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Bhardwaj PV, Dulala R, Rajappa S, Loke C. Breast Cancer in India: Screening, Detection, and Management. Hematol Oncol Clin North Am 2024; 38:123-135. [PMID: 37330342 DOI: 10.1016/j.hoc.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Breast cancer is the most common cancer in urban Indian women and the second most common cancer in all Indian women. The epidemiology as well as biology of this cancer seems to be different in the Indian subcontinent when compared with the West. The lack of population-based breast cancer screening programs and delay in seeking a medical consult due to financial and social reasons, including lack of awareness and fear related to a cancer diagnosis, results in delayed diagnosis.
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Affiliation(s)
- Prarthna V Bhardwaj
- Division of Hematology-Oncology, University of Massachusetts Chan Medical School - Baystate, 759 Chestnut Street, Springfield, MA 01199, USA
| | - Renuka Dulala
- Division of Hematology-Oncology, Holyoke Medical Center, 575 Beech Street, Holyoke, MA 01040, USA
| | - Senthil Rajappa
- Department of Medical Oncology, Basavatarakam Indo-American Cancer Hospital and Research Institute, Road no. 10, Banjara Hills, Hyderabad, Telangana 500034, India
| | - Chandravathi Loke
- Division of Hematology-Oncology, University of Massachusetts Chan Medical School - Baystate, 759 Chestnut Street, Springfield, MA 01199, USA.
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Huang X, Li S, Gao W, Shi J, Cheng M, Mi Y, Liu Y, Sang M, Li Z, Geng C. KIF20A is a Prognostic Marker for Female Patients with Estrogen Receptor-Positive Breast Cancer and Receiving Tamoxifen as Adjuvant Endocrine Therapy. Int J Gen Med 2023; 16:3623-3635. [PMID: 37637711 PMCID: PMC10455948 DOI: 10.2147/ijgm.s425918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Purpose Our aim was to verify whether KIF20A has the potential to serve as a prognostic marker for female patients with estrogen receptor (ER)-positive breast cancer (BC) and treated with tamoxifen (TAM). Patients and Methods Online tools were used to investigate the potential correlation between KIF20A gene expression and survival of patients with ER-positive BC and TAM treatment. Furthermore, immunohistochemistry (IHC) was conducted to assess the expression levels of KIF20A in patients included from our center. The prognostic value of KIF20A for disease-free survival (DFS) and overall survival (OS) was further evaluated using Cox regression analysis. Results According to the results obtained from online tools, it was found that patients with low KIF20A expression exhibited significantly better survival outcomes in terms of relapse-free survival (RFS), distant metastasis-free survival (DMFS), and OS compared to those with high KIF20A expression (P < 0.001, P < 0.001, and P = 0.008, respectively). Additionally, significantly lower gene expression of KIF20A was found in patients who responded to TAM than in those who did not respond to TAM (P < 0.001). We further included 203 patients with adjuvant TAM therapy, and IHC for KIF20A was performed on sections from paraffin-embedded blocks. Patients with low KIF20A expression had significantly better DFS and OS (P = 0.001 and 0.002, respectively, log rank test), and the expression of KIF20A was identified as an independent factor for predicting both DFS and OS (P = 0.001 and 0.008, respectively). Conclusion KIF20A expression is an independent prognostic factor for survival in patients with ER-positive BC who received adjuvant TAM therapy. In clinical practice, IHC evaluation of KIF20A expression in surgical samples before administering tamoxifen may assist in predicting the treatment outcomes of these patients.
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Affiliation(s)
- Xuchen Huang
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Sainan Li
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Wei Gao
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Jiajie Shi
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Meng Cheng
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Yunzhe Mi
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
| | - Meixiang Sang
- Research Center and Tumor Research Institute, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
| | - Ziyi Li
- Research Center and Tumor Research Institute, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
| | - Cuizhi Geng
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China
- Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China
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3
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Parikh P, Babu G, Singh R, Krishna V, Bhatt A, Bansal I, Rajappa S, Sahoo TP, Aggarwal S, Bapna A, Biswas G, Somashekhar SP, Bajpai J, Maniar V, Desai S, Raja T, Rath GK. Consensus guidelines for the management of HR-positive HER2/neu negative early breast cancer in India, SAARC region and other LMIC by DELPHI survey method. BMC Cancer 2023; 23:714. [PMID: 37525142 PMCID: PMC10391857 DOI: 10.1186/s12885-023-11121-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Precise prognostication is the key to optimum and effective treatment planning for early-stage hormone receptor (HR) positive, HER2/neu negative breast cancer patients. Differences in the breast cancer incidence and tumor anatomical features at diagnosis, pharmacogenomics data between Western and Indian women along with the vast diversity in the economic status and differences in insurance policies of these regions; suggest recommendations put forward for Western women might not be applicable to Indian/Asian women. Opinions from oncologists through a voting survey on various prognostic factors/tools to be considered for planning adjuvant therapy are consolidated in this report for the benefit of oncologists of the sub-continent, SAARC and Asia's LMIC (low and middle-income countries). METHODS A three-phase DELPHI survey was conducted to collect opinions on prognostic factors considered for planning adjuvant therapy in early-stage HR+/HER2/neu negative breast cancer patients. A panel of 25 oncologists with expertise in breast cancer participated in the survey conducted in 2021. The experts provided opinions as 'agree' or disagree' or 'not sure' in phases-1 and 2 which were conducted virtually; in the final phase-3, all the panel experts met in person and concluded the survey. RESULTS Opinions on 41 statements related to prognostic factors/tools and their implications in planning adjuvant endocrine/chemotherapy were collected. All the statements were supported by the latest data from the clinical trials (prospective/retrospective). The statements with opinions of consensus less than 66% were disseminated in phase-2, and later in phase-3 with supporting literature. In phase-3, all the opinions from panelists were consolidated and guidelines were framed. CONCLUSIONS This consensus guideline will assist oncologists of India, SAARC and LMIC countries in informed clinical decision-making on adjuvant treatment in early HR+/HER2/neu negative breast cancer patients.
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Affiliation(s)
- Purvish Parikh
- Dept of Clinical Hematology, Mahatma Gandhi Medical College Hospital, Jaipur, 302023, India.
| | - Govind Babu
- HCG Cancer Hospital, Bengaluru, 560027, India
| | - Randeep Singh
- Narayana Super speciality Hospital, Gurugram, 122002, India
| | - Vamshi Krishna
- Asian Institute of Gastroenterology, Hyderabad, 500082, India
| | - Amit Bhatt
- Avinash Cancer Clinic, Pune, 411004, India
| | - Indu Bansal
- Narayana Super speciality Hospital, Gurugram, 122002, India
| | - Senthil Rajappa
- Basavaratakam Indo American Cancer Hospital & Research Institute, Hyderabad, 500034, India
| | | | | | - Ajay Bapna
- Bhagwan Mahaveer Cancer Hospital and Research Centre, Jaipur, 302017, India
| | | | - S P Somashekhar
- Manipal Comprehensive Cancer Center, Manipal Hospital, Bengaluru, 560017, India
| | | | | | - Sharad Desai
- Mahatma Gandhi Cancer Hospital, Miraj, 416410, India
| | - T Raja
- Apollo Speciality Cancer Hospital, Chennai, 600035, India
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4
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Eshwaraiah MS, Gunda A, Kanakasetty GB, Bakre MM. The usefulness of CanAssist Breast over Ki67 in breast cancer recurrence risk assessment. Cancer Med 2023. [PMID: 37245224 DOI: 10.1002/cam4.6032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/24/2023] [Accepted: 04/23/2023] [Indexed: 05/30/2023] Open
Abstract
BACKGROUND Assessment of Ki67 by immunohistochemistry (IHC) has limited utility in clinical practice owing to analytical validity issues. According to International Ki67 Working Group (IKWG) guidelines, treatment should be guided by a prognostic test in patients expressing intermediate Ki67 range, >5%-<30%. The objective of the study is to compare the prognostic performance of CanAssist Breast (CAB) with that of Ki67 across various Ki67 prognostic groups. METHODS The cohort had 1701 patients. Various risk groups were compared for the distant relapse-free interval (DRFi) derived from Kaplan-Meier survival analysis. As per IKWG, patients are categorized into three risk groups: low-risk (<5%), intermediate risk (>5%-<30%), and high-risk (>30%). CAB generates two risk groups, low and high risk based on a predefined cutoff. RESULTS In the total cohort, 76% of the patients were low risk (LR) by CAB as against 46% by Ki67 with a similar DRFi of 94%. In the node-negative sub-cohort, 87% were LR by CAB with a DRFi of 97% against 49% by Ki67 with a DRFi of 96%. In subgroups of patients with T1 or N1 or G2 tumors, Ki67-based risk stratification was not significant while it was significant by CAB. In the intermediate Ki67 (>5%-<30%) category up to 89% (N0 sub-cohort) were LR by CAB and the percentage of LR patients was 25% (p < 0.0001) higher compared to NPI or mAOL. In the low Ki67 (≤5%) group, up to 19% were segregated as high-risk by CAB with 86% DRFi suggesting the requirement of chemotherapy in these low Ki67 patients. CONCLUSION CAB provided superior prognostic information in various Ki67 subgroups, especially in the intermediate Ki67 group.
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Affiliation(s)
| | - Aparna Gunda
- OncoStem Diagnostics Private Limited, Bangalore, India
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5
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Zhang X, Gunda A, Kranenbarg EMK, Liefers GJ, Savitha BA, Shrivastava P, Serkad CPVK, Kaur T, Eshwaraiah MS, Tollenaar RAEM, van de Velde CJH, Seynaeve CMJ, Bakre M, Kuppen PJK. Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial. Breast Cancer Res 2023; 25:40. [PMID: 37060036 PMCID: PMC10103430 DOI: 10.1186/s13058-023-01643-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Hormone receptor (HR)-positive, HER2/neu-negative breast cancers have a sustained risk of recurrence up to 20 years from diagnosis. TEAM (Tamoxifen, Exemestane Adjuvant Multinational) is a large, multi-country, phase III trial that randomized 9776 women for the use of hormonal therapy. Of these 2754 were Dutch patients. The current study aims for the first time to correlate the ten-year clinical outcomes with predictions by CanAssist Breast (CAB)-a prognostic test developed in South East Asia, on a Dutch sub-cohort that participated in the TEAM. The total Dutch TEAM cohort and the current Dutch sub-cohort were almost similar with respect to patient age and tumor anatomical features. METHODS Of the 2754 patients from the Netherlands, which are part of the original TEAM trial, 592 patients' samples were available with Leiden University Medical Center (LUMC). The risk stratification of CAB was correlated with outcomes of patients using logistic regression approaches entailing Kaplan-Meier survival curves, univariate and multivariate cox-regression hazards model. We used hazard ratios (HRs), the cumulative incidence of distant metastasis/death due to breast cancer (DM), and distant recurrence-free interval (DRFi) for assessment. RESULTS Out of 433 patients finally included, the majority, 68.4% had lymph node-positive disease, while only a minority received chemotherapy (20.8%) in addition to endocrine therapy. CAB stratified 67.5% of the total cohort as low-risk [DM = 11.5% (95% CI, 7.6-15.2)] and 32.5% as high-risk [DM = 30.2% (95% CI, 21.9-37.6)] with an HR of 2.90 (95% CI, 1.75-4.80; P < 0.001) at ten years. CAB risk score was an independent prognostic factor in the consideration of clinical parameters in multivariate analysis. At ten years, CAB high-risk had the worst DRFi of 69.8%, CAB low-risk in the exemestane monotherapy arm had the best DRFi of 92.7% [vs CAB high-risk, HR, 0.21 (95% CI, 0.11-0.43), P < 0.001], and CAB low-risk in the sequential arm had a DRFi of 84.2% [vs CAB high-risk, HR, 0.48 (95% CI, 0.28-0.82), P = 0.009]. CONCLUSIONS Cost-effective CAB is a statistically robust prognostic and predictive tool for ten-year DM for postmenopausal women with HR+/HER2-, early breast cancer. CAB low-risk patients who received exemestane monotherapy had an excellent ten-year DRFi.
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Affiliation(s)
- Xi Zhang
- Department of Surgery, Leiden University Medical Center (LUMC), Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | - Aparna Gunda
- OncoStem Diagnostics Pvt Ltd, #4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, 560027, India
| | | | - Gerrit-Jan Liefers
- Geriatric Oncology Research Group, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | | | - Payal Shrivastava
- OncoStem Diagnostics Pvt Ltd, #4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, 560027, India
| | | | - Taranjot Kaur
- OncoStem Diagnostics Pvt Ltd, #4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, 560027, India
| | | | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center (LUMC), Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | - Cornelis J H van de Velde
- Department of Surgery, Leiden University Medical Center (LUMC), Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
| | | | - Manjiri Bakre
- OncoStem Diagnostics Pvt Ltd, #4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, 560027, India.
| | - Peter J K Kuppen
- Department of Surgery, Leiden University Medical Center (LUMC), Albinusdreef 2, Leiden, 2333 ZA, The Netherlands.
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6
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Adeoye J, Akinshipo A, Koohi-Moghadam M, Thomson P, Su YX. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022; 12:976168. [DOI: 10.3389/fonc.2022.976168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
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Gunda A, Eshwaraiah MS, Gangappa K, Kaur T, Bakre MM. A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast. Breast Cancer Res Treat 2022; 196:299-310. [PMID: 36085534 PMCID: PMC9581859 DOI: 10.1007/s10549-022-06729-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 11/30/2022]
Abstract
Abstract
Aims
Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2− breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts.
Methods
Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41–5.4), and poor (PPG > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient.
Results
Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278–0.346)]/PREDICT [κ = 0.398 (0.35–0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB.
Conclusion
Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.
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Affiliation(s)
- Aparna Gunda
- OncoStem Diagnostics Pvt. Ltd., # 4 Raja Ram Mohan Roy Rd, Aanand Tower, 2nd Floor, Bangalore, 560 0027 India
| | - Mallikarjuna S. Eshwaraiah
- OncoStem Diagnostics Pvt. Ltd., # 4 Raja Ram Mohan Roy Rd, Aanand Tower, 2nd Floor, Bangalore, 560 0027 India
| | - Kiran Gangappa
- OncoStem Diagnostics Pvt. Ltd., # 4 Raja Ram Mohan Roy Rd, Aanand Tower, 2nd Floor, Bangalore, 560 0027 India
| | - Taranjot Kaur
- OncoStem Diagnostics Pvt. Ltd., # 4 Raja Ram Mohan Roy Rd, Aanand Tower, 2nd Floor, Bangalore, 560 0027 India
| | - Manjiri M. Bakre
- OncoStem Diagnostics Pvt. Ltd., # 4 Raja Ram Mohan Roy Rd, Aanand Tower, 2nd Floor, Bangalore, 560 0027 India
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Vaid A, T. R, Batra U, Krishna VM, Rangrajan B, Narayanan P, Vora A, Advani SH. Robust Guidelines for the Management of HR+ Her2− EBC: Crucial Value of CanAssist Breast. South Asian J Cancer 2022; 11:95-96. [DOI: 10.1055/s-0042-1756179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
Affiliation(s)
- Ashok Vaid
- Department of Medical & HematoOncology, Cancer Institute, Medanta Hospital, Gurugram, Haryana, India
| | - Raja T.
- Department of Medical Oncology & BMT, Apollo Hospital, Chennai, Tamil Nadu, India
| | - Ullas Batra
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute, New Delhi, India
| | | | - Bharath Rangrajan
- Department of Medical Oncology, Kovai Medical Center and Hospital, Coimbatore, Tamil Nadu, India
| | - Prasad Narayanan
- Department of Medical Oncology, Hemato Oncology and BMT, Cytecare Hospital, Bengaluru, Karnataka, India
| | - Amish Vora
- Department of Medical Oncology, Hope Cancer Clinic, New Delhi, India
| | - Suresh H. Advani
- Smita Memorial Hospital & Research Center, Thodupuza, Kerala, India
- Department of Medical Oncology, Sushrut Hospital, Jaslok Hospital, Nanavati Hospital, HN Reliance Hospital, SL Raheja Hospital, Mumbai, Maharashtra, India
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9
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Gunda A, Basavaraj C, Serkad V CP, Adinarayan M, Kolli R, Siraganahalli Eshwaraiah M, Saura C, Ruiz F, Gomez P, Peg V, Jimenez J, Sprung S, Fiegl H, Brunner C, Egle D, Bhattacharyya GS, Bakre MM. A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort. Breast 2022; 63:1-8. [PMID: 35245746 PMCID: PMC8892025 DOI: 10.1016/j.breast.2022.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/03/2022] [Accepted: 02/17/2022] [Indexed: 11/18/2022] Open
Abstract
CanAssist Breast (CAB), a prognostic test uses immunohistochemistry (IHC) approach coupled with artificial intelligence-based machine learning algorithm for prognosis of early-stage hormone-receptor positive, HER2/neu negative breast cancer patients. It was developed and validated in an Indian cohort. Here we report the first blinded validation of CAB in a multi-country European patient cohort. FFPE tumor samples from 864 patients were obtained from-Spain, Italy, Austria, and Germany. IHC was performed on these samples, followed by recurrence risk score prediction. The outcomes were obtained from medical records. The performance of CAB was analyzed by hazard ratios (HR) and Kaplan Meier curves. CAB stratified European cohort (n = 864) into distinct low- and high-risk groups for recurrence (P < 0.0001) with HR of 3.32 (1.85–5.93) like that of mixed (India, USA, and Europe) (n = 1974), 3.43 (2.34–4.93) and Indian cohort (n = 925), 3.09 (1.83–5.21). CAB provided significant prognostic information (P < 0.0001) in women aged ≤ 50 (HR: 4.42 (1.58–12.3), P < 0.0001) and >50 years (HR: 2.93 (1.44–5.96), P = 0.0002). CAB had an HR of 2.57 (1.26–5.26), P = 0.01) in women with N1 disease. CAB stratified significantly higher proportions (77%) as low-risk over IHC4 (55%) (P < 0.0001). Additionally, 82% of IHC4 intermediate-risk patients were stratified as low-risk by CAB. Accurate risk stratification of European patients by CAB coupled with its similar performance inIndian patients shows that CAB is robust and functions independent of ethnic differences. CAB can potentially prevent overtreatment in a greater number of patients compared to IHC4 demonstrating its usefulness for adjuvant systemic therapy planning in European breast cancer patients. CanAssist Breast risk (CAB) risk stratification is accurate in European breast cancer patients. CAB performance in European cohort was like that of Indian as assessed by low-risk group DMFS, HR and C-index. CAB identified significantly higher proportions as low risk compared to IHC4. Greater number of IHC4 intermediate risk patients were identified as low risk by CAB.
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Affiliation(s)
- Aparna Gunda
- OncoStem Diagnostics, Bengaluru, Karnataka, India
| | | | | | | | - Ramu Kolli
- OncoStem Diagnostics, Bengaluru, Karnataka, India
| | | | - Cristina Saura
- Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology, Passeig de La Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Fiorella Ruiz
- Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology, Passeig de La Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Patricia Gomez
- Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology, Passeig de La Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Vicente Peg
- Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology, Passeig de La Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Jose Jimenez
- Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology, Passeig de La Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | | | - Heidi Fiegl
- Medical University of Innsbruck, Innsbruck, Austria
| | | | - Daniel Egle
- Medical University of Innsbruck, Innsbruck, Austria
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10
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Akhade A, Mathew A. To Give or Not to Give Adjuvant Chemotherapy in Breast Cancer? Can CanAssist Breast Assist? South Asian J Cancer 2022; 11:1-2. [PMID: 35833050 PMCID: PMC9273325 DOI: 10.1055/s-0042-1746411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Amol Akhade
- Department of Medical Oncology, BYL Nair Hospital and T N Medical College, Mumbai, Maharashtra, India
| | - Aju Mathew
- Department of Oncology, MOSC Medical College, Kolenchery, Kerala, India
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Parikh PM, Bhattacharyya GS, Biswas G, Krishnamurty A, Doval D, Heroor A, Sharma S, Deshpande R, Chaturvedi H, Somashekhar SP, Babu G, Reddy GK, Sarkar D, Desai C, Malhotra H, Rohagi N, Bapna A, Alurkar SS, Krishna P, Deo SV, Shrivastava A, Chitalkar P, Majumdar SK, Vijay D, Thoke A, Udupa KS, Bajpai J, Rath GK, Dattatreya PS, Bondarde S, Patil S. Practical Consensus Recommendations for Optimizing Risk versus Benefit of Chemotherapy in Patients with HR Positive Her2 Negative Early Breast Cancer in India. South Asian J Cancer 2021; 10:213-219. [PMID: 34984198 PMCID: PMC8719963 DOI: 10.1055/s-0041-1742080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Breast cancer is a public health challenge globally as well as in India. Improving outcome and cure requires appropriate biomarker testing to assign risk and plan treatment. Because it is documented that significant ethnic and geographical variations in biological and genetic features exist worldwide, such biomarkers need to be validated and approved by authorities in the region where these are intended to be used. The use of western guidelines, appropriate for the Caucasian population, can lead to inappropriate overtreatment or undertreatment in Asia and India. A virtual meeting of domain experts discussed the published literature, real-world practical experience, and results of opinion poll involving 185 oncologists treating breast cancer across 58 cities of India. They arrived at a practical consensus recommendation statement to guide community oncologists in the management of hormone positive (HR-positive) Her2-negative early breast cancer (EBC). India has a majority (about 50%) of breast cancer patients who are diagnosed in the premenopausal stage (less than 50 years of age). The only currently available predictive test for HR-positive Her2-negative EBC that has been validated in Indian patients is CanAssist Breast. If this test gives a score indicative of low risk (< 15.5), adjuvant chemotherapy will not increase the chance of metastasis-free survival and should not be given. This is applicable even during the ongoing COVID-19 pandemic.
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Affiliation(s)
| | | | - Ghanshyam Biswas
- Medical Oncology, Sparsh Hospital & Critical Care, Bhubaneswar, India
| | | | - Dinesh Doval
- Medical Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Delhi, India
| | - Anil Heroor
- Surgical Oncology, Fortis Hospital, Mumbai, India
| | - Sanjay Sharma
- Surgical Oncology, Asian Cancer Institute, Mumbai, India
| | | | | | - S. P. Somashekhar
- Surgical Oncology, Manipal Comprehensive Cancer Center, Manipal Hospital, Bangalore, India
| | - Govind Babu
- Medical Oncology, HCG Cancer Hospital, Bengaluru, India
| | | | - Diptendra Sarkar
- Surgical Oncology, Institute of Post-Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial Hospital, Kolkata, India
| | - Chirag Desai
- Medical Oncology, Vedanta Institute of Medical Sciences, Ahmedabad, India
| | | | - Nitesh Rohagi
- Medical Oncology, Max Institute of Cancer Care, Delhi, India
| | - Ajay Bapna
- Medical Oncology, Bhagwan Mahaveer Cancer Hospital and Research Centre, Jaipur, India
| | | | - Prasad Krishna
- Medical Oncology, Mangalore Institute of Oncology, Mangalore, India
| | - S. V.S. Deo
- Surgical Oncology, All India Institute of Medical Sciences, Delhi, India
| | | | - Prakash Chitalkar
- Medical Oncology, Sri Aurobindo Medical College and Postgraduate Institute, Indore, India
| | | | | | - Aniket Thoke
- Radiation Oncology, Sanjeevani CBCC USA Cancer Hospital, Raipur, India
| | - K. S. Udupa
- Medical Oncology, Kasturba Medical College, Manipal, India
| | - Jyoti Bajpai
- Medical Oncology, Tata Memorial Hospital, Mumbai, India
| | - G. K. Rath
- Radiation Oncology, DR. B.R.A. Institute Rotary Cancer Hospital, Delhi, India
| | | | | | - Shekhar Patil
- Medical Oncology, HCG Cancer Hospital, Bengaluru, India
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Serkad CPV, Attuluri AK, Basavaraj C, Adinarayan M, Krishnamoorthy N, Ananthamurthy SB, Mallikarjuna SE, Bakre MM. Validation of CanAssist Breast immunohistochemistry biomarkers on an automated platform and its applicability in tissue microarray. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2021; 14:1013-1021. [PMID: 34760037 PMCID: PMC8569307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
CanAssist Breast (CAB) is a prognostic test for early-stage hormone receptor-positive invasive breast cancer. The test involves performing immunohistochemical (IHC) analysis for five biomarkers, namely CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin. In addition to IHC grading information, three clinical features, i.e., tumor size, grade, and lymph node status, serve as input into the machine learning-based algorithm to generate the CAB risk score. CAB was developed and initially validated using manual IHC. This study's objectives included: i) automate CAB IHC on an autostainer and establish its performance equivalence with manual IHC ii) validate CAB test using samples in Tissue MicroArray (TMA) format. IHC for CAB biomarkers was standardized on Ventana BenchMark XT autostainer. Two IHC methods were compared for IHC gradings and corresponding CAB risk scores/risk categories. A concordance analysis was done using MedCalcTM software. The manual and automated IHC staining methods exhibited a high level of concordance on IHC gradings for 40 cases with an Intra-class Correlation Coefficient (ICC) of >0.85 for 4 of 5 biomarkers. 100% concordance was achieved in risk categorization (low- or high-risk), with very good agreement between the risk scores demonstrated by a kappa statistic of 0.83. TMA versus whole tissue section concordance was analyzed using 45 samples on an autostainer, and the data showed 92% concordance in terms of risk category. The results confirm the equivalence between manual and automated staining methods and demonstrate the utility of TMA as an acceptable format for CanAssist Breast testing.
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Affiliation(s)
| | | | - Chetana Basavaraj
- OncoStem Diagnostics Private Limited Bangalore 560027, Karnataka, India
| | | | | | | | | | - Manjiri M Bakre
- OncoStem Diagnostics Private Limited Bangalore 560027, Karnataka, India
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13
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Chandra Doval D, Mehta A, Somashekhar SP, Gunda A, Singh G, Bal A, Khare S, Prakash V Serkad C, Adinarayan M, Krishnamoorthy N, Vijay DG, Anantakrishnan R, Bhattacharyya GS, Bakre MM. The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin. Breast 2021; 59:1-7. [PMID: 34098459 PMCID: PMC8187842 DOI: 10.1016/j.breast.2021.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 11/16/2022] Open
Abstract
Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach. We report CAB risk assessment correlating with disease outcomes in multiple clinically high- and low-risk subgroups. In this retrospective cohort of 925 patients [median age-54 (22–86)] CAB had hazard ratio (HR) of 3 (1.83–5.21) and 2.5 (1.45–4.29), P = 0.0009) in univariate and multivariate analysis. CAB's HR in sub-groups with the other determinants of outcome, T2 (HR: 2.79 (1.49–5.25), P = 0.0001); age [< 50 (HR: 3.14 (1.39–7), P = 0.0008)]. Besides application in node-negative patients, CAB's HR was 2.45 (1.34–4.47), P = 0.0023) in node-positive patients. In clinically low-risk patients (N0 tumors up to 5 cms) (HR: 2.48 (0.79–7.8), P = 0.03) and with luminal-A characteristics (HR: 4.54 (1–19.75), P = 0.004), CAB identified >16% as high-risk with recurrence rates of up to 12%. In clinically high-risk patients (T2N1 tumors (HR: 2.65 (1.31–5.36), P = 0.003; low-risk DMFS: 92.66 ± 1.88) and in women with luminal-B characteristics (HR: 3.24; (1.69–6.22), P < 0.0001; low-risk DMFS: 93.34 ± 1.34)), CAB identified >64% as low-risk. Thus, CAB prognostication was significant in women with clinically low- and high-risk disease. The data imply the use of CAB for providing helpful information to stratify tumors based on biology incorporated with clinical features for Indian patients, which can be extrapolated to regions with similarly characterized patients, South-East Asia. Prospective analysis of CAB prognostication in a retrospective cohort of ethnic Indian origin. CAB based prognostication was significant in women aged above and below 50 years. In clinically high-risk and women with luminal B features greater numbers were low-risk. In clinically low-risk and women with luminal A features greater numbers were high-risk.
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Affiliation(s)
| | | | - S P Somashekhar
- Manipal Hospital and Comprehensive Cancer Centre, Bengaluru, Karnataka, India
| | - Aparna Gunda
- OncoStem Diagnostics, Bengaluru, Karnataka, India
| | - Gurpreet Singh
- Post-Graduation Institute of Medical Education and Research, Chandigarh, India
| | - Amanjit Bal
- Post-Graduation Institute of Medical Education and Research, Chandigarh, India
| | - Siddhant Khare
- Post-Graduation Institute of Medical Education and Research, Chandigarh, India
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14
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Sankaran S, Dikshit JB, Prakash SV C, Mallikarjuna SE, Somashekhar SP, Patil S, Kumar R, Prasad K, Shet D, Bakre MM. CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario. Indian J Surg Oncol 2021; 12:21-29. [PMID: 33994724 PMCID: PMC8119549 DOI: 10.1007/s13193-019-01014-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/12/2019] [Indexed: 01/04/2023] Open
Abstract
CanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis-free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.
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Affiliation(s)
- Satish Sankaran
- OncoStem Diagnostics Private Limited, 4, Raja Ram Mohan Roy Road, Aanand Towers, 2nd Floor, Bangalore, Karnataka 560027 India
| | - Jyoti Bajpai Dikshit
- OncoStem Diagnostics Private Limited, 4, Raja Ram Mohan Roy Road, Aanand Towers, 2nd Floor, Bangalore, Karnataka 560027 India
| | - Chandra Prakash SV
- OncoStem Diagnostics Private Limited, 4, Raja Ram Mohan Roy Road, Aanand Towers, 2nd Floor, Bangalore, Karnataka 560027 India
| | - SE Mallikarjuna
- OncoStem Diagnostics Private Limited, 4, Raja Ram Mohan Roy Road, Aanand Towers, 2nd Floor, Bangalore, Karnataka 560027 India
| | | | - Shekhar Patil
- Sri Shankara Cancer Hospital and Research Center, Bangalore, India
- HCG, Bangalore, India
| | - Rajeev Kumar
- Rajiv Gandhi Cancer Institute and Research Center, New Delhi, India
| | | | - Dinesh Shet
- Father Muller Medical College, Mangalore, India
| | - Manjiri M. Bakre
- OncoStem Diagnostics Private Limited, 4, Raja Ram Mohan Roy Road, Aanand Towers, 2nd Floor, Bangalore, Karnataka 560027 India
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15
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Alzu’bi A, Najadat H, Doulat W, Al-Shari O, Zhou L. Predicting the recurrence of breast cancer using machine learning algorithms. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:13787-13800. [DOI: 10.1007/s11042-020-10448-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 08/24/2020] [Accepted: 12/22/2020] [Indexed: 08/29/2023]
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16
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Milanese JS, Tibiche C, Zaman N, Zou J, Han P, Meng Z, Nantel A, Droit A, Wang E. ETumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:973-985. [PMID: 33581336 PMCID: PMC9402585 DOI: 10.1016/j.gpb.2020.06.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 03/19/2020] [Accepted: 06/10/2020] [Indexed: 01/22/2023]
Abstract
Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn’t favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the ‘most recent common ancestor’ of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.
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Sengupta AK, Gunda A, Malpani S, Serkad CPV, Basavaraj C, Bapat A, Bakre MM. Comparison of breast cancer prognostic tests CanAssist Breast and Oncotype DX. Cancer Med 2020; 9:7810-7818. [PMID: 33027559 PMCID: PMC7643688 DOI: 10.1002/cam4.3495] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/08/2020] [Accepted: 09/10/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND CanAssist Breast (CAB) is a prognostic test for early stage hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer patients, validated on Indian and Caucasian patients. The 21-gene signature Oncotype DX (ODX) is the most widely used commercially available breast cancer prognostic test. In the current study, risk stratification of CAB is compared with that done with ODX along with the respective outcomes of these patients. METHODS A cohort of 109 early stage breast cancer patients who had previously taken the ODX test were retested with CAB, and the results respectively compared with old cut-offs of ODX as well as cut-offs suggested by TAILORx, a prospective randomized trial of ODX. Distant metastasis-free survival after 5 years was taken as the end point. RESULTS CanAssist Breast stratified 83.5% of the cohort into low-risk and 16.5% into high-risk. With the TAILORx cut-offs, ODX stratified the cohort into 89.9% low-risk and 10.1% into high-risk. The low, intermediate, and high-risk groups with ODX old cut-offs were 62.4%, 31.2%, and 6.4%, respectively. The overall concordance of CAB with ODX using both cut-offs is 75%-76%, with ~82%-83% concordance in the low-risk category of these tests. The NPV of the low-risk category of CAB was 93.4%, and of ODX with TAILORx cut-offs was 91.8% and 89.7% with old cut-offs. CONCLUSIONS Compared to the concordance reported for other tests, CAB shows high concordance with ODX, and in addition shows comparable performance in the patient outcomes in this cohort. CAB is thus an excellent and cost-effective alternative to ODX.
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18
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Govindan S, Siraganahalli Eswaraiah M, Basavaraj C, Adinarayan M, Sankaran S, Bakre M. Androgen Receptor mRNA levels determine the prognosis in triple-negative breast cancer patients. BMC Cancer 2020; 20:745. [PMID: 32778063 PMCID: PMC7419184 DOI: 10.1186/s12885-020-07218-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/26/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Anti-Androgen Receptor (AR) therapy holds promise for a subset of AR expressing triple-negative breast cancer (TNBC) patients. However, current AR assays are suboptimal in detecting the dynamic range of AR expression, contributing to its controversial role in TNBC disease prognosis. This study is aimed at evaluating the feasibility of qRT-PCR to sensitively and robustly detect AR mRNA levels for prognostication. METHODS mRNA expression profiling was performed on FFPE blocks from a retrospective cohort of 101 TNBC patients using qRT-PCR and compared with AR protein expression by immunohistochemistry . Statistical analyses included Spearman's rank correlation, Chi-square and Kaplan-Meier analyses. Distant Metastasis Free Survival was used as the end point in survival analysis. RESULTS AR mRNA expression was observed in 34/101 patients (34%) whereas 12/80 cases (15%) were positive by IHC. qRT-PCR could thus detect more AR positive patients as compared to IHC, with 75% (9/12) concordance between the two methods. Co-expression of GATA3 and FOXA1 mRNA was observed in 85 and 88% of AR mRNA positive tumors, respectively. AR mRNA positivity was significantly correlated with age at disease onset (p = 0.02), high FOXA1/GATA3 (p < 0.05) and distant recurrence. AR mRNA positive patients had poorer DMFS (43%; p = 0.002). DMFS dropped further to 26% (p = 0.006) in AR (+)/high FOXA1/GATA3 patients. AR mRNA expression together with node positivity had the worst DMFS (23%; p < 0.0001) compared to patients who were either positive for any one of these, or negative for both AR and node status. Low Ki67 mRNA with AR mRNA positivity also had poorer DMFS (39%; p = 0.001) compared to patients expressing low Ki67 with no AR mRNA expression. CONCLUSION qRT-PCR was more sensitive and reliable in detecting the dynamic expression levels of AR compared to IHC and this variation could be explained by the higher sensitivity of the former method. High AR mRNA expression was strongly associated with expression of AR protein, high FOXA1/GATA3 mRNA, and with poor prognosis. qRT-PCR was more efficient in detecting the AR positive cases compared to IHC. A distinct signature involving high GATA3/FOXA1, low Ki67, and node positivity in AR mRNA positive tumors correlated with poor prognosis. Thus, AR mRNA screening can serve as an effective prognostic marker along with offering potential targeted therapy options for TNBC.
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Affiliation(s)
- Sindhu Govindan
- OncoStem Diagnostics Private Limited, # 4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, Karnataka, 560027, India
| | | | - Chetana Basavaraj
- OncoStem Diagnostics Private Limited, # 4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, Karnataka, 560027, India
| | - Manjula Adinarayan
- OncoStem Diagnostics Private Limited, # 4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, Karnataka, 560027, India
| | - Satish Sankaran
- OncoStem Diagnostics Private Limited, # 4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, Karnataka, 560027, India
| | - Manjiri Bakre
- OncoStem Diagnostics Private Limited, # 4, Raja Ram Mohan Roy Road, Aanand Tower, 2nd Floor, Bangalore, Karnataka, 560027, India.
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Bhattacharyya GS, Doval DC, Desai CJ, Chaturvedi H, Sharma S, Somashekhar S. Overview of Breast Cancer and Implications of Overtreatment of Early-Stage Breast Cancer: An Indian Perspective. JCO Glob Oncol 2020; 6:789-798. [PMID: 32511068 PMCID: PMC7328098 DOI: 10.1200/go.20.00033] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2020] [Indexed: 12/15/2022] Open
Abstract
The prevalence and mortality of breast cancer is increasing in Asian countries, including India. With advances in medical technology leading to better detection and characterization of the disease, it has been possible to classify breast cancer into various subtypes using markers, which helps predict the risk of distant recurrence, response to therapy, and prognosis using a combination of molecular and clinical parameters. Breast cancer and its therapy, mainly surgery, systemic therapy (anticancer chemotherapy, hormonal therapy, targeted therapy, and immunotherapy), and radiation therapy, are associated with significant adverse influences on physical and mental health, quality of life, and the economic status of the patient and her family. The fear of recurrence and its devastating effects often leads to overtreatment, with a toxic cost to the patient financially and physically in cases in which this is not required. This article discusses some aspects of a breast cancer diagnosis and its impact on the various facets of the life of the patient and her family. It further elucidates the role of prognostic factors, the currently available biomarkers and prognostic signatures, and the importance of ethnically validating biomarkers and prognostic signatures.
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Affiliation(s)
| | - Dinesh C. Doval
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Chirag J. Desai
- Vedanta Institute of Medical Sciences, Ahmedabad, Gujarat, India
| | | | - Sanjay Sharma
- Asian Cancer Institute, Somaiya Ayurvihar, Mumbai, Maharashtra, India
| | - S.P. Somashekhar
- Department of Surgical Oncology, Manipal Comprehensive Cancer Center, Manipal Hospital, Bengaluru, India
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20
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Bakre MM, Ramkumar C, Attuluri AK, Basavaraj C, Prakash C, Buturovic L, Madhav L, Naidu N, R P, Somashekhar SP, Gupta S, Doval DC, Pegram MD. Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients. Cancer Med 2019; 8:1755-1764. [PMID: 30848103 PMCID: PMC6488210 DOI: 10.1002/cam4.2049] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/16/2019] [Accepted: 02/05/2019] [Indexed: 01/03/2023] Open
Abstract
CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Prathima R
- OncoStem Diagnostics Private LimitedBangaloreIndia
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21
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Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients. BMC Cancer 2019; 19:249. [PMID: 30894144 PMCID: PMC6425559 DOI: 10.1186/s12885-019-5443-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 03/07/2019] [Indexed: 01/15/2023] Open
Abstract
Background CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast. Methods All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively. Results CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested. Conclusions The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust. Electronic supplementary material The online version of this article (10.1186/s12885-019-5443-5) contains supplementary material, which is available to authorized users.
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