Predicting Leukemia Relapses Through Time Series and Other Advanced Techniques

Every three minutes, one person in the U.S. is diagnosed with leukemia or similar blood cancer. And every nine minutes, someone in the U.S. dies from these diseases.

Indeed, leukemia and related conditions are devastatingly common, even in countries with advanced medical systems. The diseases comprised around 10 percent of new cancer cases in the U.S. in 2021. More than 1.5 million people across the country either live with or are in remission from leukemia, lymphoma, myeloma, myelodysplastic syndromes (MDS), or myeloproliferative neoplasms (MPNs).

Despite these numbers, those afflicted with leukemia or other blood cancers have a better chance of survival now than ever before. The five-year survival rate for leukemia has?more than quadrupled?from 1960 to 2016.

Leukemia relapse: An all-too-common occurrence

Relapses among leukemia victims, however, are exceedingly common. Adults with acute lymphoblastic leukemia (ALL), for example, have a?50 percent chance?of relapsing. Between 10 and 20 percent of ALL sufferers of all ages will relapse.?

Relapses of other types of leukemia, such as acute myeloid leukemia (AML), also happen relatively frequently. That’s partially because chemotherapy doesn’t kill the leukemia stem cells that cause AML. “Leukemia stem cells tend to survive chemotherapy, stay in the bone marrow and regrow the disease,”?says?Dr. Jean Wang, Clinician Scientist at the Princess Margaret Cancer Centre in Toronto.

A patient’s relapse risk is currently assessed through various data points, including molecular and cytogenetic tests or clinical/biological factors such as age and white blood cell count. But these techniques are notoriously slow and often don’t produce results in time to save the patient from an aggressive relapse.

Accurate, more timely assessments are necessary to avoid over- or under-treatment and, ultimately, to ensure positive health outcomes.?

How time series and other new techniques have improved leukemia relapse prediction?

Several novel approaches for predicting leukemia relapses have recently been developed, including DNA and genetic testing and the use of machine learning models fed by time series data.

Gene signature testing: The LSC17 Test

Dr. Wang of the Princess Margaret Cancer Centre and colleagues?recently developed?a new gene signature test for AML that delivers accurate results within one to two days.?

Dr. Wang and her fellow researchers identified a signature of 17 genes – an “LSC17 score” – that can accurately predict a patient’s risk of relapse following chemotherapy. The test uses the?NanoString?platform to assess the patient and calculate an LSC17 score. The higher the score, the more likely that patient will relapse.?

Because the test is administered at the time of diagnosis, it can inform the most appropriate treatment methods – including experimental therapy or stronger initial therapy – based on the patient’s likelihood of relapse. “Using it as a correlative test in clinical trials would be really valuable to identify drugs that can benefit high-risk patients,” says Dr. Wang.

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