Table 1 Preclinical and patient data necessary to model drug combination effects across the tumor microenvironmenta
From: Looking beyond the cancer cell for effective drug combinations
| Â | Data type | Advantages | Limitations | Recommendations |
|---|---|---|---|---|
Pre-clinical | Cancer cell-line drug screens | - Cost-effective route to generate a significant amount of data - Detailed data from many cell lines is available, for example, GDSC [93] and CCLE [94] | - Limited to only modeling intracellular effects - Cell culture process affects important biology - Limited to testing existing drug targets | - Obtain more data for drug combinations - Use mixed cell assays to model the roles of other cells in the tumor and microenvironment - Develop better models of diverse mechanisms (for example, longer assays and different endpoints) |
Functional genomic screens (using siRNA, CRISPR, and mutagenesis) | - No limit to the number of combinations of targets testable - Synthetic lethalities can be readily identified | - Limited to intracellular mechanisms - Focus on loss of function - Only work in a limited number of cell backgrounds | - Use a broader range of cell contexts (including non-cancer cells) - Develop gain-of-function screens - Set up repositories that enable data to be shared and publicly accessible | |
Drug or target perturbation screens (post- treatment functional data) | - Provide information about a drug or target’s mechanistic impact and provide pharmacodynamics maps that are likely to be relevant across cell types - Large data sets are publicly available, for example, connectivity map and LINCS [63, 95] | - Typically focus on a few cancer cell types and/or global disease processes - Only a few small in vivo screens are available - Typically provide data on monotherapy only | - Obtain more data from non-tumor cell types involved in tumor biology - Carry out larger in vivo screens and/or meta-analyses - Acquire more data for drug combinations | |
Organoids (three-dimensional buds) or ex vivo screens | Can be used to obtain data about cell–cell interactions (for example, interactions between tumor cells and cells in the microenvironment) and about environmental plasticity | Few established and/or reproducible models | Develop standards to identify non-typical phenotypic parameters that are relevant to the effects of a drug on the tumor microenvironments, for example, cell-type-specific effects and cell–cell communication | |
Patient-derived tumor xenograft screens | Can model the effects of drugs on components of the tumor microenvironment | - Do not model immune interactions - Cost and ethical considerations need to be take into account when using as a discovery (versus test) tool | ||
In vivo screens in GEM, syngeneic, or humanized models | Can model immune interactions | Cost and ethical considerations need to be taken into account when used as a discovery (versus test) tool | ||
Patient | Electronic health records | Provide information about environmental exposures, immunological and metabolic measures, diagnostic assays, comorbidities and wellness, and longitudinal follow-up data | - Key data are split across isolated records in primary care and specialist hospitals, claims systems, assay providers, and others - There are currently no curation or digitization standards | - Address data confidentiality (for example, use honest brokers) and connect disparate records for patients - Improve curation and standardization |
Deconvoluting failed trials | Necessary to follow up from failed drug trials that may overlook responding populations that are mutually exclusive | Investment is rarely available to generate and mine data from failed trials | - Use a retrospective approval route in which the responding population is shown to be distinct from the comparator or standard of care | |
Profiling of cell types from healthy individuals | Projects are large and well-funded, for example, GTEx [96] and the Human Protein Atlas [97] | - Public references may not capture interpatient (or disease-influenced) variability - Findings are often assessed separately from efficacy data | - Improve integrative analyses across different types of patient data - Agree on critical measures that should be assessed for the tumor microenvironment, drug toxicity, and patient comorbidities and wellness | |
Comprehensive profiling of tumor genetics and heterogeneity | Projects are large, well-funded, and include tens of thousands of tumors, for example, projects by TCGA [98] and the ICGC [99] | - Exploratory NGS is not routine for patients - Public efforts are typically diagnostic, focus on the primary tumor, use purified tumor cell content, and have low sequencing depth | - Perform more and deeper spatial single-cell profiling of longitudinal and metastatic samples - Increase multi-omic profiling of samples | |
Longitudinal and metastatic tumor genomic profiles | Obtaining information about genetic shift after therapy could dramatically change our understanding of tumor drivers and heterogeneity | - Currently only a limited amount of such data is available - Ethical and practical considerations regarding the necessary invasive sampling procedures need to be taken into account | Continue to advance non-invasive monitoring approaches | |
Single-cell sequencing | Provide unprecedented high-resolution information about genotypic and phenotypic heterogeneity of tumors and the tumor microenvironment, including information about cell differentiation and the effects of drugs | - Current technologies require fresh tissue biopsies and obtaining these is often impractical - Mature approaches are limited to RNA profiling | - Set up central repositories for single-cell omics data from patients and models - Advance technology that enables exploration of molecules other than RNA and of non-fresh tissue samples | |
Germline genetic variation | Provide information about a patient’s inherent immunological and metabolic competencies, susceptibility to adverse events, and other aspects of wellness | Information is rarely available for patients with cancer as it is removed to avoid the risk of patients being identified and confidentiality being breached | Add functional germline variant information to public databases of tumor genetics | |
Biosensors and smart wearables | Enable real-time reactive adaptation of therapy to manage response, health, and adverse events | A limited number of devices are currently available and few molecular measures are currently possible | Develop technologies to identify the most important and relevant measures | |
Know- ledge | Pathway and interaction networks | Able to link drug target to biology | - Typically focus only on intracellular pathways and interactions - Typically limited to protein–protein interactions | Acquire more information about the cell-context specificity of interactions and about cell–cell communication |
Regulomes | Provide omics information that is indicative of active processes | - Often miss cell-context specificity of regulomes - Focus on intracellular processes - Public resources focus on the transcriptome | - Obtain more data on cell-type-specific regulomes (as in, for example, ENCODE [100]) and on extracellular communication regulomes |