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Understanding the underlying mechanisms of cancer and disease progression is the key to develop effective treatment and deploy to most responsive patients. Cancer exhibits certain common hallmarks, however it may be caused by different factors and have different molecular signatures. This changes the way it is diagnosed and how it is treated. Hence the best way of eradicating cancer is accounting all the differences at molecular, genetic and immunologic level and to apply the treatment in more ‘personalized’ way targeting the specific signatures rather than using a more generic treatment for all types of cancer. This approach relies on the identification of molecular pathways and target markers which are unique to each cancer types and even each patient.

Different strategies have been incorporated to develop therapeutics such as78 revealing the molecular profiling of the tumor, defining single or multi-gene expressions for response or resistance to a particular drug, development of targeted therapies to inhibit certain molecular pathways, and vaccine therapies79 treating the disease by patients own immune system.

As discussed before, tumor heterogeneity is suggested to be results of CSC model and/or clonal evolution. Heterogeneity has already been reported in many cancers between patients with the same type of cancer or between tumours of different tissue and cell types, (intertumor heterogeneity)80 and also within the same patient between primary and matched metastatic tumor or within the same tumor of an individual(intratumor heterogeneity)81. These distinct subtypes are different in disease progression, response and tolerance to the treatment. Hence accurate identification of subtypes with distinct molecular profile is crucial by measuring biomarkers that can relate to outcome of the disease or of the treatment. There are different kinds of biomarkers: prognostic, predictive and pharmacodynamic (Figure 5). Prognostic biomarkers are used to evaluate the disease-free or overall survival and risk or recurrence82. Predictive markers are able to assess the response to the given therapy82. On the other hand pharmacodynamic biomarkers can measure the effect of the drug on the disease. These markers should be extensively analyzed to confirm their clinical relevance and analytical validation. By selecting the patients who would likely benefit from particular therapy based on these biomarkers will not only increase the efficacy of the treatment but also help to avoid unnecessary adverse effect of drugs. Moreover, biomarker based drug selection has a substantial influence on cost. For example, it has been reported that erlotinib treatment was much more cost-effective if it is used on patients having high EGFR (Epidermal growth factor receptor)


copy number83. Though there is still a debate whether overall cost will be reduced due to the high cost of identification of biomarkers and development of targeted therapies.


Breast cancer is known to be very heterogeneous in terms of histological subtypes, treatment response and patient outcome. Thanks to microarray technologies, analyses of gene expression profile have been used to classify intrinsic subtypes of breast cancer:

luminal A, luminal B, HER2-enriched and basal like81 subtypes that can be primarily distinguished by the positive or negative expressions of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2).

Further studies have shown two more subtypes under the basal like (triple negative for ER, PR and HER2) subtype in which claudin-low tumors show a gene expression profile similar to mammary stem cell with EMT positive markers and molecular apocrine tumors are positive for androgen receptor (AR). All these six subtypes have different sensitivity against treatments but these behaviors have to be clinically validated. For example, HER2 positive tumors are found in %20-25 of the breast cancer; they are identified by the amplification of Her2 gene. This category of patients are associated with poor prognosis84 and the patients are more likely to benefit from the targeted therapy called Trastuzumab85.

To distinguish subtypes, different analytical approaches were used by Cancer Genome Atlas (TCGA) such as genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, and microRNA sequencing and reverse-phase protein arrays86. This covers different molecular profiles at genomic, epigenetic and proteomic level present in each subtype so that specific protein expression and signaling pathways could be used for a therapeutic target. Moreover a breakthrough in development of sequencing methods; next-generation sequencing, has unveiled variety of recurrent point mutations, translocations, small


insertion or deletions, duplications or amplification in DNA at a large scale that can be attained to predict prognosis or drug response87.

By accurately identifying the biomarkers and having molecular characterization of cancer relating to the distinct subtypes of cancer, several different strategies could be used for treatment. For example, therapies could be combined to target more than one pathways, one inhibiting the primary progression driver and the other inhibiting the upfront signaling mediating the resistance for the treatment81. An alternative way is that the simultaneous adaptation is made to the therapy after development of the resistant clone or any treatment related perturbations occurs, which is termed as adaptive therapy81. Another way could be by targeting the tumor environment such as inhibition of angiogenic switch or interaction between stroma and cancer cells.

A critical aspect of personalized medicine is how the patient responds to the targeted therapy at the single person level. All these identified genomic and proteomic signatures do not directly reflect the phenotype of the tumor or the activity of the drug and this limitation is addressed by assays termed as ‘next-generation functional diagnostics88. To predict the behavior of the drug response, one should consider all the components of the complex system of cancer such as proteins, metabolites, genes, interactions of gene-RNA, protein-protein and RNA-protein-protein and this cannot be provided by the only genomic information.

Already studies are performed to yield the resistance patterns by analysis on biopsies before and after treatment or monitoring the circulating tumor DNA during the treatment, yet they do not provide immediate response against the administered agents. These functional assays are now emerging to screen cytotoxicity, efficacy of potential targeted therapies, tumor responses such as FACS (Fluorescence-activated cell sorting)for measuring multiple pathways simultaneously, 3D organoid derived from patients to screen cancer-relevant drugs and CTCs for monitoring the treatment response and drug screening88. One of the promising examples of these assays is patient-derived xenograft (PDX) mouse models in which tumor biopsy from the patient is implanted into an immunodeficient mouse and expanded which gives the advantage to have some of the histological properties, gene expressions and somatic genetics from the patient. These models allow testing the drug efficacy and also started to be used in selecting therapies. Hence in the ideal world, functional assays will be used on the patients’ samples before the therapy achieving a best

‘personalized’ treatment. However, each technique still has its own limitations and has to be further tested for analytical and clinical validation and clinical utility.


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