A new technology that measures the levels of proteins in individual cells could help to identify therapy combinations that might more effectively treat people with multiple myeloma, a study suggests.
Its researchers also report that lowering levels of a particular protein on myeloma cells, MCL-1, may make them more responsive to treatments for this cancer.
Most multiple myeloma therapies work by activating apoptosis — programmed cell death — in cancer cells. However, the development of resistance remains an obstacle in this kind of treatment. This is complicated by the fact that, within a single person, not all cancer cells are identical. Indeed, there is a lot of variability cell-to-cell, possibly affecting how individual cells (and, by extension, the cancer as a whole) respond to therapies.
“We wanted to better understand the molecular differences between individual cancer cells so we could discover how these differences impact the cancer’s response to therapies — for example, whether some cells are more resistant than others to an anti-cancer drug,” Charis Teh, PhD, a researcher at Walter and Eliza Hall Institute of Medical Research, in Australia, and a study co-author, said in a press release.
“We decided that a new technology, called mass cytometry, would be an ideal approach to address this question,” Teh said.
Mass cytometry (CyTOF) basically allows researchers to measure the amount of a given protein in individual cells. The researchers used this technology to measure changes in the expression of apoptosis-associated proteins in myeloma cell lines following treatment with either dexamethasone (common brand names include Baycadron and Decadron) or Velcade (bortezomib). Both of these therapies are mainstays of multiple myeloma treatment.
By using machine learning to analyze the CyTOF data, the researchers were able to distinguish between cells in which apoptosis was activated (that is, the cell would die) and in which it wasn’t (that is, the cell was resistant).
“A distinguishing feature between the two treatments [dexamethasone or Velcade] was the induction of p53 upon treatment with [Velcade] but not dexamethasone,” the researchers wrote. p53 is a protein that regulates the cell cycle, promoting apoptosis and, in this way, functioning as a tumor suppressor.
Further analysis allowed the team to approximate the sequence of events, in terms of changes in protein expression, that preceded either outcome (cell death or cell resistance).
This analysis found the protein MCL-1 to be a key factor determining outcome: high levels of MCL-1 were predictive of cancer cell survival, while low levels were predictive of apoptosis. In other words, high MCL-1 levels were indicative of resistance to therapy.
“Therefore,” the researchers wrote in their study, “we hypothesized that selective inhibition of MCL-1 alone would enhance the apoptotic response of myeloma cells to dexamethasone.”
This idea was tested using S63845, a small molecule inhibitor of MCL-1 plus dexamethasone. They found that these two compounds synergized, with relatively low doses of each sufficient to kill over 90% of cells.
The researchers then evaluated this combination using cancer cells taken from 12 multiple myeloma patients (as opposed to the immortalized cell lines used in the above experiments). Synergistic killing was observed in 10 of these samples, with four demonstrating “very strong synergy.”
“These results indicate that combining an MCL-1 inhibitor with dexamethasone has the potential to enhance killing myeloma cells,” the researchers wrote.
“Excitingly, there are already drugs in clinical trials that inhibit MCL-1,” Teh said. One such study is a Phase 1 trial (NCT02992483) safety and tolerability trial of MIK665, by Novartis, in up to 67 adults with relapsed or refractory multiple myeloma. This open-label study may be still enrolling eligible patients at select worldwide sites.
Beyond this particular set of proteins and associated drugs, study findings show how this kind of single-cell analysis could be useful in developing cancer treatments generally.
“The panel of markers developed in this study gives researchers considerable scope to understand how cancer cells are responding to anti-cancer therapies — and as we found, it can even help to identify better drug combinations,” said Daniel Gray, PhD, a study co-author and professor at the Walter and Eliza Hall Institute.
Gray added that this technology could also help researchers and clinicians to better understand why some people respond more or less well to different therapies, which, in turn, could help guide treatment decisions.
“We’ve already started collaborations with our clinical colleagues to investigate this possibility further,” he said.
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