The future of healthcare is in accessing & using greater information about the individual. DNAmito is expanding their DNA testing platform enabling early chronic disease prediction, transforming its management; leading to earlier, more effective care.

DNAmito enables precise cancer treatment through its proprietary DNA technology and cloud platform. Our products leverage existing and open standards in the industry. DNAmito is vendor agnostic.

Who We Are

The DNAmito team is comprised of world class genomics and data scientists, accomplished entrepreneurs, software and product developers, clinical and industry experts, bringing years of experience and success to the company. They cultivate our strong innovative culture and work tirelessly to help our team to scale and succeed.

The DNAmito strategic advisory team has a wealth of experience in medical research, clinical oncology and regulatory. The members of the strategic advisory team come from leading academic institutions, medical practices as well as medical device companies.

DNAmito has a strategic partnership and licensing agreement with the Maastro Clinic and Maastricht University. Through this partnership, DNAmito has access to leading cancer research centers in Europe, Asia and the U.S.

Our Team

Syed Hamdani
Co-Founder & CEO
Kamal Gogineni
Dr. Rakesh Patel
Member of Medical Advisory
Prof. Bert Smeets
Expert in genetics in particular mitochondrial DNA
Saeed Masroor
R&D, Expert in machine learning & Artificial intelligence
Cary Oberije
R&D, Expert in statistics, in particular mitochondrial DNA
Sebastiaan Huntjens
Seasoned Business Developer with strong expertise in funding and corporate finance


Clinical Data

Already funded NIH-registered trials include:

  1. Rapid Learning for Lung Cancer
  2. Biobank Carcinoma
  3. The Head and Neck Tumor Biobank
  4. Early-Stage Breast Cancer

Select collaborators and partners:

Prospective Multicenter Trials

Maastricht University is a partner in the Requite Consortium* (15 partners)

“Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-Effects and Improve Quality of Life in Cancer Survivors” (multicenter cohort study in Lung Breast and prostate cancer: 5,300 lung, breast and prostate cancer patients



Personalized Radiation Oncology Decision Support System™

AI based platform, clinical-grade predictive models that use multiple factors to compare various treatment options:

Extensibility beyond radiation oncology

oncologists | surgeons | tumor board


Time, resources, expertise, and machine-learning in last 7 years to create Prodecis.




Data Scientists


Peer Reviewed


Disease Sites






"Optimizing outcome rather then dose"

Lambin et al. Nat Rev Clin Oncol 2013, Cheng Q, Roelofs E, Lambin P et al. Radiotherapy Oncol 2016

Multifactorial Decision Support System

Proprietary software & algorithms

Modeled and validated using data and tissue samples from partnership with 15 leading cancer centers and several already funded trials.

Lambin et al. Nat Rev Clin Oncol 2013, Cheng Q, Roelofs E, Lambin P et al. Radiotherapy Oncol 2016

Clinical Decision Support platform

Integrating Prodecis


North America

DNAmito Inc.

2225 East Bayshore Road, Suite 200

Palo Alto, CA 94303 USA


European Union

ptTheragnostic B.V.

Oxfordlaan 70

6229 EV Maastricht

The Netherlands