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Abstract Background Homologous recombination deficiency (HRD) is a phenotype that is characterized by the inability of a cell to effectively repair DNA double-strand breaks using the homologous recombination repair (HRR) pathway. Loss-of-function genes involved in this pathway can sensitize tumors to poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapy, which target the destruction of cancer cells by working in concert with HRD through synthetic lethality. However, to identify patients with these tumors, it is vital to understand how to best measure homologous repair (HR) status and to characterize the level of alignment in these measurements across different diagnostic platforms. A key current challenge is that there is no standardized method to define, measure, and report HR status using diagnostics in the clinical setting. Methods Friends of Cancer Research convened a consortium of project partners from key healthcare sectors to address concerns about the lack of consistency in the way HRD is defined and methods for measuring HR status. Results This publication provides findings from the group’s discussions that identified opportunities to align the definition of HRD and the parameters that contribute to the determination of HR status. The consortium proposed recommendations and best practices to benefit the broader cancer community. Conclusion Overall, this publication provides additional perspectives for scientist, physician, laboratory, and patient communities to contextualize the definition of HRD and various platforms that are used to measure HRD in tumors. This article reports findings of a consortium of project partners from key healthcare sectors that was convened to address concerns about the lack of consistency in the definition of homologous recombination deficiency and methods for measuring homologous repair status.

Abstract The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity. Current methods for predicting immunotherapy response are unreliable and in need of improvement. This review discusses current applications of radiomics in oncology and recent studies that use radiomics to predict immunotherapy response and toxicity.

Abstract Most gastrointestinal stromal tumors (GIST) are driven by activating mutations in Proto-oncogene c-KIT (KIT) or PDGFRA receptor tyrosine kinases (RTK). The emergence of effective therapies targeting these mutations has revolutionized the management of advanced GIST. However, following initiation of first-line imatinib, a tyrosine kinase inhibitor (TKI), nearly all patients will develop resistance within 2 years through the emergence of secondary resistance mutations in KIT, typically in the Adenosine Triphosphate (ATP)-binding site or activation loop of the kinase domain. Moreover, some patients have de novo resistance to imatinib, such as those with mutations in PDGFRA exon 18 or those without KIT or PDGFRA mutation. To target resistance, research efforts are primarily focused on developing next-generation inhibitors of KIT and/or PDGFRA, which can inhibit alternate receptor conformations or unique mutations, and compounds that impact complimentary pathogenic processes or epigenetic events. Here, we review the literature on the medical management of high-risk localized and advanced GIST and provide an update on clinical trial approaches to this disease. This article reviews the literature on the medical management of high-risk localized and advanced GIST and provides an update on clinical trial approaches to this disease.

Abstract Background One of the most common sporadic homozygous deletions in cancers is 9p21 loss, which includes the genes methylthioadenosine phosphorylase (MTAP), CDKN2A, and CDKN2B, and has been correlated with worsened outcomes and immunotherapy resistance. MTAP-loss is a developing drug target through synthetic lethality with MAT2A and PMRT5 inhibitors. The purpose of this study is to investigate the prevalence and genomic landscape of MTAP-loss in advanced gastrointestinal (GI) tumors and investigate its role as a prognostic biomarker. Materials and Methods We performed next-generation sequencing and comparative genomic and clinical analysis on an extensive cohort of 64 860 tumors comprising 5 GI cancers. We compared the clinical outcomes of patients with GI cancer harboring MTAP-loss and MTAP-intact tumors in a retrospective study. Results The prevalence of MTAP-loss in GI cancers is 8.30%. MTAP-loss was most prevalent in pancreatic ductal adenocarcinoma (PDAC) at 21.7% and least in colorectal carcinoma (CRC) at 1.1%. MTAP-loss tumors were more prevalent in East Asian patients with PDAC (4.4% vs 3.2%, P = .005) or intrahepatic cholangiocarcinoma (IHCC; 6.4% vs 4.3%, P = .036). Significant differences in the prevalence of potentially targetable genomic alterations (ATM, BRAF, BRCA2, ERBB2, IDH1, PIK3CA, and PTEN) were observed in MTAP-loss tumors and varied according to tumor type. MTAP-loss PDAC, IHCC, and CRC had a lower prevalence of microsatellite instability or elevated tumor mutational burden. Positive PD-L1 tumor cell expression was less frequent among MTAP-loss versus MTAP-intact IHCC tumors (23.2% vs 31.2%, P = .017). Conclusion In GI cancers, MTAP-loss occurs as part of 9p21 loss and has an overall prevalence of 8%. MTAP-loss occurs in 22% of PDAC, 15% of IHCC, 8.7% of gastroesophageal adenocarcinoma, 2.4% of hepatocellular carcinoma, and 1.1% of CRC and is not mutually exclusive with other targetable mutations. Methylthioadenosine phosphorylase (MTAP)-loss is a developing drug target through synthetic lethality with MAT2A and PMRT5 inhibitors. This study investigated the prevalence and genomic landscape of MTAP-loss in advanced gastrointestinal tumors and its role as a prognostic biomarker.

Abstract Background In patients with non-small cell lung cancer (NSCLC), 10%-40% will eventually develop brain metastases. We present the clinicopathologic, genomic, and biomarker landscape of a large cohort of NSCLC brain metastases (NSCLC-BM) samples. Materials and Methods We retrospectively analyzed 3035 NSCLC-BM tested with comprehensive genomic profiling (CGP) during routine clinical care. In addition, we compared the NSCLC-BM to a separate cohort of 7277 primary NSCLC (pNSCLC) specimens. Finally, we present data on 67 paired patients with NSCLC-BM and pNSCLC. Results Comprehensive genomic profiling analysis of the 3035 NSCLC-BMs found that the most frequent genomic alterations (GAs) were in the TP53, KRAS, CDKN2A, STK11, CDKN2B, EGFR, NKX2-1, RB1, MYC, and KEAP1 genes. In the NSCLC-BM cohort, there were significantly higher rates of several targetable GAs compared with pNSCLC, including ALK fusions, KRAS G12C mutations, and MET amplifications; and decreased frequency of MET exon14 skipping mutations (all P < .05). In the subset of NSCLC-BM (n = 1063) where concurrent PD-L1 immunohistochemistry (IHC) was performed, 54.7% of the patients with NSCLC-BM were eligible for pembrolizumab based on PD-L1 IHC (TPS ≥ 1), and 56.9% were eligible for pembrolizumab based on TMB-High status. In addition, in a series 67 paired pNSCLC and NSCLC-BM samples, 85.1% (57/67) had at least one additional GA discovered in the NSCLC-BM sample when compared with the pNSCLC sample. Conclusions Herein, we defined the clinicopathologic, genomic, and biomarker landscape of a large cohort of patients with NSCLC-BM which can help inform study design of future clinical studies for patients with NSCLC with BM. In certain clinical situations, metastatic NSCLC brain tissue or cerebral spinal fluid specimens may be needed to fully optimize personalized treatment. Some patients with non-small cell lung cancer (NSCLC) develop brain metastasis. This article examines the genomic landscape of NSCLC brain metastases samples with comprehensive genomic profiling in a large cohort of patients to define the potential applicability of therapeutic advances in NSCLC.

Real-world success rate of liquid and tissue-based comprehensive genomic profiling (CGP) is unknown. We analyzed real-world pan-tumor cohorts that underwent CGP during clinical care via FoundationOne CDx (F1CDx) and FoundationOne Liquid CDx (F1LCDx) to determine tissue and liquid sample adequacy based on tumor type. Pan-tumor presequencing adequacy was high (>90%) by both tissue-based F1CDx (median: 92.3%; range: 88.2%-96.9%) and liquid-based F1LCDx (median: 94.8%; range: 86.6%-96.7%). Similarly, postsequencing analysis revealed that most tissue and liquid samples yielded successful sequencing results with a median sequencing success rate of 97.9% and 98.1% for F1CDx and F1LCDx, respectively. One exception is central nervous system (CNS) tumors, for which F1CDx had dramatically higher sample sufficiency (96.9%) and postsequencing success rate (97.0%) compared with F1LCDx (86.6% and 92.9%, respectively). The pan-tumor median sample-to-success rate was 90.4% (range: 84.8%-94.4%) for F1CDx. The equivalent rate for F1LCDx was slightly higher at 93.2% (range: 80.4%-95.7%). Conversely, when examining the prevalence of F1LCDx results with high tumor fraction (TF≥1%), the sample-to-high TF results rate was dramatically lower (median: 37.7%, range: 2.1% [CNS tumors]-46.0%). In conclusion, except in CNS tumors or when accounting for liquid TF, success rates of F1CDx and F1LCDx are equivalently high. These results may guide informed decision on when to pursue tissue vs liquid testing of patients with cancer.

Background Adoption of high-throughput, gene panel-based, next-generation sequencing (NGS) into routine cancer care is widely supported, but hampered by concerns about cost. To inform policies regarding genomic testing strategies, we propose a simple metric, cost per correctly identified patient (CCIP), that compares sequential single-gene testing (SGT) vs. multiplex NGS in different tumor types. Materials and Methods A genomic testing cost calculator was developed based on clinically actionable genomic alterations identified in the European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets. Using sensitivity/specificity data for SGTs (immunohistochemistry, polymerase chain reaction, and fluorescence in situ hybridization) and NGS and marker prevalence, the number needed to predict metric was monetarized to estimate CCIP. Results At base case, CCIP was lower with NGS than sequential SGT for advanced/metastatic non-squamous non-small cell lung cancer (NSCLC), breast, colorectal, gastric cancers, and cholangiocarcinoma. CCIP with NGS was also favorable for squamous NSCLC, pancreatic, and hepatic cancers, but with overlapping confidence intervals. CCIP favored SGT for prostate cancer. Alternate scenarios using different price estimates for each test showed similar trends, but with incremental changes in the magnitude of difference between NGS and SGT, depending on price estimates for each test. Conclusions The cost to correctly identify clinically actionable genomic alterations was lower for NGS than sequential SGT in most cancer types evaluated. Decreasing price estimates for NGS and the rapid expansion of targeted therapies and accompanying biomarkers are anticipated to further support NGS as a preferred diagnostic standard for precision oncology. Adoption of next-generation sequencing (NGS) into routine cancer care is widely supported, but hampered by cost concerns. This article reports a simple metric to inform policies on genomic testing strategies: cost per correctly identified patient.

Abstract Microsatellite instability (MSI) has emerged as an important predictor of sensitivity for immunotherapy-based strategies. β-2-Microglobulin (B2M) contains microsatellites within the coding regions and is prone to somatic changes in MSI/mismatch repair deficiency (MSI/dMMR) tumors. To delineate prevalence and associations of B2M mutations in MSI-H/dMMR cancers, we investigated the mutational profile of B2M and clinical and pathological features in gastric cancer (GC), colorectal cancer (CRC), and endometrial cancer (EC) with a high incidence of microsatellite instability-high (MSI-H)/dMMR. Formalin-fixed paraffin-embedded (FFPE) tumor tissues along with matched normal tissues were collected from 108 MSI/dMMR patients with GC, CRC, and EC. Genomic profiling of tissue and blood samples were assessed next-generation sequencing (NGS). Immunohistochemistry (IHC) was used to examine the presence or absence of B2M protein. Alternations in the exonic microsatellite regions of B2M were observed at various but high frequencies (57.5% in CRC, 23.9% in GC, and 13.6% in EC) and in different forms. NGS assay revealed that genes involved in chromatin regulation, the PI3K pathway, the WNT pathway, and mismatch repair were extensively altered in the MSI-H cohort. Signature 6 and 26, 2 of 4 mutational signatures associated with defective DNA mismatch repair, featured with high numbers of small insertion/deletions (INDEL) dominated in all 3 types of cancer. Alternations in the exonic microsatellite regions of B2M were observed at various but high frequencies (57.5% in CRC, 23.9% in GC, and 13.6% in EC) and in different forms. Tumor mutational burden (TMB) was significantly higher in the patients carrying MSI-H/dMMR tumors with B2M mutation than that in patients with wild-type B2M (P = .026).The frame shift alteration occurring at the exonic microsatellite sties caused loss of function of B2M gene. In addition, a case with CRC carrying indels in B2M gene resisted the ICI treatment was reported. In conclusion, patients carrying MSI-H/dMMR tumors with B2M mutation showed significantly higher TMB. Prescription of ICIs should be thoroughly evaluated for these patients. To delineate the prevalence and associations of B2M mutations in MSI-H/dMMR cancers, the mutational profile of B2M in 3 cancer types with a high MSI-H/dMMR prevalence was investigated. This article reports the results.

Background TROP2 (TACSTD2) expression is associated with decreased overall survival (OS) in some solid tumors, and the TROP2-targeting antibody-drug conjugate (ADC) sacituzumab govitecan has been approved in breast and urothelial carcinomas. We aimed to explore the multi-omic landscape associated with TACSTD2 gene expression in various solid tumors to identify patients most likely to benefit from this approach. Methods Breast (N = 11 246), colorectal (N = 15 425), hepatocellular (N = 433), pancreatic (N = 5488), and urothelial (N = 4125) tumors were stratified into quartiles by TACSTD2 gene expression, analyzed by next-generation DNA sequencing, whole transcriptome sequencing, and immunohistochemistry at Caris Life Sciences (Phoenix, AZ). Survival data were obtained from insurance claims, and Kaplan-Meier estimates were calculated for molecularly defined cohorts. Results Several pathogenic mutations were associated with TACSTD2-high tumors, including TP53 in breast, colorectal (CRC), pancreatic, and hepatocellular cancers; KRAS in pancreatic and CRC cancers; ARID1A and FGFR3 in urothelial cancer; and CTNNB1 in hepatocellular cancer. TACSTD2-low breast tumors were enriched for copy number amplifications in CCND1 and FGF/R family member genes. TACSTD2 high was generally associated with more immune cell infiltration and greater T-cell inflammation scores. Patients with TACSTD2-high breast, CRC, and pancreatic cancers demonstrated a significantly shorter OS than TACSTD2-low tumors. This was restricted to CRC with microsatellite stable tumors and patients with pancreatic cancer with KRAS-mutant tumors. Patients with breast cancer with TACSTD2-high tumors also experienced significantly worse OS following immune checkpoint inhibitors. Conclusions TACSTD2 expression is associated with key driver alterations and a more active immune microenvironment, suggesting possible combinatorial strategies with TROP2-targeting ADCs plus immunotherapy in various solid tumors. The TROP2-targeting antibody-drug conjugate sacituzumab govitecan has been approved in breast and urothelial carcinomas. This study explored the multi-omic landscape associated with TACSTD2 gene expression in various solid tumors to identify patients most likely to benefit from this approach.

Differences in cancer genomes between racial groups may impact tumor biology and health disparities. However, the discovery of race-associated mutations is constrained by the limited representation and sample size of different racial groups in prior genomic studies. We evaluated the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing. Matched cohort analyses were used to identify histology-specific race-associated mutations including increased TERT promoter mutations in Black and Asian patients with gliomas and bladder cancers, and a decreased frequency of mutations in DNA repair pathway genes and subunits of the SWI/SNF chromatin complex in Asian and Black patients across multiple cancer types. The distribution of actionable mutations in oncogenes was also race-specific, demonstrating how targeted therapies may have a disparate impact on racial groups. Down-sampling analyses indicate that larger sample sizes are likely to discover more race-associated mutations. These results provide a resource to understand differences in cancer genomes between racial groups which may inform the design of clinical studies and patient recruitment strategies in biomarker trials. This article evaluates the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing.