It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.

The presence of B lymphocytes in tumor tertiary lymphoid structures (TLSs) is an important prognostic indicator for different types of cancers. However, whether B cell responses in the tumor microenvironment (TME) can be harnessed for immunotherapy is unclear. Here we report that a protective germline variant of human immunoglobulin heavy constant gamma 1 gene (IGHG1) containing a Gly396 to Arg396 substitution (hIgG1-G396R) confers improved survival of colorectal cancer (CRC) patients. These hIgG1-G396R homozygous CRC patients displayed elevated tumor-associated antigen (TAA)-specific IgG1 antibody production and plasma cell infiltration into tumors. In murine colon carcinoma models, mice expressing the murine functional homolog IgG2c-Gly400Arg variant (mIgG2c-G400R) also produce higher levels of tumor-specific IgG2c antibodies via enhanced plasma cell differentiation, together with alleviated tumorigenesis and progression. Mechanistically, this variant potentiates TAA-specific antibody-dependent cellular phagocytosis and antigen presentation. Comprehensive immune profiling of the TME of CRC patients revealed that hIgG1-G396R prominently promotes broad mobilization of immune cells (IgG1+ plasma cells, CD8+ T cells, CD103+ DCs) and efficient TLS formation, both key components of an anti-tumor microenvironment. Notably, adoptive transfer of tumor-primed B cells with this variant exhibited therapeutic efficacy in murine tumor models, demonstrating clinical potential. These results prompt a prospective investigation of hIgG1-G396R in CRC patients as a biomarker for clinical prognosis and demonstrate that manipulating the functionality of IgG1+ B cells in tumors could improve immunotherapy outcomes.

It is critical to identify biomarkers for neurodegenerative diseases (NDDs) to advance disease diagnosis and accelerate drug discovery for effective treatment of patients. In this work, we retrieved genotyping and clinical data from 1223 UK Biobank participants to identify genetic and clinical biomarkers for NDDs, including Alzheimer’s disease (AD), Parkinson’s disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modelling approach and Monte Carlo randomisation, we identified 16 informative clinical variables for predicting AD, PD, MND, and MG. In a multinomial model, these clinical variables could correctly predict the diagnosis of one of the four diseases with an accuracy of 88.3%. In addition to clinical biomarkers, we also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci across NDDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumour, leukaemia, and pancreatic cancer. Our analysis supports current knowledge regarding the ageing-related degeneration/cancer shift.Significance statementThis study highlights the potential for hypothesis-free mathematical modelling of easily measured clinical variables to identify diagnostic biomarkers for neurodegenerative diseases (NDDs). Prior to this study, the focus in NDD research has surrounded toxic species such as amyloid beta and α-synuclein, but this approach has not enjoyed success at clinical trial. Here, we studied Alzheimer’s disease, Parkinson’s disease, motor neuron disease, and myasthenia gravis by constructing and inspecting a multinomial based on demographics and blood and urine biochemistry. Cognitive measures were important for the predictive power of the model. Model weights correctly indicated multiple trends reported in the literature. Separately, genome-wide association indicated a shared risk profile between NDD and cancer, which has also been reported in the literature.