A Bioinformatics Pipeline For Sequence To Structure: A Case Study ...

A Bioinformatics Pipeline For Sequence To Structure: A Case Study With A Cml Patient Undergoing Treatment With Imatinib *

Anita Rachel Chacko, Shankarrao Patil, Atul K. Upadhayay,

2

Ashwat Nagrajan, Rakesh Khatri, Deepak Arya, Sudhir Krishna, R. Sowdhamini 1 *

Cecil Ross St. John's Medical College and Hospital Sarjapur Road, Bangalore, India

National Centre for Biological Sciences GKVK Campus, Bangalore, India

Abstract- Chronic Myeloid Leukaemia (CML) is a resultant

know the consequences of such off target kinases inhibition

of the 9:22 translocation event leading to the constitutive kinase

because any polymorphism or variant associated with such off

activity of BCR-ABL. Imatinib is the drug used as the first line

target kinases may have an important role in the disease

therapy in CML. We report a longitudinal case study for a CML

phenotype. The focus of the study towards kinases allows a

patient

under

treatment

with

imatinib.

The

bone

marrow

aspirate of this CML patient was used for exome sequencing. Single

nucleotide

variants

(SNVs)

unique

to

the

exome

sequencing sample datasets were analysed with an emphasis on kinases. These mutations were mapped to the structure to further understand the significance of the SNVs in the context of its stability and kinase-drug interaction. Here we present a data

better understanding of such consequences (polymorphism / variants) in CML and its role apart from BCR-ABL. Hence it becomes important to study the role of such consequences in off target kinases under treatment with imatinib. B.

Clonality in CML

structure

There always remains a clonal pool of cells (BCR­

approach. This strategy can be used to filter kinases from next

ABL positive) solely responsible for maintaining, driving and

filtering

pipeline

with

examples

for

sequence

to

relapse of the disease. Leukemic stem cells (LSCs) have been

generation sequencing data relevant to cancers. KeywordsVariants;

Chronic Myeloid Leukemia; Single Nucleotide

Kinase;

Drug

interaction;

Exome

sequencing;

longitudinal study

chromosomal 9

the biology within such clonal population becomes important heterogeneous clonal cells would allow us to control disease progression.

INTRODUCTION

Chronic myeloid leukaemia (CML) is an offshoot of the chromosome

potential to expand the leukemic cells [16, 17]. Understanding in order to target them. Any effort in order to eliminate such

I. reciprocal

shown to exist as a small clonal fraction of cells which have the

(ABL;

translocation

Abelson

kinase)

event and

between 22

(BCR;

Breakpoint Cluster Region) resulting in the hybrid BCR-ABL gene. This translates into a chimeric protein with constitutive tyrosine kinase activity that phosphorylates target proteins, leading to the expansion of hematopoietic stem and progenitor cells. The introduction of imatinib that binds to the kinase domain of BCR-ABL as a tyrosine kinase inhibitor remains the first line of therapy for CML patients [1, 2, 3, 4]. Despite the high efficiency of imatinib, approximately 30 % of patients develop resistance to it [5]. This resistance is due to the mutations in the kinase domain of BCR-ABL and can be bypassed by the second line tyrosine kinase inhibitors such as dasatinib or nilotinib [6]. Resistance to the second line therapy may also be associated with specific kinase domain mutations [7].

A. Kinases in CML Imatinib, a tyrosine kinase inhibitor specific to BCR-ABL also targets many off target kinases [8, 9] like v-Abl, c-Abl [10, 11], Tel-Abl [12], PDGFR-a, PDGFR-p, Tel-PDGFR [11,13], c-Kit [1,13], ARG [14], c-fms [15]. It becomes important to

C. }{igh t hroughput approac h The emergence of next generation sequencing technologies has facilitated for a better understanding of the cancer genome [18, 19]. Previous attempts have been made to study the exome of an atypical CML patient [20]. Analysis of the whole genome, exome or transcriptome allows a better understanding of cancers, improving further attempts towards diagnosis and therapy. Our attempt is to address a clinical case by a multidimensional

approach

involving

clinics,

genomics,

bioinformatics and structural biology. Our aim is to identify the single nucleotide variants (SNVs) in BCR-ABL and its off-target kinases and to map these mutations on the protein structure using the exome sequencing data of a CML patient under imatinib. Our pipeline will select templates of the relevant targets (predominantly kinases) such as to arrive at a structure and then map the effect of the mutations at protein level in order to understand their relevance with respect to their structure and function.

II.

threonine specific, 15 tyrosine-specific and 55 other protein

MA TERlAL AND METHODS

kinases (Fig. 1c).

A. Sample collection Bone marrow aspirate was performed over a longitudinal time

a

b

frame (OBM; old bone marrow, NBM; new bone marrow) of two years under imatinib. Inform consent was obtained before sample collection and ethical approval was obtained from Institutional Ethical Review Board (lERB), St. John's Medical

Try

College and Hospital, Bangalore, India. B.

c

Sequ encing

Ser/Thr

Others

Unique SNVs to each dataset

Exome sequencing was performed for two bone marrow aspirates

using

Illumina

Hiseq

platform

at

Genotypic,

Novel SNVs (not reported in

Bangalore, India. The coverage obtained was more than 40X by paired end processing

sequencing. was

Quality

performed

dbSNP V137)

control and raw data using

SeqQC-V2.0

Non·synonymous SNVs

(http://genotypic.co.in/SeqQC.html). Mapping was performed to the reference human genome version hg19 by ungapped alignment

using

bowtie-O.12.7

[21].

Single

nucleotide

polymorphisms (SNVs) unique in both the bone marrow aspirates were further considered for analysis in this pipeline

Kinase keyword/kinase domain

(Fig. 1a). C.

(sequence search)

Data Filtering

Check if mutation is in kinase domain

Imatinib binds to many off-target kinases;

using COD

hence, the

(sequence level) if Yes

primary goal was to look at the effect of kinases unique to these data over a period of treatment of two years with imatinib. 59,977 SNVs were analysed from the OBM and 8,616 from the NBM. The pipeline to further filter these SNVs

I

is as follows in Fig.1b. SNVs unique i.e. not overlapping between the OBM and

Check for stability/affect of the mutation/dock with ligand

dbSNP Vl37 [22, 23], a database of commonly occurring in

the

human

population.

The

SNVs

were

I

..

NBM datasets were used in the study. These were compared to SNPs

Map on structure

now

categorised as novel (not present in dbSNP V137) and known. The novel SNVs were further considered. Only the non­ synonymous SNVs in this dataset were further analysed to their

Fig. 1. a) SNVs data superset. OBM (orange). NBM (red) b) Pie chart showing the distribution of kinases c) Pipeline to map SNVs to structure.

translated gene products and splice variants. All kinase related gene products were scrutinised to see if the position of the SNVs could be mapped to the kinase domain of the protein. This was done by comparing to the conserved domain database (CDD) [24]. These SNVs were mapped to the structure (if known), modelled if otherwise. The effect of the mutation was studied with respect to the stability of the protein and binding interactions were compared if a kinase-drug complex structure were available.

The tyrosine kinases are of specific interest in the context of CML because BCR-ABL, also a tyrosine kinase is the primary target of imatinib. These 15 Tyrosine specific tyrosine kinases were categorised based on the presence or absence of SNVs in the kinase domain. The translated gene products often give rise to splice variants and heterozygosity may gives rise to two

possible

substitutions.

All

these

possibilities

were

considered and finally four unique kinases; LCK (Lymphocyte­ specific protein tyrosine kinase), CDC42 (Cell division control III.

RESULTS AND DISCUSSION

The SNVs unique to these datasets were considered in this study (Fig. 1a). The pipeline as per Fig. 1b was followed for both bone marrow aspirates (OBM and NBM). There were 9986 non-synonymous novel SNVs in the OBM and 92 in the

protein), focal adhesion kinase and a UFO tyrosine-protein kinase receptor showed SNVs in their kinase domain. The effect of these mutations was studied in the context of the structure. Two such cases are discussed.

NBM data. SNVs specific to BCR-ABL were not detected by

A. Case stu dies

the Illumina exome capture kit. The search yielded no hits for

1) LCK

kinases in the NBM. The OBM mapped to 4273 translated gene products with SNVs in them. Out of these 86 were kinases. These can be classified broadly as 16 serine or

LCK is a tyrosine kinase found in lymphocytes which phosphorylates tyrosine residues of proteins involved in the intracellular signalling pathways of these lymphocytes. It is a member of the Src family of tyrosine kinases [25]. LCK

has been seen to be a target for imatinib. The SNV in LCK is in residue 301 of the kinase domain where a valine residue is mutated to glycine. BLAST [26] against Protein data bank (PDB) yielded a binary complex of LCK and imatinib with 100% identity with the kinase domain PDB Id: 2PLO. X-ray structure of the LCK with imatinib, confirms that the conformation adopted by LCK is distinct from other structurally-characterized Src-family kinases and instead resembles kinases abll and kit (which are off target kinases for imatinib) when in complex with imatinib [27]. Local minimization till convergence fixed at 6A around the mutated residue in the context of a rigid sphere of 12A around it using SYBYL [28] yielded the following results. LCK with Val 301 yielded the energy -20.10 kcallmol after 1574 cycles of minimization using SYBYL. A point mutation to Gly 301 followed by 2047 cycles of minimization using SYBYL

yielded the energy -30.96

kcallmol. This mutation has hence led to the further stabilization of the system. There is no perturbation of the interaction

of

interaction

that

imatinib Val

had

other with

than

the

imatinib.

2) CDC42 CDC42 is a small GTPase of the Rho-subfamily involved in regulation of the cell cycle. It is known to regulate signaling pathways, cell migration and cell cycle progression. It has been shown to regulate hematopoietic stem cell aging and rejuvenation [30]. In the case of CDC42, the mutations were on the surface of the protein and BLAST yielded a hit of 100% identity to PDB: 4H2S. The possible mutations in residue Ser 166 were to GlylThr. Local minimization after 3086 cycles converged to the energy 3.575 kcallmol in the case where residue 166 was serine. This reduced to -7.681 kcallmol on convergence, after 2017 cycles of minimization, where the same residue was mutated to threonine. The energy increased to 5.186 kcallmol on convergence, after 1518 cycles of minimization when it was mutated to glycine. This shows that the serine to threonine mutation is more favorable in the context of CDC42.

hydrophobic The

distance

between the protein side chains and imatinib has increased

IV. High

throughput

CONCLUSION

experiments

like

Next

Generation

from 4.1A to 6.2A (Fig. 2).

sequencing gives rise to a large amount of data. Hence

In order to further study the interaction of LCK with

becomes crucial. Information at the level of genes can be

imatinib in the context of this mutation, we docked imatinib into LCK Gly 301 mutant structure using Autodock4 [29]. The initial binding energy of imatinib with Val301 was 12.10 and decreased to -1l.13kcallmol

in the mutant

structure. The ligand ring position was slightly perturbed to limit all contacts with the ligand within a distance of 6A.

filtering

this

extrapolated

data to

to

the

make

significant

context

to

interpretations

structure

to

make

interpretations significant. By filtering the SNVs specific to kinases from an exome dataset and following the pipeline to map the mutations up to the structure, allows to screen the significant SNVs. Effects of SNVs have been discussed earlier [31, 32] but we discuss two case studies specifically in the context of kinases.

In

the

case

of

LCK,

we

can

also

gain

a

comprehensive understanding of the kinase-drug interaction by docking studies. In this case study to review the effect of kinases over a

a

period (two years) under imatinib, we find number of kinases

showing

SNVs

have

reduced

drastically.

We

hypothesise a complete elimination of a predominant BCR­ ABL clone due to treatment. LCK though distinct from other

structurally-characterized

Src-family

kinases,

resembles kinases abll and kit (which are off target kinases for imatinib) when in complex with the immunosuppressive drug imatinib [33]. CDC42, focal adhesion kinase and UFO tyrosine-protein kinase receptor are all implicated in cancer. Hence, we have succeeded in deriving information out of the high throughput exome sequencing data. Effects at the level of the genes can hence be understood in the Gly301 Val

Fig. 2. a) LCK with imatinib (Yellow) bound b) Close up showing Val 301 c) Enenrgy minimised loop showing Gly 301 (Blue) and Val 301 (Red).

context

of

the

structure.

Altered

inter

molecular

interactions gives rise to altered disease phenotype. This would help in the further development of more efficient small molecule drugs. The bioinformatics pipeline as such can be used to map mutations from gene to structure for kinases specifically which become relevant in the next generation sequencing approach to study any type of cancer.

ACKNOWLEDGMENT ARC acknowledges the Department of Biotechnology for the DBT-Glue grant.

[17] A. Woolfson and X. Jiang, "Targeting the Chronic Myeloid Leukemia Stem Cell : A Paradigm for the Curative Treatment of Human Malignancies," 2006. [18] a Shuen and W. D. Foulkes, "Clinical implications of next-generation sequencing for cancer medicine.," Current oncology (Toronto, Ont.), vol. 17, no. 5, pp. 39-42, Oct. 2010.

REFERENCES

[19] C. Meldrum, M. A. Doyle, and R. W. Tothill, "Next-generation sequencing for cancer diagnostics: a practical perspective.," The Clinical biochemist. Reviews / Australian Association of Clinical Biochemists, vol. 32, no. 4, pp. 177-95, Nov. 2011.

[I]

B. J. Druker, et aI. , "Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells.," Nature medicine, vol. 2, no. 5, pp. 561-6, May 1996.

[2]

B. J. Druker, et aI. , "Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia.," The New Englandjournal of medicine, vol. 344, no. 14, pp. 1031-7, Apr. 2001.

[20] R. Piazza, et aI. , "Recurrent SETBPI mutations in atypical chronic myeloid leukemia.," Nature genetics, vol. 45, no. I, pp. 18-24, Jan. 2013.

[3]

C. Fausel, "Targeted chronic myeloid leukemia therapy: seeking a cure.," Journal of managed care pharmacy: JMCP, vol. 13, no. 8 Suppl A, pp. 8-12, Oct. 2007.

[21] B. Langmead, C. Trapnell, M. Pop, and S. L. Salzberg, "Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.," Genome biology, vol. 10, no. 3, p. R25, Jan. 2009.

[4]

H. M. Kantarjian, F. Giles, A. Quintas-Cardama, and J. Cortes, "Important therapeutic targets in chronic myelogenous leukemia.," Clinical cancer research: an of f icial journal of the American Association for Cancer Research, vol. 13, no. 4, pp. 1089-97, Feb. 2007.

[22] S. T. Sherry, et aI. , "dbSNP: the NCBI database of genetic variation.," Nucleic acids research, vol. 29, no. I, pp. 308-11, Jan. 2001. [23] S. T. Sherry, M. Ward, and K. Sirotkin, "dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation.," Genome research, vol. 9, no. 8, pp. 677-9, Aug. 1999.

[5]

K. Machova Polakova, J. Koblihova, and T. Stopka, "Role of epigenetics in chronic myeloid leukemia.," Current hematologic malignancy reports, vol. 8, no. I, pp. 28-36, Mar. 2013.

[24] A. Marchler-Bauer, et aI. , "CDD: a Conserved Domain Database for the functional annotation of proteins.," Nucleic acids research, vol. 39, no. Database issue, pp. D225-9, Jan. 20II.

[6]

M. E. Gorre, et aI. , "Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification.," Science (New York, N.Y.), vol. 293, no. 5531, pp. 876-80, Aug. 2001.

[25] K. C. Lee, I. Ouwehand, A. L. Giannini, N. S. Thomas, N. J. Dibb, and M. J. Bijlmakers, "Lck is a key target of imatinib and dasatinib in T-cell activation.," Leukemia, vol. 24, no. 4, pp. 896-900, Apr. 2010.

[7]

S. Soverini, et aI., "BCR-ABL kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of European LeukemiaNet.," Blood, vol. 118, no. 5, pp. 1208-15, Aug. 2011.

[26] S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, "Basic local alignment search tooL," Journal of molecular biology, vol. 215, no. 3, pp. 403-10, Oct. 1990.

[8]

M. Deininger, E. Buchdunger, and B. J. Druker, "The development of imatinib as a therapeutic agent for chronic myeloid leukemia.," Blood, vol. 105, no. 7, pp. 2640-53, Apr. 2005.

[9]

D. White, et aI. , "In vitro sensitivity to imatinib-induced inhibition of ABL kinase activity is predictive of molecular response in patients with de novo CML.," Blood, vol. I06, no. 7, pp. 2520-6, Oct. 2005.

[10] B. Nagar, "c-Abl Tyrosine Kinase and Inhibition by the Cancer Drug Imatinib (Gleevec/STI-57I)," 1. Nutr., vol. 137, no. 6, p. 1518S-1523, Jun. 2007. [11] E. Buchdunger, et aI., "Inhibition of the Abl protein-tyrosine kinase in vitro and in vivo by a 2-phenylaminopyrimidine derivative.," Cancer research, vol. 56, no. I, pp. 100-4, Jan. 1996. [12] M. Carroll, et aI. , "CGP 57148, a Tyrosine Kinase Inhibitor, Inhibits the Growth of Cells Expressing BCR-ABL, TEL-ABL, and TEL-PDGFR Fusion Proteins," Blood, vol. 90, no. 12, pp. 4947-4952, Dec. 1997. [13] E. Buchdunger, et aI. , "Abl protein-tyrosine kinase inhibitor STl571 inhibits in vitro signal transduction mediated by c-kit and platelet­ derived growth factor receptors.," The Journal of pharmacology and experimental therapeutics, vol. 295, no. I, pp. 139-45, Oct. 2000. [14] K. Okuda, E. Weisberg, D. G. Gilliland, and J. D. Griffin, "ARG tyrosine kinase activity is inhibited by STl571.," Blood, vol. 97, no. 8, pp. 2440-8, Apr. 2001. [15] A. L. Dewar, A. C. W. Zannettino, T. P. Hughes, and A. B. Lyons, "Inhibition of c-fms by imatinib: expanding the spectrum of treatment.," Cell cycle (Georgetown, Tex.), vol. 4, no. 7, pp. 851-3, Jul. 2005. [16] C. H. M. Jamieson, et aI. , "Granulocyte-macrophage progenitors as candidate leukemic stem cells in blast-crisis CML.," The New England journal of medicine, vol. 351, no. 7, pp. 657-67, Aug. 2004.

[27] M. D. Jacobs, P. R. Caron, and B. J. Hare, "ClassifYing protein kinase structures guides use of ligand-selectivity profiles to predict inactive conformations: structure of Icklimatinib complex.," Proteins, vol. 70, no. 4, pp. 1451-60, Mar. 2008.SYBYL 8. 0, Tripos International, 1699 South Hanley Rd. , St. Louis, Missouri, 63144, USA [28] SYBYL 8. 0, Tripos International, 1699 South Hanley Rd., St. Louis, Missouri, 63144, USA [29] G. M. Morris, R. Huey, W. Lindstrom, M. F. Sanner, R. K. Belew, D. S. Goodsell, and A. J. Olson, "AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.," Journal of computational chemistry, vol. 30, no. 16, pp. 2785-91, Dec. 2009. [30] M. C. Florian, et aI. , "Cdc42 activity regulates hematopoietic stem cell aging and rejuvenation.," Cell stem cell, vol. I0, no. 5, pp. 520-30, May 2012. [31] D. Chasman and R. M. Adams, "Predicting the functional consequences of non-synonymous single nucleotide polymorphisms: structure-based assessment of amino acid variation.," Journal of molecular biology, vol. 307, no. 2, pp. 683-706, Mar. 2001. [32] R. Karchin, A. N. A. Monteiro, S. V Tavtigian, M. A. Carvalho, and A. Sali, "Functional impact of missense variants in BRCAI predicted by supervised learning.," PLoS computational biology, vol. 3, no. 2, p. e26, Feb. 2007. [33] D. Wolf, H. Tilg, H. Rumpold, G. Gastl, and A. M. Wolf, "The kinase inhibitor imatinib--an immunosuppressive drug?," Current cancer drug targets, vol. 7, no. 3, pp. 251-8, May 2007.