In Selvita, one of the services we offer to our customers is to identify and validate new drug targets within the Integrated Drug Discovery platform that we have been successfully developing for some years.

One of the first steps in identifying a new drug target is to take information from public databases in order to better understand the biological context of the target of interest in the context of illness that the customer buying our services is asking us to analyze.

The existence of public data greatly facilitates this task and currently there are projects that are truly worthy of mention because the novelty and importance of the data they contain will have a decisive impact on the so-called personalized and precision medicine of the future.

What does Precision Medicine consist of?

Precision medicine, also called personalized medicine or individualized medicine, includes diagnostic, preventive and therapeutic measures that are optimally tailored to an individual.

This includes information (data) about a person’s biological background, in particular also genetic data, in the decision-making process for the adoption of therapeutic and preventive measures aimed at the treatment of the same. These tailored, data-driven treatments aim to apply more effective therapies and reduce side effects.

In the oncology field, the study of cancer cells and those derived from sick patients is reaching a stage of maturity in which it has become easier to characterize and study them. Despite this, we are still at the beginning of a process that sees the panorama of genetic alterations that occur in tumors as an incomplete and difficult to interpret picture. Understanding the biological impact of these features and how they conspire to induce specific tumor vulnerabilities is largely incomplete. Consequently, the use of genetic information from tumors to enable precision cancer medicine is limited.

There are international initiatives and projects that are having great success in creating a definitive map of cancer addiction. Such a map can serve as the basis for the entire field, leading to a blueprint for targeted therapeutic development and an acceleration of precision cancer medicine.

Figure 1 – credit: National Cancer Institute

A New Cancer Genetic Landscape: Synthetic lethality

But let’s take a step back and talk about synthetic lethality, an old concept that was heavily studied in the early years of nineteenth century as phenomenon that was historically described by several scholars who were studying the genetics of the fruit fly Drosophila melanogaster (one of the most used and studied model organism in biology in the field of genetics).

Synthetic lethality occurs between two genes when silencing one of the two genes alone allows for viability but inhibition of both is lethal. Understanding how these genes work and the context in which they work allows for therapeutic targeting in cancer. One of the examples that helped validate the concept of synthetic lethality and adopt its potential is the PARP-BRCA proteins in breast cancer. Where there is a mutation in the tumor suppressor proteins BRCA1 or BRCA2, the pharmacological targets that were validated included PARP proteins and the therapeutic effect were very high in terms of cure.

Clarifying synthetically lethal gene combinations in cancer could establish clinically relevant drug combinations and biomarkers to better treat patients.

Figure 2 – credit: National Cancer Institute


An important revolution in the medical field has been that of genomics and related technologies, which make it possible to analyze the individual genetic heritage. These technologies in the last decade have seen enormous progress, also from the point of view of performance efficiency and cost reduction. In the past, the sequencing of the human genome has required over 10 years of work and about 3 billion dollars, while currently it only takes one day to sequence several, with a unit cost of less than 1,000 dollars.

Fig. 8.1
Figure 3 – Timeline of achievements in NGS technologies

Genomic analysis of the patient allows the rapid identification of mutated genes in his DNA, which may be responsible for an ongoing disease and diagnostic for its recognition; or indicators of a predisposition, i.e. a greater probability, compared to another subject who is not a carrier, to develop a given pathology; or indicators of the probable evolution of that disease and therefore of the prognosis; or finally predictive markers, i.e. useful to predict a greater or lesser capacity to respond to a specific therapeutic treatment.

Figure 4 – Cost of sequencing a human genome from 2001 to 2020

The Cancer Dependency Map – DepMap

The DepMap project is the result of collaborative work between industry and academia in the field of characterization and study of the main tumor lines in vitro by analyzing the potential targets that represent the new pharmacological targets related to the genetic context of the disease and its mutations.

Precision medicine enables understanding the genetic diversity in a broad spectrum of human cancers in order to guide treatment based on molecular characteristics. The Cancer Cell Line Encyclopedia (CCLE) project began as a collaboration between Broad and Novartis to measure how this genomic diversity has been captured in existing cell models.

The DepMap project allowed to continue the CCLE project in a new direction by including within it data deriving from genomic characterization and screening of drug targets within the same tumor cell lines using modern biomolecular technologies such as gene inhibition using RNAi and Crispr / Cas technology.

The result of this work is a catalog of genetic vulnerabilities related to cancer cell lines that allows the identification of the best possible drug targets depending on the tumor context and the type of tumor examined.

Figure 5 – Cell lines that are part of the DepMap and CCLE project

How do we use this data in Selvita?

The bioinformatics analyses that we have developed using specific software allow us to examine the significant amount of data generated by these public projects quickly and efficiently in order to focus attention on the tumor of interest to our client.

In the field of identification of pharmacological targets for certain types of cancer, we were able to annotate in detail the dependence relationship between driver and target genes by exploiting the public data of the DepMap project and similar projects derived from literature studies.  This enabled us to draw a network of relationships between the proteins of interest to the client using modern visualization software (Cytoscape) and the most relevant database of interactions between proteins (String-DB).

The mapping of interactions through network software is also a fast and practical way to view the data of interest in order to evaluate the quantity and quality of the information generated and to also validate the analysis model that will be subsequently delivered to the customer, thus allowing them to have better results for their studies.

Figure 6 – example of an interaction network between BRCA1 / 2 and PARP1 proteins


  • Federico Malusa
    Senior Scientist I
    Computational Chemistry Team