Intratumoral microbiome of adenoid cystic carcinomas and comparison with other head and neck cancers.

Adenoid cystic carcinoma (ACC) is a rare, usually slow-growing yet aggressive head and neck malignancy. Despite its clinical significance, our understanding of the cellular evolution and microenvironment in ACC remains limited. We investigated the intratumoral microbiomes of 50 ACC tumor tissues and 33 adjacent normal tissues using 16S rRNA gene sequencing. This allowed us to characterize the bacterial communities within the ACC and explore potential associations between the bacterial community structure, patient clinical characteristics, and tumor molecular features obtained through RNA sequencing. The bacterial composition in the ACC was significantly different from that in adjacent normal salivary tissue, and the ACC exhibited diverse levels of species richness. We identified two main microbial subtypes within the ACC: oral-like and gut-like. Oral-like microbiomes, characterized by increased diversity and abundance of Neisseria, Leptotrichia, Actinomyces, Streptococcus, Rothia, and Veillonella (commonly found in healthy oral cavities), were associated with a less aggressive ACC-II molecular subtype and improved patient outcomes. Notably, we identified the same oral genera in oral cancer and head and neck squamous cell carcinomas. In both cancers, they were part of shared oral communities associated with a more diverse microbiome, less aggressive tumor phenotype, and better survival that reveal the genera as potential pancancer biomarkers for favorable microbiomes in ACC and other head and neck cancers. Conversely, gut-like intratumoral microbiomes, which feature low diversity and colonization by gut mucus layer-degrading species, such as Bacteroides, Akkermansia, Blautia, Bifidobacterium, and Enterococcus, were associated with poorer outcomes. Elevated levels of Bacteroides thetaiotaomicron were independently associated with significantly worse survival and positively correlated with tumor cell biosynthesis of glycan-based cell membrane components.

Exploratory analysis of the cervix tumoral HPV antigen-specific T-cell repertoire during chemoradiation and after brachytherapy.

BACKGROUND: Chemoradiation (CRT) may modulate the immune milieu as an in-situ vaccine. Rapid dose delivery of brachytherapy has unclear impact on T-cell repertoires. HPV-associated cancers express viral oncoproteins E6/E7, which enable tracking antigen/tumor-specific immunity during CRT. METHODS: Thirteen cervical cancer patients on a multi-institutional prospective protocol from 1/2020-1/2023 underwent standard-of-care CRT with pulsed-dose-rate brachytherapy boost (2 fractions). Cervix swabs at various timepoints underwent multiplex DNA deep sequencing of the TCR-ß/CDR3 region with immunoSEQ. Separately, HPV-responsive T-cell clones were also expanded ex vivo. Statistical analysis was via Mann-Whitney-U. RESULTS: TCR productive clonality, templates, frequency, or rearrangements increased post-brachytherapy in 8 patients. Seven patients had E6/E7-responsive evolution over CRT with increased productive templates (ranges: 1.2-50.2 fold-increase from baseline), frequency (1.2-1.7), rearrangements (1.2-40.2), and clonality (1.2-15.4). Five patients had HPV-responsive clonal expansion post-brachytherapy, without changes in HPV non-responsive clones. Epitope mapping revealed VDJ rearrangements targeting cervical cancer-associated antigens in 5 patients. The only two patients with disease recurrence lacked response in all metrics. A lack of global TCR remodeling correlated with worse recurrence-free survival, p = 0.04. CONCLUSION: CRT and brachytherapy alters the cervical cancer microenvironment to facilitate the expansion of specific T-cell populations, which may contribute to treatment efficacy.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.