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

A Retrospective Analysis of VeriStrat Status on Outcome of a Randomized Phase II Trial of First-Line Therapy with Gemcitabine, Erlotinib, or the Combination in Elderly Patients (Age 70 Years or Older) with Stage IIIB/IV Non–Small-Cell Lung Cancer

In a multicenter randomized phase II trial of gemcitabine (arm A), erlotinib (arm B), and gemcitabine and erlotinib (arm C), similar progression-free survival (PFS) and overall survival (OS) were observed in all arms. We performed an exploratory, blinded, retrospective analysis of plasma or serum samples collected as part of the trial to investigate the ability of VeriStrat (VS) to predict treatment outcomes.Ninety-eight patients were assessable, and the majority had stage IV disease (81%), adenocarcinoma histology (63%), reported current or previous tobacco use (84%), and 26% had a performance status (PS) of 2.In arm A, patients with VS Good ( = 20) compared with VS Poor status ( = 8) had similar PFS (hazard ratio [HR]: 1.21; = 0.67) and OS (HR: 0.82; = 0.64). In arm B, patients with VS Good ( = 26) compared with VS Poor ( = 12) had a statistically significantly superior PFS (HR: 0.33; = 0.002) and OS (HR: 0.40; = 0.014). In arm C, patients with VS Good ( = 17) compared with Poor ( = 1 5) had a superior PFS (HR: 0.42; = 0.027) and a trend toward superior OS (HR: 0.48; = 0.051). In the multivariate analysis for PFS, VS status was statistically significant ( = 0.011); for OS, VS status ( = 0.017) and PS ( = 0.005) were statistically significant. A statistically significant VS and treatment interaction (gemcitabine versus erlotinib) was observed for PFS and OS.Gemcitabine is the superior treatment for elderly patients with VS Poor status. First-line erlotinib for elderly patients with VS Good status may warrant further investigation

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

Mass Spectrometry-Based Multivariate Proteomic Tests for Prediction of Outcomes on Immune Checkpoint Blockade Therapy: The Modern Analytical Approach

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15–35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor–host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications

A Serum Protein Classifier Identifying Patients with Advanced Non–Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors

Purpose: Pretreatment selection of patients with non–small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. Experimental Design: In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n ¼ 289). Machine learning combined spectral and clinical data to stratify patients into three groups with good (“sensitive”), intermediate, and poor (“resistant”) outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology was investigated using protein set enrichment analyses (PSEA). Results: A signature consisting of 274 MS features derived from a development set of 116 patients was associated wit

Proteomic test for anti-PD-1 checkpoint blockade treatment of metastatic melanoma with and without BRAF mutations

Abstract The therapeutic landscape in metastatic melanoma has changed dramatically in the last decade, with the success of immune checkpoint inhibitors resulting in durable responses for a large number of patients. For patients with BRAF mutations, combinations of BRAF and MEK inhibitors demonstrated response rates and benefit comparable to those from immune checkpoint inhibitors, providing the rationale for sequential treatment with targeted and immunotherapies and raising the question of optimal treatment sequencing. Biomarkers for the selection of anti-PD-1 therapy in BRAF wild type (BRAF WT) and in BRAF mutated (BRAF MUT) patients help development of alternative treatments for patients unlikely to benefit, and might lead to better understanding of the interaction of checkpoint inhibition and targeted therapy. In this paper we evaluate the performance of a previously developed serum proteomic test, BDX008, in metastatic melanoma patients treated with anti-PD-1 agents and investigate the role of BRAF mutation status. BDX008, a pre-treatment proteomic test associated with acute phase reactants, wound healing and complement activation, stratifies patients into two groups, BDX008+ and BDX008-, with better and worse outcomes on immunotherapy. Serum samples were available from 71 patients treated with anti-PD1 inhibitors; 25 patients had BRAF mutations, 39 were wild type. Overall, BDX008+ patients had significantly better overall survival (OS) (HR = 0.50, P = 0.016) and a trend for better progression-free survival (PFS) (HR = 0.61, P = 0.060) than BDX008- patients. BDX008 classification was statistically significant in the analyses adjusted for mutation status, LDH, and line of treatment (P = 0.009 for OS and 0.031 for PFS). BRAF WT BDX008+ patients had markedly long median OS of 32.5 months and 53% landmark 2 years survival, with statistically significantly superior OS as compared to BDX008- patients (HR = 0.41, P = 0.032). The difference between BDX008+ and BDX008- in PFS in BRAF WT patients and in OS and PFS in BRAF MUT patients did not reach statistical significance, though numerically was consistent with overall results. The test demonstrated significant interaction with neutrophil-to-lymphocyte ratio (NLR) (PFS P = 0.041, OS P = 0.004). BDX008 as a biomarker selecting for benefit from immune checkpoint blockade, especially in patients with wild type BRAF and in subgroups with low NLR, warrants further evaluation

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

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.status: publishe