Chen, W. S. | bioRxiv Science

bioRxiv2020

Autoantibody landscape of advanced prostate cancer

Although the importance of T-cell immune responses is well appreciated in cancer, autoantibody responses are less well-characterized. Nevertheless, autoantibody responses are of great interest, as they may be concordant with T-cell responses to cancer antigens or predictive of response to cancer immunotherapies. We performed serum epitope repertoire analysis (SERA) on a total of 1,229 serum samples obtained from a cohort of 72 men with metastatic castration-resistant prostate cancer (mCRPC) and 1,157 healthy control patients to characterize the autoantibody landscape of mCRPC. Using whole-genome sequencing results from paired solid-tumor metastasis biopsies and germline specimens, we identified tumor-specific epitopes in 29 mutant and 11 non-mutant proteins. Autoantibody enrichments for the top candidate autoantigen (NY-ESO-1) were validated using ELISA performed on the prostate cancer cohort and an independent cohort of 106 patients with melanoma. Our study recovers antigens of known importance and identifies novel tumor-specific epitopes of translational interest in advanced prostate cancer. Statement of significanceAutoantibodies have been shown to inform treatment response and candidate drug targets in various cancers. We present the first large-scale profiling of autoantibodies in advanced prostate cancer, utilizing a new next-generation sequencing-based approach to antibody profiling to reveal novel cancer-specific antigens and epitopes. Disclosure of Potential Conflicts of InterestJJA reports receiving consulting income from Janssen Biotech and Merck and honoraria from Astellas for speakers fees. MR reports receiving commercial research support from Novartis, Johnson & Johnson, Merck, Astellas, and Medivation, and is a consultant/advisory board member for Constellation Pharmaceuticals, Amgen, Ambrx, Johnson & Johnson, and Bayer. A.R. has received honoraria from consulting with Amgen, Bristol-Myers Squibb, Chugai, Dynavax, Genentech, Merck, Nektar, Novartis, Roche and Sanofi, is or has been a member of the scientific advisory board and holds stock in Advaxis, Arcus Biosciences, Bioncotech Therapeutics, Compugen, CytomX, Five Prime, RAPT, ImaginAb, Isoplexis, Kite-Gilead, Lutris Pharma, Merus, PACT Pharma, Rgenix and Tango Therapeutics. FYF serves on the advisory board for Dendreon, EMD Serono, Janssen Oncology, Ferring, Sanofi, Blue Earth Diagnostics, Celgene, consults for Bayer, Medivation/Astellas, Genetech, and Nutcracker Therapeutics, has honoraria from Clovis Oncology, and is a founder and has an ownership stake in PFS Genomics. SGZ and FYF have patent applications with Decipher Biosciences. SGZ and FYF have a patent application licensed to PFS Genomics. SGZ and FYF have patent applications with Celgene. WAH, RW, KK, PSD, and JCS have ownership of stocks or shares at Serimmune, paid employment at Serimmune, board membership at Serimmune, and patent applications on behalf of Serimmune.

cancer biology↗

bioRxiv2018

Embedding the single-cell sample manifold to reveal insights into cancer pathogenesis and disease heterogeneity

Previously, the effect of a drug on a cell population was measured based on simple metrics such as cell viability. However, as single-cell technologies are becoming more advanced, drug screen experiments can now be conducted with more complex readouts such as gene expression profiles of individual cells. The increasing complexity of measurements from these multi-sample experiments calls for more sophisticated analytical approaches than are currently available. We developed a novel method called PhEMD (Phenotypic Earth Movers Distance) and show that it can be used to embed the space of drug perturbations on the basis of the drugs effects on cell populations. When testing PhEMD on a newly-generated, 300-sample CyTOF kinase inhibition screen experiment, we find that the state space of the perturbation conditions is surprisingly low-dimensional and that the network of drugs demonstrates manifold structure. We show that because of the fairly simple manifold geometry of the 300 samples, we can accurately capture the full range of drug effects using a dictionary of only 30 experimental conditions. We also show that new drugs can be added to our PhEMD embedding using similarities inferred from other characterizations of drugs using a technique called Nystrom extension. Our findings suggest that large-scale drug screens can be conducted by measuring only a small fraction of the drugs using the most expensive high-throughput single-cell technologies--the effects of other drugs may be inferred by mapping and extending the perturbation space. We additionally show that PhEMD can be useful for analyzing other types of single-cell samples, such as patient tumor biopsies, by mapping the patient state space in a similar way as the drug state space. We demonstrate that PhEMD is scalable, compatible with leading batch effect correction techniques, and generalizable to multiple experimental designs. Altogether, our analyses suggest that PhEMD may facilitate drug discovery efforts and help uncover the network geometry of a collection of single-cell samples.

bioinformatics↗