Cancer Research

Over a decade ago, Robert Weinberg and Douglas Hanahan defined the six hallmark traits of cancer: sustaining proliferative signaling; evading growth suppressors; activating invasion and metastasis; enabling replicative immortality; inducing angiogenesis; and resisting cell death(1). Since then, a growing body of evidence suggests two additional hallmarks—reprogramming energy metabolism and evading immune destruction—are equally important for the malignant phenotype and may prove to be true hallmarks as research continues in this area(2). These acquired capabilities, which are shared by nearly all cancer cells, are regulated by highly complex protein signaling networks.

At Cell Signaling Technology, we are dedicated to unraveling the signaling networks that underlie the cancer disease process. In addition to an active cancer research program, our production and development scientists work to provide reference material and high quality research reagents. We want to make a difference, and we want to give you the tools to make a difference too.

Key Signaling Networks

Akt Signaling

Since its initial discovery as a proto-oncogene, the serine/threonine kinase Akt (also known as protein kinase B or PKB) has become a major focus of attention because of its critical regulatory role in diverse cellular processes, including cancer progression.

Erk Signaling

Mitogen-activated protein kinases (MAPKs) are involved in many cellular programs such as cell proliferation, differentiation, motility, and death. The ERK1/2 (p44/42 MAPK) signaling pathway can be activated in response to a diverse range of extracellular stimuli including mitogens, growth factors, and cytokines, and defects in the Erk signaling pathway can lead to uncontrolled growth in cancer.

Caspase-3 Signaling

Apoptosis is programmed cell death characterized by nuclear condensation, cell shrinkage, membrane blebbing, and DNA fragmentation. Caspase-3 is a downstream effector caspase, which executes apoptosis by cleaving targeted cellular proteins.

ALK and ROS1

Anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1) are receptor tyrosine kinases (RTKs) that have been identified as mutant C-terminal fusion proteins in research studies of a wide range of human cancers.

Pivotal Tumor Immunology Targets

Tumor cells employ multiple defense strategies to evade detection and destruction by the immune system. One of these takes advantage of a natural immune mechanism that involves upregulation of immune checkpoint proteins and ligands.

  1. Hanahan, D and Weinberg, RA. (2000) Cell, 100, 57-70.
  2. Hanahan, D and Weinberg, RA. (2011) Cell, 144, 646-674.

Network Signaling

In healthy cells, complex signaling networks continually process input cues from the extracellular and intracellular environment in order to determine cell fate, such as whether or not to grow, proliferate, migrate, or die. This “signal-cue-response” model explains how input cues stimulate the signaling network to govern cell behavior(1). As we continue to understand these signaling networks, it is becoming increasingly clear that the pathways themselves do not follow a static, linear progression, but rather are highly dynamic, multivariate, and can adapt to differing inputs over time. Therefore, genetic mutations that disrupt or change the network nodes may lead to differential input processing resulting in profound effects on cell fate, such as unregulated cell proliferation and ultimately in cancer.

Cancer Diagram

In cancer cells, genetic alterations change the flow of information through the normal signal-cue-response model, resulting in the disease phenotype. Alterations that perturb the network include DNA mutations, amplifications, or changes in copy number, as well as changes in gene expression through epigenetic regulation or mRNA splicing. Understanding the functional consequences of mutations and modeling of disease networks remains a great challenge in the field of cancer research(2). Systems biology has been fundamental in the study of network signaling in both the normal and disease state. Instead of studying single pathways and nodes, many cancer researchers are now using a systems biology-based approach that combines data from genomics and proteomics to create computational models that can predict cellular response to drug therapy, mutation, or differing input cues(3). The data used in these models come from many sources. Genomic information can be generated from next generation and deep sequencing, gene expression arrays, high throughput RNAi screens, and epigenetic profiling to examine methylation state. Proteomic data from mass spectrometry-based assays for phosphorylation and other post-translational modifications are becoming more abundant as technologies advance. This information is combined with data from public repositories such as the one being created by The Cancer Genome Atlas project (also known as the Human Cancer Genome Project), which aims to sequence the genomes from 20 different cancers from various anatomical sites in an attempt to catalog mutations and identify new disease drivers (cancergenome.nih.gov). Together, this data will be invaluable for furthering our understanding of cancer signaling networks and creating predictive models that will help in the design of the most effective drug therapies for cancer patients.

  1. Pawson, T and Linding, R. (2008) FEBS Letters, 582, 1266-1270.
  2. Creixell, P, Schoof, et al. (2012) Nat. Biotechnol., 30, 842-848.
  3. Erler, JT and Linding, R. (2010) J. Pathol., 220, 290-296.

Network Medicine and Drug Resistance

Network Medicine and Drug Resistance

We now know that treating a complex disease like cancer requires more sophisticated methods than traditional chemotherapy, which indiscriminately targets rapidly dividing cells (both healthy and cancerous) and is accompanied by extensive side effects. The age of personalized medicine is upon us. Clinicians are now beginning to treat patients based on an individual’s specific cancer-causing defect. A new class of molecularly targeted drugs are available that inhibit the action of individual signaling nodes. These targeted drugs are usually directed against kinases and can come in the form of small molecule tyrosine kinase inhibitors (TKIs) or monoclonal antibodies (mAbs) that interfere with signaling. Although these molecularly targeted drugs are effective in inhibiting their intended single target, lessons learned from the complexity of network signaling remind us that these targets do not exist in isolation but rather as part of a complex cancer network. The dynamic nature of the cancer network dictates that although monotherapy with a single agent can be initially effective, the cancer almost always returns. That’s why Dr. Rune Linding at the Technical University of Denmark proposed that future drug strategies target the entire network, a strategy he called “network medicine”(1).

The single biggest challenge clinicians face when using molecularly targeted drugs is resistance. Many researchers are now taking a network medicine approach to combat drug resistance. There are several reasons why relapse occurs. First, one of the enabling characteristics of cancer cells is their genomic instability(2). Secondary mutations in the targeted node can prevent inhibitor binding to the target, making it ineffective at inhibiting kinase activity. For example, a secondary mutation in epidermal growth factor receptor (EGFR), T790M, results in resistance to the EGFR inhibitors, gefitinib and erlotinib(3). Secondly, signaling networks have the ability to “rewire”. Targeting activating mutations in receptor tyrosine kinases (RTKs) using a TKI can cause the cell to “rewire” the network so that signaling bypasses the inhibited kinase. For example, in the HCC827 non-small cell lung cancer (NSCLC) cell line, resistance to the EGFR inhibitor erlotinib was caused by amplification of c-MET, resulting in continuous signaling through PI3K(4). Resistance to EGFR inhibitors can also occur through activating mutations in PI3K or through amplification of AXL and its ligand GAS6, which signal through PI3K and may also promote epithelial-mesenchymal transition (EMT)(5). EMT is a process whereby epithelial cells acquire invasive properties of mesenchymal cells; they display stem cell-like traits, loss of E-cadherin expression, and have a more aggressive, metastatic phenotype.

As we continue to understand and appreciate the complex nature of cancer cells themselves, we must also address the fact that not all cancer cells comprising a tumor are the same. Recent work by Dr. Marco Gerlinger and colleagues at the London Research Institute (Cancer Research UK) describes extensive intratumor heterogeneity in their study of primary renal carcinoma. They performed sequencing and genomic profiling analysis on multiple samples from the same tumor and found that 63% of mutations were not present across all regions of the tumor and that cells within the tumor can be driven by the loss of different tumor suppressor genes(6). Therefore, clinicians are battling tumors that are not only variable by nature but also heterogeneous in composition, making personalized treatment strategies considerably more challenging.

Inhibitors

  Inhibitor Target Inhibitor Type For Research Involving
  Inhibitor Target Inhibitor Type For Research Involving
Molecularly Targeted Drugs
gefitinib EGFR small-molecule NSCLC
erlotinib EGFR small-molecule NSCLC; pancreatic cancer
lapatinib EGFR, HER2 small-molecule HER2+ breast cancer
cetuximab EGFR monoclonal antibody colorectal cancer (wild type KRAS); head and neck cancer
trastuzumab HER2 monoclonal antibody HER2+ breast cancer
pertuzumab HER2 dimerization monoclonal antibody HER2+ breast cancer
imatinib BCR-ABL small-molecule CML; gastrointestinal stromal tumors (GIST)
crizotinib ALK, ROS, c-Met small-molecule NSCLC
sunitinib PDGFR, VEGFR, RET, c-KIT, flt3 small-molecule renal cancer; GIST
sorafenib PDGFR, VEGFR, c-KIT, B-Raf, c-Raf small-molecule renal and liver cancers
vandetanib VEGFR, EGFR, RET small-molecule thyroid cancer
ipilimumab CTLA-4 monoclonal antibody melanoma
Chemotherapeutic Agents
doxorubicin DNA and RNA synthesis cytotoxic antibiotic  
docetaxel microtubule dynamics taxane  
paclitaxel microtubule dynamics taxane  

Combating Resistance: Combination Therapies

One of the first examples of personalized medicine was the molecularly targeted drug imatinib, a BCR-ABL kinase inhibitor used to successfully treat patients with chronic myelogenous leukemia (CML). Since then, 17 TKIs and 4 mAbs have been approved in the U.S. for use in patients(5). However, although imatinib monotherapy is successful in CML, this is not the case for most cancer. Relapse almost always occurs due to rewiring and secondary mutations. Combination therapy may be required for successful treatment.

One strategy for combination therapy is combining a TKI with traditional chemotherapy. Work from Dr. Michael Yaffe’s lab at Massachusetts Institute of Technology recently illustrated the potential effectiveness of this strategy in their study on triple negative breast cancer cells, a cancer type for which there are currently no targeted therapies and overall prognosis is poor(7,8). Dr. Yaffe used a systems biology-based approach and advanced computational models to predict drug combinations that would result in the greatest levels of synergistic killing of tumor cells. His group found that sequential application of the TKI erlotinib caused rewiring of the DNA damage response/apoptotic signaling network so that cells were more vulnerable to the chemotherapeutic agent doxorubicin when it was given 8 hours later(8). This study highlights the importance of considering the entire signaling network when designing effective cancer treatments(9).

Because of the tendency of first generation inhibitors to result in resistance, newer TKIs, called next generation inhibitors, are being created to overcome mechanisms of resistance seen in the clinic. Various combinations of molecularly targeted agents, either with or without traditional chemotherapy, are currently being used in clinical trials. One strategy is to target the same RTK in more than one way(10). For example, HER2-positive breast cancers treated with the mAb trastuzumab often relapse. In June 2012, the FDA approved combination use of trastuzumab and pertuzumab (another HER2-specific mAb that inhibits HER2/3 dimerization and continued signaling) plus the chemotherapeutic agent docetaxel. Phase 3 clinical trials evaluating this combination therapy reported increased progression-free survival(11). Other combination strategies include “vertical” targeting, which inhibits an RTK and other downstream nodes in the same pathway, and “horizontal” targeting, which combines inhibitors from parallel pathways(10).

  1. Erler, JT and Linding, R. (2010) J. Pathol., 220, 290-296.
  2. Hanahan, D and Weinberg, RA. (2011) Cell, 144, 646-674.
  3. Yun, CM. (2008) Proc. Natl. Acad. Sci., 105, 2070-2075.
  4. Engelman, JA, et al. (2007) Science, 316, 1039-1043.
  5. Gonzalez de Castro, D, et al. (2013) Clin. Pharmacol. Ther., 93, 252-259.
  6. Gerlinger, M, et al. (2012) N. Engl. J. Med., 366, 883-892.
  7. Bauer, KR, et al. (2007) Cancer, 109, 1721-1728.
  8. Lee, MJ, et al. (2012) Cell, 149, 780-794.
  9. Erler, JT and Linding, R. (2012) Cell, 149, 731-733.
  10. Al-Lazikani, et al. (2012) Nat. Biotechnol., 30, 679-692.
  11. Baselga, J, et al. (2012) N. Engl. J. Med., 366, 109-119.

Disease Drivers

PI3K
Phosphoinositide 3-kinase (PI3K) is a lipid kinase that catalyzes the phosphorylation of phosphatidylinositol-4,5-bisphosphate (PIP2) to produce phosphatidylinositol-3,4,5-triphosphate (PIP3), leading to downstream signaling through the Akt/mTOR pathway. The PIK3CA gene, which encodes the p110α catalytic subunit of PI3K, is frequently mutated in cancers of the colon, breast, lung, stomach, and brain(2). Specifically, PIK3CA is mutated in 36% of all breast cancers, with higher frequencies found in luminal and HER2+ subtypes(3). Recently, research efforts have focused on the association between activating mutations in PIK3CA and resistance to trastuzumab in HER2+ breast cancer(4). Because direct PI3K inhibitors are in early phases of development, inhibition of PI3K activity must be achieved through targeting downstream nodes(5). For example, clinical trials are currently underway to test the effectiveness of blocking the PI3K/Akt/mTOR pathway using the mTOR inhibitor everolimus in patients with trastuzumab-resistant breast cancer(6). Learn more about Akt signaling.
HER2
The HER2 (ErbB2) proto-oncogene encodes a transmembrane, receptor-like glycoprotein with intrinsic tyrosine kinase activity. While HER2 lacks an identified ligand, HER2 kinase activity can be activated in the absence of a ligand when overexpressed and through heteromeric associations with other ErbB family members(7). Amplification of the HER2 gene and overexpression of its product are detected in almost 40% of human breast cancers(8). HER2 is a key therapeutic target in the treatment of breast cancer and can be inhibited with the small moleculer inhibitor lapatinib and the monoclonal antibodies trastuzumab and pertuzumab(9).
Abl
The c-Abl proto-oncogene encodes a nonreceptor protein tyrosine kinase that is implicated in regulating cell proliferation, differentiation, apoptosis, cell adhesion, and stress responses(10). c-Abl can fuse with the Bcr gene to form a chimeric Bcr-Abl oncogene. Research studies have shown that the Bcr-Abl fusion results in production of a constitutively active tyrosine kinase, which causes CML(11). Bcr-Abl activity is inhibited by imatinib, one of the first examples of a successful molecularly targeted drug, which opened the doors to the idea of personalized medicine(12). Since the development of imatinib, the 8-year survival rate for patients with CML is 87% compared with a 15% survival rate before imatinib(13). However, the identification of imatinib-resistant tumor cells, frequently containing Abl point mutations that prevent imatinib binding, have prompted the need for the development of second generation Bcr-Abl inhibitors such as nilotinib and dasatinib(14).
EGFR
Epidermal growth factor receptor (EGFR) is an RTK that belongs to the HER/ErbB family. Research studies have shown that somatic mutations in the tyrosine kinase domain of EGFR are present in a subset of lung adenocarcinomas that respond to EGFR inhibitors, such as gefitinib and erlotinib(15,16). Two types of mutations account for approximately 90% of mutated cases: a specific point mutation, L858R, that occurs in exon 21 and short in-frame deletions in exon 19(17,18). The most frequent exon 19 deletion is E746-A750, accounting for 90% of lesions at this site, although some rare variants occur. EGFR can also be inhibited with lapatinib and the monoclonal antibody cetuximab.
KRAS
A guanine nucleotide binding protein (G protein) that cycles between active (GTP-bound) and inactive (GDP-bound) forms(19) to signal through the Raf-MEK-MAPK pathway(20). KRAS mutations in codons 12, 13, and 61 prevent GTPase-activating protein (GAP)-mediated inhibition of KRAS, resulting in constitutive activation(21). KRAS is frequently mutated in metastatic colorectal cancer (mCRC), NSCLC, and pancreatic cancers. In mCRC, KRAS mutations in codons 12 and 13, which are found in approximately 30% of mCRCs, are a predictive biomarker for a negative patient response to EGFR monoclonal antibody therapies, such as cetuximab and panitumumab(22,23). KRAS mutations are also found in 20-30% NSCLC tumors, although unlike in mCRC, they are not a biomarker for EGFR-targeted therapy(24). Since there are currently no direct inhibitors for KRAS, downstream nodes in the MAPK pathway such as Raf and MEK are being targeted for inhibition in KRAS-driven tumors(24). Learn more about MAPK signaling.
RET
An RTK that regulates cell proliferation, migration, and differentiation. Germline mutations in RET have been found in thyroid cancers, and recently, RET fusion proteins (KIF5B-RET and CCDC6-RET) have been identified in NSCLC(25,26). RET activity can be inhibited with sunitinib, sorafenib, and vandetanib, all of which are multi-targeted RTK inhibitors with specificities for multiple kinases including RET(24).
ALK
Anaplastic lymphoma kinase (ALK) is an RTK of the insulin receptor family and is similar in structure to ROS1. Although ALK signaling is not very well characterized, it is known to be expressed in the developing central and peripheral nervous system where it signals through MAPK and Akt pathways to promote cell proliferation and survival(27). ALK was originally identified as an oncogenic fusion protein with nucleophosmin (NPM) in anaplastic lymphoma(28). Since then, researchers have discovered ALK to be fused with several other proteins such as EML4, TFG, and KIF5B(29-31). In particular, the EML4-ALK fusion in NSCLC has recently become a promising new therapeutic target, as epidemiological studies suggest it is found in 3%-5% of all NSCLC patients(32). This equates to approximately 10,000 new cases per year in the U.S. ALK is inhibited by crizotinib, and next-generation ALK inhibitors are currently in clinical trials(33). Learn more about ALK.
ROS1
An RTK of the insulin receptor family that stimulates cell proliferation and survival. Like ALK, ROS1 has been shown to undergo a number of gene rearrangements that result in an oncogenic fusion protein, such as FIG-ROS1 in glioblastoma(34), and SLC34A2-ROS1 and CD74-ROS1 in NSCLC(35). In a immunohistochemistry (IHC) screening assay of >500 NSCLC samples, researchers found 1.6% of the tumors contained oncogenic ROS1 rearrangements, with the CD74-ROS1 fusion being the most prevalent(36).ROS1 activity can be inhibited with crizotinib, as shown in preclinical and early clinical studies(37). Learn more about ROS1.
  1. Garraway, LA and Landers, ES. (2013) Cell, 153, 17-37.
  2. Samuels, Y, et al. (2004) Science 304, 554.
  3. The Cancer Genome Atlas Network (2012) Nature 490, 61–70.
  4. Kataoka, Y. et al. (2010) Ann. Oncol. 21, 255–262.
  5. Lauring, J, et al. (2013) J. Natl. Compr. Canc. Netw. 11, 670-678.
  6. Al-Lazikani, et al. (2012) Nat. Biotechnol., 30, 679-692.
  7. Muthuswamy, SK, et al. (1999) Mol. Cell. Biol., 19, 6845-6857.
  8. Dittadi, R and Gion, M. (2000) J. Natl. Cancer Inst., 92, 1443-1444.
  9. Orphanos, G and Kountourakis, P. (2012) Hematol. Oncol. Stem Cell Ther., 5, 127-137.
  10. Van Etten, RA. (1999) Trends Cell Biol. 9, 179-186.
  11. Voncken, JW, et al. (1995) Cell 80, 719-728.
  12. Mahon, FX. (2012) Hematology 2012, 122-128.
  13. Kantarjian, H, et al. (2012) Blood 119, 1981-1987.
  14. Panjarian, S, et al. (2013) J. Biol. Chem. 288, 5443-5450.
  15. Lynch, TJ, et al. (2004) N. Engl. J. Med., 350, 2129-2139.
  16. Pao, W, et al. (2004) Proc. Natl. Acad. Sci. U S A., 101, 13306-13311.
  17. Kosaka, T, et al. (2004) Cancer Res., 64, 8919-8923.
  18. Riely, GJ, et al. (2006) Clin. Cancer Res., 12, 7232-7241.
  19. Boguski, MS and McCormick, F. (1993) Nature 366, 643-654.
  20. Avruch, J. et al. (1994) Trends Biochem. Sci. 19, 279-283.
  21. Bos, JL. (1989) Cancer Res. 49, 4682-4689.
  22. Gonzalez de Castro, D, et al. (2013) Clin. Pharmacol. Ther., 93, 252-259.
  23. Dempke, WC and Heinemann, V. (2010) Anticancer Res. 30, 4673-4677.
  24. Villaflor, VM and Salgia, R. (2013) J. Carcinog., 12, 7.
  25. Eng, C. (1999) J. Clin. Oncol., 17, 380-393.
  26. Takeuchi, K, et al. (2012) Nat. Med., 18, 378-381.
  27. Wellstein, A. (2012) Front. Oncol., 2, 192.
  28. Morris, SW, et al. (1994) Science, 263, 1281-1284.
  29. Soda, M, et al. (2007) Nature 448, 561-566.
  30. Rikova, K, et al. (2007) Cell, 131, 1190-1203.
  31. Takeuchi, K, et al. (2009) Clin. Cancer Res., 15, 3143-3149.
  32. Kwak, EL, et al. (2010) N. Engl. J. Med., 363, 1693-1703.
  33. Seto, T, et al. (2013) Lancet Oncol., ePub ahead of print.
  34. Charest, A. et al. (2003) Genes Chromosomes Cancer 37, 58–71.
  35. Stumpfova, M and Jänne, PA. (2012) Clin. Cancer Res., 18, 4222-4224.
  36. Rimkunas, VM, et al. (2012) Clin. Cancer Res., 18, 4449-4457.
  37. Bergethon, K, et al. (2012) J. Clin. Oncol., 30, 863-870.