i Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS and PIK3CA genes in Indian patient cohort by Harshali Anant Patil (B.E. Biotechnology) Submitted in fulfillment of the requirements for the degree of Doctor of Philosophy Deakin University January 2017
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i
Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS
and PIK3CA genes in Indian patient cohort
by
Harshali Anant Patil
(B.E. Biotechnology)
Submitted in fulfillment of the requirements for the degree of
Doctor of Philosophy
Deakin University
January 2017
sfol
Retracted Stamp
sfol
Retracted Stamp
iv
Dedicated to the Almighty and to all patients suffering from Colorectal Cancer
v
Acknowledgements
It is a pleasant task to express my thanks to all those who contributed in many
ways to the success of this study and made it an unforgettable experience for me.
At this moment of accomplishment, I gratefully acknowledge Mr. K V
Subramaniam, President of Reliance Life Sciences Pvt Ltd (RLS), for giving me
this opportunity to pursue higher degree program while working in RLS. He has
always been very supportive and has always encouraged employees to further
their academic and professional aspirations.
I would like to express my sincere gratitude to my Principal Supervisor Prof. Colin
J. Barrow, Alfred Deakin Professor and Chair In Biotechnology, School of Life &
Environmental Sciences, for his invaluable guidance and mentorship.
I would like to thank my Co-Principal Supervisor Dr. Rupinder Kanwar, Senior
Research Fellow, School of Medicine, for all the support that she provided in these
years. I really admire her zeal for perfection. Her enthusiasm and drive for best
scientific research has always motivated me and has helped me grow in my
scientific career. I feel very fortunate to have you as my mentor in this PhD journey.
My sincere thanks to Prof. Jagat Kanwar, Professor In Nanomedicine, School of
Medicine, for giving me scientific advises and the feedback on the research work.
I would also like to thank Dr. Shailaja Gada Saxena, Head, Molecular Medicine
(MME). I have been associated with Molecular Medicine group for almost 10 years
and it has been a wonderful journey. Since the time she took up this responsibility
vi
of heading Molecular Medicine, she has always provided a constant support,
encouragement and valuable suggestions, which made my journey through
doctoral program a memorable one.
Dr. Arnab Kapat, my mentor and Director, Reliance Institute of Life Sciences, thank
you so very much for the belief you had in me and the encouragement you have
given me over the last few years will stay with me. Thank you for all the scientific
advises and prompt feedbacks given on the research work. I would like to specially
thank you for the care and guidance. My journey in these last four years under your
mentorship for PhD will be cherished for all the good times spent.
I am also thankful to Ms. Anuradha, Ms. Gayathri and Ms. Ruby from Deakin India
office for their support. I am also grateful to Ms. Helen Woodall, partnerships
coordinator of Deakin-India, for making me comfortable during my stay in Deakin.
Most of the results described in this thesis would not have been obtained without a
close collaboration with few hospitals. I owe a great deal of appreciation and
gratitude to Ruby Hall Clinic, Pune, and Hinduja Hospital for helping me with the
clinical details required.
I would like to thank all MMEiets, my colleagues, Kiran, Vrunda, Smita, Deepti and
team, Sandeep and Kundanben and team specially Mr. Ganesh. Dr. Rajesh Korde,
Dhanashree, Dr. Sonia, Dr. Harshal and Asha for the histopathology work. I would
also like to thank Rehana, Mitali, Tejas Mehta and team for helping me out in
completing my PhD.
vii
Also, a big thanks goes to all ex MMEites including Darshana and Akriti for their
constant support.
My heartfelt thanks to my friends who filled my life with love and laughter –
Sheena, Urvashi, Sanjukta, Dolly, Leena, Shivani, Pavani, Kavya and Moti. The
days spent with all of you would always be cherished in my life.
Above all, I would like to thank my husband, Anant, for his personal support and
great patience at all times. “Thank you for being there always!” My parents -
mummy and papa, my in-laws-aai and dada who supported me throughout these
years, my brother Dharmu and all my family members who have given me their
unequivocal support throughout, as always, for which my mere expression of
thanks likewise does not suffice. And finally my daughter Anushree, whose smiling
face has always encouraged me.
Harshali Anant Patil
02nd January 2017
viii
Table of Content
Acknowledgements ......................................................................................................... v
Table of Content ........................................................................................................... viii
List of Publications ........................................................................................................ xii
List of Abbreviations ...................................................................................................... xv
List of Tables ..................................................................................................................xx
List of Figures ............................................................................................................... xxii
2.2.1 Colorectal cancer incidence varies globally ................................................................. 11 2.2.2 Mortality of colorectal cancer ..................................................................................... 13
2.6.2 Therapy options for colon cancer ................................................................................ 51 2.6.3 Therapy options for rectal cancer ................................................................................ 52 2.6.4 Treatment options for advanced disease i.e. mCRC .................................................... 54 2.6.5 Novel cytotoxic and targeted biologic therapeutics .................................................... 55
2.7 Targeting Vascular Endothelial Growth Factor (VEGF) .................................................. 57 2.7.1 Bevacizumab-Anti VEGF monoclonal antibody ............................................................ 60 2.7.2 Aflibercept- a novel antiangiogenic fusion protein ...................................................... 62 2.7.3 Regorafenib-small molecule inhibitor .......................................................................... 63 2.7.4 Identification of predictive biomarkers for anti-angiogenic agents: A priority for mCRC
3.2.3 Primer selection for Polymerase Chain Reaction (PCR) .............................................. 106 3.2.3.1 PCR Assay Optimization ..................................................................................................... 107
3.2.4 Detection of PCR products by agarose gel electrophoresis ........................................ 115 3.2.4.1 Method .............................................................................................................................. 115
3.2.5 Sequencing of PCR products for Detection of mutations ............................................ 115
Chapter 7 Summary and Future Work .................................................................. 258 7.1 Mutation Studies in 203 CRC patients ......................................................................... 259 7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes with clinico-
pathological data for a 203 Indian CRC patient cohort .......................................................... 261 7.3 Correlation of clinico-pathological data with survival in Indian patient cohort ............ 262
Appendix ..................................................................................................................... 268 List of Chemicals .................................................................................................................. 270 List of Instruments ............................................................................................................... 272 List of Software’s ................................................................................................................. 274
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
209
Table 5.8: Multivariate Logistic Regression Analysis for the correlation between gene mutations and clinicopathological
features in Indian CRC patients (n=203)
Clinico-pathological features
KRAS BRAF NRAS PIK3CA
OR 95%CI P OR 95%CI P OR 95%CI P OR 95%CI P
Age 2.4691 1.1154 - 5.4660
0.018* 0.4361 0.1350 - 1.4091
0.17 0.4526 0.0623 - 3.2869
0.439 0.7587 0.1757 - 3.2768
0.715
Gender 0.7083 0.3459 - 1.4505
0.339 1.0656 0.3089 - 3.6754
0.92 2.1587 0.2973, 15.6757
0.452 0.2924 0.0352 - 2.4276
0.191
Geographic location
0.9777 0.6654 - 1.4366
0.909 0.9667 0.4791 - 1.9506
0.925 0.968 0.2940 - 3.1870
0.957 0.6283 0.2346 - 1.6826
0.327
Primary or metastatic
1.0161 0.5214 - 1.9802
0.963 0.8643 0.2512 - 2.9741
0.816 1.7639 0.2433 - 12.7904
0.577 1.7857 0.4332 - 7.3616
0.425
Mucinous or Adeno or Signet
0.5534 0.2409 - 1.2712
0.123 1.5807 0.6332 - 3.9458
0.36 The model could not be fit.# The model could not be fit.#
Differentiation 0.5921 0.3852 - 0.9102
0.012* 1.2045 0.6163 - 2.3539
0.59 2.077 0.6516 - 6.6208
0.206 0.7196 0.2835 - 1.8268
0.471
Stage 0.911 0.5293 - 1.5681
0.737 1.7155 0.6019 - 4.8890
0.304 1.284 0.2274 - 7.2593
0.775 1.9522 0.5421 - 7.0306
0.297
Lymph Node Mets
0.7067 0.3236 - 1.5435
0.375 0.5796 0.1227 - 2.7376
0.468 The model could not be fit.# The model could not be fit.#
Site 1.0667 0.5223 - 2.1785
0.86 1.3365 0.3861 - 4.6269
0.652 The model could not be fit.# 2.75 0.6635 - 11.3973
0.171
#Maximum likelihood estimates of parameters may not exist due to quasi-complete separation of data points. *-Statistically significant p<0.05 OR-Odds ratio, 95%CI-95% Confidence interval
In the multivariate logistic regression analysis it was observed that mutant KRAS was directly associated with increased
age i.e above 50 years (p=0.018) and greater differentiation (p=0.012) as seen in Table 5.8.
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
210
5.4 Discussion
In this phase of the study, the mutation frequencies in KRAS, BRAF, NRAS and
PIK3CA genes in 203 Indian colorectal cancer patients was examined and also the
correlation between clinicopathological features of CRC patients with these
mutations was further investigated. To the best of my knowledge, the present study
is the first study to determine collectively the mutation status of KRAS, BRAF,
NRAS and PIK3CA genes along with clinicopathological and geographical
incidence in a cohort of 203 Indian CRC patients.
The estimated incidence of CRC worldwide is 1.3 million (Ferlay et al., 2015).
Incidence of CRC in India has been estimated as 4.2 and 3.2 per 100,000 in males
and females, respectively. Population based time trend studies show a rising trend
in incidence of CRC in India (Ferlay et al., 2015).
Significant developments have been made in the recent past in the field of treating
CRC with the use of monoclonal antibodies targeting Epidermal growth factor
receptor (EGFR) such as cetuximab and panitumumab (Velho et al., 2009). EGFR
pathway plays a very critical role in tumorigenesis and progression of CRC. EGFR
initiates cascade of downstream signalling pathways such as RAS-RAF-MAPK and
PIK3-AKT pathways, which are responsible for, cell proliferation, differentiation and
survival (Patil et al., 2016). However, these anti-EGFR monoclonal antibodies are
effective against a small subset of CRC patients. This is due to the presence of
activating oncogenic mutations downstream of EGFR like KRAS, BRAF, NRAS
and PIK3CA, which negatively predict the response to anti-EGFR therapy. Studies
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
211
have identified KRAS, BRAF, NRAS and PIK3CA gene mutations as predictive
biomarkers in response to anti-EGFR antibody therapy (D. Lambrechts, 2009, De
Roock et al., 2010).
The overall frequency of mutations in current study from 203 Indian CRC cases,
revealed the presence of one of at least gene mutation in 36% cases and
remaining 64% of cases did not have any mutations in the four genes tested. The
prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in this Indian cohort
cases was 24%, 6%, 2% and 4%, respectively. Hence, it can be seen further that
12% of KRAS wild type CRC patients had mutations in NRAS, BRAF or PIK3CA
genes.
5.4.1 KRAS
KRAS mutation frequency varies from 14% to 67% worldwide as seen in Table 5.8.
In the present study, the KRAS mutation frequency was observed to be 24%. In
Western countries such as USA, UK, France, Italy, Lithuania, Germany, Russia
and Australia, KRAS frequency ranges from 13%-67% where as in Asian countries
such as China, Korea, India, Japan and Taiwan it varies from 20%-66% as
mentioned in Table 5.8. The KRAS mutation frequency of 24% seen in the present
study is thus similar to those reported from our group as well as from Bagadi et.al.
and Bhist et.al. (20%-24%) (Patil et al., 2013, Bagadi et al., 2012, Bisht et al.,
2014) (Table 5.9). The variations seen in KRAS mutation frequency could be
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
212
attributed to ethnicity, geographical location, sample size, and techniques used and
other etiological factors.
In the current study, mutations in KRAS codons 12, 13 and 61 were evaluated.
From the past studies, it has been observed that point mutations in KRAS codon
12 are most common mutations in CRC (Vaughn et al., 2011). KRAS G12D is the
most frequent change observed which is trailed by G12V, G12C, G12S and G12A
(Vaughn et al., 2011, Neumann et al., 2009). In agreement with this, in present
study, codon 12 mutations were observed in 20.2% cases followed by codon 3.9%
in codon 13. No mutation was observed in codon 61.
The glycine residue at codon 12 has a very critical role in normal functioning of ras
protein. Hence, the single base substitutions that occur at this position cause
GTPase formation, which further gets locked at the activating site (Arrington et al.,
2012). In the current study, G12D substitution was the most frequent followed by
G12V, G12A, G12C and G12S. Similarly, G13D is the most frequent mutation
observed in codon 13, which was also seen as the only mutation observed in this
study in codon 13. (Vaughn et al., 2011)
Correlation of clinico-pathological characteristics was further investigated with
respect to KRAS mutations. It was observed that there was a significant positive
association between KRAS mutations and age of a CRC patient (p<0.05). Mutation
rate in patients with above 50 years of age was higher as compared to the patients
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
213
younger than 50 years. Furthermore, KRAS mutations were significantly
associated with tumor differentiation status and significantly associated
(moderately and poorly differentiated adenocarcinoma as compared to well-
differentiated adenocarcinoma (p<0.05) (Table 5.4). This observation is in
agreement with the previous reports (Zhang et al., 2015a). In present study, it was
further seen that KRAS mutations were more frequent in adenocarcinomas than in
mucinous or signet ring carcinoma, which is also interestingly observed in an
earlier study, however, statistically significant association was not observed in the
current study (Li et al., 2011). Other clinico-pathological characteristics showed no
significant association with KRAS mutations (p>0.05) which is in concordance with
the recent reports of Indian population study based data (Bisht et al., 2014).
5.4.2 BRAF
BRAF is a member of the serine-threonine protein kinase family i.e. RAF family. It
plays a very crucial role downstream of EGFR signalling pathway. The codon 600
Valine to Glutamic acid substitution is the most frequent alteration observed in
many human cancers including CRC (Di Nicolantonio et al., 2008). The BRAF
mutation frequency ranges from 0.2% to 25% worldwide. In the present study the
BRAF frequency is seen to be 5.9% which is in concordance with Asian studies
(Table 5.9). As mentioned earlier geographical location, etiological factors, genetic
makeup have an important role in these variations.
BRAF codon 600 V600E was the only mutation observed in our study. Other
mutations of BRAF i.e. V600K, V600Q or V600L were not observed which have
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
214
been reported in Western population (Mao et al., 2012b). Also, there was no
presence of concomitant BRAF mutation with KRAS mutant cases. This lies in
concordance with previous studies which show that BRAF and KRAS mutations
are mutually exclusive (Di Nicolantonio et al., 2008).
However, till today there is insufficient data to justify the predictive role of BRAF for
benefit from anti-EGFR monoclonal antibody therapy in KRAS wild type cases.
Few studies in past have shown worst outcome in case of BRAF mutant cases(Di
Fiore et al., 2010). In the recent past few studies have evaluated anti-BRAF/EGFR
combination regimes to elucidate the best treatment outcome (Connolly et al.,
2013, Yaeger et al., 2015). This combinatorial approach would emerge as a
potential strategy for future cancer treatment.
In the present study, it was seen that BRAF mutations were significantly observed
in moderately differentiating and poorly differentiating adenocarcinomas than in
well-differentiated adenocarcinomas. Presence of BRAF mutations in poorly
differentiated adenocarcinomas is similar to findings of previously reported studies
(Shen et al., 2013). No significant association was observed in BRAF mutations
and other clinico-pathological features which supports the reported studies (Li et
al., 2011, Mao et al., 2012b, Bisht et al., 2014).
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
215
5.4.3 NRAS
In human cancers RAS proto-oncogenes, KRAS and NRAS, are found mostly in
mutant oncogenic forms. In case of CRC unlike KRAS, which are most frequently
mutated, NRAS mutations are rare (Irahara et al., 2010). The mutation frequency
for NRAS in CRC varies from 2%-10% (Table 5.9). In the present study, NRAS
mutations were observed in 1.9% which lies in concordance with previously
reported studies from Asian countries (Bagadi et al., 2012, Zhang et al., 2015a).
There was no coexistence of KRAS , BRAF and NRAS mutations in the study. No
significant association was observed between NRAS mutations and patient
demographics.
5.4.4 PIK3CA
Alterations in phosphoinositide-3-kinase catalytic alpha; a catalytic domain in PIK3,
is seen in many cancers. In CRC these mutations range from 1%-37% and majority
of mutations are observed in exon 9 and exon 20 (Table 5.9). There are two hot
spot regions in exon 9 –codon 542 and codon 545 and one in exon 20-codon 1047.
Recently, PIK3CA has been observed as a potential predictive marker for targeted
therapy in CRC. A low response rate has been observed in patients having
PIK3CA mutations (De Roock et al., 2011). In the present study, the frequency of
PIK3CA mutations was found to be 3.9% similar to other Asian studies (Zhang et
al., 2015a, Bisht et al., 2014, Hsieh et al., 2012). Higher percentage of mutations
were observed in exon 9 which is similar to Western population (Palomba et al.,
2012). Further current study data shows that mutations were seen in exon 9 only
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
216
E545K and in exon 20 H1047R while other mutations of exon 9 namely E542K and
H1047L in exon 20 were absent. Mutations in exon 9 and 20 may affect differently
the response to anti-EGFR therapy. Mutations in exon 20 are associated with lower
response rates as seen in the study done by Mao et.al (Mao et al., 2012b, De
Roock et al., 2010).
It was also seen in the present study results that 3 cases showed overlapping
mutations in KRAS and PIK3CA. Such coexistence of KRAS and PIK3CA
mutations has been reported earlier in few studies (Mao et al., 2012b). However,
no significant correlation was seen in clinico-pathological characteristics and
PIK3CA mutations which corresponds to previous studies of Asian countries (Mao
et al., 2012b, Bisht et al., 2014) .
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
217
Table 5.9: KRAS, NRAS, BRAF and PIK3CA mutation frequencies in reported studies worldwide. Study Sample
size
Method Target Population KRAS
%
NRAS
%
BRAF
%
PIK3C
A %
(Baltruškevičienė et al.,
2016)
55 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Lithuania 67.3 0 1.8 5.5
(Zhang et al., 2015a) 1110 RT PCR and Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exon 9
China 45.4 3.9 3.1 3.5
(Foltran et al., 2015) 194 Pyro sequencing KRAS exons 2 and 3
BRAF exon 15
NRAS exons 2 and 3
PIK3CA exons 9 and 20
Italy 47.4 3.6 5.2 16.5
(Kawazoe et al., 2015) 264 Luminex assay KRAS exons 2,3 and 4
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Japan 34.1 4.2 5.4 6.4
(Normanno et al., 2015) 182 Next Generation Sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Italy 24.7 7.1 8.2 13.2
(Kriegsmann et al., 2015) 93 Mass spectrometry KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
Germany 49 2 1 Not
done
(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
Greek 46.56 9 14.4 Not
done
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
218
BRAF exon 15,
(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4
NRAS exons 2,3 and 4
BRAF exon 15
Romania 46.3 10.3 10.2 Not
done
(Bisht et al., 2014) 204 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
India 23.5 Not
done
9.8 5.9
(Guedes et al., 2013) 212 High resolution melting analysis KRAS exons 2,3 and 4
BRAF exon 15
PIK3CA exons 9 and 20
Portugal 44.1 Not
done
18.3 37.3
(Rosty et al., 2013) 757 HRM and Sanger sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Australia 28.4 Not
done
15.9 14
(Patil et al., 2013) 1323 DNA sequencing KRAS exons 2 and 3 India 20.5 Not
done
Not
done
Not
done
(Sinha et al., 2013) 62 DNA sequencing KRAS exons 2 and 3 India 62.1 Not
done
Not
done
Not
done
(Malhotra et al., 2013) 30 PCR Restriction digestion KRAS exons 2 and 3 India 26.7 Not
done
Not
done
Not
done
(Smith et al., 2013) 1976 Mass spectrometry and pyro
sequencing
KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
UK 42.3 Not
done
9 12.7
(Yanus et al., 2013) 195 HRM/COLDPCR/Allele Specific
PCR and Sequencing
KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Russia 35.9 4.1 4.1 12.3
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
219
(Derbel et al., 2013) 98 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exon 9 and 20
France 23.5 Not
done
2 4
(Neumann et al., 2013) 171 Pyro sequencing KRAS exon 2,
BRAF exon 15
PIK3CA exons 9 and 20
Germany 40.9 Not
done
11.1 18.7
(Yip et al., 2013) 44 KRAS-DNA sequencing, BRAF-
Real Time
KRAS exons 2 and 3
BRAF exon 15
Malaysia 25 Not
done
2.3 Not
done
(Nakanishi et al., 2013) 254 DNA sequencing KRAS exon 2,
BRAF exon 15
Japan 33.5 Not
done
6.7 Not
done
(Soeda et al., 2013) 43 DNA sequencing KRAS exons 2 and 3,
BRAF exon 15
PIK3CA exons 9 and 20
Japan 27.9 Not
done
4.7 4.7
(Mao et al., 2012a) 69 Sanger sequencing KRAS codons 12,13,14
BRAF codon 600
PIK3CA exons 9 and 20
China 43.9 Not
done
25.4 8.2
(Bagadi et al., 2012) 100 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
India 23 2 17 Not
done
(Liao et al., 2012) 964 Pyro sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 35 Not
done
13.7 16.7
(Bozzao et al., 2011) 209 HRM and Sanger sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 20
Italy 43.5 Not
done
0 2.3
(Palomba et al., 2012) 478 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
Sardinia 30.3 Not
done
0.26 17.4
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
220
PIK3CA exon 9 and 20
(Hsieh et al., 2012) 182 HRM KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Taiwan 33.5 Not
done
1.1 7.1
(Ling et al., 2012) 331 DNA sequencing KRAS exon 2
BRAF exon 11 and 15
PIK3CA exons 9 and 20
China 44.1 Not
done
5.8 2.6
(Balschun et al., 2011) 21 Sanger sequencing and Pyro
sequencing
KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exon 20
Germany 31.6 3.5 12.3 0
(Wong et al., 2011) 29 Real Time PCR KRAS exon 2
BRAF exon 15
PIK3CA exon 9 and 20
USA 34.9 Not
done
10.3 10.3
(Saridaki et al., 2011) 112 KRAS and PIK3CA-DNA
sequencing, BRAF - Real Time
PCR
KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Greece 33 Not
done
7.2 9.8
(Janku et al., 2011) 504 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 19 8 9 11
(Baba et al., 2011) 717 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 37.7 Not
done
15.4 16.8
(Kwon et al., 2011) 92 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Korea 20.7 Not
done
3.3 1.1
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
221
(Aoyagi et al., 2011) 134 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Japan 30.6 Not
done
0.75 13.4
(De Roock et al., 2010) 1022 Mass spectrometry KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Belgium 40 2.6 4.7 14.5
(Di Nicolantonio et al.,
2010)
43 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Italy 43.5 Not
done
0 2.3
(Lurkin et al., 2010) 294 Multiplex PCR and sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Germany 48.6 2 5.3 13.1
(Perkins et al., 2010) 42 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 9
France 45.2 Not
done
2.4 14.3
(Baldus et al., 2010) 100 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Germany 41 Not
done
7 21
(Berg et al., 2010) 181 DNA sequencing KRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Chinese 32 Not
done
16 3
(Irahara et al., 2010) 225 Pyro sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
USA 41 2.2 14 11
(Roth et al., 2010) 1404 Real Time PCR KRAS exon 2 Swiss 37 Not 7.9 Not
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
222
BRAF exon 15 done done
(Souglakos et al., 2009) 168 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
UK 36.9 Not
done
7.7 15.5
(Ogino et al., 2009) 450 Pyro sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 35.7 Not
done
15.8 18.2
(Perrone et al., 2009) 32 DNA sequencing KRAS exon 2
BRAF exon 11,15
PIK3CA exons 9 and 20
Italy 24.1 Not
done
9.7 12.9
(D. Lambrechts, 2009) 153 Squenome MALDI TOF
MassArray
KRAS exons 2,3 and 4
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
Belgium 42 5.4 9.8 12
(Velho et al., 2008) 150 PCR and sequencing KRAS exon 2
BRAF exon 15
PIK3CA exon 20
Portugal 31 Not
done
18 14
(Simi et al., 2008) 116 HRM KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Italy 43 Not
done
9.5 17.2
(Freeman et al., 2008) 62 DNA sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
USA 38.7 Not
done
5.6 3.2
(Cappuzzo et al., 2008) 80 PCR and Suveyor digestion KRAS exon 2
BRAF exon 11,15
PIK3CA exons 9 and 20
Italy 52.5 Not
done
5.06 17.7
(Barault et al., 2008) 586 DNA sequencing KRAS exon 2
BRAF exon 15
France 33.8 Not
done
13.3 16.7
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
223
PIK3CA exons 1, 2,9 and
20
(Velho et al., 2005) 150 PCR-SSCP-Sequencing KRAS exon 2
BRAF exon 15
PIK3CA exons 9 and 20
Portugal 20.7 Not
done
12 9.3
(Fransen et al., 2004) 130 PCR-SSCP-Sequencing KRAS exons 2 and 3
BRAF exon 11,15
Sweden 40 Not
done
10 Not
done
(K Servomaa, 2000) 118 PCR-SSCP-Sequencing KRAS exons 2 and 3 Finland 14 Not
done
Not
done
Not
done
Current Study 203 DNA sequencing KRAS exons 2 and 3
NRAS exons 2 and 3
BRAF exon 15
PIK3CA exons 9 and 20
India 24 2 6 4
Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients
224
These variations in the mutation patterns could be due to racial differences,
geographical differences, environmental factors, and lifestyle factors, which include
obesity or physical inactivity, or other etiological factors. Nevertheless, future
studies on large cohorts are required for in depth investigations on the genetic and
epigenetic markers involved in colorectal cancer to aid in identification of new
targets for personalised medicine.
Chapter 6. Survival Analysis
225
Chapter 6 Survival Analysis
6.1 Introduction
Several studies have been performed to identify prognostic effects of various
clinico-pathological factors in colorectal cancer (Desolneux et al., 2010, Gharbi et
al., 2010, Laohavinij et al., 2010, Moghimi-Dehkordi et al., 2008, Rath and Gandhi,
2014). In Asia, the 5-year survival rate is around 42% as compared to the USA,
where it is around 60% (Jemal et al., 2010b). Early stage detection of disease
increases the patient survival rate to around 90%. However, in developing
countries early detection is possible only in 35% of CRC patients due to lack of
screening programs. Relative survival rates of CRC patients in Asian countries
ranges from 28%-42%, with the highest being in China and lowest in India (Siegel
et al., 2015). As reviewed in Chapter 2, the survival rates in Asian countries are
lower as compared to European and Western developed countries. This has been
attributed mainly to late diagnosis. Aggressive treatment besides early detection is
another key strategy for improving overall survival.
Further as summarized in Figure 2. 15, and reviewed in Treatment strategies
section 2.6, 5-FU was the only drug used for decades for the treatment of CRC.
With the use of combination of drugs in chemotherapy, the oncologists have
achieved improvement in patient’s overall survival. Use of 5-FU plus Leucovorin
(LV) either with irinotican (FOLFIRI) or oxaliplatin (FOLFOX) has led to
improvement in survival. Further, the addition of monoclonal antibodies to standard
Chapter 6. Survival Analysis
226
chemotherapy regime has improved the survival rates. The clinicians require
accurate outcome prediction to adopt appropriate therapeutic regime.
The aim of this phase of study was to examine the correlation between
clinicopathological characteristics and survival outcome in the Indian CRC patient
cohort (n=30). This may help in disease understanding and better patient
management for the Indian patients. Hence, evaluation of progression free and
overall survival of Indian CRC patients was carried out by exploring relevant
clinicopathological factors affecting prognosis like age, sex, site, stage etc.
6.2 Patients
This study is a retrospective observational study comprising of a total of 30 Indian
CRC patients studied between January 2013 till August 2016. All clinical and follow
up data were collected from medical records. The data included age, sex, tumor
differentiation, location of tumor, lymph node involvement, depth of invasion, date
of onset, cause of recurrence if any, date of death and treatment given. The data
was collected after every 3 months. Patients were treated with either FOLFOX
(Folinic acid, 5-FU and Oxaliplatin) or CAPOX (Oxaliplatin and Capecitabine) or
CAPIRI (Capecitabine and Irinotecan). Three patients were given bevacizumab
(Avastin) and three were given cetuximab (Erbutix) in combination with
chemotherapy. The study was conducted with approval from the scientific
committee of Reliance Life Science Pvt Ltd. The study design was shared with the
hospitals for obtaining clinical information of the patients who were referred to
Chapter 6. Survival Analysis
227
Reliance Life Sciences Pvt. Ltd. for mutational analysis of the samples. Formalin
fixed specimens was stained with haematoxylin – eosin (HE) and histological
assessment was done. Direct sequencing was performed for KRAS, NRAS, BRAF
and PIK3CA mutational analysis.
6.3 Statistics
Statistical analysis was done using GraphPad Prism 7 (GraphPad Software Inc,
CA, USA) and MedCalc for Windows, version 16.0 (MedCalc Software, Ostend,
Belgium). The Kaplan- Meier method was used for plotting survival curves.
Progression free survival (PFS) is defined as the time from initial administration
of treatment until the first objective evidence of disease progression or death from
any cause.
Overall survival (OS) is defined as the time from the initiation of the treatment
until the death of the patient. Patients were censored at the time of last follow up or
if they were alive after the end of the study, which was August 2016.
To assess the differences in survival, log rank test was used. Univariate hazard
ratios were identified along with multivariate using COX proportional hazard
analysis. p value less than or equal () to 0.05 was considered as significant.
Chapter 6. Survival Analysis
228
6.4 Results:
Basic information data from the CRC patients is given in Table 6.1.
Table 6.1a: Basic Data of CRC patients (n=30)
Pt
No. Age
Date of
diagnosis
Date of
death or lost
follow up
Alive (A) or
dead (D) or
left (L)
No of
years
Round
Down
No. of
years
No. of
days
1 66 5/03/13 5/03/14 D 1 1 365
2 74 18/06/12 1/07/13 D 1 1.04 378
3 44 1/04/11 1/03/15 L 3 3.92 1430
4 70 16/07/13 1/08/14 D 1 1.04 381
5 69 24/08/13 15/07/15 L 1 1.89 690
6 58 27/11/13 3/03/15 L 1 1.26 461
7 45 6/09/13 6/07/15 D 1 1.83 668
8 70 20/11/14 6/04/16 A 1 1.38 503
9 56 2/01/14 1/08/15 D 1 1.58 576
10 62 7/06/11 12/03/16 A 4 4.77 1740
11 57 30/07/13 12/01/16 A 2 2.45 896
12 59 6/07/15 12/05/16 A 0 0.85 311
13 66 12/04/13 4/04/16 A 2 2.98 1088
14 56 30/08/14 8/05/16 A 1 1.69 617
15 52 6/08/13 10/06/15 D 1 1.84 673
16 48 31/07/13 20/05/14 D 0 0.80 293
17 63 1/03/14 11/05/15 L 1 1.19 436
18 78 27/09/12 4/12/12 D 0 0.19 68
19 60 28/02/13 1/02/15 L 1 1.93 703
20 55 7/03/13 8/11/15 D 2 2.67 976
21 45 17/12/14 29/04/16 A 1 1.37 499
Chapter 6. Survival Analysis
229
22 51 26/01/13 25/02/16 A 3 3.08 1125
23 62 8/05/13 18/06/15 D 2 2.11 771
24 48 12/02/15 10/03/16 A 1 1.07 392
25 50 6/06/13 21/07/15 D 2 2.12 775
26 52 16/02/14 24/05/16 L 2 2.27 828
27 65 26/03/13 19/07/16 A 3 3.32 1211
28 67 27/05/13 13/08/16 A 3 3.22 1174
29 71 14/07/14 6/08/16 D 2 2.07 754
30 80 21/01/14 15/07/16 D 2 2.48 906
Chapter 6. Survival Analysis
230
Table 6.1b: Clinicopathological Data of CRC patients (n=30)
Pt No Age Date of diagnosis
Date of death or lost follow up
Alive or dead or left
No.of yrs Gende
r Site
Tumor differentiation
Lymphnode mets Therapy Mutation P/M
1 66 5/03/13 5/03/14 D 1 M Colon PDA pT3N2
CAPIRI+ERBUTIX BRAF M
2 74 18/06/12 1/07/13 D 1 F Rectum PDA ypT3N2b CAPOX ND P
3 44 1/04/11 1/03/15 L 3 M Rectum PDA ypT4N2b
FOLFOX+CAPIRI ND M
4 70 16/07/13 1/08/14 D 1 M Colon MDA pT3pN0pM1 CAPIRI ND M
5 69 24/08/13 15/07/15 L 1 F Rectum MDA pT4bN1aMx FOLFOX KRASCD12 M
6 58 27/11/13 3/03/15 L 1 F Colon MDA T3N0 FOLFOX KRASCD12 P
7 45 6/09/13 6/07/15 D 1 F Colon PDA T3N2MX FOLFOX ND P
8 70 20/11/14 6/04/16 A 1 M Rectum MDA T3N1MX FOLFOX ND P
9 56 2/01/14 1/08/15 D 1 F Colon PDA T3N2
FOLFOX+CAPIRI ND M
10 62 7/06/11 12/03/16 A 4 M Rectum MDA T3NOMX CAPOX ND P
11 57 30/07/13 12/01/16 A 2 M Rectum MDA T3N1MX FOLFOX KRASCD12 P
12 59 6/07/15 12/05/16 A 0 M Rectum MDA T3N0 CAPOX ND P
13 66 12/04/13 4/04/16 A 2 M Rectum MDA T4N2M1 CAPOX+CAPIRI ND M
14 56 30/08/14 8/05/16 A 1 M Rectum MDA T3N2MX
FOLFIRI+AVASTIN KRASCD12 M
15 52 6/08/13 10/06/15 D 1 M Rectum MDA T3N0 FOLFOX ND M
16 48 31/07/13 20/05/14 D 0 F Colon PDA T4N2
FOLFOX+AVASTIN ND P
17 63 1/03/14 11/05/15 L 1 F Colon MDA T4N2
FOLFOX+ERBUTIX ND P
18 78 27/09/12 4/12/12 D 0 M Rectum MDA T3N2MX CAPOX ND P
19 60 28/02/13 1/02/15 L 1 F Colon MDA T3N1MX CAPOX ND P
20 55 7/03/13 8/11/15 D 2 F Colon PDA T3N1 FOLFOX KRASCD12 P
Chapter 6. Survival Analysis
231
21 45 17/12/14 29/04/16 A 1 F Colon MDA T3N1 FOLFOX ND M
22 51 26/01/13 25/02/16 A 3 M Colon MDA T3N0
FOLFIRI+AVASTIN ND P
23 62 8/05/13 18/06/15 D 2 F Rectum MDA T3N2MX FOLFOX KRASCD12 P
24 48 12/02/15 10/03/16 A 1 M Rectum PDA T3N0 CAPOX ND P
25 50 6/06/13 21/07/15 D 2 F Colon PDA T3N1 FOLFOX ND M
26 52 16/02/14 24/05/16 L 2 F Colon MDA T3N0 CAPOX ND P
27 65 26/03/13 19/07/16 A 3 M Rectum MDA T3N1 FOLFOX ND P
28 67 27/05/13 13/08/16 A 3 F Rectum MDA T4N2
FOLFOX+ERBUTIX ND P
29 71 14/07/14 6/08/16 D 2 M Colon PDA T3N2MX FOLFOX ND M
30 80 21/01/14 15/07/16 D 2 M Colon PDA T3N1MX FOLFOX KRAS CD12 M
Table 6.3: Univariate and Multivariate analysis of PFS and OS
Univariate
PFS OS
Clinicopathological features
Reference category
OR 95% CI p OR 95% CI p
Age >60/<60 yrs 0.834 0.3543 to 1.9636
0.65 0.94 0.3178 to 2.8032
0.72
Site Colon/Rectum 0.96 0.4061 to 2.2771
0.9 2.63 0.8756 to 7.911
0.08
Differentiation
PDA/MDA 0.503 0.2001 to 1.2643
0.08 0.25 0.07682 to 0.7499
0.009*
Lymph node involvement
Yes/No 0.55 0.2164 to 1.4007
0.23 0.53 0.1517 to 1.828
0.39
Metastatic or Primary
Primary/Metastatic 0.79 0.3264 to 1.9152
0.56 0.56 0.1875 to 1.727
0.3
Mutation status
Wild Type/ Any mutant
0.68 0.2566 to 1.8262
0.36 0.86 0.2548 to 2.918
0.81
Therapy Chemo/Chemo+biological agent
0.91 0.2984 to 2.8045
0.86 0.83 0.2021 to 3.4482
0.8
Multivariate
PFS OS
Clinicopathological features
Reference category
OR 95% CI p OR 95% CI p
Age >60/<60 yrs 1.4722
0.5944 to 3.6461
0.4033
0.456 0.1233 to 1.6866
0.2393
Site Colon/Rectum 0.6488
0.2294 to 1.8349
0.4147
1.7074
0.4122 to 7.0717
0.4606
Differentiation
PDA/MDA 0.3311
0.1047 to 1.0468
0.0598
0.1605
0.0237 to 1.0895
0.0542*
Lymph node involvement
Yes/No 0.76 0.1136 to 5.1044
0.779 0.7525
0.1133 to 4.9961
0.7684
Metastatic or Primary
Primary/Metastatic 0.9074
0.3490 to 2.3591
0.842 0.8336
0.2093 to 3.3207
0.7963
Mutation status
Wild Type/ Any mutant
1.5031
0.5732 to 3.9417
0.4073
0.6749
0.1709 to 2.6663
0.5749
Therapy Chemo/Chemo+biological agent
0.5973
0.1827 to 1.9529
0.3939
0.77 0.1450 to 4.0739
0.76
*Statistically significant
Chapter 6. Survival Analysis
246
PFS-Progression free survival, OS-Overall survival, OR-Odds ratio, 95%CI-95%Confidence interval, PDA-Poorly differentiated adenocarcinoma, MDA-Moderately differentiated adenocarcinoma Further in this study, twenty two patients had tumors with no mutations (all wild-
type tumors) and 8 had tumors with mutation in either KRAS codons 12 or 13 or
BRAF (any of the mutations). Among the 8 patients with any of the mutations, 7
had KRAS codon 12 or 13 mutations one had BRAF mutation (Table 6.4).
Patients with tumor mutations were more likely to have worse PS in comparison
with all wild-type tumors. No other significant difference was seen between the two
groups (Table 6.4).
Chapter 6. Survival Analysis
247
Table 6.4: Patient, disease and treatment characteristics (n=30).
Clinicopathological features
Wild type (n=22) Any mutant (n=8) p value
Age
>60 11 (50.0) 4 (50.0) 0.99
<60 11 (50.0) 4 (50.0)
Gender
Male 12 (54.5) 4 (50.0) 0.99
Female 10 (45.5) 4 (50.0)
Dead/Alive
Dead 9 (40.9) 4 (50.0) 0.69
Alive 13 (59.1) 4 (50.0)
Site
Colon 11 (50.0) 4 (50.0) 0.99
Rectum 11 (50.0) 4 (50.0)
Tumor Diff
MDA 14 (63.6) 5 (62.5) 0.99
PDA 8 (36.4) 3 (37.5)
Lymph node metastasis
YES 15 (68.1) 7 (87.5) 0.39
NO 7 (31.9) 1 (12.5)
Therapy
Chemo 18 (81.8) 6 (75.0) 0.64
Chemo+ Biological agent
4 (18.2) 2 (25.0)
Primary/metastasis
Primary 14 (63.6) 4 (50.0) 0.67
Metastasis 8 (36.4) 4 (50.0)
Mutation
KRAS 0 7
NRAS 0 0
BRAF 0 1
PIK3CA 0 0
Chapter 6. Survival Analysis
248
Figure 6.7: Kaplan Meier plots for comparison between patients with tumor
mutation or wild type tumors.
A
B
Figure Legend:
A- PFS plot and B-OS plot
Any mutation- KRAS +BRAF mutation
0
20
40
60
80
100
Mutation wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Wild Type
22 21 13 9 4 1 0
Group: Any Mutant
8 8 7 3 0 0 0
Mutation
Wild Type
Any Mutant
0
20
40
60
80
100
Mutationwise Overall Survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Wild type
22 17 9 5 1 0
Group: Any mutation
8 7 4 0 0 0
Mutation
Wild type
Any mutation
Chapter 6. Survival Analysis
249
Figure 6.8: Kaplan Meier plots for comparison between different therapy options.
A
B
Figure Legend: A-PFS plot and B-OS plot.
10
20
30
40
50
60
70
80
90
100
Therapy wise PFS
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Chemo + Biological agent
6 6 3 2 1 0 0
Group: Chemotherapy
24 23 17 10 3 1 0
Therapy
Chemo + Biological agent
Chemotherapy
30
40
50
60
70
80
90
100
Therapy wise Overall survival
0 1 2 3 4 5
Time in years
Surv
ival pro
babili
ty (
%)
Number at risk
Group: Chemo+ Biological agent
6 4 2 2 0 0
Group: Chemotherapy
24 20 11 3 1 0
therapy
Chemo+ Biological agent
Chemotherapy
Chapter 6. Survival Analysis
250
Among the 8 patients with mutations, one was treated with second-line anti-EGFR-
containing regimen, one was treated with second line anti VEGF treatment i. e
bevacizumab and six were treated with chemotherapy.
The median PFS of patients with KRAS or BRAF mutations (n = 8; 6 months; 95%
CI, 5-7 months) was shorter than that of patients with all wild-type tumors (n = 22; 7
months; 95% CI, 4-9 months), as verified in both univariate (HR 1.46; 95% CI,
0.5476 to 3.8978; P = 0.36) and multivariate analyses (HR 1.50; 95% CI, 0.5732 to
3.9417; P = 0.407) (Figure 6.7 A, Table 6.3).
The median OS of patients with KRAS or BRAF mutations (n = 8; 2.1 years; 95%
CI, 1.800 to 2.1 years) was shorter than that of patients with all wild-type tumors (n
= 22; 2.5 years; 95% CI, 2.100 to 2.700 years), as verified in both univariate (HR
1.1; 95% CI, 0.3316 to 3.6657; P = 0.86) and multivariate analyses (HR 0.75; 95%
CI, 0.2033 to 2.7878; P = 0.6) (Figure 6.7 B, Table 6.3).
Chapter 6. Survival Analysis
251
6.5 Discussion
In the past few years, there have been several studies done to determine the
association of a range of variables with the survival of CRC patients (Laohavinij et
al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,
Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion
of tumor, lymph node metastases and other variables have been studied. However,
determination of prognostic factor is still a challenge. The overall survival rate
currently in Asian countries is approximately 60% as the majority of
adenocarcinomas are still diagnosed at the later stages. If the disease is
diagnosed at an early stage then the survival rate is observed as 90% (Moghimi-
Dehkordi and Safaee, 2012). In this study, the effect of different clinico-pathological
features on the Indian patients survival rate was evaluated.
It was observed that overall survival was 37% with median survival of 29 months
which is similar to the survival rate observed in a study published by Yeole et al. in
2001 on the Indian population (Yeole et al., 2001). The incidence rates of colon
and rectal tumors are low in comparison to the population in Western developed
countries. In India, rectal tumors are more common than colon (Mohandas and
Desai, 1998). However, a significant increase has been noted in colon cancer
cases over the past two decades. The present study data shows that the incidence
of colon cancer in India was more in comparison to rectal cancer (colon -35% and
rectum – 28%).
Few other researchers from China have also reported the decline in rectal cancer
cases (Xu et al., 2006, Wan et al., 2001).The reason for this change is unclear, it
Chapter 6. Survival Analysis
252
could be due to improvisation in early diagnosis, changing dietary habits or
etiological changes (McMichael and Potter, 1985, Mohandas and Desai, 1998). For
example, alcohol consumption has been associated with rectal cancers while
family history is strongly associated with colon cancers (Bongaerts et al., 2008,
Andrieu et al., 2004). Literature indicates that patients having colon as the site of
tumor have a better prognosis than those having rectum (Moghimi-Dehkordi et al.,
2008, Ratto et al., 1998, Wang et al., 2008). Further, it has been observed that
poorer survival has been associated with proximal colon, rather than distal colon
and as such there is not much difference in the survival rate of distal colon and
rectal tumors (Hemminki et al., 2010, Meguid et al., 2008, Wray et al., 2009).
These differences in survival according to tumor site could instead be due to
differences in tumor aggressiveness or due to screening methodologies used. Few
studies have shown that colonoscopy and sigmoidoscopy are associated with
higher incidence and mortality with proximal colon cancers in comparison to distal
colon cancers (Newcomb et al., 2003, Atkin et al., 2010, Brenner et al., 2009,
Baxter et al., 2009). Proximal colon cancers exhibit rapid tumor progression, which
could be due to their diagnosis as interval cancers. Also these tumors frequently
show the presence of CIMP, MSS/MSI-L along with BRAF mutated status. These
all factors are associated with poorer survival in proximal tumors (Baxter et al.,
2011, Shaukat et al., 2010, Phipps et al., 2013). Similarly it has been reported MSI-
H tumors are significantly present in proximal colons and MSI-H tumors have
favourable survival outcome (Guastadisegni et al., 2010).
Chapter 6. Survival Analysis
253
In the current study the median overall survival of patients with colon tumors was
observed to be 21 months. There was however no significant association observed
for PFS and OS with respect to site of tumor origin.
The likelihood of being diagnosed with CRC increases after the age of 40 years
and the occurrence of CRC cases is higher in patients after the age of 50 years
(Fund and Research, 2007). Incidence rate is seen to higher in patients with age
above 60 years, however, the incidence of CRC is seen to be increasing in the
younger population of 40 years and below (O'Connell et al., 2004, You et al., 2012,
Siegel et al., 2009). Some studies have shown that early onset of the disease is
associated with poorer survival outcome (Fang et al., 2010, Gharbi et al., 2010,
Moghimi-Dehkordi et al., 2008, Zhang et al., 2010).
The increasing incidence of CRC in younger populations could be due to lack of
screening at a younger age, behavioural factors such as alcohol consumption,
smoking and lifestyle factor like obesity. The pesticide consumption in India has
increased several hundred folds from 154 metric tons in 1954 to 41,822 metric tons
in 2009-2010. In low income countries like India only 10% of the contaminated
water is treated rest all is discharged into water bodies. This highly contaminated
water can cause adverse health effects including cancer. In an epidemiological
study from Egypt, researchers have shown prevalence of young onset CRC in
people with exposure to pesticides (Lo et al., 2010).
Some studies that have shown the better survival in younger patients which could
be due to aggressive therapy regimes used for younger patients, low risk of
Chapter 6. Survival Analysis
254
postoperative complications and higher treatment completion rates of surgery and
adjuvant therapy. Also the younger patient group may include hereditary CRC like
Lynch syndrome which is seen to have better survival rates (O'Connell et al.,
2004).
To study the effect of age on survival the patients were divided in two categories
above 60 years and below 60 years. No significant association was observed
between the survivals of these two groups. However, the median overall survival
for patients above the age of 60 was 28 months and below 60 years was 30
months which concurs with few studies reporting poor survival rate in older patients
when compared with that of younger ones (Rosenberg et al., 2008, Laohavinij et
al., 2010). It has been observed that in the young patients CRC is more aggressive
and has poor pathological features (Chou et al., 2011).
In this study, significant association was observed between patients survival rates
and poorly differentiated adenocarcinoma (PDA). PDA is associated with poor
survival in comparison to moderately differentiated adenocarcinoma (p<0.05)
(Figure 6.8) as seen in other studies (Laohavinij et al., 2010, Moghimi-Dehkordi et
al., 2008). Histologically, PDA account for around 4.8% to 23% of all colorectal
cancer cases (Benedix et al., 2010, YOSHIDA et al., 2011, Xiao et al., 2013). PDA
is directly associated with a poor prognosis as reported in previous studies(Ishihara
et al., 2012, Bjerkeset et al., 1987). PDA cases mostly occur in advanced stages or
metastatic stages which could be the reason for poor survival.
Chapter 6. Survival Analysis
255
Lymph node metastasis is a critical predictor of survival and recurrence in CRC.
Several studies have found a significant association between numbers of lymph
node resected and improved survival (Chang et al., 2007, Gunderson et al., 2010,
Chen and Bilchik, 2006). As increased survival is noted in patients with lymph node
involvement, better therapeutic advantage is suggested in higher lymph node
retrieval. In the current study, the overall survival of patients with lymph node
involvement was 25 months, which was seen to be better than patients with no
lymph node involvement. However, the survival curves are non-significant. ASCO,
NCCN and the American College of Surgeons Commission on Cancer (CoC)
indicate that a minimum of 12 lymph node count is associated with improved
outcome in the patients (Nelson et al., 2001).The actual mechanism between
lymph node count and survival is unclear. However, there are various factors that
affect the number of lymph node examined like patient age, extent of surgical
resection and tumor location. It has been observed that right-sided tumors show
the presence of higher number of lymph nodes (Chang et al., 2007). Numbers of
lymph nodes involved reflect the patients improved immune response. More lymph
node involvement indicates a greater immune response and hence improved
survival (Pagès et al., 2005).
CRC can be prevented if detected at an early phase and if adenomatous polyps
are removed early. If the tumor is diagnosed when it is in localised stage then the
survival outcome of the patient is better than in those cases where in the diagnosis
occurs at the metastatic stage (Fatemi et al., 2010). In the current study, it was
Chapter 6. Survival Analysis
256
observed that if the tumor is localised i.e. present at the primary stage then the
median survival is 31 months in comparison to the metastatic tumors in which the
median survival is 26 months. However, the survival curve was not statistically
significant. In one study published on a Dutch population, it was observed that
metastatic tumors have significantly improved survival. This change observed
could be due to increased used and improvisation in chemotherapy, use of
adjuvant and neoadjuvant therapies and better selection of patients eligibility for
surgery (Meulenbeld et al., 2008).
Together with the clinico-pathological features, mutation in the RAS –RAF pathway
is observed in around 30-50% of colorectal cancer tumors implying that only the
remaining 50% of patients would benefit from anti-EGFR therapy. Cetuximab plus
FOLFOX helps in improving the survival rate and disease free progression
(Bokemeyer et al., 2008). Cetuximab and FOLFIRI both improve the survival and
response rate both in KRAS wild type tumors (Assenat et al., 2011). In current
study, the overall survival rate in mutated tumors with mutation in KRAS or BRAF
genes studied was 25 months and in wild type was 29 months although statistical
significance was not observed.
These experimental evidences of survival rates of CRC patients in relation to
different clinico-pathological features in this retrospective analysis of the Indian
population suggests that there are many factors which could influence the
prognosis of colorectal cancer patients. However, the present study has limitations.
Due to a smaller sample size, current study may not exactly reflect the prevalence
Chapter 6. Survival Analysis
257
of colorectal cancer in the entire Indian population, however it reflects the nature of
disease and the effect of different clinico-pathological features on survival for
Indian CRC cases. This study indicates the differences in presentation of CRC in
Indian population and also the effect of various factors on survival that may differ
from the population in Western developed nations. One of the major limitation of
the present study was that owing to the small number of patients, no data was
available specifying BRAF, NRAS and PIK3CA mutation due to which impact of
these mutations on the survival could not be studied. As seen in Chapter 5, the
frequency of BRAF, NRAS and PIK3CA mutations in Indian population is 5.9%, 2%
and 4% respectively. Further the survival analysis involved only seven patients for
KRAS and single patient for BRAF out of a small sample size selected (n=30), from
the total patients studied for mutation analysis (n=203). According to the published
literature patients with BRAF mutations are often refractory to systemic
chemotherapy and have poor prognosis hence screening for BRAF mutations has
become important. However, the study findings are extrapolative and hypothesis
generating which can be further analysed in larger cohort.
This study supports the hypothesis that clinical and pathological characteristics are
better determinants of prognosis in CRC patients. Amongst all the
clinicopathological features studied through univariate and multivariate analyses,
the feature that has the significant impact on the survival outcome, is the tumor
differentiation status. Thus, early detection and timely evaluation of tumor becomes
extremely important in CRC, which can further lead to improved survival.
Chapter 7. Summary and Future Work
258
Chapter 7 Summary and Future Work
Colorectal cancer is one of the leading causes of cancer worldwide. It is the most
common of all gastrointestinal malignancies. The chapter 2 highlights that in
existing literature wide geographical, racial and ethnic difference in incidence are
observed for this cancer. The majority of studies showing genetic and epigenetic
changes correlation with CRC have been carried out in the population of Western
developed nations. Very little data is available on the Indian population. This study
was hence undertaken with an aim to evaluate the genetic alterations in KRAS,
BRAF, NRAS and PIK3CA genes and the correlation of these molecular alterations
with clinicopathological characteristics in 203 CRC patients. Further, the correlation
between clinicopathological features and survival was studied in in a subset of
Indian population sample size (n=30). The percentage of molecular alterations
observed in this study corresponds with those reported in literature for CRC cases
described in COSMIC database.
All the molecular analysis performed in this study was according to current
recommendations of CAP and NABL guidelines. Hence, the molecular data
obtained from this study can be associated to the clinical data and errors possibly
related to technical issues are unlikely. Also, the samples analyzed in the current
study constituted a random fraction of Indian CRC patients and is hence a
balanced representation of entire Indian population.
Chapter 7. Summary and Future Work
259
7.1 Mutation Studies in 203 CRC patients
Analysis of mutation distribution in KRAS, BRAF, NRAS and PIK3CA genes was
carried out using Sanger sequencing, which is the cost effective methodology and
‘Gold Standard’ for mutation analysis. Sanger sequencing allowed evaluation of all
the hotspot regions in all four genes. Various steps were undertaken in our
laboratory to ensure optimal procedures for mutational testing through direct
sequencing. As mentioned above, these included strict adherence to current
recommendations by CAP and NABL guidelines and involvement of experienced
pathologists in representative tissue sample section and for performing tumour
macrodissection. Furthermore, mutational analyses were performed using widely
accepted protocols. The laboratory is also registered in external quality control
audits. The minimum allelic sensitivity of Sanger sequencing was established as
20% using commercially available reference standards. Samples having tumor
percentage of 20% were processed by macro dissection to enrich the tumor
content. Samples below the tumor percentage of 20 were not included in the study.
In a total of 36% of CRC cases at least one mutation in the analyzed hot spot
region was observed. The prevalence of KRAS, BRAF, NRAS and PIK3CA
mutations in the present study were 24%, 6%, 2% and 4%, respectively which
concurs well with the COSMIC database reported frequencies. Hence, it can be
seen that approximately 12% of CRC patients have mutations in NRAS, BRAF or
PIK3CA in KRAS wild type population. However, it was observed that the mutation
frequency of BRAF V600E was relatively lower in the Indian population as
Chapter 7. Summary and Future Work
260
compared to what is reported in the COSMIC database. The reason for this
difference may be the structure of COSMIC database that screens for the
information available in literature for the somatic mutations and displays its
relationship to the particular human cancer. This amino acid substitution of V to E
at codon position 600 in BRAF was observed in 6% of cases versus 10.1% in the
COSMIC database. However, this observed frequency of 6% was in concordance
with the study performed by Bagadi et. al. and Bisht et. al. on the Indian population
(Bagadi et al., 2012, Bisht et al., 2014).BRAF mutations were found to mutually
exclusive with KRAS mutations. Three cases showed coexistence of PIK3CA and
KRAS mutations together, which confirms the reported observations that PIK3CA
mutations can coexist with other molecular alterations (Thesis Chapters 4 and 5).
Chapter 7. Summary and Future Work
261
7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes
with clinico-pathological data for a 203 Indian CRC patient cohort
The statistical analysis of clinicopathological characteristics and mutation analysis
was performed using Chi-square tests. Significant positive association was
observed for KRAS mutations with age and tumor differentiation (p<0.05). The
mutation rate in patients above 50 years was higher than the rate in patients below
50 years. Also, KRAS mutations were significantly associated positively with
moderately and poorly differentiated adenocarcinoma, as compared to well-
differentiated adenocarcinoma. Other clinicopathological findings like gender,
tumor location, stage and lymph-node metastasis, showed no significant
association with KRAS mutations (p>0.05), which is in accordance with recent
reports for the Indian population.
In the case of BRAF, a statistically significant correlation was observed in
moderately differentiating and poorly differentiating adenocarcinomas, but not in
well-differentiated adenocarcinomas. This study supports previous reports that
found that BRAF mutation status is correlated with specific clinicopathological
features and hence identifies a distinctive subgroup of patients having specific
clinico-pathological features (Li et al., 2011).
No significant association was observed between any of the clinico-pathological
features with NRAS or PIK3CA mutations. However, this study agrees well with
other population based studies not only in terms of distribution of mutations and
clinical and pathological features but also in terms of association between these
mutations and the clinical data (Thesis Chapter 5).
Chapter 7. Summary and Future Work
262
7.3 Correlation of clinico-pathological data with survival in Indian patient
cohort
In the past few years, there have been many studies carried out to determine the
association of numerous variables with the survival in case of CRC (Laohavinij et
al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,
Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion
of tumor, lymph node metastases and other variables have been studied. However,
determination of prognostic factor is still a challenge. The overall survival rate
currently in Asian countries is approximately 60% as the majority of
adenocarcinomas are still diagnosed at the later stages while the highest survival
rate is observed in the USA as 64% (Moghimi-Dehkordi and Safaee, 2012).
Amongst the Asian countries, the highest survival rate is seen in China and the
lowest in India (Shiono et al., 2005, Yeole et al., 2001). In this study, it was
observed that overall survival was 37% with median survival of 25 months. In terms
of anatomic location, the median survival for colon was seen to be 21 months. The
differences in survival according to tumor site could be due to differences in tumor
aggressiveness or due to screening methodologies used. Further, the median
survival for patients above the age of 60 was 25 months and below 60 years was
30 months which concurs with few studies reporting poor survival rate in older
patients when compared with that of younger ones. However, recently it has been
observed that the incidence of CRC is rising in the younger population in India. It
has been observed that in the young patients CRC is more aggressive and has
poor pathological features (Chou et al., 2011). With regard to differentiation of
Chapter 7. Summary and Future Work
263
tumor, the current study data reveals a significant association in the survival rate of
patients with poorly differentiated adenocarcinoma (PDA). PDA is positively
associated with poor survival (median survival of 21 months) in comparison to
MDA (p0.05) as observed in previous studies. PDA cases mostly occur in
advanced stages or metastatic stages which could be the reason for poor survival.
The overall survival in the case of patients with lymph node involvement was found
to be 25 months, which was higher than patients with no lymph node involvement.
Numbers of lymph nodes involved reflect the patients’ improved immune response.
The more the lymph node involvement, the greater the immune response and
hence improved survival.
If the tumor is diagnosed when it is in a localised stage then the survival outcome
of the patient is better than those cases where in the diagnosis occurs at the
metastatic stage. In the current study, it was observed that if the tumor is
localised, i.e. present at primary stage, then the median survival was 31 months in
comparison to the metastatic tumors in which the median survival was 26 months.
The survival rate in mutated tumors was 25 months and in wild type was 29
months though statistical significance was not observed (Thesis Chapter 6).
Patients’ selection has recently entered a new era of personalised therapy. The
establishment of biomarkers and clinicopathological features prior to treatment can
lead to improved survival. The impact of different genetic and epigenetic alterations
such as mutations, SNP’s, methylation status and copy number, required for
efficacy of treatment, requires further study to determine the mechanisms of action
for the specific drug molecule used. This thesis investigated a variety of CRC
Chapter 7. Summary and Future Work
264
cases in the Indian population for studying the effect of different factors on survival.
Taken together, the findings of current study shows for the first time that at the
genetic level, mutations in one of the four genes (KRAS, BRAF, NRAF and
PIK3CA) occur at a lower frequency than in the population in Western developed
countries. Data supports the hypothesis that (i) rate of mutations in critical CRC
genes involved in the tumor growth and survival i.e. KRAS, BRAF, NRAS and
PIK3CA differ according to racial differences, and (ii) that different
clinicopathological factors would have impact on clinical outcome of the patient in
the context of Indian patient cohort. Results from therapeutic data analysis
(Chapter 6) shows that the knowledge of tumor differentiation status can influence
decision making for patient and hence improve response rate and outcome of CRC
patient. This data needs to be validated in larger cohort to potentially influence
treatment decisions in Indian patients, and hence is a step forward towards
personalised treatment.
In brief, the following conclusions can be drawn from this study:
1. The prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in CRC
patients in the present study was 24%, 6%, 2% and 4%, respectively,
which is at a much lower frequency when compared to the data available
for populations in Western developed nations.
2. BRAF mutations were found to be mutually exclusive with KRAS
mutations. However, coexistence of PIK3CA mutations with KRAS
mutant patients was observed. These results concur with the published
literature on populations in Western developed nations.
Chapter 7. Summary and Future Work
265
3. Significant statistical association (p<0.05) was observed between the
following parameters:
a. KRAS mutations with age and tumor differentiation. Mutation rate in
patients above 50 years was higher than for patients below 50 years
of age. KRAS mutations were significantly associated positively with
moderately and poorly differentiated adenocarcinoma, as compared
to well-differentiated adenocarcinoma.
b. BRAF statistically significant positive correlation was observed in
moderately differentiating and poorly differentiating adenocarcinomas
than in well-differentiated adenocarcinomas.
4. No significant association was observed between any of the
clinicopathological features with NRAS or PIK3CA mutations in Indian cases.
5. In terms of correlation between survival and clinicopathological features, the
following observations were made:
a. The 3- year overall survival in Indian patients was observed to be 37%, with
median survival of 25 months, which is much lower in comparison to that in
developed nations.
b. Significant association was observed in the survival rate of patients with
poorly differentiated adenocarcinoma (PDA). PDA is inversely associated with poor
survival (median survival of 21 months) in comparison to moderately differentiated
adenocarcinoma (MDA) (p0.05).
c. It was observed that if the tumor is localised, that is, present at the primary
stage, then the median survival is 31 months, in comparison to metastatic tumors
Chapter 7. Summary and Future Work
266
in which the median survival is 26 months. The survival rate in mutated tumors was
25 months and in wild type was 29 months, although statistical significance was
not observed.
The current study data reflects the nature of disease and the effect of different
clinicopathological features on survival in case of Indian CRC cases. These
experimental evidences of survival rates of CRC patients in relation to different
clinico-pathological features in the Indian population indicates that there are
numerous factors that influence the prognosis of colorectal cancer patients.
However, life expectancy has not increased much in these years. Though the data
has inherent limitations due to small sample size analysis, this retrospective study
supports the hypothesis (based on existed literature) that clinical and pathological
characteristics, especially tumor differentiation are good determinants of prognosis
in CRC patients.
Recently mutations in KRAS exon4 and NRAS exon 4 have been shown to have
an effect on therapeutic response. However, the reported percentage of these
mutations is low, ranging from 0.5 to 2.2%. Further studies are required to
establish the prevalence and effect of these mutations of exon 4 in the Indian
population.
In terms of a forward path, additional studies are required in the Indian CRC
population to determine the effect of additional genetic and epigenetic markers,
such as AKT, PTEN, MAPK, and other receptors molecules such as MET, MSI,
CIMP, which could provide an alternative pathway to survival. Also, larger cohort
Chapter 7. Summary and Future Work
267
needs to be studied to evaluate the effect of different mutations in correlation with
clinicopathological features on survival. Thus the future strategy to improve survival
outcomes and clinical management of CRC, lies in personalized therapy which is
still an evolving approach with a focus to identify highly specific and sensitive
predictive biomarkers. Hence, there is a strong need to identify, develop and
validate more biomarkers that will assist with clinical decision-making. As reviewed
recently (Patil et al 2016), Next Gen Sequencing and multi-gene sequencing
(parallel sequencing technology), data reveals that along with the mutations in
genes of EGFR pathway, mutations SMAD-4 and FBXW7 are also responsible for
resistance to therapy. Also, advances in imaging techniques such as FDG-PET,
DWI, DCE-MRI could potentially serve as predictive imaging biomarkers to anti-
angiogenesis inhibitors(Atreya and Goetz, 2013). Considering the current progress
and focus in personalized medicine, and with the recent genomic profiling of CRC
patient tumors and the development of new proteomic and modeling studies,
selecting and stratifying CRC patients based on their molecular profile will be
improved in future.
Appendix
268
Appendix
I. Haematoxylin and eosin (H&E) Analysis
Preparation of reagents:
1. Haematoxylin Solution (Harris):
Potassium or ammonium (alum): 100 g
Distilled water: 1000 ml
Heat to dissolve. Add 50 ml of 10% alcoholic haematoxylin solution and heat to boil
for 1 min. Remove from heat and slowly add 2.5 g of mercuric oxide (red). Heat to
the solution and until it becomes dark purple color. Cool the solution in cold water
bath and add 20 ml of glacial acetic acid (concentrated). Filter before use and store
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