A phase Ib study of pictilisib (GDC-0941) in combination with paclitaxel, with and without bevacizumab or trastuzumab, and with letrozole in advanced breast cancer

Schöffski, Patrick; Cresta, Sara; Mayer, Ingrid A.; Wildiers, Hans; Damian, Silvia; Gendreau, Steven; Rooney, Isabelle; Morrissey, Kari M.; Spoerke, Jill M.; Ng, Vivian; Singel, Stina Mui; Winer, Eric P.

doi:10.1186/s13058-018-1015-x

Cited by 60 publications

(31 citation statements)

References 42 publications

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“…Likewise, in HER2-positive BC patients, presence of a PIK3CA-mut is associated with worse response rates to neoadjuvant treatment [62]. Phase I/II studies demonstrated the overall feasibility of combining anti-HER2 treatments with PI3Ki (Table 2) [46,[50][51][52][53][54][55][56][57][58][59]63]. A phase II study including HER2-positive, trastuzumab-resistant metastatic BC patients treated with buparlisib and trastuzumab showed an overall response rate of 10%, but grade !…”

Section: With Anti-her2 Agentsmentioning

confidence: 99%

“…In most clinical studies evaluating PIK3CA-mut as a predictor of chemotherapy response in BC, inferior response rates were observed in patients with PIK3CA-mut tumours when compared with patients with PIK3CA-wild-type tumours [62,65]. Therefore, the combination of PI3Ki and chemotherapy is being investigated as an attempt to overcome treatment resistance, but no promising results have been observed so far (Table 2) [9,11,21,49,59]. Ongoing trials are evaluating the association of PI3Ki with chemotherapy in HER2-negative BC (Table 3).…”

Section: With Chemotherapymentioning

confidence: 99%

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Biomarkers of response and resistance to PI3K inhibitors in estrogen receptor-positive breast cancer patients and combination therapies involving PI3K inhibitors

et al. 2019

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In this review, we discuss biomarkers of response and resistance to PI3K inhibitors (PI3Ki) in estrogen receptor-positive breast cancer, both in the early and advanced settings. We analyse data regarding PIK3CA mutations, PI3K pathway activation, PTEN expression loss, Akt signalling, insulin levels, 18F FDG-PET/CT imaging, FGFR1/2 amplification, KRAS and TP53 mutations. Most of the discussed data comprise retrospective and exploratory studies, hence many results are not conclusive. Therefore, among all of these biomarkers, only PIK3CA mutations have proved to have a predictive value for treatment with the a-selective PI3Ki alpelisib (SOLAR-1 trial) and the b-sparing PI3Ki taselisib (SANDPIPER trial) in the advanced setting. Since the accuracy of current individual biomarkers is not optimal, a composite biomarker, including DNA, RNA and protein expression data, to more precisely assess the PI3K/AKT/mTOR pathway activation status, may arise as a promising approach. Finally, we describe the rational for new combination therapies involving PI3Ki and anti-HER2 agents, chemotherapy, CDK4/6 inhibitors, mTOR inhibitors or new endocrine treatments and discuss the ongoing trials in this field.

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Section: With Anti-her2 Agentsmentioning

confidence: 99%

Section: With Chemotherapymentioning

confidence: 99%

Biomarkers of response and resistance to PI3K inhibitors in estrogen receptor-positive breast cancer patients and combination therapies involving PI3K inhibitors

et al. 2019

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“…Moreover, PI3K and PI3K/mTOR inhibitors tested in a clinical setting (e.g. pictilisib 21 and apitolisib 22 ) have suffered from toxic effects 23,24 . Thus, identification of compounds that increase the therapeutic index for PI3K inhibitors could enable new uses for PI3K inhibition in oncology.…”

Section: Background and Summarymentioning

confidence: 99%

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

et al. 2019

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While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.

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“…PI3K mutational status is often not predictive of response to PI3K-targeted drugs. For example, only a subset of patients with mutant PI3K responded to pictilisib in clinical trials (Krop et al, 2016;Schmid et al, 2016;Schöffski et al, 2018). The edge flux and the median marker expression suggest important roles of p-MKK4 and p-STAT3 in defining pictilisib sensitivity; both have potential as markers for improving patient stratification.…”

Section: Discussionmentioning

confidence: 99%

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Tognetti

Gábor

Yang

et al. 2020

Preprint

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Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data – on more than 80 million single cells from 4,000 conditions – were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.One-linerSingle-cell proteomics coupled to perturbations improves accuracy of breast tumor drug sensitivity predictions and reveals mechanisms of sensitivity and resistance.HIGHLIGHTSMass cytometry study of signaling responses of 62 breast cancer cell lines and five lines from healthy tissue to EGF stimulation with or without perturbation with five kinase inhibitors.Single-cell signaling features and mechanistic signaling network models predicted drug sensitivity.Mechanistic signaling network models deepen the understanding of drug resistance and sensitivity mechanisms.We identify drug sensitivity-predictive genomic features via proxy signaling phenotypes.

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A phase Ib study of pictilisib (GDC-0941) in combination with paclitaxel, with and without bevacizumab or trastuzumab, and with letrozole in advanced breast cancer

Cited by 60 publications

References 42 publications

Biomarkers of response and resistance to PI3K inhibitors in estrogen receptor-positive breast cancer patients and combination therapies involving PI3K inhibitors

Biomarkers of response and resistance to PI3K inhibitors in estrogen receptor-positive breast cancer patients and combination therapies involving PI3K inhibitors

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

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