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Understanding Biomarkers for Better
Drug Discovery and Development
By Gunjan Choudharyand Laura Moriarty

In collaboration with Amplion Inc., we investigated biomarker usage within 24 companies focused onimmunotherapy. This article shares exciting insights from our analysis, including which biomarkers are themost popular and how those biomarkers are being utilized by each company.
What is a biomarker? Biomarker, or biological marker, is aterm used to describe any measurable biological indicator
or characteristic that can reflect a physiological state. Morespecifically, it has been defined as:■■■■“ a characteristic that is objectively measured and evaluatedas an indicator of normal biological processes, pathogenic
processes, or pharmacological responses to a therapeuticintervention” (NIH, Biomarkers Definitions Working Group2001)“ any substance, structure, or process that can be measured
in the body or its products and influence or predict theincidence of outcome or disease” (WHO International
Programme on Chemical Safety 2001)

One of the better known examples of biomarkers is seen indiabetes. In 1976, almost two decades after hemoglobin A1c(HbA1c) was first separated from other forms of hemoglobin,
Koenig et al. proposed that the molecule could be used toreflect the degree of glycemic control in diabetes patients. Thiswas validated in 1990 when Larsen et al. showed that diabetes
could be managed based on levels of HbA1c. Since then,
HbA1c has been touted and widely used as the biomarker ofchoice in clinical trials for assessing the efficacy of antidiabetes
drugs (Caveney and Cohen 2011) and is even used in manyover-the-counter treatment regimens. Other examples ofbiomarkers include body temperature for fever, blood pressure
for the risk of stroke, prostate-specific antigen (PSA) for
prostate cancer, and endophenotypes for schizophrenia.

Biomarkers are now being utilized in drug development andregulatory decision making as they have the potential toimprove the efficiency of the drug discovery and development
process. In particular, if specific biomarkers that monitor
or reflect the status of a disease can be identified, thenresearchers can use the changes in these biomarkers tobetter understand how effective the new therapy is against
a particular disease. Including that data in a clinical trial
would allow researchers to focus their efforts and resourceson therapies that prove to be the most effective. In turn, thiswould reduce the time and cost of bringing a new drug tomarket. All of these benefits are encouraging to companies
in the biopharmaceutical industry.
One field in the biopharmaceutical industry in whichbiomarkers are proving to have great impact isimmunotherapy. Immunotherapy or immuno-oncology isa major focus area for biopharma, and most if not all large
biopharma companies have immunotherapy programs.
The global immunotherapy drug market was valued at USD
108.4 billion in 2016 and is projected to reach USD 201.5
billion by 2021. Immunotherapy is popular because it allows
drug developers to create treatments that use an individual’s
own immune system to fight their cancer, completely changing
the way the disease is treated. The biggest advantage of
this, and the reason that immunotherapies are so useful,is their adaptive nature. As an individual’s cancer develops
and mutates, immunotherapies can help the immune
system evolve its anti-cancer response by either stimulating
components of the immune system that help fight the diseaseor suppressing procancer signals from cancer cells.

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More Than PD-L1
Within immuno-oncology, the most well-known target andcited biomarker is PD-L1 or programmed death-ligand 1. In
fact, of the top 24 companies developing immunotherapies(by market capitalization as of February 2017; Figure 1),
PD-L1 is the most cited biomarker in their clinical trials. PD-L1
is thought to help cancer cells evade the immune system.
In addition, it is also implicated in HIV, lymphoma, sepsis,
asthma, and blood disorders. Due to its potential role incancer, a number of anti-PD-L1 molecules are in development

as immunotherapies and have shown promising results inclinical trials (Ma et al. 2016). Market options already using
FDA-approved PD-L1 inhibitors include atezolizumab (forbladder cancer and non-small cell lung carcinoma) andavelumab (for non-small cell lung and Merkel cell carcinomas).
Though PD-L1 is by far the most cited biomarker in clinical
trials, 154 other biomarkers are being evaluated for theireffectiveness as proxies for prognosis in various diseases(Table 1).

Bristol-Myers Squibb
Five Prime Therapeutics Inc.
Celldex Therapeutics, Inc.
XencorMacroGenics, Inc.
Curis, Inc.
AstraZeneca
CytomX Therapeutics, Inc.
Sorrento Therapeutics
Merck & Co.
Lion Biotechnologies
Novartis
ZIOPHARM Oncology, Inc.
Kite Pharma Inc.
Aduro BioTech Inc.
bluebird bioAmgenAdvaxis
Juno Therapeutics
Incyte Corporation
Cellectis
NewLink Genetics Corporation
BeiGene Ltd.
Adaptimmune Therapeutics
2.50

2.70

2.90

3.10

3.30
Index weighting, %

3.50

3.70

3.90

4.10

Fig. 1. List of top 24 immunotherapy companies by market capitalization. This list was determined using the equal-weighted Loncar Cancer Immunotherapy
Index (LCINDX; loncarindex.com), which tracks the progress of new immunotherapies developed by major contributors in the immuno-oncology sector. Data
current as of February 2017.

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developing immunotherapies for HIV while Curis and Sorrento
Therapeutics are developing immunotherapies for hematologicalmalignancies.

Table 1. List of biomarkers and their use in clinical trials by the top 24
immuno-oncology companies.
Name of Biomarker

Number of Mentions/Trial*

PD-L1

127

HER256

CD1935

CD8a29PD-1

29

NY-ESO-1

26

HLA-A2

24

CRP

20

EGFR

16

TNFa

15Ki67

14

B7-H3

13PSA

13

Codanin 1

12KRAS

12CD4

11IFNg11

Kynurenine

11

CD68

9c-Met

9

FGFR2

9CD3e

8

ROR1

8BRCA1

7BRCA2

7CD34

7Biomarker Indication
Within clinical trials, biomarkers can be classified based on theirfunction, which can be diagnostic, prognostic, and predictive.
 iagnostic biomarkers can be used to define a population
Dwith a specific disease. For example, elevated levels of PSA
can be diagnostic of prostate cancer■■

 rognostic biomarkers correlate with the progression of aP
disease. For example, overexpression of HER2 in breast
cancer can indicate poor prognosis. These biomarkers canbe used as inclusion or exclusion criteria in a clinical trial■■

 redictive biomarkers define populations based on theirP
response to a given treatment from an efficacy or safety
perspective. For example, HbA1c levels can be predictive of
the outcome of a diabetes treatment■■

Of the biomarkers being evaluated by the top 24 immunotherapy
companies, the majority of them could be categorized as
predictive of outcome (Figure 3). This is not surprising as pharmacompanies are looking to improve the success rates of theirclinical trials. By including predictive biomarkers and stratifying
patient populations, they can identify patients who will respondto the therapy and thus increase the success rate of the trials.
Not specified: 7

Safety: 72

Inclusion: 199* Only biomarkers mentioned seven or more times are listed.
Outcome: 392

Interestingly, though cancer is the most studied disease(listed ~70 times across clinical trials carried out by the top 24
immunotherapy companies), it is not the only disease for which
immunotherapies are being developed. There are 29 otherdiseases being evaluated in which biomarkers are being used
(Figure 2). For example, Bristol-Myers Squibb and Amgen areExclusion: 84

Fig. 3. Biomarkers segmented by their specific classification.

70
69

Frequency

68
67
66
65
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Fig. 2. Diseases being studied across biomarker-associated immunotherapy clinical trials being carried out by the top 24 companies in the field.

© 2018 Bio-Rad Laboratories, Inc.

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Though immunotherapy is a relatively new field, many of theclinical trials we identified that utilize biomarkers are alreadymoving to later stages (Figure 4). This emphasizes companies’
drive to get these therapies to market in order to be the first
to offer new personalized medicines in the continuing race todevelop novel treatments for complex diseases.

250
Number of trials

Biomarkers Definitions Working Group (2001). Biomarkers and surrogate
endpoints: preferred definitions and conceptual framework. Clin Pharmacol
Ther 69, 89–95.
Caveney EJ and Cohen OJ (2011). Diabetes and biomarkers. J Diabetes Sci
Technol 5, 192–197.
Koenig RJ et al. (1976). Correlation of glucose regulation and hemoglobin AIc
in diabetes mellitus. N Engl J Med 295, 417–420.
Larsen ML et al. (1990). Effect of long-term monitoring of glycosylated
hemoglobin levels in insulin-dependent diabetes mellitus. N Engl J Med 323,1,021–1,025.

300

Ma W et al. (2016). Current status and perspectives in translational biomarker
research for PD-1/PD-L1 immune checkpoint blockade therapy. J Hematol
Oncol 9, 47.

200150WHO International Programme on Chemical Safety (2001). Biomarkers in risk
assessment: validity and validation. inchem.org/documents/ehc/ehc/ehc222.
htm, accessed April 15, 2017.

100500References

Preclinical0I

Ib

I/III/IIA

II

II/IIIIII

IVOn
market

The data presented here were generated through a collaboration between
Bio-Rad Laboratories, Inc. and Amplion Inc. Amplion’s BiomarkerBase tool
houses information about biomarkers in clinical use and provides a forum
where this information is readily accessible.

Clinical trial phase

BiomarkerBase is a trademark of Amplion Inc.
Fig. 4. Breakdown of immuno-oncology clinical trials utilizing biomarkers
by number and phase of trials.

Conclusions
Biomarker intelligence comes from having an overall
understanding of disease pathways and the characteristics
and features of biomarkers (biomarker landscape) and isrequired to truly understand the utility of biomarkers. The
use of biomarkers in drug discovery and development isincreasing. They are being used across all stages, from cellbased efficacy biomarkers in early discovery to safety markers
in preclinical studies to prognostic markers in clinical trials.
Having a thorough understanding of the biomarker landscapeand developing strong biomarker intelligence is paying off
for companies as they see their costs decrease and successrates increase. Incorporating a biomarker strategy into drug
programs is leading to better medicines for all.

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