Exploiting senescence for the treatment of cancer

Senescence is a cellular response to a variety of stress signals that is characterized by a stable withdrawal from the cell cycle and major changes in cell morphology and physiology. While most research on senescence has been performed on non-cancer cells, it is evident that cancer cells can also mount a senescence response. In this Review, we discuss how senescence can be induced in cancer cells. We describe the distinctive features of senescent cancer cells and how these changes in cellular physiology might be exploited for the selective eradication of these cells (senolysis). We discuss activation of the host immune system as a particularly attractive way to clear senescent cancer cells. Finally, we consider the challenges and opportunities provided by a 'one-two punch' sequential treatment of cancer with pro-senescence therapy followed by senolytic therapy.

PALVEN: phase Ib trial of palbociclib, letrozole and venetoclax in estrogen receptor- and BCL2-positive advanced breast cancer

The addition of a CDK4/6 inhibitor to endocrine therapy improves progression-free and overall survival in women with metastatic estrogen receptor-positive breast cancer. In that setting, CDK4/6 inhibitors induce a potent cell-cycle arrest (which may be accompanied by tumor senescence) but fail to induce apoptotic cell death. Venetoclax is a potent inhibitor of BCL2, a pro-survival protein overexpressed in the majority of estrogen receptor-positive cancers. Pre-clinical findings indicate that venetoclax augments tumor response to the CDK4/6 inhibitor palbociclib by triggering apoptosis, including in senescent cells. The PALVEN phase Ib trial will further examine this finding. The primary objective is to identify the maximum tolerated dose and determine the recommended phase II dose for palbociclib, letrozole and venetoclax combination therapy. Clinical Trial Registration: NCT03900884 (ClinicalTrials.gov).

Pooled Genomic Screens Identify Anti-apoptotic Genes as Targetable Mediators of Chemotherapy Resistance in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is often sensitive to initial treatment with platinum and taxane combination chemotherapy, but most patients relapse with chemotherapy-resistant disease. To systematically identify genes modulating chemotherapy response, we performed pooled functional genomic screens in HGSOC cell lines treated with cisplatin, paclitaxel, or cisplatin plus paclitaxel. Genes in the intrinsic pathway of apoptosis were among the top candidate resistance genes in both gain-of-function and loss-of-function screens. In an open reading frame overexpression screen, followed by a mini-pool secondary screen, anti-apoptotic genes including BCL2L1 (BCL-XL) and BCL2L2 (BCL-W) were associated with chemotherapy resistance. In a CRISPR-Cas9 knockout screen, loss of BCL2L1 decreased cell survival whereas loss of proapoptotic genes promoted resistance. To dissect the role of individual anti-apoptotic proteins in HGSOC chemotherapy response, we evaluated overexpression or inhibition of BCL-2, BCL-XL, BCL-W, and MCL1 in HGSOC cell lines. Overexpression of anti-apoptotic proteins decreased apoptosis and modestly increased cell viability upon cisplatin or paclitaxel treatment. Conversely, specific inhibitors of BCL-XL, MCL1, or BCL-XL/BCL-2, but not BCL-2 alone, enhanced cell death when combined with cisplatin or paclitaxel. Anti-apoptotic protein inhibitors also sensitized HGSOC cells to the poly (ADP-ribose) polymerase inhibitor olaparib. These unbiased screens highlight anti-apoptotic proteins as mediators of chemotherapy resistance in HGSOC, and support inhibition of BCL-XL and MCL1, alone or combined with chemotherapy or targeted agents, in treatment of primary and recurrent HGSOC. IMPLICATIONS: Anti-apoptotic proteins modulate drug resistance in ovarian cancer, and inhibitors of BCL-XL or MCL1 promote cell death in combination with chemotherapy.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.