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Systems biology approach shows MYB and FOXM1 as master regulators of germinal center proliferation

Back from a relaxing stay-cation as well as ASCO meeting--

The June 2010 issue of Molecular Systems Biology has a fascinating study identifying MYB and FOXM1 as "master regulators" of germinal center proliferation in lymph nodes.  Why should we as pathologists care about this?

What piqued my curiosity here was a connection with my previous post summarizing what I learned at USCAP on the molecular pathology of diffuse large B-cell lymphoma (DLBCL) regarding dysregulated genes in DLBCL (including MYC) and identification of three gene expression subgroups, including the "germinal center B-cell-like" group.

The authors use some fascinating and powerful techniques around a systems biology approach to find master regulator genes controlling specific cellular processes (in this case, germinal center B-cell proliferation) as well as transcriptional regulation of protein complexes whose regulation whose availability must be regulated in a context-dependent manner.

The authors developed is an interaction network specific to human B cells (called the human B-cell interactome, HBCI) by reverse-engineering of transcriptional and post-translational interactions in mature human B cells from a diverse collection of 254 B-cell gene expression profiles derived from normal and malignant mature B cells (Lefebvre et al, 2007).  They further integrated evidence from experimental assays, databases, and literature data mining, filtered by context-specific criteria, using an established evidence integration algorithm.  Then, they used a novel algorithm, the Master Regulator Interference Analysis or MARINa, to interrogate the HBCI to discover MRs of key genetic programs in the germinal center (GC) reaction of antigen-mediated immune response, that is, normal progression through the GC.

They identified MYB and FOXM1 as "master regulators" of GC B cells since 80% of the genes jointly regulated by these transcription factors are activated in the GC.  They also showed that MYB is a transcriptional activator of FOXM1 suggesting that they form a "feed-forward" loop involved in a synergistic activation of a large subset of GC-specific genes.  I don't quite understand their use of the term "feed-forward" loop here--my understanding is that a feed-forward loop is when the anticipated future effect (which hasn't yet happened) triggers the cause in the present (which would otherwise have not have happened).  An example is when there is a rumored or predicted shortage of a commodity, which leads to hoarding in anticipation of the shortage, which in turn causes a shortage to occur.  It seems that this should be called a reinforcing feedback loop but whatever.

But they also validated that common MYB/FOXM1 targets were affected by silencing either MYB or FOXM1 using gene expression profiling.  Specifically, they showed that downstream targets and other MRs are bound by MYB and FOXM1 in their promotor regions after MYB or FOXM1 silencing.  Moreover, silencing of MYB and FOXM1 showed a decreased in proliferating cells in GCs and an increase in apoptotic cells.  They further examined specific targets involved in the formation of a protein complex predicted by their model and found that about half of the MYB/FOXM1 targets cluster within a complex including novel interactions with pre-replication and mitotic proteins.  They found that two mitotic kinases inferred in the complex, BUBR1 and AURKA, physically interact with MCM3 which are all direct targets of MYB and FOXM1.

My summary doesn't do justice to this paper (due to my own shortcomings in understanding) but if you're interested in lymphomas and/or systems biology, this is well-worth a read.

Part 3. Molecular testing in lymphoma: USCAP 2010

Final recap from Dr. Adam Bagg's lecture at USCAP 2010 on molecular testing in lymphoma.  The highpoint of the lecture for me was his comments on diffuse large B-cell lymphoma (DLBCL) since this is the most common lymphoma type we see in our practice.  It so happens that there have been some recent papers that I've read that complement Dr. Bagg's lecture notes and I cite those below.  I think if you consider his comments and have a look at these papers, you should be well-prepared for the next DLBCL you see.

DLCBL is the most common lymphoma in adults but it is a heterogeneous clinical, morphological, immunophenotypic, molecular, and genetic "category."  Some differences in clinical behavior can be predicted by the recognition of certain between-defined morphological subtypes and/or clinical parameters.  This presentation focused on current attempts to use molecular approaches to more consistently predict behavior and response to specific therapies.

The most commonly dysregulated genes in DLBCL are BCL6 (~40%), BCL2(~20%), BCL10 (~15-20%), and MYC (~5-10%).

The presence of a MYC translocation predicts a poor prognosis.

t(14;18)(q32;q21)--identical to that seen in FL--is present in 20% to 30% of DLBCL and is associated with germinal-center-B-cell (GCB) type.  The detection of the translocation does not correlate with bcl2 protein expression.  Some evidence suggests that detection of bcl2 protein expression in the absence of t(14;18) predicts a poorer prognosis.

The gene BCL6 in DLBCL is promiscuous with respect to translocation partners--with at least 20 different partners described.  BCL6 protein expression associated with GCB type and a favorable prognosis.  However, the effect of BCL6 translocation on prognosis depends on the translocation partner.

Gene array data using unsupervised analysis identifies three gene expression subgroups:

1. Germinal center B-cell-like (GCB): expresses genes characteristic of germinal centers.  Detection of t(14;18) is the most specific abnormality in this group.
2. Activated B-cell-like (ABC): expresses genes characteristic of mitogenically-activated peripheral B-cells; associated with activation of the NFκB pathway.
3. Unclassified ("type 3"): neither sets of genes from GCB or ABC are highly expressed.

Although such expression profiling/gene chip array methods are not currently clinically available, this data can be translated into RT-PCR and IHC methods to further characterize DLBCL.

1. Measurement of only 6 genes by quantitative RT-PCR on routinely processed FFPE tissue appears sufficient to predict OS: expression of BCL6, LMD2, and FN1 (GCB genes) are associated with  favorable prognosis, while expression of BCL2, CCND1, and SCYA2 (ABC genes) are associated with an adverse prognosis.
2. Using just three IHC markers--CD10, BCL6, MUM1--can segregate DLBCL into GCB and non-GCB types in about 80% of cases compared with gene expression profiling.  Recently, a 5-marker panel adding GCET1 (Serpin A9) and FOXP1 improves concordance with gene expression profiling to about 93%.
3. There is currently no consensus regarding the optimal IHC panel to distinguish GCB from non=GCB DLBCLs.

Additional insights have been obtained using supervised array analyses.

Expression signatures associated with either curable or refractory disease identified protein kinase C beta (PKCβ) and c-AMP-specific phosphodiesterase (PDE4B) as associated with an adverse outcome and are potential therapeutic targets.

Numerous microRNA signatures have also been described and some of these appear prognostically relevant.

Some of the data appears contradictory and is confusing.

t(14;18) in DLBCL is considered a poor prognostic indicator but is associated with GCB-DLBCL which has a good prognosis.

There is no significant correlation with bcl2 protein expression and OS within the GCB group (which is typically associated with the t(14;18) translocation), while bcl2 protein expression does predict adverse OS in the ABC group (which has a higher frequency of 18q21 amplification--where BCL2 is located).

In addition, there are conflicting and confusing data regarding the use of IHC markers and/or surrogates for prognosis and predictive value in DLBCL.  There are multiple possible reasons for this.  Hence, there is a definite need for standardization and harmonization of these genetic and molecular markers before they can be used clinically to determine therapeutic strategies.

Recent References

Gurbaxani S, Anastasi J, Hyjek E.  Diffuse large B-cell lymphoma--more than a diffuse collection of large B cells.  Arch Pathol Lab Med 2009;133:1121-1134.

Carbone A, Gloghini A, Aiello A, Testi A, Cabras A.  B-cell lymphomas with features intermediate between distinct pathologic entities--from pathogenesis to pathology.  Hum Pathol 2010;41:621-631.

Part 2. Molecular testing in lymphoma: USCAP 2010

This is continuation from my previous "recap of USCAP" post on Dr. Adam Bagg's presentation on molecular testing in lymphoma from the Special Course on basic molecular biology at the 2010 USCAP meeting. This post deals with his comments on follicular lymphoma.

The cytogenetic hallmark of follicular lymphoma (FL) is t(14;18)(q32,;q21) involving the IgH chain gene on chromosome 14 and BCL2 on chromosome 18 and this translocation is identified in about 90% of cases of FL. This translocation results in overexpression of bcl-2 protein which protects the cell from apoptosis.  Most of the remaining 10% of cases that do not show the translocation show bcl-2 protein overexpression by other mechanisms.

This translocation is not specific for FL and can be seen in up to 25% of patients with diffuse large B-cell lymphoma (DLBCL).

• The t(14;18) typically occurs with other cytogenetic abnormalities (average of 6) at the time of diagnosis.
• FL t(14;18)-negative shows distinct clinicopathologic features including elderly age, aggressive histologic findings (grade 3, areas with diffuse growth), BCL6 genetic abnormalities, and an immunophenotypic profile which is CD10(-), bcl2(-), MUM1(+).
• Extranodal FL is less likely to show t(14;18).
• Although FL with and without t(14;18) differ at a number of genetic and molecular levels, the overall survival between the two groups appears to be the same.
• The tumor microenvironment may be even more prognostically relevant than expression profiling of the lymphoma cells themselves:

>15 CD68(+) macrophages per hpf predicts decreased OS;
Increased T-reg cells (FOXP3(+) or PD-1(+)) cells predicts improved OS;
CD4+ T cells correlate with prognosis according to their distribution: intrafollicular CD4+ T cells correlate with poor prognosis; interfollicular CD4+ T cells correlate with better prognosis.

This lecture was comprehensive and covered the other major lymphoma types but I am trying to focus on the more common lymphomas, so part 3 will cover Dr. Bagg's comments on DLBCL.

Molecular testing in lymphoma: USCAP 2010

More from the Special Course, "Basic Principles and Practice of Molecular Pathology in Cancer," that I attended at the USCAP 2010 meeting.

Dr. Adam Bagg delivered a lively presentation on molecular testing in lymphoma as part of the Special Course, "Basic Principles and Practice of Molecular Pathology in Cancer" at USCAP 2010.  This is part 1 of my take-home points:

1. Rearrangements are the most clinically relevant genetic changes in hematolymphoid malignancies and can be generally divided into two broad categories--
2. 1. Quantitative (that is, physiologic/antigen receptor gene rearrangements): detecting homogeneity (monoclonal) versus heterogeneity (polyclonal).  This is more characteristic of lymphoid malignancies.  The prototype is where a contextually active gene A (e.g. the Ig heavy chain gene in B cells) is brought into apposition with a protooncogene B (e.g. BCL2), resulting in dysregulated increased expression of gene A--for example, the t(14;18) translocation in follicular lymphoma.
   2. Qualitative (that is, non-random, recurring pathologic chromosomal translocations): detecting presence or absence of abnormality.  This is more characteristic of myeloid malignancies.  In this case, there is creation of a novel chimeric gene C (and novel mRNA transcript and novel protein) in which both gene A and gene B are disrupted with parts of each fused together--for example, BCR-ABL1 in the t(9;22) translocation in ALL.
3. Dr. Bagg did an excellent job contrasting the major methodologies used to diagnose genetic lesions that is useful not only for lymphoid neoplasms but, in general, for other cancers.  The main idea is that traditional karyotyping, nucleic acid based molecular methodologies, and FISH each have pros and cons, can complement one another, and should be selectively used depending on what you're trying to find (see #1), how much target there is, and what kind of specimen you have to work with.
4. "Since molecular diagnosis entails the assaying of sometimes-labile analytes (RNA>DNA), both the nature of the specimen and appropriate handling of the specimen become critical for valid diagnosis."  Basically, the integrity of the RNA and DNA (to a lesser extent) is the most important pre-analytic variable.

Part 2 to follow.