Multigene tests provide information that may guide the optimal treatment regimen for breast cancer (BCa) patients. However, assignment of an individual tumor to any subtype/prognostic risk group shows only moderate reproducibility depending on the assay, tumor composition, gene list and expression thresholds. This single-sample discordance impedes clinical use and raises important questions about which is the right test and whether multiple tests are better than one. We used multiplexed RNA fluorescent in situ hybridization of four BCa biomarkers, estrogen/progesterone/Her2/Ki67, to guide laser capture microdissection followed by RNAseq. This technique, called mFISHseq, ensures tumor purity, facilitates interrogation of tumor heterogeneity, permitting unbiased whole transcriptome analysis. To ascertain multigene test discordance, we applied mFISHseq on a cohort of 1,082 FFPE breast tumors with detailed clinicopathological data and derived molecular subtypes using research based PAM50, AIMS, and our own 293-gene subtyping classifier. We also assigned patients to prognostic risk groups using research based OncotypeDX, GENE70, risk of recurrence by subtype, and GGI. We observed considerable discordance with 24% and 61% of patients having at least one multigene test in disagreement for molecular subtyping and prognostic risk assignment, respectively. To improve single sample concordance, we implemented a simple voting scheme of the multigene classifiers to assign a consensus molecular subtype/risk group. Consensus subtyping reclassified 30% of patients into subtypes that better fit their transcriptomic risk and outcome, and further identified that 60% of these patients received suboptimal treatment. Likewise, our consensus prognostic risk approach mitigated discordance and provided prognostic insights for patients with high, low, and ultra-low risk. By leveraging spatially resolved, tumor enriched transcriptome profiling, mFISHseq alleviated sample-level discordance and assigned individuals to molecular subtypes/prognostic risk groups that better matched their outcome, thus resolving limitations to clinical adoption.
Current assays fail to address breast cancer's complex biology and accurately predict treatment response. On a retrospective cohort of 1082 female breast tissues, we develop and validate mFISHseq, which integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, unbiased whole transcriptome profiling, and explicitly quantifies intratumoral heterogeneity. Here we show mFISHseq has 93% accuracy compared to immunohistochemistry. Our consensus subtyping and risk groups mitigate single sample discordance, provide early and late prognostic information, and identify high risk patients with enriched immune signatures, which predict response to neoadjuvant immunotherapy in the multicenter, phase II, prospective I-SPY2 trial. We identify putative antibody-drug conjugate (ADC)-responsive patients, as evidenced by a 19-feature T-DM1 classifier, validated on I-SPY2. Deploying mFISHseq as a research-use only test on 48 patients demonstrates clinical feasibility, revealing insights into the efficacy of targeted therapies, like CDK4/6 inhibitors, immunotherapies, and ADCs. The authors develop and validate mFISHseq, a spatially informed assay that tackles several unmet needs in breast cancer, including discordance in molecular subtyping and prognostic risk and identification of biomarkers predicting response to immunotherapies and antibody-drug conjugates.
3069 Background: Breast cancer (BCa) is a heterogeneous disease requiring precise diagnostic tools to guide effective treatment strategies. Current diagnostic assays, including various multigene assays, often fail to adequately address the complex biology of BCa subtypes. To address these limitations and enhance the understanding of BCa biology, we developed and validated a novel diagnostic, prognostic, and predictive tool, called mFISHseq. Methods: Our approach, mFISHseq, integrates multiplexed fluorescent in situ hybridization (FISH) of the four main BCa biomarkers, estrogen ( ESR1)/progesterone ( PGR)/Her2 ( ERBB2) receptors and Ki67 ( MKI67), which are used to guide laser capture microdissection (LCM) of regions of interest followed by RNA-sequencing. This technique ensures tumor purity, facilitates interrogation of tumor heterogeneity, consequently permitting unbiased analysis of whole transcriptome profiling data and explicitly quantifying the variability between different tumor regions. We validated mFISHseq on a retrospective cohort study involving 1,082 FFPE breast tumors with detailed clinicopathological data, informed consent, and ethical committee approval. Results: mFISHseq demonstrated excellent analytical validity with a 93% accuracy rate compared to standard immunohistochemistry (IHC), while providing more quantitative biomarker expression. Prespecified threshold values for mFISHseq derived from a split 70:30 training/test set showed exceptional concordance with IHC as demonstrated by area under the receiver operating characteristic (ROC) curves for all markers (AUC: MKI67=0.98, ERBB2=0.95, ESR1=0.95, PGR=0.93). Both RNA-FISH and -SEQ showed moderate to very strong correlations (Spearman’s r; ERBB2=0.41, MKI67=0.61, PGR=0.66, ESR1=0.75), thus highlighting the potential to use both orthogonal methodologies to cross-validate results. To demonstrate clinical validity, we developed a 293-gene intrinsic subtype classifier, showing substantial agreement to established classifiers like PAM50 and AIMS (Cohen’s κ= 0.75 and 0.73, respectively) and superior prognostic performance. We also report that LCM is an essential component of the mFISHseq workflow, since samples that did not undergo LCM showed reduced biomarker expression, elevated non-tumor gene expression, and misclassification of samples into less aggressive molecular subtypes (e.g., normal-like) and prognostic risk groups (e.g., high to low). Conclusions: The mFISHseq method showed excellent concordance with IHC and the use of LCM provides tumor-enriched samples that are devoid of contamination from non-tumor elements, thus providing unbiased spatially resolved interrogation of tumor heterogeneity. Altogether, mFISHseq solves a long-standing challenge in the precise diagnosis and classification of breast cancer subtypes.
Abstract On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.
