**Proposal for a Human Cancer Genome Project**

**Demonstration Study**

**Prepared for the National Human Genome Research Institute**

**By**

**The J. Craig Venter Institute**

**November 25, 2005**

**Project Overview**

In response to a request from the National Human Genome Research
Institute (NHGRI), the Venter Institute is pleased to propose a
demonstration study for the Human Cancer Genome Project (HCGP). This
project builds upon our recent success in applying PCR-based directed
exon sequencing to identify somatic mutations in receptor-encoding genes
in a highly malignant form of brain cancer known as Glioblastoma
Multiforme. The effort builds upon a long-standing collaboration of Dr.
Robert Strausberg of the Venter Institute and Dr. Greg Riggins of the
Johns Hopkins University School of Medicine for the molecular
characterization of glioblastoma through genome and transcriptome
analyses(1-4). In this pilot we propose to compare the richness of data
generated by two technological approaches and to compare their
cost-effectiveness. In coordination with the other sequencing centers
and the NHGRI, we will make data submissions including trace files to
the pubic databases. We will work with the NHGRI and other centers to
develop a method for data submission including annotation of the
sequences and trace files in a manner such that they can be readily
accessed by the research community.

**Specific Aims**

**1) Re-sequence the entire coding region of approximately 70 receptor
kinase genes for 74 human glioblastoma genomes and 20 corresponding
normal blood DNA**. We will utilize the high-throughput PCR-based exon
sequencing pipeline currently in place at the Joint Technology Center of
the Venter Institute, which has been successfully adapted to detect
somatic mutations in tumors. Sequencing will be in both directions of
the coding domains in approximately 70 receptor encoding genes.

**2) Utilize 454 technology to systematically determine the somatic
mutations in selected receptor encoding genes in the 20 glioblastoma
samples that have matched normal DNA.** The samples and genes to be
sequenced will be selected based on initial results of the more
extensive sequencing performed in Specific Aim 1. This sequencing will
be performed using the 454 instrument already in place at the JTC.

**Scientific Background**

**Glioblastoma Multiforme**

### There is a predicted 0.44% lifetime risk of dying from a malignant brain tumor in the US. Over half of all astrocytic brain tumors are Glioblastoma Multiforme (glioblastomas) with an incidence of 2 to 3 new cases each per 100,000 people (5, 6). They are one of the most malignant and deadly of brain cancers. About one third of all primary CNS neoplasms are glioblastomas, but they account for close to half of brain tumor deaths in the US (7). Glioblastomas tend to occur earlier than other solid tumors and can occur across all age groups, with an incidence peak around age 50 to 60 (5, 6). Five-year survival is dismal for all ages: 20% for patients less than 20 years, 4% for middle age patients, and survival is less than 1% for patients over 65 years (7). Glioblastomas are either observed de novo in patients, or evolve as a secondary glioblastoma from lower grade astrocytomas. The primary (de novo) glioblastoma tend to occur after age 50, and are much more likely to have Epidermal Growth Factor Receptor (EGFR) amplification, p16 deletions and PTEN mutations. The classic secondary glioblastoma, is a recurrent tumor in a young patient less than age 50, and has a high probability of a p53 mutation (5, 6). 

**Kinase mutations in Glioblastoma**

## There is mounting evidence that the kinase family of genes is frequently mutated in the development of glioblastomas. Recently our group undertook a pilot study that aimed to discover potentially drug targetable alterations within receptor kinase genes in glioblastoma. This study focused on exons encoding the receptor kinase domain in 20 genes in 19 glioblastoma samples. Remarkably, even with this limited amount of sequencing we discovered two novel mutations within the kinase domain of fibroblast growth factor receptor 1 (FGFR1), representing the first catalytic domain somatic mutations within this gene in any cancer. In addition, a protein truncating somatic mutation was identified within the PDGFRA gene. Moreover, in a separate study, the Johns Hopkins group recently reported on a high rate of PI3 kinase mutations (specifically PI3KCA) in glioblastomas (8). Along with the previous reports of EGFR mutations (a receptor tyrosine kinase) and genomic amplification of CDK4, the importance of cataloging kinase mutations in he identification of potential new therapeutic targets represents a compelling opportunity. The work performed by this proposed demonstration project will be very informative with respect to strategies and choice of technological approaches in order to assure the generation of high quality data in a cost-effective manner, and to assure that mutations are found efficiently even when present in a minority of cells within a tumor. 

### 

### Specific Aim 1 - Re-sequence the entire coding region of 70 receptor tyrosine kinase genes for 74 human glioblastoma genomes and 20 corresponding normal blood DNA.

### 

We are proposing to focus this demonstration project on the detection of
mutations in genes that are potentially targets for therapeutic
intervention. In our opinion, success in this endeavor to identify new
targets would represent an immediate demonstration of the value of a
full-scale HGCP. We are proposing to sequence approximately 70 genes
encoding receptors in 74 primary glioblastoma tumors as well as matching
normal genomic DNA derived from peripheral blood cells from 20 of these
samples. For the additional 54 glioblastomas, only the tumor DNA is
available. However, putative mutations that are discovered in these
latter samples would then be available for confirmation in additional
samples that are currently being collected and that will also have
matching normal DNA. The number of genomic samples that we have chosen
provides for an opportunity to identify commonly occurring mutations at
a cost within the bounds of this demonstration project.

**Tumor samples**

Patient samples that are currently available will be provided by the
Brain Tumor Research Laboratory led by Dr. Greg Riggins at Johns Hopkins
University, Department of Neurosurgery. These samples have been
de-linked from patient identifiers and have been determined by the JHU
IRB to be exempt (see attached document from IRB JHM-IRB X).
Seventy-four glioblastoma multiforme (WHO grade IV astrocytomas) have
been selected for this study. For twenty of these samples, matching
genomic DNA from peripheral blood cells is also available. A new
protocol has been submitted by JHU, with six participating
neurosurgeons, to collect tumor and matching blood. The tumor cells will
be passaged to propagate and normal DNA propagated as transformed
lymphocytes to create DNA amounts necessary for eventual complete
sequencing of their genomes.

**Genes to be sequenced**

We are proposing to focus our studies on the subset of kinase genes that
encode cell surface receptors. Proteins encoded by such genes, including
BCR/ABL, ERBB2, and EGFR are examples of such proteins that have been
the subject of molecularly targeted therapeutic development, including
Gleevec and Herceptin. A curated list of 69 receptor kinase genes is
presented in Appendix B. We propose that this will be the primary set of
genes to be sequenced in this study. However, based on analysis of known
gene expression patterns in glioblastoma as well as regions of genomic
amplification, this list of genes might be modified. In that event, we
are proposing to sequence genes encoding additional types of cell
surface proteins expressed in glioblastoma, such as those encoding ion
channels.

**Bi-directional dideoxy sequencing**

Bi-directional dideoxy sequencing will be performed according to the
detailed methodology described by Rand et al(4) and presented in
Appendix A. In our present production process each amplicon is sequenced
bi-directionally. It has been our experience that this approach is very
effective in assuring that true positive alterations are called, and
that false positives are reduced. However, as part of this pilot we will
be evaluating the relative cost-effectiveness and accuracy of
single-stranded versus double-stranded sequencing. For example, it may
be that sequencing more samples through single-stranded sequencing might
afford a greater amount of mutation detection with similar accuracy.

**Specific aim 2 - Utilize 454 technology to systematically determine
the somatic mutations in selected receptor encoding genes in the 20
glioblastoma samples that have matched normal DNA.**

The Venter Institute was one of the first organizations to acquire a 454
sequencing instrument and has been working in a collaborative manner
with 454 to assess the applicability of this technology for various
applications including sequencing of exons from tumors. The potential
advantages of the technology are the ability to detect mutations that
are present in a relatively small fraction of cells within a tumor, and
that allele determination is quantitative. The ability to detect
mutations that occur in a small fraction of cells within a tumor is, in
our opinion, very important for eventual success in the HCGP. For
example, while ABI technology can detect mutations that comprise a
significant fraction of the tumor genome population, it may be that a
small fraction of cells within some tumors have similar mutations that
would form the basis for interventional targeting. While in some
instances these cells could be enriched for analysis through techniques
such as laser capture microdissection, it would be much preferable to
work with a detection technology that can find rare, but potentially
very important mutations within the tumor.

**Pyrosequencing-based mutation sequencing**

The 454 sequencing platform is a scalable, highly parallel and non
cloning-based sequencing system with raw throughput significantly
greater than that of state-of-the-art-capillary electrophoresis
instruments (100x). The sequencer uses disposable fibreoptic slides
containing 1.6 million wells in order to sequencer 20-40 million bases
in a 4 hour run. DNA fragments are clonally amplified on beads in
droplets of an emulsion. These template-carrying beads are then loaded
into the fibreoptic slide wells at a density that allows approximately
150 sequencing reactions/mm2.  Sequencing by synthesis is performed in
these wells using a pyrosequencing protocol, and by detecting the light
produced, 80-120 base pairs of sequence information from each well is
produced.

**Proposed pilot study with 454 technology**

We are proposing to perform a relatively small scale but potentially
highly informative pilot with the 454 technology. In this study we will
start with 20 genes for which somatic mutations will have been
identified through the ABI technology. For that small set of genes, we
will sequence all exons with the 454 technology. For this study we will
utilize the 20 glioblastoma tumors for which we also have matching DNA,
and in which somatic mutations have been identified. The study will be
informative in several ways. First, this independent dataset will serve
to validate the ABI results and to quantitate the frequency of the
mutation within the tumor. Second, we will establish if additional
somatic mutations not previously detected with the ABI technology can be
observed with this more sensitive approach.

**Detection of somatic mutations utilizing 454 technology**

454 sequencing technology potentially affords deeper coverage of a
sampling of DNA molecules. This is of particular importance when
sequencing DNA from tumor tissues that may be in small quantities
compared to normal cells. Therefore we may expect that any mutated DNA
will constitute a small sample size. Based on the data from specific aim
#1 we will select 20 genes that show the greatest concentration of
somatic mutations by comparison of tumor DNA to matched controls using
Sanger-based sequencing. These 20 genes will be the focus of PCR based
amplification, amplicon pooling, and 454 sequencing to confirmed the
presence of the previously detected somatic mutation and, more
importantly, to detect the presence of other somatic mutations below the
threshold of detection via Sanger sequencing technologies. Specifically
we will adopt the following approach to achieve this:

1.  Design PCR primers, using our existing primer design pipeline,
    targeting an amplicon size of 300 bp maximum centered on the exons
    of genes.

2.  Perform PCR using a single DNA sample (of 20 total) and a single
    primer pair. Accomplish amplification of 200 PCR reactions per DNA
    in a high throughput fashion using our existing automation and
    developed protocols.

3.  Combine all 200 PCR products for a single DNA sample using an
    automation step to be developed. This involves using an FX robot to
    combine all 200 PCR products from a 384 well PCR plate into a
    reservoir and affinity purification of the DNA pool.

4.  The DNA pool is then ligated to 454 sequencing adapters and sent
    through an established sequencing step using a picotiter plate
    enabling 4 DNA pools to be maintained distinctly through the
    sequencing step.

This approach will provide us with enough sequencing redundancy to
provide a good indication of whether mixed populations of DNA forms are
present per amplicon. Given our amplicon pooling strategy will be able
to detect the variant present from 4 DNA samples x 20 genes x 10
amplicon per gene from 2 plates of 454 sequencing. A full data set of 20
DNA samples will be achieved by 10 x 454 sequencing plates total. Our
current PCR re-sequencing indicates that we have DNA product in
approximate equivalent amounts due to our ability to detect signal
within reproducible ranges on 3730 machines. This result suggests that
pooling PCR products will provide DNA molecules from each amplicon in
equivalent amount for appropriate sampling by single molecule
technologies. We will however attempt to verify DNA concentration across
some test PCR plates and will devise strategies to alter volumes of PCR
product used for pooling to correct for significant discrepancies. We
estimate the following costs associated with accomplishing this pilot
experiment:

**References**

1\. Boon, K., Osorio, E. C., Greenhut, S. F., Schaefer, C. F.,
Shoemaker, J., Polyak, K., Morin, P. J., Buetow, K. H., Strausberg, R.
L., De Souza, S. J. & Riggins, G. J. (2002) *Proc Natl Acad Sci U S A*
**99,** 11287-92.

2\. Lal, A., Lash, A. E., Altschul, S. F., Velculescu, V., Zhang, L.,
McLendon, R. E., Marra, M. A., Prange, C., Morin, P. J., Polyak, K.,
Papadopoulos, N., Vogelstein, B., Kinzler, K. W., Strausberg, R. L. &
Riggins, G. J. (1999) *Cancer Res* **59,** 5403-7.

3\. Lal, A., Peters, H., St Croix, B., Haroon, Z. A., Dewhirst, M. W.,
Strausberg, R. L., Kaanders, J., van der Kogel, A. J. & Riggins, G. J.
(2001) *Journal Of The National Cancer Institute* **93,** 1337-1343.

4\. Rand, V., Huang, J., Stockwell, T., Ferriera, S., Buzko, O., Levy,
S., Busam, D., Li, K., Edwards, J. B., Eberhart, C., Murphy, K. M.,
Tsiamouri, A., Beeson, K., Simpson, A. J., Venter, J. C., Riggins, G. J.
& Strausberg, R. L. (2005) *Proc Natl Acad Sci U S A* **102,** 14344-9.

5\. Kleihues, P., Burger, P. C., Collins, V. P., Newcomb, E. W., Ohgaki,
H. & Cavenee, W. K. (2000) in *Pathology & Genetics: Tumours of the
Nervous System*, eds. Kleihues, P. & bCavenee, W. K. (IARC Press, Lyon),
pp. 29-39.

6\. Kleihues, P., Burger, P. C., Plate, K. H., Ohgaki, H. & Cavenee, W.
K. (2000) *Pathology & Genetics: Tumours of the Nervous System*
(International Agency for Research on Cancer, Lyon).

7\. Davis, F. G., Freels, S., Grutsch, J., Barlas, S. & Brem, S. (1998)
*J Neurosurg* **88,** 1-10.

8\. Samuels, Y., Wang, Z., Bardelli, A., Silliman, N., Ptak, J., Szabo,
S., Yan, H., Gazdar, A., Powell, S. M., Riggins, G. J., Willson, J. K.,
Markowitz, S., Kinzler, K. W., Vogelstein, B. & Velculescu, V. E. (2004)
*Science* **304,** 554.

**Appendix A**

**PCR-based capillary electrophoresis exon sequencing methodology**

The current Venter Institute exon sequencing methodology is based on
polymerase chain reaction (PCR) amplification of genomic DNA (Mullis *et
al*., 1987). The PCR primer pairs are tailed with M13 forward and M13
reverse priming sequences respectively to allow for subsequent
sequencing of the PCR products using the universal M13 primer set. The
sequencing reactions are analyzed on capillary electrophoresis Applied
Biosystems 3730*xl* DNA Analyzer units using BigDye® Terminator v3.1
Cycle Sequencing Kit chemistry (Applied Biosystems, Foster City, CA).
Custom-developed digital signal processing algorithms are used to
attenuate PCR and sequencing artifacts in the DNA signals contained in
chromatograms in order to improve the accuracy of pure and mixed base
calling. Sequences are assembled and reviewed using publicly available
software. Custom-developed software reports variants in a variety of
formats. Variations are independently verified using commercially
available software.

**1. Primer Design**

A high-throughput methodology to PCR resequence genomic regions
containing protein kinase domains has been developed. Targeted regions
were specified using Ensembl identifiers. For each targeted region, the
corresponding template sequence and annotations were retrieved using
custom software that incorporates the Ensembl API. Depending on the
calculated melting temperature (Tm) of DNA in a targeted region,
different amplicon design parameters were used:

-   For Tm \<= 82° C, amplicon size was 550-800 bps.

-   For Tm \> 82° C, amplicon size was 300-350 bps.

Since the PCR amplicon sizes could be larger or smaller than the length
of the targeted regions for a specified template, the software
automatically either

-   centered smaller targeted regions within the designed larger
    amplicons, or

-   overlapped smaller amplicons across the larger targeted regions,
    with an amplicon overlap of 150 bps.

The selection of optimal primers used in the amplicon tiling was
enumerated with the help of *primer3* (Rozen, *et al*., 2000). These
primer lists were then further screened to match our empirically-derived
success criteria from our high throughput resequencing laboratory
pipeline and our downstream trace processing algorithms requirements.
Additional criteria included searching through the reference genome to
ensure primer specificity, eliminating amplicons with stem loop
secondary structure at the primer binding sites, and rejecting primers
that bound where known variations occurred. Amplicon specific screening
eliminated primer pairs that did not center and limited known indel
variations and/or sequencing stutter motifs. The selected PCR primer
pairs were tailed with M13 forward and M13 reverse priming sequences
respectively at their 5' ends.

**2. PCR**

PCR is performed in a 384-well cycle plate using AmpliTaq Gold® DNA
Polymerase (Applied Biosystems, Foster City, CA) to amplify the regions
of interest. The PCR conditions including the input amounts and
concentrations for the DNA sample, primer pairs, polymerase, buffer and
free nucleotides as well as the cycling profiles have been optimized.
Following the PCR reactions, Shrimp Alkaline Phosphatase (USB
Corporation, Cleveland, OH) and Exonuclease I (USB Corporation,
Cleveland, OH) are added to dephosphorylate excess amounts of dNTPs and
degrade remaining amplification primers.

**3. Sequencing**

Sequencing protocols are based on dideoxynucleotide sequencing
methodology (Sanger *et* *al*., 1977). To obtain paired sequence reads
from opposite ends of each PCR fragment, two 384-well cycle sequencing
reaction plates are prepared from each plate of PCR template. Sequencing
reactions are carried out by using BigDye® Terminator v3.1 Cycle
Sequencing Kit chemistry (Applied Biosystems, Foster City, CA) and the
universal M13 primer set. Reaction products are precipitated with sodium
acetate and ethanol, dried at room temperature and stored at 4C. The
sequencing reactions are analyzed on Applied Biosystems 3730*xl* DNA
Analyzer units.

.

**4. Trace File Processing**

In order to improve the accuracy of variation detection, the trace files
are processed using a series of digital signal processing algorithms
designed to detect and attenuate various PCR and sequencing related
artifacts associated with the protocols used (Stockwell et al.,
manuscript in preparation). Dye blob, n-1/n+1, and PCR stutter artifacts
are attenuated in this fashion. Pure and mixed base calling is performed
on the processed DNA signals.

**5. Sequence Variation Detection**

The sequences from the processed traces are assembled using *phrap*
(Green, 1996). Custom software is used to generate various types of
navigation files. These navigation files assist manual reviewers, who
use *consed* (Gordon et al., 1998) to review and edit the assemblies.
The consensus sequence is extracted from the assembly. Differences
between the consensus and the reference sequences are determined using a
modified Needleman-Wunsch algorithm (Needleman *et al*., 1970). These
differences are input to custom-developed SNP classification software
that generates a report detailing the predicted functional impact of
variations, their dbSNP identifiers (if known), sample population allele
frequencies, and the positionally-associated Ensembl annotations.
Independent verification of detected variations and screen snapshots are
performed using the commercially available Mutation Explorer software
from SoftGenetics, LLC (<http://www.softgenetics.com/>).

6\. **Laboratory and computational QA/QC procedures for minimizing
errors and confirming the variations in the resequencing effort**

A variety of established quality assurance and quality control
procedures are being performed to ensure that our resequencing efforts
yield accurate and reliable information while maximizing process
efficiency. In addition to our facility-wide QA/QC procedures, we have
implemented more stringent laboratory operation procedures to deal with
the sensitive nature of PCR production, with the main concern being the
potential for any PCR and DNA contamination. We designed our pre-PCR
laboratory and our PCR pipeline workflow specifically to address those
issues. The pre- and post-PCR processes are physically separated and
performed by different personnel. We have two pre-PCR laboratories with
controlled access and strict gowning and cleaning procedures. Regular
decontamination and swipe test are also conducted to minimize
contamination. In the event that PCR product contamination is detected
steps will be taken to resolve the issue based on established
procedures.

In addition to QC testing of all raw materials, we will conduct
functional QC testing of key master mixes using a set of positive and
negative control samples. For in process monitoring, positive and
negative controls will be included for each PCR and sequencing reaction
plate processed. All PCR products generated for production runs, as well
as the positive and negative controls are evaluated prior to the
subsequent sequencing reactions.

**7. Computational approaches for evaluating all production sequences as
well as the traces for positive and negative controls.**

> *Evaluation of the Positive control-*The trace file for the positive
> control will be basecalled and the resulting sequence will be aligned
> against the known control sequence. If the positive control sequence
> aligns to the known control sequence for fewer than 100 bases or has
> less than 85% sequence identity, then the positive control will be
> considered to have failed. If a majority of positive controls fail on
> a sequencing plate a notification will be generated.
>
> *Negative control-*The trace file for the negative control will be
> basecalled. If the resulting sequence has greater then 20 unambiguous
> contiguous bases, then the resulting sequence will be aligned against
> the known control sequence. If the negative control sequence has
> greater then 20 unambiguous contiguous bases that match the known
> control sequence at greater than or equal to 85% identity, then the
> negative control will be considered to have failed. If a majority of
> negative controls fail on a sequencing plate a notification will be
> generated.
>
> *Sample-*The trace file for the sample will be basecalled and the
> resulting sequence will be aligned against the corresponding reference
> template sequence. If the basecalled sample sequence aligns to the
> reference template sequence for fewer than 100 bases or has less than
> 85% sequence identity, then the sample will be considered to have
> failed. If greater than 25% of samples fail on a sequencing plate, a
> notification will be generated.
>
> All program code written to integrate pre-existing software, such as
> phred, Tracetuner, etc, into our pipeline will be subject to rigorous
> code review. Phase I will also permit significant manual intervention
> in each aspect of the trace-processing and polymorphism detection
> process. We will also compare the results of our polymorphism
> detection process with that of submitting the same trace file to
> phred/polyphred to ensure results are either comparable or by a manual
> review process we can satisfactorily explain why differences arise.
>
> As mentioned above, the performance indicators are monitored daily by
> the QC specialist, R&D and QC manager, team leaders and the director
> of the sequencing facility. All substandard data is reviewed, possible
> causes are investigated and remedial actions are taken when
> appropriate. Since we perform QC on all critical reagents, primers and
> DNA samples and validation on all protocols and instrumentation, we
> have rarely experienced any global production failures.

**References:**

1.  Gordon D, Abajian C, Green P. Consed: a graphical tool for sequence
    finishing. Genome Res. 1998 Mar: 8(3):195-202.

2.  Green P. [Phrap/cross_match/swat
    documentation](http://www.phrap.org/phredphrap/general.html) and
    [phrap documentation](http://www.phrap.org/phredphrap/phrap.html)
    [http://www.phrap.org](http://www.phrap.org/) 1996.

3.  Mullis KB, Faloona FA. Specific synthesis of DNA in vitro via a
    polymerase-catalyzed chain reaction. Methods Enzymol. 1987; 155:
    335-50.

4.  Needleman SB, Wunsch CD. A general method applicable to the search
    for similarities in the amino acid sequence of two proteins.\
    J Mol Biol. 1970 Mar; 48(3):443-53.

5.  Rozen S, Skaletsky HJ. Primer3 on the WWW for general users and for
    biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics
    Methods and Protocols: Methods in Molecular Biology. Humana Press,
    Totowa, NJ. 2000; 365-386.

6.  Sanger F, Nicklen S, Coulson AR. DNA sequencing with
    chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977 Dec;
    74(12):5463-7.

7.  Stockwell T. Software for the high throughput, fully-automated
    detection of DNA variations using PCR, sequencing, and digital
    signal processing. Manuscript in preparation.

**Appendix B**

**Receptor kinase genes**

  -------- ---------------------------------------- ------------- -------------
  **Gene                                            **Gene        **RefSeq**
  name**                                            Symbol**      

                                                                  

  1        Activin A receptor, type I               *ACVR1*       NM_001105

  2        Activin A receptor, type IB              *ACVR1B*      NM_004302

  3        Activin A receptor, type II              *ACVR2*       NM_001616

  4        Activin A receptor, type IIB             *ACVR2B*      NM_001106

  5        Activin A receptor, type IC              *ACVR1C*      NM_145259

  6        Activin A receptor type II-like 1        *ACVRL1*      NM_000020

  7        *Anaplastic lymphoma kinase (Ki-1)*      *ALK*         *NM_004304*

  8        Anti-Mullerian hormone receptor, type    *AMHR2  *     NM_020547
           II                                                     

  9        *AXL receptor tyrosine kinase*           *AXL*         *NM_001699*

  10       Bone morphogenetic protein receptor,     *BMPR1A*      NM_004329
           type IA                                                

  11       Bone morphogenetic protein receptor,     *BMPR1B *     NM_001203
           type IB                                                

  12       Bone morphogenetic protein receptor,     *BMPR2*       NM_001204
           type II (serine/threonine kinase)                      

  13       Colony stimulating factor 1 receptor,    *CSF1R *      NM_005211
           formerly McDonough feline sarcoma viral                
           (v-fms) oncogene homolog                               

  14       *Discoidin domain receptor family,       *DDR1 *       *NM_001954*
           member 1 *                                             

  15       *Discoidin domain receptor family,       *DDR2*        *NM_006182*
           member 2*                                              

  16       *Epidermal growth factor receptor        *EGFR*        *NM_005228*
           (erythroblastic leukemia viral (v-erb-b)               
           oncogene homolog, avian)*                              

  17       *EPH receptor A1*                        *EPHA1*       *NM_005232*

  18       *EPH receptor A2*                        *EPHA2*       *NM_004431*

  19       *EPH receptor A3*                        *EPHA3*       *NM_182644*

  20       *EPH receptor A4*                        *EPHA4*       *NM_004438*

  21       *EPH receptor A5*                        *EPHA5*       *NM_004439*

  22       *EPH receptor A6*                        *EPHA6*       *NM_173655*

  23       *EPH receptor A7*                        *EPHA7*       *NM_004440*

  24       *EPH receptor A8*                        *EPHA8*       *NM_020526*

  25       *EPH receptor A10*                       *EPHA10*      *NM_173641*

  26       *EPH receptor B1*                        *EPHB1*       *NM_004441*

  27       *EPH receptor B2*                        *EPHB2*       *NM_004442*

  28       *EPH receptor B3*                        *EPHB3*       *NM_004443*

  29       *EPH receptor B4*                        *EPHB4*       *NM_004444*

  30       *EPH receptor B6*                        *EPHB6*       *NM_004445*

  31       V-erb-b2 erythroblastic leukemia viral   *ERBB2*       NM_004448
           oncogene homolog 2, neuro/glioblastoma                 
           derived oncogene homolog (avian)                       

  32       V-erb-b2 erythroblastic leukemia viral   *ERBB3*       NM_001982
           oncogene homolog 3 (avian)                             

  33       V-erb-a erythroblastic leukemia viral    *ERBB4*       NM_005235
           oncogene homolog 4 (avian)                             

  34       *Fibroblast growth factor receptor 1     *FGFR1*       *NM_015850*
           (fms-related tyrosine kinase 2, Pfeiffer               
           syndrome)*                                             

  35       *Fibroblast growth factor receptor 2     *FGFR2*       *NM_000141*
           (bacteria-expressed kinase, keratinocyte               
           growth factor receptor, craniofacial                   
           dysostosis 1, Crouzon syndrome, Pfeiffer               
           syndrome, Jackson-Weiss syndrome)*                     

  36       *Fibroblast growth factor receptor 3     *FGFR3*       *NM_000142*
           (achondroplasia, thanatophoric                         
           dwarfism)*                                             

  37       Fibroblast growth factor receptor 4      *FGFR4*       NM_002011

  38       *Fms-related tyrosine kinase 1 (vascular *FLT1*        *NM_002019*
           endothelial growth factor/vascular                     
           permeability factor receptor)*                         

  39       *Fms-related tyrosine kinase 3*          *FLT3*        *NM_004119*

  40       *Fms-related tyrosine kinase 4*          *FLT4*        *NM_182925*

  41       Insulin receptor                         *INSR*        NM_000208

  42       Insulin-like growth factor 1 receptor    *IGF1R  *     NM_000875

  43       Insulin receptor-related receptor        *INSRR*       NM_014215

  44       Kinase insert domain receptor (a type    *KDR*         NM_002253
           III receptor tyrosine kinase)                          

  45       KIT   v-kit Hardy-Zuckerman 4 feline     *KIT*         NM_000222
           sarcoma viral                                          

  46       Lemur tyrosine kinase 2                  *LMTK2*       NM_014916

  47       Lemur tyrosine kinase 3                  *LMTK3*       XM_055866

  48       Leukocyte tyrosine kinase                *LTK*         NM_002344

  49       Similar to bone morphogenetic protein    *LOC283155*   XM_208545
           receptor, type IA precursor; activin A                 
           receptor, type II-like kinase 3                        

  50       C-mer proto-oncogene tyrosine kinase     *MERTK*       NM_006343

  51       Macrophage stimulating 1 receptor        *MST1R*       NM_002447
           (c-met-related tyrosine kinase)                        

  52       Met proto-oncogene (hepatocyte growth    *MET*         NM_000245
           factor receptor)                                       

  53       Muscle, skeletal, receptor tyrosine      *MUSK*        NM_005592
           kinase                                                 

  54       Neurotrophic tyrosine kinase, receptor,  *NTRK1*       NM_002529
           type 1                                                 

  55       Neurotrophic tyrosine kinase, receptor,  *NTRK2*       NM_006180
           type 2                                                 

  56       Neurotrophic tyrosine kinase, receptor,  *NTRK3*       NM_002530
           type 3                                                 

  57       Platelet-derived growth factor receptor, *PDGFRA*      NM_006206
           alpha polypeptide                                      

  58       Platelet-derived growth factor receptor, *PDGFRB*      NM_002609
           beta polypeptide                                       

  59       *PTK7 protein tyrosine kinase 7*         *PTK7*        *NM_152883*

  60       Ret proto-oncogene (multiple endocrine   *RET*         NM_020630
           neoplasia and medullary thyroid                        
           carcinoma 1, Hirschsprung disease)                     

  61       Receptor tyrosine kinase-like orphan     *ROR1*        NM_005012
           receptor 1                                             

  62       Receptor tyrosine kinase-like orphan     *ROR2*        NM_004560
           receptor 2                                             

  63       v-ros UR2 sarcoma virus oncogene homolog *ROS1*        NM_002944
           1 (avian)                                              

  64       RYK receptor-like tyrosine kinase        *RYK*         NM_002958

  65       TEK tyrosine kinase, endothelial (venous *TEK*         NM_000459
           malformations, multiple cutaneous and                  
           mucosal)                                               

  66       Transforming growth factor, beta         *TGFBR1*      NM_004612
           receptor I (activin A receptor type                    
           II-like kinase, 53kDa)                                 

  67       Transforming growth factor, beta         *TGFBR2*      NM_003242
           receptor II (70/80kDa)                                 

  68       Tyrosine kinase with immunoglobulin and  *TIE1*        NM_005424
           epidermal growth factor homology domains               

  69       TYRO3 protein tyrosine kinase            *TYRO3*       NM_006293
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