Matteo Dugo

Improved outcomes in HER2+ female breast cancer have resulted from chemotherapy and anti-HER2 therapies. However, HER2+ER+ cancers exhibit lower response rates. The phase 2 NA-PHER2 trial (NCT02530424) investigated chemo-free preoperative HER2 blockade (trastuzumab + pertuzumab) and CDK4/6 inhibition (palbociclib) with or without endocrine therapy (fulvestrant) in HER2+ER+ breast cancer. Clinical endpoints (i.e. Ki67 dynamics and pathological complete response) were previously reported. Here we report on the biomarker analysis, secondary objective of the study. Through RNA sequencing and tumour infiltrating lymphocytes (TIL) assessment in serial biopsies, we identified biomarkers predictive of pCR or Day14 Ki67 response and unveiled treatment-induced molecular changes. High immune infiltration and low ER signalling correlated with pCR, while TP53 mutations associated with high Day14 Ki67. Stratification based on Ki67 at Day14 and at surgery defined three response groups (Ki67 HighHigh, LowHigh, LowLow), with divergent tumour and stroma expression dynamics. The HighHigh group showed dysfunctional immune infiltration and overexpression of therapeutic targets like PAK4 at baseline. The LowLow group exhibited a Luminal A phenotype by the end of treatment. This study expands our understanding of drivers and dynamics of HER2+ER+ tumour response, towards treatment tailoring.

Supplementary Figure 7. Association of TNBC subtypes and IO classifications. A) Frequency of RT-qPCR DetermaIO class by TNBC subtype; B) Frequency of RNAseq IO classes by TNBC subtype. C) Multivariable logistic regression model with TNBC subtypes and RT-qPCR IO score for association with pCR by treatment arm. Odds ratio (OR) and 95% confidence intervals are shown. D) Multivariable logistic regression model with TNBC subtypes and RNA-Seq IO score for association with pCR by treatment arm. Odds ratio (OR) and 95% confidence intervals are shown.

Supplementary Figure 5. Comparison of IO score with additional immune signatures. A) Correlation of RT-qPCR and RNA-Seq continuous IO score in the respective NeoTRIP cohorts with previously published immune signatures; B) Comparison of univariate logistic regression models for IO score and additional immune signatures evaluated in the NeoTRIP RT-qPCR and RNA-Seq cohorts. Odds ratio (OR) and 95% confidence intervals are shown. P-values for the interaction test with treatment arm are reported. Asterisks highlight a p-value < 0.05 for the univariate logistic regression model in the specific arm.

Supplementary Figure 1. Quality control of NeoTRIP RNA-Seq data. Boxplots showing the distribution of read depth of targeted coding regions by arm and response status. Dots represent the read depth of single samples. The horizontal grey dashed line represents the cut-off of 40 million paired-end reads.

Supplementary Figure 3. Continuous baseline RT-qPCR DetermaIO score. Distribution of continuous IO score by treatment arm and pCR status. P-values by logistic regression.

Supplementary Table 2. Patients’ characteristics of the NeoTRIP trial in the RNA-Seq and RT-qPCR cohorts.

Supplementary Figure 2. Comparison of RT-qPCR DetermaIO and RNA-Seq IO scores. A) Scatter plot showing the relation between continuous IO scores. Concordance and correlation coefficient = 0.94; 95% CI = 0.92 − 0.95; B) Contingency plot of binary IO classifications. Cohen’s kappa = 0.84; 95% CI = 0.77–0.91; p < 0.0001.

Supplementary Table 3. Univariate and multivariable regression of pCR according to NGS-based IO score in the RNA-Seq cohort.

Supplementary Table 4. Univariate regression of pCR according to TNBC subtypes in the RNA-Seq cohort. One-vs-all approach was applied.