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Abstract The efficacy of pembrolizumab monotherapy versus chemotherapy increased with increasing programmed death ligand 1 (PD‐L1) expression, as quantified by combined positive score (CPS; PD‐L1 expression on both tumour cells and immune cells) in patients with previously treated metastatic triple‐negative breast cancer (mTNBC) in the phase 3 KEYNOTE‐119 study. This exploratory analysis was conducted to determine whether the expression of PD‐L1 on tumour cells contributes to the predictive value of PD‐L1 CPS in mTNBC. PD‐L1 expression in tumour samples was assessed using PD‐L1 IHC 22C3 pharmDx and quantified using both CPS and tumour proportion score (TPS; PD‐L1 expression on tumour cells alone). Calculated immune cell density (CID) was defined as CPS minus TPS. The ability of each scoring method (CPS, TPS, and CID) to predict clinical outcomes with pembrolizumab was evaluated. With pembrolizumab, the area under the receiver operating characteristic curve was 0.69 (95% CI = 0.58–0.80) for CPS, 0.55 (95% CI = 0.46–0.64) for TPS, and 0.67 (95% CI = 0.56–0.77) for CID. After correction for cutoff prevalence, CPS performed as well as, if not better than, CID with respect to predicting objective response rate, progression‐free survival, and overall survival. Data from this exploratory analysis suggest that, although PD‐L1 expression on immune cells alone is predictive of response to programmed death 1 blockade in mTNBC, adding tumour PD‐L1 expression assessment (i.e. CPS, which combines immune cell and tumour cell PD‐L1 expression) may improve prediction. PD‐L1 CPS thus remains an effective and broadly applicable uniform scoring system for enriching response to programmed death 1 blockade with pembrolizumab in mTNBC as well as other tumour types.

Abstract Aim We investigated the pathologic complete response rates (pCR) and survival outcomes of early breast cancer patients who underwent neoadjuvant chemotherapy (NAC) over 14 years at a French comprehensive cancer center and reported pCR and survival outcomes by tumor subtypes and size. Methods From January 2005 to December 2018, 1150 patients receiving NAC were identified. Correlations between cT stage, breast tumor response, axillary lymph node response, pCR, surgery, and outcomes were assessed. pCR was defined as (ypT0/ypTis) and (ypN0/pN0sn). Results A pCR was reached in 31.7% (365/1150) of patients and was strongly associated with tumor subtypes, but not with tumor size (pretreatment cT category). Luminal‐B Her2‐negative and triple‐negative (TN) subtypes, cN1 status, older age, and no‐pCR had an independent negative prognostic value. Overall survival (OS), relapse‐free survival (RFS), and metastasis‐free survival (MFS) were not significantly different for cT0‐1 compared to cT2 stages. In Cox‐model adjusted on in‐breast pCR and pN status, ypN1 had a strong negative impact (OS, RFS, and MFS: HR = 3.153, 4.677, and 6.133, respectively), higher than no in‐breast pCR (HR = 2.369, 2.252, and 2.323). A negative impact of no pCR on OS was observed for cN0 patients and TN tumors (HR = 4.972) or HER2‐positive tumors (HR = 11.706), as well as in Luminal‐B Her2‐negative tumors on MFS (HR = 2.223) and for Luminal‐A on RFS (HR = 4.465) and MFS (HR = 4.185). Conclusion Achievement of pCR, but not tumor size (pretreatment cT category), has an independent prognostic impact on survival. These results suggest potential NAC benefits in patients with small tumors (

Abstract Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life‐threatening side effects. Accurately anticipating doxorubicin‐resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single‐gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin‐response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard‐scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

Inflammatory breast cancer (IBC) is the most pro‐metastatic form of breast cancer. Better understanding of its pathophysiology and identification of actionable genetic alterations (AGAs) are crucial to improve systemic treatment. We aimed to define the DNA profiles of IBC vs noninflammatory breast cancer (non‐IBC) clinical samples in terms of copy number alterations (CNAs), mutations, and AGAs. We applied targeted next‐generation sequencing (tNGS) and array‐comparative genomic hybridization (aCGH) to 57 IBC and 50 non‐IBC samples and pooled these data with four public datasets profiled using NGS and aCGH, leading to a total of 101 IBC and 2351 non‐IBC untreated primary tumors. The respective percentages of each molecular subtype [hormone receptor‐positive (HR+)/HER2−, HER2+, and triple‐negative] were 68%, 15%, and 17% in non‐IBC vs 25%, 35%, and 40% in IBC. The comparisons were adjusted for both the molecular subtypes and the American Joint Committee on Cancer (AJCC) stage. The 10 most frequently altered genes in IBCs were TP53 (63%), HER2/ERBB2 (30%), MYC (27%), PIK3CA (21%), BRCA2 (14%), CCND1 (13%), GATA3 (13%), NOTCH1 (12%), FGFR1 (11%), and ARID1A (10%). The tumor mutational burden was higher in IBC than in non‐IBC. We identified 96 genes with an alteration frequency (p

The phosphatidylinositol‐3‐kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway is frequently activated in HER2‐negative breast cancer and may play a role in taxane resistance. The phase IB/II TAKTIC trial (NCT01980277) has shown that combining a dual AKT and p70 ribosomal protein S6 kinase (p70S6K) inhibitor (LY2780301) taken orally with weekly paclitaxel in HER2‐negative advanced breast cancer is feasible, with preliminary evidence of efficacy. We wanted to explore whether circulating tumor DNA (ctDNA) may be a surrogate marker of treatment efficacy in this setting. Serial plasma samples were collected and cell‐free DNA was sequenced using low‐coverage whole‐genome sequencing, and analysis was completed with droplet digital polymerase chain reaction (PCR) for some patients with driver mutations. Baseline tumor fraction (TF) and TF after 7 weeks on treatment were compared to progression‐free survival (PFS) and the overall response rate. We also explored circulating copy number alterations associated with treatment failure. Of the 51 patients enrolled in the TAKTIC trial, at least one plasma sample was available for 44 cases (96 timepoints). All patients with tumor TP53, PI3KCA, or AKT1 mutations harbored at least one of these alterations in plasma. TF at inclusion was correlated with PFS (6m‐PFS was 92% for ctDNAneg patients vs 68% for ctDNApos cases; hazard ratio [HR] = 3.45, 95% confidence interval [CI] [1.34–8.90], P = 0.007). ctDNA status at week 7 was not correlated with prognosis. Even though most circulating copy number alterations were conserved at disease progression, some genomic regions of interest were altered in post‐progression samples. In conclusion, ctDNA detection at baseline was associated with shorter PFS in patients included in the TAKTIC trial. Plasma‐based copy number analysis may help to identify alterations involved in resistance to treatment.