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Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation., Competing Interests: Competing interests: RB declares PCT/US15/612657 (Cancer Immunotherapy), PCT/US18/36052 (Microbiome Biomarkers for Anti-PD-1/PD-L1 Responsiveness: Diagnostic, Prognostic and Therapeutic Uses Thereof), PCT/US63/055227 (Methods and Compositions for Treating Autoimmune and Allergic Disorders). AM is an equity holder in Picture Health, Elucid Bioimaging, and Inspirata. Currently, he serves on the advisory board of Picture Health, and SimBioSys. He currently consults for Takeda; He also has sponsored research agreements with AstraZeneca and Bristol Myers-Squibb; his technology has been licensed to Picture Health and Elucid Bioimaging; he is also involved in two different R01 grants with Inspirata; he also serves as a member for the Frederick National Laboratory Advisory Committee.VDJ has performed consulting for VincerX Pharmaceuticals, Providence St. John, Los Angeles; she is a founder of Bioinformatica. JSB-S, EK, DM are full time, paid employees of NCI/NIH. EJF is on the Scientific Advisory Board of Resistance Bio, a consultant for Mestag and Merck, and receives research funding from AbbVie. SG reports current and past research funding from Regeneron Pharmaceuticals, Boehringer Ingelheim, BMS (Celgene), Genentech, EMD Serono, Pfizer, and Takeda. ER is a co-founder of MedAware, Metabomed and Pangea Biomed (divested), and an unpaid member of Pangea Biomed’s and GSK Oncology scientific advisory boards. DVV is a co-founder and chief scientist of Barrier Biosciences and holds equity in the company. NV is on the Consulting/Advisory Boards of Sanofi/Genzyme (2022), Oncocyte (2021), Eli Lilly (2021), Regeneron (2022), Amgen (2023), Xencor (2023), AstraZeneca (2023), Tempus (2023), Pfizer (2024), Summit Therapeutics (2024), OncoHost (2024); she reports travel reimbursement from Regeneron; research grants from Oncocyte (2022–), Circulogene and Mirati, funding from Circulogene, and honoraria from Nebraska Oncology Society, Scienomics Group, Grace, OncLive and OMNI-Oncology. EMVA reports personal fees from Tango Therapeutics, Genome Medical, Genomic Life, Monte Rosa Therapeutics, Manifold Bio, Enara Bio, Riva Therapeutics, Foaley & Hoag, and Serinus Bio, grants and personal fees from Novartis Institute for Biomedical Research, and grants from BMS, Janssen, and Sanofi outside the submitted work; in addition, EMVA has a patent for institutional patents filed on chromatin mutations and immunotherapy response and methods for clinical interpretation pending., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome., Results: While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395)., Conclusions: NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Epidermal growth factor receptor (EGFR) signaling is frequently dysregulated in various cancers. The ubiquitin ligase Casitas B-lineage lymphoma proto-oncogene (Cbl) regulates degradation of activated EGFR through ubiquitination and acts as an adaptor to recruit proteins required for trafficking. Here, we used stable isotope labeling with amino acids in cell culture mass spectrometry to compare Cbl complexes with or without epidermal growth factor (EGF) stimulation. We identified over a hundred novel Cbl interactors, and a secondary siRNA screen found that knockdown of Flotillin-2 (FLOT2) led to increased phosphorylation and degradation of EGFR upon EGF stimulation in HeLa cells. In PC9 and H441 cells, FLOT2 knockdown increased EGF-stimulated EGFR phosphorylation, ubiquitination, and downstream signaling, reversible by EGFR inhibitor erlotinib. CRISPR knockout (KO) of FLOT2 in HeLa cells confirmed EGFR downregulation, increased signaling, and increased dimerization and endosomal trafficking. Furthermore, we determined that FLOT2 interacted with both Cbl and EGFR. EGFR downregulation upon FLOT2 loss was Cbl dependent, as coknockdown of Cbl and Cbl-b restored EGFR levels. In addition, FLOT2 overexpression decreased EGFR signaling and growth. Overexpression of wildtype (WT) FLOT2, but not the soluble G2A FLOT2 mutant, inhibited EGFR phosphorylation upon EGF stimulation in HEK293T cells. FLOT2 loss induced EGFR-dependent proliferation and anchorage-independent growth. Lastly, FLOT2 KO increased tumor formation and tumor volume in nude mice and NSG mice, respectively. Together, these data demonstrated that FLOT2 negatively regulated EGFR activation and dimerization, as well as its subsequent ubiquitination, endosomal trafficking, and degradation, leading to reduced proliferation in vitro and in vivo., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Published by Elsevier Inc.)

Background: Next-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks., Results: Here we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene-gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops., Conclusions: gcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions., (© 2021. The Author(s).)

Purpose: Whole-exome (WES) and RNA sequencing (RNA-seq) are key components of cancer immunogenomic analyses. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the Cancer Immune Monitoring and Analysis Centers (CIMAC) and the Cancer Immunologic Data Commons (CIDC) conducted a systematic harmonization study., Experimental Design: DNA and RNA were centrally extracted from fresh frozen and formalin-fixed paraffin-embedded non-small cell lung carcinoma tumors and distributed to three centers for WES and RNA-seq profiling. In addition, two 10-plex HapMap cell line pools with known mutations were used to evaluate the accuracy of the WES platforms., Results: The WES platforms achieved high precision (> 0.98) and recall (> 0.87) on the HapMap pools when evaluated on loci using > 50× common coverage. Nonsynonymous mutations clustered by tumor sample, achieving an index of specific agreement above 0.67 among replicates, centers, and sample processing. A DV200 > 24% for RNA, as a putative presequencing RNA quality control (QC) metric, was found to be a reliable threshold for generating consistent expression readouts in RNA-seq and NanoString data. MedTIN > 30 was likewise assessed as a reliable RNA-seq QC metric, above which samples from the same tumor across replicates, centers, and sample processing runs could be robustly clustered and HLA typing, immune infiltration, and immune repertoire inference could be performed., Conclusions: The CIMAC collaborating laboratory platforms effectively generated consistent WES and RNA-seq data and enable robust cross-trial comparisons and meta-analyses of highly complex immuno-oncology biomarker data across the NCI CIMAC-CIDC Network., (©2020 American Association for Cancer Research.)

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: The vast majority of metastatic cancers cannot be cured. Palliative treatment may relieve disease symptoms by stopping or slowing cancer growth and may prolong patients' lives, but almost all patients will inevitably develop disease progression after initial response. However, for reasons that are not fully understood, a very few patients will have extraordinary durable responses to standard anticancer treatments., Materials and Methods: We analyzed exceptional responders treated at Fox Chase Cancer Center between September 2009 and November 2017. An exceptional response was defined as a complete response lasting more than 1 year or a partial response or stable disease for more than 2 years. Tumor samples were analyzed using an Ambry Genetics test kit with a 142-gene panel. Messenger RNA expression was evaluated using NanoString's nCounter PanCancer Pathways Panel and Immune Profiling Panel and compared with matched controls for gender, age, and cancer type., Results: Twenty-six exceptional responders with metastatic bladder, kidney, breast, lung, ovarian, uterine, and colon cancers were enrolled. Mutations were identified in 45 genes. The most common mutation was an EPHA5 nonsynonymous mutation detected in 87.5% of patients. Mutations in DNA damage repair pathway genes were also frequent, suggesting increased genome instability. We also found varying expression of 73 genes in the Pathways panel and 85 genes in the Immune Profiling panel, many of them responsible for improvement in tumor recognition and antitumor immune response., Conclusions: The genomic instability detected in our exceptional responders, plus treatment with DNA damage compounds combined with favorable anticancer immunity, may have contributed to exceptional responses to standard anticancer therapies in the patients studied., Implications for Practice: With recent advances in the treatment of cancer, there is increased emphasis on the importance of identifying molecular markers to predict treatment outcomes, thereby allowing precision oncology. In this study, it was hypothesized that there is a "specific biologic signature" in the biology of the cancer in long-term survivors that allows sensitivity to systemic therapy and durability of response. Results showed that DNA damage repair pathway alterations, combined with favorable anticancer immunity, may have contributed to exceptional responses. It is very likely that an in-depth examination of outlier responses will become a standard component of drug development in the future., (© 2020 AlphaMed Press.)

Cancer is a complex category of diseases caused in large part by genetic or genomic, transcriptomic, and epigenetic or epigenomic alterations in affected cells and the surrounding microenvironment. Carcinogenesis reflects the clonal expansion of cells that progressively acquire these genetic and epigenetic alterations-changes that, in turn, lead to modifications at the RNA level. Gradually advancing technology and most recently, the advent of next-generation sequencing (NGS), combined with bioinformatics analytic tools, have revolutionized our ability to interrogate cancer cells. The ultimate goal is to apply these high-throughput technologies to the various aspects of clinical cancer care: cancer-risk assessment, diagnosis, as well as target identification for treatment and prevention. In this article, we emphasize how the knowledge gained through large-scale omics-oriented approaches, with a focus on variations at the level of nucleic acids, can inform the field of chemoprevention., (Published by Elsevier Inc.)

Human tumor xenografts have been used extensively for rapid screening of the efficacy of anticancer drugs for the past 35 years. The selection of appropriate xenograft models for drug testing has been largely empirical and has not incorporated a similarity to the tumor type of origin at the molecular level. This study is the first comprehensive analysis of the transcriptome of a large set of pediatric xenografts, which are currently used for preclinical drug testing. Suitable models representing the tumor type of origin were identified. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. Because a prerequisite for developing rationally designed drugs is that the target is expressed at the protein level, we developed tissue arrays from these xenografts and corroborated that high mRNA levels yielded high protein levels for two tested genes. The web database and availability of tissue arrays will allow for the rapid confirmation of the expression of potential targets at both the mRNA and the protein level for molecularly targeted agents. The database will facilitate the identification of tumor markers predictive of response to tested agents as well as the discovery of new molecular targets.