A Genomic Predictive Signature for Rigosertib in Lower Risk MDS Derived By Integrating Clinical Response, Mechanism of Action Data and Simulation

Abstract Background: Lower risk non-del5q MDS patients (pts) have limited treatment options and majority pts are transfusion dependent. Erythropoiesis stimulating agents (ESA) are first line of therapy for LR-MDS pts but almost half are resistant to ESA or become resistant after responding. Hence, LR-MDS represents an area of unmet medical need for novel agents to improve hematopoiesis and reduce transfusion dependency. Rigosertib (RIG) blocks RAS-mediated activation of proteins containing a common RAS binding domain, or RBD (Athuluri-Divaker, et al; Cell 165; 643'16). Since many pathways include proteins that employ RBD (i.e. ras, raf PI3-Kinase), interference with RBD provides a novel approach to block proliferation. While RIG showed encouraging response in LR-MDS pts (Tycko, et al; Blood 2013; 122:2745), there is a need for a biomarker that can differentiate responders from non-responders. To correlate genomics with response to RIG, we employed computational tools to integrate molecular data and observed responses. Aim: To determine predictive value of a computational biology derived genomic signature in LR-MDS pts who are treated with RIG. Methods: Materials were derived from Phase I and II studies of RIG in LR-MDS pts. Efficacy was reported as transfusion independence. Bone marrow samples were collected and processed using standard methods. DNA was extracted from bone marrow mononuclear cells or T-cells (germline control) purified from peripheral blood samples. Exome sequencing was performed using Agilent SureSelect Human All Exon v4 - 51Mb kit and HiSeq 2000. Raw sequences were aligned to the human genome build hg19 using BWA software; SNPs and In/Dels analysis was performed using PICARD, SAMTOOLS and GATK. These results, entered into predictive computational biology software (Cellworks Group), generated disease-specific protein network maps using PubMed and other online resources. Digital drug simulations are conducted by measuring drug effect on a score: a composite of cell proliferation, viability and apoptosis. Each patient-specific network map was screened for RIG reduced progression in a dose-respondent manner. Computer predictions were blindly correlated with clinical outcomes. Results: Predicted response was blindly correlated with the clinical outcome for 19 pts: Results showed 16 matches and 3 mismatches; and the following predictive test statistics: PPV - 81.25%, NPV - 100%, Sensitivity - 100%: accuracy of the predictive test at 84.21%. Modeling of LR-MDS pts predicted amplification of genes MYC, FNTA on chromosome 8; amplification of genes LGALS3, AJUBA, MAX on chromosome 14; TIAM1 on chromosome 21; HMGCR, on chromosome 5; PDPK1, MAPK3, PLK1 on chromosome 16 and PIK3CA, RAF1 on chromosome 3 correlated with increased response to RIG. Aberrations that enhanced the downstream of RAS signaling via the RAF-ERK or PI3K-AKT-MTOR pathway increased the signal flow through the drug response paths. MYC amplification on chromosome 8 via increasing the Purine synthesis pathway and production of GTP increased the RAS-ERK and RHEB-MTOR signaling. MYC also enhanced CCND1 production/proliferation. Prenylation pathway genes FNTA and HMGCR amplification also affect RAS and RHEB; enhance the drug response pathways. Another pathway that was found to be significant in the drug response, reducing proliferation, was inhibition of PLK1 and AURKA. Amplification of AJUBA, PLK1 made this pathway more significant and improved drug response via inhibition of proliferation via TIAM1-RAC1-PAK1-AURKA-PLK1 and RAF-PLK1 pathway. Deletion of TP53 and NF1 on chromosome 17 did not correlate with response. MYC amplification was seen in LR and HR MDS pts and correlated with response to RIG. However, the computational biology method showed that MYC amplification alone is not a predictor for response since there were non-responder profiles with MYC amplification and responder profiles with MYC deletion that had other aberrations in genes on chromosomes 3, 14, 16 or 21. Conclusions: A predictive method that models multiple genomic abnormalities simultaneously showed greater than 80% correlation between RIG mediated protein network perturbations and clinical outcomes in LR-MDS. The method also explained lack of response and highlighted resistance pathways that could be targeted to recover sensitivity. We also established eligibility criteria for greater precision enrollment in future trials. Disclosures Fruchtman: Onconova: Employment. Petrone:Onconova Therapeutics, Inc.: Employment. Zbyszewski:Onconova Therapeutics, Inc.: Employment. Hoffman:Onconova Therapeutics, Inc.: Employment. Vali:Cellworks Group: Employment. Singh:Cellworks Group: Employment. Usmani:Cellworks: Employment. Grover:Cellworks Group: Employment. Abbasi:Cellworks: Employment.

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Predicting Response to Dasatinib Using a Computational Model and Its Validation: A Beat AML Project Study

Blood ◽

10.1182/blood-2018-99-115678 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1541-1541

Author(s):

Jeffrey W. Tyner ◽

Brian J. Druker ◽

Cristina E. Tognon ◽

Stephen E Kurtz ◽

Leylah M. Drusbosky ◽

...

Keyword(s):

Bone Marrow ◽

Computational Biology ◽

Board Of Directors ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Patient Specific ◽

Advisory Committees ◽

Equity Ownership

Abstract Background: New prognostic factors have been recently identified in AML patient population that include frequent mutations of receptor tyrosine kinases (RTK) including KIT, PDGFR, FLT3, that are associated with higher risk of relapse. Thus, targeting RTKs could improve the therapeutic outcome in AML patients. Aim: To create a digital drug model for dasatinib and validate the predicted response in AML patient samples with ex vivo drug sensitivity testing. Methods: The Beat AML project (supported by the Leukemia & Lymphoma Society) collects clinical data and bone marrow specimens from AML patients. Bone marrow samples are analyzed by conventional cytogenetics, whole-exome sequencing, RNA-seq, and an ex vivo drug sensitivity assay. For 50 randomly chosen patients, every available genomic abnormality was inputted into a computational biology program (Cell Works Group Inc.) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated pathways. Digital drug simulations with dasatinib were conducted by quantitatively measuring drug effect on a composite AML disease inhibition score (DIS) (i.e., cell proliferation, viability, and apoptosis). Drug response was determined based on a DIS threshold reduction of > 65%. Computational predictions of drug response were compared to dasatinib IC50 values from the Beat AML ex vivo testing. Results: 23/50 (46%) AML patients had somatic mutations in an RTK gene (KIT, PDGFR, FLT3 (ITD (n=15) & TKD (n=4)), while 27/50 (54%) were wild type (WT) for the RTK genes. Dasatinib showed ex vivo cytotoxicity in 9/50 (18%) AML patients and was predicted by CBM to remit AML in 9/50 AML patients with 4 true responders and 5 false positive. Ex vivo dasatinib responses were correctly matched to the CBM prediction in 40/50 (80%) of patients (Table1), with 10 mismatches due to lack of sufficient genomic information resulting in profile creation issues and absence of sensitive loops in the profile. Only 4/23 (17%) RTK-mutant patients and 5/27(19%) RTK-WT patients were sensitive to dasatinib ex vivo, indicating that presence of somatic RTK gene mutations may not be essential for leukemia regression in response to dasatinib. Co-occurrence of mutations in NRAS, KRAS and NF1 seemed to associate with resistance as seen in 10 of the 14 profiles harboring these mutations. Conclusion: Computational biology modeling can be used to simulate dasatinib drug response in AML with high accuracy to ex vivo chemosensitivity. DNA mutations in RTK genes may not be required for dasatinib response in AML. Co-occurrence of NRAS, KRAS and NF1gene mutations may be important co-factors in modulating response to dasatinib. Disclosures Tyner: Leap Oncology: Equity Ownership; Syros: Research Funding; Seattle Genetics: Research Funding; Janssen: Research Funding; Incyte: Research Funding; Gilead: Research Funding; Genentech: Research Funding; AstraZeneca: Research Funding; Aptose: Research Funding; Takeda: Research Funding; Agios: Research Funding. Druker:Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Research Funding; Millipore: Patents & Royalties; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; McGraw Hill: Patents & Royalties; Celgene: Consultancy; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Patient True Talk: Consultancy; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; Fred Hutchinson Cancer Research Center: Research Funding; Beta Cat: Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Monojul: Consultancy. Sahu:Cellworks Research India Private Limited: Employment. Vidva:Cellworks Research India Private Limited: Employment. Kapoor:Cellworks Research India Private Limited: Employment. Azam:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Chickdipatti:Cellworks Research India Private Limited: Employment. Raveendaran:Cellworks Research India Private Limited: Employment. Gopi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry.

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The use of immunosuppressive therapy in MDS: clinical outcomes and their predictors in a large international patient cohort

Blood Advances ◽

10.1182/bloodadvances.2018019414 ◽

2018 ◽

Vol 2 (14) ◽

pp. 1765-1772 ◽

Cited By ~ 34

Author(s):

Maximilian Stahl ◽

Michelle DeVeaux ◽

Theo de Witte ◽

Judith Neukirchen ◽

Mikkael A. Sekeres ◽

...

Keyword(s):

Bone Marrow ◽

Immunosuppressive Therapy ◽

Clinical Outcomes ◽

Lower Risk ◽

Patient Cohort ◽

Transfusion Independence ◽

Key Points ◽

Rbc Transfusion

Key Points IST leads to a response in nearly half, and to RBC transfusion independence in about a third, of selected lower-risk MDS patients. Hypocellularity of bone marrow and the use of horse ATG plus cyclosporine are associated with increased rates of transfusion independence.

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Development of a Cancer Pharmacopeia-Wide Ex-Vivo Drug Sensitivity and Resistance Testing (DSRT) Platform: Identification of MEK and mTOR As Patient-Specific Molecular Drivers of Adult AML and Potent Therapeutic Combinations with Dasatinib

Blood ◽

10.1182/blood.v118.21.2487.2487 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2487-2487

Author(s):

Mika Kontro ◽

Caroline Heckman ◽

Evgeny Kulesskiy ◽

Tea Pemovska ◽

Maxim Bespalov ◽

...

Keyword(s):

Bone Marrow ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Ex Vivo ◽

Molecular Profiling ◽

Resistance Testing ◽

Patient Specific ◽

Targeted Drugs ◽

Response Profiles

Abstract Abstract 2487 Introduction: The molecular drivers of adult AML as well as the determinants of drug response are poorly understood. While AML genomes have recently been sequenced, many cases do not harbor druggable mutations. Treatment options are particularly limited for relapsed and refractory AML. Due to the molecular heterogeneity of the disease, optimal therapy would likely consist of individualized combinations of targeted and non-targeted drugs, which poses significant challenges for the conventional paradigm of clinical drug testing. In order to better understand the molecular driver signals, identify individual variability of drug response, and to discover clinically actionable therapeutic combinations and future opportunities with emerging drugs, we established a diagnostic ex-vivo drug sensitivity and resistance testing (DSRT) platform for adult AML covering the entire cancer pharmacopeia as well as many emerging anti-cancer compounds. Methods: DSRT was implemented for primary cells from adult AML patients, focusing on relapsed and refractory cases. Fresh mononuclear cells from bone marrow aspirates (>50% blast count) were screened in a robotic high-throughput screening system using 384-well plates. The primary screening panel consisted of a comprehensive collection of FDA/EMA-approved small molecule and conventional cytotoxic drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (currently n=90), such as major kinase (e.g. RTKs, checkpoint and mitotic kinases, Raf, MEK, JAKs, mTOR, PI3K), and non-kinase inhibitors (e.g. HSP, Bcl, activin, HDAC, PARP, Hh). The drugs are tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and with combinations of effective drugs explored in follow-up screens. The same samples also undergo deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. Results: DSRT data from 11 clinical AML samples and 2 normal bone marrow controls were bioinformatically processed and resulted in several exciting observations. First, overall drug response profiles of the AML samples and the controls were distinctly different suggesting multiple leukemia-selective inhibitory effects. Second, the MEK and mTOR signaling pathways emerged as potential key molecular drivers of AML cells when analyzing targets of leukemia-specific active drugs. Third, potent new ex-vivo combinations of approved targeted drugs were uncovered, such as mTOR pathway inhibitors with dasatinib. Fourth, data from ex-vivo DSRT profiles showed excellent agreement with clinical response when serial samples were analyzed from leukemia patients developing clinical resistance to targeted agents. Summary: The rapid and comprehensive DSRT platform covering the entire cancer pharmacopeia and many emerging agents has already generated powerful insights into the molecular events underlying adult AML, with significant potential to facilitate individually optimized combinatorial therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs and drug combinations. The ability to correlate response profiles of hundreds of drugs in clinical ex vivo samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for clinical hematology. Disclosures: Mustjoki: Novartis: Honoraria; Bristol-Myers Squibb: Honoraria. Porkka:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Kallioniemi:Abbot/Vysis: Patents & Royalties; Medisapiens: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bayer Schering Pharma: Research Funding; Roche: Research Funding.

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Icare 1: A Prospective Clinical Trial to Predict Treatment Response Based on Mutanome-Informed Computational Biology in Patients with AML and MDS

Blood ◽

10.1182/blood.v128.22.594.594 ◽

2016 ◽

Vol 128 (22) ◽

pp. 594-594 ◽

Cited By ~ 1

Author(s):

Leylah Drusbosky ◽

Elizabeth Wise ◽

Shireen Vali ◽

Taher Abbasi ◽

Ansu Kumar ◽

...

Keyword(s):

Computational Biology ◽

Clinical Response ◽

Treatment Response ◽

Clinical Outcomes ◽

Predictive Value ◽

Specific Protein ◽

Computer Method ◽

Protein Network ◽

Predictive Values ◽

Whole Exome

Abstract Background: Hypomethylating agents (HMAs) (e.g., azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used in the treatment of patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Despite their widespread use, HMAs fail in the majority of MDS and AML patients, and len fails in 75% of non-del(5q) MDS. Unfortunately, no method exists to predict disease response, thus the management of MDS and AML patients is challenging. Predicting treatment response would improve treatment effectiveness, restrict treatment-related adverse events to those who would benefit, and reduce health care costs. Ideally, patient prediction would be based on disease biology. Aim: To determine the biological and clinical predictive values of a genomics-informed computational biology method in patients with AML and MDS who are treated with aza, dec or len. Methods: Patients with AML or MDS were recruited in a prospective clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of treatment response to actual clinical response. Genomic profiling was conducted by conventional cytogenetics, whole exome sequencing (SureSelectXT Clinical Research Exome, Agilent), and array CGH (Agilent). These genomic results were inputted into computational biology software (Cellworks Group), which generates disease-specific protein network maps using PubMed and other online resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each patient-specific protein network map was digitally screened for the extent by which aza, dec or len reduced simulated disease growth in a dose-respondent manner. Treatment was physician's choice based on SOC. Before initiating treatment, treating physicians were masked to the results of whole exome sequencing and computational predictions. Clinical outcomes were prospectively recorded. To be eligible for efficacy assessment, patients must have had at least 4 cycles of HMA treatment or 2 cycles of len treatment. For AML, CR+PR was used to define response (IWG 2003). For MDS, CR+PR+HI was used to define response (IWG 2006). To validate the predicted protein network perturbations, Western blot assays were performed on pertinent pathway proteins. Comparisons of computer-predicted versus actual responses were performed using 2x2 tables, from which prediction values were calculated. Fisher's exact test was used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: Between June 2015 and June 2016, 80 patients were recruited. 40/80 (50%) had AML and 40/80 had MDS (50%). The median age was 66 (range 24-91). 44/80 (55%) were treatment-naïve and 36/80 (45%) were treatment-refractory. 99% completed all planned molecular tests and computational analyses. Laboratory validation study of computer-predicted, activated protein networks in 19 samples from 13 different patients showed correct prediction of 5 activated networks (Akt2, Akt3, PIK3CA, p38, Erk1/2) in 17 samples, exhibiting 89% accuracy. At the time of this report, 20/80 patients were eligible for efficacy evaluation. 6/20 patients showed clinical response to SOC therapy, while 14/20 did not achieve clinical response. 18 patients' outcome predictions were correctly matched to their actual clinical outcomes, and 2/20 were incorrectly matched, resulting in 90% prediction accuracy, 75% positive predictive value (PPV), 100% negative predictive value (NPV), 100% sensitivity, and 86% specificity. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration (p=1.664e-05). New genomic signature rules were discovered to correlate with clinical response after aza, dec or len. Conclusions: A computational method that models multiple genomic abnormalities simultaneously showed high predictive value of protein network perturbations and clinical outcomes after standard of care treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials. Disclosures Vali: Cellworks Group: Employment. Abbasi:Cellworks: Employment. Kumar:Cellworks group: Employment. Kumar Singh:Cellworks group: Employment. Basu:Cellworks Group: Employment. Kumar:Cellworks Group: Employment. Husain:Cellworks Group: Employment. Wingard:Ansun: Consultancy; Merck: Consultancy; Fate Therapeutics: Consultancy; Astellas: Consultancy; Gilead: Consultancy.

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Operative Treatment for Osteochondral Lesions of the Talus

Cartilage ◽

10.1177/1947603516644298 ◽

2016 ◽

Vol 8 (1) ◽

pp. 42-49 ◽

Cited By ~ 9

Author(s):

Youichi Yasui ◽

Adi Wollstein ◽

Christopher D. Murawski ◽

John G. Kennedy

Keyword(s):

Bone Marrow ◽

Clinical Outcomes ◽

Basic Science ◽

Clinical Evidence ◽

Surgical Techniques ◽

Biologic Agents ◽

Treatment Modalities ◽

Patient Specific ◽

Osteochondral Lesions ◽

Surgical Treatments

Objective Numerous basic science articles have published evidence supporting the use of biologic augmentation in the treatment of osteochondral lesions of the talus (OLT). However, a comprehensive evaluation of the clinical outcomes of those treatment modalities in OLT has yet to be published. The purpose of this review is to provide an evidence-based overview of clinical outcomes following biologic augmentation to surgical treatments for OLT. Design A comprehensive literature review was performed. Two commonly used surgical techniques for the treatment of OLT—bone marrow stimulation and osteochondral autograft transfer—are first introduced. The review describes the operative indications, step-by- step operative procedure, clinical outcomes, and concerns associated with each treatment. A review of the currently published basic science and clinical evidence on biologic augmentation in the surgical treatments for OLT, including platelet-rich plasma, concentrated bone marrow aspirate, and scaffold-based therapy follows. Results Biologic agents and scaffold-based therapies appear to be promising agents, capable of improving both clinical and radiological outcomes in OLT. Nevertheless, variable production methods of these biologic augmentations confound the interpretation of clinical outcomes of cases treated with these agents. Conclusions Current clinical evidence supports the use of biologic agents in OLT cases. Nonetheless, well-designed clinical trials with patient-specific, validated and objective outcome measurements are warranted to develop standardized clinical guidelines for the use of biologic augmentation for the treatment of OLT in clinical practice.

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Computational Analysis of Genomic Abnormalities from a Phase 3 Trial of Rigosertib in Higher-Risk MDS: Simulation of a Predictive Signature for Clinical Response

Blood ◽

10.1182/blood.v128.22.4324.4324 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4324-4324

Author(s):

Guillermo Garcia-Manero ◽

Michael E. Petrone ◽

Steven M. Fruchtman ◽

Bernard Brownstein ◽

Hannes Loferer ◽

...

Keyword(s):

Computational Biology ◽

Clinical Response ◽

Patient Selection ◽

Predictive Value ◽

Trisomy 21 ◽

Computational Analysis ◽

Computational Method ◽

Protein Network ◽

Predictive Test ◽

Trisomy 8

Abstract Background: MDS is a marrow stem cell disorder with limited treatments. Due to outcome heterogeneity of MDS, it is imperative to identify prognostic tools for patients in clinical trials. Rigosertib (RIG) is a RAS-mimetic that inhibits cellular signaling pathways by binding to the RAS-binding Domain found in RAS effector proteins. No approved treatment options are available for Higher-risk myelodysplastic syndromes (HR-MDS) pts after Hypomethylating Agent (HMA) failure. The "ONTIME" trial was a Phase III, randomized study of rigosertib in a heterogeneous population of HR-MDS pts who failed to respond to or progressed on HMAs. Aim: To determine the predictive value of a genomics-informed computational biology method and to design a patient selection signature in HR-MDS patients who are treated with RIG. Methods: ONTIME trial included HR-MDS pts who had progressed on (37% of total enrollment), failed to respond to (25%), or relapsed after (38%) HMA treatment. Patients were randomized 2:1 to receive RIG (199 pts) or best supportive care (BSC) (100 pts) (Garcia-Manero et al, Lancet Oncology, 2016).The primary endpoint was overall survival, analyzed on an intention-to-treat basis using the Kaplan-Meier method. Genomic DNA was isolated from single microscopic slides from 153 pts and subjected to sequence analysis of a "myeloid panel" comprising of 24 selected loci frequently mutated in MDS and AML. Standardized cytogenetic investigations were performed. The chromosome aberrations and clone definition followed the International System for Cytogenetic Nomenclature. Depending on the aberrations detected during karyotyping, further probes were applied. A complex karyotype was defined as ≥3 independent aberrations within 1 clone. These genomic results were input into predictive computational biology software (Cellworks Group, Fig. 1), which generates disease-specific dysregulated protein network maps using PubMed and other resources. Digital drug simulations are conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each patient-specific protein network map was digitally screened for the extent by which RIG reduced disease growth in a dose-respondent manner. Clinical outcomes were prospectively recorded. Results: Based on input data from cell-culture studies the predicted response was blindly correlated with the clinical outcome for 54 patients with the following predictive test statistics: positive predictive value (PPV) - 85%, negative predictive value (NPV) - 94.12%, Sensitivity - 89.47%, Specificity - 91.43% and an accuracy of the predictive test at 90.74%. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration. New genomic signature rules were discovered to correlate with clinical response after RIG treatment. The predictive computational analysis identified a novel signature for selecting HR-MDS patient's responding to RIG. We identified patients with either trisomy 8 or trisomy 21 that also do not have any of the following aberrations including: Del 5q or Del 7/7q or Del 3 or Del 14 or Del 16 or Del 20, would be responders to the drug (Trisomy 8 OR Trisomy 21) AND NOT (Del 5 q OR (Del 7/7q OR Del 3 OR Del 14 or Del 16 or Del 20). We validated this patient selection rule prospectively via statistical correlation methods and through a computer based simulation of a clinical trial generated predicted Kaplan-Meir curve with a significant p-value of 0.003 (Fig. 2). Conclusions: A predictive computational method that models multiple high-risk MDS genomic abnormalities simultaneously showed greater than 90% correlation between protein network perturbations and clinical outcomes. The computational method also helps explain reasons for lack of response to RIG and highlights resistance pathways that could be targeted to recover chemo-sensitivity. This technology also established eligibility criteria for precision enrollment in drug development trials. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures Petrone: Onconova Therapeutics, Inc.: Employment. Fruchtman:Onconova: Employment. Brownstein:Onconova Therapeutics, Inc.: Consultancy. Loferer:Onconova Therapeutics, Inc.: Employment. Azarnia:Onconova Therapeutics, Inc.: Employment. Vali:Cellworks Group: Employment. Singh:Cellworks Group: Employment. Usmani:Cellworks: Employment. Grover:Cellworks Group: Employment. Abbasi:Cellworks: Employment. Silverman:Onconova Therapeutics, Inc.: Patents & Royalties: Co-Patent holder for the combination of azacitidine and rigosertib, Research Funding.

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Use of Genomic Information to Predict Treatment Response in Multiple Myeloma Patients By Computational Mapping of Protein Network Disturbances

Blood ◽

10.1182/blood.v128.22.2099.2099 ◽

2016 ◽

Vol 128 (22) ◽

pp. 2099-2099

Author(s):

Leylah Drusbosky ◽

Mark A Fiala ◽

Justin A King ◽

Ravi Vij ◽

Shireen Vali ◽

...

Keyword(s):

Multiple Myeloma ◽

Computational Biology ◽

Treatment Response ◽

Research Funding ◽

Treatment Effectiveness ◽

Protein Network ◽

Response To Treatment ◽

Patient Specific ◽

Newly Diagnosed ◽

Predictive Values

Abstract Background: Multiple myeloma (MM) is an incurable heterogeneous hematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Bortezomib (btz) and lenalidomide (len) alone or in combination with dexamethasone (dex) or other agents, are the predominant treatments for newly diagnosed and relapsed MM. Unfortunately, no precise method exists to predict disease response, making MM patient management difficult. Predicting treatment response would improve treatment effectiveness, and potentially reduce unnecessary treatment-related adverse events and health care costs. Aim: To determine the application of a genomics-informed predictive simulation model in MM patients treated with btz or len in combination with dex. Methods: Fourteen patients were selected from two datasets. Nine relapsed MM patients were identified from Washington University and 5 newly diagnosed MM patients were identified from the publicly accessible MMRF CoMMpass dataset. In all cases, whole exome sequencing and array CGH were performed. For each patient, every available genomic abnormality was entered into a computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein pathways (Doudican, et al, J Transl Med, 2015). Digital drug simulations with HMAs were conducted by quantitatively measuring drug effect on a composite MDS disease inhibition score (i.e., cell proliferation, viability, and apoptosis). Clinically, patients received standard of care treatment and clinical responses were recorded. Predictive values were calculated based on comparisons of the computer predictions and actual clinical outcomes. Results: The models predicted that 9 patients would respond to combination treatment and 5 would not. All response predictions were properly matched to their clinical response, resulting in 100% PPV, NPV, sensitivity, specificity, and accuracy. Interestingly, the model predicted that 6 of the 9 responders would not have responded to btz or len alone; instead, response was predicted to combination therapy with dex. Conclusions: Computational biology for MM demonstrated high predictive value for response to btz and len with dex. The model may be useful in uncovering the mechanisms for treatment failure and highlight additional pathways that could be targeted to increase chemosensitivity. Disclosures Vij: Jazz: Consultancy; Shire: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Karyopharma: Consultancy. Vali:Cellworks Group: Employment. Abbasi:Cellworks: Employment. Kumar Singh:Cellworks: Employment. Kumar:Cellworks group: Employment. Gera:Cellworks: Employment.

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A Multidisciplinary Model Predicts Clinical Response in Relapsed Multiple Myeloma

Blood ◽

10.1182/blood.v126.23.501.501 ◽

2015 ◽

Vol 126 (23) ◽

pp. 501-501

Author(s):

Kenneth H. Shain ◽

Ariosto Silva ◽

Mark B Meads ◽

Allison Distler ◽

Timothy Jacobson ◽

...

Keyword(s):

Bone Marrow ◽

Partial Response ◽

Clinical Response ◽

Research Funding ◽

Drug Response ◽

Evolutionary Dynamics ◽

Ex Vivo ◽

Patient Specific ◽

Tumor Reduction ◽

The Face

Abstract The future of cancer treatment lies in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations within an individual in response to drug. Multiple myeloma (MM) is at present an incurable malignancy of bone marrow resident plasma cells with highly variable survival as a consequence of both disease- and host-specific factors. 20% of MM patients, deemed high-risk (HRMM), have shown little benefit in the era of novel agents, with an OS of less than 2 years. Intuitive treatment strategies fail to account for the complexities and evolutionary dynamics of human tumors in the face of drugs. Intuitive treatment fails to adequately account for MM evolutionary dynamics and remains a critical barrier to successful cure or, at least, long-term disease control. Reasons for therapy failure include, but are not limited to, alternation of dominant clones with each line of therapy as a consequence of Darwinian dynamics, genomic instability leading to of tumor heterogeneity, and tumor microenvironment(TME)- mediated drug resistance. We have developed an integrated computational method accounting for phenotypic tumor heterogeneity. This novel ex vivo drug screen approach, termed EMMA (evolutionary mathematical myeloma advisor), predicts patient-specific drug response in silico from fresh bone marrow biopsies within 5 days. This method utilizes longitudinal non-destructive quantification of rate and dose responses of patient-derived MM cells to drugs in an ex vivo 3D reconstruction of the bone marrow microenvironment to provide real-time personalized predictions of treatment success (percent decrease in disease burden at 90 days). The current automated 384-well plate format allows testing of 31 different drugs or combinations against a single patient sample in 5 days. An evolutionary-based computational model uses the drug sensitivity profile obtained ex vivo to detect sub-populations and their contribution to overall clinical drug response. Each drug dose is imaged once every 30 minutes for 96h. This generates 1,920 data points per drug (or combination). From these data we characterize clonal architecture as it relates to drug sensitivity as phenotypic/functional biomarker for each drug or drug combination in each MM patient sample simultaneously. We have examined the predictive accuracy of EMMA in 26 patients to date. The Pearson correlation between ex vivo model predictions and actual tumor burden changes for the 26 patients examined generated the correlation coefficient r=0.87 (P<0.0001). Further, examination of the model predictions in terms of IMWG standards revealed that 23 out of 26 patients showed agreement between model estimation and actual clinical response (88.5% concordance). The remaining 3 patients diverged by one or two stages of response: one patient presented a very good partial response (VGPR, 98.5% reduction) while the model predicted a partial response (PR, 74.5% tumor reduction); the second patient presented a partial response (PR, 74% tumor reduction) while the model predicted a complete response (CR); and the third patient presented stable disease (SD, 12% tumor reduction) and the model predicted a minimal response (MR, 30% tumor reduction). To this end, EMMA generates patient-specific clinical response predictions to individual drugs or regimens with a high degree of clinical accuracy. Beyond testing for clinical drug response, EMMA may also be used to assess dominant cell signaling pathways. We have screened 5 patients with 25 protein kinase inhibitors (PKI) representing known signaling cascades in MM. Using heatmaps representing area under the curve (AUC) of dose-response surfaces (concentration x exposure time), we have observed both common and patient-specific sensitivities to PKIs. Together, these data demonstrate that the combination of a physiological reconstruction of the TME, a non-destructive and non-invasive cell viability assay, and mathematical models, were key to overcome the major limitations of previous predictive chemosensitivity assays. EMMA has the potential to provide precise clinical insight about treatment efficacy in a timely manner and thus become a decision support tool for oncologists based on the ever-changing clonal architecture in the face of therapy. Disclosures Baz: Karyopharm: Research Funding; Celgene Corporation: Research Funding; Millennium: Research Funding; Sanofi: Research Funding.

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A 3D Bioprinted Material That Recapitulates the Perivascular Bone Marrow Structure for Sustained Hematopoietic and Cancer Models

Polymers ◽

10.3390/polym13040480 ◽

2021 ◽

Vol 13 (4) ◽

pp. 480

Author(s):

Caitlyn A. Moore ◽

Zain Siddiqui ◽

Griffin J. Carney ◽

Yahaira Naaldijk ◽

Khadidiatou Guiro ◽

...

Keyword(s):

Breast Cancer ◽

Bone Marrow ◽

Translational Medicine ◽

Drug Response ◽

Drug Approval ◽

3D Models ◽

3D Bioprinting ◽

Effective Evaluation ◽

Experimental Systems ◽

Cancer Models

Translational medicine requires facile experimental systems to replicate the dynamic biological systems of diseases. Drug approval continues to lag, partly due to incongruencies in the research pipeline that traditionally involve 2D models, which could be improved with 3D models. The bone marrow (BM) poses challenges to harvest as an intact organ, making it difficult to study disease processes such as breast cancer (BC) survival in BM, and to effective evaluation of drug response in BM. Furthermore, it is a challenge to develop 3D BM structures due to its weak physical properties, and complex hierarchical structure and cellular landscape. To address this, we leveraged 3D bioprinting to create a BM structure with varied methylcellulose (M): alginate (A) ratios. We selected hydrogels containing 4% (w/v) M and 2% (w/v) A, which recapitulates rheological and ultrastructural features of the BM while maintaining stability in culture. This hydrogel sustained the culture of two key primary BM microenvironmental cells found at the perivascular region, mesenchymal stem cells and endothelial cells. More importantly, the scaffold showed evidence of cell autonomous dedifferentiation of BC cells to cancer stem cell properties. This scaffold could be the platform to create BM models for various diseases and also for drug screening.

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Risk factors and clinical outcomes associated with blood culture contamination

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2021.111 ◽

2021 ◽

pp. 1-7

Author(s):

Justin M. Klucher ◽

Kevin Davis ◽

Mrinmayee Lakkad ◽

Jacob T. Painter ◽

Ryan K. Dare

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Blood Culture ◽

Hospital Mortality ◽

Clinical Outcomes ◽

Tertiary Care ◽

Blood Cultures ◽

Hospital Charges ◽

Patient Specific ◽

Contaminated Blood

Abstract Objective: To determine patient-specific risk factors and clinical outcomes associated with contaminated blood cultures. Design: A single-center, retrospective case-control risk factor and clinical outcome analysis performed on inpatients with blood cultures collected in the emergency department, 2014–2018. Patients with contaminated blood cultures (cases) were compared to patients with negative blood cultures (controls). Setting: A 509-bed tertiary-care university hospital. Methods: Risk factors independently associated with blood-culture contamination were determined using multivariable logistic regression. The impacts of contamination on clinical outcomes were assessed using linear regression, logistic regression, and generalized linear model with γ log link. Results: Of 13,782 blood cultures, 1,504 (10.9%) true positives were excluded, leaving 1,012 (7.3%) cases and 11,266 (81.7%) controls. The following factors were independently associated with blood-culture contamination: increasing age (adjusted odds ratio [aOR], 1.01; 95% confidence interval [CI], 1.01–1.01), black race (aOR, 1.32; 95% CI, 1.15–1.51), increased body mass index (BMI; aOR, 1.01; 95% CI, 1.00–1.02), chronic obstructive pulmonary disease (aOR, 1.16; 95% CI, 1.02–1.33), paralysis (aOR 1.64; 95% CI, 1.26–2.14) and sepsis plus shock (aOR, 1.26; 95% CI, 1.07–1.49). After controlling for age, race, BMI, and sepsis, blood-culture contamination increased length of stay (LOS; β = 1.24 ± 0.24; P < .0001), length of antibiotic treatment (LOT; β = 1.01 ± 0.20; P < .001), hospital charges (β = 0.22 ± 0.03; P < .0001), acute kidney injury (AKI; aOR, 1.60; 95% CI, 1.40–1.83), echocardiogram orders (aOR, 1.51; 95% CI, 1.30–1.75) and in-hospital mortality (aOR, 1.69; 95% CI, 1.31–2.16). Conclusions: These unique risk factors identify high-risk individuals for blood-culture contamination. After controlling for confounders, contamination significantly increased LOS, LOT, hospital charges, AKI, echocardiograms, and in-hospital mortality.

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