Ansu Kumar - Cellworks Research India Pvt Ltd

Ansu Kumar

Cellworks Research India Pvt Ltd

Bangalore, KA | India

Ansu Kumar - Cellworks Research India Pvt Ltd

Ansu Kumar

Introduction

Highly skilled and management experience in the area of Data analytics, personalised medicine of cancer treatment, cancer metabolism, tumor micro environment, diabetes, infectious disease, signaling pathway, statistical and mathematical modelling-based prediction of biological network and therapeutic implications with a strong publication track record in high impact journals. More than 5 patents in the area of cancer treatment. I gained experience in technology development, process flow automation, data analysis and predictive modelling, Enzyme kinetics, pharmacodynamics, scientific writing, providing end to end business solution and process flow development

Key Pasters and abstract published are:

1. S. Padda, Y. Gokmen-Polar, S. Badve, S. Vasista, K. Basu, A. Kumar, S. Vali, T. Abbasi, H. Wakelee.
Identification of Molecular Subtypes of Thymic Epithelial Tumors and Novel Treatments Using a
Computational Biological Model. October 2018, Journal of Thoracic Oncology 13(10): S606.
DOI: 10.1016/j.jtho.2018.08.919

2. Aneel Paulus, MD, Prachi Jani, MD, Salman Ahmed, MD, Sonikpreet Aulakh, MD, Alak Manna, PhD,
Neeraj Kumar Singh, B.Tech, Mohammed Sauban, B.E, Zakir Husain, M.Tech, Ansu Kumar, M.Sc.,
Pallavi Kumari, M.Sc., Anuj Tyagi, M.Sc., Taimur Sher, MD, Rami Manochakian, MD, Vivek Roy, MD,
Taher Abbasi, MS, MBA, Shireen Vali, PhD, Asher A. Chanan-Khan, MD and Sikander Ailawadhi.
Computational Modelling of Multiple Myeloma Patient Genomic Signatures to Predict Treatment
Outcome, American Society of Hematology Annual Meeting-2018, San Diego, CA.

3. Shireen Vali, PhD, Christopher R. Cogle, MD, Neeraj Arora, MD, Krishnan Shekhar, Ph.D., Mammen
Chandy, MD, Manju Sengar, MD, DM, Hasmukh Jain, MD, DM, Girish Chinnaswamy, MD, MRCP,
Neeraj Kumar Singh, B.Tech, Shahabuddin Usmani, M.Sc., Anusha Pampana, M.Sc., Vijayashree P
Shyamasundar, M.Sc., Huzaifa Sikora, B.Tech, Ansu Kumar, M.Sc., Taher Abbasi, MS, MBA and
Vivek S Radhakrishnan, MD, DM, MSc. Predictive Analysis on Prognostic Impact of Monosomy 7 in
AML and Identified Therapy Options for This Cohort, American Society of Hematology Annual Meeting-
2018,San Diego, CA

4. Leylah M. Drusbosky, Aneel Paulus, Anne Quillet Mary, Loic Ysebaert, Ansu Kumar, Neeraj
Kumar Singh, Yashaswini Ullal, Priyanka Bhargav, Humera Azam, Taher Abbasi, Shireen Vali, Asher
A. Chanan-Khan and Christopher R. Cogle. Predicting Mechanisms of Ibrutinib-Resistance in Chronic
Lymphocytic Leukemia (CLL) Using Computational-Based Modeling for Personalized Therapy with Clinical
Validation. Blood 2017 130:4278.


5. LeylahM. Drusbosky, Ansu Kumar, Shwetha K, Sumanth Vasista, Anuj Tyagi, Swaminathan
Rajagopalan, Aftab Alam, Taher Abbasi, Shireen Vali, Girish Chinnaswamy, Manju Sengar, Bijal
D. Shah and Christopher R. Cogle. Computational Biology Model of ETP-ALL Used to Accurately Classify
ETP-ALL Vs Non ETP-ALL Patients with Genomic Input. Blood 2017 130:1449;

6. LeylahM. Drusbosky, Ansu Kumar, Shwetha K, Priyanka Bhargav, Ashish Choudhary, Soumyashree L,
Shivgonda C. Birajdar, Kunal Ghosh Roy, Taher Abbasi, Shireen Vali, Manju Sengar, Girish
Chinnaswamy, Bijal D. Shah and Christopher R. Cogle. Predictive Simulation Modeling of Early T-Cell
Precursor Acute Lymphoblastic Leukemia (ETP-ALL) Patient Genomics Identifies Targeted Combination
Treatment Options. Blood 2017 130:2708.


7. Madeleine Turcotte, Raphael Charles Bosse, Kimberly E. Hawkins, Amy Meacham, Vindhya Vijay,
Girish Chinnaswamy, Manju Sengar, Ansu Kumar, Shwetha K, Anisha Kamath, Anjana T. Nair, Karthik
S. Raju, Taher Abbasi, Shireen Vali, Christopher R. Cogle and Leylah M. Drusbosky. Personalized
Therapy Design for Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) Using Computational
Biology Modeling with in Vitro Validation. Blood 2017 130:1448

8. Aneel Paulus, Alak Manna, Sharoon Akhtar, Neeraj Kumar Singh, Ansu Kumar, Kabya Basu, Akshita
Trivedi, Debapriya Majumdar, Taher Abbasi, Shireen Vali, Vivek Roy, Peter Martin, Morton Coleman,
Anne J Novak, Stephen M Ansell, Sikander Ailawadhi and Asher A. Chanan-Khan. The Oral Proteasome
Inhibitor Ixazomib, Alone and in Combination with Ibrutinib, Induces Lethality in Waldenstrom Macroglobulinemia
Cells That Are Resistant to Ibrutinib. Blood 2017 130:1260

9. Leylah M. Drusbosky, Kimberly E. Hawkins, Helen L. Leather, Hemant S. Murthy, Nosha Farhadfar,
John R. Wingard, Maxim Norkin, Randy A. Brown, John W. Hiemenz, Paul Castillo, Biljana N. Horn,
Ansu Kumar, Neeraj Kumar Singh, Chandan Kumar, Sumanth Vasista, Taher Abbasi, Shireen Vali,
Cristina E. Tognon, Stephen E Kurtz, Brian J. Druker, Jeffrey W. Tyner, Daniel A. Pollyea and
Christopher R. Cogle. A Genomic Signature Predicting Venetoclax Treatment Response in Acute Myeloid
Leukemia (AML) Identified By Protein Network Mapping and Validated By Ex Vivo Ddrug Sensitivity Testing.
Blood 2017 130:2707;

10. Leylah Drusbosky, Kimberly E. Hawkins, Shireen Vali, Taher Abbasi, Ansu Kumar, Neeraj Kumar
Singh, Kabya Basu, Chandan Kumar, Amjad Husain, Caitlin Tucker, Randy A. Brown, Maxim Norkin,
John Hiemenz, Jack Hsu, John Wingard, Christopher R. Cogle. Univ. of Florida, Gainesville,
FL; Cellworks Group, Inc, San Jose, CA. iCare 1: A prospective clinical trial to predict treatment
response based on mutanome-informed computational biology in patients with AML and MDS.
American Association for Cancer Research (AACR) Annual Meeting, 2017

11. Leylah Drusbosky, PhD, Elizabeth Wise, BS, Shireen Vali, PhD, Taher Abbasi, Ansu Kumar,
Neeraj Kumar Singh, Kabya Basu, Chandan Kumar, Amjad Husain, Fei Zou, PhD, Caitlin Tucker,
BS, Randy A Brown, MD, Maxim Norkin, MD, PhD, John Hiemenz, MD, John R. Wingard, MD,
Jack W Hsu,MD and Christopher R. Cogle,MD,Icare 1: A Prospective Clinical Trial to Predict
Treatment Response Based on Mutanome-Informed Computational Biology in Patients with AML
and MDS.American Society of Hematology - ASH. 58th Annual Meeting, San Diego, CA
.Blood 2016 128:594;

12. Leylah Drusbosky, Mark A Fiala, BS, CCRP, Justin A King, Ravi Vij, MD, MBA, Shireen Vali, PhD,
Taher Abbasi, Neeraj Kumar Singh, Ansu Kumar, Saji Gera and Christopher R. Cogle,MD, Use of
Genomic Information to Predict Treatment Response in Multiple Myeloma Patients By
Computational Mapping of Protein Network Disturbances. American Society of Hematology - ASH. 58th
Annual Meeting, San Diego, CA. Blood 2016 128:2099;

13. Cindy Medina, Leylah Drusbosky, Myron Chang, Shireen Vali, Ansu Kumar, Neeraj Kumar Singh,
Taher Abbasi, Mikkael A. Sekeres, Mar Mallo, Francesc Sole, Rafael Bejar, Christopher R.
Cogle,Predicting MDS Response to Drug Therapies Based on a New Method of Interpreting the MDS
Mutanome, Blood 2015 126:96;


14. Aneel Paulus, Sharoon Akhtar, Shireen Vali, Ansu Kumar, Neeraj Kumar Singh, Shahabuddin
Usmani, Himanshu Grover, Taher Abbasi, Asher Alban Chanan-Khan, In Silico Modeling of
Oncogenic Drivers in Waldenstrom Macroglobulinemia to Assess Additional Therapeutic Targets
within the BCR Signaling Pathway Identifies MEK1/2 As a Target: Potential Therapeutic Role of
Binimetinib, Blood 2015 126:1279;


15. Aneel Paulus, Kasyapa S. Chitta, Sharoon Akhtar, Hassan Yousaf, Davitte Cogen, Shireen Vali,
Ansu Kumar, Neeraj Kumar Singh, Taher Abbasi, James M. Foran, Candido E. Rivera, Taimur
Sher, Vivek Roy, Sikander Ailawadhi, Asher Alban Chanan-Khan, Aurora Kinase Is a Therapeutic
Target in Ibrutinib-Resistant Waldenstrom Macroglobulinemia: In-Silico Target Identification and in-
Vitro Validation, Blood 2015 126:2754;

16. Nicole A. Doudican, Amitabha Mazumder, Shireen Vali, Kabya Basu, Ansu Kumar, Neeraj Kumar
Singh, Zeba Sultana and Taher Abbasi, Predictive simulation-driven personalization methodology for
refractory multiple myeloma, DOI:10.1158/1538-7445.AM2015-5443 Published 1 August 2015,
Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA


17. Peter P. Sayeski, Shireen Vali, Ansu Kumar, Neeraj Singh, Susumu Kobayashi and Taher Abbasi,
Identification and therapeutic targeting of signaling pathways in Ruxolitinib resistant cells, DOI:
10.1158/1538-7445.AM2015-4978 Published 1 August 2015, AACR 106th Annual Meeting 2015; April
18-22, 2015; Philadelphia, PA


18. Sathish Kumar, Shireen Vali, Kabya Basu, Saji Gera, Neeraj Singh, Ansu Kumar, Taher Abbasi and
Shazib Pervaiz, Effectiveness of predictive simulation in identifying potential patient-specific therapeutic
targets in multiple myeloma-a pilot study, DOI: 10.1158/1538-7445.AM2015-1722 Published 1 August
2015, AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA


19. Abdel Kareem Azab, Aneel Paulus, Feda Azab, Sharoon Akhtar, Shireen Vali, Ansu Kumar,
Neeraj Kumar Singh, Shweta Kapoor, Taher Abbasi, Asher Chanan-Khan, Kasyapa S. Chitta, A
Novel and Personalized Method Using Simulation for Predicting Effective Therapeutics for
Waldenströms Macroglobulinemia, Blood 2014 124:3024;


20. Peter P. Sayeski, Shireen Vali, Ansu Kumar, Sung Park, Neeraj Kumar Singh, Anuj Tyagi, Taher
Abbasi, Christopher R. Cogle, Personalized Therapy Design for MPN Using Predictive Simulation
Methodology with in Vitro, Ex Vivo, and in Vivo Validation, Blood 2014 124:3212;


21. Nicole A Doudican, Ravi Vij, Mark A Fiala, Justin King, Shireen Vali, Kabya Basu, Ansu Kumar, Neeraj
Kumar Singh, Zeba Sultana, Taher Abbasi, Amitabha Mazumder, Therapy Personalization Using
Predictive Simulation Approach with Ex-Vivo Clinical Validations, Blood 2014 124:2232;

22. Sandeep C. Pingle, Zeba Sultana, Pengfei Jiang, Rajesh Mukthavaram, Ying Chao, Taher Abbasi,
Shweta Kapoor, Ansu Kumar, Shahabuddin Usmani, Ashish Agrawal, Shireen Vali, Santosh Kesari;
University of California San Diego, La Jolla, CA; Cellworks Research India Ltd., Bangalore, India;
Cellworks Group Inc., San Jose, CA; Cellworks Research India Ltd., San Jose, CA; University of
California, San Diego, La Jolla, CA. Predicting GBM response to targeted therapeutics using simulation
with ex vivo validations. DOI: 10.1200/jco.2014.32.15_suppl.2041 Journal of Clinical Oncology 32,
no. 15_suppl (May 2014) 2041-2041.


23. Nicole A. Doudican, Shireen Vali, Annette Leon, Ansu Kumar, Neeraj Kumar Singh, Anuj Tyagi,
Shweta Kapoor, Zeba Sultana, Taher Abbasi and Amitabha Mazumder. Individualized therapy identified
using simulation for bortezomib resistant patient with ex-vivo validation,DOI: 10.1158/1538-
7445.AM2014-1706 Published 1 October 2014, Proceedings: AACR Annual Meeting 2014; April 5-9,
2014; San Diego, CA


24. Peter P. Sayeski, Shireen Vali, Ansu Kumar, Neeraj Singh, Anuj Tyagi, Taher Abbasi, Susumu
Kobayashi; Predictive analysis of microenvironment impact on clinical outcomes to drug agents using
simulation of myeloproliferative neoplasms. DOI: 10.1200/jco.2014.32.15_suppl.7110 Journal of Clinical
Oncology 32, no. 15_suppl (May 2014) 7110-7110.


25. Shireen Vali, Shweta Kapoor, Anay Talawdekar, Ansu Kumar, Zeba Sultana, Mikhail L. Gishizky,
Pradeep Fernandes and Taher Abbasi, Computer based “virtual tumor” simulation identifies novel use
for existing drugs - implication for personalized cancer therapy. DOI: 10.1158/1538- 7445.AM2013-5215
Published 15 April 2013, Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington,
DC


26. Jia Kang, Vignette Z. Q. Ooi, Shireen Vali, Shweta Kapoor, Ansu Kumar, Taher Abbasi and Shazib
Pervaiz, STAT3 phosphorylation and Bcl-2 expression as a predictive signature for stratifying clinical
lymphomas, DOI: 10.1158/1538-7445.AM2013-5577 Published 15 April 2013, Proceedings: AACR
104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC

27. Sukhmani Padda, Yesim Gokmen, Sunil Badve, Kabya Basu, Ansu Kumar, Amjad Husain, Shireen
Vali, Taher Abbasi, Heather Wakelee. Designing Novel Therapies for Clinical Translation through
Predictive Simulation for Thymoma.ITMIG 2016, San Fransisco, USA.

28. Yesim Gokmen-Polar, Yaseswini Nellamraju, Sarath C. Janga, Xiaoping Gu, Kabya Basu, Amjad
Husain, Ansu Kumar,Shireen Vali, Sukhmani Padda, Heather Wakelee, Patrick J. Loehrer, Taher
Abbasi, Sunil Badve. Bortezomib as a Novel Targeted Therapy for Thymic Carcinoma.ITMIG 2016, San
Fransisco, USA.

Primary Affiliation: Cellworks Research India Pvt Ltd - Bangalore, KA , India

Publications

17Publications

377Reads

17Profile Views

A feedforward relationship between active Rac1 and phosphorylated Bcl-2 is critical for sustaining Bcl-2 phosphorylation and promoting cancer progression.

Cancer Lett 2019 Aug 17;457:151-167. Epub 2019 May 17.

Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore; Medical Science Cluster Cancer Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore; NUS Graduate School of Integrative Sciences and Engineering, NUS, Singapore; National University Cancer Institute, NUHS, Singapore. Electronic address:

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http://dx.doi.org/10.1016/j.canlet.2019.05.009DOI Listing
August 2019
12 Reads
5.621 Impact Factor

Predictive Analysis on Prognostic Impact of Monosomy 7 in AML and Identified Therapy Options for This Cohort

Blood 2018 132:1539; doi: https://doi.org/10.1182/blood-2018-99-116603

Background: Monosomy of chromosome 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). Monosomy 7 with complex karyotype further worsens the prognosis. Therefore, predicting response of therapies in this segment of patients is urgently needed to improve disease management by customizing therapy to the profile genomics instead of the conventional method of trial and error or one-size-fits all treatments.

Aim: Predictive analysis of disease characteristics of Monosomy 7 and Sub-clustering of this segment along with identification of therapy options for treatment of this cohort using computational biology modeling (CBM).

Methods: We selected a cohort of 1168 AML patients whose genomics and clinical data was collated from public domain datasets and literature. Using CBM, patients with (-7) were identified from this larger cohort and were sub-clustered using a machine learning approach.

The genomic data of the representative patients per sub-cluster of (-7) cohort were used to generate disease-specific protein network maps. Digital drug simulations were done by quantitatively measuring drug effect and calculating a disease inhibition score (DIS) that is a composite of proliferation, viability and apoptosis along with impact on disease specific biomarker score. Each patient-specific map was digitally screened for the extent by which standard of care (SOC) and combinations with Non-SOC inhibited the disease.

Results: CBM identified 187 (-7) patients (16%) that grouped into 8 sub-clusters (Table 1). CBM analysis of genomics of each sub-cluster representative identified combination therapy options for this cohort with poor response to SOC, supported by genomics driven disease characteristics.

Monosomy 7 aberrations would lead to decreased expression of EZH2, CARD11, EIF3, PMS2, HUS1, KMT2C (MLL3), CDK5 and IKZF1. Azacitidine (AZA) is predicted to be a non-responder in this cohort due to decreased expression of EZH2, that would impact DNA methylation via reduced recruitment of DNMTs. Lenalidomide (LEN) is also predicted to be a non-responder due to decreased expression of IKZF1, CARD11, and EIF3 despite presence of Del5q, a strong inclusion for LEN response.

This segment has shown high microsatellite instability (MSI) characteristics and this could be linked to reduced mismatch repair (MMR) pathway due to reduced expression of PMS2, HUS1 and KMT2C. This characteristic could explain certain patient cases with (-7) who have responded to Cytarabine. (Figure 1) However, the response rate of chemotherapy in (-7) cohort is <50% and relapse is high. (PMID 12393746) Loss of histone methyltransferase EZH2 through reduction of H3K27 trimethylation can be the key reason for inducing resistance to many AML cytotoxic drugs.

With (-7), CBM analysis determined an increased expression of BCL2 due to reduced methylation and HOXA9 expression. (PMID: 9562974) Proteasomal subunit PSMB5 gets down regulated due to CDK5 loss which would make the cohort more sensitive to proteasomal inhibition. This can also help with controlling EZH2 levels in the disease via regulating EZH2 proteasomal degradation. (PMID: 21289309, 27941792) CBM predicted that a combination of Cytarabine + Bortezomib and Cytarabine + Venetoclax could be effective in the (-7) AML cohort.

Conclusions: CBM analysis of AML patient genomics identified abnormal protein networks and drug resistance pathways in patients with monosomy 7, and combination therapy options in the (-7) sub-clusters. EZH2 was identified as a key reason for resistance and combinations of Cytarabine with proteasome or BCL2 inhibitor was predicted to be effective in this cohort.

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August 2019
4 Reads

Computational Modelling of Multiple Myeloma Patient Genomic Signatures to Predict Treatment Outcome

Blood 2018 132:1911; doi: https://doi.org/10.1182/blood-2018-99-119574

Background: Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited. We present a novel computational biology modelling (CBM) tool, which takes into account the combined effect of individual mutations, gene copy number abnormalities and large scale chromosomal changes in order to predict the salient molecular pathways utilized by the MM cell for survival. By reverse-engineering MM cell architecture in silico, the CBM tool is able to predict drug response and resistance mechanisms. Thus, our aim was to determine the accuracy of the CBM tool in predicting treatment response of relapsed/refractory MM patients for future management of their disease, in a more individualized manner.

Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes.

Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse.

Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities.

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August 2019
7 Reads

Identification of Molecular Subtypes of Thymic Epithelial Tumors and Novel Treatments Using a Computational Biological Model

: https://doi.org/10.1016/j.jtho.2018.08.919

https://www.jto.org/article/S1556-0864(18)31877-X/abstract

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July 2019
5 Reads

Identification of myeloproliferative neoplasm drug agents via predictive simulation modeling: assessing responsiveness with micro-environment derived cytokines.

Oncotarget 2016 Jun;7(24):35989-36001

Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, USA.

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http://dx.doi.org/10.18632/oncotarget.8540DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5094977PMC
June 2016
32 Reads
6.359 Impact Factor

Overexpression of Bcl-2 induces STAT-3 activation via an increase in mitochondrial superoxide.

Oncotarget 2015 Oct;6(33):34191-205

Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

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http://dx.doi.org/10.18632/oncotarget.5763DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4741445PMC
October 2015
24 Reads
6.359 Impact Factor

β-Caryophyllene oxide inhibits constitutive and inducible STAT3 signaling pathway through induction of the SHP-1 protein tyrosine phosphatase.

Mol Carcinog 2014 Oct 13;53(10):793-806. Epub 2013 Jun 13.

College of Korean Medicine, Kyung Hee University, Seoul, Republic of, Korea.

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http://dx.doi.org/10.1002/mc.22035DOI Listing
October 2014
18 Reads
4.808 Impact Factor

Amplification and demultiplexing in insulin-regulated Akt protein kinase pathway in adipocytes.

J Biol Chem 2012 Feb 29;287(9):6128-38. Epub 2011 Dec 29.

Diabetes and Obesity Research Program, The Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, New South Wales 2010, Australia.

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http://dx.doi.org/10.1074/jbc.M111.318238DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307283PMC
February 2012
36 Reads
4.573 Impact Factor

Predictive Simulation Modeling of Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) Patient Genomics Identifies Targeted Combination Treatment Options

Blood 2017 130:2708;

ETP-ALL, an orphan disease, is a subtype of T-ALL with poor prognosis and high risk of relapse after standard of care (SOC) chemotherapy. ETP-ALL is a genomically heterogeneous disease with many cases harboring complex karyotype and genetic mutations typically seen in myeloid neoplasias, such as acute myeloid leukemia (AML). Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Unfortunately, empirical chemotherapy trials have yet to show significant improvements in clinical outcomes of ETP-ALL patients (pts), which makes treating ETP-ALL challenging. Ideally, selecting treatment for ETP-ALL is rationally designed from the panoply of unique molecular abnormalities observed in ETP-ALL cases. Recently, we developed a predictive simulation modeling method that projects protein network maps from the hundreds of leukemia genomic mutations observed in individual pts (PMID 27855285). Our computational modeling enables multi-gene/multi-drug simulations. We hypothesized that ETP-ALL is comprised of protein network disturbances distinct from other T-ALL cases. We furthermore hypothesized these unique ETP-ALL protein networks would lead to the discovery of new treatment combinations for pts with ETP-ALL.

Aim: Identify novel combinations of drugs for ETP-ALL pts who have failed SOC therapy by using an integrated genomics and computational approach.

Methods: 66 ETP-ALL pts genomic data (whole exome sequencing, copy number variations, cytogenetics) were collected from various sources including NCBI-GEO dataset (GSE28703) (N=12), Moffitt Cancer Center (N=6) (IRB201600284) and published literature (PMID 22237106, 19147408) (N=48). The genomic data for each profile were entered into a computational biology modeling (CBM) software (Cellworks Group), which generated a disease-specific protein network map using PubMed and other online resources. Disease-specific biomarkers were identified within the protein network maps using CBM technology. A digital library of FDA-approved drugs, along with investigational agents, was simulated on every pt model both as single agent and combinations at varying doses. Drug impact was assessed by quantitatively measuring drug effect on a cell growth score (CGS), which is a composite of cell proliferation, viability and apoptosis. Unique combinations that reduced the CGS by interacting with the pt specific disease-biomarkers were selected. Synergy of drug combinations was calculated using the Co-efficient of Drug Interaction (CDI) formula. Each novel therapeutic agent or drug combination was mapped to the pts genomics, and supported by a PubMed referenced scientific mechanism of action based on disease biology.

Results: Of the 66 ETP-ALL profiles, 22/66 (33%) harbored actionable mutations, including NRASTET2FLT3 and various deletions such as del(5q) or deletions in tumor suppressor genes including CDKN2A / 2BTSC1 and others. 44/66 (67%) had many deleterious mutations that were not directly actionable by currently available drugs. The integrated genomics and CBM workflow identified targeted combination therapies for 59/66 (89.4%) of cases. Eighty-seven unique targeted combinations were identified across the 66 ETP-ALL pts in this study (Fig 1). Eleven of the identified combinations were synergistic, as calculated by CDI, and 5 of the combinations included idarubicin as one of the agents, while the most synergistic combination was lenalidomide + nelfinavir (CDI = 3.34). Analysis of CBM identified pt-specific biomarkers for 66/66 (100%) pts. Dysregulated protein networks unique to ETP-ALL pt cases included pathways involving TP53, MYC, NFKB1, FOXM1, RUNX3, STAT3, CTNNB1, E2F1 and CEBPA. Some of the key kinase biomarkers identified in the ETP-ALL profiles were AURKB, CSNK2A1, MAP3K14, RAF1, ERK and mTOR (Fig 2).

Conclusions: Computational biology analysis of complex ETP-ALL genomics found a unique protein network that may be amenable to several new treatment combinations. This CBM approach identified personalized drug regimens in all cases, including those who had no actionable genetic mutations present. This predictive technology improves the utility of cancer genetic profiling and may enhance clinical decision-making to identify treatments for ETP-ALL pts who would otherwise have no options based on limited SOC alternatives.

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November 0001
3 Reads

Identification of Molecular Subtypes of Thymic Epithelial Tumors and Novel Treatments Using a Computational Biological Model

Aug 20, 2018 publication descriptionJournal of Thoracic Oncology 13(10): S606. DOI: 10.1016/j.jtho.2018.08.919

BackgroundThe histologic classification of thymic epithelial tumors (TETs) is based on the description of both epithelial cell morphology and relative abundance of lymphocytes. Here, we used a computational biological model (CBM) approach on The Cancer Genome Atlas (TCGA) dataset to identify molecular subtypes of TETs and associated predicted therapeutic options.Jump to SectionBackgroundMethodResultConclusionKeywordsMethodWhole exome sequencing and gene expression data from the TCGA TET dataset (n = 102) along with the IUTAB-1 cell line was input into CBM software (Cellworks Group, San Jose, CA) to build an unsupervised classification model beyond molecular subtypes previously reported (Loehrer PJ ASCO 2017). The CBM generated a disease specific protein network map using PubMed and other online resources. Using computer simulation, disease biomarkers unique to each tumor were identified within the protein network maps. Among the tumors simulated, 6 molecular clusters were identified (TH1-TH6). The CBM digital drug library was tested against these molecular subtypes and the cell growth score (i.e. cell proliferation, viability, and apoptosis) was analyzed.Jump to SectionBackgroundMethodResultConclusionKeywordsResultThe CBM identified 6 molecular subtypes among 102 TET patients. Among subtypes with a GTF2I mutation, TH1, TH4, and TH6 also had chromosomal aberrations in chromosome 22 and 9. Deletion of chromosome 22 was present in TH1, deletion of chromosome 9 in TH4 and TH6, and also amplification of chromosome 22q in TH4. Among GTF2I wild type subtypes, chromosome 22q deletion and complex cytogenetics were present in TH2, trisomy of chromosome 1 in TH3, and HRAS mutations and chromosome 2 amplification in TH5. The IUTAB-1 cell line had a GTF2I mutation and mapped to the TH4 molecular subtype. The CBM predictions of sensitivity of TH4 subtype to Nelfinavir (AKT inhibitor) and Panobinostat (histone deacetylase inhibitor) along with resistance to Everolimus (MTOR inhibitor) were validated in vitro. There were two molecular subtypes for which Everolimus was predicted to be sensitive, TH1 and TH6.Jump to SectionBackgroundMethodResultConclusionKeywordsConclusionWe present an updated classification of TETs based on a CBM approach and associated potential novel therapeutic options that could be further validated in clinical trials.Jump to SectionBackgroundMethodResultConclusionKeywordsKeywordsthymic epithelial tumor, The Cancer Genome Atlas, computational biological model

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Publications publication titleComputational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer. publication dateJul 26, 2019 publication descriptionSci Rep. 2019 Jul 26;9(1):10877. doi: 10.1038/s41598-019-47381-4. publication descriptionIndividual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular bioma

Nov 4, 2017 Blood 2017 130:1260;

Abstract

Introduction: Dysregulated B-cell receptor (BCR) signaling drives proliferation of Waldenstrom macroglobulinemia (WM) cells, which characteristically secrete excessive IgM. To maintain IgM production and preserve cellular homeostasis, WM cells rely on optimally functioning proteasomes to degrade and recycle proteins. This biological dependence has been clinically exploited using the proteasome inhibitor (PI), bortezomib (Btz), which is capable of inducing remission in 85% of WM patients (Treon et al Clin Canc Res, 2007) as a single agent. Despite, its activity, Btz use is associated with a high incidence of sensory and autonomic neuropathies as well as its cumbersome dosing schedule and non-oral route of administration. Thus, new agents that 1.) build on the activity of Btz, 2.) have a more favorable toxicity profile and 3.) can be combined with standard of care agents such as, ibrutinib (Ibr), are highly attractive and should be prioritized for investigation. Here we describe the preclinical activity of ixazomib (Ixa), a PI that is administered orally and is associated with significantly less peripheral neuropathy; in combination with Ibr, in WM cells.

Methods: WM cell lines (BCWM.1 and RPCI.WM1) and their drug-resistant subclones were used. Genomic data including whole exome sequencing (WES) and copy number variations (CNV) was used in creation of digital simulation avatars of RPCI-WM1 and RPCI-WM1/IR (Ibr-resistant, Ibr-R variant), where the salient and prominently dysregulated cellular pathways were identified. Ixa +/- Ibr was tested in digital avatars and dose-dependent impact on simulated disease growth was assessed (Fig 1A). The predictive results were experimentally validated. Cell viability was measured using trypan blue or CellTiter Glo. Apoptosis was examined by annexin-V/PI staining of drug-treated cells followed by flow cytometry analysis. Mitochondrial membrane permeability (MOMP) was determined by TMRM staining of drug-treated cells followed by flow cytometry.

Results: We first profiled the growth inhibitory action of Ixa in WM cell lines (n=9); all of which were sensitive to Ixa irrespective of acquired resistance to Btz, Ibr or carfilzomib with a median IC50 of 95.59nM. We then tested whether Ixa could cooperate with Ibr to enhance WM cell kill. Indeed, the Cellworks modeling tool, predicted a significant loss of viability and induction of apoptosis in both wildtype and Ibr-R cells treated with Ibr + Ixa, as compared to either single agent (Fig 1B-D). We confirmed this in vitro, where viability of wildtype WM cells treated with Ixa (20nM), Ibr (5uM) or Ibr + Ixa for 6h significantly decreased; most notable with Ixa + Ibr (25.5% viable cells). And this effect was similar in Ibr-R cell lines (30.3% viable cells) after combination treatment. Further confirming in silico predictive modeling results, we noted a significant increase in annexin-V/PI positivity in Ixa treated wildtype (24.5%) and Ibr-R WM cell lines (33.1%). Whereas Ibr alone exhibited minimal apoptosis in WM cell lines (~11 - 15%), the combination of with Ixa + Ibr enhanced programmed cell death in wildtype (40.2%) and Ibr-R cells (50.5%). As intrinsic apoptosis is triggered through mitochondrial disruption, we also examined mitochondrial transmembrane permeability (MOMP) in cells treated with Ibr, Ixa or Ixa + Ibr. In wildtype cells, mean MOMP induced by single agent Ixa was 23.5% and in Ibr-R cells 33%; increasing to 39% and 50.4%, respectively, after treatment with Ibr + Ixa. Mechanistic studies revealed changes in proteins regulated by the ubiquitin proteasome system and those associated with BTK signaling. While Ixa alone downregulated Bcl-2 and Mcl-1 levels, this effect was more marked in cells (both wildtype and Ibr-resistant) treated with Ixa + Ibr combination. Similar effects were noted in pPLCg2, albeit with some variability between the cell lines.

Conclusion: Using a combination of in silico modeling and experimental approaches, we provide first preclinical evidence on the antitumor effects of Ibr combined with the oral PI, Ixa, in WM cells, including those resistant to Ibr. Whereas further investigation on the precise molecular mechanisms of how Ibr + Ixa cooperate to induce apoptosis in WM cell is ongoing, our studies provide the basis for clinical testing of the Ibr + Ixa regimen; with a phase II clinical study in development.

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