Personalized medicine and breast cancer





Elise DELUCHE, MD

Fabrice ANDRÉ, MD, PhD
INSERM Research Unit U981
University Paris XI, and Department of Medical Oncology
Gustave Roussy Cancer Campus
Villejuif, FRaNCE

Personalized medicine and breast cancer


by E. Deluche and F. André, France



Genomic medicine aims to identify molecular abnormalities that could act as therapeutic targets. This could ultimately result in subdividing a disease like breast cancer into multiple genomic entities, each defined by an oncogenic driver, whether mutation—as in the genes for phosphatidylinositol- 4,5-bisphosphate 3 kinase, catalytic subunit α(PIK3CA), Ak strain-transforming RAC-alpha serine/threonine-protein kinase (AKT1), phosphatase and tensin homolog (PTEN), or receptor tyrosine-protein kinase erbB- 2 (ERBB2, formerly HER2 or HER2/neu)—or amplification, as of ERBB2 or fibroblast growth factor receptor 1 (FGFR1). Targeting the oncogenic driver would then have the therapeutic effect of “oncogene dead diction.” An additional aim is to understand how the presence of single or combined genomic alterations impairs susceptibility to targeted monotherapy via mechanisms responsible for nonresponse to conventional drug effect. Genomics can also be used to detect potentially lethal subclones causing treatment resistance, as well as to analyze DNA repair defects and mechanisms of immune response suppression. Applications such as these will give oncologists a better understanding of cancer biology at the personalized level. Ongoing studies are evaluating the clinical benefits of genomic medicine in breast cancer. A longer-term prospect is that of using genomics to decrypt the mutation process and block mutagenesis within the individual patient.

Medicographia. 2015;37:243-247 (see French abstract on page 247)



Recent progress in metastatic breast cancer management includes the development of second-generation receptor tyrosine-protein kinase erbB-2 (ERBB2 [formerly HER2 or HER2/neu]) inhibitors, first-generation mammalian target of rapamycin (mTOR) inhibitors, and new cytotoxic and hormone therapies. Yet survival rates remain depressingly low, despite such apparent progress. Innovative strategies for improving outcome include immunotherapy, epigenetic modulation, and, perhaps most notably, genomic medicine, which aims to identify molecular abnormalities that could act as treatment targets in a personalized approach. This strategy employs the classic tools of genomics such as Sanger sequencing and fluorescence in situ hybridization (FISH), but also multigene panel testing and the simultaneous identification of several hundred genes on new-generation high throughput analyzers. Sequencing on this scale can identify mutations and copy number variations ranging from a few genes to an entire genome, while array comparative genomic hybridization (CGH) using DNa chip technology can quantify the number of gene copies in individual samples.

Pioneering clinical studies have shown that high-throughput genomics can be applied to metastatic breast cancer. This review details how a number of these potential applications can be used to personalize breast cancer treatment.

Identifying breast cancer drivers at the personalized level

An oncogenic driver can be defined as a single or multiple alteration generated by an oncogene that causes a cancer to progress. The cancer is said to be in a state of oncogene addiction. Targeting the oncogenic driver should therefore have the therapeutic effect of oncogene deaddiction.1 Genomic analyses of primary and metastatic breast cancers have identified a great number of potential genomic drivers,2-4 prompting the concept that breast cancer could be subdivided into multiple genomic entities, each defined by its driver.

The historic breast cancer driver is ERBB2 amplification. Targeting ERBB2 with tyrosine kinase inhibitors increases objective response rates in patients positive for ERBB2 amplification.5 The most common drivers are mutations of phosphatidylinositol- 4,5-bisphosphate 3 kinase, catalytic subunit α(PIK3Ca), but initial studies using nonselective phosphatidylinositol 3- kinase (PI3K) inhibitors have so far shown no evidence of oncogene deaddiction.6

However, recent phase 1 data suggest that targeting PI3K with specific PI3Kα inhibitors produces an objective response in patients with PIK3Ca mutations: response rates to the α-specific PI3K inhibitors BYL-719 (alpelisib) and GDC-0032 (taselisib) have been encouraging. Partial responses, in particular metabolic responses to GDC-0032, were observed in 73% of cases,7,8 confirming the role of PI3K as a breast cancer driver.





Studies are running on other genomic alterations in addition to PIK3Ca. They include amplifications of cyclin D1 (CCND1), which are fairly common, but do not correlate with any increase in efficacy by cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. amplifications of fibroblast growth factor receptor 1 (FGFR1), however, found in around 10% of estrogen receptor– positive (ER+) breast cancers, may be associated with response to nonspecific FGFR inhibitors, including dovitinib and lucitanib.9,10

More recently, much attention has focused on 2 less common genomic alterations: mutation in ak strain-transforming RaCserine/threonine-protein kinase (aKT1, also known as protein kinase B), found in around 4% of breast cancers, that could define a subset of patients susceptible to aKT1 or mTOR inhibitors; and epidermal growth factor receptor (EGFR) amplification, seen in a subset of breast cancers triple-negative for estrogen, progesterone and ERBB2 receptors, that could define patients susceptible to EGFR inhibitors.11 as for the ERBB2 mutations found in 1%-2% of breast cancers, these could define susceptibility to ERBB2 inhibitors.

Other potential drivers have also been described, but their clinical relevance remains unknown due to a dearth of clinical trial data. They include suppression of phosphatase and tensin homolog (PTEN), loss of inositol polyphosphate-4-phosphatase type II protein (INPP4B), loss of cyclin-dependent kinase inhibitor 2a (CDKN2a), amplification of fibroblast growth factor receptor 2 (FGFR2), and mutations of Kirsten rat sarcoma viral oncogene homolog (K-ras), BRaF cell signaling protein, and serine/threonine kinase 11 (STK11).9,12-14

Although multiple genomic alteration drivers have been proposed in breast cancer, we still have much to learn about how they may actually work. Three areas in particular are being studied to resolve this problem:
♦ developing and maintaining databases of cancer-related genes, then classifying the potential targets, so as to better identify the genomic alteration drivers.15 Tools need to be developed to determine whether a candidate driver is truncal (primary tumor-related) or subclonal (metastasis-related), since targeting a truncal event may be more effective (although this remains to be proved).
♦ determining whether the presence of single or combined genomic alterations impacts susceptibility to targeted monotherapy. For example, PIK3Ca mutation has been reported as a factor for resistance to ERBB2 inhibitors in patients with ERBB2 amplification.16,17
♦ determining whether oncogenic driver identification, main-ly by DNa analysis, but also by gene expression deregulation and pathway activation in the absence of DNa mutation, can also provide information on susceptibility to target therapies. Thus mTOR pathway activation, defined by the presence of phosphorylated 4E-binding protein 1 (p4EBP1) or expression of its gene,18,19 indicates disease susceptibility to mTOR inhibitors, just as ER expression in early breast cancer is a relevant target in hormone-susceptible tissues that show but few DNa alterations.20

When needing to screen for multiple genomic alterations, the aim in sequencing the regions of interest with an intermediate depth of coverage is to identify drivers in an individual (or several individuals in parallel). Gene expression analysis and protein detection can be additional tools in identifying drivers unrelated to DNa alterations, such as ER expression and mTOR activation.

Defining secondary resistance mechanisms and subclonal drivers

Genomic tests are useful for identifying the mechanisms involved in the absence or waning of response to conventional drug effect. Estrogen receptor 1 (ESR1) mutations have been reported in 10% to 30% of metastatic ER+ breast cancers resistant to hormone therapy. It is interesting to note that very few tumors are ESR1-mutation positive in the early stages. This suggests that the mutation is either present at low levels in the primary tumor or is acquired on treatment exposure. To date no other mutation has been described in breast cancer with a level of evidence indicative of resistance to a specific targeted treatment.

Two different technologies can be used to monitor and kill the lethal clone. In this regard, we hypothesize that certain mutations are associated with treatment resistance even if the lethal clone is present in only a minority of cells at diagnosis:
♦ In the scenario in which the resistant clone is present at low levels in the primary tumor, it may be detected by massively parallel whole-genome sequencing, as already successfully deployed in prostate cancer to monitor a minor lethal clone.21 This could permit presurgical “window-of-opportunity” clinical trials in which patients receive targeted treatment over several weeks to eradicate the lethal subclone.
♦ In the scenario in which the resistant clone is not detected in the primary tumor, but only during the subsequent disease course, circulating DNa could be used to monitor the development of lethal clones.22 Murtaza et al detected and characterized the emergence of a lethal clone during treatment by sequencing cancer exomes in plasma samples, identifying platelet-derived growth factor receptor- α (PDGFRa) mutation in a patient with trastuzumab resistance.23 Further research driven by the mining of massive long-term databases can be predicted to identify other mutations associated with poor response and/or resistance to standard adjuvant treatments in breast cancer.

In addition to the identification and targeting of resistant subclones, an important application of genomic testing is the quantification of intratumor heterogeneity, which in itself can be an indicator of poor prognosis, as in cancers generally. Several strategies are being developed. They include multiregion exome sequencing and copy-number analysis,24 circulating tumor-cell detection,25 and/or plasma-derived cell-free DNa sequencing.26 Recent studies point to intratumor heterogeneity in breast cancer. although similar subclonal heterogeneity has been associated with treatment resistance in lung cancer,27 chronic lymphocytic leukemia,28 and colon cancer,29 its impact in breast cancer has yet to be proven.

♦ Identifying the mechanisms of personalized tumor evolution
Mutations and DNa repair defects account for the genetic evolution of a tumor, while spatial and temporal differences in its evolution account for its internal heterogeneity. as well as identifying lethal and driver subclones, genomics has 3 further potential applications in this area: ♦ identifying DNa repair defects,
♦ quantifying genome instability and intratumor heterogeneity, and
♦ decrypting mutation mechanisms.

Identifying DNa repair defects at the individual level could lead to developing personalized synthetic lethality strategies. For example, inhibitors of poly(aDP-ribose) polymerase (PaRP) have proved useful in patients with breast cancer 1 and 2, early onset (BRCA1/2) mutations.30 Other mutations in DNa repair genes, such as ataxia-telangiectasia mutated kinase (ATM), aTM and Rad3-related protein (ATR), and DNa excision repair protein 1 (ERCC1), could also be targeted by this approach. In addition, DNa repair defects lead to genome instability, for which signatures have been developed. Such signatures can be specific to a DNa repair pathway or more general, and are identified by whole-exome sequencing on an instrument such as the affymetrix Genome-Wide SNP array 6.0 platform that detects defects in homologous repair (HR) and mismatch repair (MMR). HR-defect signatures could predict DNa susceptibility to alkylating agents and PaRP inhibitors in breast cancer patients.

More recently, accumulation of genome alterations suggestive of genome instability has been associated with everolimus resistance. In the longer term, genomics could also be used to identify mutation mechanisms, such as overexpression of apolipoprotein B messenger RNa (mRNa) editing enzyme, catalytic polypeptide-like (aPOBEC), thereby permitting personalized blockade of the mutagenesis process.

Identifying the mechanisms of immune system impact on treatment response

Morphological and molecular pathology, specifically the quantification of stromal tumor infiltrating lymphocytes, has shown the immune system to be involved in breast cancer progression and treatment response.31-33 Genomics can extend the insights gained from morphology by decrypting the molecular mechanisms enabling disease to circumvent the immune system in particular individuals and accounting for the immune system impact on drug treatment response.

Recent data suggest that certain missense somatic mutations generate proteins (neoantigens) that may be recognized by the host immune system and subsequently induce an antitumor immune response. Proof of concept in silico research found a positive correlation between the presence of immunogenic variants, increased amounts of cluster of differentiation 8a (CD8a) mRNa, and very good results in terms of antitumor immune response in many types of solid tumors.34 Elevated expression of antitumor cytotoxic T-cell exhaustion markers (programmed death-ligand 1 [PD-L1] and cytotoxic T-lymphocyte antigen 4 [CTLA4]), determined by RNa sequencing, have also been observed in conjunction with immunogenic variants, suggesting that these checkpoints were induced following an activated T-cell response. Recent mechanistic studies using mice melanoma models have borne out this concept.35 In human melanoma samples, the presence of a neoantigen signature was associated with a stronger and more durable response to the CTLa-4 antibody ipilimumab.36 If this concept can be extrapolated to breast cancer, certain mutations could also predict response to immunotherapy, such as tumor-cell inhibition at the immune (T cell) checkpoint. This hypothesis suggests that determining the spectrum of immunogenic somatic mutations in breast cancer may be important for identifying biomarkers and developing drugs in immunotherapy.

In contrast to the intrinsic cell mechanism of pathway activation at the T-cell checkpoint, oncogene addiction induces immunosuppressive mechanisms in the microenvironment.37 EGFR-driven lung cancers have been shown to upregulate the programmed cell death protein 1 (PD-1)/PDL1 pathway.38 Similarly, in gastrointestinal stromal tumors, imatinib mesylate treatment decreased tumor-cell production of the immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO).39 In these 2 examples, tumor-cell inhibition and T-cell checkpoint blockade combined synergistically with targeted therapy. These data suggest a paradigm of target inhibition combined with immunostimulation for oncogene-addicted tumors. In tumors overexpressing ERBB2, both innate and adaptive immunity certainly play an important role in the efficacy of trastuzumab. But it can be argued that combining cytotoxic chemotherapy with trastuzumab offers both signal inhibition and immune activation, and is already successful in terms of survival, with figures now exceeding 90% in stage I/II disease.40 Thus, future breast cancer genomics must not be considered independently of the immune response and there will be much to learn from integrating the evaluation of genomic drivers with the measurement of lymphocyte infiltration.

Conclusion and future prospects

The many potential applications of genomics for improving results in metastatic breast cancer include identifying the driver( s) involved, predicting resistance, decrypting DNa repair defects, and avoiding tumor-induced immune suppression. These applications should provide oncologists with a better understanding of cancer biology at the personalized level. Ongoing studies are seeking to determine the clinical usefulness of the genomic approach and hopefully demonstrate that genomic medicine improves results in breast cancer patients.

The future promises further prospects for genomic medicine in breast cancer treatment:
♦ new bioinformatics tools for personalized target identification;
♦ ever more sensitive and specific protein assays to improve target characterization;
♦ rapid translation of research insights into the clinic to inform preoperative management;
♦ and, perhaps the greatest challenge, the identification of germline mutations or polymorphisms associated with increased metastatic risk.


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Keywords: breast cancer; personalized medicine; genomics; activating mutation; oncogenic driver; subclone; T-cell checkpoint