Breast Cancer Intrinsic Subtypes Predict Outcomes in Primary and Metastatic Samples

Authors Talal Ahmed, Mark Carty, Kaveri Nadhamuni, Raphael Pelossof

Background: The prognostic and predictive value of the PAM50 intrinsic subtypes, namely Luminal A, Luminal B, Her2, and Basal-like subtypes, is well-studied in primary as well as metastatic breast cancer settings. Prosigna has emerged as a rapid PAM50 subtype predictor based on the NanoString nCounter assay. However, assay
reproducibility across various RNASeq or qRT-PCR platforms can be challenging, especially when applying the predictor on metastatic breast cancer tumors. Here, we used SpinAdapt to create an intrinsic subtype predictor that works on RNA sequencing data, and validates on multiple tumor-sites. We evaluate real-world outcomes for our intrinsic subtype predictions across various immunohistochemical (IHC) labels and metastatic sites.

Methods: We trained the subtype predictor on a cohort of 2,497 breast cancer patients using the PAM50 genes, profiled using Nanostring RNA nCounter assay (GSE148426). Approximately 5,423 de-identified records of breast cancer patients sequenced using whole-exome capture RNA-seq were included in our reference dataset. The reference dataset contained samples collected from various sites including breast (n=2440), liver (n=936), lymph node (n=577), lung (n=540), and bone (n=304). For subtype classification, we first batch-corrected the external dataset to Tempus RNA-seq reference dataset using SpinAdapt, then trained a Support-Vector Classifier (SVC) on the corrected data. A 10-fold CV experiment was performed on the corrected dataset to analytically validate the intrinsic subtype predictions. We retrospectively analyzed 7,021 de-identified breast cancer patients with known
hormone receptor (HR) or HER2 status and a matched RNASeq sample. The concordance between HR/HER2 status and PAM50 prediction was analyzed, and these patients were excluded from training. Real-world overall survival (rwOS) was evaluated from the time of first diagnosis. The outcomes across intrinsic subtypes were further assessed according to tumor collection site and HR/HER2 IHC status.

Results: The 10-fold CV experiment on the Tempus-adapted GSE148426 dataset achieved F-1 scores of 0.97, 0.86, 0.94, and 0.87 on Basal, HER2-like, Luminal A, and Luminal B PAM50 subtypes, respectively. On the Tempus evaluation dataset, 85.3% of HR+/HER2- patients (n=4,366), 65.4% of HR-/HER2+ patients (n=240), and 75.4% of HR-/HER2- patients (n=1,930) were classified as Luminal, HER2-like, and Basal, respectively. Evaluating outcomes on Tempus patients for each PAM50 group, the rwOS for the basal group was significantly shorter than patients not predicted to be basal (n=5,845, p< 2e-90). The rwOS for the predicted PAM50 basal patients remained significantly shorter than non-basal patients even when stratified by site of metastasis: breast (n=2,405; p< 1e-27), lymph node (n=577, p< 1e-7), liver (n=936; p< 1e-25), lung (n=540, p< 1e-13), and bone (n=304, p< 1e-3). Interestingly, within both HR+/HER2- (n=3,653) and HR-/HER2- (n=1,664) IHC cohorts with available outcomes data, the predicted PAM50 basal subtype could further stratify each of these populations with basal-subtype showing significantly worse prognosis than the non-basal subtype (p< 1e-27 and p< 1e-7, respectively).

Conclusions: We retrospectively analyzed Tempus multimodal RWD to validate an in-house breast intrinsic subtype predictor that is agnostic to the site of metastasis. The prognostic value of the basal subtype was significant for breast cancer patients across various sites of metastasis including lymph node, liver, lung, and bones and IHC groups. For patients in each of the HR+/HER2- and triple negative IHC groups, the intrinsic molecular subtypes provided an additional level of prognostic detail with statistical significance. These data emphasize the importance of combining molecular subtypes with IHC-based diagnostics to fully characterize clinically relevant subpopulations and risk.