Pharmaco-Pheno-Multiomic Integration Reveals Biomarker Profiles and Therapeutic Response Prediction Models in Leukemia and Ovarian Cancer

Despite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumor’s complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.

[1]  P. Neven,et al.  Randomized CLIO/BGOG-ov10 trial of olaparib monotherapy versus physician's choice chemotherapy in relapsed ovarian cancer. , 2022, Gynecologic oncology.

[2]  Joshua M. Dempster,et al.  Phosphate dysregulation via the XPR1:KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer , 2020, Nature Cancer.

[3]  Li Guo,et al.  RECQL4, Negatively Regulated by miR-10a-5p, Facilitates Cell Proliferation and Invasion via MAFB in Ovarian Cancer , 2020, Frontiers in Oncology.

[4]  D. Haselbach,et al.  Moyamoya disease factor RNF213 is a giant E3 ligase with a dynein-like core and a distinct ubiquitin-transfer mechanism. , 2020, eLife.

[5]  Jenny G. Parvani,et al.  Notch3 signaling promotes tumor cell adhesion and progression in a murine epithelial ovarian cancer model , 2020, PloS one.

[6]  Lingyun Dai,et al.  METTL7B Is Required for Cancer Cell Proliferation and Tumorigenesis in Non-Small Cell Lung Cancer , 2020, Frontiers in Pharmacology.

[7]  C. Bloomfield,et al.  EGFL7 Antagonizes NOTCH Signaling and Represents a Novel Therapeutic Target in Acute Myeloid Leukemia , 2019, Clinical Cancer Research.

[8]  John M. Ashton,et al.  Bone marrow mesenchymal stromal cells from acute myelogenous leukemia patients demonstrate adipogenic differentiation propensity with implications for leukemia cell support , 2019, Leukemia.

[9]  M. Ferrer,et al.  Target Deconvolution of a Multikinase Inhibitor with Antimetastatic Properties Identifies TAOK3 as a Key Contributor to a Cancer Stem Cell–Like Phenotype , 2019, Molecular Cancer Therapeutics.

[10]  Robert V Farese,et al.  Probing the Global Cellular Responses to Lipotoxicity Caused by Saturated Fatty Acids. , 2019, Molecular cell.

[11]  Hiromi Hirata,et al.  The AAA+ ATPase/ubiquitin ligase mysterin stabilizes cytoplasmic lipid droplets , 2019, The Journal of cell biology.

[12]  Kim-Anh Lê Cao,et al.  DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays , 2019, Bioinform..

[13]  B. Sander,et al.  The Novel Tumor Suppressor SAMHD1 Is Differentially Expressed and Partly Regulated By MYC in Peripheral T-Cell Lymphomas (PTCL) , 2018, Blood.

[14]  M. Andreeff,et al.  Low-level expression of SAMHD1 in acute myeloid leukemia (AML) blasts correlates with improved outcome upon consolidation chemotherapy with high-dose cytarabine-based regimens , 2018, Blood Cancer Journal.

[15]  D. Godfrey,et al.  Faculty Opinions recommendation of LILRB4 signalling in leukaemia cells mediates T cell suppression and tumour infiltration. , 2018, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[16]  W. Huber,et al.  Proteome-wide identification of ubiquitin interactions using UbIA-MS , 2018, Nature Protocols.

[17]  Marina Vannucci,et al.  A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. , 2018, Biostatistics.

[18]  M. Hidalgo,et al.  Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[19]  Yaqi Zhang,et al.  Role and mechanism of action of LRIG1 in ovarian cancer cell line and VP16 drug-resistant cell line , 2017, Oncology letters.

[20]  M. Carroll,et al.  Chemotherapy-Resistant Human Acute Myeloid Leukemia Cells Are Not Enriched for Leukemic Stem Cells but Require Oxidative Metabolism. , 2017, Cancer discovery.

[21]  Trevor J Pugh,et al.  Integration of Technical, Bioinformatic, and Variant Assessment Approaches in the Validation of a Targeted Next-Generation Sequencing Panel for Myeloid Malignancies. , 2017, Archives of pathology & laboratory medicine.

[22]  U. Ala,et al.  Citron Kinase Deficiency Leads to Chromosomal Instability and TP53-Sensitive Microcephaly , 2017, Cell reports.

[23]  D. Hong,et al.  Aldehyde dehydrogenase is used by cancer cells for energy metabolism , 2016, Experimental & Molecular Medicine.

[24]  Jeffrey T. Chang,et al.  Cisplatin-induced synthetic lethality to arginine-starvation therapy by transcriptional suppression of ASS1 is regulated by DEC1, HIF-1α, and c-Myc transcription network and is independent of ASS1 promoter DNA methylation , 2016, Oncotarget.

[25]  Daniel G. MacArthur,et al.  The ExAC browser: displaying reference data information from over 60 000 exomes , 2016, bioRxiv.

[26]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, Genome Biology.

[27]  Sisi Li,et al.  High expression of Midkine (MK) indicates poor prognosis in childhood acute lymphoblastic leukemia , 2015, Hematology.

[28]  Alexander Dobin,et al.  Mapping RNA‐seq Reads with STAR , 2015, Current protocols in bioinformatics.

[29]  B. Giusti,et al.  EXCAVATOR: detecting copy number variants from whole-exome sequencing data , 2013, Genome Biology.

[30]  John M. Ashton,et al.  Gene sets identified with oncogene cooperativity analysis regulate in vivo growth and survival of leukemia stem cells. , 2012, Cell stem cell.

[31]  Andrew E. Jaffe,et al.  Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .

[32]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[33]  Marcel Martin.  Cutadapt removes adapter sequences from high-throughput sequencing reads , 2011 .

[34]  S. Abdou,et al.  BAALC and ERG expression in acute myeloid leukemia with normal karyotype: impact on prognosis , 2010, International journal of laboratory hematology.

[35]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[36]  C. Brocker,et al.  Aldehyde dehydrogenase 3B1 (ALDH3B1): immunohistochemical tissue distribution and cellular-specific localization in normal and cancerous human tissues. , 2010, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[37]  H. Mayani,et al.  In vitro biology of human myeloid leukemia. , 2009, Leukemia research.

[38]  H. Hollema,et al.  MEIS and PBX homeobox proteins in ovarian cancer. , 2007, European journal of cancer.

[39]  L. Kjeldsen,et al.  Serglycin proteoglycan in hematologic malignancies: a marker of acute myeloid leukemia , 2007, Leukemia.

[40]  H. Kantarjian,et al.  Acute myeloid leukemia , 2018, Methods in Molecular Biology.

[41]  Ø. Bruserud,et al.  New Strategies in the Treatment of Acute Myelogenous Leukemia (AML): In Vitro Culture of AML Cells—The Present Use in Experimental Studies and the Possible Importance for Future Therapeutic Approaches , 2001, Stem cells.

[42]  Jianchun Dong,et al.  Distinct regulatory properties of pyruvate dehydrogenase kinase and phosphatase isoforms. , 2001, Progress in nucleic acid research and molecular biology.

[43]  B. Hankey,et al.  Surveillance, Epidemiology, and End Results Program , 1999 .

[44]  S. Orsulic,et al.  Ovarian Cancer , 1993, British Journal of Cancer.