Cracking Tumor Defiance 01-18

Why does immunotherapy achieve dramatic results in some cancer patients but fail in others? 

Scientists have elucidated the mechanism behind some tumor's ability to escape immunotherapy drugs. Image: iStock


Why does immunotherapy achieve dramatic results in some cancer patients but doesn’t help others? It is
an urgent and vexing question for many cancer specialists.

Now, two research groups from Harvard Medical School based at Dana-Farber Cancer Institute have independently discovered a genetic mechanism in cancer cells that influences whether they resist or respond to immunotherapy drugs known as checkpoint inhibitors.

The findings, the researchers say, reveal potential new drug targets and could aid efforts to extend the benefits of immunotherapy treatment to more patients and target additional types of cancer.

The two groups converged on a discovery that resistance to immune checkpoint blockade is critically controlled by changes in a group of proteins that regulate how DNA is packaged in cells. The collection of proteins, called a chromatin remodeling complex, is known as SWI/SNF. Its components are encoded by different genes, among them ARID2, PBRM1 and BRD7. SWI/SNF’s job is to open up stretches of tightly wound DNA so that its blueprints can be read by the cell to activate certain genes to make proteins. 

Researchers led by Van Allen and Choueiri sought an explanation for why some patients with a form of metastatic kidney cancer called clear cell renal cell carcinoma (ccRCC) gain clinical benefit—sometimes durable—from treatment with immune checkpoint inhibitors that block the PD-1 checkpoint, while other patients don’t.

The scientists’ curiosity was piqued by the fact that ccRCC differs from other types of cancer that respond well to immunotherapy, such as melanoma, non-small cell lung cancer and a specific type of colorectal cancer. Cells of the latter cancer types contain many DNA mutations, which are thought to make distinctive tumor antigens called, neoantigens, which help the patient’s immune system recognize and attack tumors and make the cancer cells’ microenvironment hospitable to tumor-fighting T cells. By contrast, ccRCC kidney cancer cells contain few mutations, yet some patients even with advanced, metastatic disease respond well to immunotherapy.

To search for other characteristics of ccRCC tumors that influence immunotherapy response or resistance, the researchers used whole exome DNA sequencing to analyze tumor samples from 35 patients treated in a clinical trial with the checkpoint blocker nivolumab (Opdivo). They also analyzed samples from another group of 63 patients with metastatic ccRCC treated with similar drugs.

When the data were sorted and refined, the scientists discovered that patients who benefited from the immunotherapy treatment with longer survival and progression-free survival were those whose tumors lacked a functioning PRBM1 gene. About 41 percent of patients with ccRCC kidney cancer have a nonfunctioning PBRM1 gene. That gene encodes a protein called BAF180, which is a subunit of the PBAF subtype of the SWI/SNF chromatin remodeling complex. 

Loss of the PBRM1 gene function caused the cancer cells to have increased expression of other genes, including those in the gene pathway known as IL6/JAK-STAT3, which are involved in immune system stimulation.

The finding does not directly lead to a test for immunotherapy response yet, the scientists caution, but they carry a clear therapeutic promise.

“We intend to look at these specific genomic alterations in larger, randomized controlled trials, and we hope that one day these findings will be the impetus for prospective clinical trials based on these alterations,” Choueiri said.

In the second report, the scientists led by Wucherpfennig came at the issue from a different angle. They used the gene-editing CRISPR/Cas9 tool to sift the genomes of melanoma cells for changes that made tumors resistant to being killed by immune T cells, which are the main actors in the immune system response against infections and cancer cells. 

The search turned up about 100 genes which appeared to govern melanoma cells’ resistance to being killed by T cells. Inactivating those genes rendered the cancer cells sensitive to T-cell killing. Narrowing down their search, the Wucherpfennig team identified the PBAF subtype of the SWI/SNF chromatin remodeling complex—the same group of proteins implicated by the Van Allen and Choueiri team in kidney cancer cells—as being involved in resistance to immune T cells.

When the PBRM1 gene was knocked out in experiments, the melanoma cells became more sensitive to interferon gamma produced by T cells and, in response, produced signaling molecules that recruited more tumor-fighting T cells into the tumor. The two other genes in the PBAF complex—ARID2 and BRD7—are also found mutated in some cancers, according to the researchers, and those cancers, like the melanoma lacking ARID2 function, may also respond better to checkpoint blockade. The protein products of these genes, the authors noted, “represent targets for immunotherapy, because inactivating mutations sensitize tumor cells to T-cell mediated attack.” Finding ways to alter those target molecules, they added, “will be important to extend the benefit of immunotherapy to larger patient populations, including cancers that thus far are refractory to immunotherapy.”

Research included in the report by Van Allen and Choueiri was supported by Bristol-Myers Squibb, American Association for Cancer Research Kure It Research Grant for Immunotherapy in Kidney Cancer, Kidney SPORE, and Cancer Immunologic Data Commons (National Institutes of Health grant U24CA224316).

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First draft of a genome-wide cancer ‘dependency map’ 08-22

Initial results reveal more than 760 genetic dependencies across multiple cancers

Graphic: The Broad Institute of MIT and Harvard 

In one of the largest efforts to build a comprehensive catalog of genetic vulnerabilities in cancer, researchers from the Broad Institute of MIT and Harvard and Dana-Farber Cancer Institute have identified more than 760 genes upon which multiple types of cancer cells are strongly dependent for their growth and survival.

Many of these “dependencies,” the researchers report today in the journal Cell, are specific to certain cancer types. However, about 10 percent of them are common across multiple cancers, suggesting that a relatively small number of therapies targeting these core dependencies might each hold promise for combating several tumors.

To generate these findings, the research team conducted genome-wide RNA interference (RNAi) screens on 501 cell lines representing more than 20 types of cancer, silencing more than 17,000 genes individually in each line to identify genetic dependencies unique to cancerous cells.

Cancer cells can harbor a broad variety of genetic errors, from small mutations to wholesale swaps of DNA between chromosomes. If an error shuts down a critical gene, a cancerous cell will compensate by adjusting other genes’ activity, frequently developing a dependence on such adaptations in order to persist.

Identifying these dependencies provides opportunities for scientists to gain deeper insight into cancer biology and determine new therapeutic targets.

“Much of what has been and continues to be done to characterize cancer has been based on genetics and sequencing. That’s given us the parts list,” said study co-senior author William Hahn, an institute member in the Broad Cancer Program, chief of the Division of Molecular and Cellular Oncology at Dana-Farber, and a leader in the Cancer Dependency Map initiative, a joint effort spanning the Broad Institute and Dana-Farber. “Mapping dependencies ascribes function to the parts and shows you how to reverse-engineer the processes that underlie cancer.”

RNAi silences genes using small pieces of RNA called small interfering RNAs (siRNAs). To run a genome-wide RNAi screen, researchers expose cells to pools of siRNAs and track the cells’ behavior.
“The simplest thing one can do with perturbed cells is allow them to keep growing over time and see which ones thrive,” explained study co-senior author David Root, an institute scientist and director of the Genetic Perturbation Platform at the Broad. “If cells with a certain gene silenced disappear, for example, it means that gene is essential for proliferation.”

The data revealed striking patterns in cancer cells’ dependencies. Many dependencies were cancer-specific, in that silencing each affected only a subset of the cell lines. However, more than 90 percent of the cell lines had a strong dependency on at least one of a set of 76 genes, suggesting that many cancers rely on a relatively few genes and pathways.

Using a set of molecular features (e.g., mutations, gene copy numbers, expression patterns) from each cell line, the team also generated biomarker-based models that helped explain the biology behind 426 of the 769 dependencies. Most of those biomarkers fell into four broad categories:

• Mutation(s) of a gene;
• Loss of a copy or reduced expression of a gene;
• Increased expression of a gene;
• Reliance on a gene functionally or structurally related to another, lost gene (a.k.a., a paralog dependence).

Surprisingly, more than 80 percent of the dependencies with biomarkers were associated with changes (up or down) in a gene’s expression. Mutations, often used as the grounds for pursuing a gene as a drug target, accounted for merely 16 percent of biomarker-associated dependencies.
Twenty percent of the dependencies the team discovered were associated with genes previously identified as potential drug targets.

“We can’t say we’ve found everything, but we can say that the genes we’re seeing fall into a relatively small number of bins, some of which are familiar, some less so,” Hahn said. “That initial taxonomy is a great starting point for building a full map.”

“Our results provide a starting point for therapeutic projects to decide where to focus their efforts,” said study co-first author Francisca Vazquez, a Cancer Dependency Map project leader. She added that while there was still much to do to validate the list, “It’s becoming increasingly easier to triangulate data and generate hypotheses as more genome-scale systematic data sets, like those from the Cancer Cell Line Encyclopedia, Genotype-Tissue Expression, and the Cancer Genome Atlas projects, become available.

“Bringing of all the data together will help us generate a truly comprehensive cancer dependency map.”

To eliminate false-positive results caused by seed effects — a phenomenon by which siRNAs inadvertently silence irrelevant genes — study co-first author Aviad Tsherniak led the development of a novel computational tool dubbed DEMETER.

“People sometimes take a dim view of RNAi because seed effects make the data so noisy,” said Tsherniak, leader of the Broad Cancer Program’s Data Science group. “DEMETER models gene knockdown and seed effects within the data, and computationally subtracts the seed effects. It cleans up the data and helps you find true dependencies.”

According to Hahn, the data argue that the time is ripe to pay more attention to the broader landscape of functional aspects of cancer, in addition to focusing on protein-coding gene mutations and variations.

“I think we’re close to the end of finding genes that are mutated or focally amplified in cancer,” he said. “To me, that’s a huge opportunity, because it means we have many heretofore untapped avenues for understanding cancer.”


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