“Find Me”and“Eat Me”signals:tools to drive phagocytic processes for modulating antitumor immunity

Phagocytosis,a vital defense mechanism,involves the recognition and elimination of foreign substances by cells.Phagocytes,such as neutrophils and macrophages,rapidly respond to invaders;macrophages are especially important in later stages of the immune response.They detect“find me”signals to locate apoptotic cells and migrate toward them.Apoptotic cells then send“eat me”signals that are recognized by phagocytes via specific receptors.“Find me”and“eat me”signals can be strategically harnessed to modulate antitumor immunity in support of cancer therapy.These signals,such as calreticulin and phosphatidylserine,mediate potent pro-phagocytic effects,thereby promoting the engulfment of dying cells or their remnants by macrophages,neutrophils,and dendritic cells and inducing tumor cell death.This review summarizes the phagocytic“find me”and“eat me”signals,including their concepts,signaling mechanisms,involved ligands,and functions.Furthermore,we delineate the relationships between“find me”and“eat me”signaling molecules and tumors,especially the roles of these molecules in tumor initiation,progression,diagnosis,and patient prognosis.The interplay of these signals with tumor biology is elucidated,and specific approaches to modulate“find me”and“eat me”signals and enhance antitumor immunity are explored.Additionally,novel therapeutic strategies that combine“find me”and“eat me”signals to better bridge innate and adaptive immunity in the treatment of cancer patients are discussed.

First-line penpulimab(an anti-PD1 antibody)and anlotinib(an angiogenesis inhibitor)with nab-paclitaxel/gemcitabine(PAAG)in metastatic pancreatic cancer:a prospective,multicentre,biomolecular exploratory,phase II trial

Metastatic pancreatic cancer(mPC)has a dismal prognosis.Herein,we conducted a prospective,multicentre,single-arm,phase II trial evaluating the efficacy and safety of penpulimab and anlotinib in combination with nab-paclitaxel/gemcitabine(PAAG)in patients with first-line mPC(NCT05493995).The primary endpoints included the objective response rate(ORR)and disease control rate(DCR),while secondary endpoints encompassed progression-free survival(PFS),overall survival(OS),and safety.In 66 patients analysed for efficacy,the best response,indicated by the ORR,was recorded at 50.0%(33/66)(95%CI,37.4–62.6%),with 33 patients achieving partial response(PR).Notably,the DCR was 95.5%(63/66,95%CI,87.3–99.1%).The median PFS(mPFS)and OS(mOS)were 8.8(95%CI,8.1–11.6),and 13.7(95%CI,12.4 to not reached)months,respectively.Grade 3/4 treatment-related adverse events(TRAEs)were reported in 39.4%of patients(26/66).In prespecified exploratory analysis,patients with altered SWI/SNF complex had a poorer PFS.Additionally,low serum CA724 level,high T-cell recruitment,low Th17 cell recruitment,and high NK CD56dim cell scores at baseline were potential predicative biomarkers for more favourable efficacy.In conclusion,PAAG as a first-line therapy demonstrated tolerability with promising clinical efficacy for mPC.The biomolecular findings identified in this study possess the potential to guide the precise clinical application of the triple-combo regimen.

Transformers in medical image analysis

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision.In the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and diagnosis.This paper aimed to promote awareness of the applications of transformers in medical image analysis.Specifically,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic components.Second,we reviewed various transformer architectures tailored for medical image applications and discuss their limitations.Within this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other techniques.We hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.