potential role in antitumor immunity and drug sensitivity.

MP06-07 APPLICATION OF BLADDER CANCER PATIENT-DERIVED ORGANOIDS AS PRE-CLINICAL MODEL FOR PERSONALIZED MEDICINE

You have accessJournal of UrologyCME1 May 2022MP06-07 APPLICATION OF BLADDER CANCER PATIENT-DERIVED ORGANOIDS AS PRE-CLINICAL MODEL FOR PERSONALIZED MEDICINE Martina Minoli, Mirjam Kiener, Thomas Cantore, Tarcisio Fedrizzi, Paola Gasparini, Francesca Demichelis, George N. Thalmann, Roland Seiler-Blarer, and Marianna Kruithof-de Julio Martina MinoliMartina Minoli More articles by this author , Mirjam KienerMirjam Kiener More articles by this author , Thomas CantoreThomas Cantore More articles by this author , Tarcisio FedrizziTarcisio Fedrizzi More articles by this author , Paola GaspariniPaola Gasparini More articles by this author , Francesca DemichelisFrancesca Demichelis More articles by this author , George N. ThalmannGeorge N. Thalmann More articles by this author , Roland Seiler-BlarerRoland Seiler-Blarer More articles by this author , and Marianna Kruithof-de JulioMarianna Kruithof-de Julio More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002523.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Treatment failure and/or frequent reoccurrence are the major clinical problems of bladder cancer (BLCa), suggesting that a more personal approach is mandatory. The genomic has a limited role in the identification of therapeutic options, therefore patient-derived organoids (PDOs) have been used for functional assays for predicting drug response in different cancer types. Our research aims to analyze PDOs to determine drug response profiles of BLCa patients to personalize therapy accordingly. We aim to correlate PDOs’ drug sensitivity to their molecular and genomic profiles in association with the parental tumor (PT) as well as to the clinical course of the patient. METHODS: To derive PDOs, tumor samples were dissociated into single cells and cultured in ultra-low attachment plates. The drug response was evaluated by viability assay after 48 hours of treatment. Whole exome sequencing was performed on genomic DNA from PDOs, PT, and blood (germline control). Histological evaluation of PDOs and PT morphology was performed. RESULTS: We successfully obtained PDOs from 11 NMIBC and 6 MIBC tissues. Genomic analysis of PDOs and PT showed good similarity of mutational burden with preservation of sub-clonal populations. Important BLCa mutations were found in PDOs and matched PT (e.g. loss of p53 for MIBC and amplification of FGFR3 for NMIBC). Additionally, conservation of the tumor phenotype in PDOs was confirmed. A panel of BLCa standard-of-care and FDA-approved drugs, selected based on clinical importance and the possibility of drug repurposing, were tested on PDOs. Surprisingly, a significant response to SOC (cisplatin alone or in combination with gemcitabine) was observed in 8 out of 17 analyzed samples and the non-SOC drugs were effective in few samples. Investigation of drug sensitivity showed partial association with phenotypic features, mutational profiles, and clinical outcomes. For example, Docetaxel sensitivity was associated to copy number gain in TUBB1 and MIBC organoids were significantly more sensitive to a low dose of cisplatin than NMIBC organoids. Moreover, in a case study, alteration caused by treatment was captured in organoids derived from chronologically different tumors. CONCLUSIONS: To conclude, the genetic and phenotypic profiles of PDOs and parental tumor were very similar, supporting that PDOs are preserving tumor heterogeneity and are therefore a good model of BLCa. This indicates that PDOs can represent a tool to predict drug sensitivity in a personalized manner. Source of Funding: Swiss National Science Foundation (SNF), grant number REF-18-434. Novartis, grant number REF-39-781 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e79 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Martina Minoli More articles by this author Mirjam Kiener More articles by this author Thomas Cantore More articles by this author Tarcisio Fedrizzi More articles by this author Paola Gasparini More articles by this author Francesca Demichelis More articles by this author George N. Thalmann More articles by this author Roland Seiler-Blarer More articles by this author Marianna Kruithof-de Julio More articles by this author Expand All Advertisement PDF DownloadLoading ...

Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.

Identification of Bladder Cancer Subtypes Based on Necroptosis-Related Genes, Construction of a Prognostic Model.

BackgroundNecroptosis is associated with the development of many tumors but in bladder cancer the tumor microenvironment (TME) and prognosis associated with necroptosis is unclear.MethodsWe classified patients into different necroptosis subtypes by the expression level of NRGS (necroptosis-related genes) and analyzed the relationship between necroptosis subtypes of bladder cancer and TME, then extracted differentially expressed genes (DEGS) of necroptosis subtypes, classified patients into different gene subtypes according to DEGS, and performed univariate COX analysis on DEGS to obtain prognosis-related DEGS. All patients included in the analysis were randomized into the Train and Test groups in a 1:1 ratio, and the prognostic model was obtained using the LASSO algorithm and multivariate COX analysis with the Train group as the sample, and external validation of the model was conducted using the GSE32894.ResultsTwo necroptosis subtypes and three gene subtypes were obtained by clustering analysis and the prognosis-related DEGS was subjected to the LASSO algorithm and multivariate COX analysis to determine six predictors to construct the prognostic model using the formula: riskScore = CERCAM × 0.0035 + POLR1H × −0.0294 + KCNJ15 × −0.0172 + GSDMB × −0.0109 + EHBP1 × 0.0295 + TRIM38 × −0.0300. The results of the survival curve, roc curve, and risk curve proved the reliability of the prognostic model by validating the model with the test group and the results of the calibration chart of the Nomogram applicable to the clinic also showed its good accuracy. Necroptosis subtype A with high immune infiltration had a higher risk score than necroptosis subtype B, gene subtype B with low immune infiltration had a lower risk score than gene subtypes A and C, CSC index was negatively correlated with the risk score and drug sensitivity prediction showed that commonly used chemotherapeutic agents were highly sensitive to the high-risk group.ConclusionOur analysis of NRGS in bladder cancer reveals their potential role in TME, immunity, and prognosis. These findings may improve our understanding of necroptosis in bladder cancer and provide some reference for predicting prognosis and developing immunotherapies.

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes.

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Clinically relevant orthotopic xenograft models of patient-derived glioblastoma in zebrafish.

ABSTRACTAn accurate prediction of the intracranial infiltration tendency and drug response of individual glioblastoma (GBM) cells is essential for personalized prognosis and treatment for this disease. However, the clinical utility of mouse patient-derived orthotopic xenograft (PDOX) models remains limited given current technical constraints, including difficulty in generating sufficient sample numbers from small tissue samples and a long latency period for results. To overcome these issues, we established zebrafish GBM xenografts of diverse origin, which can tolerate intracranial engraftment and maintain their unique histological features. Subsequent single-cell RNA-sequencing (scRNA-seq) analysis confirmed significant transcriptional identity to that of invading GBM microtumors observed in the proportionally larger brains of model animals and humans. Endothelial scRNA-seq confirmed that the zebrafish blood–brain barrier is homologous to the mammalian blood–brain barrier. Finally, we established a rapid and efficient zebrafish PDOX (zPDOX) model, which can predict long-term outcomes of GBM patients within 20 days. The zPDOX model provides a novel avenue for precision medicine of GBM, especially for the evaluation of intracranial infiltration tendency and prediction of individual drug sensitivity.

ITGA5 Is a Novel Oncogenic Biomarker and Correlates With Tumor Immune Microenvironment in Gliomas.

Gliomas are the most aggressive primary intracranial malignancies with poor overall survival. ITGA5 is one member of the integrin adhesion molecule family and is implicated in cancer metastasis and oncogenesis. However, few studies have explored the association between tumor immune microenvironment and ITGA5 expression level in gliomas. Firstly, we analyzed 3,047 glioma patient samples collected from the TCGA, the CGGA, and the GEO databases, proving that high ITGA5 expression positively related to aggressive clinicopathological features and poor survival in glioma patients. Then, based on the ITGA5 level, immunological characteristics and genomic alteration were explored through multiple algorithms. We observed that ITGA5 was involved in pivotal oncological pathways, immune-related processes, and distinct typical genomic alterations in gliomas. Notably, ITGA5 was found to engage in remolding glioma immune infiltration and immune microenvironment, manifested by higher immune cell infiltration when ITGA5 is highly expressed. We also demonstrated a strong correlation between ITGA5 and immune checkpoint molecules that may be beneficial from immune checkpoint blockade strategies. In addition, ITGA5 was found to be a robust and sensitive indicator for plenty of chemotherapy drugs through drug sensitivity prediction. Altogether, our comprehensive analyses deciphered the prognostic, immunological, and therapeutic value of ITGA5 in glioma, thus improving individual and precise therapy for combating gliomas.

Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution.

Different cancer patients may respond differently to cancer treatment due to the heterogeneity of cancer. It is an urgent task to develop an efficient computational method to identify drug responses in different cell lines, which guides us to design personalized therapy for an individual patient. Hence, we propose an end-to-end algorithm, namely MOFGCN, to predict drug response in cell lines based on Multi-Omics Fusion and Graph Convolution Network. MOFGCN first fuses multiple omics data to calculate the cell line similarity and then constructs a heterogeneous network by combining the cell line similarity, drug similarity, and the known cell line-drug associations. Secondly, it learns the latent features for cancer cell lines and drugs by performing graph convolution operations on the heterogeneous network. Finally, MOFGCN applies the linear correlation coefficient to reconstruct the cancer cell line-drug correlation matrix to predict drug sensitivity. To our knowledge, this is the first attempt to combine graph convolutional neural network and linear correlation coefficient for this significant task. We performed extensive evaluation experiments on the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) databases to validate MOFGCN's performance. The experimental results show that MOFGCN is superior to the state-of-the-art algorithms in predicting missing drug responses. It also leads to higher performance in predicting drug responses for new cell lines, new drugs, and targeted drugs.

Applicability of Anticancer Drugs for the Triple-Negative Breast Cancer Based on Homologous Recombination Repair Deficiency.

Triple-negative breast cancer (TNBC) is a highly aggressive disease with historically poor outcomes, primarily due to the lack of effective targeted therapies. Here, we established a drug sensitivity prediction model based on the homologous recombination deficiency (HRD) using 83 TNBC patients from TCGA. Through analyzing the effect of HRD status on response efficacy of anticancer drugs and elucidating its related mechanisms of action, we found rucaparib (PARP inhibitor) and doxorubicin (anthracycline) sensitive in HR-deficient patients, while paclitaxel sensitive in the HR-proficient. Further, we identified a HRD signature based on gene expression data and constructed a transcriptomic HRD score, for analyzing the functional association between anticancer drug perturbation and HRD. The results revealed that CHIR99021 (GSK3 inhibitor) and doxorubicin have similar expression perturbation patterns with HRD, and talazoparib (PARP inhibitor) could kill tumor cells by reversing the HRD activity. Genomic characteristics indicated that doxorubicin inhibited tumor cells growth by hindering the process of DNA damage repair, while the resistance of cisplatin was related to the activation of angiogenesis and epithelial-mesenchymal transition. The negative correlation of HRD signature score could interpret the association of doxorubicin pIC50 with worse chemotherapy response and shorter survival of TNBC patients. In summary, these findings explain the applicability of anticancer drugs in TNBC and underscore the importance of HRD in promoting personalized treatment development.

A Comprehensive Risk Assessment and Stratification Model of Papillary Thyroid Carcinoma Based on the Autophagy-Related LncRNAs.

BackgroundPapillary thyroid carcinoma (PTC) is one of the most common malignant carcinomas in the endocrine system, and it has a growing incidence worldwide. Despite the development of diagnosis and treatment modalities for thyroid carcinoma, the outcome remains uncertain. Autophagy participates in the process of cancer invasion, malignancy, metastasis, and drug resistance. Emerging research has shown that long noncoding RNAs (lncRNAs) play an important role in the process of different types of cancers. However, the interaction between the process of autophagy and lncRNA and the value of autophagy-related lncRNA for risk assessment, prediction of drug sensitivity, and prognosis prediction in PTC patients remains unknown.Materials and MethodsWe screened 1,283 autophagy-related lncRNAs and identified 144 lncRNAs with prognostic value in The Cancer Genome Atlas (TCGA) cohorts. Univariate and multivariate Cox regression analyses were used to establish the prognosis-related autophagy-related lncRNA risk classification consisting of 10 lncRNAs to indicate the level of risk, according to which the patients were grouped into high-risk group and low risk-group.ResultsThe high-risk group had dramatically worse overall survival compared with the low-risk group. Cox regression analysis was performed to confirm the independent prognostic value of the autophagy-related lncRNA risk stratification, and the time-dependent receiver operating characteristic curves of the risk stratification were 0.981 (1 year), 0.906 (3 years), and 0.963 (5 years). LncRNA CRNDE (LINC00180) is overexpressed in the tumor, and its high expression matched with poorer survival state. So, we chose it for further experiment. Finally, knockdown of the CRNDE in PTC increased the sensitivity to sorafenib.ConclusionCollectively, we successfully established a novel risk stratification for PTC based on the expression profiles of autophagy-related lncRNAs.

The Landscape of Actionable Genomic Alterations by Next-Generation Sequencing in Tumor Tissue Versus Circulating Tumor DNA in Chinese Patients With Non-Small Cell Lung Cancer.

BackgroundCirculating tumor DNA (ctDNA) sequence analysis shows great potential in the management of non-small cell lung cancer (NSCLC) and the prediction of drug sensitivity or resistance in many cancers. Here, we drew and compared the somatic mutational profile using ctDNA and tumor tissue sequence analysis in lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), and assess its potential clinical value.MethodsIn this study, 221 tumor tissues and 174 plasma samples from NSCLC patients were analyzed by hybridization capture-based next-generation sequencing (NGS) panel including 95 cancer-associated genes. Tumor response assessments were applied to 137 patients with advanced-stage (III and IV) NSCLC who first received targeted agents.ResultsTwenty significantly mutated genes were identified such as TP53, EGFR, RB1, KRAS, PIK3CA, CD3EAP, CTNNB1, ERBB2, APC, BRAF, TERT, FBXW7, and HRAS. Among them, TP53 was the most frequently mutated gene and had a higher mutation probability in male (p = 0.00124) and smoking (p < 0.0001) patients. A total of 48.35% (191/395) of NSCLC patients possessed at least one actionable alteration according to the OncoKB database. Although the sensitivity of genomic profiling from ctDNA was lower than that from tumor tissue DNA, the mutational landscape of target genes from ctDNA is similar to that from tumor tissue DNA, which led to 61.22% (30/49) of mutational concordance in NSCLC. Additionally, the mutational concordance between tissue DNA and ctDNA in LUAD differs from that in LUSC, which is 63.83% versus 46.67%, indicating that NSCLC subtypes influence the specificity of mutation detection in plasma-derived ctDNA. Lastly, patients with EGFR and TP53 co-alterations showed similar responses to Gefitinib and Icotinib, and the co-occurring TP53 mutation was most likely to be a poor prognostic factor for patients receiving Gefitinib, indicating that the distributions and types of TP53 mutations may contribute to the efficacy and prognosis of molecular targeted therapy.ConclusionsAs a promising alternative for tumor genomic profiling, ctDNA analysis is more credible in LUAD than in LUSC. Genomic subtyping has strong potential in prognostication and therapeutic decision-making for NSCLC patients, which indicated the necessity for the utility of target NGS in guiding clinical management.

Abstract P3-06-08: JAM2 inhibits breast cancer invasion and migration by inhibiting the EMT process and potentially affects the immune microenvironment by increasing CXCL9/CXCL10

Abstract Objectives: Large-scale epidemiological surveys have shown that patients with Down syndrome, which are caused by a chromosomal abnormality (an extra chromosome 21), are significantly less likely to develop solid tumors, including breast cancer than the normal population. This feature has prompted the search for oncogenes located on chromosome 21. Junctional Adhesion Molecule 2 (JAM2), which is located on chromosome 21, is lowly expressed in breast cancer and is associated with a good prognosis. These findings strongly suggest that JAM2 may be a potential oncogene for breast cancer. However, the role and function of JAM2 in breast cancer are not yet clear. Methods: Several databases such as CCLE, GTEx, METABRIC and TCGA were used to explore the expression of JAM2 in breast cancer and to analyze its diagnostic and prognostic value in breast cancer and to verify the stratification effect of JAM2 expression on the overall survival of breast cancer patients using Meta-analysis. Changes in relevant markers were examined at the genes and protein levels using RT-qPCR and Western blot techniques, cell migration and invasion ability by scratch assay and transwell assay, cell cycle and apoptosis by flow cytometry, and cell viability and chemotherapeutic drug sensitivity by CCK-8 assay. RNA-seq data of JAM2 overexpression were analyzed by bioinformatics methods, and we predicted drug sensitivity by machine learning algorithms, and finally the potential effect of JAM2 on the tumor microenvironment was investigated using liquid phase microarray technology. Results: JAM2 was found to be significantly downregulated in breast cancer tissues by our study, and the area under the curve (AUC) associated with JAM2 expression in Para-cancerous tissue and breast cancer tissues was analyzed by two algorithms, logistic regression and random forest algorithm, respectively, which calculated an AUC of 0.929 and 0.887 for the TCGA cohort, indicating that JAM2 has good clinical diagnostic value. Pearson's correlation analysis revealed that the expression of JAM2 and DNMT1 (r = -0.12, P &amp;lt; 0.01), DNMT3B (r = -0.14, P &amp;lt; 0.01) and EHMT2 (r = -0.19, P &amp;lt; 0.01) DNA methyltransferase expression showed a significant negative correlation, and after specific inhibition of DNA methyltransferase with 5-azacytidine, there was a significant increase in JAM2 mRNA levels in the JAM2-treated group compared with the control group, suggesting that the low expression of JAM2 in breast cancer is partly regulated by DNA methylation. By analyzing the database, we found that the expression of JAM2 was significantly lower in distant recurrent tumors than in primary focal tumors. Transwell assays also showed that JAM2 overexpression significantly inhibited the migration and invasion of breast cancer cells. Vimentin and EZH2 were decreased after JAM2 overexpression, and then the level of EMT process inhibitor protein E-cadherin was increased. Through the combination of RNA-seq sequencing and machine learning algorithms, we predicted the chemotherapeutic drugs that might be sensitized after JAM2 overexpression and verified that JAM2 overexpression could sensitize breast cancer cells to Doxorubicin, and we found in the follow-up experiments that JAM2 overexpression could increase BAX and inhibit BCL-2 thereby leading to apoptosis of breast cancer cells. Then we found that JAM2 could inhibit the process of EMT by increasing the expression of ATF3 and thus inhibiting FN1 through Western blot assay. Finally, by liquid-phase microarray technology, we found more chemokines of CXCL9/CXCL10 in the cell supernatant of JAM2 overexpressing cell lines compared to the null control, thus this could be a potential mechanism for JAM2 to affect the tumor immune microenvironment. Citation Format: Yang Peng. JAM2 inhibits breast cancer invasion and migration by inhibiting the EMT process and potentially affects the immune microenvironment by increasing CXCL9/CXCL10 [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-06-08.

Microorganospheres as a novel precision oncology platform in colorectal cancer.

149 Background: Patient-derived organoids (PDO) have been shown to have a high degree of similarity to the original patient tumors. PDO have also been used to perform high throughput drug screens and shown to correlate with patient response to therapy. Unfortunately, PDO require too much tissue, take too long to establish and are too inefficient and costly for adoption into the clinic. The ideal assay for clinical use would be one that could be performed in less than14 days from a core biopsy to minimize delay in therapy. We have now circumvented these barriers by leveraging recent technological advances in emulsion microfluidics and droplet generators to develop MicroOrganoSpheres (MOS) that can be established and used to predict drug sensitivity within 14 days of obtaining a biopsy. Methods: 18-gauge core biopsy specimens from patients with colorectal cancer liver metastasis who subsequently received an oxaliplatin based therapy were first obtained. Biopsy specimens were minced, enzymatically digested and mixed with components necessary to generate MOS. The mixture was then processed through a custom fabricated flow-focusing droplet microfluidic chip in our MOS Generator Device to generate MOS. After culturing for 8-10 days, MOS were then used to perform drug screen with oxaliplatin. Results: A total of twelve CRC biopsies from liver metastasis were obtained and processed to generate MOS with a success rate of 12/12 (100%). Furthermore, drug screens with oxaliplatin were performed on all twelve samples with an average time to drug screen of 10.1 days. We next wanted to determine if there was a correlation between MOS drug sensitivity and patient clinical outcome (ie. time on treatment). For the first eight patients, MOS was used to predict sensitivity to oxaliplatin and using a drug sensitivity cut-off of 1 uM, four patients were predicted to be sensitive to oxaliplatin and four patients were predicted to be resistant. Three of the four patients predicted to be sensitive to oxaliplatin continue to be on treatment (&gt; 6 months), whereas 3 of the four patients predicted to be resistant to oxaliplatin progressed on oxaliplatin based therapy within 8 weeks (sensitivity = 80%, specificity = 100%, positive predictive value = 100%, negative predictive value = 75%). Conclusions: MOS can be generated from core biopsies and correlates to time on treatment. Although further studies will need to be conducted, the ability to generate MOS and perform a drug screen in &lt; 14 days will allow for the development of a precision oncology platform that can be rapidly used in the clinic to guide therapy.

Prevalence of transmitted drug resistance among ART-naïve HIV-infected individuals, Beijing, 2015–2018

ObjectivesTransmitted drug resistance (TDR) is a critical ongoing public health challenge in HIV/AIDS therapy. We explore the prevalence of TDR, its patterns, its associated risk factors, and predicted drug sensitivity in Beijing between 2015 and 2018. MethodsRetrospective data on TDR from 3265 antiretroviral therapy (ART)-naïve patients were collected at Beijing Ditan Hospital from 1 August 2014 to 31 July 2018. TDR was defined according to the Stanford Drug Resistance Mutations Database. TDR prevalence, pattern, risk factors, and predicted drug sensitivity were analysed. ResultsThe overall prevalence of HIV-1 TDR was 6.68% (218 of 3265), including 0.77%, 3.64%, and 2.36% resistance to nucleoside reverse transcriptase inhibitors (NRTIs), non-NRTIs (NNRTIs), and protease inhibitors, respectively. The thymidine analogue mutations (TAMs) M41L/LM (4, 0.12%) and non-TAMs mutations M184V/MV/MI (8; 0.24%) were the primary NRTI-associated resistance mutations. K103N/KN (NNRTI associated) and M46L/I/IMV/IM/ML (protease inhibitor associated) were the other major resistance mutations. Patients 40–59 years old who had the CRF08_BC subtype were identified as having higher risk for drug resistance mutation. ConclusionsThe prevalence of TDR among ART-naïve individuals with HIV-1 in Beijing was at a moderate level. Long-time and continuous surveillance of HIV TDR is necessary step in the therapy of ART-naive patients.

A B-Raf V600E gene signature for melanoma predicts prognosis and reveals sensitivity to targeted therapies.

BackgroundB‐Raf V600E mutations account for about half of all skin cutaneous melanoma cases, and patients with this mutation are sensitive to BRAF inhibitors. However, aberrations in other genes in the MAPK/ERK pathway may cascade a similar effect as B‐Raf V600E mutations, rendering those patients sensitive to BRAF inhibitors. We rationalized that defining a signature based on B‐Raf pathway activity may be more informative for prognosis and drug sensitivity prediction than a binary indicator such as mutation status.MethodsIn this study, we defined a B‐Raf signature score using RNA‐seq data from TCGA. A higher score is shown to not only predict B‐Raf mutation status, but also predict other aberrations that could similarly activate the MAPK/ERK pathway, such as B‐Raf amplification, RAS mutation, and EGFR amplification.ResultsWe showed that patients dichotomized by the median B‐Raf score is more significantly stratified than by other metrics of measuring B‐Raf aberration, such as mutation status, gene expression, and protein expression. We also demonstrated that high B‐Raf score predicts higher sensitivity to B‐Raf inhibitors SB590885 and PLX4720, as expected, but also correlated with sensitivity to drugs targeting other relevant oncogenic pathways.ConclusionThe BRAF signature may better help guide targeted therapy for melanoma, and such a framework can be applied to other cancers and mutations to provide more information than mutation status alone.