Both reports we discuss herein are prognostic in their approach. They are prognostic, however, for androgen resistant PCa. Although it is always good to understand what the prognosis is, even if you cannot do anything about it, it does raise the concern of what benefit is this to either the physician or patient. The results seem to say that the prognosis is that one has 9-10 months versus 3-4 years of expected life. There is nothing that can be done, and even care of the patient is in question. No matter what it is palliative. Although the results are interesting the question is are they beneficial, to anyone. One may ask why waste the money to find out something that you can do nothing about. That is both an ethical and an economic issue.
A summary reported in the press states[1]:
The first study, demonstrating that a nine-gene signature
could distinguish between lower and higher risk castration-resistant prostate
cancer (CRPC), was led by Johann de Bono, MD, of the drug development unit at
the Royal Marsden NHS Foundation Trust in Sutton, United Kingdom, and was
conducted with colleagues in both Europe and the United States.
The second study, which found a six-gene signature that
also stratified CRPC into different risk groups, was led by William K. Oh, MD,
of the division of hematology/oncology at the Tisch Cancer Institute at Mount
Sinai School of Medicine in New York.
One of the findings was related to immune system genes not
those normally thought of in pathway control. The authors state[2]:
The result that immune function is key to prostate cancer
outcome is very surprising, said Dr. de Bono.
“The biggest surprise of this study was that the most
significant six genes which predicted survival were not primarily
cancer-related genes, but were involved in immune function,” said Dr. Oh. “In
some ways, this is not a surprise, since it suggests that the patient’s innate
immune response to cancer may be a strong predictor of the impact of the
cancer.” Dr. Oh added that the function of the identified genes in the immune
system is not yet understood. Nor is it understood how the genes may interact
and lead to a difference in survival for patients.
Both authors of the studies see the RNA analysis as
highly applicable for the clinic. Dr. Oh said that the particular six-gene
signature his study identified “could be translated fairly easily to the
clinic, since it uses simple technologies such as PCR to identify the genes of
interest.” Dr. Oh and colleagues collected the RNA in blood using a special
preservation tube (PAXgene), which are widely available. Dr. de Bono and
colleagues are currently testing whether a DNA analysis could provide the same
information.
Dr. Oh highlighted the different approaches of the two
teams: “What is interesting about the Royal Marsden paper is that they took a
very different analytic approach, which in fact looked at more genes and was
thus potentially more unbiased, and found that the most prognostic genes were
again driving immune function in patients.” Both teams ended up with a similar
result: “The blood contains a molecular signature in patients with advanced
prostate cancer which predicts survival based on the functioning of the immune
system.”
Now it must be emphasized that these studies examined
prognostic factors and not diagnostic and that further they examined patients
who were already androgen resistant, namely the PCa had progressed extensively.
Thus the implication of immune system elements is not unexpected. Also this
analysis is not diagnostic in any way and further is not prognostic in any
manner related to a watchful waiting strategy. As the authors suggest survival
in his risk is about 8 months and in “low” risk is about 35 months. In either
case the patient is terminal.
1
Recent Finding
There are two recent papers regarding this issue. The first
is a recent Lancet article by Ross et al, entitled, A whole-blood RNA transcript-based prognostic model in men with
castration-resistant prostate cancer: a prospective study[3],
the authors state:
Survival for patients with castration-resistant prostate
cancer is highly variable. We assessed the effectiveness of a whole-blood RNA
transcript-based model as a prognostic biomarker in castration-resistant
prostate cancer. Peripheral blood was prospectively collected from 62 men with
castration-resistant prostate cancer on various treatment regimens …
A six-gene model (consisting of ABL2, SEMA4D, ITGAL, and
C1QA, TIMP1, CDKN1A) separated patients with castration-resistant prostate
cancer into two risk groups: a low-risk group with a median survival of more
than 34·9 months (median survival was not reached) and a high-risk group with a
median survival of 7·8 months ....Transcriptional profiling of whole blood yields crucial
prognostic information about men with castration-resistant prostate cancer. The
six-gene model suggests possible dysregulation of the immune system, a finding
that warrants further study.
We wish to examine this in some detail. There are several
issues we wish to look at.
First, what pathways do these genes participate in and thus
how do they play a role in the management of the homeostasis of the cell. Why
would one want to consider these genes?
Second, are these genes causative or reflective of a cancer state?
If reflective are there causative genes related thereto which may merit more
detailed examination.
Third from a prognostic perspective, why are these expressed
as they are?
Fourth from a treatment perspective are these markers useful
in targeting gene aberrations so as to mitigate further uncontrolled growth and
in fact reduce what is present.
Fifth, is there a holistic picture of how most likely
metastatic growth is identified by such expression and how one may ascertain
the spread of the metastatic cells?
There is also a second paper entitled, Prognostic value of blood mRNA expression
signatures in castration-resistant prostate cancer: a prospective, two-stage
study by Olmos et al which notes[4]:
Biomarkers are urgently needed to dissect the heterogeneity
of prostate cancer between patients to improve treatment and accelerate drug
development. We analysed blood mRNA expression arrays to identify patients with
metastatic castration-resistant prostate cancer with poorer outcome.
Whole blood was collected into PAXgene tubes from patients
with castration-resistant prostate cancer and patients with prostate cancer
selected for active surveillance. In stage I (derivation set), patients with
castration-resistant prostate cancer were used as cases and patients under
active surveillance were used as controls. These patients were recruited from
The Royal Marsden Hospital NHS Foundation Trust (Sutton, UK) and The Beatson
West of Scotland Cancer Centre (Glasgow, UK).
In stage II (validation-set), patients with
castration-resistant prostate cancer recruited from the Memorial
Sloan-Kettering Cancer Center (New York, USA) were assessed. Whole-blood RNA
was hybridised to Affymetrix U133plus2 microarrays. Expression profiles were
analysed with Bayesian latent process decomposition (LPD) to identify RNA
expression profiles associated with castration-resistant prostate cancer
subgroups; these profiles were then confirmed by quantative reverse
transcriptase (qRT) PCR studies and correlated with overall survival in both
the test-set and validation-set.
LPD analyses of the mRNA expression data divided the
evaluable patients in stage I (n=94) into four groups. All patients in LPD1 (14
of 14) and most in LPD2 (17 of 18) had castration-resistant prostate cancer.
Patients with castration-resistant prostate cancer and those under active
surveillance comprised LPD3 (15 of 31 castration-resistant prostate cancer) and
LDP4 (12 of 21 castration-resistant prostate cancer).
Patients with castration-resistant prostate cancer in the
LPD1 subgroup had features associated with worse prognosis and poorer overall
survival than patients with castration-resistant prostate cancer in other LPD
subgroups (LPD1 overall survival 10·7 months [95% CI 4·1—17·2] vs non-LPD1 25·6
months [18·0—33·4]; p<0 span="span">
A nine-gene signature verified by qRT-PCR classified
patients into this LPD1 subgroup with a very low percentage of
misclassification (1·2%). The ten patients who were initially unclassifiable by
the LPD analyses were subclassified by this signature. We confirmed the
prognostic utility of this nine-gene signature in the validation
castration-resistant prostate cancer cohort, where LPD1 membership was also
associated with worse overall survival (LPD1 9·2 months [95% CI 2·1—16·4] vs
non-LPD1 21·6 months [7·5—35·6]; p=0·001), and remained an independent
prognostic factor in multivariable analyses for both cohorts.
Our results suggest that whole-blood gene profiling could
identify gene-expression signatures that stratify patients with
castration-resistant prostate cancer into distinct prognostic groups.
2
Summary of Prognostic Gene
Markers
The following Table is a summary of the prognostic gene
markers.
Gene
|
Description[5]
|
Location
|
ABL2
|
This gene encodes a member
of the Abelson family of nonreceptor tyrosine protein kinases. The protein is
highly similar to the c-abl oncogene 1 protein, including the tyrosine
kinase, SH2 and SH3 domains, and it plays a role in cytoskeletal
rearrangements through its C-terminal F-actin- and microtubule-binding
sequences. This gene is expressed in both normal and tumor cells, and is
involved in translocation with the ets variant 6 gene in leukemia. Multiple
alternatively spliced transcript variants encoding different protein isoforms
have been found for this gene.
|
1q25.2
|
SEMA4D
|
CD100; SEMAJ; coll-4;
C9orf164; M-sema-G. Semaphorin 4D (Sema 4D) is an axon guidance molecule
which is secreted by oligodendrocytes and induces growth cone collapse in the
central nervous system. By binding plexin B1 receptor it functions as an
R-Ras GTPase-activating protein (GAP) and repels axon growth cones in both
the mature central nervous system. In the immune system, CD100 binds CD72 to
activate B cells and dendritic cells, though much about this interaction is
still under investigation. During skin damage repairs, SEMA4D interacts with Plexin
B2 on gamma delta T cells to play a role in the healing process
|
9q22.2
|
ITGAL
|
ITGAL encodes the integrin
alpha L chain. Integrins are heterodimeric integral membrane proteins
composed of an alpha chain and a beta chain. This I-domain containing alpha
integrin combines with the beta 2 chain (ITGB2) to form the integrin
lymphocyte function-associated antigen-1 (LFA-1), which is expressed on all
leukocytes. LFA-1 plays a central role in leukocyte intercellular adhesion
through interactions with its ligands, ICAMs 1-3 (intercellular adhesion
molecules 1 through 3), and also functions in lymphocyte costimulatory
signaling. Two transcript variants encoding different isoforms have been
found for this gene.
|
16p11.2
|
C1QA
|
This gene encodes a major
constituent of the human complement subcomponent C1q. C1q associates with C1r
and C1s in order to yield the first component of the serum complement system.
Deficiency of C1q has been associated with lupus erythematosus and
glomerulonephritis. C1q is composed of 18 polypeptide chains: six A-chains,
six B-chains, and six C-chains. Each chain contains a collagen-like region
located near the N terminus and a C-terminal globular region. The A-, B-, and
C-chains are arranged in the order A-C-B on chromosome 1. This gene encodes
the A-chain polypeptide of human complement subcomponent C1q.
|
1p36.12
|
TIMP1
|
This gene belongs to the
TIMP gene family. The proteins encoded by this gene family are natural
inhibitors of the matrix metalloproteinases (MMPs), a group of peptidases
involved in degradation of the extracellular matrix. In addition to its
inhibitory role against most of the known MMPs, the encoded protein is able
to promote cell proliferation in a wide range of cell types, and may also
have an anti-apoptotic function. Transcription of this gene is highly
inducible in response to many cytokines and hormones. In addition, the
expression from some but not all inactive X chromosomes suggests that this
gene inactivation is polymorphic in human females. This gene is located
within intron 6 of the synapsin I gene and is transcribed in the opposite
direction.
|
Xp11.3
|
CDKN1A
|
This gene encodes a potent
cyclin-dependent kinase inhibitor. The encoded protein binds to and inhibits
the activity of cyclin-CDK2 or -CDK4 complexes, and thus functions as a
regulator of cell cycle progression at G1. The expression of this gene is
tightly controlled by the tumor suppressor protein p53, through which this
protein mediates the p53-dependent cell cycle G1 phase arrest in response to
a variety of stress stimuli. This protein can interact with proliferating
cell nuclear antigen (PCNA), a DNA polymerase accessory factor, and plays a
regulatory role in S phase DNA replication and DNA damage repair. This
protein was reported to be specifically cleaved by CASP3-like caspases, which
thus leads to a dramatic activation of CDK2, and may be instrumental in the
execution of apoptosis following caspase activation. Multiple alternatively
spliced variants have been found for this gene.
|
6q21.2
|
3
TIMP-1
TIMP-1 is a tissue inhibitor of metalloproteinases.
Metalloproteinases (matrix metalloproteinases, MMP) are zinc dependent
proteases which have the ability to cleave cell walls[6]. Transcription
of this gene and thus increase in its product are activated by cytokines and
various hormones. However, in this analysis, it is most likely the excitation
from the immune system cytokines which activate the response.
There has been extensive work performed analyzing TIMP-1
recently in various other cancers. The work of Wang et al examines Gastric
cancers, Lee examines Colorectal cancers, and Bloomston looks at pancreatic
cancers. Other detailed analyses have been done by Vaghooti et al as well as
Wang. Thus is should be no surprise as to the use of TIMP-1 in this specific
case as well.
In addition as per Marks et al[7], The
TIMP, tissue inhibitors of metalloproteases, MMP, are within the class of ADAM
proteins which are membrane bound.
The following is a summary by Bigelow et al and although it
focuses on breast cancer issues it does provide a reasonable summary as applied
to this case:
TIMP-1 (Tissue inhibitor of matrix metalloproteinase-1)
is typically associated with inhibition of matrix metalloproteinases (MMP)
induced invasion. However, TIMP-1 is overexpressed in many malignancies and is
associated with poor prognosis in breast cancer.
The mechanisms by which TIMP-1 promotes tumorigenesis are
unclear. Reduced levels of TIMP-1 mediated by shRNA in MDA-MB-231 breast cancer
cells had no effect on cellular physiology in vitro or tumor growth in
SCID mice compared to vector control MDA-MB-231 cells.
However, overexpression of TIMP-1 in MDA-MB-231 cells
resulted in inhibition of cell invasion and enhanced phosphorylation of p38
MAPK and AKT in vitro. Additionally, treatment of parental MDA-MB-231
cells with purified TIMP-1 protein led to activation of p38 MAPK and MKK 3/6.
cDNA array analysis demonstrated that high expression of TIMP-1 in MDA-MB-231
cells resulted in alterations in expression of approximately 200 genes, 1.5
fold or greater compared to vector control cells (P < 0.1).
Real-time RT-PCR confirmed changes in expression of
several genes associated with cancer progression including DAPK1, FGFR4 and
MAPK13.
In vivo, high TIMP-1 expression induced tumor growth
in SCID mice compared to vector control cells and increased tumor vessel
density. Affymetrix array analysis of vector control and TIMP-1 MDA-MB-231 xenograft
tumors revealed that TIMP-1 altered expression of approximately 600 genes
in vivo, including MMP1, MMP13, S100A14, S100P, Rab25 and ID4.
These combined observations suggest that the effects of
TIMP-1 differ significantly in a 2-D environment compared to the 3-D
environment and that TIMP-1 stimulates tumor growth.[8]
Thus we have the question that TIMP-1 at an inhibitor of MMP
is thus increased in response to cytokines which may themselves be increased as
a result of the PCa metastatic expansion. The question then becomes; is this
just a natural and expected result, is this just consistent with PCa evolution,
or is there something special here.
4
ABL2
BCR and ABL are genes closely related to CML. In a 2002
paper in NEJM by Katarjian et al we have:
Chronic myelogenous leukemia (CML) accounts for about 20
percent of newly diagnosed cases of leukemia in adults. The course of the
disease is characteristically triphasic: a chronic phase lasting three to six
years is followed by transformation to accelerated and then blast phases of
short duration. The cause of CML is the translocation of regions of the BCR and
ABL genes to form a BCR-ABL fusion gene. In at least 90 percent of cases, this
event is a reciprocal translocation termed t(9;22), which forms the Philadelphia
(Ph) chromosome. The product of the BCR-ABL gene, the BCR-ABL protein, is a
constitutively active protein tyrosine kinase with an important role in the
regulation of cell growth.
Thus this fusion product has been found to result in a
cancerous growth of the immune system. ABL2 is a product which is a tyrosine
kinase resident in the cytoplasm.
Considerable work has been done on ABL and reference is made
to that of Wong and Witte as well as O’Hare. Also there is the recent work of
Sirvent et al examining Abl in normal and cancer cells.
In the work by O’Hare et al the authors note:
The BCR-ABL signaling network and ABL kinase inhibition.
A, BCR-ABL signaling pathways activated in CML.
Dimerization of BCR-ABL triggers autophosphorylation events that activate the
kinase and generate docking sites for intermediary adapter proteins such as
GRB2. BCR-ABL– dependent signaling facilitates
activation of multiple downstream pathways that enforce enhanced survival,
inhibition of apoptosis, and perturbation of cell adhesion and migration.
A subset of these pathways and their constituent
transcription factors, serine/threonine-specific kinases, and apoptosis related
proteins are shown. A few pathways that were more recently implicated in CML
stem cell maintenance and BCR-ABL–mediated disease transformation
are shown.
Of note, this is a simplified diagram and many more
associations between BCR-ABL and signaling proteins have been reported. BCR-ABL
is unstable upon disruption of primary CML cells; therefore, pharmacodynamic
evaluation of BCR-ABL activity is performed by monitoring the tyrosine
phosphorylation status of either CRKL or STAT5, with CRKL phosphorylation
considered the most specific readout.
B, Predicted effectiveness of ABL kinase inhibitors in
three therapeutic scenarios: to inhibit native BCR-ABL , to inhibit mutated
BCR-ABL, and as a component in the control of CML involving a BCR-ABL–independent
alternate lesion[9].
Now ABL by itself has certain control mechanisms. They are
well known and reviewed extensively, refer to Wong and Witte.
5
SEMA4D
SEMA4D
is also known as CD100. The CD or cluster of determination molecules often are
receptors and frequently found on immune system sourced cells. CD100
specifically is characterized as one of Mono migration; with T and B
activation; T cell-B cell and T cell-DC interaction. Thus SEMA4D is another
immune cell related marker and not one of internal pathway control.
From the work of Gelfand et al we have:[10]
(a) Sema4D signaling in the
nervous system. Proteins in the R-Ras pathway are shown in red: in the presence
of Sema4D, Rnd1 is recruited to Plexin-B1. Plexin-B1 R-RasGAP activity is
activated and downregulates the active form of R-Ras. The decrease of active
R-Ras inhibits PI3K–Akt activity, decreasing GSK3β phosphorylation and, thus,
activating it. GSK3β then phosphorylates and deactivates CRMP2 and causes
microtubule disassembly.
(b) Sema4D signaling in the
vascular system. Proteins in the RhoA pathway are shown in blue: in the
presence of Sema4D, the receptor tyrosine kinase Met binds and phosphorylates
Plexin-B1 and then activates
PDZ–RhoGEF and LARG, which activates RhoA and leads to endothelial cell
migration through the ROCK, Pyk2 and PI3K pathway. It is not clear how this
pathway affects actin dynamics or microtubule dynamics in vascular system.
(c) Sema3A signaling in the
nervous system. Rac1-regulating proteins are shown in green: in the presence of
Sema3A, FARP2 is released from Plexin-A1 and actives Rac1. Rac1 then activates
PAK and LIMK and, as a result, phosphorylates Cofilin, which finally causes
actin depolymerization. R-Ras-regulating proteins are shown in red: in the
presence of Sema3A,
(d) Sema3A signaling in the
vascular system. Sema3A, through an unknown mechanism (possibly through Npn-1
and/or a co-receptor, shown as a dashed line and ‘?’), inhibits VEGF-induced
activation of Src and FAK and contributes to angiogenesis. Sema3A might also
function through Npn-1 to inhibit integrin-mediated adhesion of endothelial
cells to the ECM. Sema3A can induce VE-cadherin phosphorylation and causes
vascular permeability through unknown mechanisms (indicated by ‘?’), in which
PI3K–Akt is involved.
In the work of Neufeld and Kessler we have:
The main signal transduction pathways by which SEMA3A and
SEMA4D activate plexin A1 (PLEXA1) or PLEXB1…[11].
The information is derived mainly from the study of neuronal cells. The
activation of PLEXA1 by SEMA3A (left side) or PLEXB1 by SEMA4D (right side)
induces activation and sequestration of RAC1 and RND1 by the plexins.
Sequestration of RAC1 results in reduced phosphorylation
of p21-activated kinase 1 (PAK), inhibition of LIM domain kinase 1 (LIMK1)
activity and activation of cofilin, which causes actin depolymerization.
Activation of PLEXA1 by SEMA3A also results in the
activation of the tyrosine kinases FYN, FES and FER, which is followed by the
recruitment and activation of cyclin-dependent kinase 5 (CDK5), which in turn
inactivates collapsin response mediator proteins (CRMPs) such as CRMP2. CRMPs
affect microtubule dynamics and the organization of the actin cytoskeleton.
The activation of PLEXA1 also leads to the activation of
MICALs (molecules interacting with CasL), which form complexes with CRMPs and
are also essential for the effects of SEMA3A on the cytoskeleton.
In the case of SEMA4D, activation of PLEXB1 can also lead
to the inactivation of CRMPs through inhibition of phosphoinositide 3-kinase
(PI3K) and AKT activation that leads to GSK3
activation and as a result to the inactivation of CRMPs.
In addition to these short-term effects there are also
long-term effects. In the case of SEMA3A, activation of PLEXA1 induces
apoptosis of neuronal and endothelial cells, which is manifested by inhibition
of extracellular signal-regulated kinases 1 and 2 (ERK1 and ERK2)
phosphorylation and activation of caspase 3 (indicated in purple). The insert
shows the effects of SEMA3A on the actin cytoskeleton of endothelial cells.
We depict below a modified version of their pathway
description.
Also below we have from the work of Siderovski and Willard
the following discussion of pathway involvement[12]:
Membrane targeting strategies employed by multi-domain
RGS proteins.
(A) The R7 RGS proteins form obligate heterodimers
with Gβ5 via a Gγ-like sequence (the “GGL” domain) N-terminal to the RGS-box.
This GGL/Gβ5 interaction could allow R7 RGS proteins to act as conventional Gβγ
subunits in coupling Gα subunits to 7TM receptors, thereby localizing
RGS-box-mediated GAP activity to particular receptors. The DEP domain of RGS9-1
interacts with a membrane-anchoring protein (R9AP) analogous interactors may
exist for the DEP domains of other R7 subfamily members.
(B) The PDZ domain of RGS12 is able to bind the
C-terminus of the IL-8 receptor CXCR2 (at least in vitro). The RGS12 PTB domain
binds the synprint (“synaptic protein interaction”) region
of the N-type calcium channel (Cav2.2); this interaction is
dependent on neurotransmitter-mediated phosphorylation of the channel by Src.
(C) The AtRGS1 protein of Arabidopsis thaliana
(thale cress) has a unique structure for an RGS protein: an N-terminus
resembling a 7TM receptor and a C-terminal RGS-box. Although a ligand is not
known for the 7TM portion of AtRGS1, a simple sugar is most likely.
(D) The transmembrane receptor Plexin-B1 couples
binding of the membrane-bound semaphorin Sema4D to RhoA activation via an
interaction with the PDZ domain of PDZ-RhoGEF (and of the related RGS-RhoGEF
LARG). Domain abbreviations: IPT, immunoglobulin-like fold found in plexins,
Met and Ron tyrosine kinase receptors, and intracellular transcription factors;
PSI, domain found in plexins, semaphorins, and integrins; Sema, semaphorin
domain.
The pathway involvement is similar to what we have depicted
above.
6
ITGAL
ITGAL is integrin alpha L and is also known as CD11, another
CD protein and thus another immune response marker and not a pathway marker.
From the KEGG database we have the following additional
information[13]:
Gene name
|
ITGAL, CD11A,
LFA-1, LFA1A
|
||||||||||||||||||||
Definition
|
integrin,
alpha L (antigen CD11A (p180), lymphocyte function-associated antigen 1;
alpha polypeptide)
|
||||||||||||||||||||
Orthology
|
|
||||||||||||||||||||
Organism
|
hsa Homo
sapiens (human)
|
||||||||||||||||||||
Pathway
|
|
From KEGG we have the following pathway[14]:
Note the connection between the target cell and the NK or
Natural Killer sell from the immune system. ITGAL facilitates the apoptosis of
the cell. If ITGAL is defective then we have a loss of natural apoptosis.
This then is another step in the immune system failing to
manage the cell status.
7
CDKN1A
CDKN1A is controlled by SAD4. SMAD4 is an element in the
TGF-β signalling chain. TGF is a cytokine, specifically a transforming growth
factor cytokine. Like the Wnt-Apc pathway, the TGF pathway links defective
development to cancer. The pathway is shown in part below (from Bunz p 199).
Normal TGF signalling down-regulates the growth of most normal cells. Several
of the genes in the TGF/SMAD pathway activation suppress growth. Specifically
the genes CDKN1A and CDKN2B encode the cyclin dependent kinase inhibitors which
suppress growth. Activated SMAD pathways also appear to suppress the
transcription of other genes including c-Myc.
Kibel et al have recently examined CDKN1A and CDKN1B
specifically in prostate cancers with extensive insight.
We show some of the TGF SMAD signalling below along with its
control over the CDKN1A element. We will elaborate this later. Note here that
CDKN1A controls apoptosis as well.
SMAD4 controls the G1 to S transition. As stated in NCBI[15]:
This gene encodes a
member of the Smad family of signal transduction proteins. Smad proteins are
phosphorylated and activated by transmembrane serine-threonine receptor kinases
in response to TGF-beta signaling. The product of this gene forms homomeric
complexes and heteromeric complexes with other activated Smad proteins, which
then accumulate in the nucleus and regulate the transcription of target genes.
This protein binds to
DNA and recognizes an 8-bp palindromic sequence (GTCTAGAC) called the
Smad-binding element (SBE). The Smad proteins are subject to complex regulation
by post-translational modifications. Mutations or deletions in this gene have been
shown to result in pancreatic cancer, juvenile polyposis syndrome, and
hereditary hemorrhagic telangiectasia syndrome.
We use the NCI data set for its pathway[16]:
The SMAD pathway is also detailed by NCI and one is referred
to that source for further detail. From Weinberg (p 291) we also have the SMAD4 pathway showing
its immediate control of the DNA transcription.
As Weinberg states (p 292):
“… Half of all pancreatic carcinomas and more than a
quarter of all colon carcinomas carry mutant inactivated Smad4 proteins.
Without the presence of Smad4 neither Smad2-Smad4 norr Smad3-Smad4 complexes
can form. These two complexes are the chief agents dispatched by the TGF-β
receptor to the nucleus with the important assignment to shut down
proliferation.”
This control mechanism is shown above.
8
C1QA
As NCBI states[17]:
This gene encodes a major constituent of the human
complement subcomponent C1q. C1q associates with C1r and C1s in order to yield
the first component of the serum complement system. Deficiency of C1q has been
associated with lupus erythematosus and glomerulonephritis. C1q is composed of
18 polypeptide chains: six A-chains, six B-chains, and six C-chains. Each chain
contains a collagen-like region located near the N terminus and a C-terminal
globular region. The A-, B-, and C-chains are arranged in the order A-C-B on
chromosome 1. This gene encodes the A-chain polypeptide of human complement
subcomponent C1q.
Azzato et al have examined C1QA in breast cancer and they
discuss it broadly based presence. They state:
Complement is involved in the primary defence against
intravascular microorganisms and has been reported to be involved in the clearance
of tumour…. Recently, we have reported an association between expression of C1QA
and prognosis in oestrogen receptor (ER)-negative breast cancer… in more
than one cohort. We found that ER-negative tumours with overexpression of gene C1QA
were associated with a better prognosis. The C1QA
gene, located on chromosome 1p36.12, encodes for one of the components
of the C1q complex. There are seven single nucleotide polymorphisms (SNPs)
catalogued for C1QA on the NCBI database, of which
there is only one common SNP (minor allele frequency 45%)
located in an exon rs172378 is a synonymous SNP characterised by a G for A
substitution at position 361 (A361G).
Thus we have another element from the immune system. It is
part of the complement system, not the adaptive part and thus has primitive
roots.
Now we depict a selection of its pathway as below (modified
from KEGG)[18]:
Note that the expression of C1QA is controlling the chain of
complement factors which result in cell destruction. Suppression of C1QA then
results in loss of this function. C1QA is thus just another factor in the
overall control of cell proliferation.
9
Observations
There is a seemingly endless progression of genes identified
as related to various cancers. All too often they are just noted as almost an
incidental finding and as we have discussed before they are often putatively
posed with no detailed pathway implications cited.
In this case we see a preponderance of immune system genes
expressed albeit in a late stage of cancer. As indicated it is expected that
all of these patients are terminal and that we are arguing of how soon. The
range is from 10 to 40 months. Survival is not an end point; we seem to be
arguing over when death occurs. As we had indicated above although it has some
prognostic capability it has de minimis quality of care capacity. Thus one
wonders why even attempt it other than having some scientific value.
On the other hand we can always view this in a Rosenberg
manner and see the immune system kicking in in all manners and fashions. Its
failure may then result in metastatic results and rapid death. An interesting
question for treatment would be if one could re-stimulate or activate these
broken elements and see if they can restore a protective barrier against
metastatic results. Rosenberg sought this path in his years of melanoma
research. Perhaps this is a means to rejuvenate that to but a later stage of
the cancer. Namely we are seeing multiple immune elements failing so what can
we achieve to mediate that result.
The problem seen in analyses of this type is that the press
all too often exploits its ramifications. This is quite unfortunate for the
patients in that they may somehow infer that this discovery may add hope to
their plight when in reality it does nothing more than better estimate their
demise.
For example there is a quote which states[19]:
"There is an urgent need for predictive models that
help assess how aggressive the disease is in prostate cancer patients, as
survival can vary greatly," said lead investigator William K. Oh, MD,
Chief of the Division of Hematology and Medical Oncology of The Tisch Cancer
Institute at The Mount Sinai Medical Center. "Our six-gene model,
delivered in a simple blood test, will allow clinicians to better determine the
course of action for their patients, determine clinical trial eligibility, and
lead to more targeted studies in late-stage disease."
This set of tests is not what is desired. We are really
desirable of tests which can predict the aggressive nature when the Gleason score
is at 6 or less, namely when do we allow, with some sense of safety, for
watchful waiting. This report is only for ultimately terminal patients, not
those who could survive. This is a classic problem when results like this hit
the media, even the professional media. In fact the reports get more
exaggerated when we see the results in the popular media.
In summary we may pose the following:
1. There are many of these markers which are immune system
related. Is this a common cancer response in the late stages, as much of the
literature suggests. If so is the immune system attempting to isolate and
defend the body.
2. How does this progress. Somehow one sees snapshots,
namely patient A has such and such a profile and we then know when they reach
that point the prognosis is bad or very bad. But what are the details of the
evolution, do they all follow the same trajectory and if not why not and if so
why and what does that mean.
3. Is there an interaction between the pathways and the
immune system or is this just a normal, in the case of cancers, immune
response. How much of this is prostate specific and how much is common across a
wide variety of malignancies.
4. What does this tell us about potential treatment paths?
Can we activate the immune system, can we target it, and is the complement
system of special interest. Is this a call to further focus on immune system alternatives?
10 References
1. Abbas, A., A., Lichtman, Cellular and Molecular Immunology,
Saunders (New York) 2003.
2. Azzato, E., et al, Common germ line polymorphisms of C1QA and
breast cancer survival, Br Jrl Can 2010, pp 1294-1299.
3. Bloomston, M., et al, TIMP-1 antisense gene transfection
attenuates the invasive potential of pancreatic cancer cells in vitro and
inhibits tumor growth in vivo, Excerpta Medica, Am Jrl Surgery, 2005, pp
675-679.
4. Daraselia, N., et al, Molecular signature and pathway analysis
of human primary squamous and adenocarcinoma lung cancers, Am J Cancer Res,
2012.
5. DeFranco, A., et al, Immunity, Sinauer (Sunderland, MA) 2005.
6. Gorlov, I., et al, Candidate pathways and genes for prostate
cancer, BMC Med Gen 2009.
7. Kartarjian, H., et al, Hematologic and Cryptogenic Responses to
Imatinib Mesylate in Chronic Myelogenous Leukemia, NEJM, Vol 346, No 9, 2002.
8. Kibel, A., et al, CDKN1A and CDKN1B Polymorphisms and Risk of
Advanced Prostate Cancer, Can Res 2003, pp 2033-2036.
9. Lee, J., et al, Plasma or Serum TIMP-1 is a Predictor of
Survival Outcomes in Colorectal Cancer, Jrl Gastro Intest Liver Dis, 2011, pp
287-291.
10. Marks, F., et al, Cellular Signal Processing, Garland (New York)
2009.
11. Nimmerjahn, P., J., Ravetch, Translating basic mechanisms of IgG
effector activity into next generation cancer therapies, Can Immune, 2012, V 12
N 13.
12. O’Hara, T., et al, Targeting the BCR-ABL Signaling Pathway in
Therapy Resistant Philadelphia Chromosome Resistant Leukemia, Clin Can Res, Nov
2010.
13. Payne, S., C., Kemp, Tumor Suppressor Genetics, Carcinoma, 2005
pp 2031-2045.
14. Rosen, E., et al, Functional Genomic Analyses Identify Pathways
Dysregulated by Progranulin Deficiency Implicating Wnt Signalling, Cell Neuron,
2911, pp 1030-1042.
15. Rosenberg, S., The Transformed Cell: Unlocking the Mysteries of
Cancer, Harper (New York) 1993.
16. Siderovski, D., F. Willard, The GAPs, GEFs, and GDIs of
Heterotrimeric G protein alpha subunits, Int Jrl Bio Sci, 2005, pp 51-66.
17. Sirvent, A., et al, Cytoplasmic signalling by the c-Abl tyrosine
kinase in normal cancer cells, Bio Cell V 100 2008 pp 617-631.
18. Teschendorff, A., et al, Improved prognostic classification of
breast cancer defined by antagonistic activation patterns of immune response
pathway modules, BioMed, BMC Cancer, 2010.
19. Wang, C., et al, Serum TIMP-1 in Gastric Cancer Patients, Ann
Clin Lab Sci, 2006, pp 23-30.
20. Wang, X., et al, Signal transducers and activators of
transcription 3 mediates up-regulation of angiotensin II-inducted tissue
inhibitor of metalloproteinase-1 expressed in cultured human senescent
fibroblasts, Clin Med Jrl, 2006, pp 1094-1102.
21. Weinberg, R., Cancer, Garland (New York), 2008.
22. Wong, S., O. Witte, The BCR ABL Story; Bench to Bedside and
Back, Ann Rev Imm, 2004, pp 247-306.
23. Yaghooti, H., et al, Angoitensin II Differentially Induces
Matrix Matalloproteinase 9 and Tissue Inhibitor of Metalloproteinase-1
Production and Disturbs MMP/TIMP Balance, Avicenna Jrl Med Bio, 2010, pp 79-85.
24. Zsippai, A., et al, mRNA and microRNA expression patterns in
adrenocortical cancer, A, J Can Res 2011, pp 618-628.
[3]
http://www.thelancet.com/journals/lanonc/article/PIIS1470-2045%2812%2970263-2/fulltext?_eventId=login
[6]
For example the use of doxycycline as a suppressor of MMP at low doses is used
to treat corneal abrasions and certain types of dental erosions.
[7]
See Marks et al, pp. 455-459.
[8] TIMP-1 overexpression
promotes tumorigenesis of MDA-MB-231 breast cancer cells and alters expression
of a subset of cancer promoting genes in vivo distinct from those observed
in vitro, Rebecca L. H. Bigelow, Briana J. Williams, Jennifer L. Carroll, Lisa
K. Daves and James A. Cardelli, Breast Cancer Research and Treatment Volume 117, Number 1 (2009), 31-44, DOI: 10.1007/s10549-008-0170-7.
http://www.springerlink.com/content/a61k12012441l672/
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