Precision Oncology Beyond Genomics: The Future Is Here—It Is Just Not Evenly Distributed

Abstract

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient’s tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient’s treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Context Matters—Why We Need to Change From a One Size Fits all Approach to Made-to-Measure Therapies for Individual Patients With Pancreatic Cancer

“…As a potential solution, this review highlighted the combined targeted phenotypic approach as a promising strategy to identify effective compounds using a cell-based assay, allowing to take this tumor heterogeneity and the context of various unique alterations into account—given suitable cellular models are used. A powerful tool for this approach are patient-derived organoids as these are suitable models for tumor heterogeneity and personalized oncology, but also for high-throughput drug screens ( Pfohl et al, 2021 ).…”

“…Patient-derived organoids are 3D cell culture models derived from small pieces of tissue, for example from a patient’s primary tumor or a metastasis ( Silvestri et al, 2017 ; Schumacher et al, 2019 ; Pfohl et al, 2021 ). Culture conditions allow cancer stem-like cells to self-organize and form miniature versions of a patient’s tumor, i.e., organoids, that recapitulate the genetic, histologic, and molecular alterations of the tumor ( Boj et al, 2015 ; Schütte et al, 2017 ; Driehuis et al, 2019 ), including its intra-tumor heterogeneity and functional phenotype ( Schumacher et al, 2019 ; Pfohl et al, 2021 ).…”

“…Patient-derived organoids are 3D cell culture models derived from small pieces of tissue, for example from a patient’s primary tumor or a metastasis ( Silvestri et al, 2017 ; Schumacher et al, 2019 ; Pfohl et al, 2021 ). Culture conditions allow cancer stem-like cells to self-organize and form miniature versions of a patient’s tumor, i.e., organoids, that recapitulate the genetic, histologic, and molecular alterations of the tumor ( Boj et al, 2015 ; Schütte et al, 2017 ; Driehuis et al, 2019 ), including its intra-tumor heterogeneity and functional phenotype ( Schumacher et al, 2019 ; Pfohl et al, 2021 ). Further, patient-derived organoids were shown to be a suitable tool to predict treatment response in cancer patients ( Vlachogiannis et al, 2018 ; Wensink et al, 2021 ) and can be used for high-throughput drug screens ( Boehnke et al, 2016 ) and genetic manipulation ( Boj et al, 2015 ).…”

“…Reverse Clinical Engineering ® can be performed on an individual patient-derived organoid model, identifying the Achilles heel of this specific tumor, or on a collection of patient-derived organoids from a biobank of e.g., a specific cancer type (PDAC or other) to identify for example a new treatment strategy and/or biomarker for this tumor type. Either way, this approach of functionally profiling tumors in a potentially high through-put manner is of high potential for real personalized oncology ( Pfohl et al, 2021 ) and a promising strategy to finally identify effective treatment options for PDAC patients.…”

“…However, establishing distinct culture and assay conditions for each distinct tumor entity and constant supply of patient material for large screens remain a major challenge in using this combined targeted phenotypic approach with patient-derived organoids ( Boehnke et al, 2016 ). Efficient establishment of organoid cultures for different entities, drug assays including initial seeding material and density, treatment regimen, assay reproducibility, evaluation of drug response (e.g., IC50, area-under-the curve, Z-score IC50), drug validation, and assay scalability remain technical constraints and all need to be optimized ( Boehnke et al, 2016 ; Phan et al, 2019 ; Bergdorf et al, 2020 ) In addition, potential off-target toxicity cannot be assessed by organoid monocultures ( Pfohl et al, 2021 ). Nevertheless, patient derived organoids are a powerful tool which can be further expanded to take the tumor microenvironment into account, known to impact drug response, through suitable co-culture systems with e.g., cancer associated fibroblasts or immune cells ( Tsai et al, 2018 ).…”