To assess OncoTarget and OncoTreat’s ability to predict drug sensitivity in tumors from pre-treatment RNASeq profile analysis, we designed an innovative, proof-of-concept clinical study with a preclinical end point. The N of 1 study enrolled patients with advanced malignancies refractory or intolerant to standard of care treatment, including several rare tumors (Table S1). Due to trial design, selected participants had generally progressed on most if not all standard of care therapies, by time of enrollment, and lacked actionable genetic alterations suggesting potential targeted drug efficacy.

Clinically indicated biopsies or tumor resections were performed at the request of the treating oncologist; consent was required to allow a portion of the fresh tumor tissue from biopsies with ≥70% cellularity to be processed for RNASeq profiling and transplantation into immunodeficient mice. Here, we report the results of 35 distinct drug arms—including 21 OncoTarget and 22 OncoTreat-predicted drugs, 8 of which were predicted by both methods—in the first seven, consecutively established PDX models that could be expanded for in vivo drug testing. Given its research nature, the protocol did not require patients to be treated with predicted drugs. Instead, subsequent treatment was chosen by the patient’s oncologist using currently available approaches for cancers that have progressed on standard treatments, e.g., mutational profiling, off-label drug use, and referral to therapeutic clinical trials when eligible.

Note that the study was neither designed nor sufficiently powered to assess the efficacy of each individual predicted drug. Rather, our goal was to validate the overall ability of the two methodologies (tests) to predict drugs that elicit in vivo response.

The seven PDX models included three basal-like breast cancers (BLBC) (BC-32398, BC-97359, BC-50291), a pancreatic ductal carcinoma (PDA) (PAC-05647), a colon adenocarcinoma (COAD) (CAR-23659), a KIT^WT/PDGFR^WT gastrointestinal stromal tumor (GIST) harboring both a KRAS^G12D mutation and a germline SDHB deletion (GIST-81050), and a recurrent WHO grade II anaplastic meningioma (MEN) (CNS-16474). Clinical characteristics of the seven patients are summarized (Figure 1A), including extensive prior therapies and the results of targeted genomic sequencing, when performed at the discretion of the treating oncologist. For two patients (CNS-16474 and PAC-05647), mutational profiling was not available because the oncologist did not expect these tumor types to harbor clinically actionable mutations. Notably, four of the seven subjects had progressed on at least three lines of treatment before enrollment and all seven represented aggressive, drug resistant tumors.

Due to the constraints associated with the expansion and use of early PDX passages for preclinical therapeutic studies, we applied several practical selection criteria to further prioritize predicted drugs for in vivo validation. Specifically: (a) Only drugs classified as antineoplastic agents were considered, (b) Drugs were eliminated if the patient had been previously treated with them, (c) When perturbation profiles were available from suitable models—i.e. for GIST, meningioma and breast cancer—OncoTreat-predicted drugs were selected over OncoTarget-predicted ones with comparable p-values, to ensure an overall balanced number of tested predictions from the two methods, (e) Drugs predicted for the patient but not for the corresponding PDX model were eliminated, and (f) When multiple inhibitors sharing the same canonical mechanism were identified—e.g., multiple HDAC or topoisomerase II inhibitors—only the top FDA approved and/or the most statistically significant one was selected.

The 35 unique patient-specific drugs prioritized for in vivo validation, as well as prediction rationale and dosing schedule, are summarized in Table S2 (see Table S3 for further details on drug prediction and selection in each of the seven models).

We have developed two complementary, RNA-based assays to transform VIPER-based (16) tumor sample-specific protein activity profiles into actionable drug response predictions (see schematics in Figures 1B and S1A-C).

The first one (OncoTarget) identifies aberrantly activated proteins (threshold p ≤ 10⁻⁵, Bonferroni corrected, as measured by VIPER) for which a high-affinity inhibitor is currently available, see Methods. To assess target actionability, we analyzed DrugBank (RRID:SCR_002700), the SelleckChem database (RRID:SCR_003823), published literature, and public information from drug development pipelines, resulting in a curated list of 180 proteins representing validated, high-affinity targets of clinically-relevant small molecule compounds (Table S4). These include proteins that are rarely if ever mutated in cancer, such as topoisomerases, chromatin remodeling enzymes, and proteins aberrantly activated by autocrine, paracrine, or endocrine signals.

The second one (OncoTreat) leverages a large compendium of RNASeq profiles, generated to represent the transcriptional response of cell lines to a comprehensive repertoire of antineoplastic agents. These allow identification of compounds capable of inverting the transcriptional activity of a TCM module (TCM-inverters)—as defined by the 25 most activated (25↑) and 25 most inactivated (25↓) candidate MR proteins in a patient tumor—at a conservative statistical significance threshold (p ≤ 10⁻⁵, Bonferroni corrected, by 1-tailed analytic-rank based enrichment analysis — aREA), (Figures 1B and S1C) (13, 16), see Methods. As such, rather than using a priori knowledge, OncoTreat predicts TCM-inverter drugs based on drug MoA assessed de novo from experimental perturbational assays—i.e., based on the differential activity of 2,556 regulatory proteins in drug vs. vehicle control-treated cells.

The number of candidate MRs in a TCM was selected based on the average number of MRs necessary to integrate the effect of genetic alterations in their upstream pathways, as assessed in (8). Specifically, we had reported that, for the vast majority of tumor subtypes in The Cancer Genome Atlas (TCGA), there is rapid saturation of mutational events in pathways upstream of the first 1 to 100 candidate MRs of each individual tumor, with 50 MRs sufficient to account for 80% of all mutations in all but 5 of 20 tumor types analyzed (COAD, HNSC, SKCM, STAD, OV). However, we also show that drug prediction is extremely robust and reproducible for any TCM size ranging from n = 10 to 200 activated and inactivated MRs, with mean Spearman’s pairwise correlation ranging from r_s = 0.89 in GIST-81050 to r_s = 0.98 in BC-32398 (Figure S2A–E).

Drug MoA and TCM-inversion potential were assessed based on perturbational profiles in selected, high-fidelity (cognate) cell lines. These were identified based on their ability to recapitulate the TCM activity signature—i.e., the activity of the top and bottom 25 most differentially active MRs—of the greatest fraction of a histology-matched patient cohort. Thus, cognate cell lines are meant to represent a biological surrogate of the tumor of interest only in terms of providing an optimal in vitro context to assess tumor-relevant drug mechanisms of action, thus maximizing statistical power. Specifically, assessing MR protein activity decrease or increase following drug perturbation is best accomplished in cells where these are already significantly activated or inactivated, respectively. Likewise, to account for potential tumor drift effects in PDX passaging, we assessed whether the PDX models used in the study also recapitulated their corresponding patients’ TCM-activity signatures. As such, we assess model fidelity to a human tumor based on TCM MR enrichment (i.e., top 25↑+25↓) of the human tumor in differentially active and inactive proteins in the model, respectively (OncoMatch analysis (13, 20) (bioRxiv 2019.677435), threshold Bonferroni p ≤ 10⁻¹⁰, see Methods).

A total of 35 individual drugs predicted by the analysis were evaluated in individual PDX therapeutic arms—including 21 OncoTarget-predicted and 22 OncoTreat-predicted, 8 of which were predicted by both methods. In a few cases, the same drug was predicted and tested in more than one model. After expansion of PDX models for the therapeutic study, animals were enrolled once tumor volumes reached ~100 mm³. Models were treated and tumor volume measurements were recorded up to a fixed end-of-study time point with a median of 29 days (range 21 – 30 days). A waterfall plot of the response of each individual mouse, grouped by PDX model, to prioritized and Negative control drugs is shown in Figure 3B–C and comparisons summarized in Figure 3D–E; responses are summarized in Table S6. Overall, across all predictions, Disease Control—comprising stable disease (SD), partial response (PR), or complete response (CR)--was observed for 30 of 35 predictions (85.7%).

We evaluated overall treatment response, at the end of treatment, for OncoTarget-predicted, OncoTreat-predicted, and for drugs predicted by both methods. Compared to Vehicle control, there were significant differences in disease control rate (DCR)—defined as rate of SD + PR + CR—and objective response rate (ORR)—defined as rate of PR + CR—for both OncoTarget (p_DCR < 10⁻³, p_ORR = 0.03, by Fisher’s exact test) and OncoTreat-predicted drugs (p_DCR < 10⁻³, p_ORR = 0.01) (Figure 3B–E). Drugs predicted by both had response rates comparable to drugs predicted by just one methodology (DCR = 76%, n = 19/25; ORR = 20%, n = 5/25). Due to unanticipated toxicity possibly related to study treatment (e.g., tumor ulceration in a breast cancer PDX), 3 mice were unevaluable for response in the OncoTarget cohort (BC-97359, n = 2 for MK-2206 arm, n = 1 for panobinostat arm), while 1 mouse was unevaluable in the OncoTreat cohort (BC-97359, n = 1 for irinotecan arm), and 3 mice were unevaluable for response in the Both cohort (GIST-81050, n=2 for daunorubicin arm, n = 1 for topotecan arm). While cautioning that the study was neither designed nor sufficiently powered to evaluate the efficacy of individual drug predictions, for completeness, we provide growth curves for each drug (Figure S5A–C).

As a set of appropriate negative controls, we evaluated response to randomly selecting antineoplastic drugs that were not statistically significant by either OncoTarget or OncoTreat (when available) analysis (i.e., p = 1 from both methods). We note that these are not true random controls but rather potential false negative predictions meant to assess the algorithms’ predictive power. Four PDX models (GIST-81050, CAR-23659, PAC-05647, BC-50291) were thus treated with 13 Negative control drugs (Table S2) and Vehicle control. To avoid bias due to differences in tumor growth rates, these drugs were tested in conjunction with an additional Vehicle control arm in each PDX, thus providing a tumor growth-independent reference. Disease progression was observed across all models treated with Negative control drugs and statistically indistinguishable from concurrent Vehicle control (Fisher’s exact p = 0.6, Table S6).

Cumulative Kaplan-Meier analysis of animals in the OncoTarget, OncoTreat, and OncoTreat + OncoTarget cohorts was performed (Figure 4A). The analysis demonstrates highly statistically significant improvement in disease control using agents predicted by either methodology, compared to Vehicle control (p < 10⁻⁴, 2-tailed log-rank test). In contrast, there was no statistically significant difference between animals in the Negative control and passage-matched Vehicle control cohorts (p = 0.38, log-rank) (Figure 4B). Additionally, the treatment-to-control ratio (ΔT/ΔC%: the ratio of change in volumes from baseline in the treatment/control arms), which corrects for baseline differences in model-passage growth rate, was significantly improved in the OncoTarget (mean ΔT/ΔC% = 14.3%, 95% CI: −1.2 – 29.9, p = 0.004, 2-tail Mann-Whitney U-test) and OncoTreat (mean ΔT/ΔC% = 17.3%, 95% CI: −0.8 – 35.3, p = 0.014) treated cohorts, compared to the Negative control cohort (mean ΔT/ΔC% = 46.1%, 95% CI: 25.5 – 61.1), with overall statistical significance (OncoTarget vs OncoTreat vs Negative control, p = 0.002, 2-tail ANOVA) (Figure 4C).

Pharmacodynamic (PD) studies are a critical aspect of drug development to elucidate drug MoA and to characterize primary and acquired drug resistance. PD assessment from early, on-treatment samples help to determine: (a) whether effective, OncoTreat-predicted drugs recapitulate in vivo the TCM-inversion that occurs in the cognate cell line(s) (i.e., MoA conservation) and (b) whether failure of OncoTreat-predicted drugs corresponds to inability to conserve TCM-inversion in vivo, perhaps due to pharmacokinetic factors, or occurs despite TCM-inversion, for instance due to later cell adaptation or clonal selection. The opportunity to investigate the second point was limited in the study, since most OncoTreat-predicted drugs demonstrated strong anti-tumor activity.

Samples for PD assessment were procured from two mice per treatment arm, for the four PDX models treated with at least three OncoTreat-predicted drugs—GIST-81050, BC-32398, CNS-16474, and PAC-05647. Mice were randomly selected for early sacrifice, independent of tumor size, three hours following the third dose, and were excluded from response assessment. The four selected models had the highest patient tumor fidelity (p < 10⁻¹⁰, Figure 3A) and were thus well-suited to evaluate MoA conservation. TCM-inversion was assessed by VIPER analysis of RNASeq signatures comparing drug- vs. Vehicle control-treated PDX tumor samples.

Overall, the vast majority of OncoTreat-predicted drugs with available PD samples—i.e., 15 of 18 (83%)—significantly recapitulated in vivo (p < 10⁻⁵, by 1-tailed aREA) the TCM-inversion predicted from cognate cell line perturbational assays, in vitro, with p-values ranging from 10⁻⁵ (teniposide in PAC-05647) to 10⁻⁴⁰ (daunorubicin in BC-32398) (Figure 5A–D). The three drugs that failed to conserve MoA in vivo included abiraterone in CNS-16474, which was borderline for achieving disease control, belinostat in PAC-05647, which achieved disease control by end-of-study, and daunorubicin in GIST-81050, which also achieved disease control (Table S6). As belinostat was the only HDAC inhibitor evaluated for PD effect, one is tempted to speculate if the early time point was inadequate for an epigenetic modifying drug to fully implement its in vivo effect. Intriguingly, daunorubicin demonstrated strong TCM-inversion in the BC-32398 model but not GIST-81050, where it inverted the 25↓ MRs but failed to invert the 25↑ MRs, and yet had strong anti-tumor activity in both models. Conversely, one drug failed to achieve disease control (homoharringtonine in the CNS-16474 model) despite recapitulating TCM-inversion in vivo.

As expected, four of the Negative control drugs—alpelisib, selinexor, serdemetan, and pacritinib—failed to achieve significant TCM-inversion in vivo (Figure 5E) and did not achieve disease control by end-of-study. TAE684 did induce TCM-inversion in vivo (p = 10⁻¹⁶) in the early on-treatment sample, yet still failed to achieve disease control.

In summary, 15 of 18 OncoTreat predicted drugs, including 13 of 16 (81%) that induced disease control, recapitulated significant TCM-inversion in early on-treatment PD samples. This is consistent with our hypothesis that inference of drug induced TCM-inversion in carefully selected models, whether they be cell lines or PDXs, is robust and a feature of the hyper-connected and autoregulated nature of TCMs.

To support context-specific regulatory protein activity inference by VIPER, we have generated comprehensive molecular interaction networks (interactomes) using the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) (61, 62), although other suitable algorithms may be used. The networks were reverse engineered by ARACNe from ≥ 100 RNASeq profiles of human cancer tissue from (a) The Cancer Genome Atlas (TCGA) and (b) for meningioma and neuroendocrine tumors, from Columbia University collected datasets (Table S8). TCGA RNASeq level 3 data were downloaded from NCI Genomics Data Commons (63). Raw counts were normalized and variance stabilized by fitting the dispersion to a negative-binomial distribution as implemented in the DESeq2 R-package (RRID: SCR_000154) (64).

ARACNe was run with 100 bootstrap iterations using an input set of candidate regulators including: (a) 1,877 transcription factors annotated in the Gene Ontology (GO) (65) “Molecular Function database” as DNA-binding transcription factor activity (GO:0003700), DNA binding (GO:0003677), Transcription regulator activity (GO:0030528), or Regulation of transcription, DNA-templated (GO:0003677 and GO:0045449); (b) 677 transcriptional co-factors manually curated from genes annotated as Transcription Coregulator Activity (GO:0003712), Plays a Role in Regulating Transcription (GO:0030528), or Regulation of Transcription (GO:0045449); and (c) 3,895 genes encoding for signal transduction proteins, dually annotated in the GO “Biological Process database” as Signal Transduction (GO:0007165) and in the GO “Cellular Component database” as either Intracellular (GO:0005622) or Plasma membrane (GO:0005886). The Data Processing Inequality (DPI) parameter of ARACNe was set to 0 and the Mutual Information (MI) threshold was set to p = 10⁻⁸. The mode of regulation was computed based on the correlation between regulator and target gene expression as previously described (16). The version of raw counts and generated networks used in our work are provided (see Key Resources Table).

We have previously extensively validated the Virtual Proteomics by Enriched Regulon analysis (VIPER) algorithm as a highly robust and specific tool for the accurate inference of regulatory protein activity in tissue context-dependent manner (8, 15, 16). VIPER leverages accurate tissue-specific gene regulatory networks, such as those produced by ARACNe (61, 62), to measure differential protein activity from bulk or single-cell gene expression signatures (GES) (see Key Resources Table). Specifically, akin to a multiplexed gene-reporter assay, VIPER measures a protein’s differential transcriptional activity through a probabilistic enrichment framework that assesses the enrichment of its activated and repressed transcriptional targets (regulon) in genes over and under expressed in a sample of interest compared to a set of control samples (reference model).

We considered multiple options for reference models to apply VIPER to tumor samples for the purposes of predicting drug response. While organ-matched normal tissue expression profiles, such as those available from the Genotype-Tissue Expression (GTEx) resource (66), and cohort-matched tumor and tumor-adjacent profiles were considered, such references would potentially over-emphasize proliferative and cell cycle signals and under-emphasize lineage specific tumor vulnerabilities, respectively. Ultimately, we prospectively opted to use the pan-TCGA tumor dataset as our reference model to run VIPER and the downstream drug prediction algorithms.

Thus, for each cancer sample, we generate a differential gene expression signature (DGES)—computed as the gene-wise relative expression to the distribution of the expression of that gene across 11,289 TCGA samples—and expressed as its quantile relative to the reference model. Genes whose expression is not captured (quantified) in TCGA are excluded from the DGES. Next, VIPER computes enrichment scores for the targets of each regulatory protein in the DGES, using the analytic-rank based enrichment analysis (aREA) test (16), thus determining if the protein’s transcriptional footprint is over-, under-, or normally represented in the tumor’s DGES. Each enrichment score is computed by a 2-tail approach, rank-transforming the DGES for the positively regulated target genes and then inverting the signature to compute enrichment of the repressed target genes. The enrichment score is a weighted average based on targets’ ranks in the signature and the protein-target gene mutual information assignments in the network, with stronger interactions contributing more to the score. Subsequently, statistical significance for the enrichment score is computed by an analytic approach. Under the null hypothesis, the enrichment score is normally distributed with mean of zero and variance proportional to 1/(n−1), where n is the number of targets, scaled to the standard variable. This approximates shuffling the genes in the signature at random (67), outputting a normalized enrichment score (NES). While aREA does not directly account for biological correlation between the expression of various genes (i.e. covariance) in the given biological context, a very conservative NES/p-value threshold is empirically used for downstream analyses. Importantly, due to VIPER’s use of robust reporter sets to compute the activity of each protein, moderate biases in RNA profiles based on RNA extraction method, sequencing instrument, and reference transcriptome version used to map reads, all of which can affect genes captured and abundance estimates, generally have minimal effect on protein activity assessment. Likewise, due to this use of robust reporter sets, for rare cancer types not represented in TCGA, which might have lineage-specific aberrant expression of a subset of genes, we overall do not anticipate a significant impact on protein activity assessment. When cancer type specific networks are not available, we use an integrated network approach as implemented in metaVIPER (60, 68).

The most differentially active proteins identified by VIPER comprise candidate Master Regulator (MR) proteins. Critically, we have shown that MR proteins are ultra-conserved within each of 112 tumor subtypes and play a key role in integrating the effect of genomic alterations to implement a stable tumor cell state, as shown for 20 tumor types in TCGA (7). VIPER reproducibility is extremely high, such that Spearman correlation of activity profiles generated from RNASeq at 30M to as low as 50K read depth is ρ≥0.8 (16) even though correlation of the underlying gene expression profiles is low ρ≤0.3. While VIPER is uniquely suited for assessing regulatory proteins that directly control gene expression, including transcription factors, co-factors, and chromatin remodeling enzymes, we have shown that the algorithm is equally effective in monitoring activity of signaling proteins (16) and cell surface markers (60).

Through the use of (a) DrugBank (69), (b) the SelleckChem database (RRID:SCR_003823), (c) published literature, and (d) publicly available information on pharmaceutical company drug development pipelines, we have curated a refined list of 180 actionable proteins representing validated targets of high-affinity pharmacological inhibitors, either FDA approved or in clinical trials (Table S4). This manually curated target-drug(s) database is dominated by signaling proteins and established oncoproteins, as expected from the bias in druggability assessment and past focus in drug development efforts. Pharmacological agents with narrow therapeutic indices—such as those targeting neurotransmitters, ion channels, and vasoactive drugs—were purposefully removed from the database as less likely to be successfully repurposed in oncology.

OncoTarget simply analyzes the VIPER outputted protein activity measurements for these 180 actionable proteins, and provides a multiple-testing corrected significance value for the corresponding NES. We used a conservative threshold (Bonferroni p < 10⁻⁵) to identify candidate proteins eliciting essentiality when targeted by a pharmacological inhibitor, for in vivo validation. Based on this threshold, the average TCGA tumor yields 15 unique, significant OncoTarget predictions, ranging from an average of n = 4.5 in adrenocortical carcinoma (ACC) to n = 28.7 in renal clear cell carcinoma (KIRC). Tumors representing conserved subtypes tend to have conserved OncoTarget predictions, leading to identification of subsets of patients whose cancer is predicted to respond to the same set of drugs (pharmacotypes) (see Figure 7 and S6A–D for the tumor types discussed herein).

We have previously described OncoTreat (13) as a methodology to systematically elucidate compounds capable of significantly inverting the activity of the 25↑+25↓ MRs comprising the tumor checkpoint module (TCM) that regulates metastatic progression in enteropancreatic and rectal neuroendocrine tumors. In that study, OncoTreat identified entinostat, a class I histone deacetylase inhibitor, among 105 profiled drugs, as a highly effective TCM-inverter drug (13), leading to a clinical trial that is currently accruing patients (NCT03211988).

For the current study, we more broadly adapted OncoTreat to identify TCM-inverter compounds, by comparing individual tumor samples to the entire TCGA repository, as a reference model, as described above. Specifically, for each tumor type considered in the study, the analysis proceeds through the following steps:

Model fidelity was assessed based on the statistical significance of the TCM-activity conservation between a human-derived sample and a model-derived sample. For computing protein activity in cell line models, we first generated an analogous DGES comparing each cell line against a large repository of cancer cell lines (reference model), which includes both the Cancer Cell Line Encyclopedia (CCLE) (23) and the Genentech Cell Line Screening Initiative (gCSI) (24). Next, we computed the enrichment of the TCM-activity signature (25 most active and 25 most inactive patient tumor-specific MRs) in differentially active and inactive proteins in the model (OncoMatch). The aREA (16) test was again used, but any suitable enrichment analysis algorithm could be substituted. The analysis was used to (a) select optimal cell lines for the generation of perturbational profiles that effectively track the activity of tested drugs on TCM proteins and (b) to assess the fidelity of PDXs prior to validation of drugs predicted from the human sample. All cell lines used in these analyses were re-sequenced on-site, to capture any potential drift effects. A conservative threshold (Bonferroni p < 10⁻¹⁰) was used to identify high-fidelity models.

All animals were maintained under barrier conditions and all experiments were performed in accordance with and approval of the Memorial Sloan Kettering Cancer Center (MSKCC) Institutional Animal Care and Use Committee (IACUC, protocol #16-08-011) and Columbia University Irving Medical Center (CUIMC) IACUC (AAAF5850). Patient tumor tissue was collected under the CUIMC Institutional Review Board (IRB)-approved protocol AAAA7562, with written informed consent provided prospectively by the subject and conducted in accordance with recognized ethical guidelines under the U.S. Common Rule. Generation of PDX models was performed under the MSKCC IRB-approved protocol #17–387 and CUIMC IRB protocol AAAJ5811. PDX models were established as previously described (52). In summary, fresh tumor tissue was fragmented and implanted subcutaneously into nonobese/severe combined immunodeficiency IL2Rγ null, hypoxanthine phosphoribosyltransferase (HPRT)-null (NSGH) mice (Jackson Labs, IMSR catalog no. JAX:012480, RRID: IMSR_JAX:012480) (see Key Resources Table) and tumor engraftment monitored by visual and manual inspection.

Engrafted tumors were measured twice weekly with calipers and drug treatment initiated when tumor volume (TV) reached ~100 mm³ (TV = width² × ½ length) (see Key Resources Table for sourcing of drugs). Early passage animals (Passage 1 – 5) were used for all therapeutic studies. Aligned with clinical response criteria, treatment response was categorized as complete response (CR, >95% volumetric reduction from baseline or no palpable tumor), partial response (PR, >50% reduction), stable disease (SD, <50% reduction and no more than 100% increase), or progressive disease (PD, >100% increase from baseline). Disease control rate is defined as the sum of CR, PR and SD responses divided by the total responses assessed. Objective response rate is defined as the sum of CR and PR responses divided by the total responses assessed. Tumor responses were assessed at the end of the therapeutic study period. Comparisons of disease control and objective response rates across treatment groups (OncoTarget alone, OncoTreat alone, Both) were performed using a Fisher’s exact test. The Mann-Whitney-Wilcoxon method was used to compare differences in the distribution of relative tumor volume between OncoTarget or OncoTreat cohorts and Vehicle control. Vardi’s test was used to evaluate differences in area under the curve (AUC) between treatment groups across models (70, 71). Curves for Disease Control using the Kaplan-Meier estimator were compared and analyzed using the log-rank test. ΔT/ΔC%, computed as the change in tumor volume from baseline for drug treated mice divided by the change in tumor volume from baseline for Vehicle control treated mice, was determined for OncoTarget, OncoTreat, and Negative control cohorts and differences evaluated using 2-way ANOVA. All relevant statistical analyses were performed using R software (v3.5.0) or GraphPad Prism [v8.4.1 (RRID:SCR_002798)]. Statistical significance was defined as a p < 0.05.

Samples for PD assessment were procured from two mice per treatment arm, for the four PDX models treated with at least three OncoTreat-predicted drugs—GIST-81050, BC-32398, CNS-16474, and PAC-05647. Mice were randomly selected for early sacrifice, independent of tumor size, three hours following the third dose, and were excluded from response assessment. We performed RNASeq and subsequent VIPER on paired drug vs. Vehicle control-treated PDX tumor samples. TCM-inversion was assessed based on the statistical significance of the enrichment of the TCM-activity signature (i.e., 25↑+25↓ MRs of the patient tumor) in proteins inactivated and activated in drug vs. Vehicle control-treated PDX tumors, respectively, again using aREA, although alternative enrichment tests may be used. Negative NES indicates TCM-inversion (Bonferroni p < 10⁻⁵).