Molecular pathway activation – New type of biomarkers for tumor morphology and personalized selection of target drugs
Introduction
For about seven decades, chemotherapy remains a key treatment for many cancers, often with impressive success rates. For example, the use of cisplatin regimens of testicular cancer treatment turned almost complete mortality to ∼90–95% disease-specific survival [1,2]. However, many cancer types and advancer individual tumors remain incurable and/or unresponsive using standard chemotherapy approaches. Moreover, chemotherapy frequently causes severe side effects, which may dramatically decrease the quality of life of a patient [3,4]. The chemical compounds included in standard chemotherapy cocktails have numerous, sometimes poorly characterized molecular targets in both cancerous and normal proliferating cells, which makes it difficult to simulate and predict the activity of drugs to an individual patient, and in standard practice clinicians routinely use clinical or morphological predictive factors like stage, grade, proliferative activity, etc [5,6]. These predictive factors are mostly inaccurate and poorly applicable for tracing the individual patient response to chemotherapy drugs and regimens.
To address specific activities of tumor marker proteins frequently overexpressed in cancer, a new generation of drugs termed Target drugs was created that target one or a few specific molecules in a cell [7]. Drugs can also target abnormal fusion proteins, such as chimeric fusions AML1-ETO or BCR-ABL frequently formed in leukemias, and the corresponding drugs imatinib and oridonin [[8], [9], [10]].
Target drugs consist of either specific monoclonal antibodies Mabs or low molecular weight inhibitor molecules, e.g. specific kinase inhibitors Nibs [11]. Different target drugs have different mechanisms of action and are effective to different cohorts of patients. For now, more than a hundred of different target drugs have been approved for use in cancer treatment and are present on the global pharmaceutical market (e.g., see www.drugbank.ca). For several cancer types, the emergence of target drugs was highly beneficial. For example, trastuzumab (anti-HER2 monoclonal antibody) and several other new anti-HER2 medications at least doubled median survival time in patients with metastatic HER2-positive breast cancer and improved 5-year survival in early stage disease to ∼90-95% [12,13]. Interestingly, before the introduction of trastuzumab, HER2-positive cancers had the worst prognosis across all breast cancer subtypes, whereas now the situation has reverted [14]. Melanoma patients for decades had no treatment opportunities except dacarbazine chemotherapy, which resulted in only a tiny, less than 10%, chance of short-lasting (∼5-6 month long) response and very low median survival of less than a year. Now, in the case of BRAF-mutated tumor, they can receive vemurafenib (anti-BRAF target drug) and have ∼50% chance of response [15], or, irrespectively of BRAF mutation, immune checkpoint inhibitor target therapies with ∼20% chance of long-term (>5 years) disease control [16]. Target drugs were also of a great benefit for advanced kidney cancers, which used to be mostly uncurable before [17]. Compared to the previous chemotherapy treatments, target drugs often cause reduced side effects and increased efficiency, but also have higher costs.
However, many individual advancer tumor cases and even entire cancer types (e.g. glioblastoma and pancreatic cancer) remain poorly responding to the target therapies [18]. Importantly, the results of clinical trials clearly evidence that in many cases the drugs considered inefficient for an overall cancer type, show significant benefit for a small fraction of the patients. For example, anti-EGFR drugs gefitinib and erlotinib showed no overall benefit in large randomized trials on patients with non-small cell lung cancer. However, it was observed that ∼10-15% of the patients did respond to the treatment and had longer survival characteristics. Further investigation revealed that the latter group of patients had activating mutation of EGFR gene and that this mutation, therefore, can predict response to the EGFR-targeting therapies [19]. Of note, this approach doesn’t work for colorectal cancer, where EGFR-mutated status has little predictive power for anti-EGFR drugs cetuximab and panitumumab. However, discovery of the role of KRAS mutation in the resistance to the EGFR-targeting antibodies helped to identify a group of patients that can benefit from this type of treatment - patients with wild-type status of KRAS. Moreover, further investigations showed that for the KRAS-mutant tumors (∼40% of colorectal cancer), anti-EGFR antibodies cause harm and even decrease survival [20].
It is, therefore, of great importance to identify strong predictive markers of target drug efficacy, for as many cancer-drug combinations as possible. Several molecular tests have been designed to identify individualized cancer treatments [21,22]. Like the examples mentioned above, these tests mostly utilize data on single somatic mutations or expressions of single or few genes. Unfortunately, most of those predictor features are applicable only to a relatively small number of patients – because they cover only a minor fraction of target drugs, in a minor fraction of cancer types. Thus, the problem of the efficient ATD selection remains largely unsolved. The price for incorrect or inefficient treatment is losing valuable patients’ time, decreased quality and duration of life and spending excessive financial resources. It became clear nowadays that somewhat more universal methods are needed to rank the maximum number of the existing drugs for an individual patient. Solving this task would certainly require high-throughput screening of the patient-specific genetic information, including deep assessment of the patient’s tumor specific gene expression profile. In this review, we focus on the recent advancements in the application to a tumor biology of a next level of the molecular data analysis - activation of intracellular molecular pathways. We outline here the methods utilized for molecular pathway activation analysis and their applications for personalizing the use of target drugs.
Section snippets
Molecular pathways and cancer biology
Intracellular molecular pathways (IMPs) combine gene products implicated in certain molecular functions. IMPs are involved in all major events in the normal and cancer cells. The best-known IMPs are signaling, metabolic, DNA repair and cytoskeleton organization pathways [23]. The metabolic pathway combines biochemical reactions in a continuously connected network having a biologically significant output. The signaling pathway, in turn, consolidates gene products involved in signal transduction.
Pathway analysis and gene expression data
In various experimental models, molecular pathway activation can be measured through the extensive screening of protein phosphorylation, by interrogating switches of transcription factor binding sites, using specific gene expression or protein aggregation biosensors, by measuring physiological outputs and by many other methods [48,49]. However, those approaches have an obvious strong limitation that they can’t be reproduced for the biomaterial of tumor patients, especially formalin-fixed,
Pathways as tumor biomarkers
Finding reliable and accurate molecular markers of cancer remains one the major priorities of the contemporary molecular medicine. Every year, thousands of reports communicate new RNA, DNA mutation or methylation, protein and non-protein biochemical biomarkers sensitive to cancer development [83]. Most of the expression biomarkers represent RNA or protein products for the individual genes. However, there is a clear trend towards finding next-level biomarkers of cancer consisting of gene
Pathway-based scoring of cancer drug efficiencies
Overall, the pathway-based methods of predicting drug efficiency can be classified in two major groups: (i) those using PAS signatures for the IMPs as more or less simple statistical coincidence biomarkers of drug response and (ii) those using knowledge of specific drug molecular mechanisms and PAS values to simulate activities of target drugs in an individual tumor.
Translation to personalized medicine
The drug scoring methods reviewed here rely on the high throughput gene expression data. However, the first published study utilizing molecular profiling to find potential targets and select treatments was a recent pilot prospective investigation utilizing molecular profiling of tumors by immunohistochemistry, fluorescence in situ hybridization (FISH) and DNA microarrays to interrogate mutation profiles, completed by the Translational Genomics Research Institute. This multicenter (9 sites in
Challenges and opportunities
Tumor represents a mixture of different cell populations with distinct phenotypes, genotypes and epigenetic modifications. The apparent heterogeneity of high-grade tumors may be due to various factors like influence of the different cancer stem cells niches, alternative mutation profiles and differential vascularization features. This creates obvious obstacles to the molecular diagnostics of tumor, drug scoring and cancer treatment. Current RNAseq technology is an effective tool to study the
Conclusion
A rapidly growing field of molecular pathway analytics highly benefits from transition to the new level of data analysis. Studies of single genes (including expression, mutation, epigenetic regulation, molecular interactions, etc) are being replaced by the attempts of building functional models involving hundreds and thousands of gene products in their interaction networks. The emergence of the related techniques balancing on the interface of advanced computer technologies and high throughput
Acknowledgment
This study was supported by the Russian Science Foundation grant 18-15-00061.
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