Breast cancer classification divides breast cancer into categories according to different schemes criteria and serving a different purpose. The major categories are the histopathological type, the grade of the tumor, the stage of the tumor, and the expression of proteins and genes. As knowledge of cancer cell biology develops these classifications are updated.
The purpose of classification is to select the best treatment. The effectiveness of a specific treatment is demonstrated for a specific breast cancer (usually by randomized, controlled trials). That treatment may not be effective in a different breast cancer. Some breast cancers are aggressive and life-threatening, and must be treated with aggressive treatments that have major adverse effects. Other breast cancers are less aggressive and can be treated with less aggressive treatments, such as lumpectomy.
Treatment algorithms rely on breast cancer classification to define specific subgroups that are each treated according to the best evidence available. Classification aspects must be carefully tested and validated, such that confounding effects are minimized, making them either true prognostic factors, which estimate disease outcomes such as disease-free or overall survival in the absence of therapy, or true predictive factors, which estimate the likelihood of response or lack of response to a specific treatment.
Classification of breast cancer is usually, but not always, primarily based on the histological appearance of tissue in the tumor. A variant from this approach, defined on the basis of physical exam findings, is that inflammatory breast cancer (IBC), a form of ductal carcinoma or malignant cancer in the ducts, is distinguished from other carcinomas by the inflamed appearance of the affected breast, which correlates with increased cancer aggressivity.
Breast cancers can be classified by different schemata. Each of these aspects influences treatment response and prognosis. Description of a breast cancer would optimally include all of these classification aspects, as well as other findings, such as signs found on physical exam. A full classification includes histopathological type, grade, stage (TNM), receptor status, and the presence or absence of genes as determined by DNA testing:
Histopathologic classification is based upon characteristics seen upon light microscopy of biopsy specimens. They can broadly be classified into:
The 2012 World Health Organization (WHO) classification of tumors of the breast which includes benign (generally harmless) tumors and malignant (cancerous) tumors, recommends the following pathological types:
Invasive breast carcinomas
Classic Solid Mixed Alveolar Tubulolobular Pleomorphic
(Other well-accepted subtypes of metaplastic mammary carcinoma thought to have clinical significance but not included in the decade old WHO classification:
Mesenchymal tumors (including sarcoma)
Tumors of the male breast
Malignant lymphoma
Metastatic tumors to the breast from other places in the body
Precursor lesions
Benign epithelial lesions
Myoepithelial lesions
Fibroepithelial tumours
Benign tumors of the nipple
Malignant tumors of the nipple
The grading of a cancer in the breast depends on the microscopic similarity of breast cancer cells to normal breast tissue, and classifies the cancer as well differentiated (low-grade), moderately differentiated (intermediate-grade), and poorly differentiated (high-grade), reflecting progressively less normal appearing cells that have a worsening prognosis. Although grading is fundamentally based on how biopsied, cultured cells behave, in practice the grading of a given cancer is derived by assessing the cellular appearance of the tumor. The closer the appearance of the cancer cells to normal cells, the slower their growth and the better the prognosis. If cells are not well differentiated, they will appear immature, will divide more rapidly, and will tend to spread. Well differentiated is given a grade of 1, moderate is grade 2, while poor or undifferentiated is given a higher grade of 3 or 4 (depending upon the scale used).
The Nottingham system is recommended for breast cancer grading. The Nottingham system is also called the BloomâÂÂRichardsonâÂÂElston system (BRE), or the Elston-Ellis modification of the Scarff-Bloom-Richardson grading system. It grades breast carcinomas by adding up scores for tubule formation, nuclear pleomorphism, and mitotic count, each of which is given 1 to 3 points. The scores for each of these three criteria are then added together to give an overall final score and corresponding grade. It is not applicable to medullary carcinomas which are histologically high-grade by definition, while being clinically low-grade if lymph nodes are negative. It is also not applicable to metaplastic carcinomas.
The grading criteria are as follows:
This parameter assesses what percent of the tumor forms normal duct structures. In cancer, there is a breakdown of the mechanisms that cells use to attach to each other and communicate with each other, to form tissues such as ducts, so the tissue structures become less orderly.
Note: The overall appearance of the tumor has to be considered.
This parameter assesses whether the cell nuclei are uniform like those in normal breast duct epithelial cells, or whether they are larger, darker, or irregular (pleomorphic). In cancer, the mechanisms that control genes and chromosomes in the nucleus break down, and irregular nuclei and pleomorphic changes are signs of abnormal cell reproduction.
Note: The cancer areas having cells with the greatest cellular abnormalities should be evaluated.
This parameter assesses how many mitotic figures (dividing cells) the pathologist sees in 10x high power microscope field. One of the hallmarks of cancer is that cells divide uncontrollably. The more cells that are dividing, the worse the cancer.
Note: Mitotic figures are counted only at the periphery of the tumor, and counting should begin in the most mitotically active areas.
The scores for each of these three criteria are added together to give a final overall score and a corresponding grade as follows:
Lower-grade tumors, with a more favorable prognosis, can be treated less aggressively, and have a better survival rate. Higher-grade tumors are treated more aggressively, and their intrinsically worse survival rate may warrant the adverse effects of more aggressive medications.
Staging is the process of determining how much cancer there is in the body and where it is located. The underlying purpose of staging is to describe the extent or severity of an individual's cancer, and to bring together cancers that have similar prognosis and treatment. Staging of breast cancer is one aspect of breast cancer classification that assists in making appropriate treatment choices, when considered along with other classification aspects such as estrogen receptor and progesterone receptor levels in the cancer tissue, the human epidermal growth factor receptor 2 (HER2/neu) status, menopausal status, and the person's general health.
Staging information that is obtained prior to surgery, for example by mammography, x-rays and CT scans, is called clinical staging and staging by surgery is known as pathological staging.
Pathologic staging is more accurate than clinical staging, but clinical staging is the first and sometimes the only staging type. For example, if clinical staging reveals stage IV disease, extensive surgery may not be helpful, and (appropriately) incomplete pathological staging information will be obtained.
The American Joint Committee on Cancer (AJCC) and the International Union Against Cancer (UICC) recommend TNM staging, which is a two step procedure. Their TNM system, which they now develop jointly, first classifies cancer by several factors, T for tumor, N for nodes, M for metastasis, and then groups these TNM factors into overall stages.
Tumor â The tumor values (TX, T0, Tis, T1, T2, T3 or T4) depend on the cancer at the primary site of origin in the breast, as follows:
Lymph Node â The lymph node values (NX, N0, N1, N2 or N3) depend on the number, size and location of breast cancer cell deposits in various regional lymph nodes, such as the armpit (axillary lymph nodes), the collar area (supraclavicular lymph nodes), and inside the chest (internal mammary lymph nodes.) The armpit is designated as having three levels: level I is the low axilla, and is below or outside the lower edge of the pectoralis minor muscle; level II is the mid-axilla which is defined by the borders of the pectoralis minor muscle; and level III, or high () axilla which is above the pectoralis minor muscle. Each stage is as follows:
A combination of T, N and M, as follows:
The impact of different stages on outcome can be appreciated in the following table, taken from patient data in the 2013-2015 period, and using the AJCC 8th edition for staging. It does not show the influence of important additional factors such as estrogen receptor (ER) or HER2/neu receptor status, and does not reflect the impact of newer treatments.
Although TNM classification is an internationally agreed system, it has gradually evolved through its different editions; the dates of publication and of adoption for use of AJCC editions is summarized in the table in this article; past editions are available from AJCC for web download.
Several factors are important when reviewing reports for individual breast cancers or when reading the medical literature, and applying staging data.
It is crucial to be aware that the TNM system criteria have varied over time, sometimes fairly substantially, according to the different editions that AJCC and UICC have released. Readers are assisted by the provision in the table of direct links to the breast cancer chapters of these various editions.
As a result, a given stage may have quite a different prognosis depending on which staging edition is used, independent of any changes in diagnostic methods or treatments, an effect that can contribute to "stage migration". For example, differences in the 1998 and 2003 categories resulted in many cancers being assigned differently, with apparent improvement in survival rates.
As a practical matter, reports often use the staging edition that was in place when the study began, rather than the date of acceptance or publication. However, it is worth checking whether the author updated the staging system during the study, or modified the usual classification rules for specific use in the investigation.
A different effect on staging arises from evolving technologies that are used to assign patients to particular categories, such that increasingly sensitive methods tend to cause individual cancers to be reassigned to higher stages, making it improper to compare that cancer's prognosis to the historical expectations for that stage.
Finally, of course, a further important consideration is the effect of improving treatments over time as well.
Previous editions featured three metastatic values (MX, M0 and M1) which referred respectively to absence of adequate information, the confirmed absence, or the presence of breast cancer cells in locations other than the breast and regional lymph nodes, such as to bone, brain, lung.
AJCC has provided web accessible poster versions of the current versions of these copyrighted TNM descriptors and groups, and readers should refer to that up to date, accurate information or to the National Cancer Institute (NCI) or National Comprehensive Cancer Network sites which reprints these with AJCC permission.
For accurate, complete, current details refer to the accessible copyrighted documentation from AJCC, or to the authorized documentation from NCI or NCCN; for past editions refer to AJCC.
The receptor status of breast cancers has traditionally been identified by immunohistochemistry (IHC), which stains the cells based on the presence of estrogen receptors (ER), progesterone receptors (PR) and HER2. This remains the most common method of testing for receptor status, but DNA multi-gene expression profiles can categorize breast cancers into molecular subtypes that generally correspond to IHC receptor status; one commercial source is the BluePrint test, as discussed in the following section.
Receptor status is a critical assessment for all breast cancers as it determines the suitability of using targeted treatments such as tamoxifen and or trastuzumab. These treatments are now some of the most effective adjuvant treatments of breast cancer. Estrogen receptor positive (ER+) cancer cells depend on estrogen for their growth, so they can be treated with drugs to reduce either the effect of estrogen (e.g. tamoxifen) or the actual level of estrogen (e.g. aromatase inhibitors), and generally have a better prognosis. Generally, prior to modern treatments, HER+ had a worse prognosis, however HER2+ cancer cells respond to drugs such as the monoclonal antibody, trastuzumab, (in combination with conventional chemotherapy) and this has improved the prognosis significantly. Conversely, triple negative cancer (i.e. no positive receptors), lacking targeted treatments, now has a comparatively poor prognosis.
Androgen receptor is expressed in 80-90% of ER+ breast cancers and 40% of "triple negative" breast cancers. Activation of androgen receptors appears to suppress breast cancer growth in ER+ cancer while in ER- breast it appears to act as growth promoter. Efforts are underway to utilize this as prognostic marker and treatment.
Receptor status was traditionally considered by reviewing each individual receptor (ER, PR, HER2) in turn, but newer approaches look at these together, along with the tumor grade, to categorize breast cancer into several conceptual molecular classes that have different prognoses and may have different responses to specific therapies. DNA microarrays have assisted this approach, as discussed in the following section. Proposed molecular subtypes include:
Traditional DNA classification was based on the general observation that cells that are dividing more quickly have a worse prognosis, and relied on either the presence of protein Ki67 or the percentage of cancer cell DNA in S phase. These methods, and scoring systems that used DNA ploidy, are used much less often now, as their predictive and prognostic power was less substantial than other classification schemes such as the TNM stage. In contrast, modern DNA analyses are increasingly relevant in defining underlying cancer biology and in helping choose treatments.
HER2/neu status can be analyzed by fluorescent in-situ hybridization (FISH) assays. Some commentators prefer this approach, claiming a higher correlation than receptor immunohistochemistry with response to trastuzumab, a targeted therapy, but guidelines permit either testing method.
DNA microarrays have compared normal cells to breast cancer cells and found differences in the expression of hundreds of genes. Although the significance of many of those genetic differences is unknown, independent analyses by different research groups has found that certain groups of genes have a tendency to co-express. These co-expressing clusters have included hormone receptor-related genes, HER2-related genes, a group of basal-like genes, and proliferation genes. As might therefore be anticipated, there is considerable similarity between the receptor and microarray classifications, but assignment of individual tumors is by no means identical. By way of illustration, some analyses have suggested that approximately 75% of receptor classified triple-negative breast cancers (TNBC) basal-like tumors have the expected DNA expression profile, and a similar 75% of tumors with a typical basal-like DNA expression profile are receptor TNBC as well. To say this in a different way to emphasize things, this means that 25% of triple-negative breast cancer (TNBC) basal-like tumors as defined by one or other classification are excluded from the alternative classification's results. Which classification scheme (receptor IHC or DNA expression profile) more reliably assorts particular cancers to effective therapies is under investigation.
In the early 2000s, several prognostic and predictive assays were introduced as a result of research on gene expression profiling. The purpose of these tests is to stratify breast cancer patients based on their risk of recurrence, and drive decisions about adjuvant therapies. Currently, multiple such tools are used in clinical practice globally. All prognostic and predictive tools in breast cancer use machine learning methods that predict patient outcomes based on biomarkers, such as transcriptomics, tumor morphology, or basic clinicopathologic features. The most commonly used are:
The majority of these tests have been scientifically reviewed to compare them with other standard prognostic biomarkers, such as grade and receptor status. Although these gene-expression profiles look at different individual genes, they seem to classify a given tumor into similar risk groups and thus provide concordant predictions of outcome.
Although there is considerable evidence that these tests can refine the treatment decisions in a meaningful proportion of breast cancers they are fairly expensive; proposed selection criteria for which particular tumors may benefit by being interrogated by these assays remain controversial, particularly with lymph node positive cancers. One review characterized these genetic tests collectively as adding "modest prognostic information for patients with HER2-positive and triple-negative tumors, but when measures of clinical risk are equivocal (e.g., intermediate expression of ER and intermediate histologic grade), these assays could guide clinical decisions".
Oncotype DX assesses 16 cancer-related genes and 5 normal comparator reference genes, and is therefore sometimes known as the 21-gene assay. It was designed for use in estrogen receptor (ER) positive tumors. The test is run on formalin fixed, paraffin-embedded tissue. Oncotype results are reported as a recurrence score (RS), where a higher RS is associated with a worse prognosis, referring to the likelihood of recurrence without treatment. In addition to that prognostic role, a higher RS was found in translational studies of Phase III randomized controlled trials to be significantly associated with a higher probability of response to chemotherapy. In these secondary analyses, patients with low Oncotype RS were found to derive minimal benefit from adjuvant chemotherapy, suggesting it may be appropriate to choose to avoid side effects from that additional treatment. As an additional example, a neoadjuvant clinical treatment program that included initial chemotherapy followed by surgery and subsequent additional chemotherapy, radiotherapy, and hormonal therapy found a strong correlation of the Oncotype classification with the likelihood of a complete response (CR) to the presurgical chemotherapy.
Since high risk features may already be evident in many high risk cancers, for example hormone-receptor negativity or HER-2 positive disease, the Oncotype test may especially improve the risk assessment that is derived from routine clinical variables in intermediate risk disease. Results from both the US and internationally suggest that Oncotype may assist in treatment decisions.
Oncotype DX has been endorsed by the American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN). The NCCN Panel considers the 21-gene assay as an option when evaluating certain tumors to assist in estimating likelihood of recurrence and benefit from chemotherapy, emphasizing that the recurrence score should be used along with other breast cancer classification elements when stratifying risk.
Similar to most prognostic assays, Oncotype DX is offered as a laboratory-developed test through a CLIA-certified laboratory, and is not approved by the FDA.
MammaPrint is a 70-gene panel marketed by Agendia, developed in patients under age 55 years who had lymph node negative breast cancers (N0). The commercial test is marketed for use in breast cancer irrespective of estrogen receptor (ER) status. The test is run on formalin fixed, paraffin-embedded tissue. MammaPrint traditionally used rapidly frozen tissue but a room temperature, molecular fixative is available for use within 60 minutes of obtaining fresh tissue samples.
A summary of clinical trials using MammaPrint is included in the MammaPrint main article. MammaPrint was first approved for clinical use by the FDA in 2007. To be eligible for the MammaPrint gene expression profile, a breast cancer should have the following characteristics: stage 1 or 2, tumor size less than 5.0 cm, estrogen receptor positive (ER+) or estrogen receptor negative (ER-). In the US, the tumor should also be lymph node negative (N0), but internationally the test may be performed if the lymph node status is negative or positive with up to 3 nodes.
MammaPrint has been validated in a prospective Phase III randomized controlled trial, MINDACT and a prospective registry study, FLEX. Several clinical trials are ongoing investigating the role of MammaPrint in additional treatment settings, including neoadjuvant therapy.
One method of assessing the molecular subtype of a breast cancer is by BluePrint, a commercial-stage 80-gene panel marketed by Agendia, either as a standalone test, or combined with the MammaPrint gene profile. BluePrint classifies breast tumors into luminal-type, HER2-type or basal-type.
Given the limitations of gene expression profiling tests, such as long turnaround times and high costs, statistical models and more complex artificial intelligence models were developed to stratify recurrence risk and predict treatment response.
In years 2000-2020, several online calculators and nomograms were developed, including Adjuvant!,, Magee Equations, PREDICT, and CTS5. Recently, artificial intelligence tests analyzing H&E-stained pathology slides were shown to potentially improve on the accuracy of gene expression profiling tests and expand clinical utility. In real-world retrospective studies, Ataraxis Breast was found to have higher accuracy in predicting recurrence risk than Oncotype DX and refine selection of patients eligible for adjuvant CDK4/6 inhibitors.
Several biomarkers and companion diagnostics exist that are associated with relative resistance or sensitivity to specific treatment options. Various molecular pathway targets and DNA results are being incorporated in the design of clinical trials of new medicines. Specific genes such as p53, NME1, BRCA and PIK3CA/Akt may be associated with responsiveness of the cancer cells to innovative research pharmaceuticals. BRCA1 and BRCA2 polymorphic variants can increase the risk of breast cancer, and these cancers tend to express a profile of genes, such as p53, in a pattern that has been called "BRCA-ness." Cancers arising from BRCA1 and BRCA2 mutations, as well as other cancers that share a similar "BRCA-ness" profile, including some basal-like receptor triple negative breast cancers, may respond to treatment with PARP inhibitors such as olaparib. Combining these newer medicines with older agents such as 6-Thioguanine (6TG) may overcome the resistance that can arise in BRCA cancers to PARP inhibitors or platinum-based chemotherapy. mTOR inhibitors such as everolimus may show more effect in PIK3CA/Akt e9 mutants than in e20 mutants or wild types. Capivasertib and alpelisib were approved in the United States for patients with PIK3CA-mutated advanced or metastatic breast cancer following results of positive CAPItello-291 and SOLAR-1 clinical trials. Patients with ESR1 mutations are candidates for novel targeted therapies, such as camizestrant or imlunestrant.
Topoisomerase II (TOP2A) expression predicts whether doxorubicin is relatively useful. Expression of genes that regulate tubulin may help predict the activity of taxanes.
DNA methylation patterns can epigenetically affect gene expression in breast cancer and may contribute to some of the observed differences between genetic subtypes.
Tumors overexpressing the Wnt signaling pathway co-receptor low-density lipoprotein receptor-related protein 6 (LRP6) may represent a distinct subtype of breast cancer and a potential treatment target.
Numerous clinical investigations looked at whether testing for variant genotype polymorphic alleles of several genes could predict whether or not to prescribe tamoxifen; this was based on possible differences in the rate of conversion of tamoxifen to the active metabolite, endoxifen. Although some studies had suggested a potential advantage from CYP2D6 testing, data from two large clinical trials found no benefit. Testing for the CYP2C19*2 polymorphism gave counterintuitive results. The medical utility of potential biomarkers of tamoxifen responsiveness such as HOXB13, PAX2, and estrogen receptor (ER) alpha and beta isoforms interaction with SRC3 have all yet to be fully defined.