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Analysis of previous trials of polatuzumab vedotin for diffuse large B-cell lymphoma showed that the drug was more effective for tumors of one subtype (activated B cell) than those of another subtype (germinal-center B cell).

We consider a setting in which we have a treatment and a potentially large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way, and then fit a standard model using the modified covariates and no main effects. We show that coupled with an efficiency augmentation procedure, this method produces clinically meaningful estimators in a variety of settings. It can be useful for practicing personalized medicine: determining from a large set of biomarkers, the subset of patients that can potentially benefit from a treatment. We apply the method to both simulated datasets and real trial data. The modified covariates idea can be used for other purposes, for example, large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable. Supplementary materials for this article are available online.

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper. We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets ( http://cibersort.stanford.edu/ ).

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89–0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4 + T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease. A neural network trained on diverse datasets improves prediction of HLA class II epitope presentation.

A searchable pan-cancer resource generated using data from nearly 18,000 human tumors reveals links between tumor infiltration by particular leukocyte subsets, tumor expression of particular gene signatures, and patient prognosis. Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI ( FOXM1 ) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools ( http://precog.stanford.edu ) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

Circulating tumour DNA (ctDNA) analysis facilitates studies of tumour heterogeneity. Here we employ CAPP-Seq ctDNA analysis to study resistance mechanisms in 43 non-small cell lung cancer (NSCLC) patients treated with the third-generation epidermal growth factor receptor (EGFR) inhibitor rociletinib. We observe multiple resistance mechanisms in 46% of patients after treatment with first-line inhibitors, indicating frequent intra-patient heterogeneity. Rociletinib resistance recurrently involves MET , EGFR , PIK3CA , ERRB2 , KRAS and RB1 . We describe a novel EGFR L798I mutation and find that EGFR C797S, which arises in ∼33% of patients after osimertinib treatment, occurs in <3% after rociletinib. Increased MET copy number is the most frequent rociletinib resistance mechanism in this cohort and patients with multiple pre-existing mechanisms (T790M and MET ) experience inferior responses. Similarly, rociletinib-resistant xenografts develop MET amplification that can be overcome with the MET inhibitor crizotinib. These results underscore the importance of tumour heterogeneity in NSCLC and the utility of ctDNA-based resistance mechanism assessment. EGFR -mutant non-small cell lung cancer is routinely treated with EGFR inhibitors, although resistance inevitably develops. Here, the authors sequence circulating tumour DNA and show that resistance to the third-generation inhibitor rociletinib is heterogeneous and recurrently involves somatic alterations of MET , EGFR , PIK3CA , ERRB2 , and KRAS .

Immunotherapy fails to cure most cancer patients. Preclinical studies indicate that radiotherapy synergizes with immunotherapy, promoting radiation-induced antitumor immunity. Most preclinical immunotherapy studies utilize transplant tumor models, which overestimate patient responses. Here, we show that transplant sarcomas are cured by PD-1 blockade and radiotherapy, but identical treatment fails in autochthonous sarcomas, which demonstrate immunoediting, decreased neoantigen expression, and tumor-specific immune tolerance. We characterize tumor-infiltrating immune cells from transplant and primary tumors, revealing striking differences in their immune landscapes. Although radiotherapy remodels myeloid cells in both models, only transplant tumors are enriched for activated CD8+ T cells. The immune microenvironment of primary murine sarcomas resembles most human sarcomas, while transplant sarcomas resemble the most inflamed human sarcomas. These results identify distinct microenvironments in murine sarcomas that coevolve with the immune system and suggest that patients with a sarcoma immune phenotype similar to transplant tumors may benefit most from PD-1 blockade and radiotherapy. Promising results of cancer therapies in transplant tumor models often fail to predict efficacy in clinical trials. Here the authors show that, while transplant tumors are cured by radiotherapy and PD-1 blockade, autochthonous sarcomas are resistant to the identical treatment, recapitulating the immune landscape and resistance to checkpoint blockade observed in most sarcoma patients.

Hematopoietic tissues in acute myeloid leukemia (AML) patients contain both leukemia stem cells (LSC) and residual normal hematopoietic stem cells (HSC). The ability to prospectively separate residual HSC from LSC would enable important scientific and clinical investigation including the possibility of purged autologous hematopoietic cell transplants. We report here the identification of TIM3 as an AML stem cell surface marker more highly expressed on multiple specimens of AML LSC than on normal bone marrow HSC. TIM3 expression was detected in all cytogenetic subgroups of AML, but was significantly higher in AML-associated with core binding factor translocations or mutations in CEBPA. By assessing engraftment in NOD/SCID/IL2Rγ-null mice, we determined that HSC function resides predominantly in the TIM3-negative fraction of normal bone marrow, whereas LSC function from multiple AML specimens resides predominantly in the TIM3-positive compartment. Significantly, differential TIM3 expression enabled the prospective separation of HSC from LSC in the majority of AML specimens with detectable residual HSC function.

Circulating tumor DNA (ctDNA) molecular residual disease (MRD) following curative-intent treatment strongly predicts recurrence in multiple tumor types, but whether further treatment can improve outcomes in patients with MRD remains unclear. We applied CAPP-Seq ctDNA analysis to 218 samples from 65 patients receiving chemoradiation therapy (CRT) for locally advanced NSCLC, including 28 patients receiving consolidation immune checkpoint inhibition (CICI). Patients with undetectable ctDNA after CRT had excellent outcomes whether or not they received CICI. Among such patients, one died from CICI-related pneumonitis, highlighting the potential utility of only treating patients with MRD. In contrast, patients with MRD after CRT who received CICI had significantly better outcomes than patients who did not receive CICI. Furthermore, the ctDNA response pattern early during CICI identified patients responding to consolidation therapy. Our results suggest that CICI improves outcomes for NSCLC patients with MRD and that ctDNA analysis may facilitate personalization of consolidation therapy.

This phase II trial aims to determine the efficacy and safety of frontline acalabrutinib, lenalidomide and rituximab for patients with advanced stage follicular lymphoma (FL) and high tumor burden. The primary endpoint was best complete response (CR) rate; the secondary endpoints were overall response rate (ORR), duration of response measured as CR at 30 months (CR30), progression of disease at 24 months (POD24) rate, progression-free survival (PFS), overall survival and safety. Twenty-four patients with previously untreated FL were included in this phase 2 single arm study (NCT04404088). The most common grade 3-4 adverse events were neutropenia (58%) and liver function test elevation (17%). Best ORR was 100% and best CR rate was 92%. CR30 rate was 65% and POD24 rate was 17%. After a median follow-up of 43 months, median PFS and OS were not reached, 2-year PFS rate was 79% and 2-year OS rate was 92%. Here we show that the addition of acalabrutinib to R 2 is a safe and effective frontline regimen for FL patients, and further exploration in larger clinical trials is needed. Bruton tyrosine kinase (BTK) inhibitors can interrupt the crosstalk between follicular lymphoma FL cells and macrophages thereby inducing downregulation of pro-survival pathways in FL cells. Here this group reports a phase 2 single-arm trial evaluating the regimen of acalabrutinib with lenalidomide plus rituximab on twenty four patients with previously untreated FL.