Acquired resistance to anti-PD1 therapy: checkmate to checkpoint blockade?

Editorial summary Anti-programmed cell death 1 (PD1) immunotherapies are among the most effective anti-cancer immunotherapies available; however, a large number of patients present with or develop resistance to them. Unfortunately, very little is known regarding the mechanisms of resistance to such therapies. A recent study sought to identify mutations associated with resistance to anti-PD1 therapy. Results from this study demonstrated that mutations which affected the sensitivity of tumor cells to T-cell-derived interferons, and mutations limiting tumor-cell antigen presentation, could cause acquired resistance. These findings have significant implications for understanding the mechanisms by which anti-PD1 therapies exert their efficacy, comprehending why and how some patients acquire resistance over time, and ultimately guiding the development of combination therapies designed to overcome, or potentially prevent, the development of acquired immunotherapeutic resistance

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis

The Natural Products Atlas : an open access knowledge base for microbial natural products discovery

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries

Cancer immunoediting and resistance to T cell-based immunotherapy

Anticancer immunotherapies involving the use of immune-checkpoint inhibitors or adoptive cellular transfer have emerged as new therapeutic pillars within oncology. These treatments function by overcoming or relieving tumour-induced immunosuppression, thereby enabling immune-mediated tumour clearance. While often more effective and better tolerated than traditional and targeted therapies, many patients have innate or acquired resistance to immunotherapies. Cancer immunoediting is the process whereby the immune system can both constrain and promote tumour development, which proceeds through three phases termed elimination, equilibrium and escape. Throughout these phases, tumour immunogenicity is edited, and immunosuppressive mechanisms that enable disease progression are acquired. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this Review, we discuss how a deeper understanding of the mechanisms underlying the cancer immunoediting process can provide insight into the development of resistance to immunotherapies and the strategies that can be used to overcome such resistance

Additional file 1 of HTLV-1 reverse transcriptase homology model provides structural basis for sensitivity to existing nucleoside/nucleotide reverse transcriptase inhibitors

Additional file 1: Fig. S1. A Plots of predicted alignment error (PAE) for 5 different HTLV-1 reverse transcriptase models generated using Alphafold2, the model with the lowest PAE (rank_1) was used. B Cartoon representation of the Alphafold2 model theoretically complexed with DNA (green) using HERV-K (PDBID:7SR6) and MMLV (PDBID:4MH8) models. C Cartoon representation of theoretical HTLV-1 reverse transcriptase (Alphafold2) (light pink) overlayed with energy-minimized structure (GROMACS 5.3.1) (dark pink) (left). Backbone structural divergence measured as R.M.S.D. (Å) and depicted as blue (low) to grey (high) colour gradient (right). Inlay represents the active site (predicted site of reverse transcriptase inhibitor binding) amino acids for the non-energy minimized (pink) and energy minimized (light pink) structures. Fig. S2. A Molecular surface diagram of HIV-1 reverse transcriptase with non-nucleoside reverse transcriptase inhibitor (NNRTIs) binding site (allosteric site) highlighted purple (left). Interaction plots of indicated NNRTIs in the active site in their most energetically favourable conformation (1 of 10) (right). B Molecular surface diagram of HTLV-1 reverse transcriptase with non-nucleoside reverse transcriptase inhibitor (NRTIs) binding site (allosteric site) highlighted purple (left). C Data summary of molecular docking testing 10 different conformations in either the HIV-1 reverse transcriptase or HTLV-1 reverse transcriptase. D Molecular surface diagram of HTLV-1 reverse transcriptase with nucleoside reverse transcriptase inhibitor (NRTIs) binding site (active site) highlighted purple (left). Inlay of Mg2+ coordination within the active site

Timing of neoadjuvant immunotherapy in relation to surgery is crucial for outcome

Adjuvant immunotherapies targeting CTLA4 or PD-1 recently demonstrated efficacy in the treatment of earlier stages of human cancer. We previously demonstrated using mouse spontaneous metastasis models that neoadjuvant immunotherapy and surgery was superior, compared to surgery and adjuvant immunotherapy, in eradicating the lethal metastatic disease. However, the optimal scheduling between neoadjuvant immunotherapy and surgery and how it impacts on efficacy and development of immune-related adverse events (irAEs) remains undefined. Using orthotopic 4T1.2 and E0771 mouse models of spontaneously metastatic mammary cancer, we varied the schedule and duration of neoadjuvant immunotherapies and surgery and examined how it impacted on long-term survival. In two tumor models, we demonstrated that a short duration (4–5 days) between first administration of neoadjuvant immunotherapy and resection of the primary tumor was necessary for optimal efficacy, while extending this duration (10 days) abrogated immunotherapy efficacy. However, efficacy was also lost if neoadjuvant immunotherapy was given too close to surgery (2 days). Interestingly, an additional 4 adjuvant doses of treatment following a standard 2 doses of neoadjuvant immunotherapy, did not significantly improve overall tumor-free survival regardless of the combination treatment (anti-PD-1+anti-CD137 or anti-CTLA4+anti-PD-1). Furthermore, biochemical immune-related adverse events (irAEs) increased in tumor-bearing mice that received the additional adjuvant immunotherapy. Overall, our data suggest that shorter doses of neoadjuvant immunotherapy scheduled close to the time of surgery may optimize effective anti-tumor immunity and reduce severe irAEs

Batf3+ DCs and type I IFN are critical for the efficacy of neoadjuvant cancer immunotherapy

New clinical trials are now evaluating the efficacy of neoadjuvant immunotherapy in the context of primary tumor surgery. Using the orthotopic 4T1.2 mouse model of spontaneously metastatic mammary cancer, we have shown that neoadjuvant immunotherapy and surgery was superior in the generation of tumor-specific CD8 T cells and eradication of lethal metastases compared to surgery followed by adjuvant immunotherapy. However, the importance of host Batf3 and type I interferon (IFN) for long-term survival of mice following neoadjuvant immunotherapy is unknown. Here we demonstrated that loss of Batf3 DCs or type I IFN receptor blockade in 4T1.2 tumor-bearing mice treated with neoadjuvant anti-PD-1+anti-CD137 immunotherapy reduced long-term survival with a corresponding reduction in tumor-specific CD8 T cells producing effector cytokines in the primary tumor and in the periphery. Interestingly, we found all high-risk stage III melanoma patients relapsing after adjuvant or neoadjuvant ipilimumab+nivolumab within the OpACIN trial (NCT02437279) displayed low expression of Batf3 DC-associated genes in pre-treatment tumor biopsies. Further focus should now be placed on validating the requirement of an intratumoral Batf3 DC gene signature for response to neoadjuvant immunotherapy

Sex differences in oncogenic mutational processes

Funder: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology); doi: https://doi.org/10.13039/501100002790Funder: Genome Canada (Génome Canada); doi: https://doi.org/10.13039/100008762Funder: Canada Foundation for Innovation (Fondation canadienne pour l'innovation); doi: https://doi.org/10.13039/501100000196Funder: Terry Fox Research Institute (Institut de Recherche Terry Fox); doi: https://doi.org/10.13039/501100004376Abstract: Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts