Cancer is characterized by an extreme heterogeneity, both within the tumor –meaning that cancer cells within the tumor mass are different among each other’s– and among patients –meaning that cancer cells of a patients may be very different from those of another patient.
Intra-tumor heterogeneity –namely the co-existence of such different cells within a tumor– represents a serious issue in the response to treatment: for instance, as different cells may respond differently to treatment, tumors can initially respond to the treatment and regress, but eventually regrow (relapse).
The tumor microenvironment further contributes to intratumor heterogeneity. Importantly, studies have shown that intratumor heterogeneity becomes even more pronounced after treatment.
How is intratumor heterogeneity affected in response to treatment? Recent technological advances have enabled to analyze, at the molecular level, the single cells of a tumor, overcoming the issue associated with intratumor heterogeneity, by exploiting an approach called singe cell RNA sequencing (scRNAseq).
Main findings. By employing state of the art technology, the authors describe, at single cell resolution, the molecular features of cancer cells before and after targeted treatment (targeted treatment is a treatment based on the use of drugs targeting specific genes or proteins involved in cancer growth and survival). The in-depth description of the molecular characteristics of cancer cells that survive therapy would unravel the molecular pathways that these cells have initiated in order to adapt and survive treatment and might potentially offer novel pharmacological targets for a better therapy response.
Experimental details. The authors performed scRNAseq of lung cancer samples at different time points during therapy: before targeted treatment, at the end of the treatment (the so-called state of residual disease, when few cancer cells have survived treatment) and when tumor cells displayed clear drug resistance (namely when the tumor has adapted to the new condition and starts growing again).
What did the analysis reveal?
Residual cancer cells after treatment are characterized by a specific signature, namely, compared to cells before treatment, residual cancer cells overexpress 17 specific genes. Interestingly, in patients high expression of these genes is associated with better prognosis compared to patients showing low expression of these genes. Enabling cancer cell survival upon treatment, but at the same time making them less aggressive, this molecular signature represents a biomarker of good prognosis. The molecular signature featuring residual cancer cells includes genes of the WNT/beta catenin pathway, which may be therapeutically targetable. In vitro experiments have shown that inhibition of WNT/beta catenin pathway together with the targeted treatment leads to better response to treatment.
Becoming fully drug resistant, cells acquire specific features, overexpressing genes associated with invasion, cell-to-cell communication, differentiation and immune modulation. The signature associated with drug resistance of these cells includes overexpression of genes of the plasminogen activation pathway, which correlates with worse patient survival and resistance to targeted therapy; overexpression of genes of gap junctions proteins (membrane proteins constituting structures between cells that enable exchange of molecules), some of them associated with worse patient survival. Moreover, compared to cells before treatment, drug resistant cells showed overexpression of genes of the kynurenine pathway, whose expression can suppress the immune system response –thus hampering the immune system ability to eradicate cancer cells–, suggesting that drug resistant cells may actively inhibit the immune system. Higher expression of genes of the kynurenine pathway is associated with worse patient prognosis.
Concerning the tumor microenvironment, targeted treatment induced a transient stimulation of the immune system. In particular, T cells and macrophages were the most abundant immune cell types during the whole treatment, but after therapy (at the stage of residual disease), T cells increased while immunosuppressive macrophage infiltration in the tumor mass decreased. Moreover, relapse phases (when cells have acquired drug resistance) are characterized by a condition hostile to the establishment of an efficient immune system response.
Conclusions. Cancer cells evolve during treatment, likely in response to the treatment itself, adapting in order to survive. This adaptation is reflected in a specific molecular signature, meaning that cancer cells and immune cells of the tumor microenvironment show different traits before and after treatment, those traits that, conferring them drug resistance, enable them to survive. The majority of these features are biomarkers of bad patient prognosis, as they reflect cells’ ability to survive and cause relapse. The characterization of cancer cells in these specific clinical states (before treatment, after treatment, during relapse) –obtained thanks to the single cell approach employed in this study– is of key importance due to its clinical relevance: firstly, it likely explains the reason for a frequent incomplete response to targeted treatment; secondly, it supports the potential of the specific pharmacological targeting of residual disease cells aimed at a better response to treatment in lung cancer and, potentially, in other cancer types.
Reference: Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing. Maynard, McCoach, […] and Bivona. Cell 2020