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CMTM6 and CMTM4 bind to and stabilize the inhibitory receptor PD-L1 and regulate PD-L1 levels at the surface of human tumour and immune cells. Regulating immunity evasion PD-1/PD-L1 blocking antibodies are effective in the treatment of various cancers. In this study, Ton Schumacher and colleagues describe a haploid genetic screen to identify molecules and pathways that influence the cell surface expression of PD-L1. They identify chemokine-like factors CMTM6 and CMTM4 as cell endogenous regulators of PD-L1 stability, and suggest that this axis could be targeted therapeutically to improve cancer immunotherapy. Elsewhere in this issue, Mark Dawson and colleagues also identify CMTM6 as a novel regulator of PD-L1 expression, through a genome-wide CRISPR–Cas9 screen. CMTM6 functions to maintain PD-L1 at the plasma membrane by inhibiting its lysosome-mediated degradation and promoting its recycling. The clinical benefit for patients with diverse types of metastatic cancers that has been observed upon blockade of the interaction between PD-1 and PD-L1 has highlighted the importance of this inhibitory axis in the suppression of tumour-specific T-cell responses 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 . Notwithstanding the key role of PD-L1 expression by cells within the tumour micro-environment, our understanding of the regulation of the PD-L1 protein is limited 10 , 11 , 12 , 13 , 14 , 15 . Here we identify, using a haploid genetic screen, CMTM6, a type-3 transmembrane protein of previously unknown function, as a regulator of the PD-L1 protein. Interference with CMTM6 expression results in impaired PD-L1 protein expression in all human tumour cell types tested and in primary human dendritic cells. Furthermore, through both a haploid genetic modifier screen in CMTM6-deficient cells and genetic complementation experiments, we demonstrate that this function is shared by its closest family member, CMTM4, but not by any of the other CMTM members tested. Notably, CMTM6 increases the PD-L1 protein pool without affecting PD-L1 (also known as CD274 ) transcription levels. Rather, we demonstrate that CMTM6 is present at the cell surface, associates with the PD-L1 protein, reduces its ubiquitination and increases PD-L1 protein half-life. Consistent with its role in PD-L1 protein regulation, CMTM6 enhances the ability of PD-L1-expressing tumour cells to inhibit T cells. Collectively, our data reveal that PD-L1 relies on CMTM6/4 to efficiently carry out its inhibitory function, and suggest potential new avenues to block this pathway.

Preoperative immunotherapy with anti-PD1 plus anti-CTLA4 antibodies has shown remarkable pathological responses in melanoma 1 and colorectal cancer 2 . In NABUCCO (ClinicalTrials.gov: NCT03387761 ), a single-arm feasibility trial, 24 patients with stage III urothelial cancer (UC) received two doses of ipilimumab and two doses of nivolumab, followed by resection. The primary endpoint was feasibility to resect within 12 weeks from treatment start. All patients were evaluable for the study endpoints and underwent resection, 23 (96%) within 12 weeks. Grade 3–4 immune-related adverse events occurred in 55% of patients and in 41% of patients when excluding clinically insignificant laboratory abnormalities. Eleven patients (46%) had a pathological complete response (pCR), meeting the secondary efficacy endpoint. Fourteen patients (58%) had no remaining invasive disease (pCR or pTisN0/pTaN0). In contrast to studies with anti-PD1/PD-L1 monotherapy, complete response to ipilimumab plus nivolumab was independent of baseline CD8 + presence or T-effector signatures. Induction of tertiary lymphoid structures upon treatment was observed in responding patients. Our data indicate that combined CTLA-4 plus PD-1 blockade might provide an effective preoperative treatment strategy in locoregionally advanced UC, irrespective of pre-existing CD8 + T cell activity. Neoadjuvant immunotherapy combination in the NABUCCO trial elicits high pathological complete response rates in patients with locoregionally advanced (stage III) urothelial cancer and provides molecular biomarkers of treatment efficacy.

In cancer, mutually exclusive or co-occurring somatic alterations across genes can suggest functional interactions. Existing tests for such patterns make the unrealistic assumption of identical gene alteration probabilities across tumors. We present Discrete Independence Statistic Controlling for Observations with Varying Event Rates (DISCOVER), a novel test that is more sensitive than other methods and controls its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence, and most co-occurrences previously detected do not exceed expectation by chance. Many mutual exclusivities are identified involving well-known genes related to cell cycle and growth factor signaling, as well as lesser known regulators of Hedgehog signaling.

Genomic profiling is critical for the identification of treatment options for patients with metastatic cancer, but it remains unclear how frequently this procedure should be repeated during the course of the disease. To address this, we analyzed whole-genome sequencing (WGS) data of 250 biopsy pairs, longitudinally collected over the treatment course of 231 adult patients with a representative variety of metastatic solid malignancies. Within the biopsy interval (median, 6.4 months), patients received one or multiple lines of (mostly) standard-of-care (SOC) treatments, with all major treatment modalities being broadly represented. SOC biomarkers and biomarkers for clinical trial enrollment could be identified in 23% and 72% of biopsies, respectively. For SOC genomic biomarkers, we observed full concordance between the first and the second biopsy in 99% of pairs. Of the 219 biomarkers for clinical trial enrollment that were identified in the first biopsies, we recovered 94% in the follow-up biopsies. Furthermore, a second WGS analysis did not identify additional biomarkers for clinical trial enrollment in 91% of patients. More-frequent genomic evolution was observed when considering specific genes targeted by small-molecule inhibitors or hormonal therapies (21% and 22% of cases, respectively). Together, our data demonstrate that there is limited evolution of the actionable genome of treated metastases. A single WGS analysis of a metastatic biopsy is generally sufficient to identify SOC genomic biomarkers and to identify investigational treatment opportunities. Whole-genome sequencing of metastatic biopsies longitudinally sampled during the course of anticancer treatment reveals that the actionable metastatic cancer genome remains relatively stable over time.

Resistance to targeted cancer drugs is thought to result from selective pressure exerted by a high drug dose. Partial inhibition of multiple components in the same oncogenic signalling pathway may add up to complete pathway inhibition, while decreasing the selective pressure on each component to acquire a resistance mutation. We report here testing of this Multiple Low Dose (MLD) therapy model in EGFR mutant NSCLC. We show that as little as 20% of the individual effective drug doses is sufficient to completely block MAPK signalling and proliferation when used in 3D (RAF + MEK + ERK) or 4D (EGFR + RAF + MEK + ERK) inhibitor combinations. Importantly, EGFR mutant NSCLC cells treated with MLD therapy do not develop resistance. Using several animal models, we find durable responses to MLD therapy without associated toxicity. Our data support the notion that MLD therapy could deliver clinical benefit, even for those having acquired resistance to third generation EGFR inhibitor therapy. A drug used at the maximum tolerated dose can exert a strong selective pressure on cancer cells leading to resistance. In this study, the authors demonstrate the efficacy of using low dose of multiple drugs for preventing and treating resistance to EGFR tyrosine kinase inhibitors in NSCLC cells.

The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of ~ 120,000 to ~ 180,000 unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed ~ 80 (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%-33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes.

An important feature of Bayesian statistics is the opportunity to do sequential inference: the posterior distribution obtained after seeing a dataset can be used as prior for a second inference. However, when Monte Carlo sampling methods are used for inference, we only have a set of samples from the posterior distribution. To do sequential inference, we then either have to evaluate the second posterior at only these locations and reweight the samples accordingly, or we can estimate a functional description of the posterior probability distribution from the samples and use that as prior for the second inference. Here, we investigated to what extent we can obtain an accurate joint posterior from two datasets if the inference is done sequentially rather than jointly, under the condition that each inference step is done using Monte Carlo sampling. To test this, we evaluated the accuracy of kernel density estimates, Gaussian mixtures, mixtures of factor analyzers, vine copulas and Gaussian processes in approximating posterior distributions, and then tested whether these approximations can be used in sequential inference. In low dimensionality, Gaussian processes are more accurate, whereas in higher dimensionality Gaussian mixtures, mixtures of factor analyzers or vine copulas perform better. In our test cases of sequential inference, using posterior approximations gives more accurate results than direct sample reweighting, but joint inference is still preferable over sequential inference whenever possible. Since the performance is case-specific, we provide an R package mvdens with a unified interface for the density approximation methods.

Genomics has greatly improved how patients with cancer are being treated; however, clinical-grade genomic biomarkers for chemotherapies are currently lacking. Using whole-genome analysis of 37 patients with metastatic colorectal cancer (mCRC) treated with the chemotherapy trifluridine/tipiracil (FTD/TPI), we identified KRAS codon G12 ( KRAS G12 ) mutations as a potential biomarker of resistance. Next, we collected real-world data of 960 patients with mCRC receiving FTD/TPI and validated that KRAS G12 mutations were significantly associated with poor survival, also in analyses restricted to the RAS / RAF mutant subgroup. We next analyzed the data of the global, double-blind, placebo-controlled, phase 3 RECOURSE trial ( n  = 800 patients) and found that KRAS G12 mutations ( n  = 279) were predictive biomarkers for reduced overall survival (OS) benefit of FTD/TPI versus placebo (unadjusted interaction P  = 0.0031, adjusted interaction P  = 0.015). For patients with KRAS G12 mutations in the RECOURSE trial, OS was not prolonged with FTD/TPI versus placebo ( n  = 279; hazard ratio (HR) = 0.97; 95% confidence interval (CI) = 0.73–1.20; P  = 0.85). In contrast, patients with KRAS G13 mutant tumors showed significantly improved OS with FTD/TPI versus placebo ( n  = 60; HR = 0.29; 95% CI = 0.15–0.55; P  < 0.001). In isogenic cell lines and patient-derived organoids, KRAS G12 mutations were associated with increased resistance to FTD-based genotoxicity. In conclusion, these data show that KRAS G12 mutations are biomarkers for reduced OS benefit of FTD/TPI treatment, with potential implications for approximately 28% of patients with mCRC under consideration for treatment with FTD/TPI. Furthermore, our data suggest that genomics-based precision medicine may be possible for a subset of chemotherapies. A combination of real-world evidence and a reanalysis of phase 3 clinical trial data unveils KRAS codon G12 mutations as a biomarker of resistance to trifluridine/tipiracil in metastatic colorectal cancer.

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for and in the response to CDK inhibition and and in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens ( -of-many) to the distinctive contexts of individual patients ( -of-one).

DNA mismatch repair-deficient (MMR-d) cancers present an abundance of neoantigens that is thought to explain their exceptional responsiveness to immune checkpoint blockade (ICB) 1 , 2 . Here, in contrast to other cancer types 3 – 5 , we observed that 20 out of 21 (95%) MMR-d cancers with genomic inactivation of β2-microglobulin (encoded by B2M ) retained responsiveness to ICB, suggesting the involvement of immune effector cells other than CD8 + T cells in this context. We next identified a strong association between B2M inactivation and increased infiltration by γδ T cells in MMR-d cancers. These γδ T cells mainly comprised the Vδ1 and Vδ3 subsets, and expressed high levels of PD-1, other activation markers, including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors. In vitro, PD-1 + γδ T cells that were isolated from MMR-d colon cancers exhibited enhanced reactivity to human leukocyte antigen (HLA)-class-I-negative MMR-d colon cancer cell lines and B2M -knockout patient-derived tumour organoids compared with antigen-presentation-proficient cells. By comparing paired tumour samples from patients with MMR-d colon cancer that were obtained before and after dual PD-1 and CTLA-4 blockade, we found that immune checkpoint blockade substantially increased the frequency of γδ T cells in B2M-deficient cancers. Taken together, these data indicate that γδ T cells contribute to the response to immune checkpoint blockade in patients with HLA-class-I-negative MMR-d colon cancers, and underline the potential of γδ T cells in cancer immunotherapy. γδ T cells contribute to the response to immune checkpoint blockade treatment in patients with HLA-class-I-negative DNA mismatch repair-deficient colon cancers. .