Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Although aberrant EGFR signaling is widespread in cancer, EGFR inhibition is effective only in a subset of non–small cell lung cancer (NSCLC) with EGFR activating mutations. A majority of NSCLCs express EGFR wild type (EGFRwt) and do not respond to EGFR inhibition. TNF is a major mediator of inflammation-induced cancer. We find that a rapid increase in TNF level is a universal adaptive response to EGFR inhibition in NSCLC, regardless of EGFR status. EGFR signaling actively suppresses TNF mRNA levels by inducing expression of miR-21, resulting in decreased TNF mRNA stability. Conversely, EGFR inhibition results in loss of miR-21 and increased TNF mRNA stability. In addition, TNF-induced NF-κB activation leads to increased TNF transcription in a feed-forward loop. Inhibition of TNF signaling renders EGFRwt-expressing NSCLC cell lines and an EGFRwt patient-derived xenograft (PDX) model highly sensitive to EGFR inhibition. In EGFR-mutant oncogene-addicted cells, blocking TNF enhances the effectiveness of EGFR inhibition. EGFR plus TNF inhibition is also effective in NSCLC with acquired resistance to EGFR inhibition. We suggest concomitant EGFR and TNF inhibition as a potentially new treatment approach that could be beneficial for a majority of lung cancer patients.

Split-mouth designs are frequently used in dental clinical research, where a mouth is divided into two or more experimental segments that are randomly assigned to different treatments. It has the distinct advantage of removing a lot of inter-subject variability from the estimated treatment effect. Methods of statistical analyses for split-mouth design have been well developed. However, little work is available on sample size consideration at the design phase of a split-mouth trial, although many researchers pointed out that the split-mouth design can only be more efficient than a parallel-group design when within-subject correlation coefficient is substantial. In this paper, we propose to use the generalized estimating equation (GEE) approach to assess treatment effect in split-mouth trials, accounting for correlations among observations. Closed-form sample size formulas are introduced for the split-mouth design with continuous and binary outcomes, assuming exchangeable and “nested exchangeable” correlation structures for outcomes from the same subject. The statistical inference is based on the large sample approximation under the GEE approach. Simulation studies are conducted to investigate the finite-sample performance of the GEE sample size formulas. A dental clinical trial example is presented for illustration.

Cancer cells exhibit phenotypical plasticity and epigenetic reprogramming that allows them to evade lineage-dependent targeted treatments by adopting lineage plasticity. The underlying mechanisms by which cancer cells exploit the epigenetic regulatory machinery to acquire lineage plasticity and therapy resistance remain poorly understood. We identified zinc finger protein 397 (ZNF397) as a bona fide coactivator of the androgen receptor (AR), essential for the transcriptional program governing AR-driven luminal lineage. ZNF397 deficiency facilitates the transition of cancer cell from an AR-driven luminal lineage to a ten-eleven translocation 2 (TET2)-driven lineage plastic state, ultimately promoting resistance to therapies inhibiting AR signaling. Intriguingly, our findings indicate that a TET2 inhibitor can eliminate the resistance to AR-targeted therapies in ZNF397-deficient tumors. These insights uncover a novel mechanism through which prostate cancer acquires lineage plasticity via epigenetic rewiring and offer promising implications for clinical interventions designed to overcome therapy resistance dictated by lineage plasticity. Significance: This study reveals a bifurcated role of ZNF397, and a TET2–driven epigenetic mechanism regulating tumor lineage plasticity and therapy response in prostate cancer, enhances the understanding of drug resistance, and unveils a new therapeutic strategy for overcoming androgen receptor-targeted therapy resistance.

The critical roles of CD4⁺ T cells have been understudied for cancer vaccines. Here we report long-term clinical outcomes of a randomized multicenter phase II clinical trial (NCT00118274), where patients with high-risk melanoma received a multipeptide vaccine targeting CD8⁺ T cells (12MP) and were randomized to receive either of two vaccines for CD4⁺ (helper) T cells: 6MHP (6 melanoma-specific helper peptides), or tet (a nonspecific helper peptide from tetanus toxoid). Cyclophosphamide (Cy) pre-treatment was also assessed. Primary outcomes for T cell responses to 12MP, 6MHP, and tet were previously reported, suggesting immunogenicity of both vaccines but that CD8 T cell responses to 12MP were lower when tet was replaced with 6MHP. Here, in post-hoc analyses, we report durable prolongation of overall survival by adding 6MHP instead of tet. That benefit was experienced only by male patients. A favorable interaction of 6MHP and Cy is also suggested. Multivariable Cox regression analysis of the intent-to-treat population identify vaccine arm (12MP + 6MHP+Cy) and patient sex (male) as the two significant predictors of enhanced survival. These findings support the value of adding cognate T cell help to cancer vaccines and also suggest a need to assess the impact of patient sex on immune therapy outcomes.