EPCO-32. IDENTIFICATION OF PROGNOSTIC CHORDOMA SUBGROUPS USING DNA METHYLATION SIGNATURES IN TISSUE AND PLASMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi9-vi9
Author(s):  
Jeffrey Zuccato ◽  
Vikas Patil ◽  
Sheila Mansouri ◽  
Jeffrey Liu ◽  
Farshad Nassiri ◽  
...  

Abstract BACKGROUND Chordomas are malignant bone cancers arising from the skull-base and spine that are rare but cause devastating central nervous system morbidities. Survival is highly variable despite surgery and radiotherapy as 10% live under 1 year and 30-35% survive over 20 years. There are currently no reliable prognostic factors and this limits our ability to tailor patient treatment to their risk. Accordingly, this work identifies epigenetic prognostic chordoma subgroups that are detectable non-invasively through plasma methylomes to guide treatment. METHODS A total of 68 chordoma surgical specimens resected between 1996-2018 across three international centres underwent DNA methylation profiling. Cell-free methylated tumor DNA immunoprecipitation and high-throughput sequencing was performed on available matched plasma samples. RESULTS Two stable tumor clusters were identified through consensus clustering of tissue methylation data. Clusters had statistically significantly different disease-specific survivals (log-rank p=0.0062) independent of clinical factors in a multivariable Cox analysis (HR=16.5, 95%CI: 2.8-96, p=0.0018). The poorer-performing “Immune-infiltrated” cluster had genes hypomethylated at promoters, typically resulting in transcription, within immune-related pathways and higher immune cell abundance within tumors. The better-performing “Cellular” cluster showed higher tumor cellularity plus cell-to-cell interaction and extracellular matrix pathway hypomethylation. Fifty chordoma-versus-other binomial generalized linear models built using plasma methylome data distinguished chordomas from meningiomas and spinal metastases, as representative clinical differential diagnoses, in random left-out 20% testing sets (mean AUROC=0.84, 95%CI: 0.52-1.00). Plasma-based methylation signatures were highly correlated with tissue-based signals within both poor-performing (median r=0.69, 95%CI: 0.66-0.72) and better-performing cluster tumors (median r=0.67, 95%CI: 0.62-0.72). CONCLUSIONS The first identification of two distinct prognostic epigenetic chordoma subgroups is shown here with “Immune-infiltrated” tumors having a poorer prognosis than “Cellular” tumors. Plasma methylomes can be utilized for non-invasive chordoma diagnosis and subtyping. This work may transform chordoma treatment decision-making by guiding surgical planning in advance to match resection aggressiveness with patient prognosis.

2021 ◽  
Author(s):  
Jeffrey A Zuccato ◽  
Vikas Patil ◽  
Sheila Mansouri ◽  
Jeffrey C Liu ◽  
Farshad Nassiri ◽  
...  

Abstract Background Chordomas are rare malignant bone cancers of the skull-base and spine. Patient survival is variable and not reliably predicted using clinical factors or molecular features. This study identifies prognostic epigenetic chordoma subtypes that are detected non-invasively using plasma methylomes. Methods Methylation profiles of 68 chordoma surgical samples were obtained between 1996-2018 across three international centres along with matched plasma methylomes where available. Results Consensus clustering identified two stable tissue clusters with a disease-specific survival difference that was independent of clinical factors in a multivariate Cox analysis (HR=14.2, 95%CI: 2.1–94.8, p=0.0063). Immune-related pathways with genes hypomethylated at promoters and increased immune cell abundance were observed in the poor-performing “Immune-infiltrated” subtype. Cell-to-cell interaction plus extracellular matrix pathway hypomethylation and higher tumor purity was observed in the better-performing “Cellular” subtype. The findings were validated in additional DNA methylation and RNA sequencing datasets as well as with immunohistochemical staining. Plasma methylomes distinguished chordomas from other clinical differential diagnoses by applying fifty chordoma-versus-other binomial generalized linear models in random 20% testing sets (mean AUROC=0.84, 95%CI: 0.52-1.00). Tissue-based and plasma-based methylation signals were highly correlated in both prognostic clusters. Additionally, leave-one-out models accurately classified all tumors into their correct cluster based on plasma methylation data. Conclusions Here, we show the first identification of prognostic epigenetic chordoma subtypes and first use of plasma methylome-based biomarkers to non-invasively diagnose and subtype chordomas. These results may transform patient management by allowing treatment aggressiveness to be balanced with patient risk according to prognosis.


2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6075-6075
Author(s):  
P. J. Atherton ◽  
T. Smith ◽  
J. Huntington ◽  
M. Huschka ◽  
J. A. Sloan

6075 Background: Failing to meet patient expectations for participating in treatment decisions impacts satisfaction with care, but it is unknown whether this translates into QOL deficits. As part of a larger survey of cancer survivors conducted by the American Cancer Society (ACS), data were gathered on the role patients in Minnesota preferred and the role actually experienced during the treatment decision-making process. Methods: Patients who were diagnosed with one of the ten most common cancers in 2000 completed a survey containing the Profile of Mood States (POMS), the SF-36, and the Control Preferences Scale (CPS). Fisher’s exact tests compared role preference distributions across demographic categories. Two-sample t-tests compared the POMS and SF-36 scores between patients whose preferred role preference was concordant with the role experienced and those with discordant preferred and actual roles. Results: 33% of the 599 consenting patients preferred an active role in treatment decision-making, 52% preferred a collaborative role, and 13% preferred a passive role. The actual roles experienced were 33% active, 50% collaborative, 17% passive. Over 88% of patients had concordant preferred and actual roles. Patients with concordant roles had higher SF-36 physical scores (45 vs 40, p=0.004), higher vitality (50 vs 42, p=0.005), less fatigue (70.2 vs 60.1, p=0.001), better concentration (84 vs 79, p=0.008) and better overall mood (77 vs 73, p=0.006). Role preference differed across gender (p=0.0002) in that more women preferred a collaborative role than men (57.8% vs 45.5%) and fewer women preferred a passive role (9% vs 17.3%). Patients under age 50 experienced more active roles in treatment decisions than those aged 50+ (p=0.04). Patients reporting an active actual role had higher SF-36 physical scores (p=0.005) and higher POMS vigor subscale scores (p=0.04). There were no differences in QOL scores for preferred roles. Conclusions: Patients who experienced discordance between their preferred role and their experience reported substantial QOL deficits in both physical and emotional domains. Oncologists can improve patient satisfaction with care and QOL by meeting patient expectations with respect to the amount of input they have in making treatment decisions. No significant financial relationships to disclose.


Author(s):  
Brown Hannah

In this Publication Perspective, lead author Hannah Brown, Senior Research Executive at Ipsos Healthcare, London, UK, provides a summary of the recently published article 'Physician and patient treatment decision-making in relapsing-remitting multiple sclerosis in Europe and the USA', from Neurodegenerative Disease Management, that assessed factors used in treatment decision-making for relapsing-remitting multiple sclerosis in both the 5EU (UK/Germany/France/Italy/Spain) and the US.


Cancer ◽  
2013 ◽  
Vol 119 (12) ◽  
pp. 2342-2349 ◽  
Author(s):  
Pamela J. Atherton ◽  
Tenbroeck Smith ◽  
Jasvinder A. Singh ◽  
Jef Huntington ◽  
Brent B. Diekmann ◽  
...  

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Caro Fuchs ◽  
Marco S. Nobile ◽  
Guillaume Zamora ◽  
Aurélie Degeneffe ◽  
Pieter Kubben ◽  
...  

Abstract Background Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments. Methods We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient’s intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules. Results Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78–81% compared to linear models and by 71–74% compared to a model based on decision trees. Conclusion This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.


Author(s):  
Enchong Zhang ◽  
Fujisawa Shiori ◽  
Oscar YongNan Mu ◽  
Jieqian He ◽  
Yuntian Ge ◽  
...  

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.


2020 ◽  
Vol 103 (1) ◽  
pp. e1
Author(s):  
Lauren B. Barton ◽  
Kaetlyn R. Arant ◽  
Justin A. Blucher ◽  
Danielle L. Sarno ◽  
Kristin J. Redmond ◽  
...  

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