Ultra high content analyses of circulating and tumor associated hybrid cells reveal phenotypic heterogeneity.

Persistently high, worldwide mortality from cancer highlights the unresolved challenges of disease surveillance and detection that impact survival. Development of a non-invasive, blood-based biomarker would transform survival from cancer. We demonstrate the functionality of ultra-high content analyses of a newly identified population of tumor cells that are hybrids between neoplastic and immune cells in patient matched tumor and peripheral blood specimens. Using oligonucleotide conjugated antibodies (Ab-oligo) permitting cyclic immunofluorescence (cyCIF), we present analyses of phenotypes among tumor and peripheral blood hybrid cells. Interestingly, the majority of circulating hybrid cell (CHC) subpopulations were not identified in tumor-associated hybrids. These results highlight the efficacy of ultra-high content phenotypic analyses using Ab-oligo based cyCIF applied to both tumor and peripheral blood specimens. The combination of a multiplex phenotypic profiling platform that is gentle enough to analyze blood to detect and evaluate disseminated tumor cells represents a novel approach to exploring novel tumor biology and potential utility for developing the population as a blood-based biomarker in cancer.

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a ß-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the ß-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population identified in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application on PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analyses of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a ß-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC dataset including 9 patients and 2 disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and then provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the dataset and had a tendency to underestimate CHC counts for regions of interest (ROI) containing relatively large amounts of cells (>50,000) when using conventional enumeration methods. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the ß-VAE encodings achieved an F1 score of 0.80, matching the average performance of annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Circulating Neoplastic-Immune Hybrid Cells Predict Metastatic Progression in Uveal Melanoma.

Background: Uveal melanoma is an aggressive cancer with high metastatic risk. Recently, we identified a circulating cancer cell population that co-expresses neoplastic and leukocyte antigens, termed circulating hybrid cells (CHCs). In other cancers, CHCs are more numerous and better predict oncologic outcomes compared to circulating tumor cells (CTCs). We sought to investigate the potential of CHCs as a prognostic biomarker in uveal melanoma. Methods: We isolated peripheral blood monocular cells from uveal melanoma patients at the time of primary treatment and used antibodies against leukocyte and melanoma markers to identify and enumerate CHCs and CTCs by immunocytochemistry. Results: Using a multi-marker approach to capture the heterogeneous disseminated tumor cell population, detection of CHCs was highly sensitive in uveal melanoma patients regardless of disease stage. CHCs were detected in 100% of stage I-III uveal melanoma patients (entire cohort, n = 68), whereas CTCs were detected in 58.8% of patients. CHCs were detected at levels statically higher than CTCs across all stages (p = 0.05). Moreover, CHC levels, but not CTCs, predicted 3 year progression-free survival (p < 0.03) and overall survival (p < 0.04). Conclusion: CHCs are a novel and promising prognostic biomarker in uveal melanoma.

Rb analog Whi5 regulates G1 to S transition and cell size but not replicative lifespan in budding yeast.

An increase in cell size with age is a characteristic feature of replicative aging in budding yeast. Deletion of the gene encoding Whi5 results in shortened duration of G1 and reduced cell size, and has been previously suggested to increase replicative lifespan. Upon careful analysis of multiple independently derived haploid and homozygous diploid whi5Δ mutants, we find no effect on lifespan, but we do confirm the reduction in cell size. We suggest that instead of antagonizing lifespan, the elongated G1 phase of the cell cycle during aging may actually play an important role in allowing aged cells time to repair accumulating DNA damage.