Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers

Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived "healthy" normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact "differential" expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.

Distinct Traits of Structural and Regulatory Evolutional Conservation of Human Genes with Specific Focus on Major Cancer Molecular Pathways

The evolution of protein-coding genes has both structural and regulatory components. The first can be assessed by measuring the ratio of non-synonymous to synonymous nucleotide substitutions. The second component can be measured as the normalized proportion of transposable elements that are used as regulatory elements. For the first time, we characterized in parallel the regulatory and structural evolutionary profiles for 10,890 human genes and 2972 molecular pathways. We observed a ~0.1 correlation between the structural and regulatory metrics at the gene level, which appeared much higher (~0.4) at the pathway level. We deposited the data in the publicly available database RetroSpect. We also analyzed the evolutionary dynamics of six cancer pathways of two major axes: Notch/WNT/Hedgehog and AKT/mTOR/EGFR. The Hedgehog pathway had both components slower, whereas the Akt pathway had clearly accelerated structural evolution. In particular, the major hub nodes Akt and beta-catenin showed both components strongly decreased, whereas two major regulators of Akt TCL1 and CTMP had outstandingly high evolutionary rates. We also noticed structural conservation of serine/threonine kinases and the genes related to guanosine metabolism in cancer signaling: GPCRs, G proteins, and small regulatory GTPases (Src, Rac, Ras); however, this was compensated by the accelerated regulatory evolution.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers

DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers.