Predict Therapy Response Research Articles

68Ga-DOTATOC PET/CT-Based Radiomic Analysis and PRRT Outcome: A Preliminary Evaluation Based on an Exploratory Radiomic Analysis on Two Patients.

Aim: This work aims to evaluate whether the radiomic features extracted by 68Ga-DOTATOC-PET/CT of two patients are associated with the response to peptide receptor radionuclide therapy (PRRT) in patients affected by neuroendocrine tumor (NET).Methods: This is a pilot report in two NET patients who experienced a discordant response to PRRT (responder vs. non-responder) according to RECIST1.1. The patients presented with liver metastasis from the rectum and pancreas G3-NET, respectively. Whole-body total-lesion somatostatin receptor-expression (TLSREwb-50) and somatostatin receptor-expressing tumor volume (SRETV wb-50) were obtained in pre- and post-PRRT PET/CT. Radiomic analysis was performed, extracting 38 radiomic features (RFs) from the patients' lesions. The Mann–Whitney test was used to compare RFs in the responder patient vs. the non-responder patient. Pearson correlation and principal component analysis (PCA) were used to evaluate the correlation and independence of the different RFs.Results: TLSREwb-50 and SRETVwb-50 modifications correlate with RECIST1.1 response. A total of 28 RFs extracted on pre-therapy PET/CT showed significant differences between the two patients in the Mann–Whitney test (p < 0.05). A total of seven second-order features, with poor correlation with SUVmax and PET volume, were identified by the Pearson correlation matrix. Finally, the first two PCA principal components explain 83.8% of total variance.Conclusion: TLSREwb-50 and SRETVwb-50 are parameters that might be used to predict and to assess the PET response to PRRT. RFs might have a role in defining inter-patient heterogeneity and in the prediction of therapy response. It is important to implement future studies with larger and more homogeneous patient populations to confirm the efficacy of these biomarkers.

A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases.

Simple SummaryIt is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome.Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.

Bladder Tumor Subtype Commitment Occurs in Carcinoma In Situ Driven by Key Signaling Pathways Including ECM Remodeling.

Basal and luminal subtypes of invasive bladder tumors have significant prognostic and predictive impacts for patients. However, it remains unclear whether tumor subtype commitment occurs in noninvasive urothelial lesions or in carcinoma in situ (CIS) and which gene pathways are important for bladder tumor progression. To understand the timing of this commitment, we used gene expression and protein analysis to create a global overview of 36 separate tissues excised from a whole bladder encompassing urothelium, noninvasive urothelial lesions, CIS, and invasive carcinomas. Additionally investigated were matched CIS, noninvasive urothelial lesions, and muscle-invasive bladder cancers (MIBC) from 22 patients. The final stage of subtype commitment to either a luminal or basal MIBC occurred at the CIS transition. For all tissues combined, hierarchical clustering of subtype gene expression revealed three subtypes: "luminal," "basal," and a "luminal p53-/extracellular matrix (ECM)-like" phenotype of ECM-related genes enriched in tumor-associated urothelium, noninvasive urothelial lesions, and CIS, but rarely invasive, carcinomas. A separate cohort of normal urothelium from noncancer patients showed significantly lower expression of ECM-related genes compared with tumor-associated urothelium, noninvasive urothelial lesions, and CIS. A PanCancer Progression Panel of 681 genes unveiled pathways specific for the luminal p53-/ECM-like cluster, for example, ECM remodeling, angiogenesis, epithelial-to-mesenchymal transition, cellular discohesion, cell motility involved in tumor progression, and cell proliferation and oncogenic ERBB2/ERBB3 signaling for invasive carcinomas. In conclusion, this study provides insights into bladder cancer subtype commitment and associated signaling pathways, which could help predict therapy response and enhance our understanding of therapy resistance. SIGNIFICANCE: This study demonstrates that CIS is the stage of commitment for determining MIBC tumor subtype, which is relevant for patient prognosis and therapy response.

Three dimensional cultivation increases chemo- and radioresistance of colorectal cancer cell lines.

Although 2D cell cultures are commonly used to predict therapy response, it has become clear that 3D cultures may better mimic the in vivo situation and offer the possibility of tailoring translational clinical approaches. Here, we compared the response of 2D and 3D colorectal cancer (CRC) cell lines to irradiation and chemotherapy. Classic 2D cultures and 3D spheroids of CRC cell lines (CaCo2, Colo205, HCT116, SW480) were thoroughly established, then irradiated with doses of 1, 4, or 10 Gy, using a clinical-grade linear accelerator. The response was assessed by immunohistochemistry, flow cytometry, and TUNEL assays. Upon irradiation, CRC 3D spheroids were morphologically altered. After irradiation with 10 Gy, annexin V/PI staining revealed a 1.8- to 4-fold increase in the apoptosis rate in the 2D cell cultures (95% CI 3.24±0.96), and a 1.5- to 2.4-fold increase in the 3D spheroids (95% CI 1.56±0.41). Irradiation with 1 Gy caused 3- and 4-fold increases in TUNEL positive cells in the CaCo2 and HCT116 (p = 0.01) 2D cultures, respectively, compared with a 2-fold increase in the 3D spheroids. Furthermore, the 2D and 3D cultures responded differently to chemotherapy; the 3D cultures were more resistant to 5-FU and cisplatin, but not to doxorubicin and mitomycin C, than the 2D cultures. Taken together, CRC cells cultured as 3D spheroids displayed markedly higher resistance to irradiation therapy and selected chemotherapeutic drugs than 2D cultures. This in vitro difference must be considered in future approaches for determining the ideal in vitro systems that mimic human disease.

Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.

Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. We performed an in silico experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials. The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to "gold standard" CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1-3 and good prognosis for CMS2 in metastatic colorectal cancer (P < 0.001). We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.

CLIC1 Expression in Skin Biopsies from Patients With Rheumatoid and Psoriatic Arthritis as a Potential Tool to Predict Therapy Response.

Chloride intracellular channel protein 1 (CLIC1) activates inflammasomes in rheumatoid (RA) and psoriatic (PsA) arthritis. We studied CLIC1 expression in RA and PsA patients' skin with vasculitis and its variability depending on the therapy used. CLIC1 immunoexpression was evaluated in the vascular (CLIC1-V) and stromal (CLIC1-S) compartments of the RA and PsA skin biopsies of patients treated with methotrexate (MTX), leflunomid (LFN), corticotherapy (CT), or biological therapies. MTX significantly reduced CLIC1-S expression (p=0.016), whereas LFN decreased CLIC1-V (p<0.001). LFN therapy duration also correlated with CLIC1-V (p<0.001). CT decreased CLIC1-S expression (p=0.006). CLIC1-S expression persisted in skin biopsies despite of erythrocyte sedimentation rate (ESR, p=0.018) and C reactive protein (CRP, p=0.0026) normalisation. For PsA, CLIC1-S expression significantly related to MTX (p<0.022). Both CLIC1-S (p<0.001) and CLIC1-V (p=0.007) decreased by biological therapies in RA. CLIC1 expression is strongly influenced by the therapy used. Our data strongly support the extensive evaluation of CLIC1 in RA as a potential marker of inflammation and tool to predict therapy response.

Comparación de la PET/TC 18F-FDG y la DW-MRI en la evaluación de la respuesta a la radioquimioterapia neoadyuvante en pacientes con cáncer rectal localmente avanzado

ObjectiveOur aim is to evaluate if different metabolic parameters obtained by 18F-FDG PET/CT and diffusion weighted magnetic resonance imaging (DW-MRI) can aid in neoadjuvant radiochemotherapy (RCT) response assessment in locally advanced rectal cancer (LARC) patients. MethodsOut of 20 LARC patients, who were planned to receive neoadjuvant RCT, 19 were included in this prospective study. Patients had 18F-FDG PET/CT and DW-MRI at initial staging, interim (2 weeks after onset of RCT) and after completion of RCT (post-therapy). Standardized uptake value (SUV) parameters (SUVmax, SUVmean, SUVpeak, SULpeak), metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) detected on PET images and apparent diffusion coefficient (ADC) values (for b=400 and b=1000s/mm2) obtained from DW-MRI were recorded. Postoperative tumor regression grade (TRG) was used as gold-standard, except for 2 patients who were under complete remission with non-operative management 19 months post-therapy and scored as responders. ResultsOn interim PET/CT, no significant difference was found among PET parameters between responders and non-responders, whereas post-therapy SUVmax, SUVpeak, MTV, SULpeak, TLG (P=0.02, P=0.014, P=0.025, P=0.007, P=0.02, respectively) and initial MTV (P=0.034) were significantly lower in responders. ADC response index (RI) was higher in responders (interim P=0.026; post-therapy: P=0.018) and ROC analysis revealed that a threshold of ADC RI>41.6% for interim MRI and >44.6% for post-therapy MRI had sensitivity and specificity of 75.0% and 90.9%, respectively. ConclusionsWhile interim 18F-FDG PET/CT failed to predict therapy response during RCT, post-therapy PET could accurately differentiate responders. DW-MRI was found to be more promising in interim detection of RCT response.

Prospects of application of inhibitors of PD-1/PD-L1 checkpoints in malignant tumors of the stomach and esophagogastric junction

Malignant tumors of the stomach and esophagogastric junction in advanced stages progress quite aggressively, and the prospects for treatment of these patients remain unpromising. The use of checkpoint-inhibitors has proven to be an advanced treatment method for various types of cancer around the world. In theRussian Federation, nivolumab has been successfully registered as a monotherapy for common or recurrent stomach or esophagogastric junction cancer after two or more lines of systemic antitumor drug therapy. This literature review focuses on the use of registered checkpoint inhibitors (nivolumab, pembrolizumab, ipilimumab) as mono- and/or combined therapy in tumors of the stomach and esophagogastric junction, including tumors with high microsat- ellite instability (MSI-high). This review includes a description of the main therapeutic approaches using checkpoint inhibitors: prescription in mono-mode, in combination with other checkpoint inhibitors (ipilimumab) and cytotoxic drugs, and in combination with tyrosine kinase inhibitors (regorafenib). Issues of efficiency and tolerability of these combinations in patients in different therapeutic lines are considered. The role of possible predictors of therapy response is analyzed: biomarkers such as PD-Ll, MSI, dMMR and TMB expression in tumor tissues as well as immunofenotyping in fresh biopsy samples are evaluated. This article reviews and evaluates the strengths and weaknesses of checkpoint inhibitors and their possible uses.

Stem Cells and Organoid Technology in Precision Medicine in Inflammation: Are We There Yet?

Individualised cellular models of disease are a key tool for precision medicine to recapitulate chronic inflammatory processes. Organoid models can be derived from induced pluripotent stem cells (iPSCs) or from primary stem cells ex vivo. These models have been emerging over the past decade and have been used to reconstruct the respective organ-specific physiology and pathology, at an unsurpassed depth. In cancer research, patient-derived cancer organoids opened new perspectives in predicting therapy response and provided novel insights into tumour biology. In precision medicine of chronic inflammatory disorders, stem-cell based organoid models are currently being evaluated in pre-clinical pharmacodynamic studies (clinical studies in a dish) and are employed in clinical studies, e.g., by re-transplanting autologous epithelial organoids to re-establish intestinal barrier integrity. A particularly exciting feature of iPSC systems is their ability to provide insights into organ systems and inflammatory disease processes, which cannot be monitored with clinical biopsies, such as immune reactions in neurodegenerative disorders. Refinement of differentiation protocols, and next-generation co-culturing methods, aimed at generating self-organised, complex tissues in vitro, will be the next logical steps. In this mini-review, we critically discuss the current state-of-the-art stem cell and organoid technologies, as well as their future impact, potential and promises in combating immune-mediated chronic diseases.

How does Radiomics actually work? - Review.

Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades, applications of artificial intelligence are increasingly attracting scientific interest as they could substantially expand the possibility of objective quantification and diagnostic or prognostic use of image information.In this context, the term "radiomics" describes the extraction of quantitative features from imaging data such as those obtained from computed tomography or magnetic resonance imaging examinations. These features are associated with predictive goals such as diagnosis or prognosis using machine learning models. It is believed that the integrative assessment of the feature patterns thus obtained, in combination with clinical, molecular and genetic data, can enable a more accurate characterization of the pathophysiology of diseases and more precise prediction of therapy response and outcome.This review describes the classical radiomics approach and discusses the existing very large variability of approaches. Finally, it outlines the research directions in which the interdisciplinary field of radiology and computer science is moving, characterized by increasingly close collaborations and the need for new educational concepts. The aim is to provide a basis for responsible and comprehensible handling of the data and analytical methods used. KEY POINTS:: · Radiomics is playing an increasingly important role in imaging research.. · Radiomics has great potential to meet the requirements ofprecision medicine.. · Radiomics analysis is still subject to great variability.. · There is a need for quality-assured application of radiomics in medicine.. CITATION FORMAT: · Attenberger UI, Langs G, . How does Radiomics actually work? - Review. Fortschr Röntgenstr 2021; 193: 652 - 657.

F18-FET PET in pediatric brain tumors: integrative analysis of image derived parameters and clinico-pathological data.

F18-FET PET has an established diagnostic role in adult brain gliomas. In this study we analyzed image derived static and dynamic parameters with available conventional MRI, histological, clinical and follow-up data in assessment of pediatric brain tumor patients at different stages of the disease. Forty-four pediatric patients with median age 7 years, diagnosed with brain tumors and underwent forty-seven 18F-FET PET scans either initially (20 scans) or post-therapy (27 scans) were enrolled. Standardized analysis of summed FET PET images early from 10-20 min and late from 30-40 min post-injection were used for static (mean and maximum tumor to brain ratio [TBR] and biological tumor volume [BTV]) parameters evaluation as well as the time activity curve [TAC]. Nineteen out of 20 initially assessed patients had pathologically and/or clinico-radiologically proven neoplastic lesions and one patient had pathologically proven abscess. Receiver operator curve (ROC) marked early TBR max 2.95, early TBR mean 1.76, late TBR max 2.5 and late TBR mean 1.74 as discriminator points with diagnostic accuracy reaching 90% when TBR max was combined with dynamic parameters. Significant association was found between initial FET scans, early and late BTV and event free survival (EFS) (P value=0.042 and 0.005 respectively). In post-therapy assessment, the diagnostic accuracy of conventional MRI was 81.48% when used alone and 96.30% when combined with F18-FET PET scan findings. A cutoff point of 3.2 cm3 for late BTV, in post-therapy scans, was successfully marked as a predictor for therapy response (P value 0.042) and was significantly associated with EFS (P value 0.002). In FET-avid / MRI non-enhancing lesions, early TBR max was able to detect highly malignant processes (high-grade tumors in initial scans and residue/recurrence in post-therapy scans) with 80% sensitivity and 100% specificity when cutoff value of 2.25 was used (P value=0.024). In patients with FET-avid brainstem lesions, whether enhancing or non-enhancing in MRI scans, 81.8% were associated with high risk diagnoses and 68.2% of them were associated with poor therapy outcome. The degree of FET uptake matched tumor-grading, but did not show significant association with OS or EFS (P value>0.05). F18-FET PET seems to be an evolving pediatric neuro-imaging technique with valuable diagnostic and prognostic information at initial and post-therapy evaluation.

Harnessing Tissue Engineering Tools to Interrogate Host-Microbiota Crosstalk in Cancer.

SummaryRecent studies have begun to highlight the diverse and tumor-specific microbiomes across multiple cancer types. We believe this work raises the important question of whether the classical “Hallmarks of Cancer” should be expanded to include tumor microbiomes. To answer this question, the causal relationships and co-evolution of these microbiotic tumor ecosystems must be better understood. Because host-microbe interactions should be studied in a physiologically relevant context, animal models have been preferred. Yet these models are often poor mimics of human tumors and are difficult to interrogate at high spatiotemporal resolution. We believe that in vitro tissue engineered platforms could provide a powerful alternative approach that combines the high-resolution of in vitro studies with a high degree of physiological relevance. This review will focus on tissue engineered approaches to study host-microbe interactions and to establish their role as an emerging hallmark of cancer with potential as a therapeutic target.

Immature platelet dynamics correlate with ADAMTS13 deficiency and predict therapy response in immune-mediated thrombotic thrombocytopenic purpura

IntroductionThrombotic thrombocytopenic purpura (TTP) requires prompt initiation of therapeutic plasma exchange (TPE) to avoid significant morbidity and mortality. ADAMTS13 activity testing defines TTP, however, at most institutions this is a send-out test and therapy is often initiated prior to measurement availability. We describe our experience looking at absolute immature platelet counts (A-IPC) in patients suspected with TTP at presentation and in response to therapy. Materials and methodsForty-eight patients treated for suspected TTP with A-IPC measure on admission and during hospitalization met inclusion criteria. Of these patients, sixteen had new-onset TTP (ADAMTS13 < 10%), ten were relapsing patients (first diagnosis prior to study period), and 22 were classified as non-TTP (ADAMTS13 ≥ 10%). ResultsPatients with ADAMTS13 deficiency (TTP) had A-IPC different from those without deficiency. A-IPC of 1–2 × 109/L at presentation had high sensitivity and specificity with a negative predictive value of 95.5 to 100%. Two-to-three-fold increases in A-IPC from count prior to TPE initiation was limited to ADAMTS13 deficient patients who was the group responding to therapy. Increases were higher in patients with new disease onset compared to relapsing patients (p = 0.018). Likewise, relapsing patients' A-IPC appeared dependent upon platelet count at time of relapse. A-IPC predicted and correlated with ADAMTS13 deficiency in new-onset TTP (p = 0.0002). ConclusionsOnly patients with A-IPC-fold increases responded to TPE with platelet count normalization. Our results represent a proof of concept that A-IPC measurements can supplement ADAMTS13 testing and determine response to TPE. Future studies are needed to establish ways to apply these findings in the setting of suspected TTP.

Ex Vivo Culture Models to Indicate Therapy Response in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is characterized by a poor 5 year survival and varying response rates to both standard-of-care and new treatments. Despite advances in medicine and treatment methods, mortality rates have hardly decreased in recent decades. Reliable patient-derived tumor models offer the chance to predict therapy response in a personalized setting, thereby improving treatment efficacy by identifying the most appropriate treatment regimen for each patient. Furthermore, ex vivo tumor models enable testing of novel therapies before introduction in clinical practice. A literature search was performed to identify relevant literature describing three-dimensional ex vivo culture models of HNSCC to examine sensitivity to chemotherapy, radiotherapy, immunotherapy and targeted therapy. We provide a comprehensive overview of the currently used three-dimensional ex vivo culture models for HNSCC with their advantages and limitations, including culture success percentage and comparison to the original tumor. Furthermore, we evaluate the potential of these models to predict patient therapy response.

Impact of variants in ATP-binding cassettetransporters on breast cancer treatment.

There has been substantial interest in the impact of ATP-binding cassette (ABC) transporter variability on breast cancer drug resistance. Here, we provide a systematic review of ABC variants in breast cancer therapy. Notably, most studies used small heterogeneous cohorts and their identified associations lack statistical stringency, replication and mechanistic support. We conclude that commonly studied ABC polymorphisms are not suitable to accurately predict therapy response or toxicity in breast cancer patients and cannot guide treatment decisions. However, recent research shows that ABC transporters harbor a plethora of rare variants with individually small effect sizes, and we argue that a shift in strategy from target variant interrogation to comprehensive profiling might hold promise to drastically improve the predictive power of outcome models.

Treatment-related biomarkers in pulmonary hypertension patients on oral therapies

BackgroundMultiple classes of oral therapy are available for the treatment of pulmonary arterial hypertension (PAH), but there is little to guide clinicians in choosing a specific regimen or therapeutic class. We aimed to investigate whether treatment-relevant blood biomarkers can predict therapy response in prevalent PAH patients.MethodsThis prospective cohort study longitudinally assessed biomarkers along the endothelin-1 (ET-1) and nitric oxide (cGMP, ADMA, SDMA, nitrite, and S-nitrosohemoglobin) pathways along with the cGMP/NT-proBNP ratio over 12 months in patients with WHO Group 1 PAH on oral PAH-specific therapies. The relationship between biomarkers and 6MWD at the same and future visits was examined using mixed linear regression models adjusted for age. As cGMP can be elevated when NT-proBNP is elevated, we also tested the relationship between 6MWD and the cGMP/NT-pro BNP ratio. Patients with PAH with concomitant heart or lung disease or chronic thromboembolic pulmonary hypertension (CTEPH) were included in a sensitivity analysis.ResultsThe study cohort included 58 patients with PAH treated with either an endothelin receptor antagonist (27.6%), phosphodiesterase-5 inhibitor (25.9%) or a combination of the two (43.1%). Among biomarkers along the current therapeutic pathways, ET-1 and the cGMP/NT-proBNP ratio associated with same visit 6MWD (p = 0.02 and p = 0.03 respectively), and ET-1 predicted future 6MWD (p = 0.02). ET-1 (p = 0.01) and cGMP/NT-proBNP ratio (p = 0.04) also predicted future 6MWD in the larger cohort (n = 108) of PAH patients with concomitant left heart disease (n = 17), lung disease (n = 20), or CTEPH (n = 13). Finally, in the larger cohort, SDMA associated with 6MWD at the same visit (p = 0.01) in all subgroups and ADMA associated with 6MWD in PAH patients with concomitant lung disease (p = 0.03) and PAH patients on ERA therapy (p = 0.01).ConclusionsET-1, cGMP/NTproBNP ratio, and dimethylarginines ADMA and SDMA are mediators along pathways targeted by oral PAH therapies that associate with or predict 6MWD.

STUDY OF CYTOKINE BIOMARKERS FOR EARLY DETECTION OF CHRONIC GRAFT VERSUS HOST DISEASE IN ALLOGENEIC STEM CELL TRANSPLANT PATIENTS

Allogeneic transplantation represents the most efficient consolidation treatment for most cases of malignant diseases. Some progression was made in the practice of transplantation but still, GvHD, relapse, and infection are the main obstacles. The cumulative incidence of cGvHD by 1 year after transplant ranged from 30-70 %. GvHD is the most common non-relapse mortality (NRM), and accounts of about 10–25 % of all transplanted patients.CGvHD is defined by the National Institutes of Health (NIH) Consensus depending on clinical manifestations, changing the classical and rather arbitrary chronological “beyond Day 100” definition. There are two types of cGvHD: Classic and overlap. The severity of cGvHD also assessed by the NIH global severity of cGvHD.Types of Hematopoietic stem-cell transplantation (HSCT) types: Allogeneic or autologous HSC.Chronic GvHD types: Classic chronic GvHD: occurs at any time after transplantation. Overlap syndrome: May occur at any time after transplantation, with a mixture of features of both cGvHD and aGvHD.Biomarker Panel for Chronic Graft-Versus-Host Disease:NIH Biomarker Working Group in 2006 meeting, declared cGvHD biomarkers utilization for clinical trials and disease management in the following conditions to (1) predict the risk of developing cGvHD; (2) diagnose cGvHD ; (3) measure disease activity or predict prognosis; (4) predict therapy response; and (5) serve as the surrogate endpoint for therapeutic response.

Inhibition of PAK6-Mediated Survival and Cell Cycle Controls Selectively Targets Therapy-Resistant CML Stem/Progenitor Cells

The resistance of chronic myeloid leukemia (CML) leukemic stem cells (LSC) to ABL tyrosine kinase inhibitor (TKI) monotherapy remains a challenge in curing CML. We have recently identified miR-185 as a useful biomarker to predict therapy response in treatment-naïve CML stem/progenitor cells. We also demonstrated that restored miR-185 expression in LSCs impaired survival, sensitizing them to TKIs in vitro and in preclinical patient-derived xenotransplantation models, indicating that miR-185 is a critical regulator mediating TKI response/resistance of CML stem/progenitor cells. PAK6, a serine/threonine-protein kinase, was uncovered as a target gene of miR-185 by RNA-seq and was found to be upregulated in CD34+ TKI-nonresponder cells vs. TKI-responders, but its biological functions in CML are largely unknown. To investigate the biological effects of inhibiting PAK6 activity in TKI-resistant cells, we tested a pre-clinically validated pan-PAK inhibitor (PF-3758309) in vitro. PF-3758309 significantly reduced the growth of IM-resistant cell lines, including K562-resistant cells, BV173 blast cells (IC50 25-70 nM) and CD34+ TKI-nonresponder cells, as assessed by viability and CFC assays, and increased their apoptosis; these effects were significantly enhanced by TKIs (~2-fold, P&amp;lt;0.05). PF-3758309 alone, or in combination with a TKI, did not have obvious inhibitory effects on CD34+ normal bone marrow. These results were further confirmed in IM-resistant cells using a lentiviral knockdown system that specifically inhibits PAK6. Interestingly, PF-3758309 alone, or in combination with a TKI, greatly reduced mitochondrial activity in CD34+ TKI-nonresponder cells, as shown in functional assessments with MitoTracker, a dye that accumulates in active mitochondria, the site of OXPHOS; this was not seen with TKI alone (P&amp;lt;0.002). Similarly, CellROX analysis confirmed a reduction in ROS levels upon PF-3758309 treatment, or a combination of PF-3758309 with TKI, in these cells. In addtion, MDM2, a critical negative regulator of the p53 tumor suppressor, was identified as one of substrates of PAK6, by PhosphoSitePlus analysis. Its expression was found to be correlated with PF-3758309 treatment in CML cells based on CellMinerDB univariate analyses using gene-small-molecule association data from the CTRPv2 database. Indeed, Western blot analysis showed that PAK6 knockdown in K562 and IM-resistant cells led to a reduction in MDM2 protein levels. Furthermore, MDM2 downregulation by PAK6 inhibition corresponded to an increase in p21 levels, suggesting a mechanism of MDM2-mediated p21 regulation independent of p53, as these cells are p53-null. Most interestingly, PAK6 knockdown in IM-resistant cells leads to G2/M phase accumulation and increased senescence levels (2-fold, P&amp;lt;0.05), detected by senescence-associated β-galactosidase staining. PAK6 knockdown induced senescence was further supported by observations of enlarged cell size (p&amp;lt;0.05) and increased granulation, as well as changes in senescence-associated protein markers, including p21, p27, MMP-3 and the DNA damage marker pH2Ax, by Western blot analysis. Hence, our findings indicate that dual targeting of miR-185-PAK6-mediated survival, cell cycle and metabolic pathways, along with BCR-ABL, selectively eradicates drug-resistant CML stem/progenitors. Specifically, PAK6 plays roles in MDM2/p21-mediated apoptosis, senescence and cell cycle controls, offering a valuable therapeutic strategy for improved treatment and care. Disclosures No relevant conflicts of interest to declare.

Superior Therapy Response Predictions for Patients with Myelodysplastic Syndrome (MDS) Using Cellworks Singula™: Mycare-020-02

Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value &amp;lt; 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR &amp;gt; 100, p-value &amp;lt; 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

Assessment of Cellworks Omics Biosimulation Therapy Response Predictions for Patients with Acute Myeloid Leukemia (AML) Using Cellworks Singula™: Mycare-020-01

Background. In addition to clinical considerations (e.g., age, de novo vs secondary disease, comorbidities), therapy selection for AML patients is often based on information considering only cytogenetics and/or molecular aberrations and ignoring other patient-specific omics information that could potentially enable selection of more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification, the current overall outcome of AML patients remains relatively poor. The Cellworks Singula™ report predicts clinical response to physician-prescribed treatments using the novel Cellworks Omics Biology Model (CBM) that simulate in silico downstream molecular effects on cell signaling and survival of drug treatments in patient-specific diseased cells. Methods. The performance of Singula™ was evaluated in a cohort of 474 AML patients aged 2 to 85. The pre-defined Singula™ Classifier utilizes individual patients' next-generation sequencing (NGS) profiles to provide a dichotomous prediction of response or non-response to the physician prescribed treatments. The clinical outcome data for these subjects, i.e., complete response (CR) and overall survival (OS), were obtained from the TCGA and other 144 PubMed publications, each including also information on patients' cytogenetics, targeted gene mutations, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized the cytogenetic and molecular data to generate a Singula™ predicted response (i.e., CR vs non-response) classification for each patient. Statistical analyses, including assessments of accuracy, sensitivity, specificity, and negative (NPV) and positive predictive (PPV) values were performed to compare the Singula™ predicted clinical response to the actual observed clinical response. Kaplan-Meier curves, associated log rank tests and median OS are provided for patients stratified by Singula™ predicted response. Multivariate Cox proportional hazards regression was used to further test the hypothesis that Singula™ is an independent predictor for OS once adjusted for patient age and actual prescribed treatment. Results. Data are summarized in Table 1. The Singula™ classifier had 92.3% (90.6%, 95.3%) accuracy in predicting correctly observed patient complete response to the prescribed treatment. with 97.3% (95.0%, 98.8%) sensitivity. Singula™ had 83.3% (76.1%, 89.1%) specificity for the non-responder patients (n=138; 29.1%). For each of the non-responders, Singula™ provided an alternative treatment therapy predicted to produce clinical response. Assuming at least 2% of the non-responders would have responded to the alternative Singula™ prescribed treatment, Singula™ improves CR rates compared to the original physician prescribed treatment (McNemar's p-value &amp;lt; 0.05). Figure 1 provides the Kaplan-Meier curves of Singula-predicted responders vs non-responders for a subset of 292 subjects that had OS data available. In multivariate Cox proportional hazards models, the Singula Classifier remained a significant predictor of overall survival (HR = 2.171, p-value &amp;lt; 0.0001) once adjusted for patient age and physician prescribed treatment. Conclusions. Cellworks Singula™ has high accuracy and sensitivity in predicting CR for AML patient. Singula also has high specificity in identifying patients who are unlikely to respond physician and may prescribed potentially effective therapies. The Singula™ predicted responders have a significantly longer OS than the predicted non responders. Thus, Cellworks Singula™ can accurately predict AML response, be used to validate or reject a physician's therapy selection decision and, eventually, provide alternative treatment recommendations. Disclosures Marcucci: Novartis: Speakers Bureau; Abbvie: Speakers Bureau; Iaso Bio: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial). Watson:Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Amara:Cellworks Research India Private Limited: Current Employment. Pampana:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Stein:Stemline: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau.