Four intrinsic subtypes of breast cancer (BC) were identified from gene expression data in 2000: Luminal A (LumA), Luminal B (LumB), HER2-enriched (HER2-E) and Basal-like BC. These subtypes have different outcomes and variable responses to anticancer therapies. The prognosis of each subtype depends on the hormone receptor (HR) status, proliferation as per Ki-67 and HER2 protein expression. Approximately 16–18% of all BC cases are HER2+ according to immunochemistry (IHC) protein expression profile
[1]. A 50-gene signature (PAM50) was later developed to simplify the former subtype classifications through quantitative real-time reverse-transcription PCR (qRT-PCR) and NanoString nCounter
® [2]. PAM50 is currently successfully used in multiplexed gene expression platforms such as NanoString nCounter
®, which is the basis for the Prosigna
® test. This test has been approved in current practice as a prognostic and predictive tool in HER2- and positive estrogen receptor (ER+) early BC and it acts as a powerful assay for clinical trial drug development
[3]. To add complexity, all these intrinsic molecular subtypes can be identified in HER2+ tumours, with the HER2-E being the most frequent (~47%), followed by Luminal B (~18–28%), Luminal A (11–23%) and Basal-like (7–14%). Furthermore, this distribution seems to be heavily influenced by HR status, with HER2-E subtype representing 30% of the molecular subtypes within HR+/clinically HER2+ BC and 75% within HR-/clinically HER2+ tumours. Of note, the concordance rate between the pathology-based subtypes and the intrinsic subtypes is moderate (67.4%; kappa statistic: 0.50)
[4,5][4][5]. Moreover, there is enough clinical evidence to support the idea that intrinsic subtypes might identify a subgroup of clinically HER2+ tumours that highly respond to anti-HER2 therapies regardless of chemotherapy and HR status
[6]. Despite the improvement in the molecular subtyping of BC, most treatment strategies still rely on the classical pathological assessment using IHC. However, recent studies suggest that IHC is not a reliable surrogate of genomic intrinsic subtypes, and that gene expression methods have a higher predictive and prognostic value
[7,8][7][8]. This argument raises concern about whether the current IHC-based classification neglects real tumour biology, thus leading to untailored treatments
[9]. In fact, classical clinical research has been developed according to IHC BC subtypes, consigning the impact of intrinsic subtypes to exploratory analysis, instead of designing trials specifically for each molecular subtype. Therefore, it is currently the primetime for molecular based clinical trial designs.
PAM50 may be performed on both digital multiplexed gene expression and RNA-Seq platforms. The NanoString nCounter
® system enables gene expression analysis through direct multiplexed measurements. It was proven to be highly reproducible and has shown consistency between fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) derived RNAs. Therefore, it is a suitable tool to be used in clinical practice. On the other hand, full RNA-Seq is a fundamental research tool for whole transcriptome analysis. However, being very expensive and time-consuming renders it difficult to be used in routine clinical practice. Fortunately, limitations on removing partially degraded mRNA in FFPE samples with typical RNA-Seq protocols may be overcome by using Ribo-Zero-Seq
[10].
The intriguing question is whether the data of intrinsic subtypes from different clinical trials can be compared with each other, considering that the molecular assessment has not been performed with a uniform method within all these studies. As an example, the distribution of intrinsic subtypes in neoadjuvant trials differs among similarly based IHC cohorts when using different technologies. While PAMELA and APT trials reported a similar distribution of HER2-E subtype when using the nCounter platform (66.9% and 65%, respectively), this percentage was quite different in the CALGB40601 trial, which used RNAseq (31%)
[11].
This issue may be resolved by comparing the distribution of subtypes using both platforms within the same rather than different series. Consequently, some research groups have explored the correlation between gene expression data according to these two technologies
[12]. Picornell et al., compared the PAM50 intrinsic subtype determination results with both assays in the same triple negative IHC-based BC series and reported a convincing 96% positive correlation
[3]. Furthermore, a combined analysis of CALGB 40601 (Alliance) and PAMELA trials using RNAseq showed a high consistency with the nCounter platform. A similar rate of HER2-E (62%) was found in this combined cohort
[13].
An emerging paradigm for safe clinical development aims to identify patients at risk of relapse that deserve salvage treatments or, conversely, to select patients that can safely spare the intake of unnecessary drugs. The challenging issue is the accurate stratification of these categories. Based on this, Prat et al., developed a prognostic assay that integrated molecular tumour features with clinical and pathologic variables in patients with newly HER2+ BC from the adjuvant Short-HER trial. The final prognostic model was evaluated in an independent combined dataset of patients from four neo/adjuvant studies. HER2DX score was significantly associated with metastasis-free survival as a continuous variable but the HER2DX score was not associated with pCR likelihood.
[14]. To solve this issue, the same author developed and validated an additional HER2DX pCR score using clinical variables (tumour size, nodal staging) and 4 gene expression signatures (tracking immune infiltration, tumour cell proliferation, luminal differentiation, and the expression). Whereas some HER2DX variables were associated with pCR (i.e., immune, proliferation and HER2 amplicon), others were associated with non-pCR (i.e., luminal, and tumour and nodal staging). The authors concluded that both HER2DX tests (HER2Dx Risk Score and HER2Dx pCR likelihood score) provide accurate estimates of the risk of recurrence (HER2Dx Risk Score) and the likelihood to achieve a pCR (HER2Dx pCR score) in the early stage HER2+ BC
[15]. Therefore, these tools might select candidates for escalated or de-escalated systemic treatment.