|Year : 2022 | Volume
| Issue : 4 | Page : 147-151
Impact of newer technologies in cancer research and its management
Department of Radiotherapy and Oncology, PGIMER, Chandigarh, India
|Date of Submission||25-Dec-2022|
|Date of Decision||30-Dec-2022|
|Date of Acceptance||30-Dec-2022|
|Date of Web Publication||07-Jan-2023|
Department of Radiotherapy and Oncology, PGIMER, Chandigarh
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Kapoor R. Impact of newer technologies in cancer research and its management. Int J Non-Commun Dis 2022;7:147-51
| Introduction|| |
Cancer is emerging to become one of the top causes of death in both the developing and developed world, with figures increasing every day. Early diagnosis of this dreaded disease and early initiation of specific treatment are the main approaches to fight and control cancer. The early diagnosis of cancer primarily depends on various radiological imaging tools and laboratory tests, including biopsies. Conventional radiological tools such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) CT have been augmented recently by artificial intelligence (AI) applications such as deep machine learning (ML) and neural networks. Radiomics is another new application that extracts quantitative information from imaging data and converts it into mineable data for analysis. Pathology-based tools for early diagnosis are based on the identification and characterization of tumor-specific cells and cell materials which not only confirm the diagnosis of cancer but also aid in guiding the specific treatments. In this chapter, we shall discuss the recent advancements in the field of cancer research and will focus on radiological imaging-based and pathology laboratory-based newer technologies in cancer research.
| Pathology-based Newer Technologies in Cancer Research|| |
One of the reasons for the exponential increase in cancer cases is believed to be the inherent ever-changing capricious nature of cancer cells. The genetic makeup of cancer cells and their microenvironment also play a vital role in treatment failures and overall mortality. This drove drug developers and researchers into the arena of personalized medicine and therapies wherein the drugs were matched with the tumor cell biology and their corresponding microenvironment to yield better clinical outcomes.
The need to develop more effective, targeted, and definitive treatments for cancer and to be able to predict therapeutic responses is the rationale for the development of newer research methods for studying cancer cells. In this era of personalized medicine, a lot of research is being done in devolving newer technologies for the early detection of cancer and its targeted treatment. Newer pathology-based techniques in early detection include the detection of circulating biological cancer materials such as the circulating tumor cells (CTCs), their exosomes, and circulating tumor DNA (ctDNA). The newer techniques in treating cancers include cell therapy, microbiome treatment, gene editing therapy, and personalized cancer vaccines, which are beyond the scope of this chapter.
Circulating tumor cells, circulating tumor DNA, and exosomes
The traditional and most commonly used method for confirming the diagnosis of cancer is a solid core biopsy obtained from the primary site of suspicious neoplastic etiology. This core biopsy sample is not always easy to obtain as it requires invasive techniques using the image-guided placement of biopsy guns and needles in order to avoid damage to vital structures which might be present in the projected route of approach to the primary tumor site. Furthermore, many times the tumor is located deep within vital structures, which makes the core biopsy not feasible. Another issue related to core biopsies is the sampling error. This is because of the fact that the biopsy cores obtained from a particular site of the tumor may not be representative of the full tumor as the tumor tissue is known to be very heterogeneous. This heterogeneity of the tumor can lead to erroneous reporting of the degree of differentiation of the tumor as the sample obtained from one site of the tumor may be in a different stage of differentiation than that taken from the other site.
Inadequate tissue procured during these core biopsies is another area of concern that leads to repeat core biopsies. Whenever a core biopsy is repeated, the patient has to be brought to the treating facility so that the sample can be taken and the patient has to be kept under observation for any potential complication of the biopsy. This is tantamount to an increased financial burden on the patient and increased use of already scarce health-care resources. In view of all these issues, researchers wanted to come up with a tool that could diagnose cancers with accuracy and yet be easily available, repeatable, and comfortable for the patient. This led to the invention of liquid biopsies wherein freely circulating tumor-derived materials are analyzed from the blood. These materials include ctDNA, CTCs, and circulating tumor-derived exosomes. This technique can easily be repeated and is as good as taking other blood samples, to which the patients are already familiar. Since these cells represent the original tumor, their biological characteristics can predict the tumor prognosis and its progression in the due course of time.
Circulating tumor cells and circulating tumor DNA
The primary, as well as the metastatic tumor sites, has a vascular supply and drainage and a few of the tumor cells are shed into the circulating bloodstream by these tumors. Because of their direct descent from the parent tumor, these cells carry a lot of vital information about the overall behavior of the parent tumor and its prognosis. Out of the many trials that favor treatment therapies to be directed based on CTCs, the trial by Scher et al. clearly demonstrated survival benefits by choosing therapy based on CTCs in castration-resistant prostate carcinoma. The study on pancreatic ductal adenocarcinoma showed that the CTCs are of great help in predicting the extent of disease and tumor recurrence. They also concluded that CTCs can act as a guide in future monitoring of these patients and in guiding systemic therapies as well.,,
Various laboratory-based methods are employed to isolate the CTCs as these cells have a very low concentration, and it is very difficult to isolate these cells. Microfluidic application with polydimethylsiloxane is a commonly used method. These common methods used for isolation can be divided into two broad categories: positive methods and negative methods. The positive method uses the affinity of a specific antigen expressed on the cell surface to its corresponding antibody so that these specific CTCs are trapped on the device surface and the rest of the unwanted cells are not attracted or trapped. One of the drawbacks of positive selection methods is the inability to capture all the subtypes of the tumor cells, so a specific subtype of the CTCs can be isolated while the other subtypes are ignored and not captured. The negative selection methods are based on the removal of the cells of hematopoietic origin so that only the desired CTCs are isolated., Negative selection methods also can exclude certain tumor subtypes and give erroneous results.
Exosomes are cell-derived nanovesicles that are secreted in high concentrations in body fluids such as blood, urine, ascites, and saliva. These exosomes play a major role in regulating the tumor cell biology, its behavior, and its microenvironment, which in turn influences the response to therapy. Glypican-1 (GPC1)-positive exosomes are known to be present in pancreatic and breast cancer patients. Furthermore, GPC1 has a prognostic role in pancreatic carcinoma. Transforming growth factor-beta and epithelial growth factor receptor VIII is known to be associated with exosomes of patients with glioblastomas. Among the various methods used to isolate exosomes, differential ultracentrifugation, which uses a high centrifugal force to isolate the nanoparticles, is considered the best.
A better understanding of the tumor microenvironment (TME) has led scientists to devolve newer ways to attack cancer cells. Being nonstatic and ever changing in nature, the TME is difficult to understand and even tougher to predict. This makes the job of scientists difficult, as they need to create an immune response within the host organism that changes according to the nature of change in the TME. In this regard, scientists have started looking beyond the conventional T-cell-based cancer immunotherapies and are venturing into the myriad of making a part of the immune system perform the task of an effective response against cancer cells.
The tumor cells are known to have complex interactions with the nontumor cells and the extracellular matrix using a network of cytokines, growth factors, and remodeling enzymes.
Hence, these nonmalignant cells and the TME itself influence the tumor cell proliferation and its response to therapy.
Conventionally, two-dimensional (2D) models consisting of cell monolayers were used to create the TME to replicate the actual in vivo status. However, these models had their inherent disadvantages as they were not fully successful in recreating the actual tumor environment as was expected out of them. Hence, the drugs given to these tumor cells which were cultured on conventional 2D models reach the cells without encountering barriers to actual in vivo critical oxygen and nutrient gradients. This is the reason these tumor cells cultured in 2D models show a higher sensitivity to anticancer drugs.,
Furthermore, because of the 2D nature of these substrates, the cancer cells appear flattened and stretched out. These morphological changes also influence cell differentiation and multiplication, thus affecting the overall tumorigenesis. This led to an erroneous response to chemotherapeutic agents when the tumor cells in this 2D microenvironment were exposed to the chemotherapy drugs. Hence, the need for an ideal tumor model was felt that closely resembled the conditions and environment found in vivo. This model should be able to support the growth of multiple types of cells and also provide an appropriately simulated environment. Thus, various types of 3D models were created which closely imitated tumor morphology, the microenvironment, and its response to various interactions with stroma and with therapeutic agents. Newer advances in nanotechnology and biomaterials over the past have been instrumental in devolving newer 3D models which closely simulate in vivo conditions. Handholding and better coordination between chemists, cancer biologists, and physicians is a sine qua non in taking this arena of research further to completely imitate the in vivo conditions and bring the lab bench to the patient bedside.
| Radiology- and Imaging-based Newer Technologies in Cancer Research|| |
Before starting the journey of newer advances in cancer research in the field of radiology and imaging, it would be prudent to define a few terms for the readers. AI is the new buzzword in cancer research. AI is a very vast field and its purpose is to create machines that can think and perform mental tasks like humans. ML is a subset of AI, which tries to find various similar patterns in the given data that, in turn, help to arrive at a conclusion.
Deep learning is a further subset of ML where computer-based programs are used to process information that involves multiple layers of data processing based on an artificial neural network. The most commonly used deep learning method in radiology is the convolutional neural network.
Need for artificial intelligence and artificial intelligence-based applications
There has been a paradigm shift in the practice of modern medicine from “clinical skills” based practice to radiology-dependent imaging-based practices. Radiology-based tests have entered all stages in the treatment algorithms of nearly all diseases. This has made the clinicians heavily dependent on radiology and imaging results which have snowballed into a very large number of radiology tests being ordered by the clinicians. The recent developments in radiology machines such as CT and MRIs have significantly decreased the scanning time. The CT machine manufacturers have already started talking about subsecond scanning techniques for the whole body. Thus, the major bottleneck in getting timely results of these radiology-based tests is the human mind, which is busy interpreting this heavy data of radiology images. In this way, the need for an “assistant” who could see the radiology images very fast and provide an accurate report in a very small time was felt. This is where AI came into the picture. The high-processing computers started taking the place of the human brain, and they were able to provide accurate results in much lesser time.
AI-based algorithms and programs have shown tremendous progress in recognizing diseased areas from adjoining normal structures. AI-based technologies are very effective in providing a quantitative objective assessment of imaging characteristics.
Accurate detection of an abnormality on imaging, their appropriate characterization as benign or malignant, and their follow-up, are the three main tasks that are to be performed by a radiologist while interpreting the imaging. Currently, AI has shown promising results in lung and breast nodule detection and characterization.,,
Although AI-based applications have promised a lot, it is yet to deliver their potential, and there are major gaps noted between the tall promises made by AI and the actual ground-level applications in radiology. The majority of AI applications are designed to answer a very specific query or to perform a specific task. This might involve working with an image stack of a particular modality like only CT, only PET CT, or only magnetic resonance. This might also involve looking at a particular organ system (like the brain) or even looking at a suborgan system (like mapping amygdala or hippocampus) or examining a particular anatomic region (like looking for a lung nodule in the chest). It might also be used to answer a specific query like whether a calcification noted on a mammogram is benign or metastatic. This inherent “narrow” or highly specific nature of AI-based applications is not very practical as many times a broader picture of a particular modality is required to reach a diagnosis.
Hence, of late, the man behind the machine is the radiologist who has started using these narrow AI applications to answer specific tasks. This makes the radiologist a master of the AI orchestra. Such usage has helped in improving radiology workflow and thus decreasing the reporting turnaround times in the department of radiology. It is thus important for radiologists to examine the specific areas of radiology that would be targeted by AI applications.
Computer-aided diagnosis (CAD) is being used to detect lung nodules in X-rays and CT scans to find nodules that are suspicious for malignancy. Studies have shown a high rate of accurately diagnosing these suspicious lung nodules by CAD-based systems. CAD uses predefined parameters and features based on which the abnormal areas are picked up in a given image that might have escaped the eyes of a radiologist. This makes lesion detection by CAD quite accurate. However, the flip side is the overdetection of clinically insignificant findings by CAD, which need to be validated by a radiologist. To summarize, CAD is used as a tool to pick up subtle abnormalities, but these need confirmation by the radiologist. After picking up an abnormal area in a given medical image or a stack of images, the next logical step is to characterize the abnormality, i.e., to tell the probable etiology or the cause of the abnormality. The abnormality might be categorized into benign versus malignant causes or various subcategories depending on the clinical scenario and the imaging features. These clinical scenarios and the imaging features are fed into intelligent computer systems to give a final verdict for the categorization of the abnormality. Detection of change in the structure and imaging features of a particular abnormality on follow-up imaging is considered very important of imaging, particularly for cancer patients. The detection of this change is of paramount importance as it tells us whether an abnormality is increasing or decreasing in size, and it also tells us if there is an improvement or deterioration in the overall disease process. Detection of change by computer-aided systems is a relatively new area in AI. One of the intentions of using these AI-based deep learning systems for comparative studies is to remove the inter-observer variation so that uniform results can be obtained at all times.
Radiomics is the process of transformation of medical images into mineable high-dimensional data. Radiomics extracts information from routine medical images and, in the process, creates large databases of quantitative data, also known as “Big Data.” These data need to be uniform as far as the imaging and reconstruction parameters are concerned. This uniformity ensures that the algorithms can work in such a fashion so that the subtle differences in the images are easily picked up [Figure 1].
The creation of this massive database needs very high-speed processors and data processing units. Another challenge in data processing is the sharing of data across multiple sites. As is the case with other biomarker studies, radiomics also might undergo very slow progress which might be due to suboptimal study designs and the inherent technical complexity involved., There are various steps involved in radiomics data collection and interpretation [Figure 2]. Radiomics-based biomarker qualification will also require prospective multicentric trials where the biomarker should be one of the primary endpoints for large-scale commercial application and validation of this technology.
The suffix -omics is a derivative term that was first described in molecular biology to delineate various characteristics of cell molecules, such as proteins (proteomics), ribonucleic acid (transcriptomics), and deoxyribonucleic acid (genomics). This suffix is now being used in various fields of medicine to describe the process of generating high-volume data. Radiomics uses the concept that any abnormal thing contains a lot of various kinds of measurable entities such as texture, size, and shape [Figure 2]. These measurable variables are then evaluated by complex computing operations and algorithms.
| Summary|| |
Recent advancements in cancer research, especially in the area of diagnostic technologies, have created a niche area where the latest tools have not only hastened the early diagnosis of cancer but also assisted in optimal cancer-specific targeted therapies for better patient outcomes. These tools are ever evolving, and more such tools are expected shortly, which would decrease the turnaround time of diagnosing this dreaded disease and also decrease the overall suffering of cancer patients.
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