Biology subjects

Postow, M. A.

Publications and source records attributed to Postow, M. A..

8 recordsLinked to original sources

bioRxiv2026

Dendritic cell PD-L2 restrains intratumoral CD8+ T cell immunity

Blockade of inhibitory PD-1 signaling on T cells is a cornerstone of cancer immunotherapy, with current strategies targeting PD-1 or its ligand PD-L1. However, PD-1 engages an alternative ligand, PD-L2, whose role in tumor immunity remains poorly defined. Here, we show that PD-L2 is upregulated on intratumoral CCR7+ conventional dendritic cells (cDCs) in both mouse and human melanoma. Using genetic mouse models enabling selective ablation of PD-L1 or PD-L2 in cDCs, we identify a division of labor between these ligands: PD-L1 controls the size of the progenitor CD8 T cell pool in tumor-draining lymph nodes by modulating stem-like CD8 T cells, whereas PD-L2 limits progenitor exhausted CD8 T cell differentiation within the tumor microenvironment. Loss of PD-L2 in cDCs enhances cytotoxic CD8 T cell responses and suppresses tumor growth, particularly in tumors enriched for CCR7+ cDC1s. Consistent with this, increased CCR7+ cDC abundance is associated with poor prognosis in human cancers. Spatial transcriptomic analyses reveal co-localization of CCR7+ cDC1s and Tpex within CCL19hi niches, where cancer-associated fibroblasts serve as the predominant source of CCL19. Finally, intratumoral GM-CSF drives PD-L2 expression on CCR7+ cDCs, with Tpex and NK cells as major sources. Together, these findings establish cDC-associated PD-L1 and PD-L2 as spatially and functionally distinct checkpoints governing CD8 T cell differentiation. Our results suggest that the abundance of CCR7+ cDC1s may guide the choice between anti-PD-1 and anti-PD-L1 therapies and support the development of PD-L2-directed blockade.

immunology↗

bioRxiv2025

Pre-treatment naive T cells are associated with severe irAE following PD-1/CTLA4 checkpoint blockade for melanoma

Immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-CTLA-4 antibodies are used to induce an immune response against many types of tumors. However, ICIs often also induce autoimmune responses, referred to as immune-related adverse events (irAEs), which occur unpredictably and at varying levels of severity in ICI-treated patients. The immunologic factors that predispose patients to the development of severe irAE are largely unclear. Here, we utilized high dimensional mass cytometry immunophenotyping of longitudinal blood samples from patients with metastatic melanoma treated with combination anti-PD-1/CTLA4 ICI therapy in the context of a clinical trial to characterize alterations in immune profiles induced by combination ICI therapy and to identify immune features associated with development of severe irAEs. Deep T cell profiling highlighted that ICI therapy induces prominent expansions of activated, CD38hi CD4+ and CD8+ T cells, which are frequently bound by the therapeutic anti-PD-1 antibody, as well as substantial changes in regulatory T cell phenotypes. However, neither the baseline frequency nor the extent of expansion of these cell populations was associated with development of severe irAEs. Rather, single cell-association testing revealed naive CD4+ T cell abundance pre-treatment as significantly associated with the development of severe irAEs. Biaxial gating of naive CD4+ T cells confirmed a significant positive association of naive CD4+ T cell proportion and development of a severe irAE and with the number of irAEs developed in this cohort. Results from this broad profiling study indicate the abundance of naive CD4+ T cells as a predictive feature for the development of severe irAEs following combination anti-PD-1/CTLA4 ICI therapy.

immunology↗

bioRxiv2024

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for largescale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. SpatialTopic integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. We employ an efficient collapsed Gibbs sampling algorithm for model inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. We show that SpatialTopic is highly scalable on large-scale image datasets with millions of cells, along with high precision and interpretability. Our findings demonstrate that SpatialTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

bioinformatics↗

bioRxiv2024

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

bioinformatics↗

bioRxiv2023

Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach

Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the models utility, we analyzed [~]17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naive topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.

bioinformatics↗

bioRxiv2022

Whole genome error-corrected sequencing for sensitive circulating tumor DNA cancer monitoring

Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at [~]120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a [~]1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of [~]10-7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

genomics↗

bioRxiv2022

Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition.

genomics↗

bioRxiv2021

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Progression to metastatic disease remains the main cause of cancer death. Yet, the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we present MSK-MET, an integrated pan-cancer cohort of tumor genomic and clinical outcome data from more than 25,000 patients. We analyzed this dataset to identify associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma and HR-positive breast ductal carcinoma, but not in others, such as colorectal adenocarcinoma, pancreatic adenocarcinoma and high-grade serous ovarian cancer. We also identified specific somatic alterations associated with increased metastatic burden and specific routes of metastatic spread. Our data offer a unique resource for the investigation of the biological basis for metastatic spread and highlight the crucial role of chromosomal instability in cancer progression.

cancer biology↗