From a theoretical standpoint, characterization of the entire genetic background of the tumor should be considered the most comprehensive strategy to gain insight into tumor biology. Whole genome sequencing (WGS) and whole exome sequencing (WES) are NGS approaches in which virtually the entire genome (WGS) or the protein-coding regions (exons) of the genome (WES) are sequenced. Both approaches can be used in cancer research to sequence normal tissue (e.g., blood) and tumor tissue to discover new targets for therapies and biomarkers of cancer stage, predisposition, and response to therapy. In addition, sequencing of paired tumor and normal tissues allows unambiguous identification of individual germline and somatic variants as well as loss of heterozygosity and the “second hit” mutations (Mandelker and Ceyhan-Birsoy, 2020).

In the clinical setting, somatic and germline variants can be identified using both WGS and WES approaches but some important aspects should be highlighted. There are two main advantages of WGS: first, the discovery of novel genomic variants, including single nucleotide variants (SNVs) and structural variants (SVs) such as CNVs, INDELs, variable stretches in tandem repeats and balanced chromosomal translocations; second, the sequencing result includes coding, non-coding and mitochondrial DNA (Sims et al., 2014). On the other hand, WES highlights coding variants that are easier to study and whose phenotypic effects are more functional to assess.

Although it could be considered an advantage to sequence the whole genome at once, since different types of variants can be found with the same sequencing library, some limitations should be considered. First, we need to distinguish between two key parameters in sequencing, coverage depth and coverage itself. Coverage depth is a measure of how often a particular base in a sequence is seen during sequencing and can be an indicator of the reliability of the results, while coverage is the percentage of the genome that is sequenced during the experiment. These parameters are closely related and must be weighed when researchers define the goal of their studies (Sims et al., 2014; Meienberg et al., 2016). Although the coverage of WGS is higher than that of WES, the average depth of coverage in WGS experiments may be low. In contrast, WES has a higher average depth of coverage compared to WGS because WES only covers the exons that account for about 2% of the genome. Another limitation is related to the data generated by sequencing, as WGS data are very huge, and processing and storing such amount of data requires adequate computational resources, which may be a limit in some contexts. Finally, the cost of WGS experiments is usually higher than WES, especially in clinical settings.

For these reasons, WES has long been considered the gold standard for detecting genetic variants. However, comparative WGS and WES studies have recently shown that WGS is more powerful than WES in exome variant detection, providing broader coverage and better variant detection, and costs are now decreasing (Belkadi et al., 2015). In addition, the latest WGS library preparation methods are PCR-free, while the WES library preparation methods still rely on PCR amplification. This could lead to GC content bias and misidentification of variants (Meienberg et al., 2016). In addition, WES does not really cover the whole exome, so some deleterious coding SNVs might be missed. As mentioned earlier, WES is not validated for the detection of structural variants, including CNVs and translocations, and finally, by definition, it does not include non-coding intron regions. Therefore, WGS has become more attractive than WES for diagnostic purposes in recent years (Belkadi et al., 2015; Lionel et al., 2018; Hou et al., 2022).

Whole transcriptome sequencing (WTS) is an RNA-based sequencing strategy that captures the transcriptome repertoire, and its applications include quantification of gene expression, detection of alternative transcripts resulting from splice variants, detection of chromosomal rearrangements leading to chimeric gene fusions, and identification of the ever-growing family of non-coding RNAs (Lakhotia et al., 2020; Li and Wang, 2021). Depending on the research interest, total RNA extracted from samples should be treated to remove unwanted RNA species, which may be a limitation, especially in terms of time. On the other hand, the loss of valuable reads and the management of background noise are problems faced when no depletion is performed. Bulked WTS has been used in cancer research to identify pathways and genes involved in cancer development and, thanks to spatial transcriptomics and single-cell sequencing, also to understand tumor organization and interactions with the microenvironment (Chen TY et al., 2023).

Many companies that have developed high-throughput technologies have now launched numerous tumor-specific panels to study cancer. Targeted panels sequence only a small part of the genome because they are designed with probes targeting specifically regions of interest, such as sets of genes, in a specific/custom fashion. Depending on the size of the panel, they can achieve the depth of coverage required to highlight specific pathogenic variants (Lenahan et al., 2023). Targeted panels offer many advantages over WGS and WES approaches, including reduced hands-on time, ease of translation of raw data, profiling of specific tumor-associated genes and customization of the panel. These advantages can support the therapeutic decision-making process while reducing the time required (Bewicke-Copley et al., 2019).

For their part, WGS and WES approaches can be extremely useful in exploratory research and clinical trials, as they do not require “a priori” knowledge of disease mechanisms and can reveal novel molecular biomarkers. In this sense, the COGNITION study has shown that comprehensive molecular profiling using WGS and WES identifies a genomic signature in a subset of breast cancer patients at high risk of recurrence after neoadjuvant treatment, for whom targeted therapy solutions may be available (Pixberg et al., 2022).

On the other hand, targeted panels can also be employed in clinical trial design. In this case, panels could be used for many goals. First, to stratify the cohort according to known biomarkers, as in the case of the REGISTRI phase II clinical trial, in which a customized DNA panel was developed to specifically identify KIT/PDGFRA wildtype GIST patients eligible for regorafenib therapy (Martin-Broto et al., 2023); second, to support the discovery of new specific positive biomarkers, associated with response to therapy, as in the RELAY phase III trial, in which a targeted approach was used to assess ctDNA mutations and EGFR mutation dynamics after erlotinib with or without ramucirumab treatment in NSCLC patients (Garon et al., 2023); and finally, to identify actionable tumor alterations and candidate genes for molecular targeted therapies, as demonstrated in the MATCH study (Parsons et al., 2022). Of note, many targeted panels, known as PanCancer panels, are designed to cover many cancer-related genes, so the applications of these panels are widespread for many different goals.

Whole and targeted sequencing approaches can also be combined in clinical trials, as in the EVOLVE phase II study in which WES of tumor tissue and a targeted panel of cell-free DNA from blood were matched to discover novel genomic alterations responsible for resistance to PARP inhibitors in high-grade serous ovarian cancer (Lheureux et al., 2023).

In the clinical setting, things are different, as the cost of analysis is one of the limiting factors for sequencing. In this scenario, targeted panels are preferred because they have a lower cost per sample and an easier data management (Bewicke-Copley et al., 2019). Targeted panels are often designed to provide information on known biomarkers such as genomic instability score (GIS), loss of heterozygosity (LOH), microsatellite instability (MSI), and tumor mutation burden (TMB). The latter biomarker is of great interest in clinical practice, as pembrolizumab is the first FDA-approved agnostic cancer therapy that can be used in tumors with high TMB (Marcus et al., 2021); however, the sequencing method used to assess TMB may impact clinical outcomes, by excluding patients who might otherwise benefit from this treatment. Indeed, WGS, WES, and targeted-based panels have been used to measure TMB in cancer patients, with not only primary tumors but also circulating DNA in blood proposed as an alternative source material. In this context, WES of tumor and paired normal tissues is currently considered the most accurate approach to determine TMB, although this approach is both costly and time-consuming in the clinical setting (McGrail et al., 2021). On the other hand, an approach that uses targeted sequencing assays enriched in genes known to be involved in cancer appears to be more feasible in the clinic, particularly because these assays do not require paired tumor and normal samples to determine TMB. However, the type of cancer tested and the type of panel used to assess TMB can significantly affect the outcome (Merino et al., 2020). Among the many examples of targeted panels used in the clinical setting there are MyeloSeq, a 40-gene targeted panel used to determine variant and allele frequencies in patients with suspected hematologic malignancies (Barnell et al., 2021), the Oncomine Precision Assay, which tests 45 cancer-related genes (such as EGFR, KRAS, ALK, RET, BRAF, and others) used in screening for genomic alterations that can be treated with targeted therapy in NSCLC, colorectal cancer, melanoma, breast cancer, and other malignancies (Werner et al., 2022; Nindra et al., 2023), and other targeted panels such as the TruSight Oncology, the AmpliSeq, the FusionPlex, the QIASeq Multimodal Lung, which have demonstrated expertise in identifying genetic variants, and the NTRK gene fusion panel used to identify tumors sensitive to larotrectinib, an NTRK inhibitor (Drilon et al., 2018; Stockley et al., 2023).

In addition, there is also an ethical aspect to consider, as sequencing a larger portion of the genome may lead to the identification of unsolicited findings that should be better communicated to physicians and patients (Schoot et al., 2021). Targeted panels therefore have a lower chance of discovering new unsolicited findings, which facilitates clinical reports.

In summary, although WGS and WES sequencing are accurate and do not require “a priori” knowledge of disease mechanisms, their introduction into clinical practice may not be feasible, mainly because of coping with the volume of data and the cost per sample. On the other hand, targeted sequencing may facilitate the introduction of bulk sequencing into routine clinical practice where testing of multiple molecular biomarkers has become common practice, as has been the case with TMB and NTRK gene fusions. Again, the best choice between the two strategies must be balanced between cost and application.

Epigenomic sequencing has proliferated in recent years. Epigenetics is a branch of biology that studies the causal interactions between genes and their products. Basically, epigenetics studies all changes and phenotypes in gene expression that cannot be attributed to genetic causes. The most important changes can occur directly at the DNA, e.g., cytosine methylation, or at the chromatin proteins, e.g., acetylation, methylation, phosphorylation, and others (Kouzarides, 2007). The consequence of these changes is the modulation of the accessibility of the DNA sequence to enzymatic complexes, which determines the state of gene activation.

Sequencing of epigenetic modifications (epigenomics) identifies specific cancer signatures involved in tumorigenesis as well as cancer metastasis and recurrence (Huang et al., 2018; Malta et al., 2018; Sengupta et al., 2021). In particular, some histone modifications, such as reduced lysine acetylation and methylation, may act as prognostic biomarkers in breast cancer (Elsheikh et al., 2009; Zhou et al., 2022) or they may predict response to treatment, as in the case of immunotherapy (Peng et al., 2015; Hoffmann et al., 2023). In addition, dysregulation of genes involved in chromatin remodeling can also be a hallmark of a particular tumor. This is the case with mutations of histone deacetylase (HDAC) in multiple myeloma and lymphoma. HDAC inhibitors and DNA methylation inhibitors are anticancer drugs developed to target the aberrant activity of these molecules (Blumenschein et al., 2008; Galanis et al., 2009).

Proteomics and metabolomics are high-throughput screening of protein expression and metabolite abundance, respectively. Proteomics data can be considered a readout of the transcriptome, but it has been reported that only 40% of protein expression can be explained by a corresponding gene expression profile (Ideker et al., 2001; Vogel et al., 2010). Proteomic studies take pictures of the cellular protein repertoire that include protein abundance and turnover, post-translational modifications, subcellular localization, interactions with other proteins and structures, and finally protein involvement in metabolic pathways (Altelaar et al., 2013).

On the other hand, metabolomics studies investigate the presence of metabolites, their concentration and their interactions with biological systems. Unlike other “omics” approaches, metabolomics can reflect the actual biochemical activity and the state of cells and determine the true cellular phenotype (Patti et al., 2012; Tan et al., 2012).

In cancer research, proteomics and metabolomics strategies have been used not only to identify novel biomarkers involved in tumor resistance and signature that predicts treatment outcome (Dytfeld et al., 2016; Shrestha et al., 2021; Robles et al., 2022), but also to uncover cancer metabolic pathways and oncometabolites that may drive tumorigenesis and sustain tumor progression (Xu et al., 2011; Drusian et al., 2018).

In addition to targeted therapy, NGS approaches can support the concept of personalized medicine by being included in PGx studies and linked to drug safety profiles (Figure 2A). Sequencing results can thus be associated with the identification of novel biomarkers related to drug efficacy and toxicity.

The three main research strategies for PGx biomarker discovery are candidate gene studies, Genome-Wide Associated Studies (GWAS), and NGS (Figure 2B). Candidate gene studies are based on genotyping or sequencing of genes known to be involved in PK and PD processes to uncover potential variants; this is the main approach taken so far. This approach is based on “a priori” knowledge, as genes are selected based on their membership in specific pathways (Malta et al., 2018; Kwok et al., 2022; Maeda et al., 2023). A limitation of this approach is that polymorphisms that are part of unexplored pathways and may alter the phenotype of the drug response are not detected. However, this approach may have higher statistical power than other approaches even with few samples (Chan et al., 2019).

On the contrary, GWAS discovers millions of SNPs across the genome and has the potential to find variants in unexplored genes and intergenic regions not previously thought to affect drug response (Uffelmann et al., 2021). One example is the germline variants in the PRUNE2 and BARD1 genes, which have prognostic potential in advanced colorectal cancer and ovarian cancer, respectively. In addition, the variants in the AGAP1 gene may affect patient response to bevacizumab (Quintanilha et al., 2022b). Moreover, most of the SNPs detected are not the causal variants responsible for the observed phenotype, but are instead associated with the presence of functional variants in high linkage disequilibrium in a given population (Bei et al., 2010). One of the major limitations of this technique is the low statistical power in detecting associated signals for rare polymorphisms and thus the inability to find variants with small effect sizes, especially when a drug effect trait is not directly associated with drug effect or is population/region specific (Wang et al., 2019).

Finally, NGS approaches offer the possibility of generating a large amount of information on novel, common, or rare variants potentially associated with drug response, such as GWAS, but suffer less from the requirement of a large number of samples to achieve statistical power, overcoming the drawbacks of the other two SNP approaches (Sharma et al., 2014; Auwerx et al., 2022). However, NGS is not a panacea for identifying all inheritance patterns in PGx. The lack of standardization of NGS techniques and limitations in sample quality or quantity are issues that should be addressed to achieve comprehensive and robust detection and association of somatic and germline variants involved in individual drug response (Mu et al., 2019).

Although candidate gene studies, GWAS, and NGS enable the identification of variants potentially involved in individual drug response, the results obtained in these studies require internal and external validation before they can be adopted in clinical practice. Validation strategies include case/control studies, cross-validation based methods, and an independent series of patients to confirm results. In addition, orthogonal technical validation using low-throughput methods is required, with real-time PCR with TaqMan assay and pyrosequencing often being the first choice (Arbitrio et al., 2021; Hertz et al., 2021).

A detailed description of ML is beyond the scope of this review; however, we provide here an overview of the main features of the algorithms of ML.

Supervised learning, unsupervised learning and reinforcement learning are three main types of machine learning approaches (Van Der Lee and Swen, 2023). A more classical classification based on the model built using this approach is divided into supervised, unsupervised and semi-supervised models based on the type of input data, i.e., whether it is labeled, unlabeled or a combination of both (Koteluk et al., 2021; Naik et al., 2023). A graphical representation of the concept is shown in Figure 3A.

In supervised models, which account for the majority of published ML methods, each data point contains an associated label (correct/expected response) for which a ML model must be developed. Typically, the data is split into two subsets: the training data and the testing data. The training data is used to tune and train the model (Shahin et al., 2023). The testing data is used to evaluate the generality of the model. In addition, supervised learning can be used to solve regression and classification problems. In regression problems, the labels are continuous values, while in classification problems labels are discrete values. Finally, the metrics used to assess the quality of the model depend on the nature of the problem and the type of application (Boyd, 2010; Sra et al., 2012; Orozco-Arias et al., 2020; Woodman and Mangoni, 2023).

In unsupervised models, the output label measurement is usually not available. In this case, the algorithm learns relationships from data structures to provide latent patterns that need to be evaluated for utility. However, this process still requires human intervention to validate the output variable. In general, unsupervised methods deal with clustering and dimensionality reduction, leading to the identification of subgroups with common features, which is one of the main applications of unsupervised ML (Sajda, 2006; Handelman et al., 2018).

Reinforcement learning is a type of learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties for its actions and learns to maximize the cumulative reward over time (Woodman and Mangoni, 2023). Unlike supervised and unsupervised learning, reinforcement learning does not require labeled data or a training set. This type of learning is often compared to a scenario where an agent learns through trial and error (Shahin et al., 2023). Reinforcement learning can be used to solve problems that deal with complex dynamics that are influenced by changing stimuli and conditions, such as in the real clinical world (Eckardt et al., 2021; Ryan et al., 2023). In particular, reinforcement learning could help physicians to select the right therapeutic regimens for a patient, and it is able to correct its predictions based on the observation of the adverse reaction resulting from the interaction between the agent and the environment (Niraula et al., 2021; Ryan et al., 2023).

Semi-supervised approach is a method in which there is a mixture of labeled and unlabeled data (Ge et al., 2020; Shi et al., 2021; Roy et al., 2022). There have been significant developments in the field of semi-supervised learning, as researchers have proposed various techniques to make effective use of the combination of labeled and unlabeled data. These techniques aim to overcome the challenges posed by the limited amount of labeled data and the growing volume of unlabeled data (Eckardt et al., 2022; Dou et al., 2023).

In ML it is important to note the differences between prediction and inference. These two terms, often used as synonyms, are used differently in ML algorithms. Prediction is about estimating or predicting unknown outcomes, while inference is about understanding the factors and relationships that contribute to those predictions. Both aspects are crucial in ML, as prediction enables useful forecasts, while inference helps to gain insights into the underlying mechanisms and to learn from the trained models (James et al., 2013). Depending on the objective, ML algorithms have been developed to make predictions, inferences or a combination of both.

An important issue to address when discussing ML is the trade-off between model accuracy and interpretability. Some approaches are easier to interpret, but are more rigid and less accurate, because they may be based on linear functions such as linear regression. Conversely, other ML models are more flexible in estimating the functional form of the function but can be difficult to explain (Eckardt et al., 2022; Kang et al., 2023). Figure 3B illustrates the trade-off between flexibility and interpretability for some of the most commonly used ML approaches.

ML input data can be of different origins, e.g., clinical data, medical imaging, omics, time series. The use of a single type of input data is characteristic of unimodal ML, while the use of different types of input data is a feature of multimodal ML. Each type of data can be modeled in different ways, resulting in early, intermediate and late fusion (Figure 3C). In early fusion, the input data types are merged at the beginning to create a single ML model. In intermediate fusion, ML models are created interlocked, each refining the previous model. Late fusion creates separate unimodal models that are combined into a final model. The multimodal ML provides more comprehensive and accurate predictions than unimodal models. Moreover, within the multimodal approaches, the intermediate and late fusion strategies achieve better results because they take complementarity information into account when training the model (Steinberg et al., 1999; Kline et al., 2022). In cancer research these multimodal approaches are considered very useful, but their application is not so obvious. ML late fusion strategies can be used to improve the oldest diagnosis criteria, tumor classification and subtype identification, as in the case of NSCLC, where a study shows that fusion of 5 different sources of information achieves the better performance in classification compared to algorithms using only single source information (Carrillo-Perez et al., 2022). In addition, they can be used to develop software for cancer theranostics, a cancer control strategy that combines early diagnosis, accurate molecular imaging, and personalized radiation treatment (in terms of chosen agent, dose, and timing) based on the individual omics profile.

Numerous ML methods have been developed for medical research and recent applications of ML are summarized in Table 1. Here we give an overview of the main algorithms used in the field of oncology (Figure 4), namely, k-means clustering and hierarchical clustering, latent variable model, support vector machine, decision tree learning, and neural networks. For neural network algorithms, we focus on Deep Learning (DL), which is becoming increasingly important in cancer research.

In unsupervised learning, ML methods are not task-specific (i.e., they are not based on a specific predicted outcome, such as survival), provide general insights, and include methods such as k-means clustering and hierarchical clustering. These methods have been used in oncology to identify cancer subtypes, stratify patients, and create clusters from gene expression data to identify patterns and groupings (Eckardt et al., 2023). Another example of unsupervised learning is the latent variable model (LVM), which can capture unobserved variables that may affect the outcome. LVM can therefore be used to regress variables into one or more classes that would best explain the heterogeneity in the data (Miettunen et al., 2016).

Supervised methods include the support vector machine (SVM), which divides data into categories (two or more) to solve both regression and classification problems. It is based on kernel algorithms that can expand the feature space to make data more accessible. It is considered one of the most robust models to date (Tate et al., 2006; Pacurari et al., 2023).

Decision tree learning is a supervised model that is commonly used in clinical practice for decision-making processes. In this case, ML can help identify which variables have the highest separability to the desired categories and is used for both classification and regression (Podgorelec et al., 2002). Random Forest (RF) is an extension of decision tree learning that combines multiple randomly generated decision trees to improve decision making. Given the complexity of biology, data scientists usually prefer RF to decision tree (Breiman, 2001; Cui et al., 2022b).

Finally, neural networks (NNs) are models used in all three types of learning. NNs, which mimic the neural architecture of the human brain, can integrate multiple sources of information and process them in nodes and layers. Each node represents a specific feature with a specific weight, and nodes of the same level represent a layer. Moreover, nodes of different layers can be connected to each other. If the information stored in the node is valuable, the node weight exceeds a certain threshold, which means that the node is triggered and the network is active. During the training, the weight values and threshold are continuously adjusted to form the best combination of nodes and weights that results in the most informative NN (Kriegeskorte and Golan, 2019).

Deep Learning (DL) is one of the NN algorithms where the number of hidden layers and nodes is increased and the overall size of the network is very large, which allows better representation of complex relationships. The main advantage of DL is that it identifies hidden features as part of the learning process, making DL faster and more automated compared to ML (Erickson et al., 2017). These features also correlate with sensitivity and availability of cheaper computing power. Therefore, DL is now referred to as a specific subset of ML with its own algorithms and applications, and has become one of the most widely used approaches in cancer research. Thus, in the following part of this section, we discuss some applications of DL in cancer research.

The use of DL in oncology began with the analysis of medical images, because it is particularly good at identifying pathogenic features of the observed cells, and in certain cases the performance of DL is almost equal to human performance (LeCun et al., 2015; Jalloul et al., 2023). For example, the application MIA was developed to analyze images from microscopy and can be used for classification, object recognition, segmentation, and tracking (Körber, 2023). In addition, medical images of histopathological tumor sections were used to test whether DL can predict response to therapy in patients with adenocarcinoma of the gastroesophageal junction. In this work, researchers found that DL is able to distinguish patients who respond to neoadjuvant chemotherapy from those who do not by extracting certain features on the images before therapy initiation (Hörst et al., 2023). A Swedish study has developed a DL tool for detecting lymph node metastases in colorectal cancer that has excellent accuracy compared to human performance. This tool reduces the time required to assess lymph nodes, which in turn improves the diagnostic process and treatment decisions (Kindler et al., 2023). Another DL tool has been developed to assist clinicians in digital pathology by assessing the tumor cellularity of histopathologic hematoxylin and eosin sections (Altini et al., 2023). Apart from the importance that this algorithm may have in the clinical setting, it is important to point out that its use may also be useful in research, as it allows pathologists to share valuable information with researchers in an automated manner. A high percentage of tumor cells is an important requirement for researchers to perform NGS sequencing, as the biological material taken from the slice must be representative of the tumor in order to reduce the contribution of normal adjacent tissue, thereby reducing background noise and improving sequencing quality. In addition, DL has been successfully developed to predict optimal radiotherapy for patients with brain metastases using CT images and non-image clinical information (Cao et al., 2023). It has also been developed to predict pneumonitis risk in lung cancer patients treated with immune checkpoint inhibitors and to identify morphologic features that predict ERBB2 status and trastuzumab efficacy in breast cancer patients (Bychkov et al., 2021; Cheng et al., 2023). In a retrospective multicenter study, a DL algorithm was developed to help radiologists diagnose breast cancer lesions and differentiate axillary lymph node metastases based on radiological features (Zhou et al., 2023).

Although medical imaging remains the foremost application of DL, it is also used in the analysis of genomics and transcriptomics data, including data from single-cell experiments that can improve variant detection calling at cell-specific resolution. DL improvements in single-cell sequencing could enhance the ability of researchers to understand intratumoral heterogeneity and identify previously unknown cell subpopulations, making this a particularly attractive area for molecular oncology (Erfanian et al., 2023; Halawani et al., 2023; Shen et al., 2023). DeepTTA is a DL model that uses transcriptomic data to predict anticancer drug response, which can shorten the preclinical phases of drug development and drug screening. In addition, DL models which can predict cancer drug response can also be used to identify new potential clinical applications of known drugs based on target affinity and mechanism of action (Douglass et al., 2022; Jiang et al., 2022; Park et al., 2023). DeepTAP is another DL algorithm capable of predicting sequence peptides that bind to tumor neoantigens, which may be of interest to the field of cancer immunotherapy (Zhang X et al., 2023). With this in mind, DL algorithms are being used in precision medicine to predict anticancer drug response in patient-derived cancer cell lines, as in the case of the DeepDRK framework, which is freely available (Wang Y et al., 2021). A method based on DL was developed to study the uptake of targeted nanoparticles in triple-negative breast cancer, which could be useful for proper dosing in clinical practice (Ali et al., 2022). A nanodiamond biosensor platform using DL was developed to rapidly assess individual specific sensitivity to oxidative phosphorylation inhibitors in patients with hepatocellular carcinoma (Xu et al., 2023).

Finally, network pharmacology is a new approach in drug development that aims to understand the network interactions of multiple drug combinations. In this context, the network algorithms of DL may be useful to identify synergistic combinations of multiple drugs targeting a specific network that can be used to improve cancer treatment (Noor et al., 2023). DeepDTnet, for example, is a DL method for network-based target identification that reveals novel therapeutic effects of known molecules, which in turn can accelerate drug repurposing, a process aimed at finding new uses for drugs that are approved or in trials (Zeng et al., 2020). The strategy of drug repurposing offers numerous advantages over developing a completely new drug, such as a lower risk of failure because safety and risk assessment have already been tested, cost and time savings in the preclinical phases, and finally, phase I and II results are already available, thanks to sophisticated algorithms such as those used in DL and ML (Pushpakom et al., 2019). Based on this approach, many oncology and non-oncology drugs have been reviewed in recent years, and drug repurposing is particularly valuable in rare or late-stage diseases where the development of a new drug may be difficult in terms of patient recruitment and the time required for a complete clinical trial may be unreasonable.

In PGx studies, DL has also been used to predict the toxicity of specific medications. In recent works, DL methods were able to predict the toxicity of radiation-based therapy in four different cancer types (Tan et al., 2023) and identify SNP signatures associated with urinary symptoms and overall toxicity in prostate cancer patients treated with radiation therapy (Massi et al., 2020). An important implementation of DL in PGx studies may interest medical imaging with feature extraction that predicts drug response based on SNP signatures. However, this task is very difficult to accomplish because the DL algorithms used to scan medical images are designed to extract “abnormal” features, and PGx often refers to germline (i.e., “normal”) variants. However, in this way, it would be possible to reduce the number of diagnostic tests a patient has to undergo, also in the context of the most appropriate choice of therapy after diagnosis.

The use of DL, as well as ML in general, can improve healthcare and assist clinicians by shortening the time required for diagnosis and staging and facilitating decision-making in drug selection and administration of the correct dose, thus contributing to the clinical translation of precision medicine in cancer.

PGx studies have gradually shifted from reactive testing on a single gene to proactive testing on multiple genes to improve treatment outcomes. This move has been made possible by the implementation of high-throughput data generation and analysis.

With the advent of ML and computer science in cancer research, it is now possible to discover previously unknown cancer-related features and latent signatures that impact tumor development, progression and recurrence. In addition, ML offers the opportunity to gain insight into failed clinical trials to understand their limitations and potential benefits, and to prevent toxicity and other drug effects that can impact patient quality of life and treatment efficacy (Harrer et al., 2019).

One of the first constraints in screening new drugs is selecting candidate molecules from the initial bulk of drug libraries. By combining genomic features of cell lines and chemical information of molecular compounds, researchers have been able to create in silico ML multi-drug models to predict IC₅₀ values, saving cost and time (Menden et al., 2013). Furthermore, these in silico approaches enabled the identification of genomic events associated with altered drug sensitivity, optimizing drug trial design (Huang et al., 2017).

In cancer, multitherapy is often used not only to reduce the toxicity of a single anticancer agent and achieve synergistic effects, but also to overcome drug resistance (Dear et al., 2013; Lee et al., 2019; Kim et al., 2020). Screening to predict synergistic drug combinations is a computational approach that has been explored using ML technology. For example, screening multiple administrations of over 40 different drugs in melanoma cancer cells led to the identification of 11 validated, previously untested drug combinations that lead to different outcomes (Gayvert et al., 2017).

As mentioned earlier, there is growing evidence that optimal prediction of drug response relies on individualized molecular profiling (Bode and Dong, 2017). Many ML approaches in PGx studies have been developed to predict the best match between genetic alterations involved in the pathogenesis or recurrence of a given cancer and drugs targeting these alterations (Chang et al., 2018). From this perspective, therapeutic drug monitoring (TDM) is an experimental procedure that measures the plasmatic concentration of a given drug in a specific time window after administration. Recent work has shown that ML methods applied to TDM were able to predict the appropriate dosing for various drugs, e.g., lapatinib dose for patients with metastatic breast cancer (Yu et al., 2022) and cisplatin dose in cohort of patients with head and neck cancer to avoid cisplatin-related toxicity (Cauvin et al., 2022).

Integration of different omics information better captures the complexity of cancer through different molecular layers and therefore improves diagnosis, prognosis and treatment compared to using a single “omic” alone (Heo et al., 2021).

There are many examples of successful integration of multi-omics approaches. Whole genome and transcriptome data have been used to quantify the extent of specific genetic alterations at the mRNA level and derive quantitative trait loci (eQTLs). In this context, polymorphisms associated with specific phenotypes are found to be associated with eQTLs, and genetic risk factors associated with eQTLs can bona fide predict the level of the corresponding gene product (Nicolae et al., 2010).

Epigenomics and transcriptomics/proteomics data can be aligned to explain how epigenetic changes can affect protein turnover (Wang X et al., 2021). In addition, transcriptome sequencing has often been combined with miRNome sequencing to determine which microRNAs and non-coding RNAs may alter gene expression and modulate response to chemotherapy (Fazi et al., 2015; Cuttano et al., 2022). Many consortia and catalogs have been developed to promote the understanding of tumor processes with high-throughput data, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which integrates genomic and proteomic data to create a proteogenomic portrait of cancer toxicity and resistance (Ellis et al., 2013), the International Cancer Genome Consortium (ICGC), which provides cancer genomic, DNA methylation and gene expression data (Zhang et al., 2011), and The Cancer Genome Atlas (TCGA) program, which provides a collection of genomic, epigenomic, transcriptomic and proteomic data on 33 different cancer types (The Cancer Genome Atlas Research Network et al., 2013). These data collections are the tip of the iceberg of the various omics data consortia that have emerged to date, and they serve as a valuable resource for omics and ML modeling studies of cancer.

The application of ML with the integration of multi-omics data has resulted in several scores for risk prediction and diagnostic/therapeutic potential, such as the polygenic risk score. The polygenic risk score considers all genetic inheritance variants known to be associated with a particular disease and measures the risk associated with the development of the disease under investigation, thereby improving risk stratification and screening (Akdeniz et al., 2023). Other examples include the BRECADA application, which uses genetic and nongenetic risk factors for early detection of breast cancer, and the OncoNPC signature, which classifies cancer of unknown primary and accordingly tailors initial palliative treatment intent, a strategy that often leads to better patient outcomes compared with cancer treated without querying the OncoNPC signature (Moon et al., 2023; Tao et al., 2023). In addition, some radiomics features have been extracted from images of brain metastases of extracranial primary tumors and correlated with the expression level of PD-L1, allowing stratification of patients according to their sensitivity to immune checkpoint inhibitors using a ML noninvasive classifier (Meißner et al., 2022).

The ability of ML to handle multiple data structures, namely, clinical, molecular and imaging data, allows to discover hidden correlations among different input data in PGx studies to make more accurate predictions and inferences. However, there are several crucial aspects to consider when managing and using multi-omics data that should be examined. First, collection of multi-omics data requires careful evaluation of the entire experimental workflow, from tissue collection and high-quality extraction of nucleic acids and proteins to sample preparation and sequencing. Second, data analysis pipelines need to be developed to integrate individual omics approaches. In this context, early, intermediate and late ML fusions help to address the management of multimodal approaches with positive impact on clinical cancer research. Third, the expertise required for analytical and bioinformatics analyses often requires the collaboration of multiple experts to properly mediate the integration of multi-omics data. Thus, building a multidisciplinary team is therefore challenging for the success of multimodal data integration, but the positive outcomes of multimodal approaches have already been demonstrated and adopted (Kwon et al., 2015; Jing et al., 2020).

Clinical trials are later phases of drug development and incur very high costs. Clinical trials in oncology have the highest overall failure rate, mainly due to poor trial design (Wong et al., 2019). Therefore, the use of ML in clinical trials could be an opportunity to increase success. However, most applications of ML have focused on preclinical studies rather than improving clinical trial design, possibly due to the significant regulatory challenges associated with the use of ML in a clinical context (Massella et al., 2022).

ML improvements in study design can be attributed to three main strategies: cohort composition to improve suitability by reducing cohort heterogeneity, patient recruitment to improve eligibility by maximizing patient-study match, and patient monitoring to improve adherence and endpoint detection to reduce dropout rates (Harrer et al., 2019; Van Der Lee and Swen, 2023).

As mentioned earlier, oncology clinical trials often fail to meet primary endpoints due to inadequate stratification criteria, poor recruitment and evidence of severe drug toxicity (Hwang et al., 2016; Kim et al., 2023). To address these issues, the RainForest algorithm was developed. The CAIRO2 clinical trial investigated the use of cetuximab in patients with metastatic colorectal cancer and concluded that there was no benefit to using this agent in the overall population. However, the RainForest algorithm was able to identify a small subset of patients who actually benefit from cetuximab treatment based on the SNP germline profile of patients (Ubels et al., 2020). The use of the RainForest algorithm in clinical trials can save enormous resources, as the cost of a single agent is estimated to be around 2.8 million US dollars in the final stages of approval (DiMasi et al., 2016).

Severe toxicity is another major issue in clinical trials, and changes or interruptions to treatment schedules account for at least 30% of failures in phases II and III (Harrison, 2016). ML has also supported the design of clinical trials in term of drug safety. A recent work has shown that SNPs signatures can serve as genetic predictors of toxicity in personalized medicine. The germline variant rs4864950 T>A in the KDR gene increased the risk of composite toxicity (occurrence of any of hypertension, diarrhea and dermatological reactions) in patients treated with the VEGFR TKIs sorafenib and regorafenib (Quintanilha et al., 2022a). In another work, the ABCB1 rs9282564 was the variant most strongly associated with hypertension and nonhematological toxicities in ovarian cancer patients treated with bevacizumab, and SNPs in genes related to the biological oxidation pathway (CYP3A4 rs28371763 and CYP1B1 rs9341266) were the most significant variants associated with hematological toxicity in the same cohort (Polano et al., 2023).

Clinically relevant predictors of toxicity have also been found in many GWAS studies, e.g., SNPs predicting severe skin toxicity in patients with colorectal carcinoma treated with cetuximab (Baas et al., 2018) or predicting dysphagia in patients with nasopharyngeal carcinoma treated with radiotherapy (Wang et al., 2022) or predicting neurotoxicity and leukoencephalopathy in patients with lymphoblastic leukemia treated with methotrexate (Bhojwani et al., 2014). Many other correlations between SNPs and toxicity can be found in the literature. Most importantly, prediction of cancer-related toxicities can prevent deterioration in patients’ quality of life and adherence to treatment and can be used to manage chemotherapy-related adverse effects in the clinical setting (On et al., 2022).

Incorporating assessment of somatic and germline variations into treatment decisions with FOLFIRI in elderly patients with metastatic colorectal cancer has been shown to be effective. This regimen requires assessment of RAS mutations as well as DPYD and UGT polymorphisms prior to treatment with the FOLFIRI protocol (Mathijssen et al., 2003; Morel et al., 2006; Sepulveda et al., 2017; Battaglin et al., 2018). Although PGx test guidelines have already been implemented in clinical practice, another important issue in this context is the implementation of ML in clinical practice. ML has demonstrated its usefulness in retrospectively classifying patients during clinical trials to assess drug safety and prognosis (Chang et al., 2018; Quintanilha et al., 2022a; Chen et al., 2022), but the incorporation of ML into clinical trials and clinical practice is still up for debate. Standard guidelines and protocols need to be thoroughly regulated to obtain comparable information and a precise methodological approach, not only for the algorithms of ML, but also for PGx studies. It is worth noting that different ML models can be applied to the same given subject, as there is no universal applicability of ML algorithms and this could lead to different results in the same dataset.

Moreover, NGS has only recently been introduced into clinical practice to assess diagnosis and prognosis and to evaluate therapeutic strategies, but it is still a niche and there is room for improvement. In addition, sequencing of some parts of the genome remains challenging, e.g., highly polygenic regions, pseudogenes, triplet expansions, low complexity regions, short repetitive sequences, regions of high-similarity, and complex structural rearrangements (Treangen and Salzberg, 2012; Rojahn et al., 2022). It is estimated that approximately 14% of clinically relevant genetic tests are located in these genomic regions (Lincoln et al., 2021), and correct variant identification can be difficult. On the one hand, short tandem repeats and complex structural variants known to play a role in the pathogenesis of certain diseases can now be sequenced using a targeted approach with long reads sequencing technology, as longer reads are expected to generate appropriate sequence length that overlaps better during assembly (Stevanovski et al., 2022). On the other hand, these technical difficulties can be at least partially overcome by adapting NGS analysis workflows accordingly (Rojahn et al., 2022). However, detecting variants in these regions remains challenging and difficult to validate. Detection of rare and very rare mutations with low allele frequency should also be considered. Inclusion of all probes in the ML learning datasets can be helpful, because some isoforms are more informative than others, and pooling them into averages may lead to dilution or loss of this information. NGS and ML can lead to great improvements in PGx studies, as multiple samples and multiple genes can be tested simultaneously, and clinically relevant hidden patterns can be uncovered from complex data structures.

However, ML and DL also have their own limitations. In general, ML algorithms are not error-free: one of the biggest challenges is the learning model itself. Indeed, many ML approaches have problems with underfitting or overfitting, where the data follows the trained data or noise signals too closely, resulting in poor curve estimation. An appropriate size of the training dataset is also important for ML models to learn properly and make accurate predictions. The reliance on large datasets for developing accurate models is also a challenge due to the lack of sample availability. In addition, the relationship between bias and variance also plays a role in obtaining the best performance from ML models. On the other hand, the accessibility of the code used for ML in PGx studies is low (Huang et al., 2017). To further improve knowledge and sharing among distant researchers around the world, platforms for sharing data and code should be established. To this end, computer scientists could have the opportunity to address underestimated problems and find common resources to overcome them. In addition, the development and improvement of multimodal ML methods, such as late ML fusions, may encourage a more holistic view of specific patient characteristics across different input data types.

As previously reported, PGx testing has demonstrated its utility in many situations, and new variants of uncertain significance have been reported thanks to GWAS and NGS PGx studies. On the one hand, the impact of these new variants on PGx testing is still being evaluated. On the other hand, the clinical implementation of genotyping of genes known to be involved in individual drug response needs to be monitored. In particular, the development of genotyping panels should also be improved to enable the translation of new relevant findings into the clinical settings. One of the most important and unanswered questions related to the use of these genotyping panels is how representative they are of individual variability in drug response. Moreover, PGx studies often suffer from a lack of homogeneous tumor samples, which is particularly true for rare tumors and inconsistent sample ancestry origins and incomplete data are also common complaints. Harmonization of samples and data collection, as well as free and easy access to sample datasets, could facilitate PGx studies with ML. Finally, new and standardized scores to track NGS quality, ML accuracy and significance of PGx variants could also be developed to address new tasks.