Cancer discovery最新文献

Cancer Core Europe brings together the expertise, resources, and interests of seven leading cancer institutes committed to leveraging collective innovation and collaboration in precision oncology. Through targeted efforts addressing key medical challenges in cancer and partnerships with multiple stakeholders, the consortium seeks to advance cancer research and enhance equitable patient care.

Bone is the most common site of breast cancer metastasis. Bone metastasis is incurable and is associated with severe morbidity. Utilizing an immunocompetent mouse model of spontaneous breast cancer bone metastasis, we profiled the immune transcriptome of bone metastatic lesions and peripheral bone marrow at distinct metastatic stages, revealing dynamic changes during the metastatic process. We show that cross-talk between granulocytes and T cells is central to shaping an immunosuppressive microenvironment. Specifically, we identified the PD-1 and TIGIT signaling axes and the proinflammatory cytokine IL1β as central players in the interactions between granulocytes and T cells. Targeting these pathways in vivo resulted in attenuated bone metastasis and improved survival, by reactivating antitumor immunity. Analysis of patient samples revealed that TIGIT and IL1β are prominent in human bone metastasis. Our findings suggest that cotargeting immunosuppressive granulocytes and dysfunctional T cells may be a promising novel therapeutic strategy to inhibit bone metastasis. Significance: Temporal transcriptome profiling of the immune microenvironment in breast cancer bone metastasis revealed key communication pathways between dysfunctional T cells and myeloid derived suppressor cells. Cotargeting of TIGIT and IL1β inhibited bone metastasis and improved survival. Validation in patient data implicated these targets as a novel promising approach to treat human bone metastasis.

In this issue, Ryan and colleagues describe the preclinical development of a pan-RAF:MEK molecular glue with superior efficacy, brain penetrance, and tolerability in xenograft models of Ras/Raf/MAPK pathway-driven tumors. See related article by Ryan et al., p. 1190 (1).

Transforming gut microbial status from a prognostic trait to a therapeutic target is a key goal to understand and reverse resistance to anticancer immunotherapy. Glitza and colleagues propose selective manipulation of the gut microbiome with SER401 following antibiotic preconditioning and highlight multiple challenges in delivering microbiome manipulation to the clinic. See related article by Glitza et al., p. 1161 (8).

Pancreatic ductal adenocarcinoma (PDAC) therapeutic resistance is largely attributed to a unique tumor microenvironment embedded with an abundance of cancer-associated fibroblasts (CAF). Distinct CAF populations were recently identified, but the phenotypic drivers and specific impact of CAF heterogeneity remain unclear. In this study, we identify a subpopulation of senescent myofibroblastic CAFs (SenCAF) in mouse and human PDAC. These SenCAFs are a phenotypically distinct subset of myofibroblastic CAFs that localize near tumor ducts and accumulate with PDAC progression. To assess the impact of endogenous SenCAFs in PDAC, we used an LSL-KRASG12D;p53flox;p48-CRE;INK-ATTAC (KPPC-IA) mouse model of spontaneous PDAC with inducible senescent cell depletion. Depletion of senescent stromal cells in genetic and pharmacologic PDAC models relieved immune suppression by macrophages, delayed tumor progression, and increased responsiveness to chemotherapy. Collectively, our findings demonstrate that SenCAFs promote PDAC progression and immune cell dysfunction. Significance: CAF heterogeneity in PDAC remains poorly understood. In this study, we identify a novel subpopulation of senescent CAFs that promotes PDAC progression and immunosuppression. Targeting CAF senescence in combination therapies could increase tumor vulnerability to chemo or immunotherapy. See related article by Ye et al., p. 1302.

IL2 signals pleiotropically on diverse cell types, some of which contribute to therapeutic activity against tumors, whereas others drive undesired activity, such as immunosuppression or toxicity. We explored the theory that targeting of IL2 to CD8+ T cells, which are key antitumor effectors, could enhance its therapeutic index. To this aim, we developed AB248, a CD8 cis-targeted IL2 that demonstrates over 500-fold preference for CD8+ T cells over natural killer and regulatory T cells (Tregs), which may contribute to toxicity and immunosuppression, respectively. AB248 recapitulated IL2's effects on CD8+ T cells in vitro and induced selective expansion of CD8+T cells in primates. In mice, an AB248 surrogate demonstrated superior antitumor activity and enhanced tolerability as compared with an untargeted IL2Rβγ agonist. Efficacy was associated with the expansion and phenotypic enhancement of tumor-infiltrating CD8+ T cells, including the emergence of a "better effector" population. These data support the potential utility of AB248 in clinical settings. Significance: The full potential of IL2 therapy remains to be unlocked. We demonstrate that toxicity can be decoupled from antitumor activity in preclinical models by limiting IL2 signaling to CD8+ T cells, supporting the development of CD8+ T cell-selective IL2 for the treatment of cancer. See related article by Kaptein et al. p. 1226.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time.

Significance: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.