An RNA damage response network mediates the lethality of 5-FU in colorectal cancer.

5-fluorouracil (5-FU), a major anti-cancer therapeutic, is believed to function primarily by inhibiting thymidylate synthase, depleting deoxythymidine triphosphate (dTTP), and causing DNA damage. Here, we show that clinical combinations of 5-FU with oxaliplatin or irinotecan show no synergy in human colorectal cancer (CRC) trials and sub-additive killing in CRC cell lines. Using selective 5-FU metabolites, phospho- and ubiquitin proteomics, and primary human CRC organoids, we demonstrate that 5-FU-mediated CRC cell killing primarily involves an RNA damage response during ribosome biogenesis, causing lysosomal degradation of damaged rRNAs and proteasomal degradation of ubiquitinated ribosomal proteins. Tumor types clinically responsive to 5-FU treatment show upregulated rRNA biogenesis while 5-FU clinically non-responsive tumor types do not, instead showing greater sensitivity to 5-FU's DNA damage effects. Finally, we show that treatments upregulating ribosome biogenesis, including KDM2A inhibition, promote RNA-dependent cell killing by 5-FU, demonstrating the potential for combinatorial targeting of this ribosomal RNA damage response for improved cancer therapy.

Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism.

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug "CHOP" regimen in peripheral T-cell lymphoma (PTCL) cell lines and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiotherapy-the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a "single hit," to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not contingent on positive drug-drug interactions.

Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia.

Combination therapy is an important part of cancer treatment and is often employed to overcome or prevent drug resistance. Preclinical screening strategies often prioritize synergistic drug combinations; however, studies of antibiotic combinations show that synergistic drug interactions can accelerate the emergence of resistance because resistance to one drug depletes the effect of both. In this study, we aimed to determine whether synergy drives the development of resistance in cancer cell lines using live-cell imaging. Consistent with prior models of tumor evolution, we found that when controlling for activity, drug synergy is associated with increased probability of developing drug resistance. We demonstrate that these observations are an expected consequence of synergy: the fitness benefit of resisting a drug in a combination is greater in synergistic combinations than in nonsynergistic combinations. These data have important implications for preclinical strategies aiming to develop novel combinations of cancer therapies with robust and durable efficacy. SIGNIFICANCE: Preclinical strategies to identify combinations for cancer treatment often focus on identifying synergistic combinations. This study shows that in AML cells combinations that rely on synergy can increase the likelihood of developing resistance, suggesting that combination screening strategies may benefit from a more holistic approach rather than focusing on drug synergy. See related commentary by Bhola and Letai, p. 81. This article is featured in Selected Articles from This Issue, p. 80.

Tumor-Specific Activity of Precision Medicines in the NCI-MATCH Trial.

PURPOSE: National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies. EXPERIMENTAL DESIGN: To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from 10 published NCI-MATCH subprotocols (together n = 435 patients). FDR was controlled by the Benjamini-Hochberg procedure. RESULTS: Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations and MEK inhibition in lung cancers with BRAF non-V600E mutations. In addition, it identifies low-grade serious ovarian carcinoma with BRAF v600E mutation as especially sensitive to BRAF and MEK co-inhibition (dabrafenib plus trametinib), a treatment that received accelerated FDA approval for advanced solid tumors with BRAF v600E mutation. CONCLUSIONS: These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study.

Additivity predicts the efficacy of most approved combination therapies for advanced cancer.

Most advanced cancers are treated with drug combinations. Rational design aims to identify synergistic combinations, but existing synergy metrics apply to preclinical, not clinical data. Here we propose a model of drug additivity for progression-free survival (PFS) to assess whether clinical efficacies of approved drug combinations are additive or synergistic. This model includes patient-to-patient variability in best single-drug response plus the weaker drug per patient. Among US Food and Drug Administration approvals of drug combinations for advanced cancers (1995-2020), 95% exhibited additive or less than additive effects on PFS times. Among positive or negative phase 3 trials published between 2014-2018, every combination that improved PFS was expected to succeed by additivity (100% sensitivity) and most failures were expected to fail (78% specificity). This study shows synergy is neither a necessary nor common property of clinically effective drug combinations. The predictable efficacy of approved combinations suggests that additivity can be a design principle for combination therapies.

Genetically informed therapy for lymphoma: the discomfiting benefit of lumping splits.

Zhang et al. report a randomized phase 2 trial for diffuse large B cell lymphoma (DLBCL) that compared standard of care (R-CHOP) to R-CHOP combined with one of 5 agents matched to an individual lymphoma's genetics. Overall, the matching strategy significantly outperformed R-CHOP, laying the foundation for a paradigm-shifting phase 3 trial.

An RNA Damage Response Network Mediates the Lethality of 5-FU in Clinically Relevant Tumor Types.

5-fluorouracil (5-FU) is a successful and broadly used anti-cancer therapeutic. A major mechanism of action of 5-FU is thought to be through thymidylate synthase (TYMS) inhibition resulting in dTTP depletion and activation of the DNA damage response. This suggests that 5-FU should synergize with other DNA damaging agents. However, we found that combinations of 5-FU and oxaliplatin or irinotecan failed to display any evidence of synergy in clinical trials, and resulted in sub-additive killing in a panel of colorectal cancer (CRC) cell lines. In seeking to understand this antagonism, we unexpectedly found that an RNA damage response during ribosome biogenesis dominates the drug's efficacy in tumor types for which 5-FU shows clinical benefit. 5-FU has an inherent bias for RNA incorporation, and blocking this greatly reduced drug-induced lethality, indicating that accumulation of damaged RNA is more deleterious than the lack of new RNA synthesis. Using 5-FU metabolites that specifically incorporate into either RNA or DNA revealed that CRC cell lines and patient-derived colorectal cancer organoids are inherently more sensitive to RNA damage. This difference held true in cell lines from other tissues in which 5-FU has shown clinical utility, whereas cell lines from tumor tissues that lack clinical 5-FU responsiveness typically showed greater sensitivity to the drug's DNA damage effects. Analysis of changes in the phosphoproteome and ubiquitinome shows RNA damage triggers the selective ubiquitination of multiple ribosomal proteins leading to autophagy-dependent rRNA catabolism and proteasome-dependent degradation of ubiquitinated ribosome proteins. Further, RNA damage response to 5-FU is selectively enhanced by compounds that promote ribosome biogenesis, such as KDM2A inhibitors. These results demonstrate the presence of a strong RNA damage response linked to apoptotic cell death, with clear utility of combinatorially targeting this response in cancer therapy.

Tumor-specific activity of precision medicines in the NCI-MATCH trial.

Background: NCI-MATCH is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies. Methods: To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from ten published NCI-MATCH subprotocols (together n=435 patients). False discovery rate was controlled by the Benjamini-Hochberg procedure. Results: Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations, MEK inhibition in lung cancers with BRAF non-V600E mutations, and MEK inhibition in cholangiocarcinomas with NRAS mutations. Conclusions: These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study.

Ultrasensitive response explains the benefit of combination chemotherapy despite drug antagonism.

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug 'CHOP' regimen in Peripheral T-Cell Lymphoma (PTCL) cell lines, and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using month-long in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiation - the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a 'single hit', in order to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not reliant on positive drug-drug interactions.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations.

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

Drug independence and the curability of cancer by combination chemotherapy.

Combination chemotherapy can cure certain leukemias and lymphomas, but most solid cancers are only curable at early stages. We review quantitative principles that explain the benefits of combining independently active cancer therapies in both settings. Understanding the mechanistic principles underlying curative treatments, including those developed many decades ago, is valuable for improving future combination therapies. We discuss contemporary evidence for long-established but currently neglected ideas of how combination therapy overcomes tumor heterogeneity. We show that a unified model of interpatient and intratumor heterogeneity describes historical progress in the treatment of pediatric acute lymphocytic leukemia (ALL), in which increasingly intensive combination regimens ultimately achieved high cure rates. We also describe three distinct aspects of drug independence that apply at different biological scales. The ability of these principles to quantitatively explain curative regimens suggests that supra-additive (synergistic) drug interactions are not required for successful combination therapy.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects.

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology.

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy. SIGNIFICANCE: The model of independent drug action represents an effective means to predict the magnitude of benefit likely to be observed in new clinical trials for combination therapies. The "bet-hedging" strategy implicit in independent action suggests that individual patients often benefit from only a subset-sometimes one-of the drugs in a combination. Personalized, targeted combination therapy, consisting of agents likely to be active in a particular patient, will increase, perhaps substantially, the magnitude of therapeutic benefit. Precision approaches of this type will require a better understanding of variability in drug response and new biomarkers, which will entail preclinical research on diverse panels of cancer models rather than studying drug synergy in unusually sensitive models.

Predictable Clinical Benefits without Evidence of Synergy in Trials of Combination Therapies with Immune-Checkpoint Inhibitors.

PURPOSE: Combinations of immune-checkpoint inhibitors (ICI) with other cancer therapies have been approved for advanced cancers in multiple indications, and numerous trials are under way to test new combinations. However, the mechanisms that account for the superiority of approved ICI combinations relative to their constituent monotherapies remain unknown. EXPERIMENTAL DESIGN: We analyzed 13 phase III clinical trials testing combinations of ICIs with each other or other drugs in patients with advanced melanoma and lung, breast, gastric, kidney, and head and neck cancers. The clinical activity of the individual constituent therapies, measured in the same or a closely matched trial cohort, was used to compute progression-free survival (PFS) curves expected under a model of independent drug action. To identify additive or synergistic efficacy, PFS expected under this null model was compared with observed PFS by Cox regression. RESULTS: PFS elicited by approved combination therapies with ICIs could be accurately predicted from monotherapy data using the independent drug action model (Pearson r = 0.98, P < 5 × 10-9, N = 4,173 patients, 8 types of cancer). We found no evidence of drug additivity or synergy except in one trial in which such interactions might have extended median PFS by 9 days. CONCLUSIONS: Combining ICIs with other cancer therapies affords predictable and clinically meaningful benefit by providing patients with multiple chances of response to a single agent. Conversely, there exists no evidence in phase III trials that other therapies interact with and enhance the activity of ICIs. These findings can inform the design and testing of new ICI combination therapies while emphasizing the importance of developing better predictors (biomarkers) of ICI response.

Comparing the Efficacy of Cancer Therapies between Subgroups in Basket Trials.

The need to test anticancer drugs in multiple indications has been addressed by basket trials, which are Phase I or II clinical trials involving multiple tumor subtypes and a single master protocol. Basket trials typically involve few patients per type, making it challenging to rigorously compare responses across types. We describe the use of permutation testing to test for differences among subgroups using empirical null distributions and the Benjamini-Hochberg procedure to control for false discovery. We apply the approach retrospectively to tumor-volume changes and progression-free survival in published basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib and uncover examples of therapeutic benefit missed by conventional binomial testing. For example, we identify an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations. Permutation testing can be used to design basket trials but is more conservatively introduced alongside established approaches to enrollment such as Simon's two-stage design.

A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts.

Advanced ovarian cancers are a leading cause of cancer-related death in women and are currently treated with surgery and chemotherapy. This standard of care is often temporarily successful but exhibits a high rate of relapse, after which, treatment options are few. Here we investigate whether biomarker-guided use of multiple targeted therapies, including small molecules and antibody-drug conjugates, is a viable alternative. A panel of patient-derived ovarian cancer xenografts (PDX), similar in genetics and chemotherapy responsiveness to human tumors, was exposed to 21 monotherapies and combination therapies. Three monotherapies and one combination were found to be active in different subsets of PDX. Analysis of gene expression data identified biomarkers associated with responsiveness to each of the three targeted therapies, none of which directly inhibits an oncogenic driver. While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. The distribution of biomarker positivity in The Cancer Genome Atlas data suggests the potential for a similar precision approach in human patients. SIGNIFICANCE: This study exploits a panel of patient-derived xenografts to demonstrate that most ovarian tumors can be matched to effective biomarker-guided treatments.

DT2216-a Bcl-xL-specific degrader is highly active against Bcl-xL-dependent T cell lymphomas.

BACKGROUND: Patients with advanced T cell lymphomas (TCLs) have limited therapeutic options and poor outcomes in part because their TCLs evade apoptosis through upregulation of anti-apoptotic Bcl-2 proteins. Subsets of TCL cell lines, patient-derived xenografts (PDXs), and primary patient samples depend on Bcl-xL for survival. However, small molecule Bcl-xL inhibitors such as ABT263 have failed during clinical development due to on-target and dose-limiting thrombocytopenia. METHODS: We have developed DT2216, a proteolysis targeting chimera (PROTAC) targeting Bcl-xL for degradation via Von Hippel-Lindau (VHL) E3 ligase, and shown that it has better anti-tumor activity but is less toxic to platelets compared to ABT263. Here, we examined the therapeutic potential of DT2216 for TCLs via testing its anti-TCL activity in vitro using MTS assay, immunoblotting, and flow cytometry and anti-TCL activity in vivo using TCL cell xenograft and PDX model in mice. RESULTS: The results showed that DT2216 selectively killed various Bcl-xL-dependent TCL cells including MyLa cells in vitro. In vivo, DT2216 alone was highly effective against MyLa TCL xenografts in mice without causing significant thrombocytopenia or other toxicity. Furthermore, DT2216 combined with ABT199 (a selective Bcl-2 inhibitor) synergistically reduced disease burden and improved survival in a TCL PDX mouse model dependent on both Bcl-2 and Bcl-xL. CONCLUSIONS: These findings support the clinical testing of DT2216 in patients with Bcl-xL-dependent TCLs, both as a single agent and in rational combinations.

A curative combination cancer therapy achieves high fractional cell killing through low cross-resistance and drug additivity.

Curative cancer therapies are uncommon and nearly always involve multi-drug combinations developed by experimentation in humans; unfortunately, the mechanistic basis for the success of such combinations has rarely been investigated in detail, obscuring lessons learned. Here, we use isobologram analysis to score pharmacological interaction, and clone tracing and CRISPR screening to measure cross-resistance among the five drugs comprising R-CHOP, a combination therapy that frequently cures Diffuse Large B-Cell Lymphomas. We find that drugs in R-CHOP exhibit very low cross-resistance but not synergistic interaction: together they achieve a greater fractional kill according to the null hypothesis for both the Loewe dose-additivity model and the Bliss effect-independence model. These data provide direct evidence for the 50 year old hypothesis that a curative cancer therapy can be constructed on the basis of independently effective drugs having non-overlapping mechanisms of resistance, without synergistic interaction, which has immediate significance for the design of new drug combinations.

Adaptation of Human iPSC-Derived Cardiomyocytes to Tyrosine Kinase Inhibitors Reduces Acute Cardiotoxicity via Metabolic Reprogramming.

Tyrosine kinase inhibitors (TKIs) are widely used to treat solid tumors but can be cardiotoxic. The molecular basis for this toxicity and its relationship to therapeutic mechanisms remain unclear; we therefore undertook a systems-level analysis of human cardiomyocytes (CMs) exposed to four TKIs. CMs differentiated from human induced pluripotent stem cells (hiPSCs) were exposed to sunitinib, sorafenib, lapatinib, or erlotinib, and responses were assessed by functional assays, microscopy, RNA sequencing, and mass spectrometry (GEO: GSE114686; PRIDE: PXD012043). TKIs have diverse effects on hiPSC-CMs distinct from inhibition of tyrosine-kinase-mediated signal transduction; cardiac metabolism is particularly sensitive. Following sorafenib treatment, oxidative phosphorylation is downregulated, resulting in a profound defect in mitochondrial energetics. Cells adapt by upregulating aerobic glycolysis. Adaptation makes cells less acutely sensitive to sorafenib but may have long-term negative consequences. Thus, CMs exhibit adaptive responses to anti-cancer drugs conceptually similar to those previously shown in tumors to mediate drug resistance.