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.

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We integrate heterogeneous molecular data of 11,289 tumors and 1,001 cell lines

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We measure the response of 1,001 cancer cell lines to 265 anti-cancer drugs

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We uncover numerous oncogenic aberrations that sensitize to an anti-cancer drug

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Our study forms a resource to identify therapeutic options for cancer sub-populations

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

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.