ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells

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 β-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 β-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 β-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 β-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.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

Circulating Cells with Macrophage-like Characteristics in Cancer: The Importance of Circulating Neoplastic-Immune Hybrid Cells in Cancer

Simple Summary

In cancer, disseminated neoplastic cells circulating in blood are a source of tumor DNA, RNA, and protein, which can be harnessed to diagnose, monitor, and better understand the biology of the tumor from which they are derived. Historically, circulating tumor cells (CTCs) have dominated this field of study. While CTCs are shed directly into circulation from a primary tumor, they remain relatively rare, particularly in early stages of disease, and thus are difficult to utilize as a reliable cancer biomarker. Neoplastic-immune hybrid cells represent a novel subpopulation of circulating cells that are more reliably attainable as compared to their CTC counterparts. Here, we review two recently identified circulating cell populations in cancer—cancer-associated macrophage-like cells and circulating hybrid cells—and discuss the future impact for the exciting area of disseminated hybrid cells.

Abstract

Cancer remains a significant cause of mortality in developed countries, due in part to difficulties in early detection, understanding disease biology, and assessing treatment response. If effectively harnessed, circulating biomarkers promise to fulfill these needs through non-invasive “liquid” biopsy. While tumors disseminate genetic material and cellular debris into circulation, identifying clinically relevant information from these analytes has proven difficult. In contrast, cell-based circulating biomarkers have multiple advantages, including a source for tumor DNA and protein, and as a cellular reflection of the evolving tumor. While circulating tumor cells (CTCs) have dominated the circulating cell biomarker field, their clinical utility beyond that of prognostication has remained elusive, due to their rarity. Recently, two novel populations of circulating tumor-immune hybrid cells in cancer have been characterized: cancer-associated macrophage-like cells (CAMLs) and circulating hybrid cells (CHCs). CAMLs are macrophage-like cells containing phagocytosed tumor material, while CHCs can result from cell fusion between cancer and immune cells and play a role in the metastatic cascade. Both are detected in higher numbers than CTCs in peripheral blood and demonstrate utility in prognostication and assessing treatment response. Additionally, both cell populations are heterogeneous in their genetic, transcriptomic, and proteomic signatures, and thus have the potential to inform on heterogeneity within tumors. Herein, we review the advances in this exciting field.

An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy

A sensitive H2S sensing strategy has been developed based on the automated identification and enumeration algorithm.

Label-free enrichment of rare unconventional circulating neoplastic cells using a microfluidic dielectrophoretic sorting device

Cellular circulating biomarkers from the primary tumor such as circulating tumor cells (CTCs) and circulating hybrid cells (CHCs) have been described to harbor tumor-like phenotype and genotype. CHCs are present in higher numbers than CTCs supporting their translational potential. Methods for isolation of CHCs do not exist and are restricted to low-throughput, time consuming, and biased methodologies. We report the development of a label-free dielectrophoretic microfluidic platform facilitating enrichment of CHCs in a high-throughput and rapid fashion by depleting healthy peripheral blood mononuclear cells (PBMCs). We demonstrated up to 96.5% depletion of PBMCs resulting in 18.6-fold enrichment of cancer cells. In PBMCs from pancreatic adenocarcinoma patients, the platform enriched neoplastic cells identified by their KRAS mutant status using droplet digital PCR with one hour of processing. Enrichment was achieved in 75% of the clinical samples analyzed, establishing this approach as a promising way to non-invasively analyze tumor cells from patients.

Circulating Hybrid Cells: A Novel Liquid Biomarker of Treatment Response in Gastrointestinal Cancers

BACKGROUND

Real-time monitoring of treatment response with a liquid biomarker has potential to inform treatment decisions for patients with rectal adenocarcinoma (RAC), esophageal adenocarcinoma (EAC), and colorectal liver metastasis (CRLM). Circulating hybrid cells (CHCs), which have both immune and tumor cell phenotypes, are detectable in the peripheral blood of patients with gastrointestinal cancers, but their potential as an indicator of treatment response is unexplored.

METHODS

Peripheral blood specimens were collected from RAC and EAC patients after neoadjuvant therapy (NAT) or longitudinally during therapy and evaluated for CHC levels by immunostaining. Receiver operating characteristics (ROCs) and the Kaplan-Meier method were used to analyze the CHC level as a predictor of pathologic response to NAT and disease-specific survival (DSS), respectively.

RESULTS

Patients with RAC (n = 23) and EAC (n = 34) were sampled on the day of resection, and 11 patients (32%) demonstrated a pathologic complete response (pCR) to NAT. On ROC analysis, CHC levels successfully discriminated pCR from non-pCR with an area under the curve of 0.82 (95% confidence interval [CI], 0.71-0.92; P < 0.001). Additionally, CHC levels in the EAC patients correlated with residual nodal involvement (P = 0.026) and 1-year DSS (P = 0.029). The patients with RAC who were followed longitudinally during NAT (n = 2) and hepatic arterial infusion therapy for CRLM (n = 2) had CHC levels that decreased with therapy response and increased before clinical evidence of disease progression.

CONCLUSION

Circulating hybrid cells are a novel blood-based biomarker with potential for monitoring treatment response and disease progression to help guide decisions for further systemic therapy, definitive resection, and post-therapy surveillance. Additional validation studies of CHCs are warranted.

Relevance of circulating hybrid cells as a non-invasive biomarker for myriad solid tumors

Metastatic progression defines the final stages of tumor evolution and underlies the majority of cancer-related deaths. The heterogeneity in disseminated tumor cell populations capable of seeding and growing in distant organ sites contributes to the development of treatment resistant disease. We recently reported the identification of a novel tumor-derived cell population, circulating hybrid cells (CHCs), harboring attributes from both macrophages and neoplastic cells, including functional characteristics important to metastatic spread. These disseminated hybrids outnumber conventionally defined circulating tumor cells (CTCs) in cancer patients. It is unknown if CHCs represent a generalized cancer mechanism for cell dissemination, or if this population is relevant to the metastatic cascade. Herein, we detect CHCs in the peripheral blood of patients with cancer in myriad disease sites encompassing epithelial and non-epithelial malignancies. Further, we demonstrate that in vivo-derived hybrid cells harbor tumor-initiating capacity in murine cancer models and that CHCs from human breast cancer patients express stem cell antigens, features consistent with the potential to seed and grow at metastatic sites. Finally, we reveal heterogeneity of CHC phenotypes reflect key tumor features, including oncogenic mutations and functional protein expression. Importantly, this novel population of disseminated neoplastic cells opens a new area in cancer biology and renewed opportunity for battling metastatic disease.