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Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. Significance: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials.

Index of Supplementary Data

Supplementary Table 3: Transcriptomic analyses of the human patient samples A. TCGA Interactome B. SU2C Interactome C. Protein activity TCGA D. Protein Activity SU2C

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.Significance:OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.This article is highlighted in the In This Issue feature, p. 247

Detailed Materials and Methods

Figure S1: Genomic alterations in prostate cancer represented in the GEMMs (related to Fig. 2). Figure S2: Additional phenotypic analyses of the GEMMs (related to Fig. 2). Figure S3: Phenotypic analysis of allograft and organoid models (related to Fig. 2). Figure S4: Additional transcriptomic analyses of the GEMMs (related to Fig. 3). Figure S5: Analyses of AR activity in GEMMS (related to Fig. 3). Figure S6: Regulatory sub-networks of the GEMM clusters (related to Fig. 3). Figure S7. MR-match of prostate cancer cells lines to human PCa (related to Figs. 5). Figure S8. Drug perturbation protein activity profiles from DU145 cells (related to Figs. 5, 6, 7). Figure S9. LNCaP Pharmacotyping to patients and GEMMs (related to Figs. 5). Figure S10: Additional validation of drug candidates (related to Figs 6, 7). Figure S11: Additional validation of drug candidates (related to Figs 6).

ARACNe-AP transcriptional network from gene expression profiles

High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator proteins that were conserved across independent cohorts. A 10-protein transcriptional module—centered around a TEAD4–MYCN positive feedback loop—emerged as the regulatory driver of the high-risk subtype associated with MYCN amplification. Silencing of either gene collapsed MYCN-amplified (MYCNAmp) neuroblastoma transcriptional hallmarks and abrogated viability in vitro and in vivo. Consistently, TEAD4 emerged as a robust prognostic marker of poor survival, with activity independent of the canonical Hippo pathway transcriptional coactivators YAP and TAZ. These results suggest novel therapeutic strategies for the large subset of MYCN-deregulated neuroblastomas.Significance: Despite progress in understanding of neuroblastoma genetics, little progress has been made toward personalized treatment. Here, we present a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes, which can be easily extended to other tumor types. We show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4–MYCN positive feedback loop in MYCNAmp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention. Cancer Discov; 8(5); 582–99. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 517

Supplementary Experimental Procedures; Supplementary Figures S1-S10; Supplementary Table Legends S1-S9; Supplementary Tables S8-S9; Additional References

Purpose:Selinexor is an oral selective inhibitor of exportin-1 (XPO1) with efficacy in various solid and hematologic tumors. We assessed intratumoral penetration, safety, and efficacy of selinexor monotherapy for recurrent glioblastoma.Patients and Methods:Seventy-six adults with Karnofsky Performance Status ≥ 60 were enrolled. Patients undergoing cytoreductive surgery received up to three selinexor doses (twice weekly) preoperatively (Arm A; n = 8 patients). Patients not undergoing surgery received 50 mg/m2 (Arm B, n = 24), or 60 mg (Arm C, n = 14) twice weekly, or 80 mg once weekly (Arm D; n = 30). Primary endpoint was 6-month progression-free survival rate (PFS6).Results:Median selinexor concentrations in resected tumors from patients receiving presurgical selinexor was 105.4 nmol/L (range 39.7–291 nmol/L). In Arms B, C, and D, respectively, the PFS6 was 10% [95% confidence interval (CI), 2.79–35.9], 7.7% (95% CI, 1.17–50.6), and 17% (95% CI, 7.78–38.3). Measurable reduction in tumor size was observed in 19 (28%) and RANO-response rate overall was 8.8% [Arm B, 8.3% (95% CI, 1.0–27.0); C: 7.7% (95% CI, 0.2–36.0); D: 10% (95% CI, 2.1–26.5)], with one complete and two durable partial responses in Arm D. Serious adverse events (AEs) occurred in 26 (34%) patients; 1 (1.3%) was fatal. The most common treatment-related AEs were fatigue (61%), nausea (59%), decreased appetite (43%), and thrombocytopenia (43%), and were manageable by supportive care and dose modification. Molecular studies identified a signature predictive of response (AUC = 0.88).Conclusions:At 80 mg weekly, single-agent selinexor induced responses and clinically relevant PFS6 with manageable side effects requiring dose reductions. Ongoing trials are evaluating safety and efficacy of selinexor in combination with other therapies for newly diagnosed or recurrent glioblastoma.

Supplementary Data from A Phase II Study of the Efficacy and Safety of Oral Selinexor in Recurrent Glioblastoma

Supplementary Figure 3 from Secreted Protein Acidic and Rich in Cysteine Produced by Human Melanoma Cells Modulates Polymorphonuclear Leukocyte Recruitment and Antitumor Cytotoxic Capacity

The expression of secreted protein acidic and rich in cysteine (SPARC) has been associated with the malignant progression of different types of human cancer. SPARC was associated with tumor cell capacity to migrate and invade, although its precise role in tumor progression is still elusive. In the present study, we show that SPARC produced by melanoma cells modulates the antitumor activity of polymorphonuclear leukocytes (PMN). Administration to nude mice of human melanoma cells in which SPARC expression was transiently or stably knocked down by antisense RNA (SPARC-sup cells) promoted PMN recruitment and obliterated tumor growth even when SPARC-sup cells accounted for only 10% of injected malignant cells. In addition, SPARC-sup cells stimulated the in vitro migration and triggered the antimelanoma cytotoxic capacity of human PMN, an effect that was reverted in the presence of SPARC purified from melanoma cells or by reexpressing SPARC in SPARC-sup cells. Leukotrienes, interleukin 8, and growth-related oncogene, in combination with Fas ligand and interleukin 1, mediated SPARC effects. These data indicate that SPARC plays an essential role in tumor evasion from immune surveillance through the inhibition of the antitumor PMN activity.

Supplementary Figure 2 from Secreted Protein Acidic and Rich in Cysteine Produced by Human Melanoma Cells Modulates Polymorphonuclear Leukocyte Recruitment and Antitumor Cytotoxic Capacity

Supplementary Figure 1 from Secreted Protein Acidic and Rich in Cysteine Produced by Human Melanoma Cells Modulates Polymorphonuclear Leukocyte Recruitment and Antitumor Cytotoxic Capacity

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. Significance: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Background: Predicting in vivo response to antineoplastics remains an elusive challenge. We evaluated two novel transcriptomic-based, tumor-agnostic systems biology tools, OncoTarget and OncoTreat. Both methodologies predict tumor response to a diverse repertoire of clinically relevant oncology drugs, based on tumor Master Regulator (MR) analysis and de novo, experimentally-assessed drug mechanism-of-action in cognate cell lines. Specifically, OncoTarget identifies high-affinity inhibitors of individual MR proteins, while OncoTreat identifies drugs that invert the transcriptional activity of hyper-connected MR modules. Methods: We enrolled patients with several distinct, poor prognosis malignancies that had progressed on multiple standard therapies, and developed low-passage, patient-derived xenograft (PDX) models. We assessed in vivo tumor response to 35 predicted patient-drug pairings in the first seven successfully engrafted models, including three basal-like breast cancers, a pancreatic ductal carcinoma, a KIT/PDGFR wildtype gastrointestinal stromal tumor, a colon cancer, and a recurrent atypical meningioma. Results: Both OncoTarget and OncoTreat produced highly significant 30-day disease control rates (68% and 91%, respectively) and markedly delayed time to treatment failure versus vehicle control by Kaplan-Meier analysis (p < 10−4, log-rank test). Predicted drugs significantly outperformed randomly selected antineoplastic drugs that were not predicted by either methodology. Importantly, of 18 OncoTreat-predicted drugs, 15 showed MR-module inversion in vivo, demonstrating conservation of drug effect on patient tumor MR modules between carefully selected cognate cell lines and PDXs. Further, extensive benchmarking of OncoTarget and OncoTreat on TCGA tumor cohorts and prospectively profiled tumors demonstrates that candidate drugs can be identified for the vast majority of patients. Conclusions: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. We present extensive preclinical validation of two transcriptomic-based approaches. Our results suggest OncoTarget and OncoTreat may substantively complement existing PCM approaches. Importantly, as RNA-based prediction tools are relatively affordable and allow initial predictions within two weeks of receipt of specimen, OncoTarget and OncoTreat are readily scalable for the design of basket and umbrella clinical trials that would enroll diverse patient populations. Citation Format: Prabhjot S. Mundi, Filemon S. Dela Cruz, Michael V. Ortiz, Adina Grunn, Daniel Diolaiti, Mariano J. Alvarez, Andrew L. Kung, Andrea Califano. Evaluation of a transcriptomics-based precision oncology platform for patient-therapy alignment in treatment resistant malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 849.

Cell-to-cell communications are critical determinants of pathophysiological phenotypes, but methodologies for their systematic elucidation are lacking. Herein, we propose an approach for the Systematic Elucidation and Assessment of Regulatory Cell-to-cell Interaction Networks (SEARCHIN) to identify ligand-mediated interactions between distinct cellular compartments. To test this approach, we selected a model of amyotrophic lateral sclerosis (ALS), in which astrocytes expressing mutant superoxide dismutase-1 (mutSOD1) kill wild-type motor neurons (MNs) by an unknown mechanism. Our integrative analysis that combines proteomics and regulatory network analysis infers the interaction between astrocyte-released amyloid precursor protein (APP) and death receptor-6 (DR6) on MNs as the top predicted ligand-receptor pair. The inferred deleterious role of APP and DR6 is confirmed in vitro in models of ALS. Moreover, the DR6 knockdown in MNs of transgenic mutSOD1 mice attenuates the ALS-like phenotype. Our results support the usefulness of integrative, systems biology approach to gain insights into complex neurobiological disease processes as in ALS and posit that the proposed methodology is not restricted to this biological context and could be used in a variety of other non-cell-autonomous communication mechanisms., Neuron-astrocyte communication plays a key role in pathophysiology, however systematic approaches to unveil it are limited. Here, the authors propose SEARCHIN, a multi-modal integrated workflow, as a tool to identify cross-compartment ligand-receptor interactions, applied to ALS models.

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of single cell RNA-seq profiles showed that most PDA tumors comprise three coexisting lineages whose aberrant transcriptional state is mechanistically controlled by distinct regulatory programs. These lineages were characterized by the aberrant activation of either gastrointestinal lineage markers (GLS), transcriptional effectors of morphogen pathways (MOS) and acinar to ductal metaplasia markers (ALS). Each lineage was characterized by cells in two different cell states determined by the differential activation of MEK signaling (M+/M-) and high cellular plasticity. These states were confirmed in multiple cohorts, cell lines, PDX models and harmonized with bulk profile analyses. Master regulators (MRs) of GLS and MOS state were predictive of patient’s survival in bulk profiles. Cross-state plasticity was confirmed by lineage tracing assays, while pooled CRISPR/Cas9 assays confirmed the essentiality of identified MR proteins. Finally, mechanistic MR-mediated cell state control was confirmed by MR expression-mediated reprogramming of MOS cells to a GLS state. Our work provided a mechanistic model of pancreatic cancer heterogeneity and testable hypothesis to target cell state-specific pancreatic cancer dependencies. Citation Format: Pasquale Laise, Mikko Turunen, Hans Carlo Maurer, Alvaro Curiel Garcia, Ela Elyada, Bernhard Schmierer, Lorenzo Tomassoni, Jeremy Worley, Mariano J. Alvarez, Jordan Kesner, Xiangtian Tan, Somnath Tagore, Ester Calvo Fernandez, Kelly Wong, Alexander L. E. Wang, Sabrina Ge, Alina C. Iuga, Aaron T. Griffin, Winston Wong, Gulam A. Manji, Faiyaz Notta, David A. Tuveson, Kenneth P. P. Olive, Andrea Califano. Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A007.

Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors—from patients with distinct malignancies who had progressed on multiple lines of therapy—and drug perturbation transcriptional profiles predicted sensitivity to 28 of 350 drugs, 26 of which (93%) were confirmed in low-passage, patient-derived xenograft (PDX) models. Drugs were prioritized based on their ability to either invert the activity of individual Master Regulator proteins, with available high-affinity inhibitors, or of the modules they comprise (Tumor-Checkpoints), based on de novo mechanism of action analysis. Of 138 PDX mice enrolled in 16 single and 18 multi-protein treatment arms, a disease control rate (DCR) of 68% and 91 %, and an objective response rate (ORR) of 12% and 17%, were achieved respectively, compared to 6% and 0% in the negative controls arm, with multi-protein drugs achieving significantly more durable responses. Thus, these approaches may effectively complement and expand current precision oncology approaches, as also illustrated by a case study.

Selinexor, a selective inhibitor of nuclear export, has demonstrated promising activity in patients with acute myeloid leukemia (AML). This randomized, phase II study evaluated selinexor 60 mg twice weekly ( n = 118) vs. physician's choice (PC) treatment ( n = 57) in patients aged =60 years with relapsed/refractory (R/R) AML. The primary outcome was overall survival (OS). Median OS did not differ significantly for selinexor vs. PC (3.2 vs. 5.6 months; HR = 1.18 [95% CI: 0.79-1.75]; p = 0.422). Complete remission (CR) plus CR with incomplete hematologic recovery trending in favor of selinexor occurred in a minority of patients. Selinexor treated patients had an increased incidence of adverse events. The most common grade =3 adverse events were thrombocytopenia, febrile neutropenia, anemia, hyponatremia. Despite well-balanced baseline characteristics, there were numerically higher rates of TP53 mutations, prior myelodysplastic syndrome, and lower absolute neutrophil counts in the selinexor group; warranting further investigation of selinexor in more carefully stratified R/R AML patients. Registered trial: NCT02088541.

Precise characterization and targeting of host cell transcriptional machinery hijacked by SARS-CoV-2 remains challenging. To identify therapeutically targetable mechanisms that are critical for SARS-CoV-2 infection, here we elucidated the Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2. The analysis revealed coordinated inactivation of MR-proteins linked to regulatory programs potentiating efficiency of viral replication (detrimental host MR-signature) and activation of MR-proteins governing innate immune response programs (beneficial MR-signature). To identify MR-inverting compounds capable of rescuing activity of inactivated host MR-proteins, with-out adversely affecting the beneficial MR-signature, we developed the ViroTreat algorithm. Overall, >80% of drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 infection, without affecting cell viability. ViroTreat is fully generalizable and can be extended to identify drugs targeting the host cell-based MR signatures induced by virtually any pathogen.

Fimepinostat (CUDC-907), a first-in-class oral small-molecule inhibitor of histone deacetylase and phosphatidylinositol 3-kinase, demonstrated efficacy in a phase 1 study of patients with relapsed/refractory (R/R) diffuse large and high-grade B-cell lymphomas (DLBCL/HGBL), particularly those with increased MYC protein expression and/or MYC gene rearrangement/copy number gain (MYC-altered disease). Therefore, a phase 2 study of fimepinostat was conducted in this patient population with 66 eligible patients treated. The primary end-point of overall response (OR) rate for patients with MYC-IHC ≥40% (n = 46) was 15%. Subsequently, exploratory pooled analyses were performed including patients treated on both the phase 1 and 2 studies based upon the presence of MYC-altered disease as well as a biomarker identified by Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER). For these patients with MYC-altered disease (n = 63), the overall response (OR) rate was 22% with seven responding patients remaining on treatment for approximately two years or longer, and VIPER yielded a three-protein biomarker classification with positive and negative predictive values of ≥85%. Prolonged durations of response were achieved by patients with MYC-altered R/R DLBCL/HGBL treated with single-agent fimepinostat. Combination therapies and/or biomarker-based patient selection strategies may lead to higher response rates in future clinical trials.

Inhibiting individual histone deacetylase (HDAC) is emerging as well-tolerated anticancer strategy compared with pan-HDAC inhibitors. Through preclinical studies, we demonstrated that the sensitivity to the leading HDAC6 inhibitor (HDAC6i) ricolinstat can be predicted by a computational network-based algorithm (HDAC6 score). Analysis of ~3,000 human breast cancers (BCs) showed that ~30% of them could benefice from HDAC6i therapy. Thus, we designed a phase 1b dose-escalation clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in patients with metastatic BC (MBC) (NCT02632071). Study results showed that the two agents can be safely combined, that clinical activity is identified in patients with HR

Purpose:Selinexor is an oral selective inhibitor of exportin-1 (XPO1) with efficacy in various solid and hematologic tumors. We assessed intratumoral penetration, safety, and efficacy of selinexor monotherapy for recurrent glioblastoma.Patients and Methods:Seventy-six adults with Karnofsky Performance Status ≥ 60 were enrolled. Patients undergoing cytoreductive surgery received up to three selinexor doses (twice weekly) preoperatively (Arm A; n = 8 patients). Patients not undergoing surgery received 50 mg/m2 (Arm B, n = 24), or 60 mg (Arm C, n = 14) twice weekly, or 80 mg once weekly (Arm D; n = 30). Primary endpoint was 6-month progression-free survival rate (PFS6).Results:Median selinexor concentrations in resected tumors from patients receiving presurgical selinexor was 105.4 nmol/L (range 39.7–291 nmol/L). In Arms B, C, and D, respectively, the PFS6 was 10% [95% confidence interval (CI), 2.79–35.9], 7.7% (95% CI, 1.17–50.6), and 17% (95% CI, 7.78–38.3). Measurable reduction in tumor size was observed in 19 (28%) and RANO-response rate overall was 8.8% [Arm B, 8.3% (95% CI, 1.0–27.0); C: 7.7% (95% CI, 0.2–36.0); D: 10% (95% CI, 2.1–26.5)], with one complete and two durable partial responses in Arm D. Serious adverse events (AEs) occurred in 26 (34%) patients; 1 (1.3%) was fatal. The most common treatment-related AEs were fatigue (61%), nausea (59%), decreased appetite (43%), and thrombocytopenia (43%), and were manageable by supportive care and dose modification. Molecular studies identified a signature predictive of response (AUC = 0.88).Conclusions:At 80 mg weekly, single-agent selinexor induced responses and clinically relevant PFS6 with manageable side effects requiring dose reductions. Ongoing trials are evaluating safety and efficacy of selinexor in combination with other therapies for newly diagnosed or recurrent glioblastoma.

Despite extensive efforts to characterize the transcriptional landscape of pancreatic ductal adenocarcinoma (PDA), reproducible assessment of subtypes with actionable dependencies remains challenging. Systematic, network-based analysis of regulatory protein activity stratified PDA tumours into novel functional subtypes that were highly conserved across multiple cohorts, including at the single cell level and in laser capture microdissected (LCM) samples. Identified subtypes were characterized by activation of master regulator proteins representing either gastrointestinal lineage markers or transcriptional effectors of morphogen pathways. Single cell analysis confirmed the existence of Lineage and Morphogenic states but also revealed a dominant population of more differentiated Oncogenic Precursor (OP) cells , present in all sampled patients, yet not apparent from bulk tumor analysis. Master regulators were validated by pooled, CRISPR/Cas9 screens, demonstrating both subtype-specific and universal dependencies. Conversely, ectopic expression of Lineage MRs, such as OVOL2, was sufficient to reprogram Morphogenic cells, thus providing a roadmap for the future targeting of patient-specific dependencies in PDA.

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins—whose aberrant activities govern the reprogramed state of infected-coronavirus cells—presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator proteins that were conserved across independent cohorts. A 10-protein transcriptional module—centered around a TEAD4–MYCN positive feedback loop—emerged as the regulatory driver of the high-risk subtype associated with MYCN amplification. Silencing of either gene collapsed MYCN-amplified (MYCNAmp) neuroblastoma transcriptional hallmarks and abrogated viability in vitro and in vivo. Consistently, TEAD4 emerged as a robust prognostic marker of poor survival, with activity independent of the canonical Hippo pathway transcriptional coactivators YAP and TAZ. These results suggest novel therapeutic strategies for the large subset of MYCN-deregulated neuroblastomas. Significance: Despite progress in understanding of neuroblastoma genetics, little progress has been made toward personalized treatment. Here, we present a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes, which can be easily extended to other tumor types. We show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4–MYCN positive feedback loop in MYCNAmp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention. Cancer Discov; 8(5); 582–99. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 517

Introduction: Selinexor is an oral selective inhibitor of XPO1 that was recently approved by the US FDA for treatment of adult patients with relapsed or refractory DLBCL, after at least two lines of systemic therapy. Approval was based on results of the Phase 2b SADAL study, which had an overall response rate of 29% in the primary analysis population (clinicaltrials.gov: NCT02227251). We herein investigated molecular markers of response to selinexor from patients treated on the SADAL study. Methods: Exome sequencing was performed on pre-selinexor treatment biopsies from 55 patients and used to compare mutation frequencies between 21 responder patients (best overall response of complete response [CR], 6; and partial response [PR], 15) and 34 non-responder patients (stable disease [SD], 8; and progressive disease [PD], 26). Additionally, RNA sequencing was performed on a subset of 33 patients and gene expressions were used to infer activities of regulatory proteins with the VIPER algorithm. Differences in inferred protein activities between responder and non-responder patients were assessed using four machine learning algorithms: linear regression (LR), linear discriminant analysis (LDA), ridge regression (RR) and random forest (RF). Model performance was estimated using leave-one-out cross-validation (LOOCV). A separate comparison was performed in the subset of 12 patients with germinal B-cell like (GCB) DLBCL. Results: Our analysis of genes commonly mutated in DLBCL revealed that non-responder patients more frequently harbored mutations in KMT2D (35% non-responders, 14% responders). Examination of the specific types of KMT2D mutations showed that the vast majority were loss-of-function frameshift or nonsense mutations (13 of 15 mutations), indicating they could have functional relevance to disease biology. The activities of 5,742 regulatory proteins were successful inferred from RNA sequencing performed on 33 patients. Unsupervised clustering identified two outlier samples that were removed from further analysis. The remaining 31 patients consisted of 16 responders (CR, 5; and PR, 11) that were compared to 15 non-responders (PD, 15). Dimension reduction of the 5,742 protein activities (filtering proteins with low variation, poor VIPER imputation, and strong linkage) resulted in 680 independent informative regulatory protein activities used for predicting selinexor response. Different numbers of regulatory proteins were iteratively input into the machine learning models to compare responders and non-responders. The best performance model was achieved using only six proteins (ASH1L, ZNF471, RRN3, CD248, ZNF750, INHBA) (Figure 1A), and had an area under the receiver operating characteristic curve (AUC) of 0.917, 0.925, 0.883, and 0.875, for the LDA, LR, RF and RR, models, respectively (p < 0.05, permutation test) (Figure 1B). A final integrated model combining the four methods achieved an AUC = 0.929 (p < 0.05, permutation test, AUC 95% CI: [0.831-1] DeLong non-parametric method) (Figure 1C). Similar results were obtained using 5-fold cross validation with the six-protein activity signature (integrated model AUC = 0.858, AUC 95% CI: [0.72-0.997]). Finally, we focused separately on 12 patients with germinal B-cell like (GCB) DLBCL, (6 responder patients, and 6 non-responder patients). Using LOOCV with the top three protein activities associated with selinexor response in GCB-DLBCL (COL1A1, INHBA, and CNOT2) resulted in remarkably high accuracy, with an integrated model AUC = .972 (p < 0.05, permutation test, AUC 95% CI: [0.895-1]). Discussion and Conclusions: The six proteins used for defining the DLBCL selinexor response signature are not typically associated with a role in DLBCL but have been implicated in cancer biology in other contexts. Notably, INHBA was found as predictor of response in the full set and also the GCB subtype patients, suggesting that activin/inhibin signaling could be important for response to selinexor in patients with DLBCL, especially the GCB subtype. Our results produced a protein activity signature that could be useful for identifying patients with DLBCL likely to respond well to selinexor treatment, which will be validated in a larger independent sample set. Figure 1 Disclosures Walker: Vigeo Therapeutics: Consultancy; Karyopharm: Current Employment, Current equity holder in publicly-traded company. Alvarez:DarwinHealth, Inc: Current Employment, Current equity holder in private company. Chang:Karyopharm Therapeutics Inc: Current Employment. Shah:Karyopharm Therapeutics Inc: Current Employment, Current equity holder in publicly-traded company. Shacham:Karyopharm: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties: (8999996, 9079865, 9714226, PCT/US12/048319, and I574957) on hydrazide containing nuclear transport modulators and uses, and pending patents PCT/US12/048319, 499/2012, PI20102724, and 2012000928) . Califano:DarwinHealth, Inc: Consultancy, Current Employment, Current equity holder in private company, Other: Founder. Landesman:Karyopharm Therapeutics Inc: Current Employment, Current equity holder in publicly-traded company.

Despite the anticancer activity of pan-histone deacetylase (HDAC) inhibitors, their clinical use has been limited due to toxicity. However, the development of more specific inhibitors that selectively inhibit individual HDACs is emerging as a novel and well-tolerated alternative. Here, we present the results of the first clinical trial evaluating the activity of ricolinostat (the leading HDAC6 inhibitor) in breast cancer (BC) patients. We have developed a computational network-based algorithm to evaluate the activity of the HDAC6 protein, based on the enrichment of its transcriptional targets in differentially expressed genes (HDAC6 score). Through preclinical in vitro and in vivo studies, we confirmed that the HDAC6 score can stratify the sensitivity of BC cells to ricolinostat treatment and may thus have value as a predictive biomarker. Moreover, analysis of ~3,000 primary human breast cancers showed that ~30% of them present high HDAC6 scores. Based on these results, we designed a phase Ib clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in metastatic BC patients. Study results showed that the two agents can be safely combined, that clinical activity is identified specifically in patients with HR+/HER2- disease, and that the HDAC6 score was predictive of response. Expansion of our analysis to other tumor types identified multiple cohorts enriched in high HDAC6 score samples. These results suggest that the HDAC6 score may provide an effective, CLIA certified predictive biomarker of ricolinostat sensitivity in multiple human cancers. Citation Format: Qingfei Pan, Tizita Zeleke, Cody Chiuzan, Maika Onishi, Mariano Alvarez, Erin Honan, Min Yang, Pei Ling Chia, Partha Mukhopadhyay, Sean Kelly, Ruby Wu, Kathleen Fenn, Meghna Trivedi, Melissa Accordino, Katherine Crew, Dawn Hershman, Matthew Maurer, Simon Jones, Andrea Califano, Kevin Kalinsky, Jiyang Yu, Jose Silva. Network-based assessment of HDAC6 activity is highly predictive of pre-clinical and clinical responses to the HDAC6 inhibitor ricolinostat [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 645.

Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionally-distinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highly-specific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

Quantitative metrics to objectively assess the fidelity of cancer models, such as cell lines, organoids, or patient-derived xenografts (PDXs), remain elusive, with histological criteria or the presence of specific mutations often used as driving principles. We show that molecular criteria, based on the regulatory proteins responsible for maintaining transcriptional cell state and its regulatory network, are effective in identifying models that can recapitulate drug mechanism of action and drug sensitivity, independent of histological consideration.