BackgroundMany methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions.MethodsWe used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments.ResultsESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis.ConclusionsThe standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.

To identify a group of patients who might benefit from the addition of weekly paclitaxel to conventional anthracycline-containing chemotherapy as adjuvant therapy of node-positive operable breast cancer. The predictive value of PAM50 subtypes and the 11-gene proliferation score contained within the PAM50 assay were evaluated in 820 patients from the GEICAM/9906 randomized phase III trial comparing adjuvant FEC to FEC followed by weekly paclitaxel (FEC-P). Multivariable Cox regression analyses of the secondary endpoint of overall survival (OS) were performed to determine the significance of the interaction between treatment and the (1) PAM50 subtypes, (2) PAM50 proliferation score, and (3) clinical and pathological variables. Similar OS analyses were performed in 222 patients treated with weekly paclitaxel versus paclitaxel every 3 weeks in the CALGB/9342 and 9840 metastatic clinical trials. In GEICAM/9906, with a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (unadjusted HR = 0.693, p = 0.013). A benefit from paclitaxel was only observed in the group of patients with a low PAM50 proliferation score (unadjusted HR = 0.23, p < 0.001; and interaction test, p = 0.006). No significant interactions between treatment and the PAM50 subtypes or the various clinical–pathological variables, including Ki-67 and histologic grade, were identified. Finally, similar OS results were obtained in the CALGB data set, although the interaction test did not reach statistical significance (p = 0.109). The PAM50 proliferation score identifies a subset of patients with a low proliferation status that may derive a larger benefit from weekly paclitaxel.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-013-2416-2) contains supplementary material, which is available to authorized users.

Identifying mutations in the TP53 gene is important for cancer prognosis, predicting response to therapy, and determining genetic risk. We have developed a high-throughput scanning assay with automatic calling to detect TP53 mutations in DNA from fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues. The coding region of the TP53 gene (exons 2-11) was PCR-amplified from breast cancer samples and scanned by high-resolution fluorescent melting curve analyses using a 384-well format in the LightCycler 480 instrument. Mutations were confirmed by direct sequencing. Sensitivity and specificity of scanning and automatic mutation calling was determined for FF tissue (whole genome amplified [WGA] and non-WGA) and FFPE tissue. Thresholds for automatic mutation calling were established for each preparation type. Overall, we confirmed 27 TP53 mutations in 68 primary breast cancers analyzed by high-resolution melting curve scanning and direct sequencing. Using scanning and automatic calling, there was high specificity (>95%) across all DNA preparation methods. Sensitivities ranged from 100% in non-WGA DNA from fresh tissue to 86% in WGA DNA and DNA from formalin-fixed, paraffin-embedded tissue. Scanning could detect mutated DNA at a dilution of 1:200 in a background of wild-type DNA. Mutation scanning by high-resolution fluorescent melting curve analyses can be done in a high-throughput and automated fashion. The TP53 scanning assay can be performed from a variety of specimen types with high sensitivity/specificity and could be used for clinical and research purposes.

Purpose. To compare risk assignment by PAM50 Breast Cancer Intrinsic Classifier™ and Oncotype DX_Recur-rence Score (RS) in the same population.Methods. RNA was extracted from 151 estrogen receptor (ER) ؉ stage I-II breast cancers and gene expression profiled using PAM50 "intrinsic" subtyping test.Results. One hundred eight cases had complete molecular information; 103 (95%) were classified as luminal A (n ‫؍‬ 76) or luminal B (n ‫؍‬ 27). Ninety-two percent (n ‫؍‬ 98) had a low (n ‫؍‬ 59) or intermediate (n ‫؍‬ 39) RS. Among luminal A cancers, 70% had low (n ‫؍‬ 53) and the remainder (n ‫؍‬ 23) had an intermediate RS. Among luminal B cancers, nine were high (33%) and 13 were intermediate (48%) by the RS. Almost all cancers with a high RS were classified as luminal B (90%, n ‫؍‬ 9). One high RS cancer was identified as basal-like and had low ER/ESR1 and low human epidermal growth factor receptor 2 (HER2) expression by quantitative polymerase chain reaction in both assays. The majority of low RS cases were luminal A (83%, n ‫؍‬ 53). Importantly, half of the intermediate RS cancers were re-categorized as low risk luminal A subtype by PAM50.Conclusion. There is good agreement between the two assays for high (i.e., luminal B or RS > 31) and low (i.e., luminal B or RS < 18) prognostic risk assignment but PAM50 assigns more patients to the low risk category. About half of the intermediate RS group was reclassified as luminal A by PAM50. The Oncologist 2012;17:492-498

Lung cancer histologic diagnosis is clinically relevant because there are histology-specific treatment indications and contraindications. Histologic diagnosis can be challenging owing to tumor characteristics, and it has been shown to have less-than-ideal agreement among pathologists reviewing the same specimens. Microarray profiling studies using frozen specimens have shown that histologies exhibit different gene expression trends; however, frozen specimens are not amenable to routine clinical application. Herein, we developed a gene expression-based predictor of lung cancer histology for FFPE specimens, which are routinely available in clinical settings. Genes predictive of lung cancer histologies were derived from published cohorts that had been profiled by microarrays. Expression of these genes was measured by quantitative RT-PCR (RT-qPCR) in a cohort of patients with FFPE lung cancer. A histology expression predictor (HEP) was developed using RT-qPCR expression data for adenocarcinoma, carcinoid, small cell carcinoma, and squamous cell carcinoma. In cross-validation, the HEP exhibited mean accuracy of 84% and κ = 0.77. In separate independent validation sets, the HEP was compared with pathologist diagnoses on the same tumor block specimens, and the HEP yielded similar accuracy and precision as the pathologists. The HEP also exhibited good performance in specimens with low tumor cellularity. Therefore, RT-qPCR gene expression from FFPE specimens can be effectively used to predict lung cancer histology.

BackgroundGene fusion events resulting from chromosomal rearrangements play an important role in initiation of lung adenocarcinoma. The recent association of four oncogenic driver genes, ALK, ROS1, RET, and NTRK1, as lung tumor predictive biomarkers has increased the need for development of up-to-date technologies for detection of these biomarkers in limited amounts of material.MethodsWe describe here a multi-institutional study using the Ion AmpliSeq™ RNA Fusion Lung Cancer Research Panel to interrogate previously characterized lung tumor samples.ResultsReproducibility between laboratories using diluted fusion-positive cell lines was 100%. A cohort of lung clinical research samples from different origins (tissue biopsies, tissue resections, lymph nodes and pleural fluid samples) were used to evaluate the panel. We observed 97% concordance for ALK (28/30 positive; 71/70 negative samples), 95% for ROS1 (3/4 positive; 19/18 negative samples), and 93% for RET (2/1 positive; 13/14 negative samples) between the AmpliSeq assay and other methodologies.ConclusionThis methodology enables simultaneous detection of multiple ALK, ROS1, RET, and NTRK1 gene fusion transcripts in a single panel, enhanced by an integrated analysis solution. The assay performs well on limited amounts of input RNA (10 ng) and offers an integrated single assay solution for detection of actionable fusions in lung adenocarcinoma, with potential savings in both cost and turn-around-time compared to the combination of all four assays by other methods.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4736-4) contains supplementary material, which is available to authorized users.

BackgroundMultivariate assays (MVAs) for assisting clinical decisions are becoming commonly available, but due to complexity, are often considered a high-risk approach. A key concern is that uncertainty on the assay's final results is not well understood. This study focuses on developing a process to characterize error introduced in the MVA's results from the intrinsic error in the laboratory process: sample preparation and measurement of the contributing factors, such as gene expression.MethodsUsing the PAM50 Breast Cancer Intrinsic Classifier, we show how to characterize error within an MVA, and how these errors may affect results reported to clinicians. First we estimated the error distribution for measured factors within the PAM50 assay by performing repeated measures on four archetypal samples representative of the major breast cancer tumor subtypes. Then, using the error distributions and the original archetypal sample data, we used Monte Carlo simulations to generate a sufficient number of simulated samples. The effect of these errors on the PAM50 tumor subtype classification was estimated by measuring subtype reproducibility after classifying all simulated samples. Subtype reproducibility was measured as the percentage of simulated samples classified identically to the parent sample. The simulation was thereafter repeated on a large, independent data set of samples from the GEICAM 9906 clinical trial. Simulated samples from the GEICAM sample set were used to explore a more realistic scenario where, unlike archetypal samples, many samples are not easily classified.ResultsAll simulated samples derived from the archetypal samples were classified identically to the parent sample. Subtypes for simulated samples from the GEICAM set were also highly reproducible, but there were a non-negligible number of samples that exhibit significant variability in their classification.ConclusionsWe have developed a general methodology to estimate the effects of intrinsic errors within MVAs. We have applied the method to the PAM50 assay, showing that the PAM50 results are resilient to intrinsic errors within the assay, but also finding that in non-archetypal samples, experimental errors can lead to quite different classification of a tumor. Finally we propose a way to provide the uncertainty information in a usable way for clinicians.

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