Antibody Drug Conjugate Sacituzumab Govitecan Enables A Sequential TOP1/PARP Inhibitor Cancer Therapy Strategy in Breast Cancer Patients.

PURPOSE: The Antibody-Drug Conjugate (ADC) Sacituzumab govitecan (SG) comprises the topoisomerase 1 (TOP1) inhibitor SN-38, coupled to a monoclonal antibody targeting trophoblast cell surface antigen 2 (TROP-2). Poly (ADP-ribose) polymerase (PARP) inhibition may synergize with TOP1 inhibitors and SG, but previous studies combining systemic PARP and TOP1 inhibitors failed due to dose-limiting myelosuppression. Here, we assess proof-of-mechanism and clinical feasibility for SG and talazoparib employing an innovative sequential dosing schedule. PATIENTS AND METHODS: In vitro models tested pharmacodynamic endpoints, and in a phase 1b clinical trial (NCT04039230) 30 patients with metastatic Triple-Negative Breast Cancer (mTNBC) received SG and talazoparib using a concurrent (N=7) or sequential (N=23) schedule. Outcome measures included safety, tolerability, preliminary efficacy and establishment of a recommended phase 2 dose (RP2D). RESULTS: We hypothesized that tumor-selective delivery of TOP1i via SG would reduce non-tumor toxicity and create a temporal window, enabling sequential dosing of SG and PARP inhibition. In vitro, sequential SG followed by talazoparib delayed TOP1 cleavage complex clearance, increased DNA damage and promoted apoptosis. In the clinical trial, sequential SG/talazoparib successfully met primary objectives and demonstrated median PFS of 7.6 months without Dose-Limiting Toxicities (DLTs), while concurrent dosing yielded 2.3 months PFS and multiple DLTs including severe myelosuppression. CONCLUSIONS: While SG dosed concurrently with talazoparib is not tolerated clinically due to an insufficient therapeutic window, sequential dosing of SG then talazoparib proved a viable strategy. These findings support further clinical development of the combination and suggest that ADC-based therapy may facilitate novel, mechanism-based dosing strategies.

Parallel Genomic Alterations of Antigen and Payload Targets Mediate Polyclonal Acquired Clinical Resistance to Sacituzumab Govitecan in Triple-Negative Breast Cancer.

Sacituzumab govitecan (SG), the first antibody-drug conjugate (ADC) approved for triple-negative breast cancer, incorporates the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 (TOP1) inhibitor payload. We sought to identify mechanisms of SG resistance through RNA and whole-exome sequencing of pretreatment and postprogression specimens. One patient exhibiting de novo progression lacked TROP2 expression, in contrast to robust TROP2 expression and focal genomic amplification of TACSTD2/TROP2 observed in a patient with a deep, prolonged response to SG. Analysis of acquired genomic resistance in this case revealed one phylogenetic branch harboring a canonical TOP1 E418K resistance mutation and subsequent frameshift TOP1 mutation, whereas a distinct branch exhibited a novel TACSTD2/TROP2 T256R missense mutation. Reconstitution experiments demonstrated that TROP2T256R confers SG resistance via defective plasma membrane localization and reduced cell-surface binding by hRS7. These findings highlight parallel genomic alterations in both antibody and payload targets associated with resistance to SG. SIGNIFICANCE: These findings underscore TROP2 as a response determinant and reveal acquired SG resistance mechanisms involving the direct antibody and drug payload targets in distinct metastatic subclones of an individual patient. This study highlights the specificity of SG and illustrates how such mechanisms will inform therapeutic strategies to overcome ADC resistance.This article is highlighted in the In This Issue feature, p. 2355.

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.

The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.

The anti-malarial drug atovaquone potentiates platinum-mediated cancer cell death by increasing oxidative stress.

Platinum chemotherapies are highly effective cytotoxic agents but often induce resistance when used as monotherapies. Combinatorial strategies limit this risk and provide effective treatment options for many cancers. Here, we repurpose atovaquone (ATQ), a well-tolerated & FDA-approved anti-malarial agent by demonstrating that it potentiates cancer cell death of a subset of platinums. We show that ATQ in combination with carboplatin or cisplatin induces striking and repeatable concentration- and time-dependent cell death sensitization in vitro across a variety of cancer cell lines. ATQ induces mitochondrial reactive oxygen species (mROS), depleting intracellular glutathione (GSH) pools in a concentration-dependent manner. The superoxide dismutase mimetic MnTBAP rescues ATQ-induced mROS production and pre-loading cells with the GSH prodrug N-acetyl cysteine (NAC) abrogates the sensitization. Together, these findings implicate ATQ-induced oxidative stress as key mediator of the sensitizing effect. At physiologically achievable concentrations, ATQ and carboplatin furthermore synergistically delay the growth of three-dimensional avascular spheroids. Clinically, ATQ is a safe and specific inhibitor of the electron transport chain (ETC) and is concurrently being repurposed as a candidate tumor hypoxia modifier. Together, these findings suggest that ATQ is deserving of further study as a candidate platinum sensitizing agent.

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

Targeting tumour hypoxia: shifting focus from oxygen supply to demand.

Tumour hypoxia is a well-recognised barrier to anti-cancer therapy and represents one of the best validated targets in oncology. Previous attempts to tackle hypoxia have focussed primarily on increasing tumour oxygen supply; however, clinical studies using this approach have yielded only modest clinical benefit, with often significant toxicity and practical limitations. Therefore, there are currently no anti-hypoxia treatments in widespread clinical use. As an emerging alternative strategy, we discuss the relevance of inhibiting tumour oxygen metabolism to alleviate hypoxia and highlight recently initiated clinical trials using this approach.