A cancer pathway is a patient pathway:
“The patient’s journey from the initial suspicion of cancer through Clinical Investigations, patient diagnosis, and treatment” (1).
This journey captures the patient’s healthcare experience—which doctors they met with for eventual diagnosis, where they were sent for follow-up appointments, what treatment they started, how the cancer responded to treatment, among other important milestones. It also captures how they are personally impacted through the different stages of their disease and treatment. Supporting patients through this journey necessitates a deep understanding of their experiences, from their social context, cultural or religious influences, challenges to accessing healthcare, to their thoughts, fears, and emotions (2).
If this deep understanding is needed for a patient’s healthcare experience, why wouldn’t the same be true for their cancer?
The quest to understand the complexity of cancer biology and translate findings into improved therapies that can help individual patients is what motivates and underlies the efforts of cancer researchers around the world. Whether classifying the full cellular heterogeneity of a tissue system frequently plagued by cancer, identifying biomarkers that can help predict therapy response, or defining the cellular basis of treatment response in both success and failure, these research efforts can provide deep, data-driven insights into the underlying cellular and molecular biology of specific tumor types that can, in turn, inform therapeutic approaches. Thus, the cancer biology journey becomes the cancer patient journey.
At AACR 2022, the global community of cancer researchers came together to share progress that has been made towards this goal of impacting real patients by clearing the path to a deeper understanding of cancer and, ultimately, cures. Their research also demonstrated the value of employing technological innovations, such as single cell multiomics and spatial profiling, to advance this fundamental goal.
Review a few key stories from AACR in the sections below, starting with innovative research into the biology of the tumor microenvironment, all the way to novel therapeutic development coming out of clinical trials. Then, join our upcoming Cancer Symposium to learn how other leading cancer researchers are leveraging 10x Genomics technologies to advance the field.
Genes underlying tumor immune exclusion revealed by spatial CRISPR
The composition of a tumor and that of its surrounding environment, called the tumor microenvironment (TME), both play central parts in determining the progression of cancer and therapeutic response. The TME is a complex mixture of invasive cancer cells, permeated and surrounded by stromal and epithelial cells, along with extracellular matrix, from the restructured tissue. Immune cells, such as T cells, macrophages, and dendritic cells, also punctuate this mixture, and are either already present in the tissue or recruited to the tumor. Brian Brown, PhD, Director of the Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, noted in his lecture at AACR that the presence and infiltration of immune cells is a critical determinant of cancer outcomes: “If they’re not in the tumor, they’re not going to kill those cancer cells—at least, that’s what we think.”
Dr. Brown’s research team wanted to understand how tumors can regulate the cellular composition of the TME, particularly preventing infiltration of crucial immune cell populations. All of biology is encoded in the genome, so they conjectured that certain genes were underpinning the regulation and patterning of cells in the TME. However, finding and characterizing those genes, while also preserving the spatial patterning of the TME, proved another challenge. To perform this analysis, Dr. Brown’s group developed a modified CRISPR technique (3) leveraging high-dimensional spatial immunohistochemical staining and Visium Spatial Gene Expression to visualize the cellular, molecular, and architectural effects of knocking out genes associated with the tumor immune response, including immune evasion.
They identified specific genes that, when turned off, had a dramatic effect on the architecture of the TME. For example, knocking out Tgfbr2, an immune receptor for TGF-b, a regulatory cytokine associated with immune suppression, led to a highly fibromucinous tumor structure and marked exclusion of immune cells in tumor lesions bearing the knockout. This was a surprising result—removing an immune suppressor should ostensibly free up the immune response.
However, spatial transcriptomics data revealed that, despite the loss of Tgfbr2 in cancer cells, fibroblast populations in the TME were compensating, bearing an enriched gene expression signature for TGF-b activation. Activated along this gene pathway, fibroblasts could be enabling the observed immune exclusion in the tumor. This finding builds upon others that explain why immune cells don’t always effectively infiltrate a tumor to kill cancer cells, and may point to a new path for intervention.
Learn more about this study here →
Mapping single cell data to tissue with CellTrek, plus a breast cell atlas
Fully understanding cancer often starts with building a high-resolution cellular and molecular view of the tissue network where cancers emerge. Nicholas Navin, PhD, Professor at the University of Texas MD Anderson Cancer Center, shared his team’s work to build a healthy breast cell atlas, which would provide valuable information about the cellular heterogeneity and spatial organization of breast tissue in the context of cancer. Only 10 cell types had been previously identified, but using single cell and single nucleus RNA-sequencing on normal breast tissue from 100 women, plus spatial analysis on 20 additional samples, they characterized 12 cell types and 47 distinct cell states. This revealed a previously uncharacterized immune rich ecosystem in breast lobules and established one of the first references for adipose cell types in breast tissue.
Dr. Navin’s group is dedicated to implementing new genomics innovations to study breast cancer. At AACR, he introduced a computational method called CellTrek, which elevates the potential of linking single cell and Visium spatial data. The computational technique works by co-embedding single cell RNA-sequencing (scRNA-seq) and spatial gene expression data in high-dimensional space, allowing the user to map scRNA-seq data back to individual spots within the tissue morphology at single cell resolution.
Applying this approach to ductal carcinoma in situ (DCIS) tissues, Dr. Navin’s team was able to resolve the spatial distribution of tumor subclones with heterogeneous expression programs across ducts within the ductal network, as well as observe concentrations of immune cells in tertiary lymphoid structures outside the ductal regions of DCIS (4). This technique holds promise to flexibly integrate single cell and spatial gene expression data, allowing easier exploration of single cell gene signatures in tissue morphology.
Learn more about CellTrek in this publication →
Advancing clinical research with single cell sequencing
Dr. Navin also commented on the value of single cell sequencing technology for various cancer research applications, such as tracing tumor clonal evolution, defining cancer cell transcriptomic signatures, or identifying cellular subpopulations for less studied fibroblasts and endothelial cell types with an unbiased view of the TME. The technique can extend beyond basic research into tumor biology too, with the potential to advance clinical and translational applications:
“Now is a great time to start thinking about clinical applications of these tools, and how we can move them into diagnostics. There are several areas of clinical research—so early detection is a great area where we can start to look at things like urine samples for early blood cancer cells, spit for early lung cancer cells, and other bodily fluids. We can use methods to stratify which patients would progress in disease and which ones might not, based on the tumor and the tumor microenvironment profiles. We can discover lots of new drug targets, not just in the tumor cells, but also in components of the microenvironment and think about how we might treat different cancers. And we can reconstruct lineages of evolution, and understand which mutations to target…”
Several other scientists shared the same sentiment at AACR, presenting studies using single cell technology from 10x Genomics on clinical samples, aimed at improving understanding of the cellular basis of immunotherapy response and advancing personalized treatment modalities, such as cancer neoantigen vaccine development.
Deca-1 versus Deca-2: Testing two cancer vaccines
What makes a cancer vaccine effective? Lélia Delamarre, PhD, Senior Principal Scientist at Genentech, shared her research into this question, providing insights into the many layers of complex biology that determine vaccine efficacy. It starts with identifying the right neoantigens—mutated proteins presented on cancer cells, which the patient’s immune system recognizes as foreign and targets for clearance. But according to Dr. Delamarre, only 1% of mutations are immunogenic, meaning they activate the immune system against cancer. Then there is the matter of how effective those activated immune cells are at killing cancer cells. It’s not a given—there are features of T-cell biology and the TME that can actually impair or diminish T-cell killing.
These challenges to creating effective cancer vaccines ultimately require an abundance of high-resolution antigen peptide and immune response data, to not only improve prediction models to select cancer neoantigens, but also understand how T cells are enlisted to destroy cancer cells. This led Dr. Delamarre and her team to single cell immune profiling technology, which allowed them to build single cell gene expression, neoantigen specificity, and T-cell receptor libraries simultaneously from immune cells elicited after vaccination.
They first developed two mRNA-based vaccines bearing 10 cancer neoantigens each, called Deca-1 and Deca-2, and applied the vaccines to mice in a prophylactic setting, prior to introducing a tumor challenge. Single cell data revealed a comparable CD8+ T-cell response between the two vaccines, which both reduced tumor growth. However, applying the vaccines in mice that already had existing tumors led to a divergence of results: Deca-2 reduced tumor growth, while Deca-1 performed poorly. This suggested that the tumor's presence diminished the antitumor immunity of T cells elicited by Deca-1.
Through single cell analysis, they were able to identify the molecular changes driving these two results: T cells induced from Deca-1 vaccination differentiated further after entry into the tumor, becoming less functional over time. In contrast, T cells generated by Deca-2 were more active and functionally cytotoxic, showing enriched expression for granzyme and perforin.
The question remained—what features of the TME interfered with Deca-1 T cells? Dr. Delamarre pointed to the possible role of cross presentation of neoantigens from dendritic cells as an essential sustainer of T-cell antitumor immunity. In the absence of these cellular interactions, would T cells ultimately exhaust?
Dendritic cells vital to PD-1-mediated antitumor immunity
These same questions regarding the therapeutic impact of interference from within the TME are the focus of ongoing clinical research into checkpoint immunotherapy for hepatocellular carcinoma (HCC), or liver cancer, led by Miriam Merad, MD, PhD, Director of the Precision Immunology Institute at the Icahn School of Medicine at Mount Sinai. At AACR, Dr. Merad presented on a clinical trial collaboration with Regeneron Pharmaceuticals and doctors and scientists in The neoAdjuvant Research Group to Evaluate Therapeutics (TARGET), aimed at understanding the antitumor immune response to drugs applied to treatment-naive cancers.
In their phase 2 trial, 28 patients were dosed twice with PD-1 blockade prior to a resection surgery of liver lesions. 28% of patients were positive responders, showing greater than 50% tumor necrosis. High-dimensional immunohistochemical analysis of tumor-infiltrating T cells in patient liver lesions showed three distinct patterns of infiltration: low, rich, and excluded, with responders being rich in T-cell content.
To understand the molecular basis of this difference in T-cell infiltration, Dr. Merad and her team performed scRNA-seq and CITE-seq (cell surface protein profiling) and T-cell receptor sequencing on ~1 million tumor infiltrating T cells. This revealed a high ratio of helper-like CD4+ T cells in responders, with clear clonal expansion of PD-1hi progenitor CD8+ and CD4+ cells.
Staining also revealed a pattern of abundant dendritic cells clustering tightly with CD4+ and CD8+ T cells. The combined insights from this data suggested local T cell and dendritic cell interactions enabled T-cell expansion, attracting CD4+ cells which, in turn, sustained CD8+ antitumor immunity in responders. Findings from Dr. Merad’s group point to the possibility for modulation of dendritic cells at tumor sites to further strengthen the immune response after PD-1 blockade immunotherapy.
Clearing the path to a cure
It’s incredible and humbling to see how leading cancer researchers are leveraging single cell and spatial technology to resolve the complex factors influencing the composition of the tumor microenvironment, tumor infiltration, and, ultimately, therapeutic efficacy. With these multiomic, high-resolution insights into all aspects of cancer biology, scientists and doctors have a strong foundation of knowledge to build on, working towards improved therapies and providing greater confidence and support to patients through their journey with cancer.
Continue to explore innovative cancer research enabled by 10x Genomics technology at our upcoming Cancer Symposium.
- Ciria-Suarez L, et al. Breast cancer patient experiences through a journey map: A qualitative study. PLOS ONE 16: e0257680 (2021). doi: 10.1371/journal.pone.0257680
- Dhainaut M, et al. Spatial CRISPR genomics identifies regulators of the tumor microenvironment. Cell 185: 1223-1239.e20 (2022). doi: 10.1016/j.cell.2022.02.015
- Wei R, et al. Spatial charting of single-cell transcriptomes in tissues. Nat Biotechnol (2022). doi: 10.1038/s41587-022-01233-1