Predict Therapy Response Research Articles

Surgical Interest of an Accurate Real-World Prediction of Primary Systemic Therapy Response in HER2 Breast Cancers

Human epidermal growth factor receptor 2 (HER2)-enriched breast cancers (BC) present the highest rates of pathological response to primary systemic therapy (PST), but they are also the ones that tend to be larger at diagnosis, with microcalcifications and, often, with axillary involvement. If we do not have a reliable method to predict the degree of response, we may not be able to transfer the benefits of PST to surgery. The post-PST surgery planning is guided by the findings in the magnetic resonance imaging (MRI), whose predictive capacity, although high, is far from optimal. Thus, it seems interesting to find other variables to improve it. A retrospective observational study including women with HER2 BC treated with PST and further surgery was conducted. Information regarding clinical, radiological, and histopathological variables was gathered from a total of 132 patients included. Radiological complete response (rCR) was achieved in 65.9% of the sample, and pathological complete response (pCR), according to Miller and Payne criteria, in 58.3% of cases. A higher Ki67 value, the absence of Hormonal Receptors expression, and an rCR was significantly related to a pCR finding. This information impacts directly in surgery planning, as it permits adjustment of the breast resection volume.

An Image-Based Identification of Aggressive Breast Cancer Circulating Tumor Cell Subtypes.

Using previously established CTC lines from breast cancer patients, we identified different morphometric subgroups of CTCs with one of them having the highest tumorigenic potential in vivo despite the slowest cell proliferation in vitro. This subgroup represents 32% of all cells and contains cells with small cell volume, large nucleus to cell, dense nuclear areas to the nucleus, mitochondria to cell volume ratios and rough texture of cell membrane and termed "Small cell, Large mitochondria, Rough membrane" (SLR). RNA-seq analyses showed that the SLR group is enriched in pathways and cellular processes related to DNA replication, DNA repair and metabolism. SLR upregulated genes are associated with poor survival in patients with ER+ breast cancer based on the KM Plotter database. The high tumorigenic potential, slow proliferation, and enriched DNA replication/repair pathways suggest that the SLR subtype is associated with stemness properties. Our new findings provide a simple image-based identification of CTC subpopulations with elevated aggressiveness, which is expected to provide a more accurate prediction of patient survival and therapy response than total CTC numbers. The detection of morphometric and transcriptomic profiles related to the SLR subgroup of CTCs also opens opportunities for potential targeted cancer treatment.

How I approach B-lymphoblastic lymphoma in children.

There are limited data pertaining to the prognostic features and optimal therapeutic approach for the 20%-25% of children with lymphoblastic lymphoma (LLy) who have the B-lymphoblastic subtype. Outcomes are favorable following treatment modeled after acute lymphoblastic leukemia (ALL) regimens, but prognosis is dismal after relapse, and there are no established features for predicting therapy response. Ongoing US and international trials will include the largest cohort of uniformly treated patients with B-LLy to date, providing an opportunity to define clinical and molecular predictors of relapse and to establish a standard of care for treatment to improve outcomes for this rare pediatric cancer.

Identification of an individualized therapy prognostic signature for head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) are the most common cancers in the head and neck. Therapeutic response-related genes (TRRGs) are closely associated with carcinogenesis and prognosis in HNSCC. However, the clinical value and prognostic significance of TRRGs are still unclear. We aimed to construct a prognostic risk model to predict therapy response and prognosis in TRRGs-defined subgroups of HNSCC.MethodsThe multiomics data and clinical information of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA). The profile data GSE65858 and GSE67614 chip was downloaded from public functional genomics data Gene Expression Omnibus (GEO). Based on TCGA-HNSC database, patients were divided into a remission group and a non-remission group according to therapy response, and differentially expressed TRRGs between those two groups were screened. Using Cox regression analysis and Least absolute shrinkage and selection operator (LASSO) analysis, candidate TRRGs that can predict the prognosis of HNSCC were identified and used to construct a TRRGs-based signature and a prognostic nomogram.ResultA total of 1896 differentially expressed TRRGs were screened, including 1530 upregulated genes and 366 downregulated genes. Then, 206 differently expressed TRRGs that was significantly associated with the survival were chosen using univariate Cox regression analysis. Finally, a total of 20 candidate TRRGs genes were identified by LASSO analysis to establish a signature for risk prediction, and the risk score of each patient was calculated. Patients were divided into a high-risk group (Risk-H) and a low-risk group (Risk-L) based on the risk score. Results showed that the Risk-L patients had better overall survival (OS) than Risk-H patients. Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases. Moreover, for patients treated with post-operative radiotherapy, Risk-L patients had longer OS and lower recurrence than Risk-H patients. The nomogram involves risk score and other clinical factors had good performance in predicting survival probability.ConclusionsThe proposed risk prognostic signature and Nomogram based on TRRGs are novel promising tools for predicting therapy response and overall survival in HNSCC patients.

Differential nasal swab cytology represents a valuable tool for therapy monitoring but not prediction of therapy response in chronic rhinosinusitis with nasal polyps treated with Dupilumab.

Chronic Rhinosinusitis with nasal polyps (CRSwNP) is a common chronic disease with a high impact on patients' quality of life. If conservative and surgical guideline treatment cannot sufficiently control disease burden, biologicals can be considered as a comparably new treatment option that has revolutionized CRSwNP therapy since the first approval of Dupilumab in 2019. With the aim to select patients who benefit from this new treatment and to find a marker for therapy monitoring, we investigated the cellular composition of nasal mucous membranes and inflammatory cells of patients suffering from CRSwNP and undergoing Dupilumab therapy using non-invasive nasal swab cytology. Twenty CRSwNP patients with the indication for Dupilumab therapy have been included in this prospective clinical study. In total, five study visits were conducted with ambulatory nasal differential cytology using nasal swabs starting with the beginning of therapy and followed by visits every 3 months for 12 months. First, these cytology samples were stained with the May-Grunwald-Giemsa method (MGG) and the percentage of ciliated cells, mucinous cells, eosinophil cells, neutrophil cells, and lymphocytes was analyzed. Secondly, an immunocytochemical (ICC) ECP-staining was performed to detect eosinophil granulocytes. Additionally, during each study visit the nasal polyp score, SNOT20 questionnaire, olfactometry, the total IgE concentration in peripheral blood as well as the eosinophil cell count in peripheral blood were recorded. The change of parameters was evaluated over one year and the correlation between clinical effectiveness and nasal differential cytology was analyzed. In both MGG (p<0.0001) and ICC analysis (p<0.001) a significant decrease of eosinophils was seen under Dupilumab treatment. When patients were divided into a Eo-low- (<21%) and Eo-high- (≥21%) group according to the percentage eosinophils in nasal swab catology in the first study visit, the Eo-high-group showed a greater change of eosinophils over time (Δ17.82) compared to the Eo-low-group (Δ10.67) but, however, no better response to therapy. The polyp score, SNOT20 questionnaire, and total IgE concentration in peripheral blood showed a significant decrease during the observation period (p<0.0001). Nasal swab cytology as an easy-to-apply diagnostic method allows detection and quantification of the different cell populations within the nasal mucosa at a given time. The nasal differential cytology showed a significant decrease of eosinophils during Dupilumab therapy and can therefore be used as non-invasvive method for monitoring therapy success of this cost intensive therapy and potentially can allow an optimized individual therapy planning and management for CRSwNP patients. Since the validity of initial nasal swab eosinophil cell count as a predictive biomarker for therapy response was limited in our study, additional studies including larger number of participants will be necessary to further evaluate the potential benefits for clinical practice of this new diagnostic method.

A comprehensive machine learning technique for Ovarian Cancer prediction

On the list of most prevalent cancers in women, ovarian cancer comes in eighth place. Because of standard screening and surveillance limitations, it is clinically impractical to analyse tumour molecular markers to predict therapy response. A relevant dataset with the necessary features was chosen for the prediction task of whether the patient has ovarian cancer or not. This approach uses Multilayer perceptron, ELM with AdaBoost, XGboost, LSTM, and a new CNN with Random Forest algorithms to predict Ovarian Cancer. The assessment parameters for each model were calculated for accuracy, precision, recall, F1-Score, Jaccard Index, and Error rate. The algorithms are then compared based on these metrics to determine the best algorithm. The CNN with Random Forest was the best method, with 95 to 100% accuracy. The CNN with Random Forest algorithm outperformed the individual existing ensemble techniques studied.

Abstract 4524: Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations

Abstract Small cell lung cancer (SCLC) is a lethal malignancy with few therapeutic options. Tissue resection or biopsy are unusual, so the scarce sample available has limited our knowledge on the biology of SCLC tumors. Recent advances propose novel molecular subtypes (SCLC-A, SCLC-N, SCLC-P, SCLC-I, NAPI classification) to classify SCLC patients and specific treatment susceptibilities of defined subgroups. In this line, we collected 119 tumor samples from a cohort of early-stage SCLC patients with complete clinical annotation, in order to find specific molecular and immune profiles that define new patient characteristics with potential impact on prognosis or therapy selection. For this, we subjected the tumor tissue to RNA-seq targeted panel of onco-immune-related genes and to immune characterization by IHC. In addition, we analyzed molecular aberrations by whole exome sequencing. All data, including clinical annotations, were integrated by multiparametric computational analysis. Based on the computational analysis of the onco-immune transcriptomic data, we found two major groups of patients with either pro-immunogenic or pro-tumorigenic profiles. The transcriptomic pro-immunogenic group shows better survival and higher infiltration of immune cells (CD4+ T cells, CD8+ T cells, B cells and Macrophages). In contrast, the pro-tumorigenic group presents worse survival and less immune cell infiltration. Using bioinformatics, we described a gene signature that can identify both subgroups. This gene signature includes immune cell markers (MS4A1, CD3D), antigen receptor complex (CD79A), adhesion and migration of T cells (CD2) and immunomodulatory genes (IDO1, TIGIT) expressed in the pro-immunogenic group, and, proliferation (MKI67), transcription regulator (TOP2A) and epithelial and mesenchymal transition-related genes (TWIST1) in the pro-tumorigenic group. Exome sequencing analysis resulted in the expected genomic heterogeneity of SCLC tumors beyond TP53 and RB1 mutations in most patients. However, we found a significant enrichment of XIRP2 gene alterations in the pro-tumorigenic group. Consistent with an early-stage of SCLC, most of the samples were described as SCLC-A subtype by the predominant expression of ASCL1. Interestingly, our findings suggest a relevant biological heterogeneity within SCLC-A tumors that impacts immune infiltrate and disease outcome. Here we show a gene signature that can subclassify early-stage SCLC patients according to their clinical, molecular and immune features, and provides prognostic value independently of the NAPI classification. Since immunogenicity of the tumor impacts response to immunotherapy, we speculate that this gene signature might predict therapy response and therefore contribute to tailored treatment of SCLC. Citation Format: Angel Nunez-Buiza, David Gómez-Sánchez, Jose Maria Gracia-Rodríguez, Esther Conde, Jose Luis Solorzano, Eva María Garrido-Martín, Jose Carlos Machado, Susana Guimarães, Ernest Nadal, Sonia Molina-Pinelo, Miguel Ángel Piris Pinilla, Nuria Romero-Laorden, Fernando Franco, Fernando López-Ríos Moreno, Álvaro Conrado Ucero, Luis Paz-Ares. Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4524.

Abstract 3249: Circulating cytokine associations with clinical outcomes in melanoma patients treated with checkpoint inhibitors

Abstract Background: aCTLA-4/aPD-1 combination therapy has significantly improved clinical outcomes in patients with metastatic melanoma, with 50%-60% of patients responding to treatment, but predictors of response are poorly characterized. In this study, we set out to evaluate the hypothesis that circulating cytokines and peripheral white blood cell composition may predict response to therapy. Methods: We quantified cytokines and complete blood counts from 89 patients with advanced melanoma treated with combination therapy from three points in time: pre-treatment, one month and approximately three months after starting therapy. We evaluated the association between peripheral blood circulating features, cytokines and clinical labs values, and clinical outcome using predictive modeling, and investigated the prognostic values of these features using survival analysis. Results: Pre-treatment, patients with durable clinical benefit (DCB) had higher IL23, lower CXCL6, and lower IL10 levels. The ratio of neutrophils to lymphocytes (NLR) pre-treatment was not associated with response, but higher NLR one month after starting therapy predicted poorer progression free survival (PFS) and overall survival (OS), primarily driven by an increase in absolute lymphocytes. A multivariate model demonstrated that baseline CXCL6, IL10, IL23 were independent predictors of therapy response, and the combined model has reached an area under the curve (AUC) of 0.8 in prediction of response to combination therapy. Low IL10 and CXCL6 identified an excellent prognosis group, while high CXCL6 and low IL23 characterized a very poor prognosis group of patients. Conclusions: Baseline measurement of cytokines IL23, CXCL6, and IL10 can predict metastatic melanoma patients’ response to ipi/nivo combination treatment. Higher neutrophils-to-lymphocytes ratio approximately one month after therapy started associates with worse survival outcome. Our study presented circulating features from a relatively accessible and less costly avenue, peripheral blood, to be predictive of response to combination immune checkpoint blockade. Citation Format: Jiajia Chen, Giuseppe Tarantino, Mariano Severgnini, Joanna Baginska, Anita Giobbie-Hurder, Michael Manos, Janice D. Russell, Jason L. Weirather, Kathleen Pfaff, Amy Y. Huang, Scott J. Rodig, Srinika Ranashinge, David Liu, F.Stephen Hodi. Circulating cytokine associations with clinical outcomes in melanoma patients treated with checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3249.

Abstract 4517: Novel prognostic and predictive biomarker for neuroblastoma

Abstract Recently we showed that Retinal degeneration protein 3 (RD3) is constitutively expressed in healthy adult and fetal tissues beyond retina and, its gradient expression from high to low in ganglioneuroma, ganglioneuroblastoma and neuroblastoma (NB). Crucially, our sequential in vitro and in vivo studies identified that RD3 regulates NB progression and metastasis, and its loss prompts the evolution of therapy defying progressive disease (PD). Here we investigated the prognostic significance of RD3 in NB, its predictive benefit and, its association to clinical outcomes. RNA-Seq data mining (4 independent study cohorts, total n = 1013) indicated that high RD3 can predict good NB prognosis. RD3 expression (immunohistochemistry) gauged in our NB patient cohort (n = 100) showed a positive correlation with longer overall survival (OS, P = 1.0E-04) and relapse-free survival (RFS, P = 2.1E-02). Similarly, high RD3 correlated with longer OS (P = 1.2E-03) and RFS (P = 2.1-02) in children presented with high-risk NB (HR-NB), irrespective of MYCN amplification status (MYCN amplified, P = 1.4E-02; non-amplified, P = 5.5E-03) and patient age (&amp;lt;2Y age, P = 2.8E-02; &amp;gt;2Y age, P = 1.0E-04) groups. Significantly, we observed a gradient loss of RD3 with NB progression, from stage 1 through stage 4 disease. Consistently, RD3 loss was substantial in metastatic disease when compared to the primary neuroblastomas, and this loss associated with poor OS in patients presented with metastatic disease (P = 4.0E-02). More importantly, RD3 is significantly lost in therapy resistant progressive NB and corroborated significantly (P = 9.5E-03) with the poor progression free survival. Multivariate analysis identified RD3 as an independent (P = 4.4E-02) prognostic factor for NB. These results demonstrate that RD3-loss can forecast poor prognosis in NB, and further imply that assessing RD3 loss could serve as the good predictor for therapy response and/or PD evolution. Together, these data identify a novel prognostic and predictive factor for neuroblastoma. Funding: This work was partially or in full, funded by Oklahoma Center for the Advancement of Science and Technology, OCAST-HR19-045; National Institutes of Health, NIH-P20GM103639 and Department of Defense DoD-CA210339 Citation Format: Dinesh Babu Somasundaram, Zhongxin Yu, Ashley Baker, Natarajan Aravindan. Novel prognostic and predictive biomarker for neuroblastoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4517.

Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing

BackgroundImmunotherapy has revolutionized cancer treatment, but most patients are refractory to immunotherapy or acquire resistance, with the underlying mechanisms remaining to be explored.MethodsWe characterized the transcriptomes of ~92,000 single cells from 3 pre-treatment and 12 post-treatment patients with non-small cell lung cancer (NSCLC) who received neoadjuvant PD-1 blockade combined with chemotherapy. The 12 post-treatment samples were categorized into two groups based on pathologic response: major pathologic response (MPR; n = 4) and non-MPR (NMPR; n = 8).ResultsDistinct therapy-induced cancer cell transcriptomes were associated with clinical response. Cancer cells from MPR patients exhibited a signature of activated antigen presentation via major histocompatibility complex class II (MHC-II). Further, the transcriptional signatures of FCRL4+FCRL5+ memory B cells and CD16+CX3CR1+ monocytes were enriched in MPR patients and are predictors of immunotherapy response. Cancer cells from NMPR patients exhibited overexpression of estrogen metabolism enzymes and elevated serum estradiol. In all patients, therapy promoted expansion and activation of cytotoxic T cells and CD16+ NK cells, reduction of immunosuppressive Tregs, and activation of memory CD8+T cells into an effector phenotype. Tissue-resident macrophages were expanded after therapy, and tumor-associated macrophages (TAMs) were remodeled into a neutral instead of an anti-tumor phenotype. We revealed the heterogeneity of neutrophils during immunotherapy and identified an aged CCL3+ neutrophil subset was decreased in MPR patients. The aged CCL3+ neutrophils were predicted to interact with SPP1+ TAMs through a positive feedback loop to contribute to a poor therapy response.ConclusionsNeoadjuvant PD-1 blockade combined with chemotherapy led to distinct NSCLC tumor microenvironment transcriptomes that correlated with therapy response. Although limited by a small patient sample size subjected to combination therapy, this study provides novel biomarkers to predict therapy response and suggests potential strategies to overcome immunotherapy resistance.

Abstract P6-01-40: Integrative transcriptomic analysis and cohort validation identify key genes in chemotherapy treatment response in Latino breast cancer patients

Abstract Purpose: Breast cancer (BC) is one of the most frequent invasive cancers and one of the main causes of cancer mortality in women. Effective treatment interventions for BC are urgently required to improve survival rate and quality of life. Chemotherapy has been widely applied in BC treatment; however, therapeutic resistance remains an unresolved issue. Currently, only a minority of patients benefit from chemotherapy, emphasizing the need to identify more effective hub genes associated with therapy response. The overarching goal of this study is to assess hub genes correlated with BC chemotherapy treatment response via multiple databases and validate the workflow in an independent cohort of Hispanic/Latino (Colombian) women diagnosed with invasive Luminal B BC candidates for neoadjuvant chemotherapy. Design: Screening and multistep filtering of common genes correlated with chemotherapeutic response was performed by integrating differentially expressed genes between responders and non-responders in publicly available datasets. For each database, the differentially expressed genes (DEGs) between non-responders and responders were identified using GEO2R and LIMMA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted for the identified common genes using Metascape and DAVID. Functional enrichment analysis and protein-protein interaction (PPI) network for DEGs were constructed using (STRING) database. Hub genes were identified from PPI network by Cytoscape software analysis. The mRNA expression of hub genes in BC and normal tissues was subsequently explored by UALCAN. Evaluation of the effect of hub genes on survival was performed using Kaplan-Meier plotter. Hub genes were imported into the DGIdb to obtain the potential for BC chemotherapy-associated treatment drugs. The previous workflow was then applied/validated to an Illumina high-throughput RNA sequencing of 50 Luminal B cases (HER2+ and HER2-) of Hispanic/Latino patients. Results: 490 DEGs were obtained from the intersection of five public databases. Pathway enrichment analysis revealed DEGs were associated with cell cycle, estrogen response, adaptive immune response, and regulation of kinase activity, among others. Thirty-two hub genes were identified from PPI network analysis with high degree nodes and betweennesscentrality. Significant differential expression of hub genes between BC tissue and normal tissues was observed in UALCAN. These genes were significantly associated with survival probability. Fifteen potential targeted therapeutic drugs were identified through DGIdb database. Validation workflow in independent Luminal B cohort showed 238 DEGs, 90 hub genes with high degree and enrichment in the regulation of hormone levels, cellular response to EGFR, signaling by ERBB2 and MAPK. GATA3 was the hub gene found in both databases and the validation set. Both databases and validation set show hub genes, enriched pathways, and drugs that indicate their close association with tumorigenesis and would contribute to acting an important role in therapy response prediction. Conclusions: This workflow was created using public databases and applied to a patient’s cohort of different ancestries. This methodology can successfully provide potential biomarkers that correlate with therapy response. Genes were selected from PPI network. Most of them were independent biomarkers of BC treatment response, including that in underrepresented patients. Moreover, these genes may exert critical function in non-response and progression. Citation Format: Hedda Michelle Guevara-Nieto, Rafael Parra-Medina, Juan C. Mejia-Henao, Patricia López-Correa, Sandra Diaz, Jone Garai, Jovanny Zabaleta, Liliana López-Kleine, Alba L. Combita. Integrative transcriptomic analysis and cohort validation identify key genes in chemotherapy treatment response in Latino breast cancer patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-40.

Abstract P6-01-34: Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients

Abstract Background and Purpose Early prediction of neoadjuvant systemic therapy (NAST) response in triple negative breast cancer (TNBC) patients could potentially aid in the selection of alternative therapies and avoid unnecessary toxicity in patients unlikely to achieve pathologic complete response (pCR) with NAST. In this study, we investigated the radiomic features of the peritumoral and the tumoral regions from dynamic contrast enhanced (DCE) MRI acquired at different time points of NAST for early treatment response prediction in TNBC. Methods and Materials This study included 182 biopsy-confirmed stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433). All patients underwent DCE-MRI on a GE 3T MRI scanner at baseline (BL), after two (C2) and four (C4) cycles of doxorubicin/cyclophosphamide based chemotherapy and before surgery. The peritumoral and the tumoral regions were segmented manually by two fellowship-trained radiologists using early phase (2.5 min) DCE-MRI subtraction images. Ten first order radiomic features, 300 grey-level-co-occurrence matrix (GLCM) features along with their absolute and relative differences (C4/BL, C2/BL, C4/C2) between the 3 imaging time points were extracted from the peritumoral and the tumoral regions. Patients were randomly divided into training and testing sets in a 2:1 ratio. For univariate analysis, area under the receiver operating characteristics curve (AUC ROC) was measured to determine the features most predictive of pCR/non-pCR. Wilcoxon Rank Sum test was used to test the statistical significance of predictive performance. In multivariate analysis, radiomic models were established using logistic regression with elastic net regularization followed by 5-fold cross validation for performance assessment. Results Eighty-eight (48%) patients had pCR (59 training, 29 testing) and 94 (52%) patients had non-pCR (63 training, 31 testing). Twenty-five radiomic features (4 from peritumoral C4, 5 from tumoral C4, 4 from peritumoral C4/BL, 6 from tumoral C4/BL, 2 from peritumoral C4/C2 and 4 from tumoral C4/C2) were statistically significant with AUC ≥ 0.75 in both the training and the testing sets at the univariate analysis. The significant features at C4 had AUCs of 0.75-0.79 for the training set and 0.76-0.81 for the testing set. Changes measured between C4 and BL or C2 showed AUC of 0.76-0.84 in the training and 0.75-0.81 in the testing datasets. Eleven multivariate regression models comprised of radiomic features at BL, C2, C4 and their changes (C4/BL, C4/C2 and C2/BL) showed an AUC of 0.80-0.84 for cross validation and an AUC of 0.80-0.82 for independent testing. Conclusions Radiomic models using longitudinal DCE MRI parameters of peritumoral and tumoral regions during NAST have the potential to predict pCR in TNBC patients undergoing NAST. Citation Format: Bikash Panthi, Rania M. Mohamed, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-34.

Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node-positive head and neck cancer.

Prediction of survival and radiation therapy response is challenging in head and neck cancer with metastatic lymph nodes (LNs). Here we developed novel radiomics- and clinical-based predictive models. Volumes of interest of LNs were employed for radiomic features extraction. Radiomic and clinical features were investigated for their predictive value relatively to locoregional failure (LRF), progression-free survival (PFS), and overall survival (OS) and used to build multivariate models. Hundred and six subjects were suitable for final analysis. Univariate analysis identified two radiomic features significantly predictive for LRF, and five radiomic features plus two clinical features significantly predictive for both PFS and OS. The area under the curve of receiver operating characteristic curve combining clinical and radiomic predictors for PFS and OS resulted 0.71 (95%CI: 0.60-0.83) and 0.77 (95%CI: 0.64-0.89). Radiomic and clinical features resulted to be independent predictive factors, but external independent validation is mandatory to support these findings.

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.

The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way. Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker. The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

Combined scRNAseq and Bulk RNAseq Analysis to Reveal the Dual Roles of Oxidative Stress-Related Genes in Acute Myeloid Leukemia.

Oxidative stress (OS) can either lead to leukemogenesis or induce tumor cell death by inflammation and immune response accompanying the process of OS through chemotherapy. However, previous studies mainly focus on the level of OS state and the salient factors leading to tumorigenesis and progression of acute myeloid leukemia (AML), and nothing has been done to distinguish the OS-related genes with different functions. First, we downloaded single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) data from public databases and evaluated the oxidative stress functions between leukemia cells and normal cells by the ssGSEA algorithm. Then, we used machine learning methods to screen out OS gene set A related to the occurrence and prognosis of AML and OS gene set B related to treatment in leukemia stem cells (LSCs) like population (HSC-like). Furthermore, we screened out the hub genes in the above two gene sets and used them to identify molecular subclasses and construct a model for predicting therapy response. Leukemia cells have different OS functions compared to normal cells and significant OS functional changes before and after chemotherapy. Two different clusters in gene set A were identified, which showed different biological properties and clinical relevance. The sensitive model for predicting therapy response based on gene set B demonstrated predictive accuracy by ROC and internal validation. We combined scRNAseq and bulk RNAseq data to construct two different transcriptomic profiles to reveal the different roles of OS-related genes involved in AML oncogenesis and chemotherapy resistance, which might provide important insights into the mechanism of OS-related genes in the pathogenesis and drug resistance of AML.

A novel Cuprotosis-related signature predicts the prognosis and selects personal treatments for melanoma based on bioinformatics analysis.

Melanoma is a common and aggressive cutaneous malignancy characterized by poor prognosis and a high fatality rate. Recently, due to the application of Immune-checkpoint inhibitors (ICI) in melanoma treatment, melanoma patients' prognosis has been tremendously improved. However, the treatment effect varies quite differently from patient to patient. In this study, we aim to construct and validate a Cuproptosis-related risk model to improve outcome prediction of ICIs in melanoma and divide patients into subtypes with different Cuproptosis-related genes. Here, according to differentially expressed genes from four melanoma datasets in GEO (Gene Expression Omnibus), and one in TCGA (The Cancer Genome Atlas) database, a novel signature was developed through LASSO and Cox regression analysis. We used 781 melanoma samples to examine the molecular subtypes associated with Cuproptosis-related genes and studied the related gene mutation and TME cell infiltration. Patients with melanoma can be divided into at least three subtypes based on gene expression profile. Survival pan-cancer analysis was also conducted for melanoma patients. The Cuproptosis risk score can predict tumor immunity, subtype, survival, and drug sensitivity for melanoma. And Cuproptosis-associated subtypes can help predict therapeutic outcomes. Cuproptosis risk score is a promising potential biomarker in cancer diagnosis, molecular subtypes determination, TME cell infiltration characteristics, and therapy response prediction in melanoma patients.

Binding potential changes of SERT in patients with depression are associated with remission: A prospective [¹²³I]β-CIT-SPECT study.

The status of remission in patients with major depressive disorder treated with selective serotonin reuptake inhibitors (SSRIs) is mostly evaluated with clinical rating scales. Morphological correlates of the remission status remain a rare event. Addressing this challenge, we investigated functional correlates of remission by assessment of serotonin and dopamine transporter availability (SERT and DAT) using single-photon emission computed tomography (SPECT). Our purpose was to identify changes in the SERT/DAT binding potential in accordance with the clinical improvement. Nineteen drug-naïve patients with a Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) diagnosis of major depression were included. [¹²³I]2β-carbomethoxy-3β-(4-iodophenyl)tropane(β-CIT) SPECT was obtained from each participant before (baseline) and after 6 weeks (follow-up) of standardized treatment with escitalopram. The [¹²³I]β-CIT-SPECT recordings were acquired 4 hr (SERT-weighted) and 20-24 hr p.i (DAT-weighted), and binding potentials (˜BPND: baseline, follow-up, and rate of change) were calculated for thalamus, midbrain, pons (SERT), and striatum (DAT). From all study participants, neuropsychiatric symptoms were assessed using Hamilton depression (HAM-D) and Beck Depression Inventory scores. At follow-up, patients were divided into responders and nonresponders (as well as remitters and nonremitters). Compared to nonremitted, remitted patients showed over the course of 6 weeks a significantly higher loss of SERT binding potential in the thalamus (p = .036) and in the midbrain (p = .019). Additionally, the correlation of HAM-D with SERT binding potential in the thalamus showed a trend toward significance (p = .057) with higher HAM-D scores (at baseline) leading to lower SERT binding potential. No significant associations were identified for the analysis of baseline prediction of therapy response with SERT and DAT. Our results suggest that patients who remit from their depressive symptoms under escitalopram are characterized by stronger decreases of SERT, indicating that escitalopram blocking of SERT leads to clinical improvement. Therefore, this study shows that measuring SERT availability with SPECT could be an efficient and applicable technique to illustrate a possible underlying pathophysiology of symptom remission in response to treatment. In addition, the present results could help to stimulate new treatment approaches based on SERT and DAT binding. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

P411 Early ultrasound assessment predicts therapy response: an easy tool for clinical decision making

Abstract Background Intestinal ultrasound (IUS) is an effective and easy-to-use tool in monitoring Crohn’s disease (CD) lesions during different biological therapies. Aim of our study was to evaluate whether lesion improvement during biological therapy could predict transmural healing. Methods We performed a prospective study enrolling CD patients (pts) with indication to biological therapies. IUS and doppler US parameters at baseline, at 3 and 12 months were: bowel wall thickening (BWT), lesion length, echopattern, complications, blood flow according to Limberg’ score. Transmural healing (TH) was defined as normalization of all parameters; IUS responders were defined as pts with improvement of BWT associated with decreased lesion length, Limberg’ score improvement, and no worsening of the other parameters. Delta (Δ) signified the difference between IUS parameters at baseline and at 3 months. Changes in parameters and in Δ values at baseline and after 3 months were analyzed using Mann Whitney test; the area under (a ROC) curve was calculated. Differences between combination of parameters were tested by Chi-square test. Results One hundred and fifteen CD pts were enrolled (63.5% males; median age 37 years; median disease duration 96 months). Forty-nine per cent of pts had L1, 9% had L2, 42% had L3 according to the Montreal criteria. TH rate at 12 months was 20% and IUS responder rate was 43%. At baseline, no statistical differences in terms of BWT were observed between pts achieving TH vs no TH. Similarly, no differences were observed in pts defined as IUS responders vs non responders. Patients achieving TH at 12 months had a higher ΔBWT than patient without TH (p=0.0004). On ROC curve, ΔBWT improvement of 1.25 mm showed sensitivity and specificity of 73% and 61%, respectively, in predicting pts who achieved TH. Similarly, IUS responder group at 12 months had a higher ΔBWT than patients IUS non-responder group (p&amp;lt;0.0001). On ROC curve, ΔBWT improvement of 1.25 mm showed sensitivity and specificity of 83% and 57%, respectively, in predicting IUS responders. A combination of a ΔBWT (≥1 mm) and Limberg’ score improvement (1 point) was associated with a higher risk of TH at 12 months (p=0.0007; OR 5.1; 95% CI, 1.8-12.8). A combination of a ΔBWT (≥1 mm) and Limberg’ score improvement (1 point) was associated with a higher risk of IUS responders at 12 months (p&amp;lt;0.0001; OR 15.7; 95% CI, 4.5-52). Conclusion An early ΔBWT, and a combination of a ΔBWT and Limberg’ score improvement showed high diagnostic accuracy in predicting pts who achieved TH and IUS responder at 12 months. This information may help clinical decision making in terms of prompt optimization or switching/swapping therapies in CD.

DOP51 Mucosa-associated microbial signatures associate with objective response prior to the start of anti-TNFα but not vedolizumab or ustekinumab in Crohn’s disease patients

Abstract Background Crohn’s disease (CD) is a complex immune-mediated disease of the gastrointestinal tract where the gut microbiome plays an important role. Current biological treatment options in CD include the anti-TNFα agents adalimumab (ADA) and infliximab (IFX), vedolizumab (VDZ), and ustekinumab (USTE). Finding biomarkers to predict therapy response is still a clinical unmet need, as the majority of CD patients fail to reach endoscopic remission within 1 year of treatment. While the majority of studies focus on fecal samples, mucosa-adherent bacterial signature could bring forth stable biomarkers. In this study, we sought to identify signatures of the adherent microbiome in intestinal biopsies aimed at differentiating responders (R) from non-responders (NR), prior to the start of biological treatment. Methods We prospectively collected paired ileal and colonic biopsies (stored snapfrozen in -80 ºC) from adult CD patients scheduled to start anti-TNFα (IFX and ADA), VDZ and USTE treatment during baseline endoscopies. After 6-12 months of follow-up, patients were classified as either R or NR based on endoscopic response (≥50% reduction in SES-CD score) in combination with steroid-free clinical response (≥3 point drop in HBI or HBI ≤4) and/or biochemical response (≥50% reduction in C-reactive protein (CRP) and fecal calprotectin or a basal CRP ≤5 g/mL and fecal calprotectin ≤250 µg/g). Microbiome composition of the biopsies was determined using 16S RNA gene V3V4 amplicon sequencing. Results For the anti-TNFα cohort, we included in total 36 CD patients (21R and 18 NR), for VDZ cohort a total of 44 patients (28 R and 16 NR) and for USTE cohort a total of 40 patients (20R and 20 NR).When comparing α-diversity and β-diversity between R and NR, we did not find significant differences in ileal and colonic samples at baseline for the VDZ and USTE cohorts. Notable, for the anti-TNFα cohort (table 1), we obtained significant differences when comparing α-diversity (p=0.028) and β-diversity between R and NR. We demonstrated differences between colonic R and NR in taxa abundance for ASVs associated with the phyla Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria (figure 1). Furthermore, ASVs associated with Firmicutes, Bacteroidetes, Actinobacteria are different in abundance between ileal R and NR (figure 2). Figure 1: Differential taxa abundance colonic R vs NR anti-TNFα Figure 2: Differential taxa abundance ileal R vs NR anti-TNFα Figure 3: graphical summary Conclusion Here, we investigated the microbial signature of R and NR before the start of treatment of three separate cohorts and we demonstrated that the mucosa-associated microbiome differentiates R and NR to anti-TNFα therapy. Further analyses as part of the EPIC Pioneer study, are ongoing.

A prognostic model based on tumor microenvironment-related lncRNAs predicts therapy response in pancreatic cancer.

Pancreatic cancer is an aggressive malignant tumor with high mortality and a low survival rate. The immune and stromal cells that infiltrate in the tumor microenvironment (TME) significantly impact immunotherapy and drug responses. Therefore, we identify the TME-related lncRNAs to develop a prognostic model for predicting the therapy efficacy in pancreatic cancer patients. Firstly, we identified differentially expressed genes (DEGs) for weighted gene co-expression network analysis (WGCNA) to identify the TME-related module eigengenes. According to the module eigengenes, the TME-related prognostic lncRNAs were screened through the univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses to construct a prognostic risk score (RS) model. Next, the predictive power of this model was evaluated by the time-dependent receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. In addition, functional enrichment, immune cell infiltration, and somatic mutation analyses were performed. Finally, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity analyses were applied to predict therapy response. In this study, 11 TME-related prognostic lncRNAs were identified to develop the prognostic RS model. According to the RS, the low-risk patients had a better prognosis, lower rates of somatic mutation, lower TIDE scores, and higher sensitivity to gemcitabine and paclitaxel compared to high-risk patients. The findings above suggested that low-risk patients may benefit more from immunotherapy, and high-risk patients may benefit more from chemotherapy. Within this study, we established a prognostic RS model based on 11 TME-related lncRNAs, which may help improve clinical decision-making.