Nanotechnology and machine learning enable circulating tumor cells as a reliable biomarker for radiotherapy responses of gastrointestinal cancer patients

Gastrointestinal cancer resistance to treatment: the role of microbiota

The most common illnesses that adversely influence human health globally are gastrointestinal malignancies. The prevalence of gastrointestinal cancers (GICs) is relatively high, and the majority of patients receive ineffective care since they are discovered at an advanced stage of the disease. A major component of the human body is thought to be the microbiota of the gastrointestinal tract and the genes that make up the microbiome. The gut microbiota includes more than 3000 diverse species and billions of microbes. Each of them has benefits and drawbacks and been demonstrated to alter anticancer medication efficacy. Treatment of GIC with the help of the gut bacteria is effective while changes in the gut microbiome which is linked to resistance immunotherapy or chemotherapy. Despite significant studies and findings in this field, more research on the interactions between microbiota and response to treatment in GIC are needed to help researchers provide more effective therapeutic strategies with fewer treatment complication. In this review, we examine the effect of the human microbiota on anti-cancer management, including chemotherapy, immunotherapy, and radiotherapy.

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.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Circulating tumor cell abundance in head and neck squamous cell carcinoma decreases with successful chemoradiation and cetuximab treatment

Head and neck squamous cell carcinoma (HNSCC) is a common and deadly cancer. Circulating tumor cell (CTC) abundance may a valuable, prognostic biomarker in low- and intermediate-risk patients. However, few technologies have demonstrated success in detecting CTCs in these populations. We prospectively collected longitudinal CTC counts from two cohorts of patients receiving treatments at our institution using a highly sensitive device that purifies CTCs using biomimetic cell rolling and dendrimer-conjugated antibodies. In patients with intermediate risk human papillomavirus (HPV)-positive HNSCC, elevated CTC counts were detected in 13 of 14 subjects at screening with a median of 17 CTC/ml (range 0.2-2986.5). A second cohort of non-metastatic, HPV- HNSCC subjects received cetuximab monotherapy followed by surgical resection. In this cohort, all subjects had elevated baseline CTC counts median of 73 CTC/ml (range 5.4-332.9) with statistically significant declines during treatment. Interestingly, two patients with recurrent disease had elevated CTC counts during and following treatment, which also correlated with growth of size and ki67 expression in the primary tumor. The results suggest that our device may be a valuable tool for evaluating the success of less intensive treatment regimens.