Irreversible HER2 inhibitors overcome resistance to the RSL3 ferroptosis inducer in non-HER2 amplified luminal breast cancer.

Ferroptosis, a form of programed cell death, can be promoted by inhibitors of the xCT transporter (erastin) or GPX4 (RSL3). We found that GPX4, but not the xCT transporter, is selectively elevated in luminal breast cancer. Consistent with this observation, the majority of luminal breast cancer cell lines are exquisitely sensitive to RSL3 with limited sensitivity to erastin. In RSL3-resistant, but not sensitive, luminal breast cancer cell lines, RSL3 induces HER2 pathway activation. Irreversible HER2 inhibitors including neratinib reversed RSL3 resistance in constitutively RSL3-resistant cell lines. Combination treatment with RSL3 and neratinib increases ferroptosis through mitochondrial iron-dependent reactive oxygen species production and lipid peroxidation. RSL3 also activated replication stress and concomitant S phase and G2/M blockade leading to sensitivity to targeting the DNA damage checkpoint. Together, our data support the exploration of RSL3 combined with irreversible HER2 inhibitors in clinical trials.

Precision oncology for breast cancer through clinical trials.

Precision treatment for breast cancers has made several notable advances in recent decades, but challenges of tumor heterogeneity, drug resistance, and aggressive recurrence and metastases remain. To meet and overcome these challenges, we must refine our understanding of breast subtypes and treatment biomarkers according to the knowledge afforded across the spectrum of 'omics assays. A critical aspect of harnessing this knowledge into actionable biomarkers for treatment decision relies on our ability to integrate knowledge across data types and leverage our insight in evidence-based clinical trials. We review recent advances in cutting-edge clinical trials for precision treatment of breast cancer, including chemotherapies, targeted therapies, immunotherapies, and combination therapies. We comment on promising future areas of development for this exciting point in precision breast cancer research.

Functional genomic analysis identifies drug targetable pathways in invasive and metastatic cutaneous squamous cell carcinoma.

Although cutaneous squamous cell carcinoma (cSCC) is treatable in the majority of cases, deadly invasive and metastatic cases do occur. To date there are neither reliable predictive biomarkers of disease progression nor FDA-approved targeted therapies as standard of care. To address these issues, we screened patient-derived primary cultured cells from invasive/metastatic cSCC with 107 small-molecule inhibitors. In-house bioinformatics tools were used to cross-analyze drug responses and DNA mutations in tumors detected by whole-exome sequencing (WES). Aberrations in molecular pathways with evidence of potential drug targets were identified, including the Eph-ephrin and neutrophil degranulation signaling pathways. Using a screening panel of siRNAs, we identified EPHA6 and EPHA7 as targets within the Eph-ephrin pathway responsible for mitigating decreased cell viability. These studies form a plausible foundation for detecting biomarkers of high-risk progressive disease applicable in dermatopathology and for patient-specific therapeutic options for invasive/metastatic cSCC.

Illuminating biological pathways for drug targeting in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either "light" or "dark" to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations (or copy number alterations) and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations (or copy number alterations) but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35-38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development.

Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz.

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

Evidence-Based Precision Oncology with the Cancer Targetome.

A core tenet of precision oncology is the rational choice of drugs to interact with patient-specific biological targets of interest, but it is currently difficult for researchers to obtain consistent and well-supported target information for pharmaceutical drugs. We review current drug-target interaction resources and critically assess how supporting evidence is handled. We introduce the concept of a unified Cancer Targetome to aggregate drug-target interactions in an evidence-based framework. We discuss current unmet needs and the implications for evidence-based clinical omics. The focus of this review is precision oncology but the discussion is highly relevant to targeted therapies in any area.

Challenges in secondary analysis of high throughput screening data.

Repurposing an existing drug for an alternative use is not only a cost effective method of development, but also a faster process due to the drug's previous clinical testing and established pharmokinetic profiles. A potentially rich resource for computational drug repositioning approaches is publically available high throughput screening data, available in databases such as PubChem Bioassay and ChemBank. We examine statistical and computational considerations for secondary analysis of publicly available high throughput screening (HTS) data with respect to metadata, data quality, and completeness. We discuss developing methods and best practices that can help to ameliorate these issues.