A CXCL1 paracrine network links cancer chemoresistance and metastasis.

Metastasis and chemoresistance in cancer are linked phenomena, but the molecular basis for this link is unknown. We uncovered a network of paracrine signals between carcinoma, myeloid, and endothelial cells that drives both processes in breast cancer. Cancer cells that overexpress CXCL1 and 2 by transcriptional hyperactivation or 4q21 amplification are primed for survival in metastatic sites. CXCL1/2 attract CD11b(+)Gr1(+) myeloid cells into the tumor, which produce chemokines including S100A8/9 that enhance cancer cell survival. Although chemotherapeutic agents kill cancer cells, these treatments trigger a parallel stromal reaction leading to TNF-α production by endothelial and other stromal cells. TNF-α via NF-kB heightens the CXCL1/2 expression in cancer cells, thus amplifying the CXCL1/2-S100A8/9 loop and causing chemoresistance. CXCR2 blockers break this cycle, augmenting the efficacy of chemotherapy against breast tumors and particularly against metastasis. This network of endothelial-carcinoma-myeloid signaling interactions provides a mechanism linking chemoresistance and metastasis, with opportunities for intervention.

Clinical characteristics and outcomes of extranodal stage I diffuse large B-cell lymphoma in the rituximab era.

This retrospective study aimed to better define the characteristics and outcomes of extranodal stage I diffuse large B-cell lymphoma (DLBCL) in the rituximab era. Patients diagnosed with stage I DLBCL from 2001 to 2015 treated with rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP) or R-CHOP-like regimens with or without radiation (RT) were included. We identified 1955 patients with newly diagnosed DLBCL, of whom 341 had stage I and were eligible for this analysis. Extranodal presentation was observed in 224 (66%) patients, whereas 117 (34%) had nodal involvement. The most common extranodal sites were as follows: bone, 21%; stomach, 19%; testis, 9%; intestine, 8%; breast, 8%. Overall, 69% extranodal patients and 68% nodal patients received RT. Median follow-up was 5.5 years (interquartile range, 4.3-8.2). Ten-year overall survival (OS) and disease-free survival were 77% (95% confidence interval [CI], 67%-83%) and 77% (95% CI, 68%-85%). In the multivariable analyses, extranodal involvement was associated with worse OS (hazard ratio [HR], 3.44; 95% CI, 1.05-11.30) and progression-free survival (PFS; HR, 3.25; 95% CI, 1.08-9.72) compared with nodal involvement. Consolidation RT was associated with better OS (HR, 0.26; 95% CI, 0.12-0.49) and PFS (HR, 0.35; 95% CI, 0.18-0.69) in the extranodal population; however, the benefit was no longer observed in patients that were positron emission tomography (PET) negative at the end of immunochemotherapy. Relapses occurred usually late (median, 37 months), and the most common sites were the lymph nodes (31%) and the central nervous system (27%). Extranodal stage I DLBCL had a worse outcome than nodal stage 1 DLBCL. End of immunochemotherapy PET results may help select extranodal patients for consolidation RT.

Clinical and Genomic Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) Infections in mRNA Vaccinated Health Care Personnel in New York City.

BACKGROUND: Vaccine-induced clinical protection against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) variants is an evolving target. There are limited genomic level data on SARS CoV-2 breakthrough infections and vaccine effectiveness (VE) since the global spread of the B.1.617.2 (Delta) variant. METHODS: In a retrospective study from 1 November 2020 to 31 August 2021, divided as pre-Delta and Delta-dominant periods, laboratory-confirmed SARS CoV-2 infections among healthcare personnel (HCP) at a large tertiary cancer center in New York City were examined to compare the weekly infection rate-ratio in vaccinated, partially vaccinated, and unvaccinated HCP. We describe the clinical and genomic epidemiologic features of post-vaccine infections to assess for selection of variants of concern (VOC)/variants of interest (VOI) in the early post-vaccine period and impact of B.1.617.2 (Delta) variant domination on VE. RESULTS: Among 13658 HCP in our cohort, 12379 received at least 1 dose of a messenger RNA (mRNA) vaccine. In the pre-Delta period overall VE was 94.5%. Whole genome sequencing (WGS) of 369 isolates in the pre-Delta period did not reveal a clade bias for VOC/VOI specific to post-vaccine infections. VE in the Delta dominant phase was 75.6%. No hospitalizations occurred among vaccinated HCP in the entire study period, compared to 17 hospitalizations and 1 death among unvaccinated HCP. CONCLUSIONS: Findings show high VE among HCP in New York City in the pre-Delta phase, with moderate decline in VE post-Delta emergence. SARS CoV-2 clades were similarly distributed among vaccinated and unvaccinated infected HCP without apparent clustering during the pre-Delta period of diverse clade circulation. Strong vaccine protection against hospitalization was maintained through the entire study period.

Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data.

A common task for the cancer pathologist is to determine, in a patient suffering from cancer, whether a new tumor in a distinct anatomic site from the primary is an independent occurrence of cancer or a metastasis. As mutational profiling of tumors becomes more widespread in routine clinical practice, this diagnostic task can be greatly enhanced by comparing mutational profiles of the tumors to determine if they are sufficiently similar to conclude that the tumors are clonally related, that is, one is a metastasis of the other. We present here a likelihood ratio test for clonal relatedness in this setting and provide evidence of its validity. The test is unusual in that there are two possible alternative hypotheses, representing the two anatomic sites from which the single clonal cell could have initially emerged. Although evidence for clonal relatedness is largely provided by the presence of exact mutational matches in the two tumors, we show that it is possible to observe data where the test is statistically significant even when no matches are observed. This can occur when the mutational profile of one of the tumors is closely aligned with the anatomic site of the other tumor, suggesting indirectly that the tumor originated in that other site. We exhibit examples of this phenomenon and recommend a strategy for interpreting the results of these tests in practice.

NOXA genetic amplification or pharmacologic induction primes lymphoma cells to BCL2 inhibitor-induced cell death.

Although diffuse large B cell lymphoma (DLBCL) cells widely express the BCL2 protein, they rarely respond to treatment with BCL2-selective inhibitors. Here we show that DLBCL cells harboring PMAIP1/NOXA gene amplification were highly sensitive to BCL2 small-molecule inhibitors. In these cells, BCL2 inhibition induced cell death by activating caspase 9, which was further amplified by caspase-dependent cleavage and depletion of MCL1. In DLBCL cells lacking NOXA amplification, BCL2 inhibition was associated with an increase in MCL1 protein abundance in a BIM-dependent manner, causing a decreased antilymphoma efficacy. In these cells, dual inhibition of MCL1 and BCL2 was required for enhanced killing. Pharmacologic induction of NOXA, using the histone deacetylase inhibitor panobinostat, decreased MCL1 protein abundance and increased lymphoma cell vulnerability to BCL2 inhibitors in vitro and in vivo. Our data provide a mechanistic rationale for combination strategies to disrupt lymphoma cell codependency on BCL2 and MCL1 proteins in DLBCL.

Testing clonal relatedness of two tumors from the same patient based on their mutational profiles: update of the Clonality R package.

SUMMARY: The Clonality R package is a practical tool to assess the clonal relatedness of two tumors from the same patient. We have previously presented its functionality for testing tumors using loss of heterozygosity data or copy number arrays. Since then somatic mutation data have been more widely available through next generation sequencing and we have developed new methodology for comparing the tumors' mutational profiles. We thus extended the package to include these two new methods for comparing tumors as well as the mutational frequency estimation from external data required for their implementation. The first method is a likelihood ratio test that is readily available on a patient by patient basis. The second method employs a random-effects model to estimate both the population and individual probabilities of clonal relatedness from a group of patients with pairs of tumors. The package is available on Bioconductor. AVAILABILITY AND IMPLEMENTATION: Bioconductor (http://bioconductor.org/packages/release/bioc/html/Clonality.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An EM algorithm to improve the estimation of the probability of clonal relatedness of pairs of tumors in cancer patients.

BACKGROUND: We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness. In this article, using simulations, we compare two estimation approaches that we considered for our model: use of a constrained quasi-Newton algorithm to maximize the likelihood conditional on the random effect, and an Expectation-Maximization algorithm where we further condition the random-effect distribution on the data. RESULTS: In some specific settings, especially with sparse information, the estimation of the parameter of interest is at the boundary a non-negligible number of times using the first approach, while the EM algorithm gives more satisfactory estimates. This is of considerable importance for our application, since an estimate of either 0 or 1 for the proportion of cases that are clonal leads to individual probabilities being 0 or 1 in settings where the evidence is clearly not sufficient for such definitive probability estimates. CONCLUSIONS: The EM algorithm is a preferable approach for our clonality random-effect model. It is now the method implemented in our R package Clonality, making available an easy and fast way to estimate this model on a range of applications.

Identification of prognostic molecular biomarkers in 157 HPV-positive and HPV-negative squamous cell carcinomas of the oropharynx.

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been increasing due to high-risk HPV infection. We explored the significance of genetic alterations in HPV-positive (HPV-P) and HPV-negative (HPV-N) OPSCC patients on long-term outcome. A total of 157 cases of primary resected OPSCC diagnosed from 1978 to 2005 were subjected to a targeted exome sequencing by MSK-IMPACT™ interrogating somatic mutations in 410 cancer-related genes. Mutational profiles were correlated to recurrence and survival outcomes. OPSCC included 47% HPV-positive (HPV-P) and 53% HPV-negative (HPV-N) tumors arising in the base of tongue (BOT, 43%), palatine tonsil (30%) and soft palate (SP, 27%). HPV negative status, SP location and smoking were associated with poorer outcome. Poorer overall survival was found in NOTCH1-mutated HPV-P (p = 0.039), and in SOX2-amplified HPV-N cases (p = 0.036). Chromosomal arm gains in 8p and 8q, and 16q loss were more common in HPV-P (p = 0.005, 0.04 and 0.01, respectively), while 9p, 18q and 21q losses were more frequent in HPV-N OPSCC (p = 0.006, 0.002 and 0.01, respectively). Novel, potentially functional JAK3, MYC and EP300 intragenic deletions were found in HPV-P, and FOXP1, CDKN2A, CCND1 and RUNX1 intragenic deletions and one FGFR3 inversion were detected in HPV-N tumors. HPV-N/TP53-wild-type OPSCC harbored recurrent mutations in NOTCH1/3/4 (39%), PIK3CA, FAT1 and TERT. In comparison to their oral and laryngeal counterparts, HPV-N OPSCC were genetically distinct. In OPSCC, HPV status, tumor subsite and smoking determine outcome. Risk-stratification can be further refined based on the mutational signature, namely, NOTCH1 and SOX2 mutation status.

Contralateral breast cancers: Independent cancers or metastases?

A cancer in the contralateral breast in a woman with a previous or synchronous breast cancer is typically considered to be an independent primary tumor. Emerging evidence suggests that in a small subset of these cases the second tumor represents a metastasis. We sought to investigate the issue using massively parallel sequencing targeting 254 genes recurrently mutated in breast cancer. We examined the tumor archives at Memorial Sloan Kettering Cancer Center for the period 1995-2006 to identify cases of contralateral breast cancer where surgery for both tumors was performed at the Center. We report results from 49 patients successfully analyzed by a targeted massively parallel sequencing assay. Somatic mutations and copy number alterations were defined by state-of-the-art algorithms. Clonal relatedness was evaluated by statistical tests specifically designed for this purpose. We found evidence that the tumors in contralateral breasts were clonally related in three cases (6%) on the basis of matching mutations at codons where somatic mutations are rare. Clinical data and the presence of similar patterns of gene copy number alterations were consistent with metastasis for all three cases. In three additional cases, there was a solitary matching mutation at a common PIK3CA locus. The results suggest that a subset of contralateral breast cancers represent metastases rather than independent primary tumors. Massively parallel sequencing analysis can provide important evidence to clarify the diagnosis. However, given the inter-tumor mutational heterogeneity in breast cancer, sufficiently large gene panels need to be employed to define clonality convincingly in all cases.

Inference for the difference in the area under the ROC curve derived from nested binary regression models.

The area under the curve (AUC) statistic is a common measure of model performance in a binary regression model. Nested models are used to ascertain whether the AUC statistic increases when new factors enter the model. The regression coefficient estimates used in the AUC statistics are computed using the maximum rank correlation methodology. Typically, inference for the difference in AUC statistics from nested models is derived under asymptotic normality. In this work, it is demonstrated that the asymptotic normality is true only when at least one of the new factors is associated with the binary outcome. When none of the new factors are associated with the binary outcome, the asymptotic distribution for the difference in AUC statistics is a linear combination of chi-square random variables. Further, when at least one new factor is associated with the outcome and the population difference is small, a variance stabilizing reparameterization improves the asymptotic normality of the AUC difference statistic. A confidence interval using this reparameterization is developed and simulations are generated to determine their coverage properties. The derived confidence interval provides information on the magnitude of the added value of new factors and enables investigators to weigh the size of the improvement against potential costs associated with the new factors. A pancreatic cancer data example is used to illustrate this approach.

Estimating the probability of clonal relatedness of pairs of tumors in cancer patients.

Next generation sequencing panels are being used increasingly in cancer research to study tumor evolution. A specific statistical challenge is to compare the mutational profiles in different tumors from a patient to determine the strength of evidence that the tumors are clonally related, that is, derived from a single, founder clonal cell. The presence of identical mutations in each tumor provides evidence of clonal relatedness, although the strength of evidence from a match is related to how commonly the mutation is seen in the tumor type under investigation. This evidence must be weighed against the evidence in favor of independent tumors from non-matching mutations. In this article, we frame this challenge in the context of diagnosis using a novel random effects model. In this way, by analyzing a set of tumor pairs, we can estimate the proportion of cases that are clonally related in the sample as well as the individual diagnostic probabilities for each case. The method is illustrated using data from a study to determine the clonal relationship of lobular carcinoma in situ with subsequent invasive breast cancers, where each tumor in the pair was subjected to whole exome sequencing. The statistical properties of the method are evaluated using simulations, demonstrating that the key model parameters are estimated with only modest bias in small samples in most configurations.

FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing.

Allele-specific copy number analysis (ASCN) from next generation sequencing (NGS) data can greatly extend the utility of NGS beyond the identification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of 'actionable' mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy- and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS-based cancer gene copy number data in the context of clinical sequencing. We developed FACETS, an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that includes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output and integrated visualization. We demonstrate the application of FACETS using The Cancer Genome Atlas (TCGA) whole-exome sequencing of lung adenocarcinoma samples. We also demonstrate its application to a clinical sequencing platform based on a targeted gene panel.

False discovery rates for rare variants from sequenced data.

The detection of rare deleterious variants is the preeminent current technical challenge in statistical genetics. Sorting the deleterious from neutral variants at a disease locus is challenging because of the sparseness of the evidence for each individual variant. Hierarchical modeling and Bayesian model uncertainty are two techniques that have been shown to be promising in pinpointing individual rare variants that may be driving the association. Interpreting the results from these techniques from the perspective of multiple testing is a challenge and the goal of this article is to better understand their false discovery properties. Using simulations, we conclude that accurate false discovery control cannot be achieved in this framework unless the magnitude of the variants' risk is large and the hierarchical characteristics have high accuracy in distinguishing deleterious from neutral variants.

Clonal relationships between lobular carcinoma in situ and other breast malignancies.

BACKGROUND: Recent evidence suggests that lobular carcinoma in situ (LCIS) can be a clonal precursor of invasive breast cancers of both the ductal and lobular phenotypes. We sought to confirm these findings with an extensive study of fresh frozen breast specimens from women undergoing mastectomy. METHODS: Patients with a history of LCIS presenting for therapeutic mastectomy were identified prospectively. Frozen tissue blocks were collected, screened for lesions of interest, and subjected to microdissection and DNA extraction. Copy number profiling, whole-exome sequencing, or both were performed. Clonal relatedness was assessed using specialized statistical techniques developed for this purpose. RESULTS: After exclusions for genotyping failure, a total of 84 lesions from 30 patients were evaluated successfully. Strong evidence of clonal relatedness was observed between an LCIS lesion and the invasive cancer for the preponderance of cases with lobular carcinoma. Anatomically distinct in situ lesions of both ductal and lobular histology were also shown to be frequently clonally related. CONCLUSIONS: These data derived from women with LCIS with or without invasive cancer confirm that LCIS is commonly the clonal precursor of invasive lobular carcinoma and that distinct foci of LCIS frequently share a clonal origin, as do foci of LCIS and ductal carcinoma in situ.

Pattern discovery and cancer gene identification in integrated cancer genomic data.

Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [Nature (2012) 486(7403):346-352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.

Genomic investigation of etiologic heterogeneity: methodologic challenges.

BACKGROUND: The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles. METHODS: We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific sub-types, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types. RESULTS: The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery. CONCLUSIONS: Genomic profiling of tumors offers the opportunity to identify etiologically distinct sub-types, paving the way for a more refined understanding of cancer etiology.

Susceptibility of healthcare personnel with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) hybrid immunity to XBB lineage reinfection.

Among 8,678 vaccinated healthcare personnel (HCP) with previous coronavirus disease 2019 (COVID-19), by August 28, 2023, 909 (10%) had an infection of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) omicron XBB variant. Reinfection risk was comparable irrespective of previous infection type except for the omicron BQ.1 variant. Bivalent vaccination had a protective effect. COVID-19 vaccines remain vital to protect HCP, including those with hybrid immunity.

HDAC inhibitors and decitabine are highly synergistic and associated with unique gene-expression and epigenetic profiles in models of DLBCL.

Interactions between histone deacetylase inhibitors (HDACIs) and decitabine were investigated in models of diffuse large B-cell lymphoma (DLBCL). A number of cell lines representing both germinal center B-like and activated B-cell like DLBCL, patient-derived tumor cells and a murine xenograft model were used to study the effects of HDACIs and decitabine in this system. All explored HDACIs in combination with decitabine produced a synergistic effect in growth inhibition and induction of apoptosis in DLBCL cells. This effect was time dependent, mediated via caspase-3 activation, and resulted in increased levels of acetylated histones. Synergy in inducing apoptosis was confirmed in patient-derived primary tumor cells treated with panobinostat and decitabine. Xenografting experiments confirmed the in vitro activity and tolerability of the combination. We analyzed the molecular basis for this synergistic effect by evaluating gene-expression and methylation patterns using microarrays, with validation by bisulfite sequencing. These analyses revealed differentially expressed genes and networks identified by each of the single treatment conditions and by the combination therapy to be unique with few overlapping genes. Among the genes uniquely altered by the combination of panobinostat and decitabine were VHL, TCEB1, WT1, and DIRAS3.

Comparing ROC curves derived from regression models.

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely used ROC area test produces exceptionally conservative test size and extremely low power. In this article, we demonstrate that both the test statistic and its estimated variance are seriously biased when predictions from nested regression models are used as data inputs for the test, and we examine in detail the reasons for these problems. Although it is possible to create a test reference distribution by resampling that removes these biases, Wald or likelihood ratio tests remain the preferred approach for testing the incremental contribution of a new marker.