Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary: The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient's systemic status, not only the tumor's molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning. Abstract: Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Exploring the role of age as a moderator of cognitive remediation for people with schizophrenia

Background: While Cognitive Remediation (CR) is effective in reducing cognitive and functioning difficulties in people with schizophrenia, there is variability in treatment response. Previous research suggested that participants' age may be a significant moderator of CR response. Aim: To examine the impact of participants' age on CR outcomes. Method: Individual participant data were accessed from fourteen CR randomised controlled trials. We tested the moderating effect of participants' age on cognitive and functioning outcomes using multivariate linear models. Results: Data from 1084 people with a diagnosis of schizophrenia were considered. Participants had a mean age of 36.6 years (SD 11), with 11.6 years of education (SD 2.8), and an average duration of illness of 13.5 years (SD 10.7). Multivariate models showed that participants' age, when considered as a continuous variable, was not a significant moderator of treatment effect for cognitive and functioning outcomes. However, when participants were split by median age, younger participants showed higher gains in executive functions following CR compared to older participants (p=0.02). Conclusion: These results suggest that participants' age does not moderate most CR outcomes. However, larger age differences may influence the effect of CR on executive function. This may suggest some adaptation of CR practice according to participants' age. These findings inform the CR personalisation agenda.</p

Explaining multivariate molecular diagnostic tests via Shapley values

Abstract Background Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm. Methods For a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (LIME) and Shapley Additive Explanations (SHAP) based methods) and compare the results with exact Shapley values. Results Exact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contributed equally to classification for some samples, other samples showed more complex patterns of attribute importance for classification generation. Exact Shapley values and Shapley-based interaction metrics were able to provide interpretable classification explanations at the sample or patient level, while patient subgroups could be defined by comparing Shapley value profiles between patients. LIME and SHAP approximation approaches, even those seeking to include correlations between attributes, produced results that were quantitatively and, in some cases qualitatively, different from the exact Shapley values. Conclusions Shapley values can be used to determine the relative importance of input attributes to the result generated by a multivariate molecular diagnostic test for an individual sample or patient. Patient subgroups defined by Shapley value profiles may motivate translational research. However, correlations inherent in molecular data and the typically small ML training sets available for molecular diagnostic test development may cause some approximation methods to produce approximate Shapley values that differ both qualitatively and quantitatively from exact Shapley values. Hence, caution is advised when using approximate methods to evaluate Shapley explanations of the results of molecular diagnostic tests. </jats:sec

Abstract 599: A panel of mass spectrometry based serum protein tests for predicting graft-versus-host disease (GvHD) and its severity

Abstract Allogeneic hematopoietic stem cell transplantation (AHSCT) has been shown to provide longterm disease-free survival for otherwise fatal malignant or nonmalignant hematological disorders. Acute and chronic graft-versus-host disease (aGvHD and cGvHD) are a major complication following AHSCT and are associated with a substantial morbidity and mortality. Identification of patients (pts) likely to develop severe forms of GvHD could allow the selection of more aggressive therapeutic regimens for patients assessed to be at high risk. Serum samples and clinical data were available from 124 pts (age 18-70) who had received AHSCT. Five pts suffered no GvHD, 15 de novo cGvHD, 21 aGvHD but no cGvHD and 83 both aGvHD and cGvHD (overlap). Of pts with aGvHD 51% had grade I disease and of pts with cGvHD 53% had limited disease. Matrix assisted laser desorption/ionization (MALDI) mass spectra were acquired from the samples using the deep MALDI method, allowing a deep probing of the proteome. Spectra were preprocessed and spectral features defined. The integrated intensities of these features were combined with the clinical data using deep learning based machine learning to create classifiers able to stratify patients into groups depending on occurrence and severity of GvHD. Classifiers could be developed with significant power to predict occurrence and severity of GvHD. The area under the curves (AUCs) obtained for the clinical questions investigated and examples of the sensitivity and specificity achievable are summarized in the table. AUCSensitivity/SensitivityaGvHD ?0.7463%/80% or 80%/65%Grade II or severe aGvHD?0.6560%/67%cGvHD post aGvHD?0.7563%/75%severe vs mild cGvHD?0.6867%/69% It is possible to provide information on occurrence and severity of GvHD from mass spectral analysis of post-transplant serum samples. If validated, this panel of tests could provide additional information useful for clinicians choosing treatment regimens for pts following AHSCT. Citation Format: Heinrich Roder, Andreas-Claudius Hoffmann, Joanna Roder, Michael Koldehoff. A panel of mass spectrometry based serum protein tests for predicting graft-versus-host disease (GvHD) and its severity. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 599. doi:10.1158/1538-7445.AM2015-599</jats:p