Prostate cancer characterization by optical contrast enhanced photoacoustics

Nucleolin‑based targeting strategies in cancer treatment: Focus on cancer immunotherapy (Review)

The benefits of treating several types of cancers using immunotherapy have recently been established. The overexpression of nucleolin (NCL) in a number of types of cancer provides an attractive antigen target for the development of novel anticancer immunotherapeutic treatments. NCL is a multifunctional protein abundantly distributed in the nucleus, cytoplasm and cell membrane. It influences carcinogenesis, and the proliferation, survival and metastasis of cancer cells, leading to cancer progression. Additionally, the meta‑analysis of total and cytoplasmic NCL overexpression indicates a poor prognosis of patients with breast cancer. The AS1411 aptamers currently appear to have therapeutic action in the phase II clinical trial. The authors' research group has recently explored the anticancer function of NCL through the activation of T cells by dendritic cell‑based immunotherapy. The present review describes and discusses the mechanisms through which the multiple functions of NCL can participate in the progression of cancer. In addition, the studies that define the utility of NCL‑dependent anticancer therapies are summarized, with specific focus being paid to cancer immunotherapeutic approaches.

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal

Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue. In order to diagnose prostate cancer via PA spectrum assessment, this work describes the machine learning (ML) technique implementation, such as supervised classification and unsupervised hierarchical clustering. The collected PA signals were preprocessed using Pwelch method, and the features are extracted using two methods such as hierarchical cluster and correlation assessment. The extracted features are classified using four ML-methods, namely, Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA). Furthermore, as these components alter throughout the progression of prostate cancer, this study focuses on the composition and distribution of collagen, lipids, and haemoglobin. In diseased tissues compared to normal tissues, there is a stronger correlation between the various chemical components ultrasonic power spectra, suggesting that the microstructural dispersion in tumour tissues has been more uniform. The accuracy of several classifiers used in cancer tissue diagnosis was greater than 94% for all four methods, which is effective than that of benchmark medical methods. Thus, the method shows significant promise for the noninvasive, early detection of severe prostate cancer.

Overexpression of Nucleolin and Associated Genes in Prostate Cancer

Prostate cancer (PCa) is the second most frequent cancer and the fifth leading cause of cancer death in men worldwide. If local PCa presents a favorable prognosis, available treatments for advanced PCa display limiting benefits due to therapeutic resistances. Nucleolin (NCL) is a ubiquitous protein involved in numerous cell processes, such as ribosome biogenesis, cell cycles, or angiogenesis. NCL is overexpressed in several tumor types in which it has been proposed as a diagnostic and prognostic biomarker. In PCa, NCL has mainly been studied as a target for new therapeutic agents. Nevertheless, little data are available concerning its expression in patient tissues. Here, we investigated the expression of NCL using a new cohort from Mondor Hospital and data from published cohorts. Results were then compared with NCL expression using in vitro models. NCL was overexpressed in PCa tissues compared to the normal tissues, but no prognostic values were demonstrated. Nine genes were highly co-expressed with NCL in patient tissues and tumor prostate cell lines. Our data demonstrate that NCL is an interesting diagnostic biomarker and propose a signature of genes co-expressed with NCL.

Prostate cancer identification via photoacoustic spectroscopy and machine learning

Photoacoustic spectroscopy can generate abundant chemical and physical information about biological tissues. However, this abundance of information makes it difficult to compare these tissues directly. Data mining methods can circumvent this problem. We describe the application of machine-learning methods (including unsupervised hierarchical clustering and supervised classification) to the diagnosis of prostate cancer by photoacoustic spectrum analysis. We focus on the content and distribution of hemoglobin, collagen, and lipids, because these molecules change during the development of prostate cancer. A higher correlation among the ultrasonic power spectra of these chemical components is observed in cancerous than in normal tissues, indicating that the microstructural distributions in cancerous tissues are more consistent. Different classifiers applied in cancer-tissue diagnoses achieved an accuracy of 82 % (better than that of standard clinical methods). The technique thus exhibits great potential for painless early diagnosis of aggressive prostate cancer.

Photoacoustic image-guided interventions

Photoacoustic imaging has demonstrated its potential for diagnosis over the last few decades. In recent years, its unique imaging capabilities, such as detecting structural, functional and molecular information in deep regions with optical contrast and ultrasound resolution, have opened up many opportunities for photoacoustic imaging to be used during image-guided interventions. Numerous studies have investigated the capability of photoacoustic imaging to guide various interventions such as drug delivery, therapies, surgeries, and biopsies. These studies have demonstrated that photoacoustic imaging can guide these interventions effectively and non-invasively in real-time. In this minireview, we will elucidate the potential of photoacoustic imaging in guiding active and passive drug deliveries, photothermal therapy, and other surgeries and therapies using endogenous and exogenous contrast agents including organic, inorganic, and hybrid nanoparticles, as well as needle-based biopsy procedures. The advantages of photoacoustic imaging in guided interventions will be discussed. It will, therefore, show that photoacoustic imaging has great potential in real-time interventions due to its advantages over current imaging modalities like computed tomography, magnetic resonance imaging, and ultrasound imaging.

Toward Intra-Operative Prostate Photoacoustic Imaging: Configuration Evaluation and Implementation Using the da Vinci Research Kit

We compare different possible scanning geometries for prostate photoacoustic tomography (PAT) while considering a realistic reconstruction scenario in which the limited view of the prostate and the directivity effect of the transducer are considered. Simulations and experiments confirm that an intra-operative configuration in which the photoacoustic signal is received by a pickup transducer from the anterior surface of the prostate provides the best approach. We propose a PAT acquisition system that includes a da Vinci system controlled by the da Vinci Research Kit, an illumination laser, and an ultrasound machine with parallel data acquisition. The robot maneuvers the pickup transducer to form a cylindrical detection surface around the prostate. The robot is programmed to acquire trajectories in which the transducer face is parallel to and oriented toward a rotational tomography axis, while the laser is fired and PAT data are collected at regular intervals. We present our initial images acquired with this novel system.