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Background: The safety and efficacy of brentuximab vedotin (BV), an antibody-drug conjugate directed to the CD30 antigen, has been assessed in several trials in patients with peripheral T-cell lymphoma (PTCL), cutaneous T-cell lymphoma (CTCL), or B-cell non-Hodgkin lymphoma (NHL). The objective of this research was to examine the relationship between CD30 expression level and clinical response to BV., Patients and Methods: We analyzed response in patients treated with BV monotherapy in 5 prospective clinical studies in relapsed or refractory PTCL, CTCL, or B-cell NHL. CD30 expression was assessed by immunohistochemistry (IHC) using the Ber H2 antibody for 275 patients., Results: Across all 5 studies, 140 (50.9%) patients had tumors with CD30 expression <10%, including 60 (21.8%) with undetectable CD30 by IHC. No significant differences were observed for any study in overall response rates between patients with CD30 expression ≥10% or <10%. Median duration of response was also similar in the CD30 ≥10% and <10% groups for all studies., Conclusions: In this analysis of studies across a range of CD30-expressing lymphomas, CD30 expression alone, as measured by standard IHC, does not predict clinical benefit from BV, making the determination of a threshold level of expression uncertain., (© The Author(s) 2022. Published by Oxford University Press.)

Introduction: Mycosis fungoides (MF), the most common type of cutaneous T-cell lymphoma, can lead to disfiguring lesions, debilitating pruritus and frequent skin infections. This study assessed response to brentuximab vedotin in patients with MF in the phase III ALCANZA study., Methods: Baseline CD30 levels and large-cell transformation (LCT) status were centrally reviewed in patients with previously-treated CD30-positive MF using ≥2 skin biopsies obtained at screening; eligible patients required ≥1 biopsy with ≥10% CD30 expression. Patients were categorised as CD30 min  < 10% (≥1 biopsy with <10% CD30 expression), or CD30 min  ≥ 10% (all biopsies with ≥10% CD30 expression) and baseline LCT present or absent. Efficacy analyses were the proportion of patients with objective response lasting ≥4 months (ORR4) and progression-free survival (PFS)., Results: Clinical activity with brentuximab vedotin was observed across all CD30 expression levels in patients with ≥1 biopsy showing ≥10% CD30 expression. Superior ORR4 was observed with brentuximab vedotin versus physician's choice in patients: with CD30 min  < 10% (40.9% versus 9.5%), with CD30 min  ≥ 10% (57.1% versus 10.3%), with LCT (64.7% versus 17.6%) and without LCT (38.7% versus 6.5%). Brentuximab vedotin improved median PFS versus physician's choice in patients: with CD30 min  < 10% (16.7 versus 2.3 months), with CD30 min  ≥ 10% (15.5 versus 3.9 months), with LCT (15.5 versus 2.8 months) and without LCT (16.1 versus 3.5 months). Safety profiles were generally comparable across subgroups., Conclusion: These exploratory analyses demonstrated that brentuximab vedotin improved rates of ORR4 and PFS versus physician's choice in patients with CD30-positive MF and ≥1 biopsy showing ≥10% CD30 expression, regardless of LCT status., Clinical Trial Registration: Clinicaltrials.gov, NCT01578499., Competing Interests: Conflict of interest statement Y.H.K. reports advisory roles for Seagen and Takeda. H.M.P. reports advisory board for and grants or funds from Takeda. D.F. reports advisory roles for Kyowa Kirin. S.M.H. reports consulting fees from ADC Therapeutics, C4 Therapeutics, Celgene, Janssen, Kura Oncology, Kyowa Hakko Kirin, Myeloid Therapeutics, Seagen, Takeda, Verastem and Vividion Therapeutics, and research grants or funds from ADC Therapeutics, Aileron, Celgene, Daiichi Sankyo, Forty Seven, Inc., Kyowa Hakko Kirin, Millennium/Takeda, Portola Pharmaceuticals, Seagen, Trillium Therapeutics and Verastem. M.D. reports research funding from Takeda and Millennium for this study, Seagen for a physician IST, and institutional funding from Solenginex, miragene, Rhizen and Eisai. O.B. reports consulting fees from Novartis, BMS, Sanofi, Pierre, Fabre and MSD. J.A.S. reports speaker’s bureau, and consultancy/advisory roles for Takeda (Brazil). J.S. reports consulting fees from/Clinical Expert for Takeda. P.Q. reports advisory boards and speaker fee for Takeda, Kiowa, Therakos, Miragen, Helsinn-Recordati, Innate Pharma, 4SC and Actelion. P.L.Z. reports advisory roles for and honoraria from Merck, BMS, Servier, Takeda, TG Therapeutics, ADC Therapeutics, Abbvie, Incyte, Janssen, Gilead, Eusapharma, Roche, Debiopharm and Novartis. H.E. reports advisory roles for Abbvie and Genentech, honoraria from Abbvie, Genentech, Takeda and Pharmacyclics, grants or funds from Abbvie, Genentech, Pharmacyclics, Acerta, Celgene and Astrazeneca. L.C.P-B. reports advisory roles for and honoraria from Seagen. P.L.O-R. reports advisory roles for Takeda, Helsinn, 4SC, Actelion, Innate Pharma, Recordati Rare Diseases, Kyowa and miRagen, grants or funds from MEDA, and PLCG1 mutation patent. O.E.A. reports advisory roles for Trillium Therapeutics, Bioniz, Kyowa Kirin and Meivir, and research grants or funds from Actelion, Adaptive Biotechnology, Trillium Therapeutics, Pfizer and Kyowa Kirin. J.T. reports research funding from Takeda, Celgene, Roche, Beigene, Janssen and PCYC. M.W. reports honoraria from Takeda and Kyowa Kirin, and research grants or funds from Millennium. J.W. reports advisory roles for Roche, Celgene, Takeda, Janssen-Cilag, Servier, Amgen, BMS, Abbvie, Novartis and Gilead, honoraria from Roche, Celgene, Takeda, Janssen-Cilag, Servier, Amgen, Abbvie, Gilead and Novartis, grants or funds from Roche, GSK/Novartis, Takeda and Janssen-Cilag, and conference travel support from Roche. A.A.G. reports advisory roles for Seagen, Innate Pharma and StemLine Therapeutics, honoraria and grants or funds from StemLine Therapeutics, and consultancy fees from Innate Pharma and StemLine. L.B. reports employment for Zymeworks. M.C.P-W., J.L. and M.O. report employment for and ownership of stocks/shares from Seagen. V.B. reports employment for Takeda Pharmaceuticals. M.L. reports employment for and ownership of stocks/shares from Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd. W.L.T. reports employment for and ownership of stocks/shares from Takeda Pharmaceuticals. R.D. reports intermittent, project focused consulting and/or advisory relationships with Novartis, Merck Sharp & Dhome (MSD), Bristol-Myers Squibb (BMS), Roche, Amgen, Takeda, Pierre Fabre, Sun Pharma, Sanofi, Catalym, Second Genome, Regeneron and Alligator. S.W., R.S., P.W., K.T., and M.M. have no conflicts of interest to disclose., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e71; doi:10.1038/psp.2013.38; published online 4 September 2013.

ErbB3 is a critical activator of phosphoinositide 3-kinase (PI3K) signaling in epidermal growth factor receptor (EGFR; ErbB1), ErbB2 [human epidermal growth factor receptor 2 (HER2)], and [hepatocyte growth factor receptor (MET)] addicted cancers, and reactivation of ErbB3 is a prominent method for cancers to become resistant to ErbB inhibitors. In this study, we evaluated the in vivo efficacy of a therapeutic anti-ErbB3 antibody, MM-121. We found that MM-121 effectively blocked ligand-dependent activation of ErbB3 induced by either EGFR, HER2, or MET. Assessment of several cancer cell lines revealed that MM-121 reduced basal ErbB3 phosphorylation most effectively in cancers possessing ligand-dependent activation of ErbB3. In those cancers, MM-121 treatment led to decreased ErbB3 phosphorylation and, in some instances, decreased ErbB3 expression. The efficacy of single-agent MM-121 was also examined in xenograft models. A machine learning algorithm found that MM-121 was most effective against xenografts with evidence of ligand-dependent activation of ErbB3. We subsequently investigated whether MM-121 treatment could abrogate resistance to anti-EGFR therapies by preventing reactivation of ErbB3. We observed that an EGFR mutant lung cancer cell line (HCC827), made resistant to gefitinib by exogenous heregulin, was resensitized by MM-121. In addition, we found that a de novo lung cancer mouse model induced by EGFR T790M-L858R rapidly became resistant to cetuximab. Resistance was associated with an increase in heregulin expression and ErbB3 activation. However, concomitant cetuximab treatment with MM-121 blocked reactivation of ErbB3 and resulted in a sustained and durable response. Thus, these results suggest that targeting ErbB3 with MM-121 can be an effective therapeutic strategy for cancers with ligand-dependent activation of ErbB3.

Directed cell migration in response to chemical cues, also known as chemotaxis, is an important physiological process involved in wound healing, foraging, and the immune response. Cell migration requires the simultaneous formation of actin polymers at the leading edge and actomyosin complexes at the sides and back of the cell. An unresolved question in eukaryotic chemotaxis is how the same chemoattractant signal determines both the cell's front and back. Recent experimental studies have begun to reveal the biochemical mechanisms necessary for this polarized cellular response. We propose a mathematical model of neutrophil gradient sensing and polarization based on experimentally characterized biochemical mechanisms. The model demonstrates that the known dynamics for Rho GTPase and phosphatidylinositol-3-kinase (PI3K) activation are sufficient for both gradient sensing and polarization. In particular, the model demonstrates that these mechanisms can correctly localize the "front" and "rear" pathways in response to both uniform concentrations and gradients of chemical attractants, including in actin-inhibited cells. Furthermore, the model predictions are robust to the values of many parameters. A key result of the model is the proposed coincidence circuit involving PI3K and Ras that obviates the need for the "global inhibitors" proposed, though never experimentally verified, in many previous mathematical models of eukaryotic chemotaxis. Finally, experiments are proposed to (in)validate this model and further our understanding of neutrophil chemotaxis.

This paper introduces a practical data-driven method to discriminate among large-scale kinetic reaction models. The approach centers around a computable measure of model/data mismatch. We introduce two provably convergent algorithms that were developed to accommodate large ranges of uncertainty in the model parameters. The algorithms are demonstrated on a simple toy example and a methane combustion model with more than 100 uncertain parameters. They are subsequently used to discriminate between two models for a contemporarily studied biological signaling network.

We present a numerical method for computing diffusive transport on a surface derived from image data. Our underlying discretization method uses a Cartesian grid embedded boundary method for computing the volume transport in a region consisting of all points a small distance from the surface. We obtain a representation of this region from image data by using a front propagation computation based on level set methods for solving the Hamilton-Jacobi and eikonal equations. We demonstrate that the method is second-order accurate in space and time and is capable of computing solutions on complex surface geometries obtained from image data of cells.