Cancer is considered the first or second cause of death in more than 100 countries around the world in 2019 for people younger than 70 years (1). There are more than 10 million cancer deaths and more than 19 million newly diagnosed cancer cases in 2020 (2). Clinical guidelines for treating different diseases such as hypertension, diabetes, and cancer have been established by panels of experts in health organizations such as the European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) (3). The reasons why experts make these guidelines are the large number and confusion in some of the medical studies toward what is the best treatment; some cancers are rare, which makes their treatment by inexperienced physicians difficult and makes bias by physicians unavoidable, which is intrinsic in medical practice. These guidelines for cancer management should be backed up with clinical trials. However, these are often lacking, and therefore some cancer guidelines represent only a “pattern of practice,” often that of large medical institutions with a large expertise and experience in those cancers (3).

However, it has become increasingly clear over the past decade that no two patients’ cancers are exactly the same and, therefore, may have different responses to generic treatments such as radiation and chemotherapy (4). This classical model for cancer therapy is overly simplified; it results in expensive treatments and ineffectiveness and causes patients to suffer from unnecessary side effects (5).

The basic strategy in genomic medicine is to identify somatic genetic changes such as quantitative chromosomal abnormalities, amplifications, translocations, and point mutation and match it with medications targeting these defects for a patient’s advantages. Within certain conditions, a large number of patients who have specific types of tumors carrying a specific mutation have been treated with a single medication successfully (6). The utilization of imatinib to treat patients with chronic myeloid leukemia (CML) carrying the t(9;22) translocation that produces the BCR-ABL fusion kinase was the first example (7). Another example is using epidermal growth factor receptor (EGFR) inhibitors successfully to treat lung cancers that harbor mutant EGFR, also using BRAF inhibitors to treat the melanomas carrying mutated BRAF (8–11). Recently, a significant chance for an immune checkpoint inhibitor was specified in Hodgkin’s lymphoma by genetic mode (12, 13). For every patient who has lung cancer sheltering a targetable mutation in ALk or EGFR, there are four who lack any targetable mutation (14).

Although clinical trials did not evaluate the utilities, it still has gained us an assessment tool for patients to target therapies through genomic techniques. In May 2016, a release of interim analysis from the molecular analysis for therapy choice (NCI-MATCH) stated that there was good analytical achievement in their genetic screening technique, while testing was done in 87% of the 739 samples. Nonetheless, 9% of the patients of whom their testing was accomplished had tumors that harbored a mutation that may guide them to any of the 10 targeted therapy arms, and a slight percentage around 2.5% has managed to get in a treatment arm (6). The University of Texas MD Anderson Cancer Center conducted a study where 2,000 tumors from 2,000 patients were investigated with platforms containing 11, 46, or 50 genes. From these tumors, 789 (39%) had possibly targetable alterations; however, only 83 (4.2%) of the patients were included in a genotype-matched trial (15). Unfortunately, the clinical effect was not reported. A recent report by the Dana-Farber Cancer Institute where they use massively parallel sequencing assay including 282 genes for 3,727 patient tumors stated that 73% of these tumors contain an “actionable or informative alteration” (16). There are individual cases where clinical benefits appear to be clear, but still no full evaluation of the clinical benefit is reported. Marquart et al. evaluated 31 drugs with 38 Food and Drug Administration (FDA)-approved indications between 2006 and 2018. In 2006, the percentage of metastatic cancer patients who were evaluated and found to have a positive clinical outcome from the genome-targeted therapy was 0.70%. However, the percentage had risen to 4.90% in 2018 (17). The author concluded that genome-driven drugs had benefited only a small number of patients with advanced cancer (fewer than 7%) even though an increased number of patients became eligible (yet fewer than 16%) (17).

During the course of treating cancer, initially, cancer cells may respond to medications; however, after a period of time, tumor cells change to become resistant as a result of mutation of cellular genes (18). The genetic and molecular profiles of the pathological tissues such as the cancer tissues need to be adopted and analyzed prior to the interference with the new medications. Nevertheless, in some rare cases, there are unsolved genetic alterations that may be hard for medication to target (18). Ideally, prior to the start of certain treatment, it is propitious to carry out an in vitro drug sensitivity assay to investigate whether they show an inhibitory effect on the primary cancer cells from the cancer lesion itself (19).

With the acknowledgement of the limitations of existing cancer models, numerous patient-derived models of cancer (PDMCs) are in development. Recently, PDMCs like induced pluripotent stem cells (iPSCs), conditionally reprogrammed cell cultures (CR), patient-derived xenografts (PDXs), organoids, spheroids, and others have been established (Table 1) (20). And in any other prototypes or model system, every single platform has its own pros and cons when it comes to their usefulness and also their illustration of tumor structure, microenvironment, stem-differentiation states, cellular conformation, growth patterns, heterogeneity, and responses to therapeutics; all of these advantages and disadvantages are mostly dependent on the samples and specimens drawn from the patient (20). These approaches are under assessment for further use in the future in hope to understand the basic biology that lays behind cancer and the benefits in translational cancer research, specifically for target identification, drug screening, and the discovery of biomarkers that are key for detecting cancer at early stages (5).

Since the emergence of high-throughput screening (HTS) in drug discovery, it went through fast strides and established itself as one of the many premises of the present research in biology and chemistry (70). In 2006, Inglese et al. came up with a quantitative HTS (qHTS) strategy in order to enhance screening precision and accuracy; in a manner, the assays are conducted with numerous compounds’ concentrations in a magnitude ranging at more than four orders to create dose–response curves, which are in all of the tested compounds (71). This method of qHTS has led to an increase in the accuracy of screening compared with the old traditional HTS, since there is no longer a need for the primary single-concentration screening step that was known for its possibility of producing a high rate of false negatives and false positives (72).

During the past 10 years, the HTS technique has been advancing thanks to improvements in many technologies such as the establishment of innovative platforms, automation of liquid handling, and the improvement of tools that are capable of analyzing big sets of data. Hence, the HTS has become critically essential for all phases in the drug discovery journey from bench to bedside, starting from the target detection to the toxicity assessment. Miniaturization and automation help to decrease the use of reagents and the time required for analysis and also to reduce the need for labor-intensive steps, therefore resulting in decreasing the total assay cost by a large margin and making it economically and timely suitable.

The qHTS screening using numerous concentrations of thousands of different compounds constitutes a spectacular progress of success by decreasing the false-negative outcome when compared with single concentrations in the classical HTS (71). In drug discovery and toxicological studies, the generated data from qHTS have a leading role in advancing these two fields (73–75). For example, the concentration or dose-dependent response data are now being created and published for hundreds of toxicologically relevant endpoints in the second phase of the tox21 alliance between the FDA, the Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and the National Toxicology Program (NTP) (76). The results obtained from these qHTS tests could be applied for many other different purposes, such as screening of phenotypes, prediction modeling, and genome-wide association mapping (77–79).

Alkhilaiwi et al. conducted the first proof-of-concept study to merge the CR technology and robotic qHTS. In the study, we were able to propagate millions of cells from a lung tumor biopsy in 21 days and send the cells to the NCATS where they screened the cells using two libraries. The rapid expansion of 35 million cells of the RRP CR cells met the demand for a large number of testing cells.

Testing of 4,700 drugs with seven to 11 concentrations each was performed in in a week to identify the potential treatments for the patients who were alive during the study. Three medications have been identified as effective and were validated in 2D culture and 3D sphere cultures. Lowering the number of the medications tested using specific libraries based on the type of the cancer will shorten the time needed to get the result since less cells are needed (Figure 2) (80).

In spite of the clear fact that patient physiology differs considerably from one person to another, patient care and drug development depend mainly on the administration of the exact treatment options to diverse populations (19). This created a huge unmet need leading to an act that was signed into law in the United States at the end of 2016 (The 21st Century Cures Act). The act provides funding and resource to the FDA to establish new initiatives and programs that will increase its ability for fast approval of precision medicine products such as medical devices (breakthrough devices) and cell therapies.

Although there are very important features and uses of CR technology in different areas, CR still has many challenges, and the optimization of CR culture is needed when growing different types of primary cells. The first challenge with CR is the inability of maintaining a culture of certain malignant primary cells. In 2017, Yu et al. hypothesized that CR preferentially supported the outgrowth of non-malignant epithelial cells from nasopharyngeal carcinoma biopsy (81). Another group has observed similar results on primary culture of NSCLC tumor specimens (82, 83). In the study of Hynds et al., CR culture was able to establish tumor cell culture from one out of 10 primary NSCLC tumors. They advised that outgrowth of cancer epithelial cells by normal epithelial ones in late passages and cancer cells was seen at early passage only (passage 2) (84). Remarkably, these CR tumor cultures were able to form xenograft when injected in immunodeficient laboratory mice (NSG mice), and the cells that were re-cultured from tumor xenograft were found to maintain the main features of primary tumor (84). Nonetheless, many studies that have been published have described culturing tumor cells from NSCLC (68, 85). On the other hand, there were no reports for outgrowth of normal cells in neuroendocrine cervical cancer and pancreatic cancer (69, 86). The divergence in the results from the different studies could be contributed by different origins of fibroblasts, which were used as feeders in the CR culture, different methods of processing, and acquiring the primary tissues and errors in the pathology laboratory assessment.

The second challenge with the CR method is the inability to discriminate tumor cells from normal populations, which is problematic since some tumor tissues are mixed with normal tissues (47, 85). Such a problem manifests when normal cells grow with the cancer cells from the tumor/normal tissue and occasionally normal cells outgrow the cancer cells as detailed previously (47). To solve this problem, the pathologist needs to assess and designate carefully the normal and tumor tissues (47). Optimizing culture conditions can be performed to favor the growth of prostate tumor cells by removing serum from the medium, which leads to differentiating the normal cells, while prostate tumor cells grow as a mesenchymal morphologic phenotype and then reversed back to epithelial morphology in CR condition (51). Lou’s group suggested to use 3D culture and CR to reach the best results for the differentiation of normal cells to occur and therefore allowing for discrimination between normal and tumor cells (87). Another modification in the culturing condition could be the use of human fibroblasts as feeder cells rather than mouse fibroblasts since the human fibroblasts do not support the in vitro proliferation of normal epithelial cells in the long term, which will lead to selecting tumor cells to grow (62, 68, 88). Technologies such as tumor-specific antibody and next-generation DNA sequencing can be performed to differentiate between normal and cancer cells as well. One of the main advantages of CR culture is the ability to form 3D culture, allowing for imitating and recapitulating the in vivo condition and the ability to add more cells from the microenvironment of cancer such as immune cells or fibroblasts. Transwell dish-based 3D models from prostate CR cancer cells, organoid culture of prostate CR cancer cells, and multicellular spheroids of laryngeal, lung, and salivary cancer CR cells have been generated (50, 51, 80, 89). Additionally, the CR technique can be used for direct isolation of CTCs from the blood (90, 91).

Naeem et al. have used computational proteochemometric platform (DrugGenEx-Net) to identify carfilzomib for prostate cancer based on transcriptomic data from two matched pairs of benign and tumor-derived CR cells; such system can be valuable in building the library for each cancer in case the number of medications tested needs to be decreased to save more time (92). Moreover, the generated data from each screening will be fed back into the system to help enhance the addition or removal of drugs from the libraries of each disease. In conclusion, there is a huge potential for combining CR and robotic qHTS toward precision medicine based on the patient cells and therefore satisfy a huge unmet medical need.

Conception and design, development of methodology, acquisition of data, writing, review, and/or revision of the manuscript: FA.

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (G-712-166-1441). The author, therefore, acknowledges with thanks DSR for the technical and financial support.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.