Imagine cancer patient walking in a clinic. As part of diagnosis, oncologists and onco-surgeons want to see the tumor and get as much information about it as possible. A slew of tests follow. Tumor is imaged using MRI and other techniques. Many times, patient serum is evaluated to identify molecules that rise in concentration due to specific cancer. Once the surgeon is reasonably convinced of tissue mass being cancerous, s/he might take a biopsy, where a small piece of tumor is acquired and sent to histopathologist. Histopathologist is a doctor who chops this tissue into extremely thin slices and stains them with colors. Cells and nucleus within such slices are stained, which are then evaluated under microscope. Histopathologist’s power lies in his/her experience of seeing how these cells look, how they are arranged and a bunch of other features, to tell characteristics of the cancer.
To all this, let us add a new layer of complexity. Each cancer results from change in DNA which could be point mutations, amplifications, translocations etc. Research on cancer tumors are documenting more and more such changes in human genome, every passing day. Large databases are simultaneously being created which document these changes against specific tumor types. It is apparent that due to large and rapid development of new knowledge in oncology, comprehending all the information before taking crucial treatment decisions have become challenging. Also, exploitation of genomics in healthcare is relatively recent phenomenon in developing countries. Treatment algorithms have become complex and they need changes more frequently than ever before. Also, many a times an oncologist would want to test therapies off label, ie treatment not yet in the guidelines.
So, how should an oncologist be guided in such time?
Time is ripe for mobile apps to guide both oncologists and patients. Programming scripts need to be developed that compare and analyze short DNA sequences of tumor and normal tissue. This quickly reveal differences between tumor DNA and normal tissue DNA of the patient. Interestingly, this is very well worked out problem. Long before the advent of DNA sequencing, math tools were developed to solve string problems and DNA sequence is nothing but long string composed of four letters, A, T, C & G. These math tools came handy when human genome was sequenced and analysis posed a challenge. Let’s say, sequencing output of KRAS gene was fed in the program that yielded a pathogenic change at 61st position of KRAS protein. But what does that mean? How that information is related to a cancer patient? This second set of information is in formative stage. Numerous labs involved in fundamental as well as clinical research are creating new knowledge every passing day. However, existing information is already being used to develop next generation targeted therapy and to treat patients. In this case, if the patient is having thyroid cancer versus pancreatic cancer, interpretations may differ. In case of thyroid cancer, a KRAS gene mutation will mean possible follicular cell type thyroid cancer. It would be differentiated thyroid cancer where cancerous cells would retain original shape and structure. In case of pancreatic tumor, it might bring faint cheer to patient and consulting physician as few reports suggest that KRAS mutation at 61st position indicate better prognosis.
Computer program will include scripts to compare and analyze strings of DNA, while it would also have scripts that would integrate this information to clinically actionable information. Such mobile app would help oncologists to quickly conclude molecular test. With the advent of genomics era, be prepared to see physicians wielding mobile apps to fight your diseases.