Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

Prioritization of patients for germline testing based on tumor profiling of hematopoietic malignancies

Germline predisposition to hematopoietic malignancies is more common than previously appreciated, with several clinical guidelines advocating for cancer risk testing in an expanding pool of patients. As molecular profiling of tumor cells becomes a standard practice for prognostication and defining options for targeted therapies, recognition that germline variants are present in all cells and can be identified by such testing becomes paramount. Although not to be substituted for proper germline cancer risk testing, tumor-based profiling can help prioritize DNA variants likely to be of germline origin, especially when they are present on sequential samples and persist into remission. Performing germline genetic testing as early during patient work-up as possible allows time to plan allogeneic stem cell transplantation using appropriate donors and optimize post-transplant prophylaxis. Health care providers need to be attentive to the differences between molecular profiling of tumor cells and germline genetic testing regarding ideal sample types, platform designs, capabilities, and limitations, to allow testing data to be interpreted as comprehensively as possible. The myriad of mutation types and growing number of genes involved in germline predisposition to hematopoietic malignancies makes reliance on detection of deleterious alleles using tumor-based testing alone very difficult and makes understanding how to ensure adequate testing of appropriate patients paramount.

Molecular profiling of male breast cancer by multigene panel testing: Implications for precision oncology

Introduction

Compared with breast cancer (BC) in women, BC in men is a rare disease with genetic and molecular peculiarities. Therapeutic approaches for male BC (MBC) are currently extrapolated from the clinical management of female BC, although the disease does not exactly overlap in males and females. Data on specific molecular biomarkers in MBC are lacking, cutting out male patients from more appropriate therapeutic strategies. Growing evidence indicates that Next Generation Sequencing (NGS) multigene panel testing can be used for the detection of predictive molecular biomarkers, including Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI).

Methods

In this study, NGS multigene gene panel sequencing, targeting 1.94 Mb of the genome at 523 cancer-relevant genes (TruSight Oncology 500, Illumina), was used to identify and characterize somatic variants, Copy Number Variations (CNVs), TMB and MSI, in 15 Formalin-Fixed Paraffin-Embedded (FFPE) male breast cancer samples.

Results and discussion

A total of 40 pathogenic variants were detected in 24 genes. All MBC cases harbored at least one pathogenic variant. PIK3CA was the most frequently mutated gene, with six (40.0%) MBCs harboring targetable PIK3CA alterations. CNVs analysis showed copy number gains in 22 genes. No copy number losses were found. Specifically, 13 (86.7%) MBCs showed gene copy number gains. MYC was the most frequently amplified gene with eight (53.3%) MBCs showing a median fold-changes value of 1.9 (range 1.8-3.8). A median TMB value of 4.3 (range 0.8-12.3) mut/Mb was observed, with two (13%) MBCs showing high-TMB. The median percentage of MSI was 2.4% (range 0-17.6%), with two (13%) MBCs showing high-MSI. Overall, these results indicate that NGS multigene panel sequencing can provide a comprehensive molecular tumor profiling in MBC. The identification of targetable molecular alterations in more than 70% of MBCs suggests that the NGS approach may allow for the selection of MBC patients eligible for precision/targeted therapy.

Clinical and molecular features of pediatric cancer patients with Lynch syndrome

BACKGROUND

The association of childhood cancer with Lynch syndrome is not established compared with the significant pediatric cancer risk in recessive constitutional mismatch repair deficiency syndrome (CMMRD).

PROCEDURE

We describe the clinical features, germline analysis, and tumor genomic profiling of patients with Lynch syndrome among patients enrolled in pediatric cancer genomic studies.

RESULTS

There were six of 773 (0.8%) pediatric patients with solid tumors identified with Lynch syndrome, defined as a germline heterozygous pathogenic variant in one of the mismatch repair (MMR) genes (three with MSH6, two with MLH1, and one with MSH2). Tumor analysis demonstrated evidence for somatic second hits and/or increased tumor mutation burden in three of four patients with available tumor with potential implications for therapy and identification of at-risk family members. Only one patient met current guidelines for pediatric cancer genetics evaluation at the time of tumor diagnosis.

CONCLUSION

Approximately 1% of children with cancer have Lynch syndrome, which is missed with current referral guidelines, suggesting the importance of adding MMR genes to tumor and hereditary pediatric cancer panels. Tumor analysis may provide the first suggestion of an underlying cancer predisposition syndrome and is useful in distinguishing between Lynch syndrome and CMMRD.

Exome sequencing reveals a distinct somatic genomic landscape in breast cancer from women with germline PTEN variants

Germline PTEN variants (PTEN hamartoma tumor syndrome [PHTS]) confer up to 85% lifetime risk of female breast cancer (BC). BCs arising in PHTS are clinically distinct from sporadic BCs, including younger age of onset, multifocality, and an increased risk of second primary BCs. Yet, there is no previous investigation into the underlying genomic landscape of this entity. We sought to address the hypothesis that BCs arising in PHTS have a distinct genomic landscape compared to sporadic counterparts. We performed and analyzed exome sequencing data from 44 women with germline PTEN variants who developed BCs. The control cohort comprised of 497 women with sporadic BCs from The Cancer Genome Atlas (TCGA) dataset. We demonstrate that PHTS-derived BCs have a distinct somatic mutational landscape compared to the sporadic counterparts, namely second somatic hits in PTEN, distinct mutational signatures, and increased genomic instability. The PHTS group had a significantly higher frequency of somatic PTEN variants compared to TCGA (22.7% versus 5.6%; odds ratio [OR] 4.93; 95% confidence interval [CI] 2.21 to 10.98; p < 0.001) and a lower mutational frequency in PIK3CA (22.7% versus 33.4%; OR 0.59; 95% CI 0.28 to 1.22; p = 0.15). Somatic variants in PTEN and PIK3CA were mutually exclusive in PHTS (p = 0.01) but not in TCGA. Our findings have important implications for the personalized management of PTEN-related BCs, especially in the context of more accessible genetic testing.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

ATM: Functions of ATM Kinase and Its Relevance to Hereditary Tumors

Ataxia-telangiectasia mutated (ATM) functions as a key initiator and coordinator of DNA damage and cellular stress responses. ATM signaling pathways contain many downstream targets that regulate multiple important cellular processes, including DNA damage repair, apoptosis, cell cycle arrest, oxidative sensing, and proliferation. Over the past few decades, associations between germline ATM pathogenic variants and cancer risk have been reported, particularly for breast and pancreatic cancers. In addition, given that ATM plays a critical role in repairing double-strand breaks, inhibiting other DNA repair pathways could be a synthetic lethal approach. Based on this rationale, several DNA damage response inhibitors are currently being tested in ATM-deficient cancers. In this review, we discuss the current knowledge related to the structure of the ATM gene, function of ATM kinase, clinical significance of ATM germline pathogenic variants in patients with hereditary cancers, and ongoing efforts to target ATM for the benefit of cancer patients.

Germline Whole-Gene Deletion of FH Diagnosed from Tumor Profiling

Hereditary leiomyomatosis and renal cell carcinoma (HL (RCC)) entails cutaneous and uterine leiomyomatosis with aggressive type 2 papillary RCC-like histology. HLRCC is caused by pathogenic variants in the FH gene, which encodes fumarate hydratase (FH). Here, we describe an episode of young-onset RCC caused by a genomic FH deletion that was diagnosed via clinical sequencing. A 35-year-old woman was diagnosed with RCC and multiple metastases: histopathological analyses supported a diagnosis of FH-deficient RCC. Although the patient had neither skin tumors nor a family history of HLRCC, an aggressive clinical course at her age and pathological diagnosis of FH-deficient RCC suggested a germline FH variant. After counseling, the patient provided written informed consent for germline genetic testing. She was simultaneously subjected to paired tumor profiling tests targeting the exome to identify a therapeutic target. Although conventional germline sequencing did not detect FH variants, exome sequencing revealed a heterozygous germline FH deletion. As such, paired tumor profiling, not conventional sequencing, was required to identify this genetic deletion. RCC caused by a germline FH deletion has hitherto not been described in Japan, and the FH deletion detected in this patient was presumed to be of maternal European origin. Although the genotype-phenotype correlation in HLRCC-related tumors is unclear, the patient’s family was advised to undergo genetic counseling to consider additional RCC screening.

Somatic Tumor Profile Analysis in a Patient with Germline PMS2 Mutation and Synchronous Ovarian and Uterine Carcinomas

Lynch syndrome patients with synchronous endometrial and ovarian cancer (SEOC) are rare. When these cases occur, they are most often endometrioid histology and early grade. Early-grade tumors are not often sent for somatic tumor profiling. We present a 39 year old SEOC patient with germline PMS2 Lynch syndrome and clinical tumor analysis leading to insight regarding the origin and cause of these tumors, with potential therapy options. PMS2-related SEOC is less common due to lower risks for these cancers associated with germline PMS2 mutation compared to other Lynch genes. While synchronous cancers are not common, they are more likely to occur with Lynch syndrome. Tumor profiling with next-generation sequencing of 648 genes identified sixteen shared somatic actionable and biologically relevant mutations. This case is a rare example of a patient with PMS2 germline Lynch syndrome with shared somatic variants that demonstrate clonality of the two tumors arising from one common site.

Molecular Features and Clinical Management of Hereditary Gynecological Cancers

Hereditary gynecological cancers are caused by several inherited genes. Tumors that arise in the female reproductive system, such as ovaries and the uterus, overlap with hereditary cancers. Several hereditary cancer-related genes are important because they might lead to therapeutic targets. Treatment of hereditary cancers should be updated in line with the advent of various new methods of evaluation. Next-generation sequencing has led to rapid, economical genetic analyses that have prompted a concomitant and significant paradigm shift with respect to hereditary cancers. Molecular tumor profiling is an epochal method for determining therapeutic targets. Clinical treatment strategies are now being designed based on biomarkers based on tumor profiling. Furthermore, the National Comprehensive Cancer Network (NCCN) guidelines significantly changed the genetic testing process in 2020 to initially consider multi-gene panel (MGP) evaluation. Here, we reviewed the molecular features and clinical management of hereditary gynecological malignancies, such as hereditary breast and ovarian cancer (HBOC), and Lynch, Li–Fraumeni, Cowden, and Peutz–Jeghers syndromes. We also reviewed cancer-susceptible genes revealed by MGP tests.

3D monitoring of tumor volume in an in vivo model

BACKGROUND

The ability to evaluate tumor development within experimental oncology is of upmost importance. However, determining tumor volumes in 3D in vivo tumor models is challenging. The chick chorioallantoic membrane (CAM) model represents an optimized xenograft model that surpasses many disadvantages that are inherent to rodent models and provides the opportunity of real-time monitoring of tumor growth.

OBJECTIVE

The objective of this study was to introduce a new method that enables monitoring of tumor growth within the CAM model throughout the course of the experiment.

METHODS

Sarcoma cell lines and sarcoma primary tumors were grafted onto the CAM of fertilized chicken eggs. A digital microscope (Keyence VHX-6000) was used for 3D volume monitoring before and after tumor excision and compared it to tumor weight.

RESULTS

Accuracy of tumor volumes was validated through correlation with tumor weight. In and ex ovo tumor volumes correlated significantly with tumor weight values.

CONCLUSIONS

The described method can be used to assess the effects of chemotherapeutic agents on the growth of tumors that have been grafted onto the CAM and further advance personalized cancer therapy. In summary, we established a promising protocol that enables in vivo real-time tracking of tumor growth in the CAM model using a digital microscope.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

Clinical validation of the tempus xT next-generation targeted oncology sequencing assay

We developed and clinically validated a hybrid capture next generation sequencing assay to detect somatic alterations and microsatellite instability in solid tumors and hematologic malignancies. This targeted oncology assay utilizes tumor-normal matched samples for highly accurate somatic alteration calling and whole transcriptome RNA sequencing for unbiased identification of gene fusion events. The assay was validated with a combination of clinical specimens and cell lines, and recorded a sensitivity of 99.1% for single nucleotide variants, 98.1% for indels, 99.9% for gene rearrangements, 98.4% for copy number variations, and 99.9% for microsatellite instability detection. This assay presents a wide array of data for clinical management and clinical trial enrollment while conserving limited tissue.

Transcriptional profiling reveals distinct classes of parathyroid tumors in PHPT

The clinical presentation of primary hyperparathyroidism (PHPT) varies widely, although the underlying mechanistic reasons for this disparity remain unknown. We recently reported that parathyroid tumors can be functionally segregated into two distinct groups on the basis of their relative responsiveness to ambient calcium, and that patients in these groups differ significantly in their likelihood of manifesting bone disability. To examine the molecular basis for this phenotypic variation in PHPT, we compared the global gene expression profiles of calcium-sensitive and calcium-resistant parathyroid tumors. RNAseq and proteomic analysis identified a candidate set of differentially expressed genes highly correlated with calcium-sensing capacity. Subsequent quantitative assessment of the expression levels of these genes in an independent cohort of parathyroid tumors confirmed that calcium-sensitive tumors cluster in a discrete transcriptional profile group. These data indicate that PHPT is not an etiologically monolithic disorder and suggest that divergent molecular mechanisms could drive the observed phenotypic differences in PHPT disease course, provenance, and outcome.

Molecular profiling of tumours for precision oncology - high hopes versus reality

Treatment based on molecular profiling of tumor is advertised however there are very limited clinical data supporting this approach so far. Only one, relatively small, randomized clinical trial (SHIVA) have not met its primary endpoint - prolongation of PFS. Some other unpublished series were reported during ASCO 2017 and are discussed in this review. There are many issues to be resolved before the tumor profiling will enter the clinical practice with significant benefit for patients, eg. spatial and temporal heterogeneity of tumor cells in individual patient, wide access to targeted therapies, toxicity of combined targeted therapies.

Molecular profiling of advanced breast cancer tumors is beneficial in assisting clinical treatment plans

We used data obtained by Caris Life Sciences, to evaluate the benefits of tailoring treatments for a breast carcinoma cohort by using tumor molecular profiles to inform decisions. Data for 92 breast cancer patients from the commercial Caris Molecular Intelligence database was retrospectively divided into two groups, so that the first always followed treatment recommendations, whereas in the second group all patients received at least one drug after profiling that was predicted to lack benefit. The biomarker and drug associations were based on tests including fluorescent in situ hybridization and DNA sequencing, although immunohistochemistry was the main test used.

Patients whose drugs matched those recommended according to their tumor profile had an average overall survival of 667 days, compared to 510 days for patients that did not (P=0.0316). In the matched treatment group, 26% of patients were deceased by the last time of monitoring, whereas this was 41% in the unmatched group (P=0.1257). We therefore confirm the ability of tumor molecular profiling to improve survival of breast cancer patients. Immunohistochemistry biomarkers for the androgen, estrogen and progesterone receptors were found to be prognostic for survival.

TumorNext: A comprehensive tumor profiling assay that incorporates high resolution copy number analysis and germline status to improve testing accuracy

The development of targeted therapies for both germline and somatic DNA mutations has increased the need for molecular profiling assays to determine the mutational status of specific genes. Moreover, the potential of off-label prescription of targeted therapies favors classifying tumors based on DNA alterations rather than traditional tissue pathology. Here we describe the analytical validation of a custom probe-based NGS tumor panel, TumorNext, which can detect single nucleotide variants, small insertions and deletions in 142 genes that are frequently mutated in somatic and/or germline cancers. TumorNext also detects gene fusions and structural variants, such as tandem duplications and inversions, in 15 frequently disrupted oncogenes and tumor suppressors. The assay uses a matched control and custom bioinformatics pipeline to differentiate between somatic and germline mutations, allowing precise variant classification. We tested 170 previously characterized samples, of which > 95% were formalin-fixed paraffin embedded tissue from 8 different cancer types, and highlight examples where lack of germline status may have led to the inappropriate prescription of therapy. We also describe the validation of the Affymetrix OncoScan platform, an array technology for high resolution copy number variant detection for use in parallel with the NGS panel that can detect single copy amplifications and hemizygous deletions. We analyzed 80 previously characterized formalin-fixed paraffin-embedded specimens and provide examples of hemizygous deletion detection in samples with known pathogenic germline mutations. Thus, the TumorNext combined approach of NGS and OncoScan potentially allows for the identification of the “second hit” in hereditary cancer patients.

The benefit of tumor molecular profiling on predicting treatments for colorectal adenocarcinomas

We evaluated the benefit of tailoring treatments for a colorectal adenocarcinoma cancer cohort according to tumor molecular profiles, by analyzing data collected on patient responses to treatments that were guided by a tumor profiling technology from Caris Life Sciences. DNA sequencing and immunohistochemistry were the main tests that predictions were based upon, but also fragment analysis, and in situ hybridization. The status of the IHC biomarker for the thymidylate synthase receptor was a good indicator for future survival. Data collected for the clinical treatments of 95 colorectal adenocarcinoma patients was retrospectively divided into two groups: the first group was given drugs that always matched recommended treatments as suggested by the tumor molecular profiling service; the second group received at least one drug after profiling that was predicted to lack benefit. In the matched treatment group, 19% of patients were deceased at the end of monitoring compared to 49% in the unmatched group, indicating a benefit in mortality by tumor molecular profiling colorectal adenocarcinoma patients.

Investigating the benefits of molecular profiling of advanced non-small cell lung cancer tumors to guide treatments

In this study we utilized data on patient responses to guided treatments, and we evaluated their benefit for a non-small cell lung cancer cohort. The recommended therapies used were predicted using tumor molecular profiles that involved a range of biomarkers but primarily used immunohistochemistry markers. A dataset describing 91 lung non-small cell lung cancer patients was retrospectively split into two. The first group's drugs were consistent with a treatment plan whereby all drugs received agreed with their tumor's molecular profile. The second group each received one or more drug that was expected to lack benefit. We found that there was no significant difference in overall survival or mortality between the two groups. Patients whose treatments were predicted to be of benefit survived for an average of 402 days, compared to 382 days for those that did not (P = 0.7934). In the matched treatment group, 48% of patients were deceased by the time monitoring had finished compared to 53% in the unmatched group (P = 0.6094). The immunohistochemistry biomarker for the ERCC1 receptor was found to be a marker that could be used to predict future survival; ERCC1 loss was found to be predictive of poor survival.

Assessing tumor molecular profiling to guide treatments for patients with advanced female genital tract malignancy

Tumor molecular profiling has enabled selection of targeted therapies in a host of solid tumors. Here we used a retrospective clinical cohort, to evaluate the benefit of tailoring treatments for female genital tract malignancy, using tumor molecular profiles. Clinical outcome data for 112 patients was retrospectively separated into two groups. These either followed a matched treatment plan that incorporated at least one drug recommended according to their tumor profile and none that were expected to have no benefit (64 patients), or was unmatched with suggested treatments and received at least one drug that was anticipated to lack benefit for that tumor (48 patients).

In the group of patients whose drugs matched those recommended by molecular profiling of their tumor, their overall survival was 593 days on average, compared to 449 days for patients that did not; removing drugs predicted to have no benefit from treatment regimens received after profiling increased survival by 144 days on average (P = 0.0265). In the matched treatment group, 30% of patients had died by the last time of monitoring, whereas this was 40% in the unmatched group (P = 0.2778). The IHC biomarker for the progesterone receptor was demonstrated to be prognostic for survival.

Mutation based treatment recommendations from next generation sequencing data: a comparison of web tools

Interpretation of complex cancer genome data, generated by tumor target profiling platforms, is key for the success of personalized cancer therapy. How to draw therapeutic conclusions from tumor profiling results is not standardized and may vary among commercial and academically-affiliated recommendation tools. We performed targeted sequencing of 315 genes from 75 metastatic breast cancer biopsies using the FoundationOne assay. Results were run through 4 different web tools including the Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal, for drug and clinical trial recommendations. These recommendations were compared amongst each other and to those provided by FoundationOne. The identification of a gene as targetable varied across the different recommendation sources. Only 33% of cases had 4 or more sources recommend the same drug for at least one of the usually several altered genes found in tumor biopsies. These results indicate further development and standardization of broadly applicable software tools that assist in our therapeutic interpretation of genomic data is needed. Existing algorithms for data acquisition, integration and interpretation will likely need to incorporate artificial intelligence tools to improve both content and real-time status.

Multi-platform molecular profiling of a large cohort of glioblastomas reveals potential therapeutic strategies

Glioblastomas (GBM) are the most aggressive and prevalent form of gliomas with abysmal prognosis and limited treatment options. We analyzed clinically relevant molecular aberrations suggestive of response to therapies in 1035 GBM tumors. Our analysis revealed mutations in 39 genes of 48 tested. IHC revealed expression of PD-L1 in 19% and PD-1 in 46%. MGMT-methylation was seen in 43%, EGFRvIII in 19% and 1p19q co-deletion in 2%. TP53 mutation was associated with concurrent mutations, while IDH1 mutation was associated with MGMT-methylation and TP53 mutation and was mutually exclusive of EGFRvIII mutation. Distinct biomarker profiles were seen in GBM compared with WHO grade III astrocytoma, suggesting different biology and potentially different treatment approaches. Analysis of 17 metachronous paired tumors showed frequent biomarker changes, including MGMT-methylation and EGFR aberrations, indicating the need for a re-biopsy for tumor profiling to direct subsequent therapy. MGMT-methylation, PR and TOPO1 appeared as significant prognostic markers in sub-cohorts of GBM defined by age. The current study represents the largest biomarker study on clinical GBM tumors using multiple technologies to detect gene mutation, amplification, protein expression and promoter methylation. These data will inform planning for future personalized biomarker-based clinical trials and identifying effective treatments based on tumor biomarkers.

Malignant Glioma In Vitro Models: On the Utilization of Stem-like Cells

Recent publications on the molecular characterization of malignant glioma have had profound impact on the appreciation of tumoral heterogeneity within and between patients. Both these phenomena are implicated in the variability in clinical outcome between patients, as well as the inevitable recurrence of these tumors after conventional treatment. The advent of selective cell culture protocols for the propagation of patient-derived glioma stem-like cells (GSCs) provides researchers the ability to selectively study the cells that could be at the root of tumor proliferation and resistance to therapy. As these techniques are widely applied in contemporary studies and becoming the preferred in vitro model, molecular characterization of GSCs is considered pivotal for the identification and advancement of novel therapies for this devastating disease. This review aims to provide an overview of canonical molecular alterations defining subtypes of malignant glioma as derived from genotypic, transcriptomic and epigenetic profiling in relation to their representation in GSC models. The distribution of these hallmark alterations as found in characterization studies of GSCs is compared between publications. Finally, conclusions of these studies with respect to coverage of driving alterations and translational relevance are provided. By doing so, we provide a contemporary overview of scientific results derived from GSC models and hopefully create appreciation of the advantages and caveats of utilizing these models for studying malignant glioma.

Intrinsic Subtype Switching and Acquired ERBB2/HER2 Amplifications and Mutations in Breast Cancer Brain Metastases

IMPORTANCE

Patients with breast cancer (BrCa) brain metastases (BrM) have limited therapeutic options. A better understanding of molecular alterations acquired in BrM could identify clinically actionable metastatic dependencies.

OBJECTIVE

To determine whether there are intrinsic subtype differences between primary tumors and matched BrM and to uncover BrM-acquired alterations that are clinically actionable.

DESIGN, SETTING, AND PARTICIPANTS

In total, 20 cases of primary breast cancer tissue and resected BrM (10 estrogen receptor [ER]-negative and 10 ER-positive) from 2 academic institutions were included. Eligible cases in the discovery cohort harbored patient-matched primary breast cancer tissue and resected BrM. Given the rarity of patient-matched samples, no exclusion criteria were enacted. Two validation sequencing cohorts were used-a published data set of 17 patient-matched cases of BrM and a cohort of 7884 BrCa tumors enriched for metastatic samples.

MAIN OUTCOMES AND MEASURES

Brain metastases expression changes in 127 genes within BrCa signatures, PAM50 assignments, and ERBB2/HER2 DNA-level gains.

RESULTS

Overall, 17 of 20 BrM retained the PAM50 subtype of the primary BrCa. Despite this concordance, 17 of 20 BrM harbored expression changes (<2-fold or >2-fold) in clinically actionable genes including gains of FGFR4 (n = 6 [30%]), FLT1 (n = 4 [20%]), AURKA (n = 2 [10%]) and loss of ESR1 expression (n = 9 [45%]). The most recurrent expression gain was ERBB2/HER2, which showed a greater than 2-fold expression increase in 7 of 20 BrM (35%). Three of these 7 cases were ERBB2/HER2-negative out of 13 ERBB2/HER2-negative in the primary BrCa cohort and became immunohistochemical positive (3+) in the paired BrM with metastasis-specific amplification of the ERBB2/HER2 locus. In an independent data set, 2 of 9 (22.2%) ERBB2/HER2-negative BrCa switched to ERBB2/HER2-positive with 1 BrM acquiring ERBB2/HER2 amplification and the other showing metastatic enrichment of the activating V777L ERBB2/HER2 mutation. An expanded cohort revealed that ERBB2/HER2 amplification and/or mutation frequency was unchanged between local disease and metastases across all sites; however, a significant enrichment was appreciated for BrM (13% local vs 24% BrM; P < .001).

CONCLUSIONS AND RELEVANCE

Breast cancer BrM commonly acquire alterations in clinically actionable genes, with metastasis-acquired ERBB2/HER2 alterations in approximately 20% of ERBB2/HER2-negative cases. These observations have immediate clinical implications for patients with ERBB2/HER2-negative breast cancer and support comprehensive profiling of metastases to inform clinical care.

Classifying the Progression of Ductal Carcinoma from Single-Cell Sampled Data via Integer Linear Programming: A Case Study

Ductal Carcinoma In Situ (DCIS) is a precursor lesion of Invasive Ductal Carcinoma (IDC) of the breast. Investigating its temporal progression could provide fundamental new insights for the development of better diagnostic tools to predict which cases of DCIS will progress to IDC. We investigate the problem of reconstructing a plausible progression from single-cell sampled data of an individual with synchronous DCIS and IDC. Specifically, by using a number of assumptions derived from the observation of cellular atypia occurring in IDC, we design a possible predictive model using integer linear programming (ILP). Computational experiments carried out on a preexisting data set of 13 patients with simultaneous DCIS and IDC show that the corresponding predicted progression models are classifiable into categories having specific evolutionary characteristics. The approach provides new insights into mechanisms of clonal progression in breast cancers and helps illustrate the power of the ILP approach for similar problems in reconstructing tumor evolution scenarios under complex sets of constraints.

Inference of transcriptional regulation in cancers

Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.

Molecular profiling is not the future: it is now!

Bobby Reddy speaks to Gemma Westcott, Commissioning Editor: Dr Reddy graduated from the UCLA School of Medicine in 1996. Shortly after, he obtained an internship and did his residency in Internal Medicine at Harbor UCLA Medical Center. He then went on to do his fellowship in Hematology and Oncology at City of Hope. Since then, he has been working in private practice (full and part time) for the past 11 years and has had an academic appointment as teaching faculty at Harbor UCLA. Prior to his current role, Dr Reddy worked as a senior medical director as Caris Life Sciences.

Integrative genomic identification of genes on 8p associated with hepatocellular carcinoma progression and patient survival

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is an aggressive malignancy; its mechanisms of development and progression are poorly understood. We used an integrative approach to identify HCC driver genes, defined as genes whose copy numbers associate with gene expression and cancer progression.

METHODS

We combined data from high-resolution, array-based comparative genomic hybridization and transcriptome analysis of HCC samples from 76 patients with hepatitis B virus infection with data on patient survival times. Candidate genes were functionally validated using in vitro and in vivo models.

RESULTS

Unsupervised analyses of array comparative genomic hybridization data associated loss of chromosome 8p with poor outcome (reduced survival time); somatic copy number alterations correlated with expression of 27.3% of genes analyzed. We associated expression levels of 10 of these genes with patient survival times in 2 independent cohorts (comprising 319 cases of HCC with mixed etiology) and 3 breast cancer cohorts (637 cases). Among the 10-gene signature, a cluster of 6 genes on 8p, (DLC1, CCDC25, ELP3, PROSC, SH2D4A, and SORBS3) were deleted in HCCs from patients with poor outcomes. In vitro and in vivo analyses indicated that the products of PROSC, SH2D4A, and SORBS3 have tumor-suppressive activities, along with the known tumor suppressor gene DLC1.

CONCLUSIONS

We used an unbiased approach to identify 10 genes associated with HCC progression. These might be used in assisting diagnosis and to stage tumors based on gene expression patterns.

A statistical modeling approach to the analysis of spatial patterns of FDG-PET uptake in human sarcoma

Clinical experience with positron emission tomography (PET) scanning of sarcoma, using fluorodeoxyglucose (FDG), has established spatial heterogeneity in the standardized uptake values within the tumor mass as a key prognostic indicator of patient survival. But it may be that a more detailed quantitation of the tumor FDG uptake pattern could provide additional insights into risk. The present work develops a statistical model for this purpose. The approach is based on a tubular representation of the tumor mass with a simplified radial analysis of uptake, transverse to the tubular axis. The technique provides novel ways of characterizing the overall profile of the tumor, including the introduction of an approach for the measurement of its phase of development. The phase measure can distinguish between early phase tumors, in which the uptake is highest at the core, and later stage masses, in which there can often be central voids in FDG uptake. Biologically, these voids arise from necrosis and fluid, fat or cartilage accumulations. The tumor profiling technique is implemented using open-source software tools and illustrations are provided with clinically representative scans. A series of FDG-PET studies from 185 patients is used to formally evaluate the prognostic benefit. Significant improvements in the prediction of patient survival and progression are obtained from the tumor profiling analysis. After adjustment for other factors including heterogeneity, a typical one standard deviation increase in phase (as determined by the analysis) is associated with close to 20% more risk of progression or death. The work confirms that more detailed quantitative assessments of the spatial pattern of PET imaging data of tumor masses, beyond the maximum FDG uptake (SUV(max)) and previously considered measures of heterogeneity, provide improved prognostic information for potential input to treatment decisions for future patients.

MicroRNA-based classification of hepatocellular carcinoma and oncogenic role of miR-517a

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is a heterogeneous tumor that develops via activation of multiple pathways and molecular alterations. It has been a challenge to identify molecular classes of HCC and design treatment strategies for each specific subtype. MicroRNAs (miRNAs) are involved in HCC pathogenesis, and their expression profiles have been used to classify cancers. We analyzed miRNA expression in human HCC samples to identify molecular subclasses and oncogenic miRNAs.

METHODS

We performed miRNA profiling of 89 HCC samples using a ligation-mediated amplification method. Subclasses were identified by unsupervised clustering analysis. We identified molecular features specific for each subclass using expression pattern (Affymetrix U133 2.0; Affymetrix, Santa Clara, CA), DNA change (Affymetrix STY Mapping Array), mutation (CTNNB1), and immunohistochemical (phosphor[p]-protein kinase B, p-insulin growth factor-IR, p-S6, p-epidermal growth factor receptor, β-catenin) analyses. The roles of selected miRNAs were investigated in cell lines and in an orthotopic model of HCC.

RESULTS

We identified 3 main clusters of HCCs: the wingless-type MMTV integration site (32 of 89; 36%), interferon-related (29 of 89; 33%), and proliferation (28 of 89; 31%) subclasses. A subset of patients with tumors in the proliferation subclass (8 of 89; 9%) overexpressed a family of poorly characterized miRNAs from chr19q13.42. Expression of miR-517a and miR-520c (from ch19q13.42) increased proliferation, migration, and invasion of HCC cells in vitro. MiR-517a promoted tumorigenesis and metastatic dissemination in vivo.

CONCLUSIONS

We propose miRNA-based classification of 3 subclasses of HCC. Among the proliferation class, miR-517a is an oncogenic miRNA that promotes tumor progression. There is rationale for developing therapies that target miR-517a for patients with HCC.