Method For Stratification Of Patients Research Articles

Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multicenter Study for Development and Validation.

The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight CT findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Furthermore, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.

In Silico Identification and Functional Characterization of Genetic Variations across DLBCL Cell Lines.

Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma and frequently develops through the accumulation of several genetic variations. With the advancement in high-throughput techniques, in addition to mutations and copy number variations, structural variations have gained importance for their role in genome instability leading to tumorigenesis. In this study, in order to understand the genetics of DLBCL pathogenesis, we carried out a whole-genome mutation profile analysis of eleven human cell lines from germinal-center B-cell-like (GCB-7) and activated B-cell-like (ABC-4) subtypes of DLBCL. Analysis of genetic variations including small sequence variants and large structural variations across the cell lines revealed distinct variation profiles indicating the heterogeneous nature of DLBCL and the need for novel patient stratification methods to design potential intervention strategies. Validation and prognostic significance of the variants was assessed using annotations provided for DLBCL samples in cBioPortal for Cancer Genomics. Combining genetic variations revealed new subgroups between the subtypes and associated enriched pathways, viz., PI3K-AKT signaling, cell cycle, TGF-beta signaling, and WNT signaling. Mutation landscape analysis also revealed drug-variant associations and possible effectiveness of known and novel DLBCL treatments. From the whole-genome-based mutation analysis, our findings suggest putative molecular genetics of DLBCL lymphomagenesis and potential genomics-driven precision treatments.

Assessing Putative Markers of Colorectal Cancer Stem Cells: From Colonoscopy to Gene Expression Profiling.

Cancer stem cells (CSCs) are proposed to be involved in colorectal cancer (CRC) initiation, growth, and metastasis. The aim of our pilot study was to assess possible correlations between the clinicopathological characteristics of CRC patients and CSCs gene expression patterns, in order to provide insight into new methods for patient stratification and targeted therapeutic strategies. Our study involved 60 CRC patients, and the following three specific CSC genes were targeted: PROM1/CD133, ALCAM/CD166 and HCAM /CD44. Data are presented as relative mRNA expression of target genes to GAPDH. The expression of total CD133 and CD166 was assessed in paired samples of CRC tumors and adjacent tissue, while CD44 was assessed in similar samples. The qRT-PCR analysis detected all three targeted genes to different extents, in both normal and tumor tissue. In nine cases (15.69%), total CD133 had a higher expression in tumor tissue, whilst in 28 cases (47.06%) the expression was higher in non-malignant peritumor tissue. The total CD166 expression was increased in tumor tissue compared with paired non-invaded peritumor samples in eight cases (13.73%), whilst in eight cases (13.73%) the expression was higher in non-malignant peritumor tissue. Total CD44 expression was higher in tumor tissue compared with paired non-invaded peritumor samples in 47 cases (78.95%). In the remaining cases the difference between paired samples was biologically insignificant. In conclusion, our study suggests that qRT-PCR is feasible in assessing the gene expression profiles of CSCs from CRC, and a promising pathway to be followed for determining how often a person needs screening by colonoscopy and at which age to start. This could improve CRC diagnosis and early patient stratification, and open the way for new oncologic treatment development.

579P A multicenter evaluation of a low pass whole genome sequencing-based solution for homologous recombination deficiency detection

Poly (ADP-ribose) polymerase inhibitors (PARPi) induce synthetic lethality in homologous recombination deficient (HRD) tumors and have revolutionized ovarian cancer treatment. Patient stratification methods currently used are based on genome-wide enumeration of known HRD biomarkers and require deep sequence profiles (> 30x), in some cases from tumor-normal pairs. The cost and challenges introduced by implementing the current solutions hinder the clinical adoption of HRD testing. Here we present a multicenter performance evaluation study of an alternative deep learning-based decentralized solution for HRD detection, the SOPHiA DDM Dx HRD Solution (SOPHiA GENETICS SA), which leverages the impact of HRD on the coverage profiles obtained from low-pass whole-genome sequencing (lpWGS) data (X1 fold coverage). We generated and sequenced, in 5 independent clinical laboratories, 153 low pass WGS libraries (X1, ∼10 million reads, Illumina) for 150 Formalin-Fixed Paraffin-Embedded ovarian cancer samples. HRD classification was performed according to the manufacturer’s recommendations. The analytical performance was evaluated as overall (OPA), positive (PPA), and negative (NPA) percentage of agreement with Myriad myChoice CDx status (Myriad Genetic Laboratories, Inc.; US FDA-approved). The Quality Control criteria for SOPHiA DDM Dx HRD Solution analysis were met for 97,3% of the libraries (n=149). We found a high overall percentage agreement, 93.7% (97.1% NPA and 90.4% PPA) between SOPHiA DDM Dx HRD Solution and Myriad myChoice CDx classification (Table). Additionally, SOPHiA DDM Dx HRD Solution results for samples analyzed in duplicate were 100% reproducible.Table: 579PConfusion matrix of comparison between Myriad myChoice CDx and SOPHiA DDM Dx HRD Solution results (149 unique samples)SOPHiA DDM Dx HRD SolutionPositiveNegativeFailMyriad myChoice CDxPositive669-Negative2673Fail-11 Open table in a new tab We conclude that SOPHiA DDM Dx HRD Solution supports accurate HRD detection from low pass whole genome sequencing data, which is amongst the most cost-effective and easy to implement genome profiling methods.

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection.

Simple SummaryPatient stratification is clinically important because it allows us to understand the characteristics and establish treatment strategies for a group. Transcriptomic data play an important role in determining molecular subtypes and predicting survival. In the case of breast cancer, although the order of prognosis according to molecular subtypes is well known, there is heterogeneity even within a subtype. Therefore, patient stratification considering both molecular subtypes and survival outcomes is required. In this study, a methodology to handle this problem is presented. A genetic algorithm is used to select a set of genes, and a risk score is assigned to each patient using their expression level. According to the risk score, patients are ordered and stratified considering molecular subtypes and survival outcomes. Consequently, informative genes for patient stratification with respect to both aspects could be nominated, and the usefulness of the risk score was shown through comparison with other indicators.Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Enamine N-Oxides: Synthesis and Application to Hypoxia-Responsive Prodrugs and Imaging Agents.

Tumor hypoxia induces the large-scale adaptive reprogramming of cancer cells, promoting their transformation into highly invasive and metastatic species that lead to highly negative prognoses for cancer patients. We describe the synthesis and application of a hypoxia-responsive trigger derived from previously inaccessible enamine N-oxide structures. Hypoxia-dependent reduction of this motif by hemeproteins results in the concomitant activation of a caged molecule and a latent electrophile. We exploit the former in a hypoxia-activated prodrug application using a caged staurosporine molecule as a proof-of-principle. We demonstrate the latter in in vivo tumor labeling applications with enamine-N-oxide-modified near-infrared probes. Hypoxia-activated prodrug development has long been complicated by the heterogeneity of tumor hypoxia in patients. The dual drug release and imaging modalities of the highly versatile enamine N-oxide motif present an attractive opportunity for theranostic development that can address the need not only for new therapeutics but paired methods for patient stratification.

Prognostic value of resting coronary sinus flow determined by phase-contrast cine cardiovascular magnetic resonance in patients with known or suspected coronary artery disease

BackgroundPhase-contrast cine cardiovascular magnetic resonance (CMR) of the coronary sinus has emerged as a non-invasive method to measure coronary sinus blood flow (CSBF) and coronary flow reserve (CFR). We aimed to compare the prognostic value of resting CSBF and CFR for predicting major adverse cardiac events (MACE) in patients with known or suspected coronary artery disease (CAD) who underwent vasodilator stress CMR.MethodsWe studied 693 patients with known CAD and 519 patients with suspected CAD admitted to our hospital between 2009 and 2019. The CFR was calculated as the CSBF during adenosine triphosphate infusion divided by CSBF at rest. MACE was defined as composite of cardiovascular death, acute coronary syndrome, heart failure hospitalization, and sustained ventricular tachyarrhythmia.ResultsDuring a median follow-up of 4.6 years, 92 patients (8%) experienced MACE. The resting CSBF was significantly higher in patients with MACE than in patients without MACE (114.7 ± 44.9 mL/min vs. 84.7 ± 30.9 mL/min, p < 0.001 for known CAD; 122.2 ± 33.3 mL/min vs. 86.6 ± 36.7 mL/min, p < 0.001 for suspected CAD). The resting CSBF remained a significant predictor for MACE after adjusting clinical and CMR variables (hazard ratio [HR] of resting CSBF higher than the median: 3.18, p = 0.0083 for known CAD; HR: 23.3, p < 0.001 for suspected CAD). The area under the curve for predicting MACE was 0.73 for resting CSBF, 0.72 for CFR (p = 0.78) in patients with known CAD, and 0.82 for resting CSBF, 0.83 (p = 0.58) for CFR in patients with suspected CAD.ConclusionsThe resting CSBF may be a useful non-invasive method for the risk stratification of patients with known or suspected CAD without any radiation exposure, contrast media, or pharmacological vasodilator agents.

Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.

Improving clinical trial outcomes in amyotrophic lateral sclerosis.

Individuals who are diagnosed with amyotrophic lateral sclerosis (ALS) today face the same historically intransigent problem that has existed since the initial description of the disease in the 1860s — a lack of effective therapies. In part, the development of new treatments has been hampered by an imperfect understanding of the biological processes that trigger ALS and promote disease progression. Advances in our understanding of these biological processes, including the causative genetic mutations, and of the influence of environmental factors have deepened our appreciation of disease pathophysiology. The consequent identification of pathogenic targets means that the introduction of effective therapies is becoming a realistic prospect. Progress in precision medicine, including genetically targeted therapies, will undoubtedly change the natural history of ALS. The evolution of clinical trial designs combined with improved methods for patient stratification will facilitate the translation of novel therapies into the clinic. In addition, the refinement of emerging biomarkers of therapeutic benefits is critical to the streamlining of care for individuals. In this Review, we synthesize these developments in ALS and discuss the further developments and refinements needed to accelerate the introduction of effective therapeutic approaches.

Stroma Transcriptomic and Proteomic Profile of Prostate Cancer Metastasis Xenograft Models Reveals Prognostic Value of Stroma Signatures.

Simple SummaryCurrently, there is a need for prognostic tools that can stratify patients, who present with primary disease, based on whether they are at low or high risk for drug resistant and hormone-independent lethal metastatic prostate cancer. The aim of our study was to assess the potentially added value of tumor microenvironment (stroma) components for the characterisation of prostate cancer. By utilising patient derived-xenograft models we show that the molecular properties of the stroma cells are highly responsive to androgen hormone levels, and considerable ECM remodelling processes take place not only in androgen-dependent but also in androgen-independent tumor models. Transcriptomic mechanisms linked to osteotropism are conserved in bone metastatic xenografts, even when implanted in a different microenvironment. A stroma-specific gene list signature was identified, which highly correlates with Gleason score, metastasis progression and progression-free survival, and thus could potentially complement current patient stratification methods. Resistance acquisition to androgen deprivation treatment and metastasis progression are a major clinical issue associated with prostate cancer (PCa). The role of stroma during disease progression is insufficiently defined. Using transcriptomic and proteomic analyses on differentially aggressive patient-derived xenografts (PDXs), we investigated whether PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern. RNA sequencing was performed on the PCa PDXs BM18 (castration-sensitive) and LAPC9 (castration-resistant), representing different disease stages. Using organism-specific reference databases, the human-specific transcriptome (tumor) was identified and separated from the mouse-specific transcriptome (stroma). To identify proteomic changes in the tumor (human) versus the stroma (mouse), we performed human/mouse cell separation and subjected protein lysates to quantitative Tandem Mass Tag labeling and mass spectrometry. Tenascin C (TNC) was among the most abundant stromal genes, modulated by androgen levels in vivo and highly expressed in castration-resistant LAPC9 PDX. The tissue microarray of primary PCa samples (n = 210) showed that TNC is a negative prognostic marker of the clinical progression to recurrence or metastasis. Stroma markers of osteoblastic PCa bone metastases seven-up signature were induced in the stroma by the host organism in metastatic xenografts, indicating conserved mechanisms of tumor cells to induce a stromal premetastatic signature. A 50-gene list stroma signature was identified based on androgen-dependent responses, which shows a linear association with the Gleason score, metastasis progression and progression-free survival. Our data show that metastatic PCa PDXs, which differ in androgen sensitivity, trigger differential stroma responses, which show the metastasis risk stratification and prognostic biomarker potential.

Characterization of progression-related alternative splicing events in testicular germ cell tumors.

Accumulating evidence supports the significance of aberrant alternative splicing (AS) events in cancer; however, genome-wide profiling of progression-free survival (PFS)-related AS events in testicular germ cell tumors (TGCT) has not been reported. Here, we analyzed high-throughput RNA-sequencing data and percent-spliced-in values for 150 patients with TGCT. Using univariate and multivariate Cox regression analysis and a least absolute shrinkage and selection operator method, we identified the top 15 AS events most closely associated with disease progression. A risk-associated AS score (ASS) for the 15 AS events was calculated for each patient. ASS, pathological stage, and T stage were significantly associated with disease progression by univariate analysis, but only ASS and pathological stage remained significant by multivariate analysis. The ability of these variables to predict 5-year progression was assessed using receiver operating characteristic curve analysis. ASS had stronger predictive value than a combination of age, pathological stage, and T stage (area under the curve = 0.899 and 0.715, respectively). Furthermore, Kaplan–Meier analysis of patients with low and high ASS demonstrated that high ASS was associated with significantly worse PFS than low ASS (P = 1.46 × 10−7). We also analyzed the biological functions of the PFS-related AS-related genes and found enrichment in pathways associated with DNA repair and modification. Finally, we identified a regulatory network of splicing factors with expression levels that correlated significantly with AS events in TGCT. Collectively, this study identifies a novel method for risk stratification of patients and provides insight into the molecular events underlying TGCT.

Abstract 4689: Co-treatment of MEKi and BRD4 inhibitors lead to synergistic repression of MYC-associated enhancer RNAs

Abstract Recent evidence suggests that co-treatment with BRD4 and MEK inhibitors can lead to synergistic suppression of MAPK signaling, however, little is known about the mechanism of action of this synergy. In a triple negative breast cancer cell line model, we harvested nascent RNA at 45 minutes following repression of BRD4 and MEK via a variant of the precision run-on followed by sequencing (PRO-seq) assay. We noted dose-dependent repression of enhancer RNAs over MYC binding sites as well as enhancer RNAs upstream known ERK targets such as DUSP6. Using new machine learning techniques, we were able to link synergy-specific enhancer RNAs to their target genes. This work will inform on new methods for patient stratification and biomarker selection of the BETi and MEKi treatment regimes. Citation Format: Joseph Azofeifa, Joel Basken, Maria Lai, Ryan Langendorf, Laura Norris, Tim Read, Adam Robbins-Pianka. Co-treatment of MEKi and BRD4 inhibitors lead to synergistic repression of MYC-associated enhancer RNAs [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4689.

Microfluidic Platform for Next-Generation Sequencing Library Preparation with Low-Input Samples.

Advances in next-generation sequencing (NGS) have made available a wealth of information that had previously been inaccessible to researchers and clinicians. NGS has been applied to understand genomic, transcriptomic, and epigenomic changes and gained traction as a significant tool capable of accelerating diagnosis, prognosis, and biomarker discovery. However, these NGS assays have yet to be practical methods for patient stratification or diagnosis because of the gap between the tiny quantities of biomaterials provided by a clinical sample and the large DNA input required by most of these assays. Current library preparation methodologies typically require large input amounts of DNA and a long and complicated manual process. Here, we present a microfluidic droplet-based system for NGS library preparation, capable of reducing the number of pipetting steps significantly, reducing reagent consumption by 10×, andautomating much of the process, while supporting an extremely low DNA input requirement (10 pg per library). This semiautomated technology will allow for low-input preparations of 8 libraries simultaneously while reducing batch-to-batch variation and operator hands-on time.

SUBSTRA: Supervised Bayesian Patient Stratification.

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection. The R code of SUBSTRA is available at https://github.com/sahandk/SUBSTRA. Supplementary data are available at Bioinformatics online.

Abstract 2634: Predictive value of 18F-FDG PET/CT scanning in combination with clinical parameters in patients with newly diagnosed multiple myeloma

Abstract Objective: To evaluate the association of 18F-2`-deoxy 2`-fluorodeoxyglucose (18F-FDG) PET/CT with clinical parameters in predicting patients with newly diagnosed multiple myeloma (MM). Methods: One hundred and twenty MM patients undergoing 18F-FDG PET/CT scanning were analyzed in a retrospective cohort study. Results: Based on multivariate analysis, β2M, LDH, number of focal lesions (FLs) and SUVmax were significantly correlated with overall survival (OS). These 4 variables were used to construct a new staging system (NSS) based on the number of risk factors. NSS provided a better discrimination of risk between stages III and II than International staging system (ISS) (p &amp;lt; 0.001 vs. p = 0.086). For OS, there was no significant difference among risk groups in Durie-Salmon (DS) stage (p &amp;gt; 0.05). Based on Spearman correlation analysis, presence of lesions in appendicular skeleton, number of FLs and SUVmax appeared to indicate advanced stage of MM. ROC curves which showed the combination of β2M with Calcium got a specificity of 96.3% for the presence of lesions in appendicular skeleton, and LDH alone had 100% specificity in predicting the number of FLs, although the sensitivity was only 50%. Conclusions: 18F-FDG PET/CT in combination with clinical parameters provided an accurate and simple method for risk stratification of patients with newly diagnosed MM. Citation Format: Honglei Tu, Fuling Zhou, Yunbao Pan, Francois X Claret. Predictive value of 18F-FDG PET/CT scanning in combination with clinical parameters in patients with newly diagnosed multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2634.

Reprogramming glioma cell cultures with retinoic acid: Additional arguments for reappraising the potential of retinoic acid in the context of personalized glioma therapy

Background: Glioma, notably glioblastoma multiforme, is characterized by extensive inter-and intra-tumoral heterogeneity. Surprisingly, the potential for differentiation of glioma cells has not been systematically analyzed and included in patient stratification methods. In the current study, retinoic acid (RA), a neuronal differentiation agent, was assessed for the pro-differentiative and anti-proliferative effects on glioma cells. Methods: Using RA-responsive glioma culture as an experimental paradigm, we analyzed the differentiation process both by videomicroscopy and at the mRNA (RNA-seq and reverse transcription-quantitative-polymerase chain reaction) and proteomic levels. Results: Glioma cells can differentiate into neurons in response to RA by (i) extending ultra-long cytoplasmic extensions, (ii) using these extensions to move from cell to cell either by perikaryal translocation or in a spider-flight like process, (iii) slowing their cell cycling, (iv) acquiring several neuronal differentiation markers such as MAPT, GAP43, DCX, NRCAM, NeuroD2, NeuroG2, and NeuN, and (v) decreasing the expression of several genes associated with glioma aggressiveness. Conclusion: These results indicate the existence of a subgroup of patients harboring RA-responsive glioma cells amenable to differentiation therapy, and stratifying such patients with a functional test is easily achievable. This provides the first step to reassess the potential of RA in the context of personalized medicine.

Predictive value of 18 F-FDG PET/CT scanning in combination with clinical parameters in patients with newly diagnosed multiple myeloma.

To evaluate the association of 18 F-2'-deoxy 2'-fluorodeoxyglucose (18 F-FDG) PET/CT with clinical parameters in predicting patients with newly diagnosed multiple myeloma (MM). A total of 120 MM patients undergoing 18 F-FDG PET/CT scanning were analyzed in a retrospective cohort study. Based on multivariate analysis, β2M, LDH, number of focal lesions (FLs), and SUVmax were significantly correlated with OS. These 4 variables were used to construct a new staging system (NSS) based on the number of risk factors. NSS provided a better discrimination of risk between stages III and II than International staging system (ISS) (P<.001 vs P=.086). For OS, there was no significant difference among risk groups in Durie-Salmon (DS) stage (P>.05). Based on Spearman correlation analysis, the presence of lesions in appendicular skeleton, number of FLs, and SUVmax appeared to indicate advanced stage of MM. ROC curves which showed the combination of β2M with calcium got a specificity of 96.3% for lesions in appendicular skeleton, and LDH alone had 100% specificity in predicting the number of FLs, although the sensitivity was only 50%. 18 F-FDG PET/CT in combination with clinical parameters provided an accurate and simple method for risk stratification of patients with newly diagnosed MM.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BackgroundMolecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients. Materials and MethodsWe reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER+ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER+ and ER− breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival. ResultsGenes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies. ConclusionA biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.

Stratification of responders towards eculizumab using a structural epitope mapping strategy.

The complement component 5 (C5)-binding antibody eculizumab is used to treat patients with paroxysmal nocturnal hemoglobinuria (PNH) and atypical haemolytic uremic syndrome (aHUS). As recently reported there is a need for a precise classification of eculizumab responsive patients to allow for a safe and cost-effective treatment. To allow for such stratification, knowledge of the precise binding site of the drug on its target is crucial. Using a structural epitope mapping strategy based on bacterial surface display, flow cytometric sorting and validation via haemolytic activity testing, we identified six residues essential for binding of eculizumab to C5. This epitope co-localizes with the contact area recently identified by crystallography and includes positions in C5 mutated in non-responders. The identified epitope also includes residue W917, which is unique for human C5 and explains the observed lack of cross-reactivity for eculizumab with other primates. We could demonstrate that Ornithodorus moubata complement inhibitor (OmCI), in contrast to eculizumab, maintained anti-haemolytic function for mutations in any of the six epitope residues, thus representing a possible alternative treatment for patients non-responsive to eculizumab. The method for stratification of patients described here allows for precision medicine and should be applicable to several other diseases and therapeutics.