Back

Lack of TGFβ signaling competency predicts immune poor cancer conversion to immune rich and response to checkpoint blockade

Moore, J.; Gkantalis, J.; Guix, I.; Chou, W.; Yuen, K.; Lazar, A.; Spitzer, M. H.; combes, a.; Barcellos-Hoff, M. H.

2024-03-08 cancer biology
10.1101/2024.03.06.583752 bioRxiv
Show abstract

BackgroundThe efficacy of immune checkpoint blockade (ICB) depends on restoring immune recognition of cancer cells that have evaded immune surveillance. At the time of diagnosis, patients with lymphocyte-infiltrated cancers are the most responsive to ICB, yet a considerable fraction of patients have immune-poor tumors. MethodsWe analyzed transcriptomic data from IMvigor210, TCGA, and TISMO datasets to evaluate the predictive value of {beta}Alt, a score representing the negative correlation of signatures consisting of transforming growth factor beta (TGF{beta}) targets and genes involved in error-prone DNA repair. The immune context of {beta}Alt was assessed by evaluating tumor-educated immune signatures. An ICB-resistant, high {beta}Alt preclinical tumor model was treated with a TGF{beta} inhibitor, radiation, and/or ICB and assessed for immune composition and tumor control. ResultsHere, we show that high {beta}Alt is associated with an immune-poor context yet is predictive of ICB response in both humans and mice. A high {beta}Alt cancer in which TGF{beta} signaling is compromised generates a TGF{beta} rich, immunosuppressive tumor microenvironment. Accordingly, preclinical modeling showed that TGF{beta} inhibition followed by radiotherapy could convert an immune-poor, ICB-resistant tumor to an immune-rich, ICB-responsive tumor. Mechanistically, TGF{beta} blockade in irradiated tumors activated natural killer cells that were required to recruit lymphocytes to respond to ICB. In support of this, natural killer cell activation signatures were also increased in immune-poor mouse and human tumors that responded to ICB. ConclusionsThese studies suggest that loss of TGF{beta} competency identifies a subset of cold tumors that are candidates for ICB. Our mechanistic studies show that inhibiting TGF{beta} activity converts high {beta}Alt, cold tumors into ICB-responsive tumors via NK cells. Thus, a biomarker consisting of combined TGF{beta}, DNA repair, and immune context signatures provides a means to prospectively identify patients whose cancers may be converted from cold to hot, which could be exploited for therapeutic treatment. O_LIWhat is already known on this topic - For some cancer patients, response to ICB provides durable tumor control. Current biomarkers are insufficient to reliably predict the immunotherapy response for most patients, particularly those whose tumors lack lymphocytic infiltration. C_LIO_LIWhat this study adds - The {beta}Alt score, which reports a DNA damage deficiency caused by lack of TGF{beta} signaling, predicts response to ICB in clinical trial data from IMvigor210 metastatic bladder cancer patients and for melanoma patients. Notably, transcriptomic assessment of the immune context shows that these are immune-poor, so-called "cold" tumors. Preclinical modeling indicates that alleviating TGF{beta} inhibition of NK cells is critical to relieving immunosuppression. C_LIO_LIHow this study might affect research, practice or policy - Our work identifies a novel tumor phenotype consisting of cancer cells that have lost TGF{beta} signaling and gained error-prone DNA repair embedded in a TGF{beta} rich, immune-poor microenvironment, which is conserved across cancer types in humans and among preclinical tumor models. Patients whose immune-poor tumors have high {beta}Alt scores are strong candidates for ICB and radiotherapy combinations that may be further augmented by TGF{beta} inhibition. Hence, the {beta}Alt score can be used to stratify immune-poor cancer patients for optimal therapeutic strategies. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.