Polarization Observables in Double Pion Photo-Production with Circularly Polarized Photons off Transversely Polarized Protons

The study of excited states of the nucleon facilitates the understanding of the nucleon structure and its underlying symmetry and couplings. A main goal of the NL program at the Thomas Jefferson National Accelerator Facility is to investigate the excitation and decays of the baryon resonances and assist in identifying the “missing” nucleon resonances that are predicted by theoretical models. One way to study the nucleon resonances is by extracting polarization observables, which provide more information than the unpolarized cross-section studies, e.g. access to the transition amplitudes of the reaction. Double-pion photoproduction contributes strongly to the total cross section at high energies and thus it plays an important role in probing the nucleon resonance spectrum. The CLAS g9b (FROST) experiment at Thomas Jefferson National Accelerator Facility provided double-pion photoproduction data using transversely polarized protons and circularly polarized photons, with energies up to 3.0 GeV. Beam- and target-polarization asymmetries were measured and the polarization observables I,Px+,Py+,Px,Py were extracted for the yp=p pi+ pi- reaction. The results are reported and compared with the calculations of an effective Lagrangian model. The data will help deepen the current knowledge of hadronic resonance decays and possibly assist in identifying new baryon resonances via PWA (Partial Wave Analysis) and in this way will contribute to a more comprehensive understanding of the strong interaction

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

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

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.</jats:p

Additional file 1 of A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data

Table S1. Classifier Development Parameters: Prognosis of Prostate Cancer Patients. Table S2. Classifier Development Parameters: Prognosis of Prostate Cancer Patients with 10,000 additional randomly generated features. Table S3. Classifier Development Parameters: Prognosis of Lung Cancer Patients After Surgery (DOCX 20 kb

831 Exact Shapley values for explaining complex machine learning based molecular tests of checkpoint inhibitors: potential utility for patients, physicians, and translational research

BackgroundModern machine learning (ML) models based on highly multivariate attribute sets (e.g. unbiased -omics data) can be very successful at generating clinically useful predictions, but at the price of less transparency in how individual attributes are used to make those predictions. In short, ML test algorithms tend to be ”black boxes”. Shapley values (SVs)1 describe the relative importance of the attributes used within a multivariate test to the generation of the test result for an individual patient.2 While typically the calculation of SVs is computationally prohibitive, our ML architecture permits the generation of SVs for large patient cohorts. In this study, we evaluate SVs for the Anti-PD-L1 Response Test (ART), that was shown in independent validation to predict outcomes for patients treated with atezolizumab,3 for the POPLAR Ph2 and OAK Ph3 studies of non-small cell lung cancer (NSCLC) patients .4 5Abstract 831 Figure 1Radar plots illustrating the values of the 10 most important SVs for test classification generation for two samples classified as poor (A and B) and two samples classified as good (C and D). Positive SVs are shown in red and negative SVs are shown in blueAbstract 831 Figure 2Heatmaps of the SVs for samples classified as poor showing two subgroups (top and bottom, separated by horizontal line) with different patterns of SVs for (A) POPLAR and (B) OAKAbstract 831 Figure 3Heatmaps of the SVs for samples classified as good showing three subgroups, top, middle, and bottom, separated by horizontal lines) with different patterns of SVs for (A) POPLAR and (B) OAKMethodsART results, Good or Poor had been produced for 262 patients in POPLAR (NCT01903993) and 786 patients in OAK (NCT02008227). Exact SVs were generated for each pretreatment serum sample for each of the 93 attributes (proteomic features) used in the test. The distribution of SVs across the cohort was investigated to assess the relative importance of each feature to test classification. Subgroups of patients with similar patterns of SVs were identified using t-sne plots and ML methods in the POPLAR cohort and validated in the OAK cohort.ResultsThe SV distributions showed that the features influencing ART classification most were similar in both POPLAR and OAK. The relative importance of features to test classification differed between patients (figure 1), but subgroups of patients within test classification groups showed similar patterns of SVs (figures 2 and 3). Such patient subgroups, identified within POPLAR, were also found in the OAK cohort and were associated with differences in outcome and/or differences in patient characteristics.ConclusionsSVs can explain how complex ML-based tests combine molecular attributes to produce individual patient results. Exact SVs can be obtained for certain ML architectures used in molecular test development, revealing the overall relative importance of attributes used in such molecular tests. Subgrouping of patients with the same test classification by different patterns of SVs is possible. This may reveal different biologies contributing to a Good or Poor phenotype and inform translational studies.Trial RegistrationClinicalTrialsgov NCT01903993 and NCT02008227ReferencesShapley L. A value for n-person games. Contributions to the Theory of Games. 1953;2.28:307–317.Roder J, Maguire L, Georgantas R, Roder H. Explaining multivariate molecular diagnostic tests via Shapley values. BMC Med Inform Decis Mak 2021;21(1):211.Kowanetz M, Leng N, Roder J, et al. Evaluation of immune-related markers in the circulating proteomic and their association with atezolizumab efficacy in patients with 2L+ NSCLC. J Immunother Cancer 2018;6(Suppl1):114.Fehrenbacher L, Spira A, Ballinger M, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 2016;387(10030):1837–1846.Rittmeyer A, Barlesi F, Waterkamp D, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicenter randomized controlled trial. Lancet 2017;389(10066):255–265.Ethics ApprovalThe OAK study that was done in 194 academic medical centers and community oncology practices across 31 countries worldwide. The study was done in full accordance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. All patients gave written informed consent.The POPLAR trial was done at 61 academic medical centers and community oncology practices across 13 countries in Europe and North America. The study was done in full accordance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. Protocol (and modification) approval was obtained from anindependent ethics committee for each site. Patients gave written informed consent.</jats:sec