Q2 Solutions supports the battle against cancer by offering a number of services to support clinical trials. These offerings include cutting edge genomics technologies to power and enhance immuno- oncology (IO) trials where reporting transcriptional response pairs a person to therapy without multiple biopsies. This kind of approach extends treatment utility because of it enables continual refinement of the most useful biomarkers found within the trial results. Of the 189 oncology therapies under development in the three year period leading up to 2015, 56% had an associated biomarker.1 Nearly 60% of the oncology drugs expected to launch in 2017 will be targeted to biomarker-defined patients.1 Currently, while (69%) IO clinical trials are focused on single-protein biomarkers, 42% have a genomic component (many in complement to the known protein markers).2
Biomarkers for IO trials
Some of the greatest treatment challenges facing oncologists are matching individuals to therapies while abating treatment resistance. Biomarkers built of the transcriptome can help elucidate which patients have the highest probability of responding to a treatment, and which patients are likely to develop resistance or adverse events based on biochemical pathway incompatibility.
At Q2 Solutions, we use a diverse set of genomic tools for optimizing the use of biomarkers for IO studies. These applications help elucidate the complex interactions between an individual’s cancer and their immune system and include:
- targeted gene expression profiling
- human leukocyte antigen (HLA) allele determination
- immunoglobulin heavy chain variable region (IGHV) mutation status
- T cell receptor and B cell receptor immune repertoire
- neoantigen/neoepitope candidate lead identification,
- microbiome analysis
- tumor mutation burden and
- gene mutation signatures
In practice, finding the right biomarkers can be quite challenging. One way to overcome some of the barriers involved is to identify modules of genes that are observed to be expressed reliably together in response to the disease and/or treatment of interest. This starts with staying current on the latest drug response research and trial data in order to immediately translate relevant transcripts into assays that maximize clinical utility without the need for additional testing. Once identified, these expression signatures can be further reduced to a smaller, clinically-relevant, panel required to characterize the predictive response.
That clinically relevant panel not only powers the trial, but also contains relevant controls that can be processed consistently in a regulated fashion, thus increasing throughput, reducing turnaround-time, and reducing costs. Additionally, phenotypes measured with gene expression panels can later be analyzed for associations with matched orthogonal genomic data to find novel associative biomarkers.
Recent advances in gene expression
Recently, scientists have demonstrated use of RNA-Sequencing (RNA-Seq) to measure gene expression and find markers of immune response and survivability.3 Bindea et al.4 used complementary microarray types to measure and validate the heterogeneous landscape of colorectal tumor microenvironments for 105 patients. These researchers measured adaptive and innate immune cell mRNA transcripts with whole transcriptome approaches, thereby identifying the full complement of immune response sub-populations and showed that immune cells have their own, unique expression characteristics. This really helps identify the immune response to the cancer in an unbiased approach and provides insights into the state of adaptive immunity and the tumors’ expression landscape within a single assay. They recapitulated the finding that spatiotemporal factors are very prognostic of survival, and that the types of cells present and cell density are also important, even if discrimination is difficult between high and medium expression of gene signatures.5
Given the recent successes and potential of understanding a patient’s immune system to cancer and potential for immunotherapy, these results have extreme potential.
In extension, Iglesia et al.3 evaluated a B-cell gene expression signature in TCGA ovarian cancer using RNA-Seq and demonstrated a strong prognostic effect. Furthermore, they measured hyper mutation in B-cell receptor gene expression signatures, an analysis that would be impossible using the same gene expression microarrays that Bindea et al. used and an afterthought for protein expression assays. Iglesia et al. showed that analysis is also prognostic. In fact, these researchers found that B-cell gene signature is associated more strongly with improved survival than even classical clinical variables such as node status.2
By measuring all transcribed gene products instead of a focused panel of known proteins, RNA-Seq can shed light on previously unknown mechanisms of immune response. Measuring the immune repertoire presents its own challenges, however multiple parallel efforts are underway to optimize assays and utilize sequence inference algorithms that can accommodate skewing in areas of overlap with the reference transcriptome. Overall, gene expression assays, including the examples presented above, hold great promise for measuring restricted clonal repertoire in the pursuit of diagnostic biomarkers of immune response.
Moving the fight forward
As the possibilities for the use of biomarkers increase, so does our dedication and drive to find ways to translate those possibilities into hope for patients with cancer. Biomarkers have already taken us leaps and bounds beyond where we were a decade ago. More patients expect to receive targeted medicines with increased probability for a successful outcome. More physicians can feel sure that they are offering their patients a treatment that can improve their health without causing adverse effects.
At Q2 Solutions, we are committed to continue doing our part in this fight against cancer, and we look forward to seeing what a difference we all can make together.
To learn more, see Gene expression for keeping pace with immuno-oncology breakthroughs and biomarker identification.
1 McKesson Specialty Health/US Oncology Network (MSH). http://www.nxtbook.com/nxtbooks/pharmcomm/ 20130910/?utm_source=Campaigner&utm _campaign=SeptOct_Digital_Edition_eBlast_8-28-13&campaigner=1&utm_medium=HTMLEmail#/0
3 Iglesia MD, Vincent BG, Parker JS, Hoadley KA, Carey LA, Perou CM, et al. Prognostic B-cell signatures using mRNA-seq in patients with subtype-specific breast and ovarian cancer. Clin Cancer Res. 2014;20(14):3818-2910.1158/1078-0432.CCR-13-3368. http://www.ncbi.nlm.nih.gov/pubmed/24916698.
4 Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782-9510.1016/j.immuni.2013.10.003 http://www.ncbi.nlm.nih.gov/pubmed/24138885.
5 Angell H, Galon J. From the immune contexture to the Immunoscore: The role of prognostic and predictive immune markers in cancer. Curr Opin Immunol 2013;25:261-7. https://www.ncbi.nlm.nih.gov/pubmed/23579076