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Background and objective: Previous germline studies on renal cell carcinoma (RCC) have usually pooled clear and non–clear cell RCCs and have not adequately accounted for population stratification, which might have led to an inaccurate estimation of genetic risk. Here, we aim to analyze the major germline drivers of RCC risk and clinically relevant but underexplored germline variant types. Methods: We first characterized germline pathogenic variants (PVs), cryptic splice variants, and copy number variants (CNVs) in 1436 unselected RCC patients. To evaluate the enrichment of PVs in RCC, we conducted a case-control study of 1356 RCC patients ancestry matched with 16 512 cancer-free controls using approaches accounting for population stratification and histological subtypes, followed by characterization of secondary somatic events. Key findings and limitations: Clear cell RCC patients (n = 976) exhibited a significant burden of PVs in VHL compared with controls (odds ratio [OR]: 39.1, p = 4.95e-05). Non–clear cell RCC patients (n = 380) carried enrichment of PVs in FH (OR: 77.9, p = 1.55e-08) and MET (OR: 1.98e11, p = 2.07e-05). In a CHEK2-focused analysis with European participants, clear cell RCC (n = 906) harbored nominal enrichment of low-penetrance CHEK2 variants—p.Ile157Thr (OR: 1.84, p = 0.049) and p.Ser428Phe (OR: 5.20, p = 0.045), while non–clear cell RCC (n = 295) exhibited nominal enrichment of CHEK2 loss of function PVs (OR: 3.51, p = 0.033). Patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset than patients without germline PVs (mean: 46.0 vs 60.2 yr, p

Summary: Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and a heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are the limited understanding of its gene regulatory programs and the low diagnostic yield of tumor biopsies due to spatial heterogeneity. Herein, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries from tissue and plasma samples from 50 patients with RCC and healthy volunteers. By profiling histone modifications and DNA methylation, we identified highly recurrent epigenomic reprogramming enriched in sRCC. Furthermore, CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs, and FOSL1 expression was associated with the response to ICIs in RCC in two randomized clinical trials. Finally, we established a blood-based diagnostic approach using detectable sRCC epigenomic signatures in patient plasma, providing a framework for discovering epigenomic correlates of tumor histology via liquid biopsy.

Abstract Background Breast cancer patients from the indigenous Arab population present much earlier than patients from Western countries and have traditionally been underrepresented in cancer genomics studies. The contribution of polygenic and Mendelian risk toward the earlier onset of breast cancer in the population remains elusive. Methods We performed low-pass whole genome sequencing (lpWGS) and whole-exome sequencing (WES) from 220 female breast cancer patients unselected for positive family history from the indigenous Arab population. Using publicly available resources, we imputed population-specific variants and calculated breast cancer burden-sensitive polygenic risk scores (PRS). Variant pathogenicity was also evaluated on exome variants with high coverage. Results Variants imputed from lpWGS showed high concordance with paired exome (median dosage correlation: 0.9459, Interquartile range: 0.9410–0.9490). After adjusting the PRS to the Arab population, we found significant associations between PRS performance in risk prediction and first-degree relative breast cancer history prediction (Spearman rho=0.43, p = 0.03), where breast cancer patients in the top PRS decile are 5.53 (95% CI 1.76–17.97, p = 0.003) times more likely also to have a first-degree relative diagnosed with breast cancer compared to those in the middle deciles. In addition, we found evidence for the genetic liability threshold model of breast cancer where among patients with a family history of breast cancer, pathogenic rare variant carriers had significantly lower PRS than non-carriers (p = 0.0205, Mann-Whitney U test) while for non-carriers every standard deviation increase in PRS corresponded to 4.52 years (95% CI 8.88–0.17, p = 0.042) earlier age of presentation. Conclusions Overall, our study provides a framework to assess polygenic risk in an understudied population using lpWGS and identifies common variant risk as a factor independent of pathogenic variant carrier status for earlier age of onset of breast cancer among indigenous Arab breast cancer patients.

Abstract Background Longitudinal data on key cancer outcomes for clinical research, such as response to treatment and disease progression, are not captured in standard cancer registry reporting. Manual extraction of such outcomes from unstructured electronic health records is a slow, resource-intensive process. Natural language processing (NLP) methods can accelerate outcome annotation, but they require substantial labeled data. Transfer learning based on language modeling, particularly using the Transformer architecture, has achieved improvements in NLP performance. However, there has been no systematic evaluation of NLP model training strategies on the extraction of cancer outcomes from unstructured text. Results We evaluated the performance of nine NLP models at the two tasks of identifying cancer response and cancer progression within imaging reports at a single academic center among patients with non-small cell lung cancer. We trained the classification models under different conditions, including training sample size, classification architecture, and language model pre-training. The training involved a labeled dataset of 14,218 imaging reports for 1112 patients with lung cancer. A subset of models was based on a pre-trained language model, DFCI-ImagingBERT, created by further pre-training a BERT-based model using an unlabeled dataset of 662,579 reports from 27,483 patients with cancer from our center. A classifier based on our DFCI-ImagingBERT, trained on more than 200 patients, achieved the best results in most experiments; however, these results were marginally better than simpler “bag of words” or convolutional neural network models. Conclusion When developing AI models to extract outcomes from imaging reports for clinical cancer research, if computational resources are plentiful but labeled training data are limited, large language models can be used for zero- or few-shot learning to achieve reasonable performance. When computational resources are more limited but labeled training data are readily available, even simple machine learning architectures can achieve good performance for such tasks.

Background Recent trials suggest that programmed cell death 1 (PD-1)-directed immunotherapy may be beneficial for some patients with anal squamous cell carcinoma and biomarkers predictive of response are greatly needed.Methods This multicenter phase II clinical trial (NCT02919969) enrolled patients with metastatic or locally advanced incurable anal squamous cell carcinoma (n=32). Patients received pembrolizumab 200 mg every 3 weeks. The primary endpoint of the trial was objective response rate (ORR). Exploratory objectives included analysis of potential predictive biomarkers including assessment of tumor-associated immune cell populations with multichannel immunofluorescence and analysis of circulating tumor tissue modified viral-human papillomavirus DNA (TTMV-HPV DNA) using serially collected blood samples. To characterize the clinical features of long-term responders, we combined data from our prospective trial with a retrospective cohort of patients with anal cancer treated with anti-PD-1 immunotherapy (n=18).Results In the phase II study, the ORR to pembrolizumab monotherapy was 9.4% and the median progression-free survival was 2.2 months. Despite the high level of HPV positivity observed with circulating TTMV-HPV DNA testing, the majority of patients had low levels of tumor-associated CD8+PD-1+ T cells on pretreatment biopsy. Patients who benefited from pembrolizumab had decreasing TTMV-HPV DNA scores and a complete responder’s TTMV-HPV DNA became undetectable. Long-term pembrolizumab responses were observed in one patient from the trial (5.3 years) and three patients (2.5, 6, and 8 years) from the retrospective cohort. Long-term responders had HPV-positive tumors, lacked liver metastases, and achieved a radiological complete response.Conclusions Pembrolizumab has durable efficacy in a rare subset of anal cancers. However, despite persistence of HPV infection, indicated by circulating HPV DNA, most advanced anal cancers have low numbers of tumor-associated CD8+PD-1+ T cells and are resistant to pembrolizumab.

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Summary: The success of precision oncology—which aims to match the right therapies to the right patients based on molecular status—is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, the authors train natural language processing to extract outcomes for participants in a precision oncology study.

Neuroendocrine carcinomas (NECs) arise from different anatomic sites, but have similar histological and clinical features. Here, the authors show that the epigenetic landscape of a range of NECs converges towards a common epigenetic state, while distinct subtypes occur within neuroendocrine prostate cancer contributing to intratumor heterogeneity in clinical samples.

A randomized phase 2 clinical trial has recently shown no benefit of the combination eribulin and pembrolizumab over pembrolizumab alone in HR + metastatic breast cancer patients (NCT03051659). Here, the authors are reporting the final OS data and biomarker analyses on a subset of samples to analyze molecular correlates

Background Preclinical and clinical data support potential synergy between anti-HER2 therapy plus immune checkpoint blockade. The safety and tolerability of trastuzumab emtansine (T-DM1) combined with pembrolizumab is unknown.Methods This was a single-arm phase Ib trial (registration date January 26, 2017) of T-DM1 plus pembrolizumab in metastatic, human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Eligible patients had HER2-positive, metastatic breast cancer previously treated with taxane, trastuzumab, and pertuzumab, and were T-DM1-naïve. A dose de-escalation design was used, with a dose-finding cohort followed by an expansion cohort at the recommended phase 2 dose (RP2D), with mandatory baseline biopsies. The primary endpoint was safety and tolerability. Secondary endpoints included objective response rate (ORR) and progression-free survival (PFS). Immune biomarkers were assessed using histology, protein/RNA expression, and whole exome sequencing. Associations between immune biomarkers and treatment response, and biomarker changes before and during treatment, were explored.Results 20 patients received protocol therapy. There were no dose-limiting toxicities. The RP2D was 3.6 mg/kg T-DM1 every 21 days plus 200 mg pembrolizumab every 21 days. 85% of patients experienced treatment-related adverse events (AEs) ≥grade 2, 20% of patients experienced grade 3 AEs, and no patients experienced grade >4 AEs. Four patients (20%) experienced pneumonitis (three grade 2 events; one grade 3 event). ORR was 20% (95% CI 5.7% to 43.7%), and median PFS was 9.6 months (95% CI 2.8 to 16.0 months). Programmed cell death ligand-1 and tumor infiltrating lymphocytes did not correlate with response in this small cohort.Conclusions T-DM1 plus pembrolizumab was a safe and tolerable regimen. Ongoing trials will define if there is a role for checkpoint inhibition in the management of HER2-positive metastatic breast cancer.Trial registration number NCT03032107.

The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 531 localized and 143 metastatic prostate cancers profiled by whole genome sequencing, 125 metastatic samples of which were also profiled via whole transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined 9 subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.

Metastatic castration-resistant prostate cancers (mCRPCs) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ARTs). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins, we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein–protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found that CREB5 was associated with Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in CREB5/FOXA1-mediated ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

Fatma-Elzahraa Eid et al. illustrate a principled approach for identifying biases that can inflate the performance of biological machine learning models. When applied to three biomedical prediction problems, they identify previously unrecognized biases and ultimately show that models are likely to learn primarily from data biases when there is insufficient learnable signal in the data.

Sarcomatoid and rhabdoid tumours are highly aggressive forms of renal cell carcinoma that are also responsive to immunotherapy. In this study, the authors perform a comprehensive molecular characterization of these tumours discovering an enrichment of specific alterations and an inflamed phenotype.

Multiple myeloma (MM) is treated with induction chemotherapy, autologous stem cell transplant (ASCT) and long-term immunomodulatory drug (IMiD) maintenance. Here, the authors show that the presence of clonal haematopoiesis of indeterminate potential (CHIP) at time of ASCT is associated with adverse outcomes in MM patients.

Abstract Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5–10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

Immune-checkpoint inhibition therapy has achieved success in a subset of patients. Here the authors profiled about 200 relevant metabolites in patient serum samples from three independent immunotherapy trials and found the serum kynurenine/tryptophan ratio increases to be associated with worse overall survival.

Summary: The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8+ T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration. : The role of inherited variants in influencing the immune composition of the tumor microenvironment is not fully characterized. Shahamatdar et al. identify germline variants, genes, and pathways associated with immune infiltration phenotypes in cancer, which may offer insights into determinants of response to immunotherapy. Keywords: somatic, germline, immune, SNPs, gwas, cancer, immunotherapy

Summary: Androgen-receptor (AR) inhibitors, including enzalutamide, are used for treatment of all metastatic castration-resistant prostate cancers (mCRPCs). However, some patients develop resistance or never respond. We find that the transcription factor CREB5 confers enzalutamide resistance in an open reading frame (ORF) expression screen and in tumor xenografts. CREB5 overexpression is essential for an enzalutamide-resistant patient-derived organoid. In AR-expressing prostate cancer cells, CREB5 interactions enhance AR activity at a subset of promoters and enhancers upon enzalutamide treatment, including MYC and genes involved in the cell cycle. In mCRPC, we found recurrent amplification and overexpression of CREB5. Our observations identify CREB5 as one mechanism that drives resistance to AR antagonists in prostate cancers. : Advanced prostate cancers develop resistance to androgen receptor (AR)-targeting therapies. Hwang et al. show that resistant prostate cancers overexpress or amplify CREB5, mediating resistance to AR inhibition. CREB5 suppression reduces the viability of therapy-resistant patient-derived models, suggesting that CREB5 is a target in prostate cancer patients with limited treatment options. Keywords: metastatic castration-resistant prostate cancer, androgen deprivation therapy, therapy resistance, CREB5, androgen receptor, ORF screen

Abstract Background An elevated Neutrophil-to-lymphocyte ratio (NLR) is associated with worse outcomes in several malignancies. However, its role with contemporary immune checkpoint blockade (ICB) is unknown. We investigated the utility of NLR in metastatic renal cell carcinoma (mRCC) patients treated with PD-1/PD-L1 ICB. Methods We examined NLR at baseline and 6 (±2) weeks later in 142 patients treated between 2009 and 2017 at Dana-Farber Cancer Institute (Boston, USA). Landmark analysis at 6 weeks was conducted to explore the prognostic value of relative NLR change on overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Cox and logistic regression models allowed for adjustment of line of therapy, number of IMDC risk factors, histology and baseline NLR. Results Median follow up was 16.6 months (range: 0.7–67.8). Median duration on therapy was 5.1 months (

The impact of cisplatin-based chemotherapy on tumor genomes is complex. Here, the authors study matched pre- and post-chemotherapy primary samples in muscle-invasive bladder cancer, finding a cisplatin-based mutational signature, and highlighting the impact of intratumor heterogeneity on survival.

Identifying the mutational landscape of tumours from cell-free DNA in the blood could help diagnostics in cancer. Here, the authors present ichorCNA, software that quantifies tumour content in cell free DNA, and they demonstrate that cell-free DNA whole-exome sequencing is concordant with metastatic tumour whole-exome sequencing.

The development of sensitive and non-invasive “liquid biopsies” presents new opportunities for longitudinal monitoring of tumor dissemination and clonal evolution. The number of circulating tumor cells (CTCs) is prognostic in multiple myeloma (MM), but there is little information on their genetic features. Here, we have analyzed the genomic landscape of CTCs from 29 MM patients, including eight cases with matched/paired bone marrow (BM) tumor cells. Our results show that 100% of clonal mutations in patient BM were detected in CTCs and that 99% of clonal mutations in CTCs were present in BM MM. These include typical driver mutations in MM such as in KRAS, NRAS, or BRAF. These data suggest that BM and CTC samples have similar clonal structures, as discordances between the two were restricted to subclonal mutations. Accordingly, our results pave the way for potentially less invasive mutation screening of MM patients through characterization of CTCs.

Abstract Background To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. Methods We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Results Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. Conclusions We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.