Multiple myeloma is a type of blood cancer that affects the plasma cells located in the bone marrow of the body. During an infection, when B cells or B lymphocytes mature, they become plasma cells, which, in turn, are responsible for the production of antibodies or immunoglobulins. Therefore, multiple myeloma is a condition when these plasma cells grow uncontrollably and become cancerous, also producing abnormal proteins like monoclonal immunoglobulin. Ultimately, the excessive proliferation of cells leads to organ failure and death.
Multiple myeloma is the second-most common type of blood cancer, but its detection, diagnosis, and treatment are often challenging because it could resemble several other conditions in the initial stages, and there is no way of precisely distinguishing whether cells will become malignant or not. Statistics show that current blood tests are mostly inaccurate in identifying patients presenting with early symptoms of the disease. For instance, those in the precancerous stages are typically made to “watch and wait,” but, in fact, it was observed that 1% of individuals did actually develop the full disease.
Therefore, researchers from the Weizmann Institute of Science and clinicians from Israeli hospitals developed a technology wherein the genetic makeup of cells of cancer patients were profiled to yield a faster diagnosis and treatment.
Their full paper was published in a recent issue of Nature Medicine.
Forty individuals with suspected multiple myeloma and 11 healthy controls participated in the study. A machine-learning approach was applied, which created ‘blueprints,’ that sequenced RNA from the patients’ cells. From this, the specific genes that were active in individual cells were identified.
On comparing the models created from the two groups (normal vs. myeloma), it was seen that the healthy individuals had plasma cells that were similar to each other, showing a common blueprint. On the other hand, the myeloma cells were noticed to be highly heterogeneous with every patient displaying a unique blueprint. Some myeloma patients, who had several tumors, showed more than one blueprint.
Image showing the difference between normal cell development (left) and the multiplication of myeloma cells in the bones of the body (right). (Source: Patient Resource LLC)
This technique could make it possible to identify cancer early in the blood. When the cells are recognized in this pre-cancerous stage, they can be precisely diagnosed and treated.
Weizmann Institute of Science’s Dr. Assaf Weiner said, “We are entering an era in which measuring big data and using machine learning will provide clinicians new insights and understanding into devastating diseases like multiple myeloma.”
Prof. Amit of the Institute’s Immunology department described the bright potential of single-cell genomic analysis, not just confined to laboratories, but also as a diagnostic tool and in clinical discoveries. He added that this kind of research and profiling could lead the way to personalize medicine, where patients can make better and informed choices about their treatment plans.
Lead author of the paper, Dr. Guy Ledergor, also substantiated these inferences from the study. He hopes that physicians use this method to track multiple myeloma in real-time and then treat patients accordingly, possibly even before the condition worsens.
Experts have also pointed out that collaborations with other hospitals and research labs to advance diagnosis and treatment, by incorporating technologies like single-cell RNA sequencing in clinical research, could prove useful in distancing patients from the progression of this cancer.
This innovation, in the future, could lead to the earlier diagnosis and treatment of multiple myeloma patients. It could also be useful in avoiding relapses of the condition that usually follows chemotherapy. Another area that this technology could contribute is in replacing painful bone marrow biopsies with a blood test.
Top Image: Stained multiple myeloma cells of the bone marrow. (Source: Wikimedia Commons)