Role of Antigen Spread and Distinctive Characteristics of Immunotherapy in Cancer Treatment

Contains fulltext : 174163.pdf (publisher's version ) (Open Access)Immunotherapy is an important breakthrough in cancer. US Food and Drug Administration-approved immunotherapies for cancer treatment (including, but not limited to, sipuleucel-T, ipilimumab, nivolumab, pembrolizumab, and atezolizumab) substantially improve overall survival across multiple malignancies. One mechanism of action of these treatments is to induce an immune response against antigen-bearing tumor cells; the resultant cell death releases secondary (nontargeted) tumor antigens. Secondary antigens prime subsequent immune responses (antigen spread). Immunotherapy-induced antigen spread has been shown in clinical studies. For example, in metastatic castration-resistant prostate cancer patients, sipuleucel-T induced early immune responses to the immunizing antigen (PA2024) and/or the target antigen (prostatic acid phosphatase). Thereafter, most patients developed increased antibody responses to numerous secondary proteins, several of which are expressed in prostate cancer with functional relevance in cancer. The ipilimumab-induced antibody profile in melanoma patients shows that antigen spread also occurs with immune checkpoint blockade. In contrast to chemotherapy, immunotherapy often does not result in short-term changes in conventional disease progression end points (eg, progression-free survival, tumor size), which may be explained, in part, by the time taken for antigen spread to occur. Thus, immune-related response criteria need to be identified to better monitor the effectiveness of immunotherapy. As immunotherapy antitumor effects take time to evolve, immunotherapy in patients with less advanced cancer may have greater clinical benefit vs those with more advanced disease. This concept is supported by prostate cancer clinical studies with sipuleucel-T, PSA-TRICOM, and ipilimumab. We discuss antigen spread with cancer immunotherapy and its implications for clinical outcomes

Family Composition and Stability for Orphans: A Longitudinal Study of Well-Being in 5 Low- and Middle-Income Countries

Objectives: Many orphaned children in low- and middle-income countries live with family. Yet, their household composition and its stability are not well-characterized, nor is impact of stability on longer-term outcomes. Methods: We used the longitudinal, multi-country Positive Outcomes for Orphans cohort to describe adult family living with orphans. Stability was measured by changes in presence of six familial relations over time, and related to three outcomes: 1) incident abuse, 2) cognitive functioning, 3) emotional difficulties. Associations were estimated using generalized linear models fit with generalized estimating equations. For abuse, Poisson regression estimated risk ratios. For continuous scores of cognitive functioning and emotional difficulties, linear models estimated mean differences (MDs) with 95% confidence intervals. Results: Among 1,359 orphans, 53–61% reported living with their mother each year; 7–13% with father; nearly 60% reported ≥1 change in composition over follow-up. Compared to 0 changes, difficulties increased with 1 change [MD: 0.23 (−0.33, 0.79)], 2 changes [MD: 0.57 (0.00, 1.16)] and ≥3 changes [MD: 0.73 (0.18, 1.29)]. No associations were found with abuse or cognitive functioning. Conclusion: Orphan well-being may be improved through supports stabilizing household composition or targeting emotional resilience

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics