Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy.

Combination cancer therapies aim to improve the probability and magnitude of therapeutic responses and reduce the likelihood of acquired resistance in an individual patient. However, drugs are tested in clinical trials on genetically diverse patient populations. We show here that patient-to-patient variability and independent drug action are sufficient to explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or additivity. This is also true for combinations tested in patient-derived tumor xenografts. In a combination exhibiting independent drug action, each patient benefits solely from the drug to which his or her tumor is most sensitive, with no added benefit from other drugs. Even when drug combinations exhibit additivity or synergy in pre-clinical models, patient-to-patient variability and low cross-resistance make independent action the dominant mechanism in clinical populations. This insight represents a different way to interpret trial data and a different way to design combination therapies.

Biomarker-driven strategy for MCL1 inhibition in T-cell lymphomas.

There is a pressing need for more effective therapies to treat patients with T-cell lymphomas (TCLs), including first-line approaches that increase the response rate to cyclophosphamide, adriamycin, vincristine, and prednisone (CHOP) chemotherapy. We characterized the mitochondrial apoptosis pathway in cell lines and patient-derived xenograft (PDX) models of TCL and assessed the in vitro efficacy of BH3 mimetics, including the BCL2 inhibitor venetoclax, the BCL2/BCL-xL inhibitor navitoclax, and the novel MCL1 inhibitor AZD5991. The abundance of antiapoptotic BCL2 family members based on immunoblotting or RNA transcript levels correlated poorly with the activity of BH3 mimetics. In contrast, the functional approach BH3 profiling reliably predicted sensitivity to BH3 mimetics in vitro and in vivo. We used BH3 profiling to select TCL PDX that were dependent on MCL1. Mice xenografted with these PDX and treated with AZD5991 had markedly improved survival. The combination of AZD5991 and CHOP achieved synergy based on survival improvement beyond a mathematical "sum of benefits" model. Thus, MCL1 inhibition is a promising strategy as both a single agent and in combination with chemotherapy for patients with TCL and functional dependence on MCL1.

Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.

Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.

Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance.

The evolution of antibiotic resistance can now be rapidly tracked with high-throughput technologies for bacterial genotyping and phenotyping. Combined with new approaches to evolve resistance in the laboratory and to characterize clinically evolved resistant pathogens, these methods are revealing the molecular basis and rate of evolution of antibiotic resistance under treatment regimens of single drugs or drug combinations. In this Progress article, we review these new tools for studying the evolution of antibiotic resistance and discuss how the genomic and evolutionary insights they provide could transform the diagnosis, treatment and predictability of antibiotic resistance in bacterial infections.

The role of repressor kinetics in relief of transcriptional interference between convergent promoters.

Transcriptional interference (TI), where transcription from a promoter is inhibited by the activity of other promoters in its vicinity on the same DNA, enables transcription factors to regulate a target promoter indirectly, inducing or relieving TI by controlling the interfering promoter. For convergent promoters, stochastic simulations indicate that relief of TI can be inhibited if the repressor at the interfering promoter has slow binding kinetics, making it either sensitive to frequent dislodgement by elongating RNA polymerases (RNAPs) from the target promoter, or able to be a strong roadblock to these RNAPs. In vivo measurements of relief of TI by CI or Cro repressors in the bacteriophage λ PR-PRE system show strong relief of TI and a lack of dislodgement and roadblocking effects, indicative of rapid CI and Cro binding kinetics. However, repression of the same λ promoter by a catalytically dead CRISPR Cas9 protein gave either compromised or no relief of TI depending on the orientation at which it binds DNA, consistent with dCas9 being a slow kinetics repressor. This analysis shows how the intrinsic properties of a repressor can be evolutionarily tuned to set the magnitude of relief of TI.

Potent transcriptional interference by pausing of RNA polymerases over a downstream promoter.

Elongating RNA polymerases (RNAPs) can interfere with transcription from downstream promoters by inhibiting DNA binding by RNAP and activators. However, combining quantitative measurement with mathematical modeling, we show that simple RNAP elongation cannot produce the strong asymmetric interference observed between a natural face-to-face promoter pair in bacteriophage lambda. Pausing of elongating polymerases over the RNAP-binding site of the downstream promoter is demonstrated in vivo and is shown by modeling to account for the increased interference. The model successfully predicts the effects on interference of treatments increasing or reducing pausing. Gene regulation by pausing-enhanced occlusion provides a general and potentially widespread mechanism by which even weak converging or tandem transcription, either coding or noncoding, can bring about strong in cis repression.

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.

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.

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.

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.

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.

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.

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.

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.

Chemical decay of an antibiotic inverts selection for resistance.

Antibiotics are often unstable and can decay into various compounds with potential biological activities. We found that as tetracycline degrades, the competitive advantage conferred to bacteria by resistance not only diminishes but actually reverses to become a prolonged disadvantage due to the activities of more stable degradation products. Tetracycline decay can lead to net selection against resistance, which may help explain the puzzling coexistence of sensitive and resistant strains in natural environments.

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.

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.