Proximal signaling events activated by TCR-peptide/MHC (TCR-pMHC) binding have been the focus of intense ongoing study, but understanding how the consequent downstream signaling networks integrate to govern ultimate avidity-appropriate TCR-pMHC T cell responses remains a crucial next challenge. We hypothesized that a quantitative combination of key downstream network signals across multiple pathways must encode the information generated by TCR activation, providing the basis for a quantitative model capable of interpreting and predicting T cell functional responses. To this end, we measured 11 protein nodes across six downstream pathways, along five time points from 10 min to 4 h, in a 1B6 T cell hybridoma stimulated by a set of three myelin proteolipid protein 139–151 altered peptide ligands. A multivariate regression model generated from this data compendium successfully comprehends the various IL-2 production responses and moreover successfully predicts a priori the response to an additional peptide treatment, demonstrating that TCR binding information is quantitatively encoded in the downstream network. Individual node and/or time point measurements less effectively accounted for the IL-2 responses, indicating that signals must be integrated dynamically across multiple pathways to adequately represent the encoded TCR signaling information. Of further importance, the model also successfully predicted a priori direct experimental tests of the effects of individual and combined inhibitors of the MEK/ERK and PI3K/Akt pathways on this T cell response. Together, our findings show how multipathway network signals downstream of TCR activation quantitatively integrate to translate pMHC stimuli into functional cell responses.

Purpose: Bcl-2 overexpression is frequently detected in lymphoid malignancies, being associated with poor prognosis and reduced response to therapy. Here, we evaluated whether Bcl-2 overexpression affects the cytotoxic activity of proteasome inhibitors taken alone or in association with conventional anticancer drugs or tumor necrosis factor–related apoptosis-inducing ligand (TRAIL). Experimental Design: Jurkat cells engineered to overexpress Bcl-2 were treated with proteasome inhibitors (MG132, epoxomicin, and bortezomib), anticancer drugs (etoposide and doxorubicin), TRAIL, or combinations of these compounds. Cell death and loss of mitochondrial transmembrane potential were detected by flow cytometry. Cytosolic relocalization of cytochrome c and SMAC/Diablo, caspase cleavage, and Bcl-2 and Mcl-1 levels were determined by immunoblotting. Nuclear factor-κB inhibition was done by retroviral transduction with a dominant-negative mutant of IκBα. Results: Bcl-2 overexpression results in significant inhibition of apoptosis in response to proteasome inhibitors, antiblastics, and TRAIL. Addition of TRAIL to proteasome inhibitors results in a synergistic cytotoxic effect in Bcl-2-overexpressing cells, whereas this result is not reproduced by the combination of proteasome inhibitors with antiblastic drugs. Importantly, proteasome inhibitors plus TRAIL induce mitochondrial dysfunction irrespective of up-regulated Bcl-2. Bcl-2 cleavage to a fragment with putative proapoptotic activity and elimination of antiapoptotic Mcl-1 may both play a role in proteasome inhibitors-TRAIL cooperation. Conversely, nuclear factor-κB inhibition by proteasome inhibitors is per se insufficient to explain the observed synergy. Conclusions: Combined proteasome inhibitors and TRAIL overcome the apoptotic threshold raised by Bcl-2 and may prove useful in the treatment of chemoresistant malignancies with up-regulated Bcl-2.

Introduction Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. Methods Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque. Results The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half‐life of 2.75 years. This is likely why beta‐secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody‐dependent cellular phagocytosis is the best approach for plaque reduction. Discussion A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

Heregulin-driven ERBB3 signaling has been implicated as a mechanism of resistance to cytotoxic and antiendocrine therapies in preclinical breast cancer models. In this study, we evaluated the effects of seribantumab (MM-121), a heregulinblocking anti-ERBB3 monoclonal antibody, alone and in combination with the aromatase inhibitor letrozole, on cell signaling and tumor growth in a preclinical model of postmenopausal estrogen receptor-positive (ER þ ) breast cancer. In vitro, heregulin treatment induced estrogen receptor phosphorylation in MCF-7Ca cells, and long-term letrozole-treated (LTLT-Ca) cells had increased expression and activation levels of EGFR, HER2, and ERBB3. Treatment with seribantumab, but not letrozole, inhibited basal and heregulin-mediated ERBB receptor phosphorylation and downstream effector activation in letrozole-sensitive (MCF-7Ca) and -refractory (LTLT-Ca) cells. Notably, in MCF-7Ca-derived xenograft tumors, cotreatment with seribantumab and letrozole had increased antitumor activity compared with letrozole alone, which was accompanied by downregulated PI3K/MTOR signaling both prior to and after the development of resistance to letrozole. Moreover, the addition of an MTOR inhibitor to this treatment regimen did not improve antitumor activity and was not well tolerated. Our results demonstrate that heregulin-driven ERBB3 signaling mediates resistance to letrozole in a preclinical model of ER þ breast cancer, suggesting that heregulin-expressing ER þ breast cancer patients may benefit from the addition of seribantumab to antiendocrine therapy.