Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Abstract

Using pre-treatment gene expression, protein/phosphoprotein, and clinical data from the I-SPY2 neoadjuvant platform trial (NCT01042379), we create alternative breast cancer subtypes incorporating tumor biology beyond clinical hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status to better predict drug responses. We assess the predictive performance of mechanism-of-action biomarkers from ∼990 patients treated with 10 regimens targeting diverse biology. We explore >11 subtyping schemas and identify treatment-subtype pairs maximizing the pathologic complete response (pCR) rate over the population. The best performing schemas incorporate Immune, DNA repair, and HER2/Luminal phenotypes. Subsequent treatment allocation increases the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. pCR gains from reclassification and improved patient selection are highest in HR⁺ subsets (>15%). As new treatments are introduced, the subtyping schema determines the minimum response needed to show efficacy. This data platform provides an unprecedented resource and supports the usage of response-based subtypes to guide future treatment prioritization.

Keywords: DNA repair; Immune; Luminal; breast cancer; clinical trial; immunotherapy; multiple arms; platinum; response prediction; subtyping.

Figure 1.. Trial design and data.

a) I-SPY2 trial schematic, b) Timeline of I-SPY2 investigational regimens, c) pCR rate across arms by receptor subtype (blue arrows=graduated; grey arrows=graduated in all HER2+, d) ISPY2–990 mRNA/RPPA Data Resource consort.

Figure 2.. Clustered heatmap of mechanism-of-action ‘qualifying’ biomarkers across 10 arms.

Unsupervised clustering of mechanism-of-action biomarkers (rows) and 987 patient samples (columns), with biomarkers annotated by platform and pathway; and samples annotated by HR/HER2, MP1/2 class, response, receptor subtype, PAM50, TN subtypes (7- and 4-classes), and arm. See also Table S1 and S2.

Figure 3.. pCR association analysis of continuous mechanism-of-action biomarkers across 10 arms.

Dot-plot showing the level and direction of association between each signature (column) and pCR as labeled (rows): All patients (rows 1–11), HR+HER2− (rows 12–20), TN (rows 21–29), HR+HER2+ (rows 30–36) and HR-HER2+ (rows 37–42). Row labels denote treatment arm. Red/blue dot indicates higher/lower levels associate with pCR; darker intensity reflects larger effect size; size of dot reflects strength of association (1/p); white background indicates LR p<0.05; X denotes missing data. See also Table S3 and Figure S1.

Figure 4. Clinically motivated response-based biomarker-subsets.

a) Overall prevalence and pCR rates in Pembro by immune subtype in TN. b) Overall prevalence and pCR rates in VC by DRD subtype in TN. p-values shown are from Fisher’s exact test. c) Sankey plot showing Immune/DRD subsets in TN, with barplots of pCR rates in VC, Pembro and control. d) Sankey plot showing Immune/DRD subsets in HR+HER2-. e) Sankey plot of HER2+/BP_Luminal and HER2+/BP_Her2_or_Basal in HER2+, with barplots of pCR rates in Ctr, TDM1/P and MK2206 arms. f) Sankey plot showing the collapse of Immune/DRD subtypes in HER2− from 8 to 3 classes. # denotes patient subset too small to be evaluable (<5). See also Figure S2.

Figure 5. Integrated treatment response-predictive subtyping 5 (RPS-5) schema combining Immune, DRD, HER2, and BP_subtype phenotypes.

a) Sankey plot between receptor subtype and RPS-5 subtypes, with pCR rate barplots for each subtype (highest pCR rate labeled in blue). These pCR rates may differ from the reported estimated pCR in Figure 1c from Bayesian efficacy analyses. b) In silico experiment comparing pCR rates in I-SPY2’s control arm (black bar), experimental arms (orange bar); and estimated pCR rates if treatments had been ‘optimally’ assigned using receptor subtype (red bar;) or RPS-5 subtyping (blue bar). c) Hazard-ratio (HR) for Distant Recurrence-Free Survival (DRFS) for pCR versus non-pCR by RPS-5 subtype (box size=power; whiskers=95% CI). # denotes subsets with <5 patients, * denotes arm not open in subtype. p-values are from Fisher’s exact test. See also Figure S3.

Figure 6.. Response-predictive subtyping schema characteristics diagram for 11+ example schemas.

a) Pie charts showing the number (3–8) and prevalence of subtypes in each schema (column), b) Grid of constituent biomarkers (purple=present, white=absent), c) treatment arms with the highest pCR rate in one or more subtype (turquoise=selected, cream=not selected), and d) in silico experiment barplot showing pCR rates achieved in the control arm (black), experimental arms (orange); and estimated pCR rates if treatments had been optimally assigned using receptor subtype (red) or by the response-predictive schema in the column (blue). e) Barplot showing gain in pCR relative to receptor subtype. See also Figure S4.

Figure 7.. Impact of subtyping schema on minimum required efficacy of new agent (HER2low example).

a) Sankey plot showing a variety of ways to combine HER2low status with HR and Immune/DRD. b) Scatter plot showing prevalence of HER2low subsets (x-axis) vs. the minimum pCR rate required for an anti-HER2low agent to equal that of the I-SPY2 agent with the highest response (minimum efficacy; y-axis).