Genome-wide screen for anticancer drug resistance in haploid human embryonic stem cells.

Anticancer drugs are at the frontline of cancer therapy. However, innate resistance to these drugs occurs in one-third to one-half of patients, exposing them to the side effects of these drugs with no meaningful benefit. To identify the genes and pathways that confer resistance to such therapies, we performed a genome-wide screen in haploid human embryonic stem cells (hESCs). These cells possess the advantage of having only one copy of each gene, harbour a normal karyotype, and lack any underlying point mutations. We initially show a close correlation between the potency of anticancer drugs in cancer cell lines to those in hESCs. We then exposed a genome-wide loss-of-function library of mutations in all protein-coding genes to 10 selected anticancer drugs, which represent five different mechanisms of drug therapies. The genetic screening enabled us to identify genes and pathways which can confer resistance to these drugs, demonstrating several common pathways. We validated a few of the resistance-conferring genes, demonstrating a significant shift in the effective drug concentrations to indicate a drug-specific effect to these genes. Strikingly, the p53 signalling pathway seems to induce resistance to a large array of anticancer drugs. The data shows dramatic effects of loss of p53 on resistance to many but not all drugs, calling for clinical evaluation of mutations in this gene prior to anticancer therapy.

The temporal behavior of the murine T cell receptor repertoire following Immunotherapy.

Immunotherapy is now an essential tool for cancer treatment, and the unique features of an individual's T cell receptor repertoire are known to play a key role in its effectiveness. The repertoire, famously vast due to a cascade of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire over several time points following treatment with anti-CTLA-4, in a syngeniec mouse model for colorectal cancer, generating a longitudinal dataset of T cell clones and their abundance. The dynamics of the repertoire can be observed in response to treatment over a period of four weeks, as clonal expansion of specific clones ascends and descends. The data made available here can be used to determine treatment and predict its effect, while also providing a unique look at the behavior of the immune system over time.

A T cell repertoire timestamp is at the core of responsiveness to CTLA-4 blockade.

Biology of the response to anti-CTLA-4 involves the dynamics of specific T cell clones. Reasons for clinical success and failure of this treatment are still largely unknown. Here, we quantified the dynamics of the T cell receptor (TCR) repertoire, throughout 4 weeks involving treatment with anti-CTLA-4, in a syngeneic mouse model for colorectal cancer. These dynamics show an initial increase in clonality in tandem with a decrease in diversity, effects which gradually subside. Furthermore, response to treatment is tightly connected to the shared and public parts of the T cell repertoire. We were able to recognize time-dependent behaviors of specific TCR sequences and cell types and to show the response is dominated by specific motifs. We see that a single, specific time point might be useful to inform a physician of the true response to treatmentThe research further highlights the importance of temporal analyses of the immune response.

Breast cancer is marked by specific, Public T-cell receptor CDR3 regions shared by mice and humans.

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires.

A single nucleotide variant of human PARP1 determines response to PARP inhibitors.

The introduction of novel cancer drugs and innovative treatments brings great hope for cancer patients, but also an urgent need to match drugs to suitable patients, since certain drugs that benefit one patient may actually harm others. The newly developed poly-ADP ribose polymerase (PARP) inhibitors (PARPis) are a group of pharmacological enzyme inhibitors used clinically for multiple indications. Several forms of cancer tend to be PARP dependent, making PARP an attractive target for cancer therapy. Specifically, PARPis are commonly used in BRCA-associated breast cancers patients, since unrepaired single-strand breaks are converted into double-strand breaks and BRCA-associated tumors cannot repair them by homologous recombination so that PARPi leads to tumor cell death, by a mechanism called "Synthetic Lethality". Unfortunately, not all patients respond to PARPi, and it is not currently possible to predict who will or will not respond. Here, we present a specific genomic marker, which reflects a single-nucleotide polymorphism of human PARP1 and correlates in vitro with response to PARPi, throughout all indications. In addition, we report that this SNP is associated with re-shaping mRNA, and mRNA levels, and influences the final protein structure to expose new binding sites while hiding others. The status of the SNP is therefore critical to patients' care, as it relates responses to PARPi to the PARP1-SNP carried.

Network Representation of T-Cell Repertoire- A Novel Tool to Analyze Immune Response to Cancer Formation.

The T cell repertoire potentially presents complexity compatible, or greater than, that of the human brain. T cell based immune response is involved with practically every part of human physiology, and high-throughput biology needed to follow the T-cell repertoire has made great leaps with the advent of massive parallel sequencing [1]. Nevertheless, tools to handle and observe the dynamics of this complexity have only recently started to emerge [e.g., 2, 3, 4] in parallel with sequencing technologies. Here, we present a network-based view of the dynamics of the T cell repertoire, during the course of mammary tumors development in a mouse model. The transition from the T cell receptor as a feature, to network-based clustering, followed by network-based temporal analyses, provides novel insights to the workings of the system and provides novel tools to observe cancer progression via the perspective of the immune system. The crux of the approach here is at the network-motivated clustering. The purpose of the clustering step is not merely data reduction and exposing structures, but rather to detect hubs, or attractors, within the T cell receptor repertoire that might shed light on the behavior of the immune system as a dynamic network. The Clone-Attractor is in fact an extension of the clone concept, i.e., instead of looking at particular clones we observe the extended clonal network by assigning clusters to graph nodes and edges to adjacent clusters (editing distance metric). Viewing the system as dynamical brings to the fore the notion of an attractors landscape, hence the possibility to chart this space and map the sample state at a given time to a vector in this large space. Based on this representation we applied two different methods to demonstrate its effectiveness in identifying changes in the repertoire that correlate with changes in the phenotype: (1) network analysis of the TCR repertoire in which two measures were calculated and demonstrated the ability to differentiate control from transgenic samples, and, (2) machine learning classifier capable of both stratifying control and trangenic samples, as well as to stratify pre-cancer and cancer samples.

System-wide Analysis of the T Cell Response.

The T cell receptor (TCR) controls the cellular adaptive immune response to antigens, but our understanding of TCR repertoire diversity and response to challenge is still incomplete. For example, TCR clones shared by different individuals with minimal alteration to germline gene sequences (public clones) are detectable in all vertebrates, but their significance is unknown. Although small in size, the zebrafish TCR repertoire is controlled by processes similar to those operating in mammals. Thus, we studied the zebrafish TCR repertoire and its response to stimulation with self and foreign antigens. We found that cross-reactive public TCRs dominate the T cell response, endowing a limited TCR repertoire with the ability to cope with diverse antigenic challenges. These features of vertebrate public TCRs might provide a mechanism for the rapid generation of protective T cell immunity, allowing a short temporal window for the development of more specific private T cell responses.