Ten months ago, the physicians of an enthusiastic 76-year-old sales clerk from New Jersey who had an advanced carcinoma in his urinary tract decided to try unconventional therapy. A few weeks ago, he sent a sample of his tumor to my team at the Institute of Precision Medicine at Weill Cornell Medical College and New York-Presbyterian Hospital in New York City. Genetic sequencing revealed that he had more than normal copies of the HER2 gene (also known as ERBB2) 1,2.
After years of failure in the usual arsenal of surgery, chemotherapy, and radiation, physicians incorporated the drug Herceptin (trastuzumab) into the woman’s treatment. Herceptin is more commonly used for breast cancer, but it targets the HER2 mutation. He is free from the disease since taking the medicine.
Advances in sequencing have dramatically increased the possibility of discovering mutations that promote tumor growth in some people and in some tumors – even in specific cells within a tumor.
Yet mountains of genomic data are accumulating that are of little use because they are not tied to clinical information, such as family medical history. Furthermore, genomic data is generally limited to documents that cannot be easily discovered, shared or even understood by most clinicians.
To achieve the level of success US President Barack Obama and others are anticipating in precision medicine for cancer care, sequence data must be linked, in real time, to a patient sitting in front of their doctor.
Integrated genomic and clinical data should also be available in a searchable manner to the broader community of clinicians and researchers. Prototypes for centralized data banks are showing promise, but growing them requires serious and sustained investment.
Physicians are used to evaluating 20–50 measurements from routine laboratory tests, such as for blood-sugar levels. Such data can be easily entered into patients’ electronic health records. Genomic data introduces a new level of complexity.
To get an idea of the scale, it would take more than 25 days to transfer 2.5 petabytes (a petabyte is 1,000 terabytes) of data from one computer server to another, prepared by The Cancer Genome Atlas – a US project that was developed in 2005. Catalog was started in Mutations that drive cancer. That’s according to my colleague Toby Bloom, deputy director of informatics at the New York Genome Center, a consortium that specializes in large-scale human genome sequencing.
Highly complex genomic reports are rarely available in electronic form and rarely contain basic patient information. For example, whole-genome sequencing by the International Cancer Genome Consortium (ICGC) on tumor samples from nearly 14,000 people has revealed nearly 13 million mutations in the genome.
But many factors other than mutations in a person’s DNA will affect whether a single patient will respond to a particular treatment. Unfortunately, in the ICGC attempt – and many prefer it – only the most minimal clinical data, such as tumor type and size, are available (see ‘Missing metrics’).
Since 2013, working with a team of computational biologists from Weil Cornell and the Center for Integrative Biology at the University of Trento in Italy, my colleagues and I have been working to determine the feasibility of linking genomic data to clinical data in real time. A pilot program has been organized for So far, we’ve created easy-to-read reports for 250 people living with cancer.
Each report contains a barcode, allowing patients to be identified and re-identified as needed, and is designed to be easily integrated into New York-Presbyterian Weill Cornell Medical Center’s electronic health-record system.
The data, which are presented like results of pathology, capture clinical information (family history, drug use and so on), information about mutations for which specific drugs exist, and unknown effects. Conclusions about genetic anomalies with.
We have found that more than 90% of our patients carry a mutation that may be responsive to a known drug – although less than 10% of patients may be eligible for clinical testing either for logistical reasons or Because there is insufficient evidence to warrant trying an unapproved drug.
To be more widely useful, these data need to be shared across institutions. Take, for example, current efforts to investigate the efficacy and safety of the drug neratinib in patients whose tumor growth is driven by different mutations in HER2 or EGFR3.
Except for lung cancer (in which EGFR mutations are common), the frequency of these mutations is in the range of 1–6%, so achieving the required numbers for a phase II clinical trial would mean the number of patients from multiple medical centers. to recruit.