Volume 11 Supplement 5

Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: systems biology

Open Access

A systematic analysis of FDA-approved anticancer drugs

  • Jingchun Sun1,
  • Qiang Wei1,
  • Yubo Zhou2,
  • Jingqi Wang1,
  • Qi Liu3 and
  • Hua Xu1Email author
BMC Systems BiologyBMC series – open, inclusive and trusted201711(Suppl 5):87

https://doi.org/10.1186/s12918-017-0464-7

Published: 3 October 2017

Abstract

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

Keywords

Anticancer drugs Drug-cancer network Cancer-drug-target network Drug repurposing

Background

In the last 50 years, numerous remarkable achievements have been made in the fight against cancer, starting from understanding cancer mechanisms to patient treatment. However, cancer remains as one of the leading causes of death in the world, which places a heavy burden on health services and society. Cancer involves abnormal cell growth with the potential to invade or spread to other parts of the body and encompasses more than 100 distinct diseases with diverse risk factors and epidemiology. Over the past five decades, scientific discoveries and technological advances, including modern molecular biology methods, high-throughput screening, structure-based drug design, combinatorial and parallel chemistry, and the sequencing of the human genomes have improved the drug discovery. However, the increasing cost of new drug development and decreasing number of truly efficient medicines approved by the US Food and Drug Administration (FDA) present unprecedented challenges for the pharmaceutical industry and patient healthcare, including the oncology [1, 2]. As the increasing availability of FDA-approved drugs and quantitative biological data from the human genome project, multiple strategies have been proposed to shorten the drug development process and significantly lower costs, including drug repurposing [3, 4] and network pharmacology [5, 6].

With advances in anticancer drug discovery and development in the last several decades, more than 100 anticancer drugs have been discovered and approved by the FDA [7, 8]. These drugs can be broadly classified into two basic categories: cytotoxic and targeted agents based on their mechanisms of action [911]. The cytotoxic agents can kill rapidly dividing cells by targeting components of the mitotic and/or DNA replication pathways. The targeted agents block the growth and spread of cancer through interacting with molecular targets that are involved in the pathways relevant to cancer growth, progression, and spread [12]. Those successful agents and their related data may provide valuable clues for further identification of novel drug targets, the discovery of novel anticancer drug combinations, drug repurposing, and computational pharmacology. Several reviews have provided the historical summary of these drugs, which revealed the trends of increasing proportion of targeted agents, particularly monoclonal antibodies [7, 8]. Recently network pharmacology has successfully applied in multiple fields such as target identification, prediction of side effects, and investigation of general patterns of drug actions [5, 13, 14]. Therefore, besides of updating the FDA-approved anticancer drugs, analysis of drug-disease/target networks will significantly increase our understanding of the molecular mechanisms underlying drug actions and provide valuable clues for drug discovery.

Thus, in this study, we first comprehensively collected the FDA-approved anticancer drugs by the end of 2014 and curated their related data, such as initial approval years, action mechanisms, indications, delivery methods, and targets from multiple data sources. According to their action mechanisms, we classified them into two groups: cytotoxic and targeted drugs. Then, we analyzed these data to reveal the different trends between the two groups. Besides, we analyzed the drug targets by investigating their subcellular locations, functional classifications, and genetic mutations. Finally, we generated anticancer drug-disease and drug-target networks to capture the common anticancer drugs across different types of cancer and to reveal how strongly the anticancer drugs and targets interact or drug-target networks. The network-assisted investigation provides us with novel insights into the relationships among anticancer drugs and disease or drugs and targets, which may provide valuable information for further understanding anticancer drugs and the development of more efficient treatments.

Methods

Collection of FDA-approved anticancer drugs and their relation information

We have collected anticancer drugs approved by FDA since 1949 to the end of 2014 from multiple data sources. We started the collection of the anticancer drugs from anticancer drug-focused websites, including National Cancer Institute (NCI) drug information [15], MediLexicon cancer drug list [16], and NavigatingCancer [17]. Then, we employed the tool MedEx-UIMA, a new natural language processing system, to retrieve the generic names for these drugs [18]. Using the generic names, we searched Drug@FDA [19] and downloaded their FDA labels. For those that cannot be found in the drugs@FDA, we obtained their labels from Dailymed [20] or DrugBank [21]. From the drug label, we manually retrieved the initial approval year, drug action mechanism, drug target, delivery method, and indication for each drug. We further checked the multiple sources such as the MyCancerGenome [22], DrugBank, and the several publications [4, 23] to obtain the drug targets. For drug category, we manually checked the ChemoCare [24] to assign the drugs as cytotoxic or targeted agents. In our curated drug list, we did not include the medicines to treat drug side effects, cancer pain, other conditions, or cancer prevention.

Classes of drug targets and cancer

For these targeted agents, we collected their targets from FDA drug labels, DrugBank, and MyCancerGenome. We then manually curated the primary effect-mediating targets for each drug. We further retrieved the gene annotation from Ingenuity Pathway Analysis (IPA) [25] to obtain their subcellular location and family classes. For the indication, we first collected the detail information from FDA drug labels and then manually classified them into higher-level class for the purpose of data analysis. For example, drug idelalisib can be used to treat relapsed chronic lymphocytic leukemia (CLL), relapsed follicular B-cell non-Hodgkin lymphoma (FL), relapsed small lymphocytic lymphoma (SLL) from FDA labels. In our data analysis, we recorded the drug’s therapeutic classes as leukemia and lymphoma.

Cancer genes and somatic mutations of the cancer genome

The cancer gene set contains 594 genes from the Cancer Gene Census, which have been implicated in tumorigenesis by experimental evidence in the literature (July 14, 2016) [26]. We obtained 50 oncogenes (OCGs) and 50 tumor suppressor genes (TSGs) with high confidence from Davioli et al. [27]. The somatic mutations were obtained from Supplementary Table 2 in one previous work [28]. The table contains the somatic mutations in 3268 patients across 12 types of cancer. They are bladder urothelial carcinoma (BLCA), breast adenocarcinoma (BRCA), colon and rectal adenocarcinoma (COAD/READ), glioblastoma (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), acute myeloid leukemia (LAML), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian cancer (OV), and uterine corpus endometrioid carcinoma (UCEC). The mutations include missense, silent, nonsense, splice site, readthrough, frameshift indels (insertions/deletions) and inframe indels [28].

Network analysis

We built two networks based on our curated data, drug-cancer and drug-cancer-target networks. In the drug-cancer network, there are two types of nodes representing drug or cancer types and edges suggesting drug as the approved treatment for the cancer. In the drug-cancer-target network, there are three types of nodes representing cancer types, drug or drug target and edges indicating cancer-drug associations or drug-target interactions. The network degree is used to assess the toplogical feature of each cancer type and drug, i.e., the number of edges of each node in the network.

Common target-based approach

We used common target-based approach to discover novel drug-cancer associations [29]. It is one of the “guilt-by-association” strategies based on the knowledge that whether the drugs shared common targets or not. If two drugs A and B have a common target, drug A is in current use for treating cancer type C and drug B is used for cancer type D, it is highly likely to be effective for drug A-cancer type D and drug B-cancer type C associations.

Results and Discussion

FDA-approved anticancer drugs

From 1949 to 2014, a total of 150 medicines has been approved with an indication for at least one type of cancer (Table 1). Notably, in this study, we did not include the drugs used to treat side effects of cancer treatment, cancer pain, and other conditions. Based on the mechanism of action (MOA), we grouped them into two groups: 61 cytotoxic drugs and 89 targeted drugs. Most of the cytotoxic drugs are alkylating agents, anti-microtubule agents, topoisomerase inhibitors while most of the targeted drugs belong to signal transduction inhibitors, gene expression modulators, apoptosis induces, hormone therapies, and monoclonal antibodies. Figure 1 shows that the number of approved drugs in cancer treatment had a gradual increase. In the later years (1991–2014), the number of approved anticancer (116 drugs) extremely increased compared to that of the previous five decades (1941–1990, 34 drugs). Even in the recent years (2011–2014), the annual average number was 9, which was about 2.5 times of that in 1991–2000 (3.8) or 2001–2010 (4.2). From 1991 to 2000, the number of anticancer targeted drugs (17) was similar to that of cytotoxic drugs (21). However, since the 2000s, the number of targeted drugs (65) was significantly higher than that of the cytotoxic drugs (13), which was about five times.
Table 1

Summary of FDA-approved anticancer drugs from 1949 to 2014

Drug

Approval year

Therapeutic class

Target gene

Delivery type

Cytotoxic

 Mechlorethamine

1949

Lung cancer; Leukemia; Lymphoma

DNA synthesis

Single

 Leucovorin

1952

Colorectal cancer; Bone cancer

TYMS

Both

 Methotrexate

1953

Leukemia; Breast cancer; Head and neck cancer; Lung cancer; Lymphoma; Bone cancer; Gestational trophoblastic disease

DHFR

Both

 Mercaptopurine

1953

Leukemia

HPRT1

Combination

 Busulfan

1954

Leukemia

DNA synthesis

Combination

 Chlorambucil

1957

Leukemia; Lymphoma

DNA synthesis

Single

 Cyclophosphamide

1959

Lymphoma; Multiple myeloma; Leukemia; Brain cancer; Ovarian cancer; Retinoblastoma; Breast cancer

DNA synthesis

Both

 Vincristine sulfate

1963

Leukemia

TUBA4A; TUBB

Single

 Dactinomycin

1964

Sarcoma; Gestational trophoblastic disease; Testicular cancer; Kidney cancer

RNA synthesis

Both

 Vinblastine sulfate

1965

Lymphoma; Testicular cancer; Choriocarcinoma; Breast cancer

TUBA1A; TUBB; TUBD1; TUBE1; TUBG1

Combination

 Thioguanine

1966

Leukemia

DNA synthesis

Combination

 Procarbazine hydrochloride

1969

Lymphoma

DNA synthesis

Combination

 Floxuridine

1970

Stomach cancer

DNA synthesis

Single

 Fluorouracil

1970

Breast cancer; Colorectal cancer; Stomach cancer; Pancreatic cancer

DNA synthesis

Single

 Mitotane

1970

Adrenal cortical carcinoma

Unknown

Single

 Bleomycin

1973

Head and neck cancer; Lymphoma; Penile cancer; Cervical cancer; Vulvar cancer; Testicular cancer

DNA synthesis

Both

 Doxorubicin hydrochloride

1974

Leukemia; Breast cancer; Stomach cancer; Lymphoma; Ovarian cancer; Lung cancer; Sarcoma; Thyroid cancer; Bladder cancer; Kidney cancer; Brain cancer

TOP2A; DNA synthesis

Single

 Dacarbazine

1975

Melanoma; Lymphoma

DNA synthesis

Both

 Lomustine

1976

Brain cancer; Lymphoma

DNA synthesis

Both

 Carmustine

1977

Brain cancer; Lymphoma; Multiple myeloma

DNA synthesis

Both

 Cisplatin

1978

Testicular cancer; Ovarian cancer; Bladder cancer

DNA synthesis

Both

 Asparaginase

1978

Leukemia

Unknown

Combination

 Streptozocin

1982

Pancreatic cancer

DNA synthesis; SLC2A2

Single

 Etoposide

1983

Testicular cancer; Lung cancer

TOP2A; TOP2B

Combination

 Ifosfamide

1988

Testicular cancer

DNA synthesis

Combination

 Carboplatin

1989

Ovarian cancer

DNA synthesis

Both

 Altretamine

1990

Ovarian cancer

DNA synthesis

Single

 Fludarabine

1991

Leukemia

DNA synthesis

Single

 Pentostatin

1991

Leukemia

ADA

Single

 Paclitaxel

1992

Breast cancer; Lung cancer; Pancreatic cancer; Ovarian cancer; Sarcoma

TUBA4A; TUBB1

Both

 Melphalan

1992

Multiple myeloma; Ovarian cancer

DNA synthesis

Combination

 Teniposide

1992

Leukemia

TOP2A

Combination

 Cladribine

1993

Leukemia

DNA synthesis

Single

 Vinorelbine tartrate

1994

Lung cancer

TUBB

Both

 Pegaspargase

1994

Leukemia

Biological

Combination

 Thiotepa

1994

Breast cancer; Ovarian cancer; Bladder cancer

DNA synthesis

Single

 Docetaxel

1996

Prostate cancer; Breast cancer; Head and neck cancer; Stomach cancer; Lung cancer; Brain cancer

TUBA4A; TUBB1

Both

 Gemcitabine

1996

Ovarian cancer; Pancreatic cancer; Lung cancer; Breast cancer

DNA synthesis; RRM1; TYMS

Both

 Irinotecan

1996

Colorectal cancer

TOP1; TOP1MT

Both

 Topotecan hydrochloride

1996

Ovarian cancer; Lung cancer; Cervical cancer

TOP1; TOP1MT

Both

 Idarubicin

1997

Leukemia

DNA synthesis; TOP2A

Combination

 Capecitabine

1998

Colorectal cancer; Breast cancer

DNA synthesis; RNA synthesis; Protein synthesis; TYMS

Both

 Daunorubicin hydrochloride

1998

Leukemia

DNA synthesis; TOP2A; TOP2B

Combination

 Valrubicin

1998

Bladder cancer

DNA synthesis; TOP2A

Single

 Temozolomide

1999

Brain cancer

DNA synthesis

Both

 Cytarabine

1999

Leukemia

DNA synthesis

Single

 Epirubicin

1999

Breast cancer

CHD1; DNA synthesis; TOP2A

Single

 Arsenic trioxide

2000

Leukemia

Unknown

Single

 Mitomycin

2002

Stomach cancer; Pancreatic cancer

DNA synthesis

Both

 Oxaliplatin

2002

Colorectal cancer

DNA synthesis

Combination

 Pemetrexed disodium

2004

Lung cancer; Mesothelioma

DHFR; GART; TYMS

Both

 Clofarabine

2004

Leukemia

DNA synthesis

Single

 Nelarabine

2005

Leukemia; Lymphoma

DNA synthesis

Single

 Ixabepilone

2007

Breast cancer

TUBB3

Both

 Bendamustine hydrochloride

2008

Leukemia; Lymphoma

DNA synthesis

Single

 Pralatrexate

2009

Lymphoma

DHFR; TYMS

Single

 Cabazitaxel

2010

Prostate cancer

TUBA4A; TUBB1

Combination

 Eribulin mesylate

2010

Breast cancer

TUBA4A; TUBB1

Single

 Asparaginase erwinia chrysanthemi

2011

Leukemia

Biological

Combination

 Omacetaxine mepesuccinate

2012

Leukemia

RPL3

Single

 Radium 223 dichloride

2013

Prostate cancer

Unknown

Single

Targeted

 Fluoxymesterone

1956

Breast cancer

AR; ESR1; NR3C1; PRLR

Single

 Methyltestosterone

1973

Breast cancer

AR

Single

 Tamoxifen citrate

1977

Breast cancer

ESR1; ESR2

Single

 Estramustine

1981

Prostate cancer

ESR1; ESR2; MAP1A; MAP2

Single

 Interferon Alfa-2b, recombinant

1986

Sarcoma; Leukemia; Melanoma; Lymphoma

IFNAR1; IFNAR2

Single

 Goserelin

1989

Prostate cancer; Breast cancer

GNRHR; LHCGR

Both

 Flutamide

1989

Prostate cancer

AR

Combination

 Aldesleukin

1992

Melanoma; Kidney cancer

IL2RA; IL2RB; IL2RG

Single

 Bicalutamide

1995

Prostate cancer

AR

Combination

 Anastrozole

1995

Breast cancer

CYP19A1

Single

 Porfimer

1995

Esophageal cancer; Lung cancer

FCGR1A; LDLR

Single

 Nilutamide

1996

Prostate cancer

AR

Combination

 Imiquimod

1997

Basal cell carcinoma

TLR7; TLR8

Single

 Letrozole

1997

Breast cancer

CYP19A1

Single

 Rituximab

1997

Lymphoma; Leukemia

MS4A1

Single

 Toremifene

1997

Breast cancer

ESR1

Single

 Thalidomide

1998

Multiple myeloma

CRBN

Combination

 Trastuzumab

1998

Breast cancer; Stomach cancer

ERBB2

Single

 Alitretinoin

1999

Kaposi’s sarcoma

RARA; RARB; RARG; RXRA; RXRB; RXRG

Single

 Bexarotene

1999

Lymphoma

RXRA; RXRB; RXRG

Single

 Denileukin diftitox

1999

Lymphoma

IL2RA; IL2RB; IL2RG; protein synthesis

Single

 Exemestane

1999

Breast cancer

CYP19A1

Single

 Gemtuzumab ozogamicin

2000

Leukemia

CD33; DNA synthesis

Single

 Triptorelin

2000

Prostate cancer

GNRH1

Single

 Alemtuzumab

2001

Leukemia

CD52

Single

 Imatinib mesylate

2001

Leukemia; Stomach cancer

BCR-ABL

Single

 Peginterferon Alfa-2b

2001

Melanoma

IFNAR1; IFNAR2

Single

 Fulvestrant

2002

Breast cancer

ESR1

Single

 Ibritumomab tiuxetan

2002

Lymphoma

MS4A1

Single

 Leuprolide acetate

2002

Prostate cancer

GNRHR

Single

 Abarelix

2003

Prostate cancer

GNRHR

Single

 Bortezomib

2003

Multiple myeloma; Lymphoma

PSMB1; PSMB2; PSMB5; PSMD1; PSMD2

Single

 Gefitinib

2003

Lung cancer

EGFR

Single

 Tositumomab and Iodine I 131 Tositumomab

2003

Lymphoma

MS4A1

Single

 Bevacizumab

2004

Colorectal cancer; Lung cancer; Brain cancer; Kidney cancer

VEGFA

Both

 Cetuximab

2004

Head and neck cancer; Colorectal cancer

EGFR

Both

 Erlotinib hydrochloride

2004

Pancreatic cancer; Lung cancer

EGFR

Both

 Azacitidine

2004

Leukemia

DNMT1

Single

 Lenalidomide

2005

Multiple myeloma; Lymphoma

CRBN

Both

 Sorafenib tosylate

2005

Liver cancer; Kidney cancer; Thyroid cancer

BRAF; FGFR1; FLT1; FLT3; FLT4; KDR; KIT; PDGFRB; RAF1; RET

Single

 Dasatinib

2006

Leukemia

BCR-ABL

Single

 Decitabine

2006

Leukemia

DNMT1

Single

 Panitumumab

2006

Colorectal cancer

EGFR

Single

 Sunitinib malate

2006

Stomach cancer; Kidney cancer; Pancreatic cancer

CSF1R; FLT1; FLT3; FLT4; KDR; KIT; PDGFRA; PDGFRB

Single

 Vorinostat

2006

Lymphoma

HDAC1; HDAC2; HDAC3; HDAC6

Single

 Lapatinib ditosylate

2007

Breast cancer

EGFR; ERBB2

Combination

 Nilotinib

2007

Leukemia

BCR-ABL

Single

 Temsirolimus

2007

Kidney cancer

MTOR

Single

 Degarelix

2008

Prostate cancer

GNRHR

Single

 Everolimus

2009

Breast cancer; Brain cancer; Kidney cancer; Pancreatic cancer

MTOR

Both

 Ofatumumab

2009

Leukemia

MS4A1

Single

 Pazopanib hydrochloride

2009

Kidney cancer; Sarcoma

FGF1; FGFR3; FLT1; FLT4; ITK; KDR; KIT; PDGFRA; PDGFRB; SH2B3

Single

 Romidepsin

2009

Lymphoma

HDAC1; HDAC2; HDAC3; HDAC6

Single

 Denosumab

2010

Bone cancer

TNFSF11

Single

 Hydroxyurea

2010

Melanoma; Leukemia; Ovarian cancer; Head and neck cancer

RRM1

Single

 Sipuleucel-T

2010

Prostate cancer

ACPP

Single

 Abiraterone acetate

2011

Prostate cancer

CYP17A1

Single

 Brentuximab vedotin

2011

Lymphoma

TNFRSF8

Single

 Crizotinib

2011

Lung cancer

ALK; MET

Single

 Ipilimumab

2011

Melanoma

CTLA4

Single

 Ruxolitinib phosphate

2011

Myelofibrosis

JAK1; JAK2

Single

 Vandetanib

2011

Thyroid cancer

EGFR; PTK6; TEK; VEGFA

Single

 Vemurafenib

2011

Melanoma

BRAF

Single

 Pertuzumab

2012

Breast cancer

ERBB2

Both

 Axitinib

2012

Kidney cancer

FLT1; FLT4; KDR

Single

 Bosutinib

2012

Leukemia

BCR-ABL

Single

 Cabozantinib

2012

Thyroid cancer

KDR; MET; RET

Single

 Carfilzomib

2012

Multiple myeloma

PSMB1; PSMB10; PSMB2; PSMB5; PSMB8; PSMB9

Single

 Enzalutamide

2012

Prostate cancer

AR

Single

 Ponatinib hydrochloride

2012

Leukemia

BCR-ABL

Single

 Regorafenib

2012

Colorectal cancer; Stomach cancer

RET; FLT1; KDR; FLT4; KIT; PDGFRA; PDGFRB; FGFR1; FGFR2; TEK; DDR2; NTRK1; EPHA2; RAF1; BRAF; MAPK11; FRK; ABL1

Single

 Vismodegib

2012

Basal cell carcinoma

SMO

Single

 Ziv-aflibercept

2012

Colorectal cancer

PGF; VEGFA; VEGFB

Single

 Dabrafenib

2013

Melanoma

BRAF; LIMK1; NEK11; RAF1; SIK1

Both

 Trametinib

2013

Melanoma

MAP2K1; MAP2K2

Both

 Obinutuzumab

2013

Leukemia

MS4A1

Combination

 Ado-trastuzumab emtansine

2013

Breast cancer

ERBB2

Single

 Afatinib

2013

Lung cancer

EGFR; ERBB2; ERBB4

Single

 Ibrutinib

2013

Lymphoma

BTK

Single

 Pomalidomide

2013

Multiple myeloma

CRBN

Single

 Idelalisib

2014

Leukemia; Lymphoma

PIK3CD

Both

 Belinostat

2014

Lymphoma

HDAC1; HDAC2; HDAC3; HDAC6

Single

 Ceritinib

2014

Lung cancer

ALK

Single

 Pembrolizumab

2014

Melanoma

PDCD1

Single

 Ramucirumab

2014

Stomach cancer

KDR

Single

 Lanreotide

2014

Gastroenteropancreatic neuroendocrine tumor

SSTR2; SSTR5

Single

 Blinatumomab

2014

Leukemia

CD19; CD3D

Single

 Nivolumab

2014

Melanoma

PDCD1

Single

 Olaparib

2014

Ovarian cancer

PARP1; PARP2; PARP3

Single

Fig. 1

Number of anticancer drugs approved by FDA from 1949 to 2014. Approval dates were retrieved from FDA drug labels. Drugs were divided into two categories according to their action mechanisms. The inserted table is the summary of drug numbers for each decade

Among 89 targeted drugs, 18 are antibodies, of which two (rituximab and trastuzumab) were approved in 1990, eight in the 2000s (gemtuzumab ozogamicin, alemtuzumab, ibritumomab tiuxetan, tositumomab and iodine I 131 tositumomab, bevacizumab, cetuximab, panitumumab, and ofatumumab) and seven from 2010 to 2014 (denosumab, brentuximab vedotin, ipilimumab, pertuzumab, ado-trastuzumab emtansine, obinutuzumab, and pembrolizumab). The trend was consistent with previous observations [7], which indicated that the advanced molecular understanding of cancer during the period had contributed substantially to the development of the anticancer drug, especially targeted drugs [30].

According to the drug delivery method administered to the patient, one drug can be categorized as a cancer single (individual) drug or a cancer combination drug. A combination drug is a drug that makes up a cancer drug combination that several individual drugs are administered to the patient. Though the targeted agents have become the primary focus of the therapeutic cancer research, investigation of their combined use with other targeted drugs or with cytotoxic drugs has become promising for the development of the effective cancer treatment [31, 32]. Among the 150 drugs, 96 drugs could be given to patients one at a time, 22 could be given in combination with other cancer drugs to patients, and 32 drugs could be delivered to patients as the combination drugs or single drugs (Fig. 2). The targeted drugs tended to be delivered as signal drugs (Pearson’s correlation: r = 0.92, P < 2.2 × 10−26) while cytotoxic drug tended to be delivered as combination drugs (r = 0.43, P = 0.002) or by both methods (r = 0.44, P = 0.001).
Fig. 2

Delivery methods of anticancer drugs approved by FDA from 1949 to 2014

Subcellular location and function of drug targets

In our curated data set, among the 150 anticancer FDA-approved drugs, 89 were targeted drugs that could be used to treat 23 types of cancer and acted on 102 protein targets (Tables 1, 2). To comprehensively understand the target functions and their genetic roles in cancer, we performed a survey from the perspectives of subcellular location, functional classification, and genetic mutations. These insights might be valuable for further understanding of molecular mechanisms of cancer and the advanced development of cancer therapy [30, 33, 34].
Table 2

Subcellular location and function classification of targeted drug targets

Subcellular location

Family

Subfamily

Number of targets

Cytoplasm (27)

 

Enzyme (23)

E3 ligase

1

Epigenetic enzyme

1

Monooxygenases

2

Peptidase

6

Phosphatidyl Inositol Kinases

1

Serine/threonine kinase

5

Threonine/tyrosine-protein kinase

2

Tyrosine kinase

5

Other (4)

Other

4

Extracellular space (7)

 

Cytokine (1)

Cytokine

1

Enzyme (1)

Phosphatase

1

Growth factor (4)

Growth factor

4

Hormone (1)

Hormone

1

Nucleus (23)

 

Enzyme (13)

Epigenetic enzyme

4

Polymerase

3

Ribonucleotide diphosphate reductase

1

Serine/threonine kinase

3

Tyrosine kinase

2

Receptor(10)

Ligand-dependent nuclear receptor

10

Plasma membrane (45)

 

Antigen (5)

Antigen

5

Enzyme(21)

Tyrosine kinase

21

Receptor(17)

Transmembrane receptor

12

 

G-protein coupled receptor

5

Transporter (1)

Transporter

1

Other (1)

Other

1

We retrieved the target’s subcellular information and function classification from IPA and manually reviewed for each target (Table 2). The result shows that most of the drug targets (45, 44%) located in the plasma membrane, 27 (26%) in the cytoplasm, 23 (23%) in the cell nucleus, and only seven (7%) in the extracellular space (Fig. 3a). Among the 45 targets in the plasma membrane, 21 were tyrosine kinases, 12 were transmembrane receptors, five were antigens, and five were G-protein coupled receptors. Among the 27 targets in the cytoplasm, 23 were enzymes and four were others. Among the 23 targets in the nucleus, 13 were enzymes and 10 were receptors. The observation indicates that, to date, the most successful anticancer drugs target the plasma membrane proteins.
Fig. 3

Anticancer drug target percentage of subcellular locations a and function families b and c

The data set showed that enzymes made up the largest groups of drug targets (58, 57%) while receptors were the second largest group of anticancer target proteins (27, 26%) (Fig. 3b). Of these enzymes, 28 (27%) were tyrosine kinases, eight (8%) were the serine/threonine kinases, six (6%) were peptidases, and five (5%) were epigenetic enzymes (Fig. 3c). Of these receptors, 12 (12%) were transmembrane receptors, 10 (10%) were ligand-dependent nuclear receptors, and five (5%) were G-protein coupled receptors (Fig. 3c).

Genetic pattern of targeted anticancer targets

To check if these targets are the cancer candidate genes, we compared them with the cancer gene set which contains 594 genes from the Cancer Gene Census [26]. Among 102 target genes, 32 genes are cancer genes. Compared to all the protein-coding genes in the human (20,729), the anticancer drug targets were significantly enriched with cancer genes (Hypergeometric test, P-value = 3.57 × 10−25). Among the 32 cancer genes, 16 were oncogenes while none were tumor suppressor genes according to the the high confidence TSGs and OCGs from Davioli et al. [27].

To further explore the mutation pattern of the anticancer drug targets, we utilized the somatic mutations in 3268 patients across 12 types of cancer from TCGA Pan-Cancer [28]. Among 102 drug targets, 32 were cancer genes. Thus we compared the mutation frequency of four gene sets: 32 genes belonging to drug targets and cancer genes (TargetCancer genes), 70 genes only belonging to genes encoding drug targets (TargetOnly genes), 537 cancer genes only belonging to cancer genes and with mutation data (CancerOnly genes), and 20,308 genes with mutation data excluding the genes from above three gene sets (Other genes). To compare the distribution of mutation frequency of the tumor samples among the four gene sets, we performed the Kolmogorov-Smirnor (K-S) tests. Figure 4a shows the comparison of mutation percentage of all samples in each gene set. The TargetCancer genes had the highest average mutation frequency (2.41%), which was significantly higher than that of TargetOnly (1.19%, K-S test: P = 4.79 × 10−5), CancerOnly (1.85%, P = 0.0005), and Other genes (0.97%, P = 1.31 × 10−9). The CancerOnly genes had the second highest average mutation frequency (1.85%), which was significantly higher than that of of TargetOnly (P = 0.0275) and other genes (P < 2.2× 10−17). The TargetOnly genes had the third highest average mutation frequency, which was significantly higher than that of other genes (P = 0.0134).
Fig. 4

Mutation pattern of drug target genes belonging to cancer genes. The TargetCancer represented the common genes between anticancer drug targets and cancer genes. The TargetOnly represented the genes only belonging to genes encoding drug targets with mutation data. The CancerOnly represented the genes only belonging to cancer genes with mutation data. The Other represented genes with mutation data excluding the genes from above three gene sets. a Comparison of average mutation frequency of four gene sets. b Percentage of genes with at least 2% mutation frequency in the Pan-Cancer. c The function classification, mutation frequency in individual cancer type and Pan-Cancer, and numbers of drugs of 32 TargetCancer genes. We highlighted the mutation frequency higher than 5% of samples in “TargetCancer” genes with red color

Notably, among the 32 TargetCancer genes, 18 genes (56%) had at least 2% mutation frequency across the Pan-Cancer collection (Fig. 4b). Compared to that of TargetOnly genes (39%), CancerOnly (29%), or Other gene sets (10%), the percentage was significantly higher (Chi-squared test P-values: 0.0002, 0.002, 2.48 × 10−16, respectively). Figure 4c shows the percentage of samples with mutations of the 32 TargetCancer genes, their function classification, and number of targeting drugs. Indeed, for the 32 Target Cancer genes, there was a significant correlation between the percentages of samples with mutations and numbers of targeted drugs (Pearson’s correlation: r = 0.40, P = 0.0230). Among the 32 genes, the most frequently mutated gene in the Pan-Cancer cohort was EGFR (6.2%). Its mutations significantly occur in the brain cancer GBM (27.1%), lung cancer (13.5%), COAD/READ (5.8%), HNSC (6.2%). Among the seven drugs targeting the gene, three (afatinib, erlotinib, and gefitinib) were used to treat lung cancer, two (cetuximab and panitumumab) were used to treat colorectal cancer, and one (cetuximab) was used to treat head and neck cancer.

Drug-cancer network

To explore the associations between the drugs and cancer types, we generated a drug-cancer network, which comprised 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations (Fig. 5) based on the FDA-approved drug-cancer associations in our curated data.
Fig. 5

Drug-cancer network. The red ellipse represents the cancer; the green rectangle represents the cytotoxic drug; the green diamond represents the targeted drug. The cancer abbreviations included in the Table 3

In the drug-cancer network, the degree (number of cancer types) of the 150 drugs ranged from one to eleven, and the average degree was 1.65. The degree distribution of these drugs was strongly right-skewed, indicating that most drugs had a low degree and only a small portion of the nodes had a high degree. The degree of the cytotoxic drugs was 2.13, which was significantly higher than that of the targeted drugs (1.33, K-S test: P = 0.0378). Most of them (105, 70%) could be used to treat only one cancer type. Among the 105 drugs, 35 belonged to the cytotoxic drugs while 70 belonged to the targeted drugs. Among the rest 45 drugs, 24 (16%) could be used to treat two cancer types and 21 drugs (14%) could be used to treat at least three cancer types. Among the 21 drugs, 15 were cytotoxic drugs while six were targeted drugs. Most of the 21 drugs (16, 76%) were approved by FDA before 2000. The most commonly used drug was doxorubicin that could be used to treat 11 cancer types, including leukemia, breast cancer, stomach cancer, lymphoma, ovarian cancer, lung cancer, sarcoma, thyroid cancer, bladder cancer, kidney cancer, and brain cancer. Doxorubicin is a cytotoxic anthracycline antibiotic isolated from cultures of Streptomyces peucetius var. caesius, which binds to nucleic acids, presumably by specific intercalation of the planar anthracycline nucleus with the DNA double helix [35]. The result indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs.

In the drug-cancer network, the degree (number of drugs) of the 33 cancer types ranged from one to 40 and the average degree was 7.52. The degree distribution of the cancer types was not obviously right-skewed. Among the 33 cancer types, 11 had one drug, 12 had at least two drugs and less than 10 drugs, and ten had at least ten drugs (Table 3). They were leukemia (number of drugs: 40), lymphoma (28), breast cancer (27), lung cancer (17), prostate cancer (15), ovarian cancer (12), melanoma (11), colorectal cancer (10), kidney cancer (10), and stomach cancer (10). Among the 40 drugs used to treat leukemia, 24 belonged to cytotoxic drugs while 16 drugs were the targeted drugs. Similarly, the numbers of cytotoxic drugs and targeted drugs were similar to each other for lymphoma, breast cancer, and lung cancer. However, for prostate cancer, melanoma, and kidney cancer, the numbers of targeted drugs were significantly higher than those of cytotoxic drugs.
Table 3

Cancer classes, their abbreviations, and number of anticancer drugs

Cancer

Abbreviation

Number of drugs

Number of targeted drugs

Number of cytotoxic drugs

Leukemia

Leukemia

40

16

24

Lymphoma

Lymphoma

28

14

14

Breast cancer

BRCA

27

14

13

Lung cancer

Lung cancer

17

7

10

Prostate cancer

PCa

15

12

3

Ovarian cancer

OV

12

2

10

Melanoma

Melanoma

11

10

1

Colorectal cancer

CRC

10

5

5

Kidney cancer

KNC

10

8

2

Stomach cancer

GCA

10

5

5

Brain cancer

BrainC

8

2

6

Multiple myeloma

MM

8

5

3

Pancreatic cancer

PACA

8

3

5

Testicular cancer

TC

6

0

6

Head and neck cancer

HNC

5

2

3

Sarcoma

Sarcoma

5

2

3

Bladder cancer

BCA

4

0

4

Thyroid cancer

THC

4

3

1

Bone cancer

BoneC

3

1

2

Basal cell carcinoma

BCC

2

2

0

Cervical cancer

CC

2

0

2

Gestational trophoblastic disease

GTD

2

0

2

Adrenal cortical carcinoma

ACR

1

0

1

Choriocarcinoma

CCA

1

0

1

Esophageal cancer

EC

1

1

0

Gastroenteropancreatic neuroendocrine tumor

GEP-NET

1

1

0

Kaposi’s sarcoma

KS

1

1

0

Liver cancer

Liver cancer

1

1

0

Mesothelioma

Mesothelioma

1

0

1

Myelofibrosis

MF

1

1

0

Penile cancer

PC

1

0

1

Retinoblastoma

RB

1

0

1

Vulvar cancer

VUC

1

0

1

Network of targeted drugs, targets, and cancer

Besides the drug-cancer network, we generated a specific network for targeted drugs, their targets, and their indications. The network contained 214 nodes (89 drugs, 102 targets, and 23 cancer types) and 313 edges (118 drug-cancer associations and 195 drug-target associations) (Fig. 6) based on the FDA-approved targeted drug-cancer associations and targeted drug-target associations in our curated data.
Fig. 6

Network of targeted drugs, targets, and cancer types. The red rectangle represents the cancer; the green rectangle represents the targeted drug, the blue rectangle represents the drug target. The cancer abbreviations included in the Table 3

In the network, drugs had two types of neighbors: drug target and drug indication (cancer type). The target degree (number of targets) of the 89 drugs ranged from one to 18, and the average degree was 2.19. The cancer degree (number of cancer types) of the 89 drugs ranged from one to four and the average degree was 1.33. Among the 89 drugs, 22 had more than two targets. The drug regorafenib had 18 targets, which was approved by FDA to treat gastrointestinal stromal tumors and metastatic colorectal cancer. Among the 89 drugs, 19 drugs could be used to treat more than one cancer types. Four drugs bevacizumab, everolimus, hydroxyurea, and recombinant interferon Alfa-2b could be used to treat four types of cancer. The degree (number of drugs) of targets ranged from one to seven and the average degree was 1.91. The EGFR (epidermal growth factor receptor) and KDR (kinase insert domain receptor) were the most popular targets and both could be targeted by seven drugs, separately. The EGFR-related seven drugs could be used to treat six cancer types, while KDR-related drugs could be used to treat seven types of cancer. There were three common cancer types: colorectal cancer, thyroid cancer, pancreatic cancer. The degree (number of drugs) of cancer types ranged from one to 16 and the average degree was 5.13. As we discussed before, leukemia had 16 targeted drugs can be used to treat.

The common target-based approach, namely, the drugs that shared common targets could be used to treat the same disease, is one of the “guilt-by-association” strategies to identify the novel drug-disease associations [29]. During the analysis, we noticed that, among the 89 drugs, 70 drugs had at least one common target. Applying the common target-based approach, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types. To evaluate the novel drug-cancer associations, we utilized the clinical trial studies to see if the drug had been investigated in the corresponding cancer type. After searching using the 52 drugs and their predicted cancer types against ClinivalTrials.gov, we found that most of the drug-cancer associations (116) have been investigated in at least one clinical trial (Table 4) while the 17 had not been investigated in clinical trials. The later part of novel drug-cancer associations might provide valuable clues for drug repurposing. The most well-studied association was the thalidomide-lymphoma, which had 174 clinical trial studies, including 15 Phase III clinical trial studies and one Phase IV clinical trial study. The drug thalidomide was approved to treat multiple myeloma. Recently its combination with other drugs entered to treat the peripheral T-cell lymphoma in the Phase 4 study (ClinicalTrials.gov Identifier: NCT01664975).
Table 4

Potential drug-cancer associations with numbers of clinical trials

Drug

Possible indication

Number of clinical trialsa

Drug

Possible indication

Number of clinical trialsa

Thalidomide

Lymphoma

174

Ziv-aflibercept

Lung cancer

5

Temsirolimus

BRCA

129

Afatinib

CRC

4

Cetuximab

Lung cancer

77

Axitinib

THC

4

Ofatumumab

Lymphoma

64

Gefitinib

PACA

4

Erlotinib

HNC

62

Pazopanib

GCA

4

Temsirolimus

PACA

50

Pazopanib

CRC

4

Aldesleukin

Lymphoma

44

Pertuzumab

GCA

4

Obinutuzumab

Lymphoma

44

Regorafenib

KNC

4

Gefitinib

HNC

43

Regorafenib

PACA

4

Temsirolimus

BrainC

40

Regorafenib

Melanoma

4

Axitinib

KNC

39

Tamoxifen citrate

PCa

4

Cetuximab

PACA

31

Tositumomab and Iodine I 131 Tositumomab

Leukemia

4

Erlotinib

CRC

31

Vandetanib

KNC

4

Panitumumab

HNC

31

Vemurafenib

THC

4

Sorafenib

Melanoma

30

Ziv-aflibercept

KNC

4

Vandetanib

Lung cancer

29

Ado-trastuzumab emtansine

GCA

3

Erlotinib

BRCA

25

Afatinib

PACA

3

Sorafenib

PACA

25

Bevacizumab

THC

3

Sorafenib

CRC

23

Cabozantinib

PACA

3

Trastuzumab

Lung cancer

22

Cabozantinib

Sarcoma

3

Vandetanib

HNC

22

Dabrafenib

THC

3

Carfilzomib

Lymphoma

20

Fulvestrant

PCa

3

Lapatinib

HNC

20

Pertuzumab

Lung cancer

3

Afatinib

HNC

17

Vandetanib

PACA

3

Sunitinib

Sarcoma

17

Ziv-aflibercept

BrainC

3

Panitumumab

Lung cancer

16

Axitinib

Sarcoma

2

Sorafenib

Sarcoma

16

Axitinib

PACA

2

Sunitinib

Liver cancer

16

Axitinib

GCA

2

Cetuximab

BRCA

14

Cabozantinib

Liver cancer

2

Peginterferon Alfa-2b

Leukemia

14

Denileukin diftitox

KNC

2

Sunitinib

CRC

14

Estramustine

BRCA

2

Lapatinib

GCA

13

Gefitinib

THC

2

Gefitinib

CRC

12

Vandetanib

GCA

2

Gefitinib

BRCA

12

Bexarotene

KS

1

Leuprolide

BRCA

12

Cabozantinib

CRC

1

Vandetanib

BRCA

11

Cabozantinib

GCA

1

Sorafenib

GCA

10

Cetuximab

THC

1

Bicalutamide

BRCA

9

Crizotinib

THC

1

Denileukin diftitox

Melanoma

9

Dabrafenib

KNC

1

Enzalutamide

BRCA

9

Dabrafenib

Liver cancer

1

Ibritumomab tiuxetan

Leukemia

9

Dabrafenib

CRC

1

Cabozantinib

Lung cancer

8

Degarelix

BRCA

1

Regorafenib

Liver cancer

8

Erlotinib

THC

1

Lapatinib

Lung cancer

7

Lapatinib

THC

1

Lapatinib

CRC

7

Peginterferon Alfa-2b

Sarcoma

1

Panitumumab

PACA

7

Ramucirumab

PACA

1

Ramucirumab

Liver cancer

7

Ramucirumab

Sarcoma

1

Vandetanib

CRC

7

Regorafenib

THC

1

Vandetanib

BrainC

7

Ziv-aflibercept

THC

1

Ado-trastuzumab emtansine

Lung cancer

6

Abarelix

BRCA

0

Axitinib

Liver cancer

6

Afatinib

THC

0

Cabozantinib

KNC

6

Alitretinoin

Lymphoma

0

Pazopanib

PACA

6

Bosutinib

GCA

0

Pazopanib

THC

6

Dabrafenib

GCA

0

Ramucirumab

CRC

6

Dasatinib

GCA

0

Sunitinib

THC

6

Fluoxymesterone

PCa

0

Afatinib

GCA

5

Flutamide

BRCA

0

Axitinib

CRC

5

Methyltestosterone

PCa

0

Lapatinib

PACA

5

Nilotinib

GCA

0

Panitumumab

BRCA

5

Nilutamide

BRCA

0

Pazopanib

Liver cancer

5

Panitumumab

THC

0

Peginterferon Alfa-2b

Lymphoma

5

Ponatinib

GCA

0

Pomalidomide

Lymphoma

5

Ramucirumab

THC

0

Ramucirumab

KNC

5

Vemurafenib

GCA

0

Regorafenib

Sarcoma

5

Vemurafenib

KNC

0

Toremifene

PCa

5

Vemurafenib

Liver cancer

0

Vemurafenib

CRC

5

   

aobtained from ClinivalTrials.gov

Conclusion

FDA-approved anticancer medicines play important roles in the successful cancer treatment and novel anticancer drug development. In this study, we comprehensively collected 150 FDA-approved anticancer drugs from 1949 to 2014. According to their action mechanisms, we groups them into two sets: cytotoxic and targeted agency. Then we performed a comprehensive analysis from the perspective of drugs, drug indications, drug targets, and their relationships. For drugs, we summarized their historical characteristics and delivery methods. For targets, we surveyed their cellular location, functional classification, genetic patterns. We further applied network methodology to investigate their relationships. In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to discover novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

Abbreviations

BLCA: 

Bladder urothelial carcinoma

BRCA: 

Breast adenocarcinoma

CLL: 

Chronic lymphocytic leukemia

COAD/READ: 

Colon and rectal adenocarcinoma

EGFR: 

Epidermal growth factor receptor

FDA: 

Food and drug administration

FL: 

Follicular B-cell non-Hodgkin lymphoma

GBM: 

Glioblastoma

HNSC: 

Head and neck squamous cell carcinoma

IPA: 

Ingenuity pathway analysis

KDR: 

Kinase insert domain receptor

KIRC: 

Kidney renal clear cell carcinoma

K-S: 

Kolmogorov-Smirnor

LAML: 

Acute myeloid leukemia

LUAD: 

Lung adenocarcinoma

LUSC: 

Lung squamous cell carcinoma

MOA: 

Mechanism of action

NCI: 

National cancer institute

OCGs: 

Oncogenes

OV: 

Ovarian cancer

SLL: 

Small lymphocytic lymphoma

TSGs: 

Tumor suppressor genes

UCEC: 

Uterine corpus endometrioid carcinoma

Declarations

Acknowledgements

We thank Dr. Anupama E. Gururaj for manually check cancer classification.

Funding

This project was supported by Cancer Prevention & Research Institute of Texas (CPRIT R1307) Rising Star Award to Dr. Hua Xu.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

About this supplement

This article has been published as part of BMC Systems Biology Volume 11 Supplement 5, 2017: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: systems biology. The full contents of the supplement are available online at <https://bmcsystbiol.biomedcentral.com/articles/supplements/volume-11-supplement-5>.

Authors’ contributions

JS and YZ collected data for the study. JS and QL performed data analysis. JS and HX conceived and designed the study. QW prepared the figs. JS and HX wrote the manuscript. JS, QW, YZ, QL and HX revised the manuscript. All the authors have read and approved the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Biomedical Informatics, The University of Texas Health Science Center at Houston
(2)
National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences
(3)
Department of Biomedical Informatics, Vanderbilt University

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Copyright

© The Author(s). 2017

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