Our Story

Trucan is an omics-based oncology platform development company with a mission to revolutionize cancer detection and care by implementing true personalization.

Globally, oncologists face challenging choices when dealing with insufficient or incomplete information. We acknowledge that identical diagnoses may not always yield identical responses to medications. Oncologists' decisions are based on historical aggregated data that often does not translate at the individual patient level. These decisions carry significant weight, affecting both patients and their families. Our objective is to establish strong correlates between omics and clinical behaviour using carefully curated patient cohorts. This will help make clinical decision-making more objective and scientific. We acknowledge the inadequacy of the current “one size fits all” approach, and have therefore invented the “cluster mutation discovery engine”. Our ultra-personalized approach revolutionizes cancer diagnosis and care. By identifying patterns that are not obvious to others, we can define the cluster to which a patient belongs thus being able to detect &/or predict with greater efficacy than all other current methods.

Clinicians employ our methodology for diagnosing and treating patients. We collaborate with pharmaceutical companies to optimize their drug development process by aiding the careful selection of patients for Ph II and III clinical trials - such selection leads to faster recruitment of possible responders and leads to lower cost, reduced time for clinical trials, and much-conserved patent life. This approach also offers companion diagnostics by the end of the trial phase. Pharmaceutical companies and academics are presented with validated targets, guiding them to concentrate their disease biology and drug discovery investigations specifically on challenging-to-treat, resistant, and metastatic cancers.

Innovation is in our DNA. Our team of biologists, computer scientists, and clinicians use a combination of medical, scientific, and technological expertise to ask “How else and why not” constantly, the results of which are evident in our inventions and approaches.

Patient Stories

The three main unmet needs in cancer care today are late diagnosis, lack of predictability of treatment, and high cost of treatment. The chances of survival are greatly reduced when a patient is diagnosed with cancer at a later/advanced stage. Furthermore, 20-60% of cancer patients do not respond to standard of care and are subject to toxic therapies with neither guarantee of recovery nor eectiveness. Consequently, these ineffective treatments significantly increase the financial burden upon the patient and reduce the quality of life.

Here are examples of real patient stories where the usage of our platform could have eliminated risk, increased chances of survival, and improved quality of life.

A 30-year-old female from Bellary was diagnosed with TNBC in 2017. Given NACT; had a partial response, and underwent a mastectomy and adjuvant chemo and radiation therapy. On follow- up, chances of recurrence are high in view of the partial response to NACT.

A case where the response to neoadjuvant chemotherapy could have been predicted with accuracy and additional chemotherapy given initially this likely increasing chances of long-term survival.

A 53-year-old female from Pune; had invasive ductal carcinoma in 2009(ER+PR+). Treated with NACT followed by surgery; she was on endocrine therapy for five years after which she was advised routine follow-up. In March 2015, she presented with metastasis of cancer in her bones.

A case where periodic blood-based monitoring would have detected recurrence earlier than symptom-led investigation.

A 46-year-old presented with oral ulcer, diagnosed as squamous cell carcinoma, treated with surgery and chemotherapy in 2020; recurrence in 2 years. Advised pembrolizumab every 6 weeks for 1 year. Disease progression was seen after 4 months. The cost of pembrolizumab was 2 lacs plus hospital charges~2.5 lacs x 3 = 7.5 lacs with no clinical benefit

Asha
Bindu
Patil

A case where ineffective treatments had led to large costs that could have been avoided. Prediction of response could have avoided this line of treatment.