Biology subjects

Tibshirani, R.

Publications and source records attributed to Tibshirani, R..

11 recordsLinked to original sources

bioRxiv2026

Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs

Cell cycle (CC) dynamics are reflected in diverse T cell processes such as TCR activation, expansion, contraction, differentiation, senescence, anergy, and exhaustion; linking CC behaviors to functional and dysfunctional T cell states in development and disease. Progression through CC checkpoints is also tightly linked to cell fate decisions across development. Yet, much remains unknown about the connection between CC sensing and T cell differentiation programs. To disentangle the relationship across T cell state, time-since-activation, receptor signaling, division, and CC, we leverage high-throughput single-cell mass cytometry for parallel measurement of these diverse biological states. By modulating CC progression and receptor signaling with inhibitors as well as tonic signaling Chimeric Antigen Receptor (CAR) models of T cell exhaustion, we reveal that earlier G1/S CC programs crosstalk with receptor signaling to control T cell fate, and that exhaustion programs are downstream to aberrant, S-G2 phase CC arrest signatures in tonic CAR signaling in vitro, in situ, and in vivo across human cancers in association with CD8 T-lymphocyte dysfunction.

immunology↗

bioRxiv2025

Deep single-cell immune and signaling profiles predict long term therapy response in chronic myeloid leukemia within hours

Chronic myeloid leukemia (CML) is effectively treated with small molecule BCR::ABL1 tyrosine kinase inhibitors (TKIs) but like in all cancers, early identification of suboptimal- and non-responders is a challenge. Through mass cytometry analysis of peripheral blood leukocytes, we collected high-dimensional data from de novo chronic phase CML patients enrolled in two multicenter clinical trials (clinicaltrials.gov NCT01725204 and NCT01061177). In leukocytes, dasatinib and nilotinib inhibited intracellular signaling within one or three hours after first per oral dose, and each TKI had a unique signaling signature reflecting its kinase specificity profile beyond BCR::ABL1. An immune and signaling profile was constructed for each patient, predicting the treatment response (BCR::ABL1IS, %) the first 12 months of treatment. These results show that single cell immune and signaling profiles within hours of first dose can predict 12 months treatment response, anticipating future optimalization of kinase inhibitor treatment within days rather than months.

cancer biology↗

bioRxiv2025

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

cancer biology↗

bioRxiv2022

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

bioRxiv2022

Mechanical competition promotes selective listening to receptor inputs to resolve directional dilemmas in neutrophil migration

As neutrophils navigate complex environments to reach sites of infection, they may encounter obstacles that force them to split their front into multiple leading edges, raising the question of how the cell selects which front to maintain and which front(s) to abandon. Here we challenge chemotaxing HL60 neutrophil-like cells with symmetric bifurcating microfluidic channels, enabling us to probe the cell-intrinsic properties of their decision-making process. Using supervised statistical learning, we demonstrate that cells commit to one leading edge late in the decision- making process, rather than amplifying early pre-existing asymmetries. Furthermore, we use optogenetic tools to show that receptor inputs only bias the decision similarly late, once mechanical stretching begins to weaken each front. Finally, optogenetic attempts to reverse cell decisions reveal that, once an edge begins retracting, it commits to this fate, with the kinase ROCK limiting its sensitivity to inputs until the retraction is complete. Collectively our results suggest a "selective listening" model in which both actively protruding cell fronts and actively retracting cell rears have strong commitment to their current migratory program.

cell biology↗

bioRxiv2021

The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS Development of a new classifier for DCIS recurrence or progression Outcome associated pathways identified across multiple data types and compartments Four stroma-specific signatures identified CNAs characterize DCIS subgroups associated with high risk invasive cancers

cancer biology↗

bioRxiv2020

Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank

In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimensional features, and have access to a large number of outcomes (phenotypes): lifestyle measures, biomarkers, and disease outcomes. We are hence led to fit sparse reduced-rank regression models, using computational strategies that allow us to scale to problems of this size. We use an iterative algorithm that alternates between solving the sparse regression problem and solving the reduced rank decomposition. For the sparse regression component, we propose a scalable iterative algorithm based on adaptive screening that leverages the sparsity assumption and enables us to focus on solving much smaller sub-problems. The full solution is reconstructed and tested via an optimality condition to make sure it is a valid solution for the original problem. We further extend the method to cope with practical issues such as the inclusion of confounding variables and imputation of missing values among the phenotypes. Experiments on both synthetic data and the UK Biobank data demonstrate the effectiveness of the method and the algorithm. We present multiSnpnet package, available at http://github.com/junyangq/multiSnpnet that works on top of PLINK2 files, which we anticipate to be a valuable tool for generating polygenic risk scores from human genetic studies.

bioinformatics↗

bioRxiv2019

A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems

The UK Biobank (Bycroft et al., 2018) is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with GWAS, have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso (Tibshirani, 1996), since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet (Friedman et al., 2010a) and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports{ell} 1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with{ell} 1/{ell}2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve superior predictive performance on quantitative and qualitative traits including height, body mass index, asthma and high cholesterol. Author SummaryWith the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesnt limit to the identification of genotypes that are significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe improved prediction performance on height, body mass index (BMI), asthma and high cholesterol by the lasso over other univariate and multiple regression methods. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers.

bioinformatics↗

bioRxiv2019

Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGF{beta}-treatment and identify through TGF{beta}-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.

systems biology↗

bioRxiv2018

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features. Authors SummaryCancer is a genetic disease, occurring as a result of mutagenic processes causing DNA somatic mutations in genes controlling cellular growth and division. These somatic mutations arise from processes such as defective DNA repair and environmental mutagens, which massively increase the rate of somatic variants. As a result, due to the specificity of molecular lesions caused by such processes, and the specific repair mechanisms deployed by the cell to mitigate the damage, mutagenic processes generate characteristic point mutation rate spectra which are called mutational signatures. These signatures can indicate which mutagenic processes are active in a tumor, reveal biological differences between cancer subtypes, and may be useful markers for therapeutic response. Here, we develop SparseSignatures, a novel framework for mutational signature discovery capable of both identifying the active signatures in a dataset of point mutations and calculating their exposure values, i.e., the number of mutations originating from each signature in each patient. We show that our approach outperforms current state-of-the-art methods on simulated data using a variety of standard metrics and then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms.

bioinformatics↗

bioRxiv2017

A Noninvasive Molecular Clock for Fetal Development Predicts Gestational Age and Preterm Delivery

We performed a high time-resolution, longitudinal study of normal pregnancy development by measuring cell-free RNA (cfRNA) in blood from women during each week of pregnancy. Analysis of tissue-specific transcripts in these samples enabled us to follow fetal and placental development with high resolution and sensitivity, and also to detect gene-specific responses of the maternal immune system to pregnancy. We established a \"clock\" for normal pregnancy development and enabled a direct molecular approach to determine expected delivery dates with comparable accuracy to ultrasound, creating the basis for a portable, inexpensive fetal dating method. We also identified a related gene set that accurately discriminated women at risk for spontaneous preterm delivery up to two months in advance of labor, forming the basis of a potential screening test for risk of preterm delivery.

bioengineering↗