Cancer discovery最新文献

Multiple factors in the design of a chimeric antigen receptor (CAR) influence CAR T-cell activity, with costimulatory signals being a key component. Yet, the impact of costimulatory domains on the downstream signaling and subsequent functionality of CAR-engineered natural killer (NK) cells remains largely unexplored. Here, we evaluated the impact of various costimulatory domains on CAR-NK cell activity, using a CD70-targeting CAR. We found that CD28, a costimulatory molecule not inherently present in mature NK cells, significantly enhanced the antitumor efficacy and long-term cytotoxicity of CAR-NK cells both in vitro and in multiple xenograft models of hematologic and solid tumors. Mechanistically, we showed that CD28 linked to CD3Z creates a platform that recruits critical kinases, such as LCK and ZAP70, initiating a signaling cascade that enhances CAR-NK cell function. Our study provides insights into how CD28 costimulation enhances CAR-NK cell function and supports its incorporation in NK-based CARs for cancer immunotherapy.

Over-consumption of iron-rich red meat and hereditary or genetic iron overload are associated with increased risk of colorectal carcinogenesis, yet the mechanistic basis of how metal-mediated signaling leads to oncogenesis remains enigmatic. Using fresh colorectal cancer (CRC) samples we identify Pirin, an iron sensor, that overcomes a rate-limiting step in oncogenesis, by re-activating the dormant human-reverse-transcriptase (hTERT) subunit of telomerase holoenzyme in an iron-(Fe3+)-dependent-manner and thereby drives CRCs. Chemical genetic screens combined with isothermal-dose response fingerprinting and mass-spectrometry identified a small molecule SP2509, that specifically inhibits Pirin-mediated hTERT reactivation in CRCs by competing with iron-(Fe3+) binding. Our findings, first to document how metal ions reactivate telomerase, provide a molecular mechanism for the well-known association between red meat, and increased incidence of CRCs. Small molecules like SP2509 represent a novel modality to target telomerase that acts as driver of 90% human cancers and is yet to be targeted in clinic.

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.

Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.

Colorectal cancer is a highly heterogeneous disease, with well-characterized subtypes based on genome, DNA methylome, and transcriptome signatures. To chart the epigenetic landscape of colorectal cancers, we generated a high-quality single-cell chromatin accessibility atlas of epithelial cells for 29 patients. Abnormal chromatin states acquired in adenomas were largely retained in colorectal cancers, which were tightly accompanied by opposite changes of DNA methylation. Unsupervised analysis on malignant cells revealed two epigenetic subtypes, exactly matching the iCMS classification, and key iCMS-specific transcription factors (TFs) were identified, including HNF4A and PPARA for iCMS2 tumors and FOXA3 and MAFK for iCMS3 tumors. Notably, subtype-specific TFs bind to distinct target gene sets and contribute to both interpatient similarities and diversities for both chromatin accessibilities and RNA expressions. Moreover, we identified CpG-island methylator phenotypes and pinpointed chromatin state signatures and TF regulators for the CIMP-high subtype. Our work systematically revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers.

Significance: Our work revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. Moreover, interpatient minor similarities and major diversities of chromatin accessibility signatures of TF target genes can faithfully explain the corresponding interpatient minor similarities and major diversities of RNA expression signatures of colorectal cancers, respectively. This article is featured in Selected Articles from This Issue, p. 897.

Summary: Advances in cancer biology and diagnostics have led to the recognition of a multitude of rare cancer subtypes, emphasizing the pressing need for strategies to accelerate drug development for patients with these cancers. This paper addresses the unique challenges of dose finding in trials that accrue small numbers of patients with rare cancers; strategies for dose optimization are proposed, in line with evolving approaches to dose determination in the age of the US Food and Drug Administration's Project Optimus.

Summary: Drug-tolerant residual disease (DTRD) after the initial maximal response to a systemic therapy can serve as a tumor reservoir for the development of acquired drug resistance and represents a major clinical challenge across various cancers and types of therapies. To unlock the next frontier in precision oncology, we propose a fundamental paradigm shift in the treatment of metastatic cancers with a sharpened focus towards defining, monitoring, and therapeutically targeting the DTRD state.

Recent clinical trials have highlighted the limited efficacy of T cell-based immunotherapy in patients with glioblastoma (GBM). To better understand the characteristics of tumor-infiltrating lymphocytes (TIL) in GBM, we performed cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing with paired V(D)J sequencing, respectively, on TILs from two cohorts of patients totaling 15 patients with high-grade glioma, including GBM or astrocytoma, IDH-mutant, grade 4 (G4A). Analysis of the CD8+ TIL landscape reveals an enrichment of clonally expanded GZMK+ effector T cells in the tumor compared with matched blood, which was validated at the protein level. Furthermore, integration with other cancer types highlights the lack of a canonically exhausted CD8+ T-cell population in GBM TIL. These data suggest that GZMK+ effector T cells represent an important T-cell subset within the GBM microenvironment and may harbor potential therapeutic implications.

Significance: To understand the limited efficacy of immune-checkpoint blockade in GBM, we applied a multiomics approach to understand the TIL landscape. By highlighting the enrichment of GZMK+ effector T cells and the lack of exhausted T cells, we provide a new potential mechanism of resistance to immunotherapy in GBM. This article is featured in Selected Articles from This Issue, p. 897.

Summary: In this issue, a study by Kazansky and colleagues explored resistance mechanisms after EZH2 inhibition in malignant rhabdoid tumors (MRT) and epithelioid sarcomas (ES). The study identified genetic alterations in EZH2 itself, along with alterations that converge on RB1-E2F-mediated cell-cycle control, and demonstrated that inhibition of cell-cycle kinases, such as Aurora Kinase B (AURKB) could bypass EZH2 inhibitor resistance to enhance treatment efficacy. See related article by Kazansky et al., p. 965 (6).

Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.

Significance: Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors. This article is featured in Selected Articles from This Issue, p. 897.