Therapeutic resistance continues to be an indominable foe in our ambition for curative cancer treatment. Recent insights into the molecular determinants of acquired treatment resistance in the ...clinical and experimental setting have challenged the widely held view of sequential genetic evolution as the primary cause of resistance and brought into sharp focus a range of non-genetic adaptive mechanisms. Notably, the genetic landscape of the tumour and the non-genetic mechanisms used to escape therapy are frequently linked. Remarkably, whereas some oncogenic mutations allow the cancer cells to rapidly adapt their transcriptional and/or metabolic programme to meet and survive the therapeutic pressure, other oncogenic drivers convey an inherent cellular plasticity to the cancer cell enabling lineage switching and/or the evasion of anticancer immunosurveillance. The prevalence and diverse array of non-genetic resistance mechanisms pose a new challenge to the field that requires innovative strategies to monitor and counteract these adaptive processes. In this Perspective we discuss the key principles of non-genetic therapy resistance in cancer. We provide a perspective on the emerging data from clinical studies and sophisticated cancer models that have studied various non-genetic resistance pathways and highlight promising therapeutic avenues that may be used to negate and/or counteract the non-genetic adaptive pathways.
Among patients with MCL, many of whom were at high risk, ibrutinib plus venetoclax led to a complete response in 62% at week 16. Among patients with a response, response was ongoing in 78% at 15 ...months. Low-grade diarrhea, fatigue, and nausea or vomiting were side effects.
Loss of MHC class I (MHC-I) antigen presentation in cancer cells can elicit immunotherapy resistance. A genome-wide CRISPR/Cas9 screen identified an evolutionarily conserved function of polycomb ...repressive complex 2 (PRC2) that mediates coordinated transcriptional silencing of the MHC-I antigen processing pathway (MHC-I APP), promoting evasion of T cell-mediated immunity. MHC-I APP gene promoters in MHC-I low cancers harbor bivalent activating H3K4me3 and repressive H3K27me3 histone modifications, silencing basal MHC-I expression and restricting cytokine-induced upregulation. Bivalent chromatin at MHC-I APP genes is a normal developmental process active in embryonic stem cells and maintained during neural progenitor differentiation. This physiological MHC-I silencing highlights a conserved mechanism by which cancers arising from these primitive tissues exploit PRC2 activity to enable immune evasion.
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•PRC2 maintains bivalency at MHC-I antigen-processing genes silencing MHC-I expression•Cancer cells co-opt this conserved, lineage-specific PRC2 function to evade T cells•Pharmacological inhibition of PRC2 in MHC-I low cancers restores anti-tumor immunity•Immunotherapy resistance may arise via non-genomic routes that exploit PRC2 activity
Burr et al. show that cancer cells co-opt PRC2 to evade immune surveillance. PRC2 maintains bivalency at the promoters of MHC-I antigen-processing pathway (MHC-I APP) genes to repress their basal and cytokine-activated expression. Inhibition of PRC2 restores the MHC-I APP and T cell-mediated anti-tumor immunity.
Breast cancer is a group of heterogeneous diseases that show substantial variation in their molecular and clinical characteristics. This heterogeneity poses significant challenges not only in breast ...cancer management, but also in studying the biology of the disease. Recently, rapid progress has been made in understanding the genomic diversity of breast cancer. These advances led to the characterisation of a new genome‐driven integrated classification of breast cancer, which substantially refines the existing classification systems currently used. The novel classification integrates molecular information on the genomic and transcriptomic landscapes of breast cancer to define 10 integrative clusters, each associated with distinct clinical outcomes and providing new insights into the underlying biology and potential molecular drivers. These findings have profound implications both for the individualisation of treatment approaches, bringing us a step closer to the realisation of personalised cancer management in breast cancer, but also provide a new framework for studying the underlying biology of each novel subtype.
This review highlights significant advances in the classification of breast cancer that integrate genomic and transcriptomic signatures and might thus enable personalised therapeutic management.
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment
. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic ...therapy
. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery
were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
All cancers emerge after a period of clonal selection and subsequent clonal expansion. Although the evolutionary principles imparted by genetic intratumour heterogeneity are becoming increasingly ...clear
, little is known about the non-genetic mechanisms that contribute to intratumour heterogeneity and malignant clonal fitness
. Here, using single-cell profiling and lineage tracing (SPLINTR)-an expressed barcoding strategy-we trace isogenic clones in three clinically relevant mouse models of acute myeloid leukaemia. We find that malignant clonal dominance is a cell-intrinsic and heritable property that is facilitated by the repression of antigen presentation and increased expression of the secretory leukocyte peptidase inhibitor gene (Slpi), which we genetically validate as a regulator of acute myeloid leukaemia. Increased transcriptional heterogeneity is a feature that enables clonal fitness in diverse tissues and immune microenvironments and in the context of clonal competition between genetically distinct clones. Similar to haematopoietic stem cells
, leukaemia stem cells (LSCs) display heritable clone-intrinsic properties of high, and low clonal output that contribute to the overall tumour mass. We demonstrate that LSC clonal output dictates sensitivity to chemotherapy and, although high- and low-output clones adapt differently to therapeutic pressure, they coordinately emerge from minimal residual disease with increased expression of the LSC program. Together, these data provide fundamental insights into the non-genetic transcriptional processes that underpin malignant clonal fitness and may inform future therapeutic strategies.
The human microbiome plays an important role in cancer. Accumulating evidence indicates that commensal microbiome-derived DNA may be represented in minute quantities in the cell-free DNA of human ...blood and could possibly be harnessed as a new cancer biomarker. However, there has been limited use of rigorous experimental controls to account for contamination, which invariably affects low-biomass microbiome studies.
We apply a combination of 16S-rRNA-gene sequencing and droplet digital PCR to determine if the specific detection of cell-free microbial DNA (cfmDNA) is possible in metastatic melanoma patients. Compared to matched stool and saliva samples, the absolute concentration of cfmDNA is low but significantly above the levels detected from negative controls. The microbial community of plasma is strongly influenced by laboratory and reagent contaminants introduced during the DNA extraction and sequencing processes. Through the application of an in silico decontamination strategy including the filtering of amplicon sequence variants (ASVs) with batch dependent abundances and those with a higher prevalence in negative controls, we identify known gut commensal bacteria, such as Faecalibacterium, Bacteroides and Ruminococcus, and also other uncharacterised ASVs. We analyse additional plasma samples, highlighting the potential of this framework to identify differences in cfmDNA between healthy and cancer patients.
Together, these observations indicate that plasma can harbour a low yet detectable level of cfmDNA. The results highlight the importance of accounting for contamination and provide an analytical decontamination framework to allow the accurate detection of cfmDNA for future biomarker studies in cancer and other diseases.
This study evaluated the sensitivity of assaying tumor DNA circulating in the plasma to monitor breast cancer. This assay is compared with three other approaches: radiographic imaging, assay of ...cancer antigen 15-3 (CA 15-3) levels, and assay of circulating tumor cells.
Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide.
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Metastatic breast cancer remains an incurable disease but is treatable by means of serial administration of endocrine, cytotoxic, or biologic therapies. The monitoring of treatment response is essential to avoid continuing ineffective therapies, to prevent unnecessary side effects, and to determine the benefit of new therapeutics. Treatment response is generally assessed with the use of serial imaging, but radiographic measurements often fail to detect changes in tumor burden. Therefore, there is an urgent need for biomarkers that measure tumor burden with high sensitivity . . .
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