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MD Anderson Building Proteomic Assays to Guide Cancer Therapy


NEW YORK (GenomeWeb) – Researchers at the University of Texas MD Anderson Cancer Center continue to move proteomic assays for guiding cancer therapy toward the clinic.

MD Anderson researcher Gordon Mills told GenomeWeb this week that assays based on his lab's Cancer Proteome Atlas project, could be used in patient treatment at the medical center within the next 18 months.

Relatedly, Mills noted that as work on the Cancer Proteome Atlas continues, the project has increasingly shifted from profiling treatment-naïve patients and toward analyses looking at questions like tumor changes and evolution in response to treatment as well as differences between primary tumors and metastases.

He and his colleagues have also changed and expanded the proteomic assays they run on their samples to account for emerging treatment options including immuno-oncology and targeting DNA damage repair. Currently, the researchers routinely measure 302 targets per sample, and plan in the near future to increase that to 500 to 600 targets, Mills said. The samples are analyzed primarily via reverse phase protein arrays, which uses cell lysates spotted in array formats that can then be probed with antibodies to proteins of interest.

The Cancer Proteome Atlas collects data from a variety of initiates Mills and his colleagues are involved in. His lab has served as the primary proteomic resource for a number of National Cancer Institute initiatives including The Cancer Genome Atlas, the Exceptional Responders Initiative, the ALCHEMIST precision medicine trials, the Cancer Driver Discovery Project, the Cancer Trials Support Unit, and the Cancer Therapy Evaluation Program.

This week, Mills and other TCGA researchers published a paper in Oncogene detailing the initiative's genomic and proteomic analyses of 1,023 non-small-cell lung cancer cases. 

Among the most notable findings of this work, Mills said, was the identification of four molecular subtypes offering different "therapeutic opportunities." One large subset appeared potentially vulnerable to checkpoint inhibitors like anti-PD-1 or PD-L1 therapies. Another group would potentially benefit from CAR T-cell therapy, while a third could benefit from targeted DNA damage response mechanisms.

The final group exhibited p38/MAPK and mTOR signaling patterns "that really had not been clearly identified as associated with any particular subset of lung cancer," Mills said, noting that this signaling could potentially be targeted.

More generally, he said, that data like that presented in the Oncogene study indicates that therapies need "to move from drugging single genomic aberrations to targeting the integration of information from multiple genomic aberrations."

That integration "occurs at the protein level," he said. "[It] simply can't be achieved by looking at the DNA and RNA — and so we are going to need that additional protein information to help us do a better job of benefitting our patients."

Mills noted that while earlier efforts like his TCGA work focused on characterizing tumors before treatment, he is now shifting to an emphasis on analyzing tumors as they change over time and in response to therapy.

"The TCGA studies are absolutely critical and incredibly important, but what those are is a set of previously untreated patients, and they provide us with sort of the baseline of what is happening across different cancers," he said. "Much more of our emphasis now is on sample sets and types not captured by the TCGA. That would be things like looking at tumor evolution upon treatment and at metastatic versus primary, asking what changes are happening at the local and distant levels."

"For the relatively common cancers where we have adequate sample numbers I think the initial [baseline] analysis work is done," he said.

He added that, in addition to exploration of new sample types, the field was now bringing more advanced analyses to bear on the existing data sets.

"What you are going to see are [studies] going back to that information and integrating across DNA, RNA, and protein in novel manners," Mills said, noting that having large amounts of genomic and proteomic data across a wide range of cancers has enabled the development of increasingly sophisticated tools for their analysis.

"The incredible data trove that is available allows development of the algorithms and also provides the incentive to build them, because you have something to work on," he said. "Our ability to [for instance] call mutations, to determine tumor content versus stroma and different types of stroma from transcriptional profiling and looking at DNA copy number changes, has really greatly improved."

The ongoing development of new analysis tools and their application to existing cancer datasets "will be an ongoing process probably for the next generation," Mills said.

Nearer term, Mills and his colleagues hope to begin using insights gleaned from their analyses to guide patient treatment. For this they are moving from the reverse phase protein array platform to NanoString's nCounter Analysis System, which will allow them to make gene and protein measurements simultaneously. NanoString and MD Anderson announced last year that they are collaborating on such assays.

Mills said he and his colleagues decided to move to the nCounter platform for clinical versions of their assays due to what they saw as the platform's greater robustness compared to RPPA.

"We took a careful look at the [RPPA] technology and really felt that it would be hard to do this on a broad basis," he said. "So with that in mind, we approached NanoString to develop a barcoding antibody approach that would allow multiplex analysis on the NanoString platform to go into the clinic."

Mills noted that Theranostics Health, a clinical proteomics firm founded by George Mason University researchers Lance Liotta and Emanuel Petricoin, inventors of the RPPA technique, was using the platform for clinical assays, and that he and his colleagues "are carefully watching what is coming out of Theranostics to see if [RPPA] is a viable and useful [clinical] platform."

"But while they are doing that, we have decided to take a different approach," he said, adding that he was an inventor of some of the technology being used in the NanoString-based workflow.

Assays on the nCounter platform are currently available by contract with NanoString, Mills said, noting that he and his colleagues are currently running them as part of their validation work.

He said that it would likely take six months to a year for he and his colleagues to validate them sufficiently for implementation in a CLIA facility and then another six months or so of additional validation after that.

"An 18-month horizon to be able to use this information in patient management is reasonable," he said.