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Advocacy >  Biomarkers for Lymphoma

Last update: 09/01/2016


| Reports and Resources | Biomarker Index and Resources | Search PubMed

In the News

A biomarker is broadly defined as any test that guides (personalizes) therapy or research based on a biologic measure, such as a factor in the blood or tumor sample. 

Types of biomarkers: 

Biomarkers may be grouped as follows by type:


Mechanism-based, for predicting activity of the drug  - to predict who is most likely to benefit from a specific therapy -
before treatment begins, such to identify that the target of a drug exists in the tumor.


For toxicity - to guide the optimal dose and schedule of treatment during treatment, or who should not received a specific kind of treatment. 


For prognosis -  to identify patients with high- or low-risk disease - helping to choose the appropriate approach to treatment - perhaps focusing on novel therapies instead of standard approaches for high-risk lymphoma, or observation for patients with low-risk lymphoma.


For response assessment - to measure the quality of the response to treatment that might predict the duration of the response

to detect minimal residual disease in the blood or marrow by PCR after treatment

to detect the metabolic status in remaining lesions by PET imaging mid-treatment or after treatment

An advocate's perspective:

There's a need to reduce the risk of unproductive toxicity – the patients experiencing only the side effects of the treatment with minimal or no benefit.  Getting it wrong, choosing an ineffective therapy can narrow the patient’s future treatment options.  The uncertainty that a toxic treatment will provide benefit adds to the burden of living with the disease. 

Expectations are rising that we can do better - that tests will become available to help physicians to select the most effective therapy for their patients.   Further, the use of predictors of response will also help to foster participation in trials that make use of what we might call companion tests that determine eligibility to receive a drug.

Arguably, this is our biggest need at this time (not to approve yet another active drug) but to identify who that drug is for and how to best use it.   The poor alternative is care based on our biases ... or by trial and error.

Presently, assessing the risk of the lymphoma is guided by clinical indexes (IPI, FLIPI, etc.), such as age, some blood factors, and stage of the disease. These can be very useful for comparing outcomes across studies  but do not appear to predict response to specific therapies.  So the search is on to identify biological risk factors that can add precision to these indexes or help to identify who will benefit from a specific drug.

However, a bad test can be as harmful as a bad drug - leading to under- or over-treating - or choosing the wrong treatment.  So validation of a biomarker is essential - defined as the “efforts to confirm the accuracy, precision, and effectiveness of results.” Once validated, biomarkers can help to further personalize medicine -- so that the patient is treated optimally with a better chance for benefit. 

Why has progress been slow for identify biomarkers that can guide the use of therapy for lymphoma?

The parable of the blind men describing the same elephant by touch comes to mind. There may be thousands of factors influencing the behavior of the disease that are not yet recognized – needed to have the whole picture of the elephant. ZAP70 for CLL, for example, might only be predictive when an unknown factor is also in effect.

Analogy:  A man has a lower risk of falling if he uses a cane.  If we don’t know about canes yet, what explains the different rate of falling among men with weak legs remains a mystery. 

Adding to the challenge of this science-based endeavor is that each subtype of lymphoma is unique – is a different kind of elephant.  Further, there can be significant variation in the same type of lymphoma from patient to patient.  For follicular lymphoma there are at least 4 prognostic types associated with the immune signatures in the tumor microenvironment. So Joanne’s FL is not the same as Mike’s or Linda’s, or Jama’s at the molecular level – or in how the microenvironment interacts with the tumor cells.

Standardization of methods for giving laboratory tests is also critical to the validation of biomarkers.  Confidence in the compiled reports made by different groups depends on everyone using the same tests in the same way.

Because of the complexity of the biology of lymphoma (an avalanche of interactions)... clinical factors remains the bottom line way to guide clinical decisions – such as how the lymphoma actually behaves over time. The key prognostic risk factor for indolent FL appears to be clinical: the response to initial primary therapy. 

Important to identifying and validating biomarkers is the design of the studies and the study size. Small studies will lack the power to answer the question – will have false positive and negative associations.

Analogy:  a study of 30 may show that those of us born in August do better with a treatment … when we know that our month of birth has no plausible bearing on the outcome. A study of 1,000 would show a more balanced distribution of outcomes by our month of birth. 

We must bear in mind that there are limited resources and many competing projects that merit funding. Biomarker studies are very expensive -- requiring additional resources to store and analyze the tissue.  Such study will require patients to participate and consent to give samples of blood or tumor cells, which might add to the challenge of achieving full enrollment.

There is also a need to increase the financial incentive to develop and evaluate biomarkers --- that's equivalent to the incentives for developing new drugs. 

The bar is rising for NCI-funded clinical research - and will require increasingly integral (validated) markers for patient selection in clinical trials.  For lymphoma it seems that we have dozens of candidate markers but few that are "ready for prime time" --  that can be used reliably to select patients for specific drugs or approaches to treatment.  

The emerging emphasis on the use of markers to determine eligibility in a trial is a positive trend - it is good for patients ... because it's research focused on the unique biological profile of the tumors (patient and tumor specific).  An initial challenge will be to fund studies that are large enough to validate biomarkers in a time of austerity.   (Notably, the larger studies are often funded by drug sponsors who may not have an incentive to carry out or publish results on biomarkers related to their drug products.) 

So in a time of increasing scientific opportunities to do better there are also substantial challenges -including the fundamental challenge of the enormous biological complexity of lymphoma a -- driven by genetic mutations that can accumulate over time and might also vary by area of presentation within the same patient. 

Given the complexity of lymphoma and how it interacts with the body, markers that predict response to specific targeted therapies may be more feasible to identify than prognostic markers -- such as a feature of the tumor that is present or absent, relating to how the drug works - its mechanism of action; or a factor in the tumor that confers resistance to a given treatment.  

So we can anticipate that clinical trials will increasingly ask more of patients - to provide tissue samples to determine our eligibility for a study and to provide samples after we receive a treatment to evaluate what happened with more precision. 

See also Giving Tissue  and an advocates perspective on  Giving-blood-and-tissue-2014.pdf

Clinical Trials evaluating biomarkers

Lymphoma or CLL B-cell T-cell
List lymphoma OR CLL studies
List DLBCL studies
List t-cell lymphoma studies

In the News

Aug 2016: Dr. Julie, Brahmer,
Immunotherapy Biomarkers and Patient Selection - YouTube
* Elevated serum levels of IL-2R, IL-1RA and CXCL9 are associated with poor survival in follicular lymphoma
* AACR journal: Catch-22 for Cancer Tests

It seems that a similar "catch" prevents researchers from moving forward with biomarkers:  it won't be used as an integral part of the study (to select patients) unless previously validated, and can't be validated unless previously tested in a prospective study. (Karl)
*ASCO Post 2013:
“The Quest to Optimize Personalized Therapies for Cancer --
A Conversation With Brian J. Druker, MD” 
* PET Compared With CT After Rituximab Induction Therapy In Follicular Lymphoma: National Lymphocare Study
MRD in the News at ASH 2013:

Find Trials that apply MRD testing

* ASH Paper, 2013: Minimal Residual Disease (MRD) Predicts PFS In Mantle Cell #Lymphoma: CALGB 50403 (Alliance)
* ASH Paper, 2013:  Improved Igh-Based MRD Detection By Using Droplet Digital PCR: a Comparison With Real Time Quantitative PCR In MCL and MM
ASH Paper: Comparative Analysis Of Next-Gen Sequencing and Real-Time Quantitative PCR For MRD in Follicular Lymphomas
* ASH Paper, 2013: Minimal Residual Disease Measurement By Deep Sequencing Reflects Changes In Disease Load During Therapy In Diffuse Large B Cell Lymphoma Patients
* ASH Paper, 2013: Treatment Of Early Stage Follicular Lymphoma With Involved Field Radiotherapy and Rituximab. Role Of Bcl-2 Molecular Monitoring
* ASH Paper, 2013: DNA Sequencing-Based Monitoring Of Serum Predicts Clinical Relapse Before CT Imaging in Diffuse Large B-Cell Lymphoma
* ASH Paper, 2013: Detection Of Classical Hodgkin Lymphoma In Peripheral Blood Using High-Throughput Sequencing Assay
* ASH Paper, 2013: Comparison Of Deep Sequencing and Allele-Specific Oligonucleotide PCR Methods For MRD Quantitation In Acute Lymphoblastic Leukemia and Mantle Cell Lymphoma: CALGB 10403 and CALGB 59909 (Alliance)
*For Traders 2013:
Sequenta's ClonoSIGHT MRD Test Can Detect Relapse of Diffuse Large B-Cell Lymphoma More Than Seven Months Before CT Scan

Biomarker Reports and Resources

Follicular (and similar indolent b-cell) lymphomas

An important question for follicular lymphoma, and perhaps all indolent lymphomas, is when to treat, which is often based on clinical symptoms and evidence of steady progression.

When there's a current need to treat advanced stage indolent b-cell lymphoma, the standard approach is Rituxan-based chemotherapy - however, how much additional treatment to give remains controversial.  The controversy was not resolved by PRIMA which showed that 43% of the participants who were observed (did not get maintenance therapy for 2 years and were not exposed to additional risks and costs) have not relapsed at 6 years:

With a median follow-up of 73 months from randomization, 6-year progression free survival estimate was
42.7% (95% CI 38 – 46.9%) in the observation arm
59.2% (95% CI 54.7 – 63.7%) in the rituximab maintenance arm

We propose that the study of maintenance/consolidation protocols following the initial therapy of indolent lymphoma should be based on emerging and novel biomarkers for efficacy in order to limit the risk of over treating patients. Patients would be eligible to participate in the study based on the response to induction therapy - so called response-adapted protocols - similar to the approach for Hodgkins lymphoma using PET imaging. 

... The quality of the response to induction therapy might be estimated by a combination of standard imaging (CT) and a biomarker - such as PET imaging (metabolic biomarker) or Minimal Residual Disease (MRD) detection in the blood and marrow by sensitive PCR testing (traces of tumor-specific factors in the blood). 

... Participants who have PET or MRD positive status after induction therapy would be eligible for studies testing promising consolidation protocols and can be monitored for response with the same tests.  The results of the large PRIMA study testing maintenance Rituxan would serve as the reference point for judging success - with consideration that results in PRIMA were based on all patients - independent of the response to induction.

Ethics:  This approach to selecting patients for the study of consolidation therapy after induction with R-chemo appears ethical because there is uncertainty regarding the benefit of maintenance for the unselected population (Cheson), emerging evidence that PET negative status after induction therapy is correlated with long remissions without maintenance, and encouraging indications that MRD status  could be more sensitive than PET for predicting a benefit without maintenance/consolidation.  Patients who are not eligible for the study can receive consolidation therapy off-study, if desired, or may do just as well using the treatment as needed (such as the finding for RESORT).   These considerations should make participation in the studies attractive to patients and to referring physicians. 

Biomarkers predicting response to the specific agents might be used to narrow eligibility further to patients who have disease that can be effectively targeted by the investigational agent - such as cereblon tumor expression as a potentially predictive marker of response to lenalidomide (needed to bind the study drug). 

Biomarker Index and Resources

General  PubMed Query
* Cancer Genetics Incorporated
Complete Program- CLL and DLBCL Testing Services

A handy list of biomarkers for CLL and DLBCL as of this date.  However, the need to validate the risk factor is very important ... so what is lacking in the resource are Level of Evidence scores .. as some markers may be exploratory at this point ... or the significance can vary because there are other (perhaps not yet known) biological risk factors that are at work influencing how the disease behaves / responds to therapy.      So actual behavior and staging could well trump the results of a test.   For example,  the IHC method for determining ABC / GCB DLBCL is not considered reliable as a way to select patients for investigational protocols based on cell of origin.  (Karl)      
Treatment-specific biomarkers
* Lenalidomide / Cereblon
Biomarker for response to Lenalidomide for lymphoma PubMed Query


Candidates cytogenetic biomarkers?  ... there are many, but none that are considered ready for prime time - to select patients with the highest risk of FL for the study of novel approaches.  
RESOURCES for potential cytogenetic biomarkers for FL
* Blood 1994:
Prognostic value of chromosomal abnormalities in follicular lymphoma
H Tilly, et al
* Follicular lymphoma Atlas of Genetics
* J Clin Invest. Oct 1, 2012
Pathogenesis of follicular lymphoma
Robert Kridel, Laurie H. Sehn, and Randy D. Gascoyne”

Unexpectedly, it was shown that germinal center–derived lymphomas are characterized by frequent mutations of histone-modifying genes. In FL in particular, recurrent mutations have been reported in the histone methyltransferases MLL2 (89%) and EZH2 (7.2%), the histone acetylases CREBBP (32.6%), EP300 (8.7%), and MEF2B (15.3%) (refs. 47, 48, 50, 51, and Table Table1).1). The high recurrence of these mutations illustrates that FL is likely a disease of the epigenome as well as the genome (52).
* ASH Education, 2013:
Dissecting follicular lymphoma: high versus low risk
Carbohydrate antigen-125
Int J Hematol. 2012
High serum carbohydrate antigen-125 (CA-125) level predicts poor outcome in patients with follicular lymphoma independently of the FLIPI score.
Prognostic value of MUM-1  PubMed Query

Prognostic value of regulatory T cells, lymphoma-associated macrophages, and MUM-1 expression in follicular lymphoma treated before and after the introduction of monoclonal antibody therapy: a Southwest Oncology Group Study 
Serum thymidine kinase 1 level
High baseline serum thymidine kinase 1 level predicts unfavorable outcome in patients with follicular lymphoma. PubMed - NCBI
Mantle Cell (coming soon)
DLBCL (coming soon)


Resources and Related News Items

Minimal Residual Disease testing  PAL
AACR: technical
Biomarkers for Cancer Risk, Early Detection, and Prognosis: The Validation Conundrum

We have considered three fundamental concerns related to validation.

(a) Overfitting. This refers to the tendency of models trained on large numbers of variables measured on small numbers of samples to produce extraordinarily high sensitivity and specificity and then fail on independent validation sets.

 (b) Bias. Are results due to differences between the cancer and the control samples that do not exist in the cancer and control populations?

(c) Robustness. Are results generalizable to appropriate clinical populations?


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For all medical concerns, you should always consult your doctor. 
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