Kaufmann, Scott H.

Abstract Background: Large scale genetic perturbations screens have been instrumental for many key discoveries. In the last decade sequence specific genetic perturbations created RNAi and CRISPR (PMID: 25446316). Recently, specific genome targeting approaches using single guide RNAs (sgRNAs) for the CRISPR-Cas9 system have been repurposed to induce sequence-specific repression or activation of gene expression at a genome-wide scale. Different perturbation approaches have different effects and encompass a trade-off between specificity and duration in establishing a measurable perturbation which can result in different biological readouts. However, bench-side strategies are time consuming and expensive. The method described here aims to build a systems-based computational platform to assess pharmacological and biological perturbations at genome-wide network level.Methods: Our network tool NetDecoder (Nucleic Acids Res. 2016; 44(10):e100) that models genome-wide information flow will be used to build context specific protein-protein interaction networks. These networks will be subsequently subjected to in silico perturbations and their impacts on network properties will be assessed. AVANA and Demeter datasets will be used during method validation procedures. Results: Using publicly available expression datasets we generated context specific Breast Cancer networks and individually manipulated nodes and edges of context-specific networks associated with pharmacological FDA approved targets. Our perturbation networks captured cellular rewiring by engulfing new gene players, alterations of the network size and its connectivity. Conclusions: Our in-silico genome wide perturbation platform provides a flexible approach to fine-tune single and tandem manipulations, allowing for inexpensively monitoring a range of pharmacological interventions using large omics data. Citation Format: Cristina Correia, Choong Yong Ung, Cheng Zhang, Scott H. Kaufmann, Hu Li. A genome wide pharmacological perturbation platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1226.

Abstract Senescent cells drive age-related tissue dysfunction partially via the induction of a chronic senescence-associated secretory phenotype (SASP). Mitochondria are major regulators of the SASP; however, the underlying mechanisms have not been elucidated. Mitochondria also control apoptosis, a cell fate generally perceived as distinct from cellular senescence, which is apoptosis resistant. Here, we show that mitochondrial outer membrane permeability (MOMP) occurring in a small subset of mitochondria is a feature of cellular senescence. This process, called minority MOMP (miMOMP), depends on the formation of BAX and BAK macropores and results in release of mitochondrial DNA (mtDNA) into the cytosol, which in turn activates the cGAS/STING pathway, a major regulator of the SASP. Importantly, we found that inhibition of MOMP in vivo decreases inflammatory markers and improves healthspan parameters in aged mice. Our results propose the novel concept that apoptosis and senescence are regulated by similar mitochondrial-dependent mechanisms, and that sub-lethal mitochondrial apoptotic stress is a major driver of the SASP. We also provide proof-of-concept that inhibition of miMOMP may be a new therapeutic avenue to improve healthspan.

Despite recent discoveries and therapeutic advances in aggressive myeloid neoplasms, there remains a pressing need for improved therapies. For instance, in acute myeloid leukemia (AML), while most patients achieve a complete remission with conventional chemotherapy or the combination of a hypomethylating agent and venetoclax, de novo or acquired drug resistance often presents an insurmountable challenge, especially in older patients. Poly(ADP-ribose) polymerase (PARP) enzymes, PARP1 and PARP2, are involved in detecting DNA damage and repairing it through multiple pathways, including base excision repair, single-strand break repair, and double-strand break repair. In the context of AML, PARP inhibitors (PARPi) could potentially exploit the frequently dysfunctional DNA repair pathways that, similar to deficiencies in homologous recombination in BRCA-mutant disease, set the stage for cell killing. PARPi appear to be especially effective in AML with certain gene rearrangements and molecular characteristics (RUNX1-RUNX1T1 and PML-RARA fusions, FLT3- and IDH1-mutated). In addition, PARPi can enhance the efficacy of other agents, particularly alkylating agents, TOP1 poisons, and hypomethylating agents, that induce lesions ordinarily repaired via PARP1-dependent mechanisms. Conversely, emerging reports suggest that long-term treatment with PARPi for solid tumors is associated with an increased incidence of myelodysplastic syndrome (MDS) and AML. Here, we (i) review the pre-clinical and clinical data on the role of PARPi, specifically olaparib, talazoparib, and veliparib, in aggressive myeloid neoplasms and (ii) discuss the reported risk of MDS/AML with PARPi, especially as the indications for PARPi use expand to include patients with potentially curable cancer.

The poly(ADP-ribose) binding protein CHFR regulates cellular responses to mitotic stress. The deubiquitinase UBC13, which regulates CHFR levels, has been associated with better overall survival in paclitaxel-treated ovarian cancer. Despite the extensive use of taxanes in the treatment of ovarian cancer, little is known about expression of CHFR itself in this disease. In the present study, tissue microarrays containing ovarian carcinoma samples from 417 women who underwent initial surgical debulking were stained with anti-CHFR antibody and scored in a blinded fashion. CHFR levels, expressed as a modified H-score, were examined for association with histology, grade, time to progression (TTP) and overall survival (OS). In addition, patient-derived xenografts from 69 ovarian carcinoma patients were examined for CHFR expression and sensitivity to paclitaxel monotherapy. In clinical ovarian cancer specimens, CHFR expression was positively associated with serous histology (p = 0.0048), higher grade (p = 0.000014) and higher stage (p = 0.016). After correction for stage and debulking, there was no significant association between CHFR staining and overall survival (p = 0.62) or time to progression (p = 0.91) in patients with high grade serous cancers treated with platinum/taxane chemotherapy (N = 249). Likewise, no association between CHFR expression and paclitaxel sensitivity was observed in ovarian cancer PDXs treated with paclitaxel monotherapy. Accordingly, differences in CHFR expression are unlikely to play a major role in paclitaxel sensitivity of high grade serous ovarian cancer.

Abstract Background: Outcomes of patients with relapsed/refractory AML (RR-AML) have remained poor. Therapy with venetoclax based combinations in this setting leads to CR/CRi rates of 21-49% (DiNardo CD et al. Am J Hematol 2018, Aldoss I et al. Haematologica 2018, Stahl M et al. Blood Adv 2020). Preclinical studies with BCL-2 inhibitors indicate potential mechanisms of drug resistance including overexpression of the anti-apoptotic protein MCL-1 (Konopleva et al. Cancer Cell. 2006). Pevonedistat is a first in class inhibitor of Nedd8 activating enzyme that induces the pro-apoptotic protein NOXA leading to neutralization of MCL-1 and apoptosis. Preclinical studies evaluating the combination of pevonedistat and venetoclax against AML cell lines have demonstrated a synergistic effect (Knorr KL et al. Cell Death Differ. 2015). Hence, we designed a phase I study to assess the safety and tolerability of adding pevonedistat to the combination of azacitidine and venetoclax in patients with RR-AML. Study design and methods: We conducted a phase I study with the combination of pevonedistat, venetoclax and azacitidine in patients with RR-AML. Patients aged 18 years or above with morphologically documented RR-AML, ECOG performance status 0-2 and adequate organ function were eligible. Major exclusion criteria were isolated extramedullary relapse, hematopoietic cell transplantation (HCT) within 100 days of enrollment, and active acute GVHD. Previous therapy with hypomethylating agents (HMA) or venetoclax was not an exclusion criterion. The dose escalation phase was conducted using 3+3 design. Treatment included azacitidine (75 mg/m 2 daily x 7 days), venetoclax (400 mg daily x 28 days), and pevonedistat in escalating doses (10-20 mg/m 2 IV days 1,3,5 of each cycle) with a cycle length of 28 days. Pevonedistat was given at 10 mg/m 2 dose in cohort 1, 15 mg/m 2 dose in cohort 2 and 20 mg/m 2 dose in cohort 3. The primary endpoint is to determine the recommended phase 2 dose (RP2D) and toxicity profile of pevonedistat, azacitidine and venetoclax. Other endpoints included determination of response rates, duration of response, survival, pharmacokinetics, correlation of response rates with AML genomic profile, correlation of pretreatment levels of BCL2, BCLXL, MCL1, BAX or BAK with response, determination of changes in NOXA (PMAIP1) mRNA and protein expression pre-and post-pevonedistat treatment, evaluation of BH3 mimetic profiling on bone marrow samples by flow cytometry and assessing the sensitivity of leukemia and leukemic stem/progenitor cells to pevonedistat ex vivo. Results: Thirteen patients participated in the dose escalation phase, 12 of whom were evaluable. Median age was 69 years (61-91), 30.8% had secondary/therapy related AML, 69.2% with adverse risk disease, 53.8% previously received venetoclax/HMA and 23.1% had relapse after prior allogeneic hematopoietic cell transplantation (HCT) (Table 1). Seven patients were enrolled into cohort 1 (pevonedistat 10 mg/m 2 dose) of which one was not evaluable, three patients enrolled into cohort 2 (pevonedistat 15 mg/m 2 dose), and three patients enrolled into cohort 3 (pevonedistat 20 mg/m 2 dose). Grade 3 or higher AEs included febrile neutropenia (23%), infection (15%), anemia (38%), neutropenia (54%), thrombocytopenia (38%). There was 1 dose limiting toxicity (DLT) in cohort 1 (atrial fibrillation) that triggered cohort expansion. However, subsequent patients did not experience DLT despite planned dose escalation. Of the 12 evaluable total patients, CR/CRi was observed in 5 (41.6%) patients. Notably, patients with RR-AML who were venetoclax/HMA naïve had a CR/CRi 83.3% (5/6 patients). The response rates for each cohort are summarized in Table 2. Three of the five patients with CR/CRi (60%) achieved MRD negativity by flow cytometry. Four patients who achieved CR/CRi proceeded to allogeneic HCT after therapy. Median OS of the cohort was 5.4 months (1.8-14) and median OS was not reached in patients with CR/CRi. Conclusions: The addition of pevonedistat to venetoclax and azacitidine backbone is safe and well tolerated in patients with RR-AML. Dose escalation yielded encouraging efficacy in venetoclax/HMA naïve RR-AML patients. The study is currently in the dose expansion phase. Details on correlative studies examining mechanisms of therapeutic efficacy and resistance will be reported in the main meeting. Figure 1 Figure 1. Disclosures Guru Murthy: Cardinal health: Honoraria; TG Therapeutics: Other: Advisory board meeting; DAVA Oncology: Honoraria; CancerExpertNow: Honoraria; Qessential: Honoraria; Techspert: Consultancy; Curio Sciences: Honoraria; Guidepoint: Consultancy. Abedin: AltruBio: Research Funding; Helsinn: Research Funding; Amgen: Honoraria; Pfizer: Research Funding; Agios: Honoraria; Actinium: Research Funding; Astellas Pharma Inc.: Research Funding. Litzow: Jazz: Other: Advisory Board; Pluristem: Research Funding; Actinium: Research Funding; Amgen: Research Funding; Astellas: Research Funding; AbbVie: Research Funding; Omeros: Other: Advisory Board; Biosight: Other: Data monitoring committee. Atallah: Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Research Funding; BMS: Honoraria, Speakers Bureau; Takeda: Consultancy, Research Funding; Abbvie: Consultancy, Speakers Bureau; Amgen: Consultancy.

Abstract PARP inhibitors (PARPi) have activity in homologous recombination (HR) repair-deficient, high-grade serous ovarian cancers (HGSOC). However, even responsive tumors develop PARPi resistance, highlighting the need to delay or prevent the appearance of PARPi resistance. Here, we showed that the ALK kinase inhibitor ceritinib synergizes with PARPis by inhibiting complex I of the mitochondrial electron transport chain, which increases production of reactive oxygen species (ROS) and subsequent induction of oxidative DNA damage that is repaired in a PARP-dependent manner. In addition, combined treatment with ceritinib and PARPi synergized in HGSOC cell lines irrespective of HR status, and a combination of ceritinib with the PARPi olaparib induced tumor regression more effectively than olaparib alone in HGSOC patient-derived xenograft (PDX) models. Notably, the ceritinib and olaparib combination was most effective in PDX models with preexisting PARPi sensitivity and was well tolerated. These findings unveil suppression of mitochondrial respiration, accumulation of ROS, and subsequent induction of DNA damage as novel effects of ceritinib. They also suggest that the ceritinib and PARPi combination warrants further investigation as a means to enhance PARPi activity in HGSOC, particularly in tumors with preexisting HR defects. Significance: The kinase inhibitor ceritinib synergizes with PARPi to induce tumor regression in ovarian cancer models, suggesting that ceritinib combined with PARPi may be an effective strategy for treating ovarian cancer.

Abstract In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 12 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic “scarring,” indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after six lines of therapy (four platinum-based), whereas another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neoadjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy. Significance: Homozygous RAD51C methylation is a positive predictive biomarker for sensitivity to PARP inhibitors, whereas a single unmethylated gene copy is sufficient to confer resistance.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.