Precisium AI 

Programmatic survey of clinically tested targets in Oncology

Host Organization

Precisium AI is a medtech company developing an AI-driven platform for precision target identification in complex diseases. The company focuses on improving early target selection in drug discovery through large-scale integration of biological and clinical data. The platform integrates diverse data types, including single-cell and spatial omics, functional genomics, protein interaction networks, and clinical evidence. By modeling disease at cellular resolution, Precisium AI supports data-driven target prioritization for pharmaceutical and biotechnology partners and is currently expanding its platform into oncology.

Background and Problem Description

Despite major scientific and technological advances, oncology drug development continues to face high failure rates in late-stage clinical trials. A key contributing factor is suboptimal target selection, often based on bulk-level analyses that do not fully capture tumor heterogeneity or cell-type–specific biology. In breast cancer, lung cancer, and prostate cancer, numerous molecular targets have been proposed, yet many fail to translate into effective therapies. There is a need for systematic, data-driven approaches that integrate high-resolution omics data with structured clinical evidence to support more informed and biologically grounded target prioritization in oncology.

Project Description and Objectives

The aim of this internship project is to support the expansion of Precisium AI’s oncology platform by systematically analyzing therapeutic targets and clinical trial outcomes in breast cancer, lung cancer, and prostate cancer. The primary objective of this project is to establish disease-specific target landscapes using R or Python, based on structured analysis of molecular targets evaluated in clinical development. The candidate will support the compilation and analysis of information on target genes or proteins, mechanisms of action, clinical trial phase, and trial outcomes, with the objective of distinguishing successful, failed, and discontinued therapeutic strategies across oncology indications. All analyses will be performed using reproducible computational workflows appropriate for a one-month internship project. The outputs generated through these R- or Python-based analyses will directly contribute to the deliverables of the internship, including disease-specific target landscape summaries and documented computational pipelines that can be reused for future oncology indications.

Project Tasks and Responsibilities

During the one-month internship, the candidate’s primary responsibility will be to independently generate disease-specific oncology target landscapes for breast cancer, lung cancer, and prostate cancer using R or Python, with scientific guidance from Precisium AI.

Primary Task 

• Design and implement reproducible R or Python workflows to generate structured, disease-specific target landscapes summarizing therapeutic targets evaluated in oncology clinical development, including target identity, mechanism of action, clinical trial phase, and trial outcome.

Supporting Tasks:

In support of the primary objective and within the time constraints of the internship, the candidate will also:

• Support data collection and curation of oncology clinical trial information from public biomedical sources
•  Assist with data harmonization, including standardization of target identifiers and outcome
  classifications
•  Document analysis workflows, data sources, and assumptions to ensure reproducibility and facilitate future reuse

Expected Deliverables

By the end of the one-month internship, the candidate is expected to deliver the following.

Primary Deliverables 

1. Three disease-specific oncology target landscapes, generated using R or Python, covering
   breast cancer, lung cancer, and prostate cancer
2.  Reproducible R or Python analysis scripts, clearly structured, documented, and reusable for future oncology indications within Precisium AI’s platform

Supporting Deliverables 

    3. Curated and harmonized oncology datasets, linking clinical trial outcomes to molecular              targets

    4. Concise technical documentation, including data sources, analytical approach, key                       assumptions, and notes on limitations and potential extensions

This project provides a research-intensive internship experience at the interface of cancer biology, data science, and translational medicine. The internship offers exposure to real-world industry workflows and addresses clinically relevant challenges in oncology drug development, aligning well with the objectives of the KI Career Service Internship Program.

Contact Information 

Name: Gozde Gucluler Akpina

Email: gozde.gucluler@precisium.ai