Calibrating Observational Health Record Data Against a Randomized Trial.

Importance: The conditions required for health record data sources to accurately assess treatment effectiveness remain unclear. Emulation of randomized clinical trials (RCTs) with health record data and subsequent calibration of the results can help elucidate this.

Objective: To pilot an emulation of the KEYNOTE-189 RCT using a commercially available electronic health record (EHR) data source.

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients.

A Phase Ib Trial of Personalized Neoantigen Therapy Plus Anti-PD-1 in Patients with Advanced Melanoma, Non-small Cell Lung Cancer, or Bladder Cancer.

Neoantigens arise from mutations in cancer cells and are important targets of T cell-mediated anti-tumor immunity. Here, we report the first open-label, phase Ib clinical trial of a personalized neoantigen-based vaccine, NEO-PV-01, in combination with PD-1 blockade in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. This analysis of 82 patients demonstrated that the regimen was safe, with no treatment-related serious adverse events observed.

Harnessing Real-World Evidence for the Development of Novel Cancer Therapies.

Resourcing real-world evidence (RWE) is becoming an increasingly important asset in developing novel therapies for cancer. In this article, an overview of the benefits and challenges of using these data is provided. Through several case examples we highlight future applications and potential.

Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction.

Increasing evidence indicates CD4 T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway.

High-Order Drug Combinations Are Required to Effectively Kill Colorectal Cancer Cells.

Like classical chemotherapy regimens used to treat cancer, targeted therapies will also rely upon polypharmacology, but tools are still lacking to predict which combinations of molecularly targeted drugs may be most efficacious. In this study, we used image-based proliferation and apoptosis assays in colorectal cancer cell lines to systematically investigate the efficacy of combinations of two to six drugs that target critical oncogenic pathways. Drug pairs targeting key signaling pathways resulted in synergies across a broad spectrum of genetic backgrounds but often yielded only cytostatic responses.

Targeted Exome Sequencing of the Cancer Genome in Patients with Very High-risk Bladder Cancer.

Unlabelled: We completed targeted exome sequencing of the tumors of 50 patients with pTis-pT4b bladder cancer. Mutations were categorized by type, stratified against previously identified cancer loci in the Catalogue of Somatic Mutations in Cancer and The Cancer Genome Atlas databases, and evaluated in pathway analysis and comutation plots. We analyzed mutation associations with receipt of neoadjuvant chemotherapy, nodal involvement, metastatic disease development, and survival.

Activation of MET via diverse exon 14 splicing alterations occurs in multiple tumor types and confers clinical sensitivity to MET inhibitors.

Unlabelled: Focal amplification and activating point mutation of the MET gene are well-characterized oncogenic drivers that confer susceptibility to targeted MET inhibitors. Recurrent somatic splice site alterations at MET exon 14 (METex14) that result in exon skipping and MET activation have been characterized, but their full diversity and prevalence across tumor types are unknown. Here, we report analysis of tumor genomic profiles from 38,028 patients to identify 221 cases with METex14 mutations (0.

A functional genomic approach identifies FAL1 as an oncogenic long noncoding RNA that associates with BMI1 and represses p21 expression in cancer.

In a genome-wide survey on somatic copy-number alterations (SCNAs) of long noncoding RNA (lncRNA) in 2,394 tumor specimens from 12 cancer types, we found that about 21.8% of lncRNA genes were located in regions with focal SCNAs. By integrating bioinformatics analyses of lncRNA SCNAs and expression with functional screening assays, we identified an oncogene, focally amplified lncRNA on chromosome 1 (FAL1), whose copy number and expression are correlated with outcomes in ovarian cancer.

Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis.

Background: Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach.

Quinoline 3-sulfonamides inhibit lactate dehydrogenase A and reverse aerobic glycolysis in cancer cells.

Background: Most normal cells in the presence of oxygen utilize glucose for mitochondrial oxidative phosphorylation. In contrast, many cancer cells rapidly convert glucose to lactate in the cytosol, a process termed aerobic glycolysis. This glycolytic phenotype is enabled by lactate dehydrogenase (LDH), which catalyzes the inter-conversion of pyruvate and lactate.

Tumor genetic analyses of patients with metastatic melanoma treated with the BRAF inhibitor dabrafenib (GSK2118436).

Purpose: Dabrafenib is a selective inhibitor of V600-mutant BRAF kinase, which recently showed improved progression-free survival (PFS) as compared with dacarbazine, in metastatic melanoma patients. This study examined potential genetic markers associated with response and PFS in the phase I study of dabrafenib.

Experimental Design: Baseline (pretreatment or archival) melanoma samples were evaluated in 41 patients using a custom genotyping melanoma-specific assay, sequencing of PTEN, and copy number analysis using multiplex ligation amplification and array-based comparative genomic hybridization.

Lapatinib inhibits the growth of esophageal squamous cell carcinoma and synergistically interacts with 5-fluorouracil in patient-derived xenograft models.

Lapatinib is a dual tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR) and human EGFR-2 (HER2) tyrosine kinase domains. To explore the potential utility of lapatinib for the treatment of esophageal squamous cell carcinoma (ESCC), we examined the expression profiles of EGFR and HER2 in tumor tissues and in paired adjacent non-neoplastic tissues from patients with ESCC. We evaluated the antitumor effects of lapatinib alone or in combination with oxaliplatin or 5-fluorouracil (5-FU) on a panel of primary ESCC cells in vitro with various levels of EGFR and HER2 expression.

Loss and reduced expression of PTEN correlate with advanced-stage gastric carcinoma.

Phosphatase and tensin homolog (PTEN) is a tumor suppressor involved in multiple cell processes. To investigate the role of PTEN in the development of gastric carcinoma, we determined the expression pattern of PTEN in primary gastric carcinoma and in paired adjacent non-neoplastic tissue. We also determined the correlation of PTEN expression with clinicopathological characteristics and patient survival.

High overexpression of fatty acid synthase is associated with poor survival in Chinese patients with gastric carcinoma.

Fatty acid synthase (FAS) is the key enzyme regulating de novo biosynthesis of fatty acids. FAS overexpression has been found in many types of tumors and is associated with poor survival. However, the expression of FAS and its relationship with prognosis in Chinese patients with gastric carcinoma are still unknown.

Genomic DNA copy-number alterations of the let-7 family in human cancers.

In human cancer, expression of the let-7 family is significantly reduced, and this is associated with shorter survival times in patients. However, the mechanisms leading to let-7 downregulation in cancer are still largely unclear. Since an alteration in copy-number is one of the causes of gene deregulation in cancer, we examined copy number alterations of the let-7 family in 2,969 cancer specimens from a high-resolution SNP array dataset.

Combined phosphatase and tensin homolog (PTEN) loss and fatty acid synthase (FAS) overexpression worsens the prognosis of Chinese patients with hepatocellular carcinoma.

We aimed to investigate the expression pattern of phosphatase and tensin homolog (PTEN), to evaluate the relationship between PTEN expression and clinicopathological characteristics, including fatty acid synthase (FAS) expression, and to determine the correlations of PTEN and FAS expression with survival in Chinese patients with hepatocellular carcinoma (HCC). The expression patterns of PTEN and FAS were determined using tissue microarrays and immunohistochemistry. The expression of PTEN was compared with the clinicopathological characteristics of HCC, including FAS expression.

Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis.

We developed a novel tool for microarray data analysis that can parsimoniously discover highly predictive genes by finding the optimal trade off between fold change and t-test p value through rigorous cross validation. In addition to find a small set of highly predictive genes, the tool also has a procedure that recursively discovers and removes predictive genes from the dataset until no such genes can be found. We applied our tool to a public breast cancer dataset with the goal to discover genes that can predict patient’s response to a preoperative chemotherapy.

Systematic analysis of genotype-specific drug responses in cancer.

A systematic understanding of genotype-specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor.

Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood.

Purpose: We sought to evaluate the feasibility of detecting PIK3CA mutations in circulating tumor DNA (ctDNA) from plasma of patients with metastatic breast cancer using a novel technique called BEAMing.

Experimental Design: In a retrospective analysis, 49 tumor and temporally matched plasma samples from patients with breast cancer were screened for PIK3CA mutations by BEAMing. We then prospectively screened the ctDNA of 60 patients with metastatic breast cancer for PIK3CA mutations by BEAMing and compared the findings with results obtained by screening corresponding archival tumor tissue DNA using both sequencing and BEAMing.

Comprehensive predictive biomarker analysis for MEK inhibitor GSK1120212.

The MEK1 and MEK2 inhibitor GSK1120212 is currently in phase II/III clinical development. To identify predictive biomarkers, sensitivity to GSK1120212 was profiled for 218 solid tumor cell lines and 81 hematologic malignancy cell lines. For solid tumors, RAF/RAS mutation was a strong predictor of sensitivity.

A combined array-based comparative genomic hybridization and functional library screening approach identifies mir-30d as an oncomir in cancer.

Oncomirs are microRNAs (miRNA) that acts as oncogenes or tumor suppressor genes. Efficient identification of oncomirs remains a challenge. Here we report a novel, clinically guided genetic screening approach for the identification of oncomirs, identifying mir-30d through this strategy.

Subtype and pathway specific responses to anticancer compounds in breast cancer.

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses.

High chromosome number in hematological cancer cell lines is a negative predictor of response to the inhibition of Aurora B and C by GSK1070916.

Background: Aurora kinases play critical roles in mitosis and are being evaluated as therapeutic targets in cancer. GSK1070916 is a potent, selective, ATP competitive inhibitor of Aurora kinase B and C. Translation of predictive biomarkers to the clinic can benefit patients by identifying the tumors that are more likely to respond to therapies, especially novel inhibitors such as GSK1070916.