Spatial dynamics of tertiary lymphoid aggregates in head and neck cancer: insights into immunotherapy response.

BACKGROUND: Recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) generally has a poor prognosis for patients with limited treatment options. While incorporating immune checkpoint inhibitors (ICIs) has now become the standard of care, the efficacy is variable, with only a subset of patients responding. The complexity of the tumor microenvironment (TME) and the role of tertiary lymphoid structures (TLS) have emerged as critical determinants for immunotherapeutic response. METHODS: In this study, we analyzed two independently collected R/M HNSCC patient tissue cohorts to better understand the role of TLS in response to ICIs. Utilizing a multi-omics approach, we first performed targeted proteomic profiling using the Nanostring GeoMx Digital Spatial Profiler to quantify immune-related protein expression with spatial resolution. This was further characterized by spatially resolved whole transcriptome profiling of TLSs and germinal centers (GCs). Deeper single-cell resolved proteomic profiling of the TLSs was performed using the Akoya Biosciences Phenocycler Fusion platform. RESULTS: Our proteomic analysis revealed the presence of T lymphocyte markers, including CD3, CD45, and CD8, expressing cells and upregulation of immune checkpoint marker PD-L1 within tumor compartments of patients responsive to ICIs, indicative of 'hot tumor' phenotypes. We also observed the presence of antigen-presenting cells marked by expression of CD40, CD68, CD11c, and CD163 with upregulation of antigen-presentation marker HLA-DR, in patients responding to ICIs. Transcriptome analysis of TLS and GCs uncovered a marked elevation in the expression of genes related to immune modulation, diverse immune cell recruitment, and a potent interferon response within the TLS structure. Notably, the distribution of TLS-tumor distance was found to be significantly different across response groups (H = 9.28, p = 0.026). The proximity of TLSs to tumor cells was found to be a critical indicator of ICI response, implying that patients with TLSs located further from tumor cells have worse outcomes. CONCLUSION: The study underscores the multifaceted role of TLSs in modulating the immunogenic landscape of the TME in R/M HNSCC, likely influencing the efficacy of ICIs. Spatially resolved multi-omics approaches offer valuable insights into potential biomarkers for ICI response and highlight the importance of profiling the TME complexity when developing therapeutic strategies and patient stratification.

Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI.

RESEARCH QUESTION: Can artificial intelligence (AI) improve the efficiency and efficacy of sperm searches in azoospermic samples? DESIGN: This two-phase proof-of-concept study began with a training phase using eight azoospermic patients (>10,000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify spermatozoa. Second, side-by-side testing was undertaken on two cohorts of non-obstructive azoospermia patient samples: an embryologist versus the AI identifying all the spermatozoa in the still images (cohort 1, n = 4), and a side-by-side test with a simulated clinical deployment of the AI model with an intracytoplasmic sperm injection microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, n = 4). RESULTS: In cohort 1, the AI model showed an improvement in the time taken to identify all the spermatozoa per field of view (0.02 ± 0.30  ×  10-5s versus 36.10 ± 1.18s, P < 0.0001) and improved recall (91.95 ± 0.81% versus 86.52 ± 1.34%, P < 0.001) compared with an embryologist. From a total of 2660 spermatozoa to find in all the samples combined, 1937 were found by an embryologist and 1997 were found by the AI in less than 1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet (98.90 ± 3.19 s versus 168.7 ± 7.84 s, P < 0.0001) and found 1396 spermatozoa, while 1274 were found without AI, although no significant difference was observed. CONCLUSIONS: AI-powered image analysis has the potential for seamless integration into laboratory workflows, to reduce the time to identify and isolate spermatozoa from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.

In situ characterization of the tumor microenvironment.

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

Characterisation of circulating tumor-associated and immune cells in patients with advanced-stage non-small cell lung cancer.

Objectives: Globally, non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and the leading cause of cancer-related deaths. Tumor-associated circulating cells in NSCLC can have a wide variety of morphological and phenotypic characteristics, including epithelial, immunological or hybrid subtypes. The distinctive characteristics and potential clinical significance of these cells in patients with NSCLC are explored in this study. Methods: We utilised a spiral microfluidic device to enrich large cells and cell aggregates from the peripheral blood samples of NSCLC patients. These cells were characterised through high-resolution immunofluorescent imaging and statistical analysis, correlating findings with clinical information from our patient cohort. Results: We have identified varied populations of heterotypic circulating tumor cell clusters with differing immune cell composition that included a distinct class of atypical tumor-associated macrophages that exhibits unique morphology and cell size. This subtype's prevalence is positively correlated with the tumor stage, progression and metastasis. Conclusions: Our study reveals a heterogeneous landscape of circulating tumor cells and their clusters, underscoring the complexity of NSCLC pathobiology. The identification of a unique subtype of atypical tumor-associatedmacrophages that simultaneously express both tumor and immune markers and whose presence correlates with late disease stages, poor clinical outcomes and metastatic risk infers  the potential of these cells as biomarkers for NSCLC staging and prognosis. Future studies should focus on the role of these cells in the tumor microenvironment and their potential as therapeutic targets. Additionally, longitudinal studies tracking these cell types through disease progression could provide further insights into their roles in NSCLC evolution and response to treatment.

Whole transcriptome profiling of placental pathobiology in SARS-CoV-2 pregnancies identifies placental dysfunction signatures.

Objectives: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a 'preeclampsia-like syndrome'. However, the mechanisms underpinning SARS-CoV-2-induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS-CoV-2 infection. Methods: We utilised whole-transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS-CoV-2 in the third trimester of their pregnancy (n = 7) and those collected prior to the start of the coronavirus disease 2019 (COVID-19) pandemic (n = 9). Results: Through comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified SARS-CoV-2 to promote signatures associated with hypoxia and placental dysfunction. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH) and preeclampsia (FLT1, EGFR, KISS1, PAPPA2) were enriched with SARS-CoV-2. Pathways related to increased nutrient uptake, vascular tension, hypertension and inflammation were also enriched in SARS-CoV-2 samples compared to uninfected controls. Conclusions: Our findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of SARS-CoV-2 in pregnancy, particularly its role in placental dysfunction. Furthermore, this study highlights the significance of digital spatial profiling in mapping the intricate crosstalk between trophoblasts and villous core stromal cells, thus shedding light on pathways associated with placental dysfunction in pregnancies with SARS-CoV-2 infection.