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Customizable Chemo

Anna Sandler
Lake Forest College
Lake Forest, Illinois 60045

It is January 2018. Julia Louis-Dreyfus, former star of Seinfeld, is being welcomed with a serenade of Michael Jackson’s “Beat It” by her sons as she takes the stage to mark the debut on her final chemotherapy treatment (6). As Louis-Dreyfus embraces this award-winning moment, she takes the time to reflect to her audience: “The bad news is that not all women are so lucky, so let’s fight all cancers and make universal healthcare a reality” (6).


Breast cancer is the second leading cause of cancer-related deaths in women and is expected to rob the lives of about 40,000 women this year alone. To catch the thief in its tracks, scientists and health professionals must target the defining markers of cancer: abnormal cell division and invasion. Normally, the cell cycle abides by a system of checkpoints before dividing. Like the three branches of government, a cell’s rate of division is limited by a system of checks and balances; checks for proper environment, successful DNA replication, and correct assembly of chromosomes in mitosis are crucial to maintain the genetic and environmental integrity of daughter cells. The rise of cancer occurs when the cycle experiences dysfunctional governing; cells bypass checkpoints and fail to carry out proper law enforcement to halt division, leading to excessive growth and the potential to invade neighboring tissues. Nonetheless, treatments can attempt to restore order in the court, such as chemotherapy.


First introduced in the 1940s, chemotherapy (chemo) uses drugs to slow the growth of cancer; specific combinations of drugs are used in different chemo regimens. In fact, an alphabet soup of various drug combinations solely for breast cancer exists: MFL (methotrexate, 5-fluorouracil, leucovorin), AC (doxorubicin, cyclophosphamide), CAF (cyclophosphamide, doxorubicin, 5-fluorouracil), etc. Although doctors can narrow down the list of regimens based on the stage of cancer a patient exhibits, the body’s response to treatment is an emergent property; success depends on the body as a system of unique, individual components, one of which is genetic variation. As a result, the same treatment can elicit various responses from different people, making the course of chemo difficult to choose. However, pharmacogenomics is an emerging field that exploits these genetic variations to predict the success of specific treatments. Indeed, researchers recently studied gene variants of a drug-metabolizing enzyme and their relation to differing breast cancer chemo responses. The team’s results show that a specific gene variation can serve as a biomarker for predicting treatment outcomes.


The most common variances in the genetic code are single nucleotide polymorphisms (SNPs); one nucleotide at a specific location in a gene can differ among individuals. As these SNPs occur about once in every 300 nucleotides, there are approximately 10 million locations in the genome where SNPs prevail. The gene at the center of the researchers’ study was Cytochrome P450 1A1 (CYP1A1); its extensive study stems from the fact that a SNP exists at codon 462 that is implicated in cancers. At this codon, a nucleotide mutation from A to G results in an amino acid change from isoleucine to valine (Ile462Val), a SNP strongly associated with susceptibility to different cancers (1-2, 4). Building on from their previous work, Zhou et al. (2018) sought to determine the effects of the wild type (AA)[1] and Ile462Val polymorphism (AG/GG)[2] on clinical outcomes of metastatic breast cancer (MBC) patients receiving chemo.


Zhou et al. (2018) studied two different chemo regimens for MBC: DC (docetaxel, capecitabine) and DT (docetaxel, thiotepa) and analyzed how gene variants of the CYP1A1 gene influenced the patients’ responses to the treatments. The researchers had previously found that the Ile462Val polymorphism was the only SNP of the CYP1A1 gene that was significantly associated with progression-free survival (PFS) ¾ the length of time during treatment of a disease that a patient does not get worse despite still living with the disease ¾  in patients treated with DC (3). Although the team needed to perform more studies to validate the finding, the results of the experiment provided insight into the potential role of this variant in predicting breast cancer treatment outcomes. To commence their more recent study, Zhou et al. (2018) recruited female MBC patients over the age of 18 years who had an estimated life expectancy of three months and had gone at least six months without chemo[3]. The patients were then randomly assigned to the DT or DC treatment group, where cycles of treatment repeated every 21 days until unacceptable toxicity, disease progression, or patient refusal inhibited the continuation of the study. Prior to chemo, genomic DNA was extracted from the participants’ blood samples and sequenced to determine each individual’s genotype. Before starting the cycles, it was critical to operationally define the various outcomes to the treatments. The research team measured patient outcomes via PFS and overall survival (OS), the length of time (in months) from the start of treatment the patients were alive. Additionally, T tests and chi-square analyses were conducted to determine whether the differences between the two groups and genotypes were significant. The clinical outcomes in this study indicated the prevalent role of the CYP1A1 variants in predicting which regimen worked better in a certain individual.


            Without factoring genotypes into account, Zhou et al. (2018) found that PFS and OS were not significantly different between the DT and DC groups (Figure 1). Factoring genetics, though, patients homozygous for the wild-type CYP1A1 genotype (AA) in the DT group exhibited significantly longer PFS (median 8.4 months vs. 5.5 months, p = .004) and OS (median 33.4 months vs. 19.6 months, p = .045) than those under DC treatment (Figure 2). On the other hand, patients harboring the mutated form of CYP1A1 (AG/GG) displayed significantly longer PFS (median 8.4 months vs. 6.4 months, p = .045) and OS (median 28.5 months vs. 15.8 months,

p = .005) in the DC treatment group compared with those being treated with DT (Figure 3). Thus, compared with the mutant genotypes (AG/GG), patients in the DT group with the wildtype genotype (AA) had significantly longer PFS (8.4 months vs. 6.4 months, P = .019) and OS (33.4 vs. 15.4 months, P = .018). Conversely, patients carrying the mutant AG/GG genotype displayed better clinical outcomes in PFS (8.4 vs. 5.5 months, P = .005) and OS (28.5 vs. 19.6 months, P = .010) in the DC group compared to the wildtype (AA) patients. Following these measurements, Zhou et al. (2018) tested the predictive power of the AG/GG polymorphism on PFS and OS by performing additional statistical analyses: multivariable adjustment to eliminate outside variables and Cox proportional hazard models (HR) to determine if a specific genotype raised or reduced the risk for progression and death (<1 = reduces risk, 1 = no effect, >1 = increases risk). It was confirmed that the mutant genotypes (AG/GG) were independent risk predictors of PFS (HR 1.90, P = .038) and OS (HR 2.24, P = .042) for the DT group but reduced the risk for both progression (HR .406, P = .014) and death (HR .406, P = .014) in the DC group. All the results are compiled in Table 1.


            Synthesizing everything together, Zhou et al. (2018) showed that patients with the wildtype genotype (AA) of the drug metabolizing enzyme CYP1A1 benefited more from DT treatment while the mutant genotypes (AG/GG) benefited more from DC treatment. Further analyses revealed that the mutant genotypes reduced risk for progression and death, independent of other variables. Still, the study had some limitations. For instance, the researchers did not consider patient compliance, how accurately the patients adhered to the prescribed regimen, nor did they examine variants in other genes implicated in drug metabolism. Thus, it would be worth repeating the study with these additional variables at play, as well as conducting a validation study with a greater number of patients. Nonetheless, Zhou et al. (2018) revealed the power of pharmacogenomics and the potential of personalized prescriptions, catered based on an individual’s unique genome. So, let’s welcome pharmacogenomics onto the stage with the hopes that one day more people will be walking down the red carpet to mark their final chemo treatments.


Figures and Tables (Adapted from Zhou et al. (2018))

Sandler Fig 1


Figure 1: PFS(A) and OS(B) of the two different treatment groups


Sandler Fig 2


Figure 2: PFS(A) and OS(B) of the two different treatment groups for patients with the wild type (AA) CYP1A1 gene


Sandler Fig 3


Figure 3: PFS(A) and OS(B) of the two different treatment groups for patients with the mutated forms (AG/GG) of the CYP1A1 gene




PFS (months)




OS (months)




DT group










CYP1A1 genotypes




1.00 (ref.)




(1.00 ref)






















DC group










CYP1A1 genotypes




1.00 (ref)




1.00 (ref.)












Table 1: Summary of clinical results and statistical analyses based on patient genotypes and treatment groups

Note: Table layout is constructed similarly to Zhou et al. (2018) but omits a few rows and confidence intervals for the sake of this article




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