Biology subjects

Gu, S. S.

Publications and source records attributed to Gu, S. S..

3 recordsLinked to original sources

bioRxiv2026

Circadian misalignment underlies immune escape in breast cancer

Circadian regulation shapes tissue physiology, yet how it organizes cellular and molecular dynamics within the tumor microenvironment (TME) and influences tumor immunity remains unclear. Using temporal single-nucleus multiomic profiling of mouse breast tumors, we uncovered extensive circadian programs that are both cell-type-specific and shared across the TME, governing proliferation and immune responses. Notably, cancer epithelial cells exhibited global acrophase misalignment relative to immune populations. This intercellular desynchrony manifests as temporal decoupling between tumor proliferation and immune activation, discordant antigen presentation and T cell recognition with intrinsic activation-exhaustion overlap in T cells, and asynchronous PD-1/PD-L1 oscillations that sustain checkpoint-mediated suppression. Similar patterns were observed in human triple-negative breast cancer (TNBC). Together, these findings establish intercellular circadian misalignment as a mechanism of tumor immune evasion and position the circadian architecture of the tumor-immune ecosystem as a previously underappreciated determinant of tumor development and therapeutic response. HIGHLIGHTSO_LISingle-cell multiomics maps circadian regulation of tumor-immune programs. C_LIO_LICircadian regulation in cancer epithelial cells is misaligned with immune cell populations. C_LIO_LITumor-immune temporal misalignment undermines antitumor immunity. C_LIO_LICircadian misalignment in human TNBC suggests relevance for immunotherapy timing. C_LI

cancer biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2020

In Vivo CRISPR Screens Identify E3 Ligase Cop1 as a Modulator of Macrophage Infiltration and Cancer Immunotherapy Target

Despite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, and shows synergy in anti-tumor immunity with ICB. Transcriptomics, epigenomics, and proteomics analyses revealed Cop1 functions through proteasomal degradation of the C/ebp{delta} protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebp{delta}, which leads to polyubiquitination of C/ebp{delta}. Cop1 inhibition stabilizes C/ebp{delta} to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage levels in the TNBC tumor microenvironment. HighlightsO_LILarge-scale in vivo CRISPR screens identify new immune targets regulating the tumor microenvironment C_LIO_LICop1 knockout in cancer cells enhances anti-tumor immunity C_LIO_LICop1 modulates chemokine secretion and macrophage infiltration into tumors C_LIO_LICop1 targets C/ebp{delta} degradation via Trib2 and influences ICB response C_LI

cancer biology↗