Effect Of Tumor Heterogeneity Research Articles

Enhancement of Acetate-Induced Apoptosis of Colorectal Cancer Cells by Cathepsin D Inhibition Depends on Oligomycin A-Sensitive Respiration.

Colorectal cancer (CRC) is a leading cause of death worldwide. Conventional therapies are available with varying effectiveness. Acetate, a short-chain fatty acid produced by human intestinal bacteria, triggers mitochondria-mediated apoptosis preferentially in CRC but not in normal colonocytes, which has spurred an interest in its use for CRC prevention/therapy. We previously uncovered that acetate-induced mitochondrial-mediated apoptosis in CRC cells is significantly enhanced by the inhibition of the lysosomal protease cathepsin D (CatD), which indicates both mitochondria and the lysosome are involved in the regulation of acetate-induced apoptosis. Herein, we sought to determine whether mitochondrial function affects CatD apoptotic function. We found that enhancement of acetate-induced apoptosis by CatD inhibition depends on oligomycin A-sensitive respiration. Mechanistically, the potentiating effect is associated with an increase in cellular and mitochondrial superoxide anion accumulation and mitochondrial mass. Our results provide novel clues into the regulation of CatD function and the effect of tumor heterogeneity in the outcome of combined treatment using acetate and CatD inhibitors.

Intrapatient Tumor Heterogeneity in IHC Interpretation Using PD-L1 IHC 22C3 pharmDx.

Tumor heterogeneity may impact immunohistochemical (IHC) interpretation, thus potentially affecting decision making by treating oncologists for cancer patient management. Programmed cell death ligand-1 (PD-L1) IHC 22C3 pharmDx is a companion diagnostic used as an aid in identifying patient eligibility for treatment with pembrolizumab (KEYTRUDA). This study aims to investigate tumor heterogeneity impact on IHC staining when evaluating PD-L1 expression using PD-L1 IHC 22C3 pharmDx. The effect of tumor heterogeneity was evaluated based on the PD-L1 diagnostic status of PD-L1 IHC 22C3 pharmDx stained tumor tissue sections at relevant diagnostic cutoffs for non–small cell lung carcinoma, gastric or gastroesophageal junction adenocarcinoma, urothelial carcinoma, head and neck squamous cell carcinoma, esophageal cancer and triple negative breast cancer. Overall agreement for the PD-L1 diagnostic status was assessed for each tumor type within a given specimen block (Intra-Block), between specimen blocks from the same surgical resection (Intra-Case), and between intrapatient primary and metastatic specimens. Intrablock and intracase point estimates were above 75%, and primary versus metastatic point estimates were above 50%. The results suggest that PD-L1 expression is consistent across cut sections through a minimum of 150 µm within a tissue block and between blocks from the same surgical resection and is significantly maintained across primary and metastatic blocks from the same patient despite changes to the tissue microenvironment. These data provide confidence for histopathologists and oncologists that evaluation of PD-L1 expression at clinically relevant cutoffs is reproducible among different assessments (or samplings) of a single tumor specimen.

Neutrophil-to-lymphocyte ratio (NLR) as a prognostic indicator of treatment response to novel hormones in metastatic castrate-resistant prostate cancer: Real-world data from a single institution.

52 Background: Prostate cancer is the most common solid malignancy in men. Despite advances in radical management, a proportion of patients develop castrate resistant metastatic disease (mCRPC). Management options in mCRPC include the novel hormones Enzalutamide (E) or Abiraterone Acetate (AA). There is limited knowledge of clinical factors suggesting improved response to either E or AA to guide clinician choice. Systemic inflammation caused by tumor activity may predict response to E or AA. The NLR is a marker of systemic inflammation which has been assessed previously. We present findings from a single institution evaluation of NLR for E and AA. Methods: Men receiving E or AA for mCRPC between March 2018 - February 2020 at our institution were identified. Baseline characteristics and pre-specified metrics including hematology, age and disease burden as well as time on treatment and survival data were retrospectively taken from hospital systems and collated into a single dataset. Time on treatment was used as a surrogate for progression free survival (PFS). Patients could switch therapies if they developed dose limiting toxicity within 3 months of treatment as per UK guidelines. AUROC curve analysis was applied to determine NLR cut off for E and AA cohorts specific for PFS. Results: 128 patients are included in the final analysis; 65 patients received E, 63 AA. Cohorts were well balanced with a median age at initial treatment of 75 (range 46-102) for AA and 74 (range 48-91) for E. The median Gleason score for AA was 8 and 9 for E. Patients receiving AA were more likely to have greater than 3 bone metastases (85% vs 74%). Patients receiving E were more likely to have visceral disease, 13% vs 10%. NLR cutoff, determined by AUROC curve analysis was 3.09 for E with an AUC of 0.782 (p <0.001). For AA, NLR cutoff was 3.1 with a non-statistically significant AUC of 0.588 (p=0.704). Median PFS was 239 days (range 13-1997) for E, 175 days (range 22-2037) for AA. Conclusions: The data does not support the hypothesis that NLR can be used to determine between the therapeutic option of E or AA in individual patients. Regardless of NLR, both E and AA remain good therapeutic options for men with mCRPC. This single institution real world data provides evidence of the role of NLR in determining response to novel hormone agents in the mCRPC setting for patients commencing E. No statistically significant NLR could be calculated for patients receiving AA. Although well balanced, in this non-randomized population, patients with multiple bony metastases were more likely to receive AA and those with visceral metastases, E. It is possible that this imbalance explains the lack of statistical significance in the AA cohort. We aim to expand our study cohort and evaluate OS and other potential metrics including the effects of tumor heterogeneity on the prognostic value of NLR.

Effects of Molecular Heterogeneity on Survival of Patients With BRAFV600-Mutated Melanoma Treated With Vemurafenib With or Without Cobimetinib in the coBRIM Study.

The treatment of advanced BRAFV600-mutated melanomas with BRAF inhibitors (BRAFi) has improved survival, but the efficacy of BRAFi varies among individuals and the development of acquired resistance to BRAFi through reactivation of mitogen-activated protein kinase (MAPK) signaling is common. We performed an exploratory, retrospective analysis to investigate the effects of BRAFV600 allelic balance, coexisting oncogene mutations, cell proliferation signaling levels, and loss of PTEN expression on progression-free survival (PFS) in patients in the phase III coBRIM study, which compared the combination of the MEK inhibitor cobimetinib with the BRAFi vemurafenib versus vemurafenib as monotherapy. Baseline tumor samples from the intention-to-treat population were analyzed by targeted deep sequencing at a median coverage of 3,600× and by immunohistochemistry for cell proliferation markers, BRAFV600E, and PTEN. The association of these biomarkers with PFS was assessed by Cox proportional hazards modeling. Gene expression in relation to loss of PTEN was profiled by RNA sequencing in 205 patient samples and 42 BRAFV600-mutated melanoma cell lines. Neither BRAFV600 allelic balance nor coexisting mutations in the RAS/RAF/RTK pathway affected PFS in either treatment group. Increased baseline MAPK signaling and cell proliferation did not affect PFS in patients treated with cobimetinib combined with vemurafenib. PTEN loss was associated with reduced PFS in patients treated with vemurafenib alone but not in patients treated with cobimetinib combined with vemurafenib. Deeper inhibition of the MAPK pathway through targeting of both MEK and BRAF may override the effects of tumor heterogeneity and improve PFS in all patients with advanced BRAFV600 melanoma.

Abstract 1307: Effect of tumor purity on somatic mutation detection using next-generation sequencing technology: A benchmarking study

Abstract Next generation sequencing (NGS) is becoming an essential tool for clinical cancer care. Whole genome sequencing (WGS), whole exome sequencing (WES), and targeted gene sequencing (TGS) have emerged as important tools and are critical for advancing precision oncology to meaningfully impact patient lives. To enable this goal, we must first thoroughly characterize the complexities of using NGS to identify somatic mutations in samples with tumor heterogeneity and variable tissue sample purity. Here, we applied both experiment and in silico approaches to determine the effects of tumor heterogeneity on somatic mutation detection. First, we systematically investigated somatic mutation events in paired cell lines (breast carcinoma and matched normal) using a myriad of NGS platforms and protocols. Then, we applied machine learning algorithms to define high confidence mutation calls with datasets from the combination of all platforms and pipelines. Finally, we performed whole genome sequencing from tumor DNA samples titrated with matched normal cells to create a gradient of tumor purities from 100%, 75%, 50%, 20%, 10%, 5%, and 0%. We also created a series of in silico mixes by pooling reads from tumor and normal cell lines at respective ratios. With somatic variation “ground truth” established by multiple orthogonal sequencing platforms and multiple analysis tools, we evaluated performance of individual software tools for tumor purity assessment and for mutation detection. Our experimental design allowed us to systematically evaluate accuracy, specificity and sensitivity of mutation detection across a range of tumor purities, as well as conduct a head-to-head comparison of in silico approaches versus experimental approaches. This comprehensive study will help to clarify our understanding of experimental and analytical challenges that arise when confronted with tumor heterogeneity in patient care clinical decisions. In addition, data generated in this study will be used as a benchmark resource for the community to develop and improve tools to meet clinical challenges. Citation Format: Wendell Jones, Wenming Xiao, Somatic Mutation Working Group, SEQC2 Consortium. Effect of tumor purity on somatic mutation detection using next-generation sequencing technology: A benchmarking study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1307.

Immunotherapy in esophageal cancer

Tumor progression is often associated with immune suppression or the ability of the tumors to avoid immune surveillance. Immunotherapy improves the ability of the immune system to recognize and clear tumor cells with a little influence on the normal tissues. Immunotherapy is a hot spot in the research of advanced esophageal cancer. Immunotherapy of esophageal cancer includes immune checkpoint inhibitors, adoptive cellular immunotherapy, tumor vaccines and antibody therapy. At present, a large number of clinical trials are underway to evaluate the role of immunotherapy in esophageal cancer. Checkpoint inhibitors represented by Pembrolizumab and Nivolumab, has achieved initial success in the treatment of advanced esophageal cancer to improve the prognosis and life quality of esophageal cancer patients. In the future, further studies are needed to have a research on the effects of tumor heterogeneity, prediction of therapeutic targets, and immune tolerance. Key words: Esophageal neoplasms; Immunotherapy; Immune checkpoint blockades

Breast tumor subgroups reveal diverse clinical prognostic power

Predicting the outcome of cancer therapies using molecular features and clinical observations is a key goal of cancer biology, which has been addressed comprehensively using whole patient datasets without considering the effect of tumor heterogeneity. We hypothesized that molecular features and clinical observations have different prognostic abilities for different cancer subtypes, and made a systematic study using both clinical observations and gene expression data. This analysis revealed that (1) gene expression profiles and clinical features show different prognostic power for the five breast cancer subtypes; (2) gene expression data of the normal-like subgroup contains more valuable prognostic information and survival associated contexts than the other subtypes, and the patient survival time of the normal-like subtype is more predictable based on the gene expression profiles; and (3) the prognostic power of many previously reported breast cancer gene signatures increased in the normal-like subtype and reduced in the other subtypes compared with that in the whole sample set.

Effect of Tumor Heterogeneity on the Assessment of Ki67 Labeling Index in Well-differentiated Neuroendocrine Tumors Metastatic to the Liver

The Ki67 labeling index is known to correlate with survival in patients with neuroendocrine tumors (NETs). A grading scheme recently endorsed by the World Health Organization for gastroenteropancreatic NETs classifies well-differentiated NETs into 2 categories based on the Ki67 labeling index: low (G1) and intermediate grades (G2). Tumor heterogeneity is a common finding in many tumors including NETs. Metastatic NETs to the liver are often diagnosed by radiographically guided needle core biopsy from which the Ki67 index is determined, which randomly samples the lesion without being targeted to regions that may show a higher proliferative rate. Whether the Ki67 index obtained from this type of limited material represents the whole tumor has been questioned. Forty-five surgically resected liver metastases of well-differentiated NETs were retrieved. A 9 core (3 core-triplets) tissue microarray (TMA) was constructed from the paraffin blocks of each tumor, each triplet considered to represent a virtual biopsy. Immunohistochemical staining for Ki67 was performed on TMA and whole slides, and the Ki67 labeling indices were determined by digital image analysis. Correlation of the Ki67 index with patient survival was analyzed. Forty-seven percent of cases showed intratumoral heterogeneity in Ki67 index that translated into discrepant grades among subsections on the whole slide. A similar trend was recapitulated on the virtual biopsies, although to a lesser degree. When the definitive grade of the tumor was based on the highest Ki67 index identified on the whole slide, the virtual biopsies perfectly predicted G1 cases (100%), but were much less accurate for G2 cases (47.8% with 3 biopsies and 34.8% with single biopsy). Accordingly, the predictive value for G1 on the virtual biopsies was low (64.7% and 59.5% for 3 and 1 biopsy, respectively), but was perfect for G2 (100%). By Kaplan-Meier survival analysis, there was a statistically significant difference between G1 and G2 in terms of overall survival, disease-free survival, and progression-free survival when graded on either whole-slide subsections or virtual biopsies. On the whole slides, the highest Ki67 grade showed a better correlation with overall survival than the mean Ki67 grade. In summary, by image analysis, we found that about half of the NETs metastatic to the liver show intratumoral heterogeneity resulting in discrepant Ki67 grade. In most cases, in particular G1, the virtual biopsy is representative of the whole slide, but for G2 the representation is <50%. Nevertheless, grades based on virtual biopsy had statistically significant prognostic values on patient survival, and there is no clear difference between the 3 and single virtual biopsy. Ki67 staining of core biopsies usually provides an adequately reliable method of proliferation assessment for prognosis of metastatic NETs to the liver, although the choice of treatment may be affected by intratumoral grade heterogeneity.

Abstract 4847: Purifying tumor gene expression profiles leads to predictive models of patient outcome

Abstract INTRODUCTION: Molecular-based diagnostics tools such as gene expression signatures for predicting patient outcome or response to treatment are cornerstones of personalized medicine. However, they have a reputation for poor validation performance on patient cohorts independent of those on which they were developed, a combined result of small patient cohorts and tumor heterogeneity. We quantitatively assess the impact of tumor heterogeneity on gene-expression based predictive models of patient outcome for non small cell lung cancer. EXPERIMENTS: We developed a fully automated pipeline to computationally purify tumor gene expression profiles using a tool called ISOLATE (Quon et al., 2009), identify a prognostic gene signature on a training cohort (Friedman et al., 2010), and validate the signature on independent cohorts. ISOLATE accepts heterogeneous tumor profiles and healthy tissue profiles, and outputs purified tumor profiles and percentage tumor content for each heterogeneous sample. We used 1 training cohort of 86 patients (Beer et al., 2002), and 4 independent validation cohorts totaling 443 patients (Shedden et al., 2008). The computational pipeline was run on the training and validation cohorts in two identical scenarios with the exception of either performing computational purification on all samples or not. RESULTS: We first validated the estimates of % tumor content of ISOLATE on 32 lung adenocarcinomas on which two pathologists independently estimated % content. For the 23 samples on which the two pathologists agreed strongly, ISOLATE estimates had 0.51 correlation with their average, compared with an overall correlation of 0.59 between the two pathologists. Our computational pipeline identified an 82-gene signature (‘unpurified-sig’) on the unpurified tumor profiles, and a 110-gene signature (‘ISOLATE-sig’) on the purified tumor profiles. We used these signatures to identify low and high risk patient groups within the validation cohorts of 443 patients. ISOLATE-sig achieved a very significant difference in survival between the identified low and high risk patients (Hazard Ratio (HR)=1.92, p=10⁁(−6)), compared to unpurified-sig (HR=1.53, p=10⁁(−3)). We separately examined all 227 Stage I patients, and found consistent results: ISOLATE-sig performance (HR=1.80, p=10⁁(−3)) compared very favorably to the original profiles (HR=1.45, p=10⁁(−2)). Here, only ISOLATE-sig achieves a hazard ratio whose 95% CI strictly exceeds 1. CONCLUSION: This is the first study to date that quantitatively assesses the effect of tumor heterogeneity on models for predicting patient outcome. We also present a fully automated computational pipeline for purifying, identifying, and validating gene expression based signatures. We showed purification lead to much better prognostic models of lung cancer, and anticipate similar results for other solid cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4847. doi:10.1158/1538-7445.AM2011-4847

Further Radiobiologic Modeling of Palliative Radiotherapy: Use of Virtual Trials

To study duration of response in palliative radiotherapy in a population of tumors. Models of dynamic changes in cell number with time were used to develop a function for the remission time (T(rem)) after palliative radiotherapy: [See Equation], where BED is the biologically effective dose, t(1) the duration of symptoms (i.e., the time between the onset of symptoms and the initiation of radiotherapy), K the daily BED repopulation equivalent, alpha the linear radiosensitivity parameter in the linear-quadratic model, and z the tumor regression rate. Simulations of clinical trials show marked variations in remission statistics depending on the tumor characteristics and are highly compatible with the results of clinical trials. Dose escalation produces both a higher proportion and extended duration of remissions, especially in tumors with high alpha/beta ratios and K values, but the predicted dose responses of acute and late side effects show that caution is necessary. The prospect of using particle beam therapy to reduce normal tissue radiation exposures or using hypoxic sensitizers to improve the tumor cell kill might significantly improve the results of palliative radiotherapy in carefully selected patients and could also be used for safer palliative re-treatments in patients with the potential for prolonged survival. The effect of tumor heterogeneity in determining palliative responses probably exceeds that in radical radiotherapy; as few as 100 patients in each treatment arm produce statistically unreliable results. Virtual trials of palliative radiotherapy can be useful to test the effects of competing schedules and better determine future strategies, including improved design of clinical trials as well as combinations of radiotherapy with other anticancer modalities.