Citations

PURPOSECurrent tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy.METHODSUsing an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors.RESULTSAmong 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features.CONCLUSIONDiet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.

Abstract Purpose: Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. Experimental Design: We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. Results: We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Conclusions: Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Abstract Background Combination irinotecan and cetuximab is approved for irinotecan-refractory metastatic colorectal cancer (mCRC). It is unknown if adding bevacizumab improves outcomes. Patients and Methods In this multicenter, randomized, double-blind, placebo-controlled phase II trial, patients with irinotecan-refractory RAS-wildtype mCRC and no prior anti-EGFR therapy were randomized to cetuximab 500 mg/m2, bevacizumab 5 mg/kg, and irinotecan 180 mg/m2 (or previously tolerated dose) (CBI) versus cetuximab, irinotecan, and placebo (CI) every 2 weeks until disease progression or intolerable toxicity. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and adverse events (AEs). Results The study closed early after the accrual of 36 out of a planned 120 patients due to changes in funding. Nineteen patients were randomized to CBI and 17 to CI. Baseline characteristics were similar between arms. Median PFS was 9.7 versus 5.5 months for CBI and CI, respectively (1-sided log-rank P = .38; adjusted hazard ratio [HR] = 0.64; 95% confidence interval [CI], 0.25-1.66). Median OS was 19.7 versus 10.2 months for CBI and CI (1-sided log-rank P = .02; adjusted HR = 0.41; 95% CI, 0.15-1.09). ORR was 36.8% for CBI versus 11.8% for CI (P = .13). Grade 3 or higher AEs occurred in 47% of patients receiving CBI versus 35% for CI (P = .46). Conclusion In this prematurely discontinued trial, there was no significant difference in the primary endpoint of PFS between CBI and CI. There was a statistically significant improvement in OS in favor of CBI compared with CI. Further investigation of CBI for the treatment of irinotecan-refractory mCRC is warranted. Clinical Trial Registration: NCT02292758

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have dramatically changed the landscape of cancer therapy. Both remarkable and durable responses have been observed in patients with melanoma, non-small-cell lung cancer (NSCLC), and other malignancies. However, the PD-1/PD-L1 blockade has demonstrated meaningful clinical responses and benefits in only a subset of patients. In addition, several severe and life-threatening adverse events were observed in these patients. Therefore, the identification of predictive biomarkers is urgently needed to select patients who are more likely to benefit from ICI therapy. PD-L1 expression level is the most commonly used biomarker in clinical practice for PD-1/PD-L1 inhibitors. However, negative PD-L1 expression cannot reliably exclude a response to a PD-1/PD-L1 blockade. Other factors, such as tumor microenvironment and other tumor genomic signatures, appear to impact the response to ICIs. In this review, we examine emerging data for novel biomarkers that may have a predictive value for optimizing the benefit from anti-PD-1/PD-L1 immunotherapy.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value <0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P<0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Introduction: Immune checkpoint receptor (ICR) blockade has emerged as an effective anti-tumour modality, but only in a subset of cancer patients. Moreover, in Multiple myeloma (MM), single-agent activity has not been observed, highlighting the need to better understand the mechanism of action of this class of drugs. We recently showed that combinatorial ICR blockade using αLAG3 and αPD-1 delays disease progression and improves survival in the transplantable Vκ*MYC model of MM (Croucher et al. ASH 2018). However, despite this being a controlled study with genetically-homogeneous tumours, anti-tumour immune responses were heterogeneous, with only a subset of mice demonstrating a delay in tumour progression (17/29 mice, response rate = 58.6%). Thus, using this model, we set out to define mechanisms underlying variability in response to ICR blockade. Methods: We established a cohort of mice by engrafting 5-week-old C57BL/6 mice with Vκ12598 cells via tail vein injection. Treatment with αLAG3/αPD-1 or Ig-control was initiated 1-week post-engraftment and bone marrow (BM) samples were collected 3 weeks after the start of treatment. Following FACS-enrichment of T cells and plasma cells (PCs), single cell suspensions were subjected to matched single-cell gene expression (5' scRNA-seq) and T cell receptor (TCR)/B cell receptor (BCR) profiling (10x Genomics). Results: Samples were selected for profiling based on response to treatment, with responders (n=4) defined by significantly lower disease burden compared to non-responders (n=3) and control-treated mice (n=5), as measured by serum M-protein and %PCs in BM/spleen at sacrifice. Unsupervised clustering of scRNA-seq data from PCs (n=3,318 cells) identified no gene expression or BCR repertoire differences between control and treated, or between responder and non-responder samples, supporting that variability in response was not related to malignant Vκ12598 cells themselves. Across all samples, a statistically significant difference was not detected between the total number of unique TCR sequences (clonotypes) comparing control-treated (351-2369), non-responders (1185-2327) and responders (1378-1698), with no overlapping TCR sequences between top clonotypes. Evaluation of TCR repertoire diversity revealed that αLAG3/αPD-1 treatment induces clonal T cell expansion in control versus treated mice, but this was not significantly different between responders and non-responders. Analysis of paired scRNA-seq data (n=21,520 cells) revealed that expanded T cells from αLAG3/αPD-1-treated mice occupy a different cell state in responder vs. non-responder mice. We speculate that underlying differences in the TCR repertoire may dictate the downstream phenotype of expanded, anti-tumour T cells in mice treated with combinatorial αLAG3/αPD-1. Tumour control following treatment was associated with clonal expansion of T cells expressing genes related to cytoxicity and activation (Ccl5, Ifng, Fasl, Gzmb), whereas tumour progression was associated with clonal expansion of proliferative T cells (Cdkn3, Birc5, Ccna2, Aurka, Mki67). Although T cell proliferation is typically a phenotype ascribed to effector T cells, recent studies have similarly observed this proliferative cell state in dysfunctional T cells within melanoma tumours. Moreover, emerging evidence supports suppression of T cell proliferation by CDK4/6 inhibitors as a means to augment anti-tumour activity of ICR-based therapy. Thus, studies exploring whether reversal of the observed proliferative T cell state can restore response to αLAG3/αPD-1 treatment in non-responding Vκ12598 mice are ongoing and will be reported. Conclusions: ICR inhibitors demonstrate significant activity in some cancers, however many patients fail to respond and a similarly promising level of efficacy has not been achieved in MM. Studies aimed at unraveling the mechanisms of response and resistance to ICR inhibitors are therefore needed to improve the utility of this class of drugs for all patients. Our approach of using paired single-cell gene expression and TCR repertoire profiling has enabled identification of molecular cell states specifically in expanded T cells of responder vs. non-responder mice. In turn, our work nominates novel mechanisms that may be used as potential biomarkers for anti-tumour immune responses as well as potential targets to augment responses to ICR blockade therapy. Disclosures Chesi: Abcuro: Patents & Royalties: Genetically engineered mouse model of myeloma; Novartis: Consultancy, Patents & Royalties: human CRBN transgenic mouse; Pfizer: Consultancy; Pi Therapeutics: Patents & Royalties: Genetically engineered mouse model of myeloma; Palleon Pharmaceuticals: Patents & Royalties: Genetically engineered mouse model of myeloma. Bergsagel: GSK: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Sebag: Janssen: Research Funding; Bristol Myers-Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Karyopharm Therapeutics: Consultancy, Honoraria. Trudel: BMS/Celgene: Consultancy, Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Roche: Consultancy; Sanofi: Honoraria; Pfizer: Honoraria, Research Funding; Genentech: Research Funding.

Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults, responsible for approximately 225,000 deaths per year. Despite pre-clinical successes, therapeutic interventions have failed to extend patient survival by more than a few months. Anti-PD-1 checkpoint inhibition monotherapy has had efficacy against some tumor types but not GBM. The aim of this study was to determine whether supplementing anti-PD-1 checkpoint blockade with an engineered extended half-life IL-2 could improve outcomes in a preclinical model of disease. Our enhanced checkpoint blockade (ECB) strategy reliably cures approximately50% of C57BL/6 mice bearing orthotopic GL261 gliomas and extended median survival even in the mice that eventually succumbed. This therapy generates robust immunologic responses, features of which include secondary lymphoid organ enlargement and increased activation status of both CD4 and CD8 T cells. Further, many of the characteristics of brain-tumor mediated peripheral immunosuppression, including MHC class II downregulation on APCs, are prevented by ECB combination therapy. This immunity is durable, with long-term ECB survivors able to resist GL261 rechallenge. Notably, ECB’s efficacy is independent of host MHC class I restricted antigen presentation, being equally efficacious in MHC class I and CD8 T cell deficient mice. Conversely, ECB combination therapy is reliant on CD4 T cells and their depletion abrogates the therapy’s survival benefit. Our data shows ECB combination immunotherapy to be efficacious against the GL261 glioma model through an MHC class I independent mechanism, enhancing its off-the-shelf translational appeal relative to strategies requiring extensive knowledge of tumor-specific antigens.

Abstract Glioblastoma is associated with severe and multifaceted immunosuppression affecting all immune organs. Immunosuppression in GBM is a critical barrier to the success of immunotherapies and patient survival. We demonstrated that immunosuppression in the GL261-model of experimental GBM presents with significant thymic and spleen atrophy, MHCII downregulation, presence of potent immunosuppressive factors in serum, and sequestration of T-cells in the bone marrow. Parabiosis studies determined that soluble factors mediate immunosuppression by inhibiting T-cell proliferation, thymic involution, and loss of peripheral T-cells. In contrast, bone marrow T-cell sequestration was not mediated through soluble factors. While the immunosuppression in GBM is severe, a causative link between each facet of immunosuppression and overall survival is lacking. We used two strategies to block T-cell sequestration into the bone marrow and evaluated the extent survival was impacted in experimental GBM. First, we evaluated the extent a novel and off-the-shelf combination immunotherapy that uses extended 1/2-life IL-2 and anti-PD-1 reverses bone marrow T-cell sequestration. Sham treatment or anti-PD1 monotherapy did not alter T-cell sequestration in the bone marrow and animals had no enhanced survival. Extended 1/2-life IL-2 monotherapy and combination strategy both prevented T-cell sequestration into the bone marrow. However, only combined therapy, which also prevented MHC class II downregulation, improved survival. Second, we determined that glioma-bearing adrenalectomized mice do not present with bone marrow T-cell sequestration. However, sera of glioma-bearing adrenalectomized mice is as immunosuppressive as glioma-bearing controls. Blocking bone marrow T-cell sequestration in the presences of serum immunosuppression led to no survival benefit in glioma-bearing adrenalectomized mice compared to controls. In short, bone marrow T-cell sequestration alone does not correspond with overall survival in experimental glioma. Importantly, a concerted effort to reverse MHC class II downregulation and define inhibitory circulating factors may have the highest impact in immunotherapeutic efficacy and improving patient survival.

Objective We aimed to measure provider perspectives on the acceptability, appropriateness, and feasibility of teleneonatology in neonatal intensive care units (NICUs) and community hospitals. Study Design Providers from five academic tertiary NICUs and 27 community hospitals were surveyed using validated implementation measures to assess the acceptability, appropriateness, and feasibility of teleneonatology. For each of the 12 statements, scale values ranged from 1 to 5 (1 = strongly disagree; 5 = strongly agree), with higher scores indicating greater positive perceptions. Survey results were summarized, and differences across respondents assessed using generalized linear models. Results The survey response rate was 56% (203/365). Respondents found teleneonatology to be acceptable, appropriate, and feasible. The percent of respondents who agreed with each of the twelve statements ranged from 88.6 to 99.0%, with mean scores of 4.4 to 4.7 and median scores of 4.0 to 5.0. There was no difference in the acceptability, appropriateness, and feasibility of teleneonatology when analyzed by professional role, years of experience in neonatal care, or years of teleneonatology experience. Respondents from Level I well newborn nurseries had greater positive perceptions of teleneonatology than those from Level II special care nurseries. Conclusion Providers in tertiary NICUs and community hospitals perceive teleneonatology to be highly acceptable, appropriate, and feasible for their practices. The wide acceptance by providers of all roles and levels of experience likely demonstrates a broad receptiveness to telemedicine as a tool to deliver neonatal care, particularly in rural communities where specialists are unavailable. Key Points