Validation of the Lung Immune Prognostic Index (LIPI) as a prognostic biomarker in metastatic renal cell carcinoma.

BACKGROUND: The Lung Immune Prognostic Index (LIPI) is associated with immune checkpoint inhibitors (ICI) outcomes across different solid tumors, particularly in non-small cell lung cancer. Data regarding the prognostic and/or predictive role of LIPI in metastatic renal cell carcinoma (mRCC) are still scarce. The aim of this study was to evaluate whether LIPI could be predictive of survival in mRCC patients. METHODS: We used patient level data from three different prospective studies (NIVOREN trial: nivolumab; TORAVA trial: VEGF/VEGFR-targeted therapy (TT); CheckMate 214: nivolumab-ipilimumab vs sunitinib). LIPI was calculated based on a derived neutrophils/(leukocyte-neutrophil) ratio > 3 and lactate-dehydrogenase >upper limit of normal, classifying patients into three groups (LIPI good, 0 factors;LIPI intermediate (int), 1 factor;LIPI poor, 2 factors) and/or into two groups (LIPI good, 0 factors;LIPI int/poor, 1-2 factors) according to trial sample size. Primary and secondary endpoints were overall survival (OS) and progression-free survival (PFS). RESULTS: In the Nivolumab dataset (n = 619), LIPI was significantly associated with OS (LIPI-good 30.1 vs 13.8 months in the LIPI int/poor; HR= 0.47) and PFS (HR=0.74). In the VEGF/VEGFR-TT dataset (n = 159), only a correlation with PFS was observed. In the CheckMate214 dataset (n = 1084), LIPI was significantly associated with OS (nivolumab-ipilimumab OS LIPI good vs int/poor: HR=0.55, p < 0.0001; sunitinib: OS LIPI good vs int/poor: 0.38, p < 0.0001) in both treatment groups in univariate and multivariate analysis. CONCLUSIONS: Pretreatment-LIPI correlated with worse survival outcomes in mRCC treated with either ICI or antiangiogenic therapy, confirming LIPI's prognostic role in mRCC irrespective of systemic treatment used.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Biochemical and transcriptional profiling to triage additional activities in a series of IGF-1R/IR inhibitors.

Therapeutic development of a targeted agent involves a series of decisions over additional activities that may be ignored, eliminated or pursued. This paper details the concurrent application of two methods that provide a spectrum of information about the biological activity of a compound: biochemical profiling on a large panel of kinase assays and transcriptional profiling of mRNA responses. Our mRNA profiling studies used a full dose range, identifying subsets of transcriptional responses with differing EC(50)s which may reflect distinct targets. Profiling data allowed prioritization for validation in xenograft models, generated testable hypotheses for active compounds, and informed decisions on the general utility of the series.

Identification of a nonkinase target mediating cytotoxicity of novel kinase inhibitors.

In developing inhibitors of the LIM kinases, the initial lead molecules combined potent target inhibition with potent cytotoxic activity. However, as subsequent compounds were evaluated, the cytotoxic activity separated from inhibition of LIM kinases. A rapid determination of the cytotoxic mechanism and its molecular target was enabled by integrating data from two robust core technologies. High-content assays and gene expression profiling both indicated an effect on microtubule stability. Although the cytotoxic compounds are still kinase inhibitors, and their structures did not predict tubulin as an obvious target, these results provided the impetus to test their effects on microtubule polymerization directly. Unexpectedly, we confirmed tubulin itself as a molecular target of the cytotoxic kinase inhibitor compounds. This general approach to mechanism of action questions could be extended to larger data sets of quantified phenotypic and gene expression data.

Chemical genetics identifies Rab geranylgeranyl transferase as an apoptotic target of farnesyl transferase inhibitors.

A chemical genetics approach identified a cellular target of several proapoptotic farnesyl transferase inhibitors (FTIs). Treatment with these FTIs caused p53-independent apoptosis in Caenorhabditis elegans, which was mimicked by knockdown of endosomal trafficking proteins, including Rab5, Rab7, the HOPS complex, and notably the enzyme Rab geranylgeranyl transferase (RabGGT). These FTIs were found to inhibit mammalian RabGGT with potencies that correlated with their proapoptotic activity. Knockdown of RabGGT induced apoptosis in mammalian cancer cell lines, and both RabGGT subunits were overexpressed in several tumor tissues. These findings validate RabGGT, and by extension endosomal function, as a therapeutically relevant target for modulation of apoptosis, and enhance our understanding of the mechanism of action of FTIs.