In the last two decades, the field cancer therapy has undergone a revolution, thanks to the emergence of immune-based treatments. Yet, while these therapies initially showed great promise, when used to treat several types of cancers, some of the hype has since faded, due to a lack of response on the part of most patients.
That said, all is not lost. As the success of immune-oncology (IO) drugs are associated with the tumor microenvironment (TME) state, many scientists are now using the TME as a predictive tool for immunotherapy response. Moreover, new drugs targeting the TME for better efficacy of immune-oncology treatment have recently emerged, reigniting the medical community’s hope for a cure.
The importance of the TME
In 2000, Hanan and Weinberg conceived the ‘hallmark of cancer,’ a set of six hallmark characteristics defining cancer (1). The list consisted of undisputed fundamental aspects pertaining to cancer cells, such as limitless replicative potential and insensitivity to anti-growth signals. Today, 22 years later, we know that cancerous tissue is not merely a collection of cells gone wrong, but rather a complex ecosystem of multiple types of cells that are reprogramed by to promote cancer cell growth and survival. Indeed, the tumor microenvironment (TME) has become part of the revamped ‘hallmark of cancer,’ published in 2011 and 2022 (2,3).
Over the past decade and with the emergence of immune-based cancer therapies, a clear association between the tumor immune microenvironment (TIME) and the success of immunotherapy treatments has been demonstrated (4). Broadly speaking, the tumor microenvironment can be divided into ‘hot’ and ‘cold’ tumors, which are characterized by a high or low abundance of immune cells in the TME, respectively. This separation holds an essential prognostic value as ‘hot’ tumors have been found to respond to immunotherapy in multiple types of cancers. As many immune cells In the TIME are actually tumor-protective and express immune inhibitory proteins such as PD-1 and PDL-1, it is not surprising that ‘cold’ tumors depleted of those cells are less responsive to immunotherapies that target those same proteins.
Deciphering the TME
The TME’s important role in predicting immunotherapy response led many scientists to rigorously analyze it, so as to discover novel markers that will predict patients more likely to positively respond to immunotherapy treatments. In the last decade, proteins that protect tumors from immune elimination, such as PD-1 and PDL-1, were correlated with immunotherapy response. However, it has since been proven that, as single markers, they lack the specificity and sensitivity required to accurately predict immunotherapy response (6, 7). Today, efforts are being made to develop new technologies that could enable us to capture the TME more extensively. These include new ‘omics’ analysis methods for genomic and transcriptomic data, improved imaging tools, and multiplex staining. These methods now focus on analyzing the TME in its natural spatial form. As The TME entails many types of cells and multiple cell-cell interactions, in-depth analysis of the TME using these advanced tools will unquestionably reveal better marker-based prediction tools for immunotherapy, as we move forward into the future.
Targeting the TME
Revealing those tumors that are considered to be ‘hot’ and, therefore, more likely to respond to immunotherapy, is essential, but what can be done to help those patients who are not good candidates for immunotherapy?
In recent years, new drugs have been proposed to modulate the tumor microenvironment by turning those ‘cold’ tumors into ‘hot’ tumors. Presumably, reprogramming aspects of the immune compartment, such as immunosuppressive cells, may overcome resistance mechanisms in the TME and enhance antitumor immunity. Moreover, as the TME is more penetrable and accessible than tumor cells, therapeutic strategies targeting the tumor microenvironment are appealing. An example can be seen in the growing number of clinical trials combining anti-VEGF and immunotherapy. For instance, anti-VEGF has been shown to modulate the TME either indirectly, by enhancing tumor perfusion in the TME, or directly, by inhibiting immunosuppressive cells (8).
Fig1. Vascular Normalization Can Reprogram the Immunosuppressive Tumor Microenvironment (M. Datta, et al. 2019)
Most importantly, in the phase 3 clinical trial, the addition of immunotherapy (atezolizumab) to anti-VEGF (bevacizumab) plus chemotherapy significantly improved progression-free survival and overall survival among patients with metastatic nonsquamous NSCLC (9). These early successes will defiantly lead to many other clinical trials testing the effective combination of immunotherapies with TME-targeting agents.
The biggest challenge for personalized cancer immunotherapies lies in the lack of comprehensive knowledge of the cancer-immune or immune-immune interactions in the TME. Once improved, it will become possible to define classes of the TME and determine which cells or cell-cell interactions are essential for suppressing antitumor immunity and in what contexts. While the emergence of new technologies, such as CODEX multiplex staining and spatial multi-omics, are getting us closer to grasping the complexity of the TME spatially, they are still highly costly and demand months if not years of work to analyze the data.
Also, once achieved, the enormous data generated by these analyses will mandate AI-powered technological tools for accurate measurements of all parameters across multiple heterogeneous tumors.
Ultimately, when we surpass these obstacles, we will hold better tools for classification strategy for personalized therapy.
In Nucleai, we have developed a platform allowing unique, in-depth analysis of the TME from tissue slides. By using an advanced AI-powered tool to analyze multiple types of cells, our platform aims to bridge the growing data of various parameters representing the TME to the final purpose of actionable clinical decisions.
Fig 2. Turning ‘cold’ tumors actionable
(a)Today, TME analysis of cold and hot tumors is mainly used for prognostic and predictive analysis of immunotherapy response. With the growing advancement in drugs modulating the TME from cold to hot tumors (b) we are close to turn the TME analysis to a clinical tool allowing patient specific drug selection.
- Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
- Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: The next generation. Cell 144, 646–674 (2011).
- Hanahan, D. Hallmarks of Cancer: New DimensionsHallmarks of Cancer: New Dimensions. Cancer Discov. 12, 31–46 (2022).
- Bagaev, A. et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 39, 845-865.e7 (2021).
- Ofek, E. et al. Predicting response to pembrolizumab in non-small cell lung cancer, by analyzing the spatial arrangement of tumor infiltrating lymphocytes using deep learning. https://doi.org/10.1200/JCO.2021.39.15_suppl.9045 39, 9045–9045 (2021).
- Gibney, G. T., Weiner, L. M. & Atkins, M. B. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet. Oncol. 17, e542 (2016).
- Datta, M., Coussens, L. M., Nishikawa, H., Hodi, F. S. & Jain, R. K. Reprogramming the Tumor Microenvironment to Improve Immunotherapy: Emerging Strategies and Combination Therapies. Am. Soc. Clin. Oncol. Educ. book. Am. Soc. Clin. Oncol. Annu. Meet. 39, 165 (2019).
- Socinski, M. A. et al. Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC. N. Engl. J. Med. 378, 2288–2301 (2018).